Identification of the high risk emergency surgical patient: Which risk prediction model should be used?

Science.gov (United States)

Stonelake, Stephen; Thomson, Peter; Suggett, Nigel

2015-09-01

National guidance states that all patients having emergency surgery should have a mortality risk assessment calculated on admission so that the 'high risk' patient can receive the appropriate seniority and level of care. We aimed to assess if peri-operative risk scoring tools could accurately calculate mortality and morbidity risk. Mortality risk scores for 86 consecutive emergency laparotomies, were calculated using pre-operative (ASA, Lee index) and post-operative (POSSUM, P-POSSUM and CR-POSSUM) risk calculation tools. Morbidity risk scores were calculated using the POSSUM predicted morbidity and compared against actual morbidity according to the Clavien-Dindo classification. The actual mortality was 10.5%. The average predicted risk scores for all laparotomies were: ASA 26.5%, Lee Index 2.5%, POSSUM 29.5%, P-POSSUM 18.5%, CR-POSSUM 10.5%. Complications occurred following 67 laparotomies (78%). The majority (51%) of complications were classified as Clavien-Dindo grade 2-3 (non-life-threatening). Patients having a POSSUM morbidity risk of greater than 50% developed significantly more life-threatening complications (CD 4-5) compared with those who predicted less than or equal to 50% morbidity risk (P = 0.01). Pre-operative risk stratification remains a challenge because the Lee Index under-predicts and ASA over-predicts mortality risk. Post-operative risk scoring using the CR-POSSUM is more accurate and we suggest can be used to identify patients who require intensive care post-operatively. In the absence of accurate risk scoring tools that can be used on admission to hospital it is not possible to reliably audit the achievement of national standards of care for the 'high-risk' patient.

Does diagnosis affect the predictive accuracy of risk assessment tools for juvenile offenders: Conduct Disorder and Attention Deficit Hyperactivity Disorder.

Science.gov (United States)

Khanna, Dinesh; Shaw, Jenny; Dolan, Mairead; Lennox, Charlotte

2014-10-01

Studies have suggested an increased risk of criminality in juveniles if they suffer from co-morbid Attention Deficit Hyperactivity Disorder (ADHD) along with Conduct Disorder. The Structured Assessment of Violence Risk in Youth (SAVRY), the Psychopathy Checklist Youth Version (PCL:YV), and Youth Level of Service/Case Management Inventory (YLS/CMI) have been shown to be good predictors of violent and non-violent re-offending. The aim was to compare the accuracy of these tools to predict violent and non-violent re-offending in young people with co-morbid ADHD and Conduct Disorder and Conduct Disorder only. The sample included 109 White-British adolescent males in secure settings. Results revealed no significant differences between the groups for re-offending. SAVRY factors had better predictive values than PCL:YV or YLS/CMI. Tools generally had better predictive values for the Conduct Disorder only group than the co-morbid group. Possible reasons for these findings have been discussed along with limitations of the study. Copyright © 2014 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Risk analysis for dengue suitability in Africa using the ArcGIS predictive analysis tools (PA tools).

Science.gov (United States)

Attaway, David F; Jacobsen, Kathryn H; Falconer, Allan; Manca, Germana; Waters, Nigel M

2016-06-01

Risk maps identifying suitable locations for infection transmission are important for public health planning. Data on dengue infection rates are not readily available in most places where the disease is known to occur. A newly available add-in to Esri's ArcGIS software package, the ArcGIS Predictive Analysis Toolset (PA Tools), was used to identify locations within Africa with environmental characteristics likely to be suitable for transmission of dengue virus. A more accurate, robust, and localized (1 km × 1 km) dengue risk map for Africa was created based on bioclimatic layers, elevation data, high-resolution population data, and other environmental factors that a search of the peer-reviewed literature showed to be associated with dengue risk. Variables related to temperature, precipitation, elevation, and population density were identified as good predictors of dengue suitability. Areas of high dengue suitability occur primarily within West Africa and parts of Central Africa and East Africa, but even in these regions the suitability is not homogenous. This risk mapping technique for an infection transmitted by Aedes mosquitoes draws on entomological, epidemiological, and geographic data. The method could be applied to other infectious diseases (such as Zika) in order to provide new insights for public health officials and others making decisions about where to increase disease surveillance activities and implement infection prevention and control efforts. The ability to map threats to human and animal health is important for tracking vectorborne and other emerging infectious diseases and modeling the likely impacts of climate change. Copyright © 2016 Elsevier B.V. All rights reserved.

Optimising preoperative risk stratification tools for prostate cancer using mpMRI

Energy Technology Data Exchange (ETDEWEB)

Reisaeter, Lars A.R.; Losnegaard, Are; Biermann, Martin; Roervik, Jarle [Haukeland University Hospital, Department of Radiology, Bergen (Norway); University of Bergen, Department of Clinical Medicine, Bergen (Norway); Fuetterer, Jurgen J. [Radboud University Nijmegen Medical Centre, Department of Radiology, Nijmegen (Netherlands); Nygaard, Yngve [Haukeland University Hospital, Department of Urology, Bergen (Norway); Monssen, Jan [Haukeland University Hospital, Department of Radiology, Bergen (Norway); Gravdal, Karsten [Haukeland University Hospital, Department of Pathology, Bergen (Norway); Halvorsen, Ole J.; Akslen, Lars A. [Haukeland University Hospital, Department of Pathology, Bergen (Norway); Centre for Cancer Biomarkers CCBIO, Department of Clinical Medicine, University of Bergen (Norway); Haukaas, Svein; Beisland, Christian [University of Bergen, Department of Clinical Medicine, Bergen (Norway); Haukeland University Hospital, Department of Urology, Bergen (Norway)

2018-03-15

To improve preoperative risk stratification for prostate cancer (PCa) by incorporating multiparametric MRI (mpMRI) features into risk stratification tools for PCa, CAPRA and D'Amico. 807 consecutive patients operated on by robot-assisted radical prostatectomy at our institution during the period 2010-2015 were followed to identify biochemical recurrence (BCR). 591 patients were eligible for final analysis. We employed stepwise backward likelihood methodology and penalised Cox cross-validation to identify the most significant predictors of BCR including mpMRI features. mpMRI features were then integrated into image-adjusted (IA) risk prediction models and the two risk prediction tools were then evaluated both with and without image adjustment using receiver operating characteristics, survival and decision curve analyses. 37 patients suffered BCR. Apparent diffusion coefficient (ADC) and radiological extraprostatic extension (rEPE) from mpMRI were both significant predictors of BCR. Both IA prediction models reallocated more than 20% of intermediate-risk patients to the low-risk group, reducing their estimated cumulative BCR risk from approximately 5% to 1.1%. Both IA models showed improved prognostic performance with a better separation of the survival curves. Integrating ADC and rEPE from mpMRI of the prostate into risk stratification tools improves preoperative risk estimation for BCR. (orig.)

Dutch Risk Assessment tools

NARCIS (Netherlands)

Venema, A.

2015-01-01

The ‘Risico- Inventarisatie- en Evaluatie-instrumenten’ is the name for the Dutch risk assessment (RA) tools. A RA tool can be used to perform a risk assessment including an evaluation of the identified risks. These tools were among the first online risk assessment tools developed in Europe. The

Prospective validation of American Diabetes Association risk tool for predicting pre-diabetes and diabetes in Taiwan-Taichung community health study.

Directory of Open Access Journals (Sweden)

Chia-Ing Li

Full Text Available BACKGROUND: A simple diabetes risk tool that does not require laboratory tests would be beneficial in screening individuals at higher risk. Few studies have evaluated the ability of these tools to identify new cases of pre-diabetes. This study aimed to assess the ability of the American Diabetes Association Risk Tool (ADART to predict the 3-year incidence of pre-diabetes and diabetes in Taiwanese. METHODS: This was a 3-year prospective study of 1021 residents with normoglycemia at baseline, gathered from a random sample of residents aged 40-88 years in a metropolitan city in Taiwan. The areas under the curve (AUCs of three models were compared: ADART only, ADART plus lifestyle behaviors at baseline, and ADART plus lifestyle behaviors and biomarkers at baseline. The performance of ADART was compared with that of 16 tools that had been reported in the literature. RESULTS: The AUCs and their 95% confidence intervals (CIs were 0.60 (0.54-0.66 for men and 0.72 (0.66-0.77 for women in model 1; 0.62 (0.56-0.68 for men and 0.74 (0.68-0.80 for women in model 2; and 0.64 (0.58-0.71 for men and 0.75 (0.69-0.80 for women in model 3. The AUCs of these three models were all above 0.7 in women, but not in men. No significant difference in either women or men (p = 0.268 and 0.156, respectively was observed in the AUC of these three models. Compared to 16 tools published in the literature, ADART had the second largest AUC in both men and women. CONCLUSIONS: ADART is a good screening tool for predicting the three-year incidence of pre-diabetes and diabetes in females of a Taiwanese population. The performance of ADART in men was similar to the results with other tools published in the literature. Its performance was one of the best among the tools reported in the literature.

Systematic review of fall risk screening tools for older patients in acute hospitals.

Science.gov (United States)

Matarese, Maria; Ivziku, Dhurata; Bartolozzi, Francesco; Piredda, Michela; De Marinis, Maria Grazia

2015-06-01

To determine the most accurate fall risk screening tools for predicting falls among patients aged 65 years or older admitted to acute care hospitals. Falls represent a serious problem in older inpatients due to the potential physical, social, psychological and economic consequences. Older inpatients present with risk factors associated with age-related physiological and psychological changes as well as multiple morbidities. Thus, fall risk screening tools for older adults should include these specific risk factors. There are no published recommendations addressing what tools are appropriate for older hospitalized adults. Systematic review. MEDLINE, CINAHL and Cochrane electronic databases were searched between January 1981-April 2013. Only prospective validation studies reporting sensitivity and specificity values were included. Recommendations of the Cochrane Handbook of Diagnostic Test Accuracy Reviews have been followed. Three fall risk assessment tools were evaluated in seven articles. Due to the limited number of studies, meta-analysis was carried out only for the STRATIFY and Hendrich Fall Risk Model II. In the combined analysis, the Hendrich Fall Risk Model II demonstrated higher sensitivity than STRATIFY, while the STRATIFY showed higher specificity. In both tools, the Youden index showed low prognostic accuracy. The identified tools do not demonstrate predictive values as high as needed for identifying older inpatients at risk for falls. For this reason, no tool can be recommended for fall detection. More research is needed to evaluate fall risk screening tools for older inpatients. © 2014 John Wiley & Sons Ltd.

Predicting recidivism among adult male child pornography offenders: Development of the Child Pornography Offender Risk Tool (CPORT).

Science.gov (United States)

Seto, Michael C; Eke, Angela W

2015-08-01

In this study, we developed a structured risk checklist, the Child Pornography Offender Risk Tool (CPORT), to predict any sexual recidivism among adult male offenders with a conviction for child pornography offenses. We identified predictors of sexual recidivism using a 5-year fixed follow-up analysis from a police case file sample of 266 adult male child pornography offenders in the community after their index offense. In our 5-year follow-up, 29% committed a new offense, and 11% committed a new sexual offense, with 3% committing a new contact sexual offense against a child and 9% committing a new child pornography offense. The CPORT items comprised younger offender age, any prior criminal history, any contact sexual offending, any failure on conditional release, indication of sexual interest in child pornography material or prepubescent or pubescent children, more boy than girl content in child pornography, and more boy than girl content in other child depictions. The CPORT was significantly associated with any sexual recidivism, with moderate predictive accuracy, and thus has promise in the risk assessment of adult male child pornography offenders with further cross-validation. (c) 2015 APA, all rights reserved).

Cardiovascular risk prediction: the old has given way to the new but at what risk-benefit ratio?

Directory of Open Access Journals (Sweden)

Yeboah J

2014-10-01

Full Text Available Joseph Yeboah Heart and Vascular Center of Excellence, Wake Forest University School of Medicine, Winston-Salem, NC, USA Abstract: The ultimate goal of cardiovascular risk prediction is to identify individuals in the population to whom the application or administration of current proven lifestyle modifications and medicinal therapies will result in reduction in cardiovascular disease events and minimal adverse effects (net benefit to society. The use of cardiovascular risk prediction tools dates back to 1976 when the Framingham coronary heart disease risk score was published. Since then a lot of novel risk markers have been identified and other cardiovascular risk prediction tools have been developed to either improve or replace the Framingham Risk Score (FRS. In 2013, the new atherosclerotic cardiovascular disease risk estimator was published by the American College of Cardiology and the American Heart Association to replace the FRS for cardiovascular risk prediction. It is too soon to know the performance of the new atherosclerotic cardiovascular disease risk estimator. The risk-benefit ratio for preventive therapy (lifestyle modifications, statin +/− aspirin based on cardiovascular disease risk assessed using the FRS is unknown but it was assumed to be a net benefit. Should we also assume the risk-benefit ratio for the new atherosclerotic cardiovascular disease risk estimator is also a net benefit? Keywords: risk prediction, prevention, cardiovascular disease

Become the PPUPET Master: Mastering Pressure Ulcer Risk Assessment With the Pediatric Pressure Ulcer Prediction and Evaluation Tool (PPUPET).

Science.gov (United States)

Sterken, David J; Mooney, JoAnn; Ropele, Diana; Kett, Alysha; Vander Laan, Karen J

2015-01-01

Hospital acquired pressure ulcers (HAPU) are serious, debilitating, and preventable complications in all inpatient populations. Despite evidence of the development of pressure ulcers in the pediatric population, minimal research has been done. Based on observations gathered during quarterly HAPU audits, bedside nursing staff recognized trends in pressure ulcer locations that were not captured using current pressure ulcer risk assessment tools. Together, bedside nurses and nursing leadership created and conducted multiple research studies to investigate the validity and reliability of the Pediatric Pressure Ulcer Prediction and Evaluation Tool (PPUPET). Copyright © 2015 Elsevier Inc. All rights reserved.

Tools for assessing fall risk in the elderly: a systematic review and meta-analysis.

Science.gov (United States)

Park, Seong-Hi

2018-01-01

The prevention of falls among the elderly is arguably one of the most important public health issues in today's aging society. The aim of this study was to assess which tools best predict the risk of falls in the elderly. Electronic searches were performed using Medline, EMBASE, the Cochrane Library, CINAHL, etc., using the following keywords: "fall risk assessment", "elderly fall screening", and "elderly mobility scale". The QUADAS-2 was applied to assess the internal validity of the diagnostic studies. Selected studies were meta-analyzed with MetaDisc 1.4. A total of 33 studies were eligible out of the 2,321 studies retrieved from selected databases. Twenty-six assessment tools for fall risk were used in the selected articles, and they tended to vary based on the setting. The fall risk assessment tools currently used for the elderly did not show sufficiently high predictive validity for differentiating high and low fall risks. The Berg Balance scale and Mobility Interaction Fall chart showed stable and high specificity, while the Downton Fall Risk Index, Hendrich II Fall Risk Model, St. Thomas's Risk Assessment Tool in Falling elderly inpatients, Timed Up and Go test, and Tinetti Balance scale showed the opposite results. We concluded that rather than a single measure, two assessment tools used together would better evaluate the characteristics of falls by the elderly that can occur due to a multitude of factors and maximize the advantages of each for predicting the occurrence of falls.

Can we predict Acute Medical readmissions using the BOOST tool? A retrospective case note review.

Science.gov (United States)

Lee, Geraldine A; Freedman, Daniel; Beddoes, Penelope; Lyness, Emily; Nixon, Imogen; Srivastava, Vivek

2016-01-01

Readmissions within 30-days of hospital discharge are a problem. The aim was to determine if the Better Outcomes for Older Adults through Safe Transitions (BOOST) risk assessment tool was applicable within the UK. Patients over 65 readmitted were identified retrospectively via a casenote review. BOOST assessment was applied with 1 point for each risk factor. 324 patients were readmitted (mean age 77 years) with a median of 7 days between discharge and readmission. The median BOOST score was 3 (IQR 2-4) with polypharmacy evident in 88% and prior hospitalisation in 70%. The tool correctly predicted 90% of readmissions using two or more risk factors and 99.1% if one risk factor was included. The BOOST assessment tool appears appropriate in predicting readmissions however further analysis is required to determine its precision.

The REDUCE metagram: a comprehensive prediction tool for determining the utility of dutasteride chemoprevention in men at risk for prostate cancer

Directory of Open Access Journals (Sweden)

Carvell eNguyen

2012-10-01

Full Text Available Introduction: 5-alpha reductase inhibitors can reduce the risk of prostate cancer but can be associated with significant side effects. A library of nomograms which predict the risk of clinical endpoints relevant to dutasteride treatment may help determine if chemoprevention is suited to the individual patient. Methods: Data from the REDUCE trial was used to identify predictive factors for nine endpoints relevant to dutasteride treatment. Using the treatment and placebo groups from the biopsy cohort, Cox proportional hazards and competing risks regression models were used to build 18 nomograms, whose predictive ability was measured by concordance index and calibration plots. Results: A total of 18 nomograms assessing the risks of cancer, high-grade cancer, high grade prostatic intraepithelial neoplasia (HGPIN, atypical small acinar proliferation (ASAP, erectile dysfunction (ED, acute urinary retention (AUR, gynecomastia, urinary tract infection (UTI and BPH-related surgery either on or off dutasteride were created. The nomograms for cancer, high grade cancer, ED, AUR, and BPH-related surgery demonstrated good discrimination and calibration while those for gynecomastia, UTI, HGPIN, and ASAP predicted no better than random chance. Conclusions: To aid patients in determining whether the benefits of dutasteride use outweigh the risks, we have developed a comprehensive metagram that can generate individualized risks of 9 outcomes relevant to men considering chemoprevention. Better models based on more predictive markers are needed for some of the endpoints but the current metagram demonstrates potential as a tool for patient counseling and decision making that is accessible, intuitive, and clinically relevant.

The REDUCE metagram: a comprehensive prediction tool for determining the utility of dutasteride chemoprevention in men at risk for prostate cancer

Energy Technology Data Exchange (ETDEWEB)

Nguyen, Carvell T.; Isariyawongse, Brandon [Department of Urology, Glickman Urological and Kidney Institute, Cleveland Clinic Foundation, Cleveland, OH (United States); Yu, Changhong; Kattan, Michael W., E-mail: kattanm@ccf.org [Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, OH (United States)

2012-10-11

Introduction: 5-alpha reductase inhibitors can reduce the risk of prostate cancer (PCa) but can be associated with significant side effects. A library of nomograms which predict the risk of clinical endpoints relevant to dutasteride treatment may help determine if chemoprevention is suited to the individual patient. Methods: Data from the REDUCE trial was used to identify predictive factors for 9 endpoints relevant to dutasteride treatment. Using the treatment and placebo groups from the biopsy cohort, Cox proportional hazards (PH) and competing risks regression (CRR) models were used to build 18 nomograms, whose predictive ability was measured by concordance index (CI) and calibration plots. Results: A total of 18 nomograms assessing the risks of cancer, high grade cancer, high grade prostatic intraepithelial neoplasia (HGPIN), atypical small acinar proliferation (ASAP), erectile dysfunction (ED), acute urinary retention (AUR), gynecomastia, urinary tract infection (UTI) and BPH-related surgery either on or off dutasteride were created. The nomograms for cancer, high grade cancer, ED, AUR, and BPH-related surgery demonstrated good discrimination and calibration while those for gynecomastia, UTI, HGPIN, and ASAP predicted no better than random chance. Conclusions: To aid patients in determining whether the benefits of dutasteride use outweigh the risks, we have developed a comprehensive metagram that can generate individualized risks of 9 outcomes relevant to men considering chemoprevention. Better models based on more predictive markers are needed for some of the endpoints but the current metagram demonstrates potential as a tool for patient counseling and decision-making that is accessible, intuitive, and clinically relevant.

The REDUCE metagram: a comprehensive prediction tool for determining the utility of dutasteride chemoprevention in men at risk for prostate cancer

International Nuclear Information System (INIS)

Nguyen, Carvell T.; Isariyawongse, Brandon; Yu, Changhong; Kattan, Michael W.

2012-01-01

Introduction: 5-alpha reductase inhibitors can reduce the risk of prostate cancer (PCa) but can be associated with significant side effects. A library of nomograms which predict the risk of clinical endpoints relevant to dutasteride treatment may help determine if chemoprevention is suited to the individual patient. Methods: Data from the REDUCE trial was used to identify predictive factors for 9 endpoints relevant to dutasteride treatment. Using the treatment and placebo groups from the biopsy cohort, Cox proportional hazards (PH) and competing risks regression (CRR) models were used to build 18 nomograms, whose predictive ability was measured by concordance index (CI) and calibration plots. Results: A total of 18 nomograms assessing the risks of cancer, high grade cancer, high grade prostatic intraepithelial neoplasia (HGPIN), atypical small acinar proliferation (ASAP), erectile dysfunction (ED), acute urinary retention (AUR), gynecomastia, urinary tract infection (UTI) and BPH-related surgery either on or off dutasteride were created. The nomograms for cancer, high grade cancer, ED, AUR, and BPH-related surgery demonstrated good discrimination and calibration while those for gynecomastia, UTI, HGPIN, and ASAP predicted no better than random chance. Conclusions: To aid patients in determining whether the benefits of dutasteride use outweigh the risks, we have developed a comprehensive metagram that can generate individualized risks of 9 outcomes relevant to men considering chemoprevention. Better models based on more predictive markers are needed for some of the endpoints but the current metagram demonstrates potential as a tool for patient counseling and decision-making that is accessible, intuitive, and clinically relevant.

Nutritional Risk Screening 2002, Short Nutritional Assessment Questionnaire, Malnutrition Screening Tool, and Malnutrition Universal Screening Tool Are Good Predictors of Nutrition Risk in an Emergency Service.

Science.gov (United States)

Rabito, Estela Iraci; Marcadenti, Aline; da Silva Fink, Jaqueline; Figueira, Luciane; Silva, Flávia Moraes

2017-08-01

There is an international consensus that nutrition screening be performed at the hospital; however, there is no "best tool" for screening of malnutrition risk in hospitalized patients. To evaluate (1) the accuracy of the MUST (Malnutrition Universal Screening Tool), MST (Malnutrition Screening Tool), and SNAQ (Short Nutritional Assessment Questionnaire) in comparison with the NRS-2002 (Nutritional Risk Screening 2002) to identify patients at risk of malnutrition and (2) the ability of these nutrition screening tools to predict morbidity and mortality. A specific questionnaire was administered to complete the 4 screening tools. Outcomes measures included length of hospital stay, transfer to the intensive care unit, presence of infection, and incidence of death. A total of 752 patients were included. The nutrition risk was 29.3%, 37.1%, 33.6%, and 31.3% according to the NRS-2002, MUST, MST, and SNAQ, respectively. All screening tools showed satisfactory performance to identify patients at nutrition risk (area under the receiver operating characteristic curve between 0.765-0.808). Patients at nutrition risk showed higher risk of very long length of hospital stay as compared with those not at nutrition risk, independent of the tool applied (relative risk, 1.35-1.78). Increased risk of mortality (2.34 times) was detected by the MUST. The MUST, MST, and SNAQ share similar accuracy to the NRS-2002 in identifying risk of malnutrition, and all instruments were positively associated with very long hospital stay. In clinical practice, the 4 tools could be applied, and the choice for one of them should be made per the particularities of the service.

Risk prediction of hepatotoxicity in paracetamol poisoning.

Science.gov (United States)

Wong, Anselm; Graudins, Andis

2017-09-01

Paracetamol (acetaminophen) poisoning is the most common cause of acute liver failure in the developed world. A paracetamol treatment nomogram has been used for over four decades to help determine whether patients will develop hepatotoxicity without acetylcysteine treatment, and thus indicates those needing treatment. Despite this, a small proportion of patients still develop hepatotoxicity. More accurate risk predictors would be useful to increase the early detection of patients with the potential to develop hepatotoxicity despite acetylcysteine treatment. Similarly, there would be benefit in early identification of those with a low likelihood of developing hepatotoxicity, as this group may be safely treated with an abbreviated acetylcysteine regimen. To review the current literature related to risk prediction tools that can be used to identify patients at increased risk of hepatotoxicity. A systematic literature review was conducted using the search terms: "paracetamol" OR "acetaminophen" AND "overdose" OR "toxicity" OR "risk prediction rules" OR "hepatotoxicity" OR "psi parameter" OR "multiplication product" OR "half-life" OR "prothrombin time" OR "AST/ALT (aspartate transaminase/alanine transaminase)" OR "dose" OR "biomarkers" OR "nomogram". The search was limited to human studies without language restrictions, of Medline (1946 to May 2016), PubMed and EMBASE. Original articles pertaining to the theme were identified from January 1974 to May 2016. Of the 13,975 articles identified, 60 were relevant to the review. Paracetamol treatment nomograms: Paracetamol treatment nomograms have been used for decades to help decide the need for acetylcysteine, but rarely used to determine the risk of hepatotoxicity with treatment. Reported paracetamol dose and concentration: A dose ingestion >12 g or serum paracetamol concentration above the treatment thresholds on the paracetamol nomogram are associated with a greater risk of hepatotoxicity. Paracetamol elimination half

Risk terrain modeling predicts child maltreatment.

Science.gov (United States)

Daley, Dyann; Bachmann, Michael; Bachmann, Brittany A; Pedigo, Christian; Bui, Minh-Thuy; Coffman, Jamye

2016-12-01

As indicated by research on the long-term effects of adverse childhood experiences (ACEs), maltreatment has far-reaching consequences for affected children. Effective prevention measures have been elusive, partly due to difficulty in identifying vulnerable children before they are harmed. This study employs Risk Terrain Modeling (RTM), an analysis of the cumulative effect of environmental factors thought to be conducive for child maltreatment, to create a highly accurate prediction model for future substantiated child maltreatment cases in the City of Fort Worth, Texas. The model is superior to commonly used hotspot predictions and more beneficial in aiding prevention efforts in a number of ways: 1) it identifies the highest risk areas for future instances of child maltreatment with improved precision and accuracy; 2) it aids the prioritization of risk-mitigating efforts by informing about the relative importance of the most significant contributing risk factors; 3) since predictions are modeled as a function of easily obtainable data, practitioners do not have to undergo the difficult process of obtaining official child maltreatment data to apply it; 4) the inclusion of a multitude of environmental risk factors creates a more robust model with higher predictive validity; and, 5) the model does not rely on a retrospective examination of past instances of child maltreatment, but adapts predictions to changing environmental conditions. The present study introduces and examines the predictive power of this new tool to aid prevention efforts seeking to improve the safety, health, and wellbeing of vulnerable children. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Risk assessment tools to identify women with increased risk of osteoporotic fracture. Complexity or simplicity?

DEFF Research Database (Denmark)

Rubin, Katrine Hass; Friis-Holmberg, Teresa; Hermann, Anne Pernille

2013-01-01

A huge number of risk assessment tools have been developed. Far from all have been validated in external studies, more of them have absence of methodological and transparent evidence and few are integrated in national guidelines. Therefore, we performed a systematic review to provide an overview...... of existing valid and reliable risk assessment tools for prediction of osteoporotic fractures. Additionally, we aimed to determine if the performance each tool was sufficient for practical use and lastly to examine whether the complexity of the tools influenced their discriminative power. We searched Pub......Med, Embase and Cochrane databases for papers and evaluated these with respect to methodological quality using the QUADAS checklist. A total of 48 tools were identified, 20 had been externally validated, however only 6 tools had been tested more than once in a population-based setting with acceptable...

About Using Predictive Models and Tools To Assess Chemicals under TSCA

Science.gov (United States)

As part of EPA's effort to promote chemical safety, OPPT provides public access to predictive models and tools which can help inform the public on the hazards and risks of substances and improve chemical management decisions.

Mechanisms, Prediction, and Prevention of ACL Injuries: Cut Risk With Three Sharpened and Validated Tools

Science.gov (United States)

Hewett, Timothy E.; Myer, Gregory D.; Ford, Kevin R.; Paterno, Mark V.; Quatman, Carmen E.

2017-01-01

Economic and societal pressures influence modern medical practice to develop and implement prevention strategies. Anterior cruciate ligament (ACL) injury devastates the knee joint leading to short term disability and long term sequelae. Due to the high risk of long term osteoarthritis in all treatment populations following ACL injury, prevention is the only effective intervention for this life-altering disruption in knee health. The “Sequence of Prevention” Model provides a framework to monitor progress towards the ultimate goal of preventing ACL injuries. Utilizing this model, our multidisciplinary collaborative research team has spent the last decade working to delineate injury mechanisms, identify injury risk factors, predict which athletes are at-risk for injury, and develop ACL injury prevention programs. Within this model of injury prevention, modifiable factors (biomechanical and neuromuscular) related to injury mechanisms likely provide the best opportunity for intervention strategies aimed to decrease the risk of ACL injury, particularly in female athletes. Knowledge advancements have led to the development of potential solutions that allow athletes to compete with lowered risk of ACL injury. Design and integration of personalized clinical assessment tools and targeted prevention strategies for athletes at high risk for ACL injury may transform current prevention practices and ultimately significantly reduce ACL injury incidence. This 2016 OREF Clinical Research Award focuses on the authors' work and contributions to the field. The author's acknowledge the many research groups who have contributed to the current state of knowledge in the fields of ACL injury mechanisms, injury risk screening and injury prevention strategies. PMID:27612195

Nutritional Risk in Emergency-2017: A New Simplified Proposal for a Nutrition Screening Tool.

Science.gov (United States)

Marcadenti, Aline; Mendes, Larissa Loures; Rabito, Estela Iraci; Fink, Jaqueline da Silva; Silva, Flávia Moraes

2018-03-13

There are many nutrition screening tools currently being applied in hospitals to identify risk of malnutrition. However, multivariate statistical models are not usually employed to take into account the importance of each variable included in the instrument's development. To develop and evaluate the concurrent and predictive validities of a new screening tool of nutrition risk. A prospective cohort study was developed, in which 4 nutrition screening tools were applied to all patients. Length of stay in hospital and mortality were considered to test the predictive validity, and the concurrent validity was tested by comparing the Nuritional Risk in Emergency (NRE)-2017 to the other tools. A total of 748 patients were included. The final NRE-2017 score was composed of 6 questions (advanced age, metabolic stress of the disease, decreased appetite, changing of food consistency, unintentional weight loss, and muscle mass loss) with answers yes or no. The prevalence of nutrition risk was 50.7% and 38.8% considering the cutoff points 1.0 and 1.5, respectively. The NRE-2017 showed a satisfactory power to indentify risk of malnutrition (area under the curve >0.790 for all analyses). According to the NRE-2017, patients at risk of malnutrition have twice as high relative risk of a very long hospital stay. The hazard ratio for mortality was 2.78 (1.03-7.49) when the cutoff adopted by the NRE-2017 was 1.5 points. NRE-2017 is a new, easy-to-apply nutrition screening tool which uses 6 bi-categoric features to detect the risk of malnutrition, and it presented a good concurrent and predictive validity. © 2018 American Society for Parenteral and Enteral Nutrition.

Validation and inter-rater reliability of a three item falls risk screening tool

Directory of Open Access Journals (Sweden)

Catherine Maree Said

2017-11-01

Full Text Available Abstract Background Falls screening tools are routinely used in hospital settings and the psychometric properties of tools should be examined in the setting in which they are used. The aim of this study was to explore the concurrent and predictive validity of the Austin Health Falls Risk Screening Tool (AHFRST, compared with The Northern Hospital Modified St Thomas’s Risk Assessment Tool (TNH-STRATIFY, and the inter-rater reliability of the AHFRST. Methods A research physiotherapist used the AHFRST and TNH-STRATIFY to classify 130 participants admitted to Austin Health (five acute wards, n = 115 two subacute wards n = 15; median length of stay 6 days IQR 3–12 as ‘High’ or ‘Low’ falls risk. The AHFRST was also completed by nursing staff on patient admission. Falls data was collected from the hospital incident reporting system. Results Six falls occurred during the study period (fall rate of 4.6 falls per 1000 bed days. There was substantial agreement between the AHFRST and the TNH-STRATIFY (Kappa = 0.68, 95% CI 0.52–0.78. Both tools had poor predictive validity, with low specificity (AHFRST 46.0%, 95% CI 37.0–55.1; TNH-STRATIFY 34.7%, 95% CI 26.4–43.7 and positive predictive values (AHFRST 5.6%, 95% CI 1.6–13.8; TNH-STRATIFY 6.9%, 95% CI 2.6–14.4. The AHFRST showed moderate inter-rater reliability (Kappa = 0.54, 95% CI = 0.36–0.67, p < 0.001 although 18 patients did not have the AHFRST completed by nursing staff. Conclusions There was an acceptable level of agreement between the 3 item AHFRST classification of falls risk and the longer, 9 item TNH-STRATIFY classification. However, both tools demonstrated limited predictive validity in the Austin Health population. The results highlight the importance of evaluating the validity of falls screening tools, and the clinical utility of these tools should be reconsidered.

Early Antenatal Prediction of Gestational Diabetes in Obese Women: Development of Prediction Tools for Targeted Intervention.

Directory of Open Access Journals (Sweden)

Sara L White

Full Text Available All obese women are categorised as being of equally high risk of gestational diabetes (GDM whereas the majority do not develop the disorder. Lifestyle and pharmacological interventions in unselected obese pregnant women have been unsuccessful in preventing GDM. Our aim was to develop a prediction tool for early identification of obese women at high risk of GDM to facilitate targeted interventions in those most likely to benefit. Clinical and anthropometric data and non-fasting blood samples were obtained at 15+0-18+6 weeks' gestation in 1303 obese pregnant women from UPBEAT, a randomised controlled trial of a behavioural intervention. Twenty one candidate biomarkers associated with insulin resistance, and a targeted nuclear magnetic resonance (NMR metabolome were measured. Prediction models were constructed using stepwise logistic regression. Twenty six percent of women (n = 337 developed GDM (International Association of Diabetes and Pregnancy Study Groups criteria. A model based on clinical and anthropometric variables (age, previous GDM, family history of type 2 diabetes, systolic blood pressure, sum of skinfold thicknesses, waist:height and neck:thigh ratios provided an area under the curve of 0.71 (95%CI 0.68-0.74. This increased to 0.77 (95%CI 0.73-0.80 with addition of candidate biomarkers (random glucose, haemoglobin A1c (HbA1c, fructosamine, adiponectin, sex hormone binding globulin, triglycerides, but was not improved by addition of NMR metabolites (0.77; 95%CI 0.74-0.81. Clinically translatable models for GDM prediction including readily measurable variables e.g. mid-arm circumference, age, systolic blood pressure, HbA1c and adiponectin are described. Using a ≥35% risk threshold, all models identified a group of high risk obese women of whom approximately 50% (positive predictive value later developed GDM, with a negative predictive value of 80%. Tools for early pregnancy identification of obese women at risk of GDM are described

Youth Actuarial Risk Assessment Tool (Y-ARAT): The development of an actuarial risk assessment instrument for predicting general offense recidivism on the basis of police records.

Science.gov (United States)

van der Put, Claudia E

2014-06-01

Estimating the risk for recidivism is important for many areas of the criminal justice system. In the present study, the Youth Actuarial Risk Assessment Tool (Y-ARAT) was developed for juvenile offenders based solely on police records, with the aim to estimate the risk of general recidivism among large groups of juvenile offenders by police officers without clinical expertise. On the basis of the Y-ARAT, juvenile offenders are classified into five risk groups based on (combinations of) 10 variables including different types of incidents in which the juvenile was a suspect, total number of incidents in which the juvenile was a suspect, total number of other incidents, total number of incidents in which co-occupants at the youth's address were suspects, gender, and age at first incident. The Y-ARAT was developed on a sample of 2,501 juvenile offenders and validated on another sample of 2,499 juvenile offenders, showing moderate predictive accuracy (area under the receiver-operating-characteristic curve = .73), with little variation between the construction and validation sample. The predictive accuracy of the Y-ARAT was considered sufficient to justify its use as a screening instrument for the police. © The Author(s) 2013.

New risk metrics and mathematical tools for risk analysis: Current and future challenges

International Nuclear Information System (INIS)

Skandamis, Panagiotis N.; Andritsos, Nikolaos; Psomas, Antonios; Paramythiotis, Spyridon

2015-01-01

The current status of the food safety supply world wide, has led Food and Agriculture Organization (FAO) and World Health Organization (WHO) to establishing Risk Analysis as the single framework for building food safety control programs. A series of guidelines and reports that detail out the various steps in Risk Analysis, namely Risk Management, Risk Assessment and Risk Communication is available. The Risk Analysis approach enables integration between operational food management systems, such as Hazard Analysis Critical Control Points, public health and governmental decisions. To do that, a series of new Risk Metrics has been established as follows: i) the Appropriate Level of Protection (ALOP), which indicates the maximum numbers of illnesses in a population per annum, defined by quantitative risk assessments, and used to establish; ii) Food Safety Objective (FSO), which sets the maximum frequency and/or concentration of a hazard in a food at the time of consumption that provides or contributes to the ALOP. Given that ALOP is rather a metric of the public health tolerable burden (it addresses the total ‘failure’ that may be handled at a national level), it is difficult to be interpreted into control measures applied at the manufacturing level. Thus, a series of specific objectives and criteria for performance of individual processes and products have been established, all of them assisting in the achievement of FSO and hence, ALOP. In order to achieve FSO, tools quantifying the effect of processes and intrinsic properties of foods on survival and growth of pathogens are essential. In this context, predictive microbiology and risk assessment have offered an important assistance to Food Safety Management. Predictive modelling is the basis of exposure assessment and the development of stochastic and kinetic models, which are also available in the form of Web-based applications, e.g., COMBASE and Microbial Responses Viewer), or introduced into user

New risk metrics and mathematical tools for risk analysis: Current and future challenges

Energy Technology Data Exchange (ETDEWEB)

Skandamis, Panagiotis N., E-mail: pskan@aua.gr; Andritsos, Nikolaos, E-mail: pskan@aua.gr; Psomas, Antonios, E-mail: pskan@aua.gr; Paramythiotis, Spyridon, E-mail: pskan@aua.gr [Laboratory of Food Quality Control and Hygiene, Department of Food Science and Technology, Agricultural University of Athens, Iera Odos 75, 118 55, Athens (Greece)

2015-01-22

The current status of the food safety supply world wide, has led Food and Agriculture Organization (FAO) and World Health Organization (WHO) to establishing Risk Analysis as the single framework for building food safety control programs. A series of guidelines and reports that detail out the various steps in Risk Analysis, namely Risk Management, Risk Assessment and Risk Communication is available. The Risk Analysis approach enables integration between operational food management systems, such as Hazard Analysis Critical Control Points, public health and governmental decisions. To do that, a series of new Risk Metrics has been established as follows: i) the Appropriate Level of Protection (ALOP), which indicates the maximum numbers of illnesses in a population per annum, defined by quantitative risk assessments, and used to establish; ii) Food Safety Objective (FSO), which sets the maximum frequency and/or concentration of a hazard in a food at the time of consumption that provides or contributes to the ALOP. Given that ALOP is rather a metric of the public health tolerable burden (it addresses the total ‘failure’ that may be handled at a national level), it is difficult to be interpreted into control measures applied at the manufacturing level. Thus, a series of specific objectives and criteria for performance of individual processes and products have been established, all of them assisting in the achievement of FSO and hence, ALOP. In order to achieve FSO, tools quantifying the effect of processes and intrinsic properties of foods on survival and growth of pathogens are essential. In this context, predictive microbiology and risk assessment have offered an important assistance to Food Safety Management. Predictive modelling is the basis of exposure assessment and the development of stochastic and kinetic models, which are also available in the form of Web-based applications, e.g., COMBASE and Microbial Responses Viewer), or introduced into user

New risk metrics and mathematical tools for risk analysis: Current and future challenges

Science.gov (United States)

Skandamis, Panagiotis N.; Andritsos, Nikolaos; Psomas, Antonios; Paramythiotis, Spyridon

2015-01-01

The current status of the food safety supply world wide, has led Food and Agriculture Organization (FAO) and World Health Organization (WHO) to establishing Risk Analysis as the single framework for building food safety control programs. A series of guidelines and reports that detail out the various steps in Risk Analysis, namely Risk Management, Risk Assessment and Risk Communication is available. The Risk Analysis approach enables integration between operational food management systems, such as Hazard Analysis Critical Control Points, public health and governmental decisions. To do that, a series of new Risk Metrics has been established as follows: i) the Appropriate Level of Protection (ALOP), which indicates the maximum numbers of illnesses in a population per annum, defined by quantitative risk assessments, and used to establish; ii) Food Safety Objective (FSO), which sets the maximum frequency and/or concentration of a hazard in a food at the time of consumption that provides or contributes to the ALOP. Given that ALOP is rather a metric of the public health tolerable burden (it addresses the total `failure' that may be handled at a national level), it is difficult to be interpreted into control measures applied at the manufacturing level. Thus, a series of specific objectives and criteria for performance of individual processes and products have been established, all of them assisting in the achievement of FSO and hence, ALOP. In order to achieve FSO, tools quantifying the effect of processes and intrinsic properties of foods on survival and growth of pathogens are essential. In this context, predictive microbiology and risk assessment have offered an important assistance to Food Safety Management. Predictive modelling is the basis of exposure assessment and the development of stochastic and kinetic models, which are also available in the form of Web-based applications, e.g., COMBASE and Microbial Responses Viewer), or introduced into user-friendly softwares

Tools for Microbiological risk assessment

DEFF Research Database (Denmark)

Bassett, john; Nauta, Maarten; Lindqvist, Roland

can increase the understanding of microbiological risks in foods. It is timely to inform food safety professionals about the availability and utility of MRA tools. Therefore, the focus of this report is to aid the food safety manager by providing a concise summary of the tools available for the MRA......Microbiological Risk Assessment (MRA) has emerged as a comprehensive and systematic approach for addressing the risk of pathogens in specific foods and/or processes. At government level, MRA is increasingly recognised as a structured and objective approach to understand the level of risk in a given...... food/pathogen scenario. Tools developed so far support qualitative and quantitative assessments of the risk that a food pathogen poses to a particular population. Risk can be expressed as absolute numbers or as relative (ranked) risks. The food industry is beginning to appreciate that the tools for MRA...

Evaluation of an inpatient fall risk screening tool to identify the most critical fall risk factors in inpatients.

Science.gov (United States)

Hou, Wen-Hsuan; Kang, Chun-Mei; Ho, Mu-Hsing; Kuo, Jessie Ming-Chuan; Chen, Hsiao-Lien; Chang, Wen-Yin

2017-03-01

To evaluate the accuracy of the inpatient fall risk screening tool and to identify the most critical fall risk factors in inpatients. Variations exist in several screening tools applied in acute care hospitals for examining risk factors for falls and identifying high-risk inpatients. Secondary data analysis. A subset of inpatient data for the period from June 2011-June 2014 was extracted from the nursing information system and adverse event reporting system of an 818-bed teaching medical centre in Taipei. Data were analysed using descriptive statistics, receiver operating characteristic curve analysis and logistic regression analysis. During the study period, 205 fallers and 37,232 nonfallers were identified. The results revealed that the inpatient fall risk screening tool (cut-off point of ≥3) had a low sensitivity level (60%), satisfactory specificity (87%), a positive predictive value of 2·0% and a negative predictive value of 99%. The receiver operating characteristic curve analysis revealed an area under the curve of 0·805 (sensitivity, 71·8%; specificity, 78%). To increase the sensitivity values, the Youden index suggests at least 1·5 points to be the most suitable cut-off point for the inpatient fall risk screening tool. Multivariate logistic regression analysis revealed a considerably increased fall risk in patients with impaired balance and impaired elimination. The fall risk factor was also significantly associated with days of hospital stay and with admission to surgical wards. The findings can raise awareness about the two most critical risk factors for falls among future clinical nurses and other healthcare professionals and thus facilitate the development of fall prevention interventions. This study highlights the needs for redefining the cut-off points of the inpatient fall risk screening tool to effectively identify inpatients at a high risk of falls. Furthermore, inpatients with impaired balance and impaired elimination should be closely

A generic rabies risk assessment tool to support surveillance.

Science.gov (United States)

Ward, Michael P; Hernández-Jover, Marta

2015-06-01

The continued spread of rabies in Indonesia poses a risk to human and animal populations in the remaining free islands, as well as the neighbouring rabies-free countries of Timor Leste, Papua New Guinea and Australia. Here we describe the development of a generic risk assessment tool which can be used to rapidly determine the vulnerability of rabies-free islands, so that scarce resources can be targeted to surveillance activities and the sensitivity of surveillance systems increased. The tool was developed by integrating information on the historical spread of rabies, anthropological studies, and the opinions of local animal health experts. The resulting tool is based on eight critical parameters that can be estimated from the literature, expert opinion, observational studies and information generated from routine surveillance. In the case study presented, results generated by this tool were most sensitive to the probability that dogs are present on private and fishing boats and it was predicted that rabies-infection (one infected case) might occur in a rabies-free island (upper 95% prediction interval) with a volume of 1000 boats movements. With 25,000 boat movements, the median of the probability distribution would be equal to one infected case, with an upper 95% prediction interval of six infected cases. This tool could also be used at the national-level to guide control and eradication plans. An initial recommendation from this study is to develop a surveillance programme to determine the likelihood that boats transport dogs, for example by port surveillance or regularly conducted surveys of fisherman and passenger ferries. However, the illegal nature of dog transportation from rabies-infected to rabies-free islands is a challenge for developing such surveillance. Copyright © 2014 Elsevier B.V. All rights reserved.

Prediction of absolute risk of fragility fracture at 10 years in a Spanish population: validation of the WHO FRAX ™ tool in Spain

Directory of Open Access Journals (Sweden)

Solà Sílvia

2011-01-01

Full Text Available Abstract Background Age-related bone loss is asymptomatic, and the morbidity of osteoporosis is secondary to the fractures that occur. Common sites of fracture include the spine, hip, forearm and proximal humerus. Fractures at the hip incur the greatest morbidity and mortality and give rise to the highest direct costs for health services. Their incidence increases exponentially with age. Independently changes in population demography, the age - and sex- specific incidence of osteoporotic fractures appears to be increasing in developing and developed countries. This could mean more than double the expected burden of osteoporotic fractures in the next 50 years. Methods/Design To assess the predictive power of the WHO FRAX™ tool to identify the subjects with the highest absolute risk of fragility fracture at 10 years in a Spanish population, a predictive validation study of the tool will be carried out. For this purpose, the participants recruited by 1999 will be assessed. These were referred to scan-DXA Department from primary healthcare centres, non hospital and hospital consultations. Study population: Patients attended in the national health services integrated into a FRIDEX cohort with at least one Dual-energy X-ray absorptiometry (DXA measurement and one extensive questionnaire related to fracture risk factors. Measurements: At baseline bone mineral density measurement using DXA, clinical fracture risk factors questionnaire, dietary calcium intake assessment, history of previous fractures, and related drugs. Follow up by telephone interview to know fragility fractures in the 10 years with verification in electronic medical records and also to know the number of falls in the last year. The absolute risk of fracture will be estimated using the FRAX™ tool from the official web site. Discussion Since more than 10 years ago numerous publications have recognised the importance of other risk factors for new osteoporotic fractures in addition to

Violence risk prediction. Clinical and actuarial measures and the role of the Psychopathy Checklist.

Science.gov (United States)

Dolan, M; Doyle, M

2000-10-01

Violence risk prediction is a priority issue for clinicians working with mentally disordered offenders. To review the current status of violence risk prediction research. Literature search (Medline). Key words: violence, risk prediction, mental disorder. Systematic/structured risk assessment approaches may enhance the accuracy of clinical prediction of violent outcomes. Data on the predictive validity of available clinical risk assessment tools are based largely on American and North American studies and further validation is required in British samples. The Psychopathy Checklist appears to be a key predictor of violent recidivism in a variety of settings. Violence risk prediction is an inexact science and as such will continue to provoke debate. Clinicians clearly need to be able to demonstrate the rationale behind their decisions on violence risk and much can be learned from recent developments in research on violence risk prediction.

The utility of absolute risk prediction using FRAX® and Garvan Fracture Risk Calculator in daily practice.

Science.gov (United States)

van Geel, Tineke A C M; Eisman, John A; Geusens, Piet P; van den Bergh, Joop P W; Center, Jacqueline R; Dinant, Geert-Jan

2014-02-01

There are two commonly used fracture risk prediction tools FRAX(®) and Garvan Fracture Risk Calculator (GARVAN-FRC). The objective of this study was to investigate the utility of these tools in daily practice. A prospective population-based 5-year follow-up study was conducted in ten general practice centres in the Netherlands. For the analyses, the FRAX(®) and GARVAN-FRC 10-year absolute risks (FRAX(®) does not have 5-year risk prediction) for all fractures were used. Among 506 postmenopausal women aged ≥60 years (mean age: 67.8±5.8 years), 48 (9.5%) sustained a fracture during follow-up. Both tools, using BMD values, distinguish between women who did and did not fracture (10.2% vs. 6.8%, respectively for FRAX(®) and 32.4% vs. 39.1%, respectively for GARVAN-FRC, pbetter for women who sustained a fracture (higher sensitivity) and FRAX(®) for women who did not sustain a fracture (higher specificity). Similar results were obtained using age related cut off points. The discriminant value of both models is at least as good as models used in other medical conditions; hence they can be used to communicate the fracture risk to patients. However, given differences in the estimated risks between FRAX(®) and GARVAN-FRC, the significance of the absolute risk must be related to country-specific recommended intervention thresholds to inform the patient. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Mini-Nutritional Assessment, Malnutrition Universal Screening Tool, and Nutrition Risk Screening Tool for the Nutritional Evaluation of Older Nursing Home Residents.

Science.gov (United States)

Donini, Lorenzo M; Poggiogalle, Eleonora; Molfino, Alessio; Rosano, Aldo; Lenzi, Andrea; Rossi Fanelli, Filippo; Muscaritoli, Maurizio

2016-10-01

Malnutrition plays a major role in clinical and functional impairment in older adults. The use of validated, user-friendly and rapid screening tools for malnutrition in the elderly may improve the diagnosis and, possibly, the prognosis. The aim of this study was to assess the agreement between Mini-Nutritional Assessment (MNA), considered as a reference tool, MNA short form (MNA-SF), Malnutrition Universal Screening Tool (MUST), and Nutrition Risk Screening (NRS-2002) in elderly institutionalized participants. Participants were enrolled among nursing home residents and underwent a multidimensional evaluation. Predictive value and survival analysis were performed to compare the nutritional classifications obtained from the different tools. A total of 246 participants (164 women, age: 82.3 ± 9 years, and 82 men, age: 76.5 ± 11 years) were enrolled. Based on MNA, 22.6% of females and 17% of males were classified as malnourished; 56.7% of women and 61% of men were at risk of malnutrition. Agreement between MNA and MUST or NRS-2002 was classified as "fair" (k = 0.270 and 0.291, respectively; P < .001), whereas the agreement between MNA and MNA-SF was classified as "moderate" (k = 0.588; P < .001). Because of the high percentage of false negative participants, MUST and NRS-2002 presented a low overall predictive value compared with MNA and MNA-SF. Clinical parameters were significantly different in false negative participants with MUST or NRS-2002 from true negative and true positive individuals using the reference tool. For all screening tools, there was a significant association between malnutrition and mortality. MNA showed the best predictive value for survival among well-nourished participants. Functional, psychological, and cognitive parameters, not considered in MUST and NRS-2002 tools, are probably more important risk factors for malnutrition than acute illness in geriatric long-term care inpatient settings and may account for the low predictive

Testing the Predictive Validity of the Hendrich II Fall Risk Model.

Science.gov (United States)

Jung, Hyesil; Park, Hyeoun-Ae

2018-03-01

Cumulative data on patient fall risk have been compiled in electronic medical records systems, and it is possible to test the validity of fall-risk assessment tools using these data between the times of admission and occurrence of a fall. The Hendrich II Fall Risk Model scores assessed during three time points of hospital stays were extracted and used for testing the predictive validity: (a) upon admission, (b) when the maximum fall-risk score from admission to falling or discharge, and (c) immediately before falling or discharge. Predictive validity was examined using seven predictive indicators. In addition, logistic regression analysis was used to identify factors that significantly affect the occurrence of a fall. Among the different time points, the maximum fall-risk score assessed between admission and falling or discharge showed the best predictive performance. Confusion or disorientation and having a poor ability to rise from a sitting position were significant risk factors for a fall.

Laboratory-based and office-based risk scores and charts to predict 10-year risk of cardiovascular disease in 182 countries

DEFF Research Database (Denmark)

Ueda, Peter; Woodward, Mark; Lu, Yuan

2017-01-01

BACKGROUND: Worldwide implementation of risk-based cardiovascular disease (CVD) prevention requires risk prediction tools that are contemporarily recalibrated for the target country and can be used where laboratory measurements are unavailable. We present two cardiovascular risk scores, with and ...

Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder

DEFF Research Database (Denmark)

Maier, Robert; Moser, Gerhard; Chen, Guo-Bo

2015-01-01

Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk...... number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low...

Fall Risk Assessment Tools for Elderly Living in the Community: Can We Do Better?

Science.gov (United States)

Palumbo, Pierpaolo; Palmerini, Luca; Bandinelli, Stefania; Chiari, Lorenzo

2015-01-01

Falls are a common, serious threat to the health and self-confidence of the elderly. Assessment of fall risk is an important aspect of effective fall prevention programs. In order to test whether it is possible to outperform current prognostic tools for falls, we analyzed 1010 variables pertaining to mobility collected from 976 elderly subjects (InCHIANTI study). We trained and validated a data-driven model that issues probabilistic predictions about future falls. We benchmarked the model against other fall risk indicators: history of falls, gait speed, Short Physical Performance Battery (Guralnik et al. 1994), and the literature-based fall risk assessment tool FRAT-up (Cattelani et al. 2015). Parsimony in the number of variables included in a tool is often considered a proxy for ease of administration. We studied how constraints on the number of variables affect predictive accuracy. The proposed model and FRAT-up both attained the same discriminative ability; the area under the Receiver Operating Characteristic (ROC) curve (AUC) for multiple falls was 0.71. They outperformed the other risk scores, which reported AUCs for multiple falls between 0.64 and 0.65. Thus, it appears that both data-driven and literature-based approaches are better at estimating fall risk than commonly used fall risk indicators. The accuracy-parsimony analysis revealed that tools with a small number of predictors (~1-5) were suboptimal. Increasing the number of variables improved the predictive accuracy, reaching a plateau at ~20-30, which we can consider as the best trade-off between accuracy and parsimony. Obtaining the values of these ~20-30 variables does not compromise usability, since they are usually available in comprehensive geriatric assessments.

Fall Risk Assessment Tools for Elderly Living in the Community: Can We Do Better?

Directory of Open Access Journals (Sweden)

Pierpaolo Palumbo

Full Text Available Falls are a common, serious threat to the health and self-confidence of the elderly. Assessment of fall risk is an important aspect of effective fall prevention programs.In order to test whether it is possible to outperform current prognostic tools for falls, we analyzed 1010 variables pertaining to mobility collected from 976 elderly subjects (InCHIANTI study. We trained and validated a data-driven model that issues probabilistic predictions about future falls. We benchmarked the model against other fall risk indicators: history of falls, gait speed, Short Physical Performance Battery (Guralnik et al. 1994, and the literature-based fall risk assessment tool FRAT-up (Cattelani et al. 2015. Parsimony in the number of variables included in a tool is often considered a proxy for ease of administration. We studied how constraints on the number of variables affect predictive accuracy.The proposed model and FRAT-up both attained the same discriminative ability; the area under the Receiver Operating Characteristic (ROC curve (AUC for multiple falls was 0.71. They outperformed the other risk scores, which reported AUCs for multiple falls between 0.64 and 0.65. Thus, it appears that both data-driven and literature-based approaches are better at estimating fall risk than commonly used fall risk indicators. The accuracy-parsimony analysis revealed that tools with a small number of predictors (~1-5 were suboptimal. Increasing the number of variables improved the predictive accuracy, reaching a plateau at ~20-30, which we can consider as the best trade-off between accuracy and parsimony. Obtaining the values of these ~20-30 variables does not compromise usability, since they are usually available in comprehensive geriatric assessments.

RNA-SSPT: RNA Secondary Structure Prediction Tools.

Science.gov (United States)

Ahmad, Freed; Mahboob, Shahid; Gulzar, Tahsin; Din, Salah U; Hanif, Tanzeela; Ahmad, Hifza; Afzal, Muhammad

2013-01-01

The prediction of RNA structure is useful for understanding evolution for both in silico and in vitro studies. Physical methods like NMR studies to predict RNA secondary structure are expensive and difficult. Computational RNA secondary structure prediction is easier. Comparative sequence analysis provides the best solution. But secondary structure prediction of a single RNA sequence is challenging. RNA-SSPT is a tool that computationally predicts secondary structure of a single RNA sequence. Most of the RNA secondary structure prediction tools do not allow pseudoknots in the structure or are unable to locate them. Nussinov dynamic programming algorithm has been implemented in RNA-SSPT. The current studies shows only energetically most favorable secondary structure is required and the algorithm modification is also available that produces base pairs to lower the total free energy of the secondary structure. For visualization of RNA secondary structure, NAVIEW in C language is used and modified in C# for tool requirement. RNA-SSPT is built in C# using Dot Net 2.0 in Microsoft Visual Studio 2005 Professional edition. The accuracy of RNA-SSPT is tested in terms of Sensitivity and Positive Predicted Value. It is a tool which serves both secondary structure prediction and secondary structure visualization purposes.

Primary Sclerosing Cholangitis Risk Estimate Tool (PREsTo) Predicts Outcomes in PSC: A Derivation & Validation Study Using Machine Learning.

Science.gov (United States)

Eaton, John E; Vesterhus, Mette; McCauley, Bryan M; Atkinson, Elizabeth J; Schlicht, Erik M; Juran, Brian D; Gossard, Andrea A; LaRusso, Nicholas F; Gores, Gregory J; Karlsen, Tom H; Lazaridis, Konstantinos N

2018-05-09

Improved methods are needed to risk stratify and predict outcomes in patients with primary sclerosing cholangitis (PSC). Therefore, we sought to derive and validate a new prediction model and compare its performance to existing surrogate markers. The model was derived using 509 subjects from a multicenter North American cohort and validated in an international multicenter cohort (n=278). Gradient boosting, a machine based learning technique, was used to create the model. The endpoint was hepatic decompensation (ascites, variceal hemorrhage or encephalopathy). Subjects with advanced PSC or cholangiocarcinoma at baseline were excluded. The PSC risk estimate tool (PREsTo) consists of 9 variables: bilirubin, albumin, serum alkaline phosphatase (SAP) times the upper limit of normal (ULN), platelets, AST, hemoglobin, sodium, patient age and the number of years since PSC was diagnosed. Validation in an independent cohort confirms PREsTo accurately predicts decompensation (C statistic 0.90, 95% confidence interval (CI) 0.84-0.95) and performed well compared to MELD score (C statistic 0.72, 95% CI 0.57-0.84), Mayo PSC risk score (C statistic 0.85, 95% CI 0.77-0.92) and SAP statistic 0.65, 95% CI 0.55-0.73). PREsTo continued to be accurate among individuals with a bilirubin statistic 0.90, 95% CI 0.82-0.96) and when the score was re-applied at a later course in the disease (C statistic 0.82, 95% CI 0.64-0.95). PREsTo accurately predicts hepatic decompensation in PSC and exceeds the performance among other widely available, noninvasive prognostic scoring systems. This article is protected by copyright. All rights reserved. © 2018 by the American Association for the Study of Liver Diseases.

Dementia Population Risk Tool (DemPoRT): study protocol for a predictive algorithm assessing dementia risk in the community.

Science.gov (United States)

Fisher, Stacey; Hsu, Amy; Mojaverian, Nassim; Taljaard, Monica; Huyer, Gregory; Manuel, Douglas G; Tanuseputro, Peter

2017-10-24

The burden of disease from dementia is a growing global concern as incidence increases dramatically with age, and average life expectancy has been increasing around the world. Planning for an ageing population requires reliable projections of dementia prevalence; however, existing population projections are simple and have poor predictive accuracy. The Dementia Population Risk Tool (DemPoRT) will predict incidence of dementia in the population setting using multivariable modelling techniques and will be used to project dementia prevalence. The derivation cohort will consist of elderly Ontario respondents of the Canadian Community Health Survey (CCHS) (2001, 2003, 2005 and 2007; 18 764 males and 25 288 females). Prespecified predictors include sociodemographic, general health, behavioural, functional and health condition variables. Incident dementia will be identified through individual linkage of survey respondents to population-level administrative healthcare databases (1797 and 3281 events, and 117 795 and 166 573 person-years of follow-up, for males and females, respectively, until 31 March 2014). Using time of first dementia capture as the primary outcome and death as a competing risk, sex-specific proportional hazards regression models will be estimated. The 2008/2009 CCHS survey will be used for validation (approximately 4600 males and 6300 females). Overall calibration and discrimination will be assessed as well as calibration within predefined subgroups of importance to clinicians and policy makers. Research ethics approval has been granted by the Ottawa Health Science Network Research Ethics Board. DemPoRT results will be submitted for publication in peer-review journals and presented at scientific meetings. The algorithm will be assessable online for both population and individual uses. ClinicalTrials.gov NCT03155815, pre-results. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No

Identification of high-risk cutaneous melanoma tumors is improved when combining the online American Joint Committee on Cancer Individualized Melanoma Patient Outcome Prediction Tool with a 31-gene expression profile-based classification.

Science.gov (United States)

Ferris, Laura K; Farberg, Aaron S; Middlebrook, Brooke; Johnson, Clare E; Lassen, Natalie; Oelschlager, Kristen M; Maetzold, Derek J; Cook, Robert W; Rigel, Darrell S; Gerami, Pedram

2017-05-01

A significant proportion of patients with American Joint Committee on Cancer (AJCC)-defined early-stage cutaneous melanoma have disease recurrence and die. A 31-gene expression profile (GEP) that accurately assesses metastatic risk associated with primary cutaneous melanomas has been described. We sought to compare accuracy of the GEP in combination with risk determined using the web-based AJCC Individualized Melanoma Patient Outcome Prediction Tool. GEP results from 205 stage I/II cutaneous melanomas with sufficient clinical data for prognostication using the AJCC tool were classified as low (class 1) or high (class 2) risk. Two 5-year overall survival cutoffs (AJCC 79% and 68%), reflecting survival for patients with stage IIA or IIB disease, respectively, were assigned for binary AJCC risk. Cox univariate analysis revealed significant risk classification of distant metastasis-free and overall survival (hazard ratio range 3.2-9.4, P risk by GEP but low risk by AJCC. Specimens reflect tertiary care center referrals; more effective therapies have been approved for clinical use after accrual. The GEP provides valuable prognostic information and improves identification of high-risk melanomas when used together with the AJCC online prediction tool. Copyright © 2016 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.

From the lab - Predicting Autism in High-Risk Infants | NIH MedlinePlus the Magazine

Science.gov (United States)

... High-Risk Infants Follow us Photo: iStock Predicting Autism in High-Risk Infants AN NIH-SUPPORTED STUDY ... high-risk, 6-month-old infants will develop autism spectrum disorder by age 2. Such a tool ...

Common features of microRNA target prediction tools

Directory of Open Access Journals (Sweden)

Sarah M. Peterson

2014-02-01

Full Text Available The human genome encodes for over 1800 microRNAs, which are short noncoding RNA molecules that function to regulate gene expression post-transcriptionally. Due to the potential for one microRNA to target multiple gene transcripts, microRNAs are recognized as a major mechanism to regulate gene expression and mRNA translation. Computational prediction of microRNA targets is a critical initial step in identifying microRNA:mRNA target interactions for experimental validation. The available tools for microRNA target prediction encompass a range of different computational approaches, from the modeling of physical interactions to the incorporation of machine learning. This review provides an overview of the major computational approaches to microRNA target prediction. Our discussion highlights three tools for their ease of use, reliance on relatively updated versions of miRBase, and range of capabilities, and these are DIANA-microT-CDS, miRanda-mirSVR, and TargetScan. In comparison across all microRNA target prediction tools, four main aspects of the microRNA:mRNA target interaction emerge as common features on which most target prediction is based: seed match, conservation, free energy, and site accessibility. This review explains these features and identifies how they are incorporated into currently available target prediction tools. MicroRNA target prediction is a dynamic field with increasing attention on development of new analysis tools. This review attempts to provide a comprehensive assessment of these tools in a manner that is accessible across disciplines. Understanding the basis of these prediction methodologies will aid in user selection of the appropriate tools and interpretation of the tool output.

Development and Validation of a Prediction Model to Estimate Individual Risk of Pancreatic Cancer.

Science.gov (United States)

Yu, Ami; Woo, Sang Myung; Joo, Jungnam; Yang, Hye-Ryung; Lee, Woo Jin; Park, Sang-Jae; Nam, Byung-Ho

2016-01-01

There is no reliable screening tool to identify people with high risk of developing pancreatic cancer even though pancreatic cancer represents the fifth-leading cause of cancer-related death in Korea. The goal of this study was to develop an individualized risk prediction model that can be used to screen for asymptomatic pancreatic cancer in Korean men and women. Gender-specific risk prediction models for pancreatic cancer were developed using the Cox proportional hazards model based on an 8-year follow-up of a cohort study of 1,289,933 men and 557,701 women in Korea who had biennial examinations in 1996-1997. The performance of the models was evaluated with respect to their discrimination and calibration ability based on the C-statistic and Hosmer-Lemeshow type χ2 statistic. A total of 1,634 (0.13%) men and 561 (0.10%) women were newly diagnosed with pancreatic cancer. Age, height, BMI, fasting glucose, urine glucose, smoking, and age at smoking initiation were included in the risk prediction model for men. Height, BMI, fasting glucose, urine glucose, smoking, and drinking habit were included in the risk prediction model for women. Smoking was the most significant risk factor for developing pancreatic cancer in both men and women. The risk prediction model exhibited good discrimination and calibration ability, and in external validation it had excellent prediction ability. Gender-specific risk prediction models for pancreatic cancer were developed and validated for the first time. The prediction models will be a useful tool for detecting high-risk individuals who may benefit from increased surveillance for pancreatic cancer.

International multicenter tool to predict the risk of nonsentinel node metastases in breast cancer

DEFF Research Database (Denmark)

Meretoja, Tuomo J; Leidenius, Marjut H K; Heikkilä, Päivi S

2012-01-01

predicting nonsentinel node involvement were identified in logistic regression analysis. A multivariable predictive model was developed and validated by area under the receiver operating characteristics curve (AUC), first internally in 500 additional patients and then externally in 1068 patients from other...... centers. All statistical tests were two-sided. Results Nine tumor- and sentinel node-specific variables were identified as statistically significant factors predicting nonsentinel node involvement in logistic regression analysis. A resulting predictive model applied to the internal validation series...... resulted in an AUC of 0.714 (95% confidence interval [CI] = 0.665 to 0.763). For the external validation series, the AUC was 0.719 (95% CI = 0.689 to 0.750). The model was well calibrated in the external validation series. Conclusions We present a novel, international, multicenter, predictive tool...

Mortality Risk After Transcatheter Aortic Valve Implantation: Analysis of the Predictive Accuracy of the Transcatheter Valve Therapy Registry Risk Assessment Model.

Science.gov (United States)

Codner, Pablo; Malick, Waqas; Kouz, Remi; Patel, Amisha; Chen, Cheng-Han; Terre, Juan; Landes, Uri; Vahl, Torsten Peter; George, Isaac; Nazif, Tamim; Kirtane, Ajay J; Khalique, Omar K; Hahn, Rebecca T; Leon, Martin B; Kodali, Susheel

2018-05-08

Risk assessment tools currently used to predict mortality in transcatheter aortic valve implantation (TAVI) were designed for patients undergoing cardiac surgery. We aim to assess the accuracy of the TAVI dedicated American College of Cardiology / Transcatheter Valve Therapies (ACC/TVT) risk score in predicting mortality outcomes. Consecutive patients (n=1038) undergoing TAVI at a single institution from 2014 to 2016 were included. The ACC/TVT registry mortality risk score, the Society of Thoracic Surgeons - Patient Reported Outcomes (STS-PROM) score and the EuroSCORE II were calculated for all patients. In hospital and 30-day all-cause mortality rates were 1.3% and 2.9%, respectively. The ACC/TVT risk stratification tool scored higher for patients who died in-hospital than in those who survived the index hospitalization (6.4 ± 4.6 vs. 3.5 ± 1.6, p = 0.03; respectively). The ACC/TVT score showed a high level of discrimination, C-index for in-hospital mortality 0.74, 95% CI [0.59 - 0.88]. There were no significant differences between the performance of the ACC/TVT registry risk score, the EuroSCORE II and the STS-PROM for in hospital and 30-day mortality rates. The ACC/TVT registry risk model is a dedicated tool to aid in the prediction of in-hospital mortality risk after TAVI.

Using social media as a tool to predict syphilis.

Science.gov (United States)

Young, Sean D; Mercer, Neil; Weiss, Robert E; Torrone, Elizabeth A; Aral, Sevgi O

2018-04-01

Syphilis rates have been rapidly rising in the United States. New technologies, such as social media, might be used to anticipate and prevent the spread of disease. Because social media data collection is easy and inexpensive, integration of social media data into syphilis surveillance may be a cost-effective surveillance strategy, especially in low-resource regions. People are increasingly using social media to discuss health-related issues, such as sexual risk behaviors, allowing social media to be a potential tool for public health and medical research. This study mined Twitter data to assess whether social media could be used to predict syphilis cases in 2013 based on 2012 data. We collected 2012 and 2013 county-level primary and secondary (P&S) and early latent syphilis cases reported to the Center for Disease Control and Prevention, along with >8500 geolocated tweets in the United States that were filtered to include sexual risk-related keywords, including colloquial terms for intercourse. We assessed the relationship between syphilis-related tweets and actual case reports by county, controlling for socioeconomic indicators and prior year syphilis cases. We found a significant positive relationship between tweets and cases of P&S and early latent syphilis. This study shows that social media may be an additional tool to enhance syphilis prediction and surveillance. Copyright © 2017 Elsevier Inc. All rights reserved.

External validation of approaches to prediction of falls during hospital rehabilitation stays and development of a new simpler tool

Directory of Open Access Journals (Sweden)

Angela Vratsistas-Curto

2017-12-01

Full Text Available Objectives: To test the external validity of 4 approaches to fall prediction in a rehabilitation setting (Predict_FIRST, Ontario Modified STRATIFY (OMS, physiotherapists’ judgement of fall risk (PT_Risk, and falls in the past year (Past_Falls, and to develop and test the validity of a simpler tool for fall prediction in rehabilitation (Predict_CM2. Participants: A total of 300 consecutively-admitted rehabilitation inpatients. Methods: Prospective inception cohort study. Falls during the rehabilitation stay were monitored. Potential predictors were extracted from medical records. Results: Forty-one patients (14% fell during their rehabilitation stay. The external validity, area under the receiver operating characteristic curve (AUC, for predicting future fallers was: 0.71 (95% confidence interval (95% CI: 0.61–0.81 for OMS (Total_Score; 0.66 (95% CI: 0.57–0.74 for Predict_FIRST; 0.65 (95% CI 0.57–0.73 for PT_Risk; and 0.52 for Past_Falls (95% CI: 0.46–0.60. A simple 3-item tool (Predict_CM2 was developed from the most predictive individual items (impaired mobility/transfer ability, impaired cognition, and male sex. The accuracy of Predict_CM2 was 0.73 (95% CI: 0.66–0.81, comparable to OMS (Total_Score (p = 0.52, significantly better than Predict_FIRST (p = 0.04, and Past_Falls (p < 0.001, and approaching significantly better than PT_Risk (p = 0.09. Conclusion: Predict_CM2 is a simpler screening tool with similar accuracy for predicting fallers in rehabilitation to OMS (Total_Score and better accuracy than Predict_FIRST or Past_Falls. External validation of Predict_CM2 is required.

The predictive and external validity of the STarT Back Tool in Danish primary care.

Science.gov (United States)

Morsø, Lars; Kent, Peter; Albert, Hanne B; Hill, Jonathan C; Kongsted, Alice; Manniche, Claus

2013-08-01

The STarT Back Tool (SBT) was recently translated into Danish and its concurrent validity described. This study tested the predictive validity of the Danish SBT. Danish primary care patients (n = 344) were compared to a UK cohort. SBT subgroup validity for predicting high activity limitation at 3 months' follow-up was assessed using descriptive proportions, relative risks, AUC and odds ratios. The SBT had a statistically similar predictive ability in Danish primary care as in UK primary care. Unadjusted relative risks for poor clinical outcome on activity limitation in the Danish cohort were 2.4 (1.7-3.4) for the medium-risk subgroup and 2.8 (1.8-3.8) for the high-risk subgroup versus 3.1 (2.5-3.9) and 4.5 (3.6-5.6) for the UK cohort. Adjusting for confounders appeared to explain the lower predictive ability of the Danish high-risk group. The Danish SBT distinguished between low- and medium-risk subgroups with a similar predictive ability of the UK SBT. That distinction is useful information for informing patients about their expected prognosis and may help guiding clinicians' choice of treatment. However, cross-cultural differences in the SBT psychosocial subscale may reduce the predictive ability of the high-risk subgroup in Danish primary care.

Predictive Validity of the STarT Back Tool for Risk of Persistent Disabling Back Pain in a U.S Primary Care Setting.

Science.gov (United States)

Suri, Pradeep; Delaney, Kristin; Rundell, Sean D; Cherkin, Daniel C

2018-04-03

To examine the predictive validity of the Subgrouping for Targeted Treatment (STarT Back) tool for classifying people with back pain into categories of low, medium, and high risk of persistent disabling back pain in U.S. primary care. Secondary analysis of data from participants receiving usual care in a randomized clinical trial. Primary care clinics. Adults (N = 1109) ≥18 years of age with back pain. Those with specific causes of back pain (pregnancy, disc herniation, vertebral fracture, spinal stenosis) and work-related injuries were not included. Not applicable. The original 9-item version of the STarT Back tool, administered at baseline, stratified patients by their risk (low, medium, high) of persistent disabling back pain (STarT Back risk group). Persistent disabling back pain was defined as Roland-Morris Disability Questionnaire scores of ≥7 at 6-month follow-up. The STarT Back risk group was a significant predictor of persistent disabling back pain (PSTarT Back risk groups successfully separated people with back pain into distinct categories of risk for persistent disabling back pain at 6-month follow-up in U.S. primary care. These results were very similar to those in the original STarT Back validation study. This validation study is a necessary first step toward identifying whether the entire STarT Back approach, including matched/targeted treatment, can be effectively used for primary care in the United States. Published by Elsevier Inc.

A prognostic tool to identify adolescents at high risk of becoming daily smokers

Directory of Open Access Journals (Sweden)

Paradis Gilles

2011-08-01

Full Text Available Abstract Background The American Academy of Pediatrics advocates that pediatricians should be involved in tobacco counseling and has developed guidelines for counseling. We present a prognostic tool for use by health care practitioners in both clinical and non-clinical settings, to identify adolescents at risk of becoming daily smokers. Methods Data were drawn from the Nicotine Dependence in Teens (NDIT Study, a prospective investigation of 1293 adolescents, initially aged 12-13 years, recruited in 10 secondary schools in Montreal, Canada in 1999. Questionnaires were administered every three months for five years. The prognostic tool was developed using estimated coefficients from multivariable logistic models. Model overfitting was corrected using bootstrap cross-validation. Goodness-of-fit and predictive ability of the models were assessed by R2, the c-statistic, and the Hosmer-Lemeshow test. Results The 1-year and 2-year probability of initiating daily smoking was a joint function of seven individual characteristics: age; ever smoked; ever felt like you needed a cigarette; parent(s smoke; sibling(s smoke; friend(s smoke; and ever drank alcohol. The models were characterized by reasonably good fit and predictive ability. They were transformed into user-friendly tables such that the risk of daily smoking can be easily computed by summing points for responses to each item. The prognostic tool is also available on-line at http://episerve.chumontreal.qc.ca/calculation_risk/daily-risk/daily_smokingadd.php. Conclusions The prognostic tool to identify youth at high risk of daily smoking may eventually be an important component of a comprehensive tobacco control system.

On-Line, Self-Learning, Predictive Tool for Determining Payload Thermal Response

Science.gov (United States)

Jen, Chian-Li; Tilwick, Leon

2000-01-01

This paper will present the results of a joint ManTech / Goddard R&D effort, currently under way, to develop and test a computer based, on-line, predictive simulation model for use by facility operators to predict the thermal response of a payload during thermal vacuum testing. Thermal response was identified as an area that could benefit from the algorithms developed by Dr. Jeri for complex computer simulations. Most thermal vacuum test setups are unique since no two payloads have the same thermal properties. This requires that the operators depend on their past experiences to conduct the test which requires time for them to learn how the payload responds while at the same time limiting any risk of exceeding hot or cold temperature limits. The predictive tool being developed is intended to be used with the new Thermal Vacuum Data System (TVDS) developed at Goddard for the Thermal Vacuum Test Operations group. This model can learn the thermal response of the payload by reading a few data points from the TVDS, accepting the payload's current temperature as the initial condition for prediction. The model can then be used as a predictive tool to estimate the future payload temperatures according to a predetermined shroud temperature profile. If the error of prediction is too big, the model can be asked to re-learn the new situation on-line in real-time and give a new prediction. Based on some preliminary tests, we feel this predictive model can forecast the payload temperature of the entire test cycle within 5 degrees Celsius after it has learned 3 times during the beginning of the test. The tool will allow the operator to play "what-if' experiments to decide what is his best shroud temperature set-point control strategy. This tool will save money by minimizing guess work and optimizing transitions as well as making the testing process safer and easier to conduct.

An ensemble model of QSAR tools for regulatory risk assessment.

Science.gov (United States)

Pradeep, Prachi; Povinelli, Richard J; White, Shannon; Merrill, Stephen J

2016-01-01

Quantitative structure activity relationships (QSARs) are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR predictions can be used for chemical risk assessment for protection of human and environmental health, which makes them interesting to regulators, especially in the absence of experimental data. For compatibility with regulatory use, QSAR models should be transparent, reproducible and optimized to minimize the number of false negatives. In silico QSAR tools are gaining wide acceptance as a faster alternative to otherwise time-consuming clinical and animal testing methods. However, different QSAR tools often make conflicting predictions for a given chemical and may also vary in their predictive performance across different chemical datasets. In a regulatory context, conflicting predictions raise interpretation, validation and adequacy concerns. To address these concerns, ensemble learning techniques in the machine learning paradigm can be used to integrate predictions from multiple tools. By leveraging various underlying QSAR algorithms and training datasets, the resulting consensus prediction should yield better overall predictive ability. We present a novel ensemble QSAR model using Bayesian classification. The model allows for varying a cut-off parameter that allows for a selection in the desirable trade-off between model sensitivity and specificity. The predictive performance of the ensemble model is compared with four in silico tools (Toxtree, Lazar, OECD Toolbox, and Danish QSAR) to predict carcinogenicity for a dataset of air toxins (332 chemicals) and a subset of the gold carcinogenic potency database (480 chemicals). Leave-one-out cross validation results show that the ensemble model achieves the best trade-off between sensitivity and specificity (accuracy: 83.8 % and 80.4 %, and balanced accuracy: 80.6 % and 80.8 %) and highest inter-rater agreement [kappa ( κ ): 0

Metal Vapor Arcing Risk Assessment Tool

Science.gov (United States)

Hill, Monika C.; Leidecker, Henning W.

2010-01-01

The Tin Whisker Metal Vapor Arcing Risk Assessment Tool has been designed to evaluate the risk of metal vapor arcing and to help facilitate a decision toward a researched risk disposition. Users can evaluate a system without having to open up the hardware. This process allows for investigating components at risk rather than spending time and money analyzing every component. The tool points to a risk level and provides direction for appropriate action and documentation.

The Stroke Assessment of Fall Risk (SAFR): predictive validity in inpatient stroke rehabilitation.

Science.gov (United States)

Breisinger, Terry P; Skidmore, Elizabeth R; Niyonkuru, Christian; Terhorst, Lauren; Campbell, Grace B

2014-12-01

To evaluate relative accuracy of a newly developed Stroke Assessment of Fall Risk (SAFR) for classifying fallers and non-fallers, compared with a health system fall risk screening tool, the Fall Harm Risk Screen. Prospective quality improvement study conducted at an inpatient stroke rehabilitation unit at a large urban university hospital. Patients admitted for inpatient stroke rehabilitation (N = 419) with imaging or clinical evidence of ischemic or hemorrhagic stroke, between 1 August 2009 and 31 July 2010. Not applicable. Sensitivity, specificity, and area under the curve for Receiver Operating Characteristic Curves of both scales' classifications, based on fall risk score completed upon admission to inpatient stroke rehabilitation. A total of 68 (16%) participants fell at least once. The SAFR was significantly more accurate than the Fall Harm Risk Screen (p Fall Harm Risk Screen, area under the curve was 0.56, positive predictive value was 0.19, and negative predictive value was 0.86. Sensitivity and specificity of the SAFR (0.78 and 0.63, respectively) was higher than the Fall Harm Risk Screen (0.57 and 0.48, respectively). An evidence-derived, population-specific fall risk assessment may more accurately predict fallers than a general fall risk screen for stroke rehabilitation patients. While the SAFR improves upon the accuracy of a general assessment tool, additional refinement may be warranted. © The Author(s) 2014.

Evaluation of the validity of osteoporosis and fracture risk assessment tools (IOF One Minute Test, SCORE, and FRAX) in postmenopausal Palestinian women.

Science.gov (United States)

Kharroubi, Akram; Saba, Elias; Ghannam, Ibrahim; Darwish, Hisham

2017-12-01

The need for simple self-assessment tools is necessary to predict women at high risk for developing osteoporosis. In this study, tools like the IOF One Minute Test, Fracture Risk Assessment Tool (FRAX), and Simple Calculated Osteoporosis Risk Estimation (SCORE) were found to be valid for Palestinian women. The threshold for predicting women at risk for each tool was estimated. The purpose of this study is to evaluate the validity of the updated IOF (International Osteoporosis Foundation) One Minute Osteoporosis Risk Assessment Test, FRAX, SCORE as well as age alone to detect the risk of developing osteoporosis in postmenopausal Palestinian women. Three hundred eighty-two women 45 years and older were recruited including 131 women with osteoporosis and 251 controls following bone mineral density (BMD) measurement, 287 completed questionnaires of the different risk assessment tools. Receiver operating characteristic (ROC) curves were evaluated for each tool using bone BMD as the gold standard for osteoporosis. The area under the ROC curve (AUC) was the highest for FRAX calculated with BMD for predicting hip fractures (0.897) followed by FRAX for major fractures (0.826) with cut-off values ˃1.5 and ˃7.8%, respectively. The IOF One Minute Test AUC (0.629) was the lowest compared to other tested tools but with sufficient accuracy for predicting the risk of developing osteoporosis with a cut-off value ˃4 total yes questions out of 18. SCORE test and age alone were also as good predictors of risk for developing osteoporosis. According to the ROC curve for age, women ≥64 years had a higher risk of developing osteoporosis. Higher percentage of women with low BMD (T-score ≤-1.5) or osteoporosis (T-score ≤-2.5) was found among women who were not exposed to the sun, who had menopause before the age of 45 years, or had lower body mass index (BMI) compared to controls. Women who often fall had lower BMI and approximately 27% of the recruited postmenopausal

Predicting risk and outcomes for frail older adults: an umbrella review of frailty screening tools

Science.gov (United States)

Apóstolo, João; Cooke, Richard; Bobrowicz-Campos, Elzbieta; Santana, Silvina; Marcucci, Maura; Cano, Antonio; Vollenbroek-Hutten, Miriam; Germini, Federico; Holland, Carol

2017-01-01

EXECUTIVE SUMMARY Background A scoping search identified systematic reviews on diagnostic accuracy and predictive ability of frailty measures in older adults. In most cases, research was confined to specific assessment measures related to a specific clinical model. Objectives To summarize the best available evidence from systematic reviews in relation to reliability, validity, diagnostic accuracy and predictive ability of frailty measures in older adults. Inclusion criteria Population Older adults aged 60 years or older recruited from community, primary care, long-term residential care and hospitals. Index test Available frailty measures in older adults. Reference test Cardiovascular Health Study phenotype model, the Canadian Study of Health and Aging cumulative deficit model, Comprehensive Geriatric Assessment or other reference tests. Diagnosis of interest Frailty defined as an age-related state of decreased physiological reserves characterized by an increased risk of poor clinical outcomes. Types of studies Quantitative systematic reviews. Search strategy A three-step search strategy was utilized to find systematic reviews, available in English, published between January 2001 and October 2015. Methodological quality Assessed by two independent reviewers using the Joanna Briggs Institute critical appraisal checklist for systematic reviews and research synthesis. Data extraction Two independent reviewers extracted data using the standardized data extraction tool designed for umbrella reviews. Data synthesis Data were only presented in a narrative form due to the heterogeneity of included reviews. Results Five reviews with a total of 227,381 participants were included in this umbrella review. Two reviews focused on reliability, validity and diagnostic accuracy; two examined predictive ability for adverse health outcomes; and one investigated validity, diagnostic accuracy and predictive ability. In total, 26 questionnaires and brief assessments and eight frailty

A clinical risk stratification tool for predicting treatment resistance in major depressive disorder.

Science.gov (United States)

Perlis, Roy H

2013-07-01

Early identification of depressed individuals at high risk for treatment resistance could be helpful in selecting optimal setting and intensity of care. At present, validated tools to facilitate this risk stratification are rarely used in psychiatric practice. Data were drawn from the first two treatment levels of a multicenter antidepressant effectiveness study in major depressive disorder, the STAR*D (Sequenced Treatment Alternatives to Relieve Depression) cohort. This cohort was divided into training, testing, and validation subsets. Only clinical or sociodemographic variables available by or readily amenable to self-report were considered. Multivariate models were developed to discriminate individuals reaching remission with a first or second pharmacological treatment trial from those not reaching remission despite two trials. A logistic regression model achieved an area under the receiver operating characteristic curve exceeding .71 in training, testing, and validation cohorts and maintained good calibration across cohorts. Performance of three alternative models with machine learning approaches--a naïve Bayes classifier and a support vector machine, and a random forest model--was less consistent. Similar performance was observed between more and less severe depression, men and women, and primary versus specialty care sites. A web-based calculator was developed that implements this tool and provides graphical estimates of risk. Risk for treatment resistance among outpatients with major depressive disorder can be estimated with a simple model incorporating baseline sociodemographic and clinical features. Future studies should examine the performance of this model in other clinical populations and its utility in treatment selection or clinical trial design. Copyright © 2013 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Prediction of Outcome After Emergency High-Risk Intra-abdominal Surgery Using the Surgical Apgar Score

DEFF Research Database (Denmark)

Cihoric, Mirjana; Toft Tengberg, Line; Bay-Nielsen, Morten

2016-01-01

BACKGROUND: With current literature quoting mortality rates up to 45%, emergency high-risk abdominal surgery has, compared with elective surgery, a significantly greater risk of death and major complications. The Surgical Apgar Score (SAS) is predictive of outcome in elective surgery, but has nev...... emergency high-risk abdominal surgery. Despite its predictive value, the SAS cannot in its current version be recommended as a standalone prognostic tool in an emergency setting....

Development of computerized risk management tool

International Nuclear Information System (INIS)

Kil Yoo Kim; Mee Jung Hwang; Seung Cheol Jang; Sang Hoon Han; Tae Woon Kim

1997-01-01

The author describes the kinds of efforts for the development of computerized risk management tool; (1) development of a risk monitor, Risk Monster, (2) improvement of McFarm (Missing Cutsets Finding Algorithm for Risk Monitor) and finally (3) development of reliability database management system, KwDBMan. Risk Monster supports for plant operators and maintenance schedulers to monitor plant risk and to avoid high peak risk by rearranging maintenance work schedule. Improved McFarm significantly improved calculation speed of Risk Monster for the cases of supporting system OOS (Out Of Service). KwDBMan manages event data, generic data and CCF (Common Cause Failure) data to support Risk Monster as well as PSA tool, KIRAP (KAERI Integrated Reliability Analysis Package)

The Development of a Plant Risk Evaluation (PRE) Tool for Assessing the Invasive Potential of Ornamental Plants

OpenAIRE

Conser, Christiana; Seebacher, Lizbeth; Fujino, David W.; Reichard, Sarah; DiTomaso, Joseph M.

2015-01-01

Weed Risk Assessment (WRA) methods for evaluating invasiveness in plants have evolved rapidly in the last two decades. Many WRA tools exist, but none were specifically designed to screen ornamental plants prior to being released into the environment. To be accepted as a tool to evaluate ornamental plants for the nursery industry, it is critical that a WRA tool accurately predicts non-invasiveness without falsely categorizing them as invasive. We developed a new Plant Risk Evaluation (PRE) too...

Dairy farmer use of price risk management tools.

Science.gov (United States)

Wolf, C A

2012-07-01

Volatility in milk and feed prices can adversely affect dairy farm profitability. Many risk management tools are available for use by US dairy farmers. This research uses surveys of Michigan dairy farmers to examine the extent to which price risk management tools have been used, the farm and operator characteristics that explain the use of these tools, and reasons farmers have not used these tools. A 1999 survey was used to benchmark the degree to which dairy producers had used milk and feed price risk management instruments to compare with 2011 use rates. The surveys collected information about the farm characteristics such as herd size, farmland operated, business organization, and solvency position. Farm operator characteristics collected include age, education, and experience. Dairy farmer use of both milk and feed price risk management tools increased between 1999 and 2011. In 2011, herd size was positively related to the use of milk price risk management tools, whereas farms organized as a sole proprietorship were less likely to use them. Also in 2011, herd size and land operated were positively related to feed price risk management tools, whereas operator age was negatively related. Reasons why farmers had not used price risk management tools included basis risk, cost, lack of management time, cooperative membership, and lack of understanding. Conclusions include the need for educational programming on price risk management tools and a broader exploration of dairy farm risk management programs. Copyright © 2012 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Risk D and D Rapid Prototype: Scenario Documentation and Analysis Tool

International Nuclear Information System (INIS)

Unwin, Stephen D.; Seiple, Timothy E.

2009-01-01

Report describes process and methodology associated with a rapid prototype tool for integrating project risk analysis and health and safety risk analysis for decontamination and decommissioning projects. The objective of the Decontamination and Decommissioning (D and D) Risk Management Evaluation and Work Sequencing Standardization Project under DOE EM-23 is to recommend or develop practical risk-management tools for decommissioning of nuclear facilities. PNNL has responsibility under this project for recommending or developing computer-based tools that facilitate the evaluation of risks in order to optimize the sequencing of D and D work. PNNL's approach is to adapt, augment, and integrate existing resources rather than to develop a new suite of tools. Methods for the evaluation of H and S risks associated with work in potentially hazardous environments are well-established. Several approaches exist which, collectively, are referred to as process hazard analysis (PHA). A PHA generally involves the systematic identification of accidents, exposures, and other adverse events associated with a given process or work flow. This identification process is usually achieved in a brainstorming environment or by other means of eliciting informed opinion. The likelihoods of adverse events (scenarios) and their associated consequence severities are estimated against pre-defined scales, based on which risk indices are then calculated. A similar process is encoded in various project risk software products that facilitate the quantification of schedule and cost risks associated with adverse scenarios. However, risk models do not generally capture both project risk and H and S risk. The intent of the project reported here is to produce a tool that facilitates the elicitation, characterization, and documentation of both project risk and H and S risk based on defined sequences of D and D activities. By considering alternative D and D sequences, comparison of the predicted risks can

Utilizing Dental Electronic Health Records Data to Predict Risk for Periodontal Disease.

Science.gov (United States)

Thyvalikakath, Thankam P; Padman, Rema; Vyawahare, Karnali; Darade, Pratiksha; Paranjape, Rhucha

2015-01-01

Periodontal disease is a major cause for tooth loss and adversely affects individuals' oral health and quality of life. Research shows its potential association with systemic diseases like diabetes and cardiovascular disease, and social habits such as smoking. This study explores mining potential risk factors from dental electronic health records to predict and display patients' contextualized risk for periodontal disease. We retrieved relevant risk factors from structured and unstructured data on 2,370 patients who underwent comprehensive oral examinations at the Indiana University School of Dentistry, Indianapolis, IN, USA. Predicting overall risk and displaying relationships between risk factors and their influence on the patient's oral and general health can be a powerful educational and disease management tool for patients and clinicians at the point of care.

Evaluation of the efficacy of six nutritional screening tools to predict malnutrition in the elderly.

Science.gov (United States)

Poulia, Kalliopi-Anna; Yannakoulia, Mary; Karageorgou, Dimitra; Gamaletsou, Maria; Panagiotakos, Demosthenes B; Sipsas, Nikolaos V; Zampelas, Antonis

2012-06-01

Malnutrition in the elderly is a multifactorial problem, more prevalent in hospitals and care homes. The absence of a gold standard in evaluating nutritional risk led us to evaluate the efficacy of six nutritional screening tools used in the elderly. Two hundred forty eight elderly patients (129 men, 119 female women, aged 75.2 ± 8.5 years) were examined. Nutritional screening was performed on admission using the following tools: Nutritional Risk Index (NRI), Geriatric Nutritional Risk Index (GNRI), Subjective Global Assessment (SGA), Mini Nutritional Assessment - Screening Form (MNA-SF), Malnutrition Universal Screening Tool (MUST) and Nutritional Risk Screening 2002 (NRS 2002). A combined index for malnutrition was also calculated. Nutritional risk and/or malnutrition varied greatly, ranging from 47.2 to 97.6%, depending on the nutritional screening tool used. MUST was the most valid screening tool (validity coefficient = 0.766, CI 95%: 0.690-0.841), while SGA was in better agreement with the combined index (κ = 0.707, p = 0.000). NRS 2002 although was the highest in sensitivity (99.4%), it was the lowest in specificity (6.1%) and positive predictive value (68.2%). MUST seem to be the most valid in the evaluation of the risk for malnutrition in the elderly upon admission to the hospital. NRS 2002 was found to overestimate nutritional risk in the elderly. Copyright © 2011 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

Prediction of Machine Tool Condition Using Support Vector Machine

International Nuclear Information System (INIS)

Wang Peigong; Meng Qingfeng; Zhao Jian; Li Junjie; Wang Xiufeng

2011-01-01

Condition monitoring and predicting of CNC machine tools are investigated in this paper. Considering the CNC machine tools are often small numbers of samples, a condition predicting method for CNC machine tools based on support vector machines (SVMs) is proposed, then one-step and multi-step condition prediction models are constructed. The support vector machines prediction models are used to predict the trends of working condition of a certain type of CNC worm wheel and gear grinding machine by applying sequence data of vibration signal, which is collected during machine processing. And the relationship between different eigenvalue in CNC vibration signal and machining quality is discussed. The test result shows that the trend of vibration signal Peak-to-peak value in surface normal direction is most relevant to the trend of surface roughness value. In trends prediction of working condition, support vector machine has higher prediction accuracy both in the short term ('One-step') and long term (multi-step) prediction compared to autoregressive (AR) model and the RBF neural network. Experimental results show that it is feasible to apply support vector machine to CNC machine tool condition prediction.

Longitudinal Evaluation of Johns Hopkins Fall Risk Assessment Tool and Nurses' Experience.

Science.gov (United States)

Hur, Eun Young; Jin, Yinji; Jin, Taixian; Lee, Sun-Mi

The Johns Hopkins Fall Risk Assessment Tool (JHFRAT) is relatively new in Korea, and it has not been fully evaluated. This study revealed that the JHFRAT had good predictive validity throughout the hospitalization period. However, 2 items (fall history and elimination patterns) on the tool were not determinants of falls in this population. Interestingly, the nurses indicated those 2 items were the most difficult items to assess and needed further training to develop the assessment skills.

New Tool to Predict Glaucoma

Science.gov (United States)

... In This Section A New Tool to Predict Glaucoma email Send this article to a friend by ... Close Send Thanks for emailing that article! Tweet Glaucoma can be difficult to detect and diagnose. Measurement ...

Suicide Risk Screening Tools and the Youth Population.

Science.gov (United States)

Patterson, Sharon

2016-08-01

The use of suicide risk screening tools is a critical component of a comprehensive approach to suicide risk assessment. Since nurses frequently spend more time with patients than any other healthcare professional, they are in key positions to detect and prevent suicidal behavior in youth. To inform nurses about suicide risk screening tools for the youth population. Suicide risk screening tools are research-based standardized instruments that are used to identify people who may be at risk for suicide. A literature search was performed using the Athabasca University Library Resource, the databases of the Cumulative Index to Nursing and Allied Health Literature, ScienceDirect, and Google Scholar. Nurses are cautioned to utilize suicide risk screening tools as only part of the suicide risk assessment in youth populations and avoid the danger of relying on tools that may result in a blind application of evidence to the detriment of clinical experience and judgement. © 2016 Wiley Periodicals, Inc.

Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups

OpenAIRE

Marschollek, Michael; Gövercin, Mehmet; Rust, Stefan; Gietzelt, Matthias; Schulze, Mareike; Wolf, Klaus-Hendrik; Steinhagen-Thiessen, Elisabeth

2012-01-01

Abstract Background Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients' assessment data and to evaluate their predictive performance (aim#1), and to identify high-risk subgroups from the data (aim#2). Methods A ...

Habitat Modeling and Preferences of Marine Mammals as Function of Oceanographic Characteristics: Development of Predictive Tools for Assessing the Risks and the Impacts Due to Sound Emissions

Science.gov (United States)

2011-09-30

evaluate WEC projects in the perspective of the environmental cost-benefit analysis. Proceedings of the ISOPE 2011, Maui, Hawaii, USA 19-24 June, 2011...Function of Oceanographic Characteristics: Development of Predictive Tools for Assessing the Risks and the Impacts Due to Sound Emissions Dr...detections) and the available environmental predictors; - Creating the knowledge-based background about potential mitigation measures appropriate for

Predicting the 10-Year Risks of Atherosclerotic Cardiovascular Disease in Chinese Population: The China-PAR Project (Prediction for ASCVD Risk in China).

Science.gov (United States)

Yang, Xueli; Li, Jianxin; Hu, Dongsheng; Chen, Jichun; Li, Ying; Huang, Jianfeng; Liu, Xiaoqing; Liu, Fangchao; Cao, Jie; Shen, Chong; Yu, Ling; Lu, Fanghong; Wu, Xianping; Zhao, Liancheng; Wu, Xigui; Gu, Dongfeng

2016-11-08

The accurate assessment of individual risk can be of great value to guiding and facilitating the prevention of atherosclerotic cardiovascular disease (ASCVD). However, prediction models in common use were formulated primarily in white populations. The China-PAR project (Prediction for ASCVD Risk in China) is aimed at developing and validating 10-year risk prediction equations for ASCVD from 4 contemporary Chinese cohorts. Two prospective studies followed up together with a unified protocol were used as the derivation cohort to develop 10-year ASCVD risk equations in 21 320 Chinese participants. The external validation was evaluated in 2 independent Chinese cohorts with 14 123 and 70 838 participants. Furthermore, model performance was compared with the Pooled Cohort Equations reported in the American College of Cardiology/American Heart Association guideline. Over 12 years of follow-up in the derivation cohort with 21 320 Chinese participants, 1048 subjects developed a first ASCVD event. Sex-specific equations had C statistics of 0.794 (95% confidence interval, 0.775-0.814) for men and 0.811 (95% confidence interval, 0.787-0.835) for women. The predicted rates were similar to the observed rates, as indicated by a calibration χ 2 of 13.1 for men (P=0.16) and 12.8 for women (P=0.17). Good internal and external validations of our equations were achieved in subsequent analyses. Compared with the Chinese equations, the Pooled Cohort Equations had lower C statistics and much higher calibration χ 2 values in men. Our project developed effective tools with good performance for 10-year ASCVD risk prediction among a Chinese population that will help to improve the primary prevention and management of cardiovascular disease. © 2016 American Heart Association, Inc.

RISK IDENTIFICATION TOOLS – POLISH MSMES COMPANIES PRACTICES

Directory of Open Access Journals (Sweden)

Iwona Gorzeń-Mitka

2013-07-01

Full Text Available The purpose of this study is to present risk identification tools in Polish micro, small and medium-sized enterprises (MSMEs. Risk identification is a key element of the risk management process in companies. Correctly fitting risk identification tools affect the accuracy of management decisions. The result of research is to identify the leading risk identification tools used by MSMEs. The study was conducted in 2010-2012 using a mixed survey-monographic method and questionnaires. The qualitative data were obtained during the study.

Risk predictive modelling for diabetes and cardiovascular disease.

Science.gov (United States)

Kengne, Andre Pascal; Masconi, Katya; Mbanya, Vivian Nchanchou; Lekoubou, Alain; Echouffo-Tcheugui, Justin Basile; Matsha, Tandi E

2014-02-01

Absolute risk models or clinical prediction models have been incorporated in guidelines, and are increasingly advocated as tools to assist risk stratification and guide prevention and treatments decisions relating to common health conditions such as cardiovascular disease (CVD) and diabetes mellitus. We have reviewed the historical development and principles of prediction research, including their statistical underpinning, as well as implications for routine practice, with a focus on predictive modelling for CVD and diabetes. Predictive modelling for CVD risk, which has developed over the last five decades, has been largely influenced by the Framingham Heart Study investigators, while it is only ∼20 years ago that similar efforts were started in the field of diabetes. Identification of predictive factors is an important preliminary step which provides the knowledge base on potential predictors to be tested for inclusion during the statistical derivation of the final model. The derived models must then be tested both on the development sample (internal validation) and on other populations in different settings (external validation). Updating procedures (e.g. recalibration) should be used to improve the performance of models that fail the tests of external validation. Ultimately, the effect of introducing validated models in routine practice on the process and outcomes of care as well as its cost-effectiveness should be tested in impact studies before wide dissemination of models beyond the research context. Several predictions models have been developed for CVD or diabetes, but very few have been externally validated or tested in impact studies, and their comparative performance has yet to be fully assessed. A shift of focus from developing new CVD or diabetes prediction models to validating the existing ones will improve their adoption in routine practice.

Clinical prediction of fall risk and white matter abnormalities: a diffusion tensor imaging study

Science.gov (United States)

The Tinetti scale is a simple clinical tool designed to predict risk of falling by focusing on gait and stance impairment in elderly persons. Gait impairment is also associated with white matter (WM) abnormalities. Objective: To test the hypothesis that elderly subjects at risk for falling, as deter...

Korean risk assessment model for breast cancer risk prediction.

Science.gov (United States)

Park, Boyoung; Ma, Seung Hyun; Shin, Aesun; Chang, Myung-Chul; Choi, Ji-Yeob; Kim, Sungwan; Han, Wonshik; Noh, Dong-Young; Ahn, Sei-Hyun; Kang, Daehee; Yoo, Keun-Young; Park, Sue K

2013-01-01

We evaluated the performance of the Gail model for a Korean population and developed a Korean breast cancer risk assessment tool (KoBCRAT) based upon equations developed for the Gail model for predicting breast cancer risk. Using 3,789 sets of cases and controls, risk factors for breast cancer among Koreans were identified. Individual probabilities were projected using Gail's equations and Korean hazard data. We compared the 5-year and lifetime risk produced using the modified Gail model which applied Korean incidence and mortality data and the parameter estimators from the original Gail model with those produced using the KoBCRAT. We validated the KoBCRAT based on the expected/observed breast cancer incidence and area under the curve (AUC) using two Korean cohorts: the Korean Multicenter Cancer Cohort (KMCC) and National Cancer Center (NCC) cohort. The major risk factors under the age of 50 were family history, age at menarche, age at first full-term pregnancy, menopausal status, breastfeeding duration, oral contraceptive usage, and exercise, while those at and over the age of 50 were family history, age at menarche, age at menopause, pregnancy experience, body mass index, oral contraceptive usage, and exercise. The modified Gail model produced lower 5-year risk for the cases than for the controls (p = 0.017), while the KoBCRAT produced higher 5-year and lifetime risk for the cases than for the controls (pKorean women, especially urban women.

PISCES: A Tool for Predicting Software Testability

Science.gov (United States)

Voas, Jeffrey M.; Miller, Keith W.; Payne, Jeffery E.

1991-01-01

Before a program can fail, a software fault must be executed, that execution must alter the data state, and the incorrect data state must propagate to a state that results directly in an incorrect output. This paper describes a tool called PISCES (developed by Reliable Software Technologies Corporation) for predicting the probability that faults in a particular program location will accomplish all three of these steps causing program failure. PISCES is a tool that is used during software verification and validation to predict a program's testability.

Development of configuration risk management tool

International Nuclear Information System (INIS)

Masuda, Takahiro; Doi, Eiji

2003-01-01

Tokyo Electric Power Company (referred to as TEPCO hereinafter), and other Japanese utilities as well, have been trying to improve the capacity factor of their Nuclear Power Plants (NPPs) through modernization of Operation and Maintenance strategy. TEPCO intends to apply risk information to O and M field with maintaining or even improving both safety and production efficiency. Under these situations, TEPCO with some BWR utilities started to develop a Configuration Risk Management (CRM) tool that can estimate risk in various plant conditions due to configuration changes during outage. Moreover, we also intend to apply CRM to on-line maintenance (OLM) in the near future. This tool can calculate the Core Damage Frequency (CDF) according to given plant condition, such as SSCs availability, decay heat level and the inventory of coolant in both outage state and full-power operation. From deterministic viewpoint, whether certain configuration meet the related requirements of Technical Specifications. User-friendly interface is one of the important features of this tool because this enables the site engineers with little experience in PSA to quantify and utilize the risk information by this tool. (author)

Predictive value of general movements' quality in low-risk infants for minor neurological dysfunction and behavioural problems at preschool age

NARCIS (Netherlands)

Bennema, Anne N; Schendelaar, Pamela; Seggers, Jorien; Haadsma, Maaike L; Heineman, Maas Jan; Hadders-Algra, Mijna

Background: General movement (GM) assessment is a well-established tool to predict cerebral palsy in high-risk infants. Little is known on the predictive value of GM assessment in low-risk populations. Aims: To assess the predictive value of GM quality in early infancy for the development of the

Predictive validity of the identification of seniors at risk screening tool in a German emergency department setting.

Science.gov (United States)

Singler, Katrin; Heppner, Hans Jürgen; Skutetzky, Andreas; Sieber, Cornel; Christ, Michael; Thiem, Ulrich

2014-01-01

The identification of patients at high risk for adverse outcomes [death, unplanned readmission to emergency department (ED)/hospital, functional decline] plays an important role in emergency medicine. The Identification of Seniors at Risk (ISAR) instrument is one of the most commonly used and best-validated screening tools. As to the authors' knowledge so far there are no data on any screening tool for the identification of older patients at risk for a negative outcome in Germany. To evaluate the validity of the ISAR screening tool in a German ED. This was a prospective single-center observational cohort study in an ED of an urban university-affiliated hospital. Participants were 520 patients aged ≥75 years consecutively admitted to the ED. The German version of the ISAR screening tool was administered directly after triage of the patients. Follow-up telephone interviews to assess outcome variables were conducted 28 and 180 days after the index visit in the ED. The primary end point was death from any cause or hospitalization or recurrent ED visit or change of residency into a long-term care facility on day 28 after the index ED visit. The mean age ± SD was 82.8 ± 5.0 years. According to ISAR, 425 patients (81.7%) scored ≥2 points, and 315 patients (60.5%) scored ≥3 points. The combined primary end point was observed in 250 of 520 patients (48.1%) on day 28 and in 260 patients (50.0%) on day 180. Using a continuous ISAR score the area under the curve on day 28 was 0.621 (95% confidence interval, CI 0.573-0.669) and 0.661 (95% CI 0.615-0.708) on day 180, respectively. The German version of the ISAR screening tool acceptably identified elderly patients in the ED with an increased risk of a negative outcome. Using the cutoff ≥3 points instead of ≥2 points yielded better overall results.

Application of Risk Assessment Tools in the Continuous Risk Management (CRM) Process

Science.gov (United States)

Ray, Paul S.

2002-01-01

Marshall Space Flight Center (MSFC) of the National Aeronautics and Space Administration (NASA) is currently implementing the Continuous Risk Management (CRM) Program developed by the Carnegie Mellon University and recommended by NASA as the Risk Management (RM) implementation approach. The four most frequently used risk assessment tools in the center are: (a) Failure Modes and Effects Analysis (FMEA), Hazard Analysis (HA), Fault Tree Analysis (FTA), and Probabilistic Risk Analysis (PRA). There are some guidelines for selecting the type of risk assessment tools during the project formulation phase of a project, but there is not enough guidance as to how to apply these tools in the Continuous Risk Management process (CRM). But the ways the safety and risk assessment tools are used make a significant difference in the effectiveness in the risk management function. Decisions regarding, what events are to be included in the analysis, to what level of details should the analysis be continued, make significant difference in the effectiveness of risk management program. Tools of risk analysis also depends on the phase of a project e.g. at the initial phase of a project, when not much data are available on hardware, standard FMEA cannot be applied; instead a functional FMEA may be appropriate. This study attempted to provide some directives to alleviate the difficulty in applying FTA, PRA, and FMEA in the CRM process. Hazard Analysis was not included in the scope of the study due to the short duration of the summer research project.

Using an Internet-Based Breast Cancer Risk Assessment Tool to Improve Social-Cognitive Precursors of Physical Activity.

Science.gov (United States)

Fowler, Stephanie L; Klein, William M P; Ball, Linda; McGuire, Jaclyn; Colditz, Graham A; Waters, Erika A

2017-08-01

Internet-based cancer risk assessment tools might serve as a strategy for translating epidemiological risk prediction research into public health practice. Understanding how such tools affect key social-cognitive precursors of behavior change is crucial for leveraging their potential into effective interventions. To test the effects of a publicly available, Internet-based, breast cancer risk assessment tool on social-cognitive precursors of physical activity. Women (N = 132) aged 40-78 with no personal cancer history indicated their perceived risk of breast cancer and were randomly assigned to receive personalized ( www.yourdiseaserisk.wustl.edu ) or nonpersonalized breast cancer risk information. Immediately thereafter, breast cancer risk perceptions and physical activity-related behavioral intentions, self-efficacy, and response efficacy were assessed. Personalized information elicited higher intentions, self-efficacy, and response efficacy than nonpersonalized information, P values Internet-based risk assessment tools can produce beneficial effects on important social-cognitive precursors of behavior change, but lingering skepticism, possibly due to defensive processing, needs to be addressed before the effects can be maximized.

Utility of Eating Assessment Tool-10 in Predicting Aspiration in Patients with Unilateral Vocal Fold Paralysis.

Science.gov (United States)

Zuniga, Steven A; Ebersole, Barbara; Jamal, Nausheen

2018-03-01

Objective Examine the incidence of penetration/aspiration in patients with unilateral vocal fold immobility and investigate the relationship with self-reported perception of dysphagia. Study Design Case series with chart review. Setting Academic cancer center. Subjects and Methods Adult patients with unilateral vocal fold immobility diagnosed between 2014 and 2016 were reviewed. Patients were stratified into an aspiration group and a nonaspiration group using objective findings on flexible endoscopic evaluation of swallowing, as scored using Rosenbek's Penetration Aspiration Scale. Objective findings were compared to patient perception of dysphagia. Bivariate linear correlation analysis was performed to evaluate correlation between Eating Assessment Tool-10 scores and presence of aspiration. Tests of diagnostic accuracy were calculated to investigate the predictive value of Eating Assessment Tool-10 scores >9 on aspiration risk. Results Of the 35 patients with new-onset unilateral vocal fold immobility were evaluated, 25.7% (9/35) demonstrated tracheal aspiration. Mean ± SD Eating Assessment Tool-10 scores were 19.2 ± 13.7 for aspirators and 7.0 ± 7.8 for nonaspirators ( P = .016). A statistically significant correlation was demonstrated between increasing Eating Assessment Tool-10 scores and Penetration Aspiration Scale scores ( r = 0.511, P = .002). Diagnostic accuracy analysis for aspiration risk in patients with an Eating Assessment Tool-10 score >9 revealed a sensitivity of 77.8% and a specificity of 73.1%. Conclusion Patient perception of swallowing difficulty may have utility in predicting aspiration risk. An EAT-10 of >9 in patients with unilateral vocal fold immobility may portend up to a 5 times greater risk of aspiration. Routine swallow testing to assess for penetration/aspiration may be indicated in patients with unilateral vocal fold immobility.

Long-Term Survival Prediction for Coronary Artery Bypass Grafting: Validation of the ASCERT Model Compared With The Society of Thoracic Surgeons Predicted Risk of Mortality.

Science.gov (United States)

Lancaster, Timothy S; Schill, Matthew R; Greenberg, Jason W; Ruaengsri, Chawannuch; Schuessler, Richard B; Lawton, Jennifer S; Maniar, Hersh S; Pasque, Michael K; Moon, Marc R; Damiano, Ralph J; Melby, Spencer J

2018-05-01

The recently developed American College of Cardiology Foundation-Society of Thoracic Surgeons (STS) Collaboration on the Comparative Effectiveness of Revascularization Strategy (ASCERT) Long-Term Survival Probability Calculator is a valuable addition to existing short-term risk-prediction tools for cardiac surgical procedures but has yet to be externally validated. Institutional data of 654 patients aged 65 years or older undergoing isolated coronary artery bypass grafting between 2005 and 2010 were reviewed. Predicted survival probabilities were calculated using the ASCERT model. Survival data were collected using the Social Security Death Index and institutional medical records. Model calibration and discrimination were assessed for the overall sample and for risk-stratified subgroups based on (1) ASCERT 7-year survival probability and (2) the predicted risk of mortality (PROM) from the STS Short-Term Risk Calculator. Logistic regression analysis was performed to evaluate additional perioperative variables contributing to death. Overall survival was 92.1% (569 of 597) at 1 year and 50.5% (164 of 325) at 7 years. Calibration assessment found no significant differences between predicted and actual survival curves for the overall sample or for the risk-stratified subgroups, whether stratified by predicted 7-year survival or by PROM. Discriminative performance was comparable between the ASCERT and PROM models for 7-year survival prediction (p validated for prediction of long-term survival after coronary artery bypass grafting in all risk groups. The widely used STS PROM performed comparably as a predictor of long-term survival. Both tools provide important information for preoperative decision making and patient counseling about potential outcomes after coronary artery bypass grafting. Copyright © 2018 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

Empirical comparison of web-based antimicrobial peptide prediction tools.

Science.gov (United States)

Gabere, Musa Nur; Noble, William Stafford

2017-07-01

Antimicrobial peptides (AMPs) are innate immune molecules that exhibit activities against a range of microbes, including bacteria, fungi, viruses and protozoa. Recent increases in microbial resistance against current drugs has led to a concomitant increase in the need for novel antimicrobial agents. Over the last decade, a number of AMP prediction tools have been designed and made freely available online. These AMP prediction tools show potential to discriminate AMPs from non-AMPs, but the relative quality of the predictions produced by the various tools is difficult to quantify. We compiled two sets of AMP and non-AMP peptides, separated into three categories-antimicrobial, antibacterial and bacteriocins. Using these benchmark data sets, we carried out a systematic evaluation of ten publicly available AMP prediction methods. Among the six general AMP prediction tools-ADAM, CAMPR3(RF), CAMPR3(SVM), MLAMP, DBAASP and MLAMP-we find that CAMPR3(RF) provides a statistically significant improvement in performance, as measured by the area under the receiver operating characteristic (ROC) curve, relative to the other five methods. Surprisingly, for antibacterial prediction, the original AntiBP method significantly outperforms its successor, AntiBP2 based on one benchmark dataset. The two bacteriocin prediction tools, BAGEL3 and BACTIBASE, both provide very good performance and BAGEL3 outperforms its predecessor, BACTIBASE, on the larger of the two benchmarks. gaberemu@ngha.med.sa or william-noble@uw.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

Selecting a risk-based tool to aid in decision making

Energy Technology Data Exchange (ETDEWEB)

Bendure, A.O.

1995-03-01

Selecting a risk-based tool to aid in decision making is as much of a challenge as properly using the tool once it has been selected. Failure to consider customer and stakeholder requirements and the technical bases and differences in risk-based decision making tools will produce confounding and/or politically unacceptable results when the tool is used. Selecting a risk-based decisionmaking tool must therefore be undertaken with the same, if not greater, rigor than the use of the tool once it is selected. This paper presents a process for selecting a risk-based tool appropriate to a set of prioritization or resource allocation tasks, discusses the results of applying the process to four risk-based decision-making tools, and identifies the ``musts`` for successful selection and implementation of a risk-based tool to aid in decision making.

Predicting readmission risk of patients with diabetes hospitalized for cardiovascular disease: a retrospective cohort study.

Science.gov (United States)

Rubin, Daniel J; Golden, Sherita Hill; McDonnell, Marie E; Zhao, Huaqing

2017-08-01

To develop and validate a tool that predicts 30d readmission risk of patients with diabetes hospitalized for cardiovascular disease (CVD), the Diabetes Early Readmission Risk Indicator-CVD (DERRI-CVD™). A cohort of 8189 discharges was retrospectively selected from electronic records of adult patients with diabetes hospitalized for CVD. Discharges of 60% of the patients (n=4950) were randomly selected as a training sample and the remaining 40% (n=3219) were the validation sample. Statistically significant predictors of all-cause 30d readmission risk were identified by multivariable logistic regression modeling: education level, employment status, living within 5miles of the hospital, pre-admission diabetes therapy, macrovascular complications, admission serum creatinine and albumin levels, having a hospital discharge within 90days pre-admission, and a psychiatric diagnosis. Model discrimination and calibration were good (C-statistic 0.71). Performance in the validation sample was comparable. Predicted 30d readmission risk was similar in the training and validation samples (38.6% and 35.1% in the highest quintiles). The DERRI-CVD™ may be a valid tool to predict all-cause 30d readmission risk of patients with diabetes hospitalized for CVD. Identifying high-risk patients may encourage the use of interventions targeting those at greatest risk, potentially leading to better outcomes and lower healthcare costs. Copyright © 2017 Elsevier Inc. All rights reserved.

A clinical tool to predict Plasmodium vivax recurrence in Malaysia.

Science.gov (United States)

Mat Ariffin, Norliza; Islahudin, Farida; Kumolosasi, Endang; Makmor-Bakry, Mohd

2017-12-08

Recurrence rates of Plasmodium vivax infections differ across various geographic regions. Interestingly, South-East Asia and the Asia-Pacific region are documented to exhibit the most frequent recurrence incidences. Identifying patients at a higher risk for recurrences gives valuable information in strengthening the efforts to control P. vivax infections. The aim of the study was to develop a tool to identify P. vivax- infected patients that are at a higher risk of recurrence in Malaysia. Patient data was obtained retrospectively through the Ministry of Health, Malaysia, from 2011 to 2016. Patients with incomplete data were excluded. A total of 2044 clinical P. vivax malaria cases treated with primaquine were included. Data collected were patient, disease, and treatment characteristics. Two-thirds of the cases (n = 1362) were used to develop a clinical risk score, while the remaining third (n = 682) was used for validation. Using multivariate analysis, age (p = 0.03), gametocyte sexual count (p = 0.04), indigenous transmission (p = 0.04), type of treatment (p = 0.12), and incomplete primaquine treatment (p = 0.14) were found to be predictors of recurrence after controlling for other confounding factors; these predictors were then used in developing the final model. The beta-coefficient values were used to develop a clinical scoring tool to predict possible recurrence. The total scores ranged between 0 and 8. A higher score indicated a higher risk for recurrence (odds ratio [OR]: 1.971; 95% confidence interval [CI]: 1.562-2.487; p ≤ 0.001). The area under the receiver operating characteristic (ROC) curve of the developed (n = 1362) and validated model (n = 682) was of good accuracy (ROC: 0.728, 95% CI: 0.670-0.785, p value useful tool in targeting patients at a higher risk for recurrence for closer monitoring during follow-up, after treatment with primaquine.

Tail Risk Premia and Return Predictability

DEFF Research Database (Denmark)

Bollerslev, Tim; Todorov, Viktor; Xu, Lai

The variance risk premium, defined as the difference between actual and risk-neutralized expectations of the forward aggregate market variation, helps predict future market returns. Relying on new essentially model-free estimation procedure, we show that much of this predictability may be attribu......The variance risk premium, defined as the difference between actual and risk-neutralized expectations of the forward aggregate market variation, helps predict future market returns. Relying on new essentially model-free estimation procedure, we show that much of this predictability may......-varying economic uncertainty and changes in risk aversion, or market fears, respectively....

Proarrhythmia risk prediction using human induced pluripotent stem cell-derived cardiomyocytes.

Science.gov (United States)

Yamazaki, Daiju; Kitaguchi, Takashi; Ishimura, Masakazu; Taniguchi, Tomohiko; Yamanishi, Atsuhiro; Saji, Daisuke; Takahashi, Etsushi; Oguchi, Masao; Moriyama, Yuta; Maeda, Sanae; Miyamoto, Kaori; Morimura, Kaoru; Ohnaka, Hiroki; Tashibu, Hiroyuki; Sekino, Yuko; Miyamoto, Norimasa; Kanda, Yasunari

2018-04-01

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are expected to become a useful tool for proarrhythmia risk prediction in the non-clinical drug development phase. Several features including electrophysiological properties, ion channel expression profile and drug responses were investigated using commercially available hiPSC-CMs, such as iCell-CMs and Cor.4U-CMs. Although drug-induced arrhythmia has been extensively examined by microelectrode array (MEA) assays in iCell-CMs, it has not been fully understood an availability of Cor.4U-CMs for proarrhythmia risk. Here, we evaluated the predictivity of proarrhythmia risk using Cor.4U-CMs. MEA assay revealed linear regression between inter-spike interval and field potential duration (FPD). The hERG inhibitor E-4031 induced reverse-use dependent FPD prolongation. We next evaluated the proarrhythmia risk prediction by a two-dimensional map, which we have previously proposed. We determined the relative torsade de pointes risk score, based on the extent of FPD with Fridericia's correction (FPDcF) change and early afterdepolarization occurrence, and calculated the margins normalized to free effective therapeutic plasma concentrations. The drugs were classified into three risk groups using the two-dimensional map. This risk-categorization system showed high concordance with the torsadogenic information obtained by a public database CredibleMeds. Taken together, these results indicate that Cor.4U-CMs can be used for drug-induced proarrhythmia risk prediction. Copyright © 2018 The Authors. Production and hosting by Elsevier B.V. All rights reserved.

Identification of seniors at risk (ISAR) screening tool in the emergency department: implementation using the plan-do-study-act model and validation results.

Science.gov (United States)

Asomaning, Nana; Loftus, Carla

2014-07-01

To better meet the needs of older adults in the emergency department, Senior Friendly care processes, such as high-risk screening are recommended. The identification of Seniors at Risk (ISAR) tool is a 6-item validated screening tool for identifying elderly patients at risk of the adverse outcomes post-ED visit. This paper describes the implementation of the tool in the Mount Sinai Hospital emergency department using a Plan-Do-Study-Act model; and demonstrates whether the tool predicts adverse outcomes. An observational study tracked tool implementation. A retrospective chart audit was completed to collect data about elderly ED patients during 2 time periods in 2010 and 2011. Data analysis compared the characteristics of patients with positive and negative screening tool results. The identification of Seniors at Risk tool was completed for 51.6% of eligible patients, with 61.2% of patients having a positive result. Patients with positive screening results were more likely to be over age 79 (P = .003); be admitted to hospital (P Risk tool was challenged by problematic compliance with tool completion. Strategies to address this included tool adaptation; and providing staff with knowledge of ED and inpatient geriatric resources and feedback on completion rates. Positive screening results predicted adverse outcomes in elderly Mount Sinai Hospital ED patients. © 2014. Published by Elsevier Inc. All rights reserved.

Recurrent epistaxis: predicting risk of 30-day readmission, derivation and validation of RHINO-ooze score.

Science.gov (United States)

Addison, A; Paul, C; Kuo, R; Lamyman, A; Martinez-Devesa, P; Hettige, R

2017-06-01

To derive and validate a predictive scoring tool (RHINO-ooze score) with good sensitivity and specificity in identifying patients with epistaxis at high risk of 30 day readmission and to enable risk stratification for possible definitive intervention. Using medical databases, we searched for factors influencing recurrent epistaxis. The information ascertained together with our analysis of retrospective data on patients admitted with epistaxis between October 2013 and September 2014, was used as the derivation cohort to develop the predictive scoring model (RHINO-ooze score). The tool was validated by performing statistical analysis on the validation cohort of patients admitted with epistaxis between October 2014 and October 2015. Multiple linear regressions with backwards elimination was used to derive the predictive model. The area under the curve (AUC), sensitivity and specificity were calculated. 834 admissions were encountered within the study period. Using the derivative cohort (n= 302) the RHINO-ooze score with a maximum score of 8 from five variables (Recent admission, Haemorrhage point unidentified, Increasing age over 70, posterior Nasal packing, Oral anticoagulant) was developed. The RHINO-ooze score had a chi-square value of 99.72 with a significance level of smaller than 0.0001 and hence an overall good model fit. Comparison between the derivative and validation groups revealed similar rates of 30-day readmission between the cohorts. The sensitivity and specificity of predicting 30-day readmission in high risk patients with recurrent epistaxis (RHINO-ooze score equal/larger than 6) was 81% and 84%, respectively. The RHINO-ooze scoring tool demonstrates good specificity and sensitivity in predicting the risk of 30 day readmission in patients with epistaxis and can be used as an adjunct to clinical decision making with regards to timing of operative intervention in order to reduce readmission rates.

Quantifying prognosis with risk predictions.

Science.gov (United States)

Pace, Nathan L; Eberhart, Leopold H J; Kranke, Peter R

2012-01-01

Prognosis is a forecast, based on present observations in a patient, of their probable outcome from disease, surgery and so on. Research methods for the development of risk probabilities may not be familiar to some anaesthesiologists. We briefly describe methods for identifying risk factors and risk scores. A probability prediction rule assigns a risk probability to a patient for the occurrence of a specific event. Probability reflects the continuum between absolute certainty (Pi = 1) and certified impossibility (Pi = 0). Biomarkers and clinical covariates that modify risk are known as risk factors. The Pi as modified by risk factors can be estimated by identifying the risk factors and their weighting; these are usually obtained by stepwise logistic regression. The accuracy of probabilistic predictors can be separated into the concepts of 'overall performance', 'discrimination' and 'calibration'. Overall performance is the mathematical distance between predictions and outcomes. Discrimination is the ability of the predictor to rank order observations with different outcomes. Calibration is the correctness of prediction probabilities on an absolute scale. Statistical methods include the Brier score, coefficient of determination (Nagelkerke R2), C-statistic and regression calibration. External validation is the comparison of the actual outcomes to the predicted outcomes in a new and independent patient sample. External validation uses the statistical methods of overall performance, discrimination and calibration and is uniformly recommended before acceptance of the prediction model. Evidence from randomised controlled clinical trials should be obtained to show the effectiveness of risk scores for altering patient management and patient outcomes.

Distribution of Short-Term and Lifetime Predicted Risks of Cardiovascular Diseases in Peruvian Adults

Science.gov (United States)

Quispe, Renato; Bazo-Alvarez, Juan Carlos; Burroughs Peña, Melissa S; Poterico, Julio A; Gilman, Robert H; Checkley, William; Bernabé-Ortiz, Antonio; Huffman, Mark D; Miranda, J Jaime

2015-01-01

Background Short-term risk assessment tools for prediction of cardiovascular disease events are widely recommended in clinical practice and are used largely for single time-point estimations; however, persons with low predicted short-term risk may have higher risks across longer time horizons. Methods and Results We estimated short-term and lifetime cardiovascular disease risk in a pooled population from 2 studies of Peruvian populations. Short-term risk was estimated using the atherosclerotic cardiovascular disease Pooled Cohort Risk Equations. Lifetime risk was evaluated using the algorithm derived from the Framingham Heart Study cohort. Using previously published thresholds, participants were classified into 3 categories: low short-term and low lifetime risk, low short-term and high lifetime risk, and high short-term predicted risk. We also compared the distribution of these risk profiles across educational level, wealth index, and place of residence. We included 2844 participants (50% men, mean age 55.9 years [SD 10.2 years]) in the analysis. Approximately 1 of every 3 participants (34% [95% CI 33 to 36]) had a high short-term estimated cardiovascular disease risk. Among those with a low short-term predicted risk, more than half (54% [95% CI 52 to 56]) had a high lifetime predicted risk. Short-term and lifetime predicted risks were higher for participants with lower versus higher wealth indexes and educational levels and for those living in urban versus rural areas (PPeruvian adults were classified as low short-term risk but high lifetime risk. Vulnerable adults, such as those from low socioeconomic status and those living in urban areas, may need greater attention regarding cardiovascular preventive strategies. PMID:26254303

Algorithm for predicting death among older adults in the home care setting: study protocol for the Risk Evaluation for Support: Predictions for Elder-life in the Community Tool (RESPECT).

Science.gov (United States)

Hsu, Amy T; Manuel, Douglas G; Taljaard, Monica; Chalifoux, Mathieu; Bennett, Carol; Costa, Andrew P; Bronskill, Susan; Kobewka, Daniel; Tanuseputro, Peter

2016-12-01

Older adults living in the community often have multiple, chronic conditions and functional impairments. A challenge for healthcare providers working in the community is the lack of a predictive tool that can be applied to the broad spectrum of mortality risks observed and may be used to inform care planning. To predict survival time for older adults in the home care setting. The final mortality risk algorithm will be implemented as a web-based calculator that can be used by older adults needing care and by their caregivers. Open cohort study using the Resident Assessment Instrument for Home Care (RAI-HC) data in Ontario, Canada, from 1 January 2007 to 31 December 2013. The derivation cohort will consist of ∼437 000 older adults who had an RAI-HC assessment between 1 January 2007 and 31 December 2012. A split sample validation cohort will include ∼122 000 older adults with an RAI-HC assessment between 1 January and 31 December 2013. Predicted survival from the time of an RAI-HC assessment. All deaths (n≈245 000) will be ascertained through linkage to a population-based registry that is maintained by the Ministry of Health in Ontario. Proportional hazards regression will be estimated after assessment of assumptions. Predictors will include sociodemographic factors, social support, health conditions, functional status, cognition, symptoms of decline and prior healthcare use. Model performance will be evaluated for 6-month and 12-month predicted risks, including measures of calibration (eg, calibration plots) and discrimination (eg, c-statistics). The final algorithm will use combined development and validation data. Research ethics approval has been granted by the Sunnybrook Health Sciences Centre Review Board. Findings will be disseminated through presentations at conferences and in peer-reviewed journals. NCT02779309, Pre-results. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to

FRAT-up, a Web-based fall-risk assessment tool for elderly people living in the community.

Science.gov (United States)

Cattelani, Luca; Palumbo, Pierpaolo; Palmerini, Luca; Bandinelli, Stefania; Becker, Clemens; Chesani, Federico; Chiari, Lorenzo

2015-02-18

About 30% of people over 65 are subject to at least one unintentional fall a year. Fall prevention protocols and interventions can decrease the number of falls. To be effective, a prevention strategy requires a prior step to evaluate the fall risk of the subjects. Despite extensive research, existing assessment tools for fall risk have been insufficient for predicting falls. The goal of this study is to present a novel web-based fall-risk assessment tool (FRAT-up) and to evaluate its accuracy in predicting falls, within a context of community-dwelling persons aged 65 and up. FRAT-up is based on the assumption that a subject's fall risk is given by the contribution of their exposure to each of the known fall-risk factors. Many scientific studies have investigated the relationship between falls and risk factors. The majority of these studies adopted statistical approaches, usually providing quantitative information such as odds ratios. FRAT-up exploits these numerical results to compute how each single factor contributes to the overall fall risk. FRAT-up is based on a formal ontology that enlists a number of known risk factors, together with quantitative findings in terms of odds ratios. From such information, an automatic algorithm generates a rule-based probabilistic logic program, that is, a set of rules for each risk factor. The rule-based program takes the health profile of the subject (in terms of exposure to the risk factors) and computes the fall risk. A Web-based interface allows users to input health profiles and to visualize the risk assessment for the given subject. FRAT-up has been evaluated on the InCHIANTI Study dataset, a representative population-based study of older persons living in the Chianti area (Tuscany, Italy). We compared reported falls with predicted ones and computed performance indicators. The obtained area under curve of the receiver operating characteristic was 0.642 (95% CI 0.614-0.669), while the Brier score was 0.174. The Hosmer

Predictive Data Tools Find Uses in Schools

Science.gov (United States)

Sparks, Sarah D.

2011-01-01

The use of analytic tools to predict student performance is exploding in higher education, and experts say the tools show even more promise for K-12 schools, in everything from teacher placement to dropout prevention. Use of such statistical techniques is hindered in precollegiate schools, however, by a lack of researchers trained to help…

TCF7L2 variant genotypes and type 2 diabetes risk in Brazil: significant association, but not a significant tool for risk stratification in the general population

Directory of Open Access Journals (Sweden)

Mill JG

2008-12-01

Full Text Available Abstract Background Genetic polymorphisms of the TCF7L2 gene are strongly associated with large increments in type 2 diabetes risk in different populations worldwide. In this study, we aimed to confirm the effect of the TCF7L2 polymorphism rs7903146 on diabetes risk in a Brazilian population and to assess the use of this genetic marker in improving diabetes risk prediction in the general population. Methods We genotyped the single nucleotide polymorphisms (SNP rs7903146 of the TCF7L2 gene in 560 patients with known coronary disease enrolled in the MASS II (Medicine, Angioplasty, or Surgery Study Trial and in 1,449 residents of Vitoria, in Southeast Brazil. The associations of this gene variant to diabetes risk and metabolic characteristics in these two different populations were analyzed. To access the potential benefit of using this marker for diabetes risk prediction in the general population we analyzed the impact of this genetic variant on a validated diabetes risk prediction tool based on clinical characteristics developed for the Brazilian general population. Results SNP rs7903146 of the TCF7L2 gene was significantly associated with type 2 diabetes in the MASS-II population (OR = 1.57 per T allele, p = 0.0032, confirming, in the Brazilian population, previous reports of the literature. Addition of this polymorphism to an established clinical risk prediction score did not increased model accuracy (both area under ROC curve equal to 0.776. Conclusion TCF7L2 rs7903146 T allele is associated with a 1.57 increased risk for type 2 diabetes in a Brazilian cohort of patients with known coronary heart disease. However, the inclusion of this polymorphism in a risk prediction tool developed for the general population resulted in no improvement of performance. This is the first study, to our knowledge, that has confirmed this recent association in a South American population and adds to the great consistency of this finding in studies around the world

Predicting Sepsis Risk Using the "Sniffer" Algorithm in the Electronic Medical Record.

Science.gov (United States)

Olenick, Evelyn M; Zimbro, Kathie S; DʼLima, Gabrielle M; Ver Schneider, Patricia; Jones, Danielle

The Sepsis "Sniffer" Algorithm (SSA) has merit as a digital sepsis alert but should be considered an adjunct to versus an alternative for the Nurse Screening Tool (NST), given lower specificity and positive predictive value. The SSA reduced the risk of incorrectly categorizing patients at low risk for sepsis, detected sepsis high risk in half the time, and reduced redundant NST screens by 70% and manual screening hours by 64% to 72%. Preserving nurse hours expended on manual sepsis alerts may translate into time directed toward other patient priorities.

Risk predicting of macropore flow using pedotransfer functions, textural maps and modeling

DEFF Research Database (Denmark)

Iversen, Bo Vangsø; Børgesen, Christen Duus; Lægdsmand, Mette

2011-01-01

of this study were first to develop pedotransfer functions (PTFs) predicting near-saturated [k(−1)] and saturated (Ks) hydraulic conductivity using simple soil parameters as predictors and second to use this information and a newly developed rasterbased soil property map of Denmark to identify risk areas...... modeling were used to construct a new map dividing Denmark into risk categories for macropore flow. This map can be combined with other tools to identify areas where there is a high risk of contaminants leaching out of the root zone....

Risk indicators as a tool for risk control

International Nuclear Information System (INIS)

Oien, K.

2001-01-01

This paper presents a general methodology for the establishment of risk indicators that can be used as a tool for risk control during operation of offshore petroleum installations. The risk indicators established are based on the platform specific quantitative risk analysis (QRA). The general methodology is evaluated against comparable approaches both in offshore and nuclear industry. There are two distinct features of this methodology. The first is that it is truly risk-based with the intention of covering the total risk picture. The second is that the identification of the risk factors contributing most to the total risk is based on realistic changes of each factor assessed by the platform personnel, not a theoretically assumed change. The set of risk indicators for one specific installation is presented along with test results

Cardiovascular Disease Population Risk Tool (CVDPoRT): predictive algorithm for assessing CVD risk in the community setting. A study protocol.

Science.gov (United States)

Taljaard, Monica; Tuna, Meltem; Bennett, Carol; Perez, Richard; Rosella, Laura; Tu, Jack V; Sanmartin, Claudia; Hennessy, Deirdre; Tanuseputro, Peter; Lebenbaum, Michael; Manuel, Douglas G

2014-10-23

Recent publications have called for substantial improvements in the design, conduct, analysis and reporting of prediction models. Publication of study protocols, with prespecification of key aspects of the analysis plan, can help to improve transparency, increase quality and protect against increased type I error. Valid population-based risk algorithms are essential for population health planning and policy decision-making. The purpose of this study is to develop, evaluate and apply cardiovascular disease (CVD) risk algorithms for the population setting. The Ontario sample of the Canadian Community Health Survey (2001, 2003, 2005; 77,251 respondents) will be used to assess risk factors focusing on health behaviours (physical activity, diet, smoking and alcohol use). Incident CVD outcomes will be assessed through linkage to administrative healthcare databases (619,886 person-years of follow-up until 31 December 2011). Sociodemographic factors (age, sex, immigrant status, education) and mediating factors such as presence of diabetes and hypertension will be included as predictors. Algorithms will be developed using competing risks survival analysis. The analysis plan adheres to published recommendations for the development of valid prediction models to limit the risk of overfitting and improve the quality of predictions. Key considerations are fully prespecifying the predictor variables; appropriate handling of missing data; use of flexible functions for continuous predictors; and avoiding data-driven variable selection procedures. The 2007 and 2009 surveys (approximately 50,000 respondents) will be used for validation. Calibration will be assessed overall and in predefined subgroups of importance to clinicians and policymakers. This study has been approved by the Ottawa Health Science Network Research Ethics Board. The findings will be disseminated through professional and scientific conferences, and in peer-reviewed journals. The algorithm will be accessible

Automatic generation of bioinformatics tools for predicting protein-ligand binding sites.

Science.gov (United States)

Komiyama, Yusuke; Banno, Masaki; Ueki, Kokoro; Saad, Gul; Shimizu, Kentaro

2016-03-15

Predictive tools that model protein-ligand binding on demand are needed to promote ligand research in an innovative drug-design environment. However, it takes considerable time and effort to develop predictive tools that can be applied to individual ligands. An automated production pipeline that can rapidly and efficiently develop user-friendly protein-ligand binding predictive tools would be useful. We developed a system for automatically generating protein-ligand binding predictions. Implementation of this system in a pipeline of Semantic Web technique-based web tools will allow users to specify a ligand and receive the tool within 0.5-1 day. We demonstrated high prediction accuracy for three machine learning algorithms and eight ligands. The source code and web application are freely available for download at http://utprot.net They are implemented in Python and supported on Linux. shimizu@bi.a.u-tokyo.ac.jp Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

RISK REDUCTION WITH A FUZZY EXPERT EXPLORATION TOOL

Energy Technology Data Exchange (ETDEWEB)

William W. Weiss

2000-06-30

Incomplete or sparse information on geologic or formation characteristics introduces a high level of risk for oil exploration and development projects. Expert systems have been developed and used in several disciplines and industries, including medical diagnostics, with favorable results. A state-of-the-art exploration ''expert'' tool, relying on a computerized data base and computer maps generated by neural networks, is proposed through the use of ''fuzzy'' logic, a relatively new mathematical treatment of imprecise or non-explicit parameters and values. This project will develop an Artificial Intelligence system that will draw upon a wide variety of information to provide realistic estimates of risk. ''Fuzzy logic,'' a system of integrating large amounts of inexact, incomplete information with modern computational methods to derive usable conclusions, has been demonstrated as a cost-effective computational technology in many industrial applications. During project year 1, 90% of geologic, geophysical, production and price data were assimilated for installation into the database. Logs provided geologic data consisting of formation tops of the Brushy Canyon, Lower Brushy Canyon, and Bone Springs zones of 700 wells used to construct regional cross sections. Regional structure and isopach maps were constructed using kriging to interpolate between the measured points. One of the structure derivative maps (azimuth of curvature) visually correlates with Brushy Canyon fields on the maximum change contours. Derivatives of the regional geophysical data also visually correlate with the location of the fields. The azimuth of maximum dip approximately locates fields on the maximum change contours. In a similar manner the second derivative in the x-direction of the gravity map visually correlates with the alignment of the known fields. The visual correlations strongly suggest that neural network architectures will be

Deep learning architectures for multi-label classification of intelligent health risk prediction.

Science.gov (United States)

Maxwell, Andrew; Li, Runzhi; Yang, Bei; Weng, Heng; Ou, Aihua; Hong, Huixiao; Zhou, Zhaoxian; Gong, Ping; Zhang, Chaoyang

2017-12-28

Multi-label classification of data remains to be a challenging problem. Because of the complexity of the data, it is sometimes difficult to infer information about classes that are not mutually exclusive. For medical data, patients could have symptoms of multiple different diseases at the same time and it is important to develop tools that help to identify problems early. Intelligent health risk prediction models built with deep learning architectures offer a powerful tool for physicians to identify patterns in patient data that indicate risks associated with certain types of chronic diseases. Physical examination records of 110,300 anonymous patients were used to predict diabetes, hypertension, fatty liver, a combination of these three chronic diseases, and the absence of disease (8 classes in total). The dataset was split into training (90%) and testing (10%) sub-datasets. Ten-fold cross validation was used to evaluate prediction accuracy with metrics such as precision, recall, and F-score. Deep Learning (DL) architectures were compared with standard and state-of-the-art multi-label classification methods. Preliminary results suggest that Deep Neural Networks (DNN), a DL architecture, when applied to multi-label classification of chronic diseases, produced accuracy that was comparable to that of common methods such as Support Vector Machines. We have implemented DNNs to handle both problem transformation and algorithm adaption type multi-label methods and compare both to see which is preferable. Deep Learning architectures have the potential of inferring more information about the patterns of physical examination data than common classification methods. The advanced techniques of Deep Learning can be used to identify the significance of different features from physical examination data as well as to learn the contributions of each feature that impact a patient's risk for chronic diseases. However, accurate prediction of chronic disease risks remains a challenging

Application of cardiovascular disease risk prediction models and the relevance of novel biomarkers to risk stratification in Asian Indians.

Science.gov (United States)

Kanjilal, S; Rao, V S; Mukherjee, M; Natesha, B K; Renuka, K S; Sibi, K; Iyengar, S S; Kakkar, Vijay V

2008-01-01

The increasing pressure on health resources has led to the emergence of risk assessment as an essential tool in the management of cardiovascular disease (CVD). Concern exists regarding the validity of their generalization to all populations. Existing risk scoring models do not incorporate emerging 'novel' risk factors. In this context, the aim of the study was to examine the relevance of British, European, and Framingham predictive CVD risk scores to the asymptomatic high risk Indian population. Blood samples drawn from the participants were analyzed for various 'traditional' and 'novel' biomarkers, and their CVD risk factor profiling was also done. The Framingham model defined only 5% of the study cohort to be at high risk, which appears to be an underestimation of CVD risk in this genetically predisposed population. These subjects at high risk had significantly elevated levels of lipid, pro-inflammatory, pro-thrombotic, and serological markers. It is more relevant to develop risk predictive scores for application to the Indian population. This study substantiates the argument that alternative approaches to risk stratification are required in order to make them more adaptable and applicable to different populations with varying risk factor and disease patterns.

In silico tools to aid risk assessment of endocrine disrupting chemicals

International Nuclear Information System (INIS)

Jacobs, M.N.

2004-01-01

In silico or computational tools could be used more effectively in endocrine disruptor risk assessment for prescreening potential endocrine disruptors, improving experimental in vitro screening assay design and facilitating more thorough data analyses. The in silico tools reviewed here are three-fold and include the use of: (1) nuclear receptor (NR) crystal structures and homology models to examine potential modes of ligand binding by different representative compounds; (2) multivariate principal component analyses (PCA) techniques to select best predicted cell lines for endocrine disrupting chemicals (EDC) risk assessment purposes; (3) NR quantitative structure-activity relationships (QSARs) that can be constructed from varied biological data sources, using multivariate partial least squares (PLS) techniques and specific descriptors. The cytosolic and NR examples discussed here include the Ah receptor (AhR), the human oestrogen receptor α (hERα) and the human pregnane X receptor (PXR). The varied biological data sets can be compared to give a more integrated dimension to receptor cross talk mechanisms, with further support from molecular modelling studies

Distribution of Short-Term and Lifetime Predicted Risks of Cardiovascular Diseases in Peruvian Adults.

Science.gov (United States)

Quispe, Renato; Bazo-Alvarez, Juan Carlos; Burroughs Peña, Melissa S; Poterico, Julio A; Gilman, Robert H; Checkley, William; Bernabé-Ortiz, Antonio; Huffman, Mark D; Miranda, J Jaime

2015-08-07

Short-term risk assessment tools for prediction of cardiovascular disease events are widely recommended in clinical practice and are used largely for single time-point estimations; however, persons with low predicted short-term risk may have higher risks across longer time horizons. We estimated short-term and lifetime cardiovascular disease risk in a pooled population from 2 studies of Peruvian populations. Short-term risk was estimated using the atherosclerotic cardiovascular disease Pooled Cohort Risk Equations. Lifetime risk was evaluated using the algorithm derived from the Framingham Heart Study cohort. Using previously published thresholds, participants were classified into 3 categories: low short-term and low lifetime risk, low short-term and high lifetime risk, and high short-term predicted risk. We also compared the distribution of these risk profiles across educational level, wealth index, and place of residence. We included 2844 participants (50% men, mean age 55.9 years [SD 10.2 years]) in the analysis. Approximately 1 of every 3 participants (34% [95% CI 33 to 36]) had a high short-term estimated cardiovascular disease risk. Among those with a low short-term predicted risk, more than half (54% [95% CI 52 to 56]) had a high lifetime predicted risk. Short-term and lifetime predicted risks were higher for participants with lower versus higher wealth indexes and educational levels and for those living in urban versus rural areas (PPeruvian adults were classified as low short-term risk but high lifetime risk. Vulnerable adults, such as those from low socioeconomic status and those living in urban areas, may need greater attention regarding cardiovascular preventive strategies. © 2015 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

Performance of the HEMORR(2)HAGES, ATRIA, and HAS-BLED Bleeding Risk-Prediction Scores in Patients With Atrial Fibrillation Undergoing Anticoagulation

NARCIS (Netherlands)

Apostolakis, Stavros; Lane, Deirdre A.; Guo, Yutao; Buller, Harry; Lip, Gregory Y. H.

2012-01-01

Objectives The objective of this study was to compare the predictive performance of bleeding risk-estimation tools in a cohort of patients with atrial fibrillation (AF) undergoing anticoagulation. Background Three bleeding risk-prediction schemes have been derived for and validated in patients with

Thyroid Cancer Risk Assessment Tool

Science.gov (United States)

The R package thyroid implements a risk prediction model developed by NCI researchers to calculate the absolute risk of developing a second primary thyroid cancer (SPTC) in individuals who were diagnosed with a cancer during their childhood.

Analysis of Alternatives for Risk Assessment Methodologies and Tools

Energy Technology Data Exchange (ETDEWEB)

Nachtigal, Noel M. [Sandia National Lab. (SNL-CA), Livermore, CA (United States). System Analytics; Fruetel, Julia A. [Sandia National Lab. (SNL-CA), Livermore, CA (United States). Systems Research and Analysis; Gleason, Nathaniel J. [Sandia National Lab. (SNL-CA), Livermore, CA (United States). Systems Research and Analysis; Helms, Jovana [Sandia National Lab. (SNL-CA), Livermore, CA (United States). Systems Research and Analysis; Imbro, Dennis Raymond [Sandia National Lab. (SNL-CA), Livermore, CA (United States). Systems Research and Analysis; Sumner, Matthew C. [Sandia National Lab. (SNL-CA), Livermore, CA (United States). Systems Research and Analysis

2013-10-01

The purpose of this document is to provide a basic overview and understanding of risk assessment methodologies and tools from the literature and to assess the suitability of these methodologies and tools for cyber risk assessment. Sandia National Laboratories (SNL) performed this review in support of risk modeling activities performed for the Stakeholder Engagement and Cyber Infrastructure Resilience (SECIR) division of the Department of Homeland Security (DHS) Office of Cybersecurity and Communications (CS&C). The set of methodologies and tools covered in this document is not intended to be exhaustive; instead, it focuses on those that are commonly used in the risk assessment community. The classification of methodologies and tools was performed by a group of analysts with experience in risk analysis and cybersecurity, and the resulting analysis of alternatives has been tailored to address the needs of a cyber risk assessment.

Draught risk index tool for building energy simulations

DEFF Research Database (Denmark)

Vorre, Mette Havgaard; Jensen, Rasmus Lund; Nielsen, Peter V.

2014-01-01

Flow elements combined with a building energy simulation tool can be used to indicate areas and periods when there is a risk of draught in a room. The study tests this concept by making a tool for post-processing of data from building energy simulations. The objective is to show indications...... of draught risk during a whole year, giving building designers a tool for the design stage of a building. The tool uses simple one-at-a-time calculations of flow elements and assesses the uncertainty of the result by counting the number of overlapping flow elements. The calculation time is low, making...... it usable in the early design stage to optimise the building layout. The tool provides an overview of the general draught pattern over a period, e.g. a whole year, and of how often there is a draught risk....

Gaussian process regression for tool wear prediction

Science.gov (United States)

Kong, Dongdong; Chen, Yongjie; Li, Ning

2018-05-01

To realize and accelerate the pace of intelligent manufacturing, this paper presents a novel tool wear assessment technique based on the integrated radial basis function based kernel principal component analysis (KPCA_IRBF) and Gaussian process regression (GPR) for real-timely and accurately monitoring the in-process tool wear parameters (flank wear width). The KPCA_IRBF is a kind of new nonlinear dimension-increment technique and firstly proposed for feature fusion. The tool wear predictive value and the corresponding confidence interval are both provided by utilizing the GPR model. Besides, GPR performs better than artificial neural networks (ANN) and support vector machines (SVM) in prediction accuracy since the Gaussian noises can be modeled quantitatively in the GPR model. However, the existence of noises will affect the stability of the confidence interval seriously. In this work, the proposed KPCA_IRBF technique helps to remove the noises and weaken its negative effects so as to make the confidence interval compressed greatly and more smoothed, which is conducive for monitoring the tool wear accurately. Moreover, the selection of kernel parameter in KPCA_IRBF can be easily carried out in a much larger selectable region in comparison with the conventional KPCA_RBF technique, which helps to improve the efficiency of model construction. Ten sets of cutting tests are conducted to validate the effectiveness of the presented tool wear assessment technique. The experimental results show that the in-process flank wear width of tool inserts can be monitored accurately by utilizing the presented tool wear assessment technique which is robust under a variety of cutting conditions. This study lays the foundation for tool wear monitoring in real industrial settings.

Risk monitor - a tool for operational safety assessment risk monitor - user's manual

International Nuclear Information System (INIS)

Hari Prasad, M.; Vinod, Gopika; Saraf, R.K.; Ghosh, A.K.

2006-06-01

Probabilistic Safety Assessment has become a key tool as on today to identify and understand Nuclear Power Plant vulnerabilities. As a result of the availability of these PSA studies, there is a desire to use them to enhance plant safety and to operate the nuclear stations in the most efficient manner. Risk Monitor is a PC based tool, which computes the real time safety level and assists plant personnel to manage day-to-day activities. Risk Monitor is a PC based user friendly software tool used for modification and re-analysis of a nuclear Power plant. Operation of Risk Monitor is based on PSA methods for assisting in day to day applications. Risk Monitoring programs can assess the risk profile and are used to optimize the operation of Nuclear Power Plants with respect to a minimum risk level over the operating time. This report presents the background activities of Risk Monitor, its application areas and the step by step procedure for the user.to interact with the software. This software can be used with the PSA model of any Nuclear Power Plant. (author)

Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups.

Science.gov (United States)

Marschollek, Michael; Gövercin, Mehmet; Rust, Stefan; Gietzelt, Matthias; Schulze, Mareike; Wolf, Klaus-Hendrik; Steinhagen-Thiessen, Elisabeth

2012-03-14

Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients' assessment data and to evaluate their predictive performance (aim#1), and to identify high-risk subgroups from the data (aim#2). A data set of n = 5,176 single in-patient episodes covering 1.5 years of admissions to a geriatric hospital were extracted from the hospital's data base and matched with fall incident reports (n = 493). A classification tree model was induced using the C4.5 algorithm as well as a logistic regression model, and their predictive performance was evaluated. Furthermore, high-risk subgroups were identified from extracted classification rules with a support of more than 100 instances. The classification tree model showed an overall classification accuracy of 66%, with a sensitivity of 55.4%, a specificity of 67.1%, positive and negative predictive values of 15% resp. 93.5%. Five high-risk groups were identified, defined by high age, low Barthel index, cognitive impairment, multi-medication and co-morbidity. Our results show that a little more than half of the fallers may be identified correctly by our model, but the positive predictive value is too low to be applicable. Non-fallers, on the other hand, may be sorted out with the model quite well. The high-risk subgroups and the risk factors identified (age, low ADL score, cognitive impairment, institutionalization, polypharmacy and co-morbidity) reflect domain knowledge and may be used to screen certain subgroups of patients with a high risk of falling. Classification models derived from a large data set using data mining methods can compete with current dedicated fall risk screening tools, yet lack diagnostic precision. High-risk subgroups may be identified

Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups

Directory of Open Access Journals (Sweden)

Marschollek Michael

2012-03-01

Full Text Available Abstract Background Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients' assessment data and to evaluate their predictive performance (aim#1, and to identify high-risk subgroups from the data (aim#2. Methods A data set of n = 5,176 single in-patient episodes covering 1.5 years of admissions to a geriatric hospital were extracted from the hospital's data base and matched with fall incident reports (n = 493. A classification tree model was induced using the C4.5 algorithm as well as a logistic regression model, and their predictive performance was evaluated. Furthermore, high-risk subgroups were identified from extracted classification rules with a support of more than 100 instances. Results The classification tree model showed an overall classification accuracy of 66%, with a sensitivity of 55.4%, a specificity of 67.1%, positive and negative predictive values of 15% resp. 93.5%. Five high-risk groups were identified, defined by high age, low Barthel index, cognitive impairment, multi-medication and co-morbidity. Conclusions Our results show that a little more than half of the fallers may be identified correctly by our model, but the positive predictive value is too low to be applicable. Non-fallers, on the other hand, may be sorted out with the model quite well. The high-risk subgroups and the risk factors identified (age, low ADL score, cognitive impairment, institutionalization, polypharmacy and co-morbidity reflect domain knowledge and may be used to screen certain subgroups of patients with a high risk of falling. Classification models derived from a large data set using data mining methods can compete with current dedicated fall risk screening tools, yet lack

Daylight prediction techniques in energy design tools

Energy Technology Data Exchange (ETDEWEB)

Milne, M.; Zurick, J. [California Univ., Los Angeles, Dept. of Architecture, CA (United States)

1998-09-01

Four different whole-building energy design tool systems that calculate energy savings from daylighting and that display annual performance on an-hour-by-hour basis, have been tested. The nature of design tools, the sources of hourly outdoor illuminance data, the ways of predicting indoor illumination, the assumptions of each tool, and the resulting energy savings of the design tools tested are discussed. The tests were carried out with the essential criteria for evaluating whole-building daylighting and energy design tools in mind. These have been identified as user confidence, accuracy, response time, and the amount of detail. Results of the tests, all four of them run on a single elementary school classroom for the sake of comparability, were provided. 9 refs., 2 figs.

A predictive tool to estimate the risk of axillary metastases in breast cancer patients with negative axillary ultrasound

DEFF Research Database (Denmark)

Meretoja, T J; Heikkilä, P S; Mansfield, A S

2014-01-01

of this study was to evaluate the risk factors for axillary metastases in breast cancer patients with negative preoperative axillary ultrasound. METHODS: A total of 1,395 consecutive patients with invasive breast cancer and SNB formed the original patient series. A univariate analysis was conducted to assess...... risk factors for axillary metastases. Binary logistic regression analysis was conducted to form a predictive model based on the risk factors. The predictive model was first validated internally in a patient series of 566 further patients and then externally in a patient series of 2,463 patients from......BACKGROUND: Sentinel node biopsy (SNB) is the "gold standard" in axillary staging in clinically node-negative breast cancer patients. However, axillary treatment is undergoing a paradigm shift and studies are being conducted on whether SNB may be omitted in low-risk patients. The purpose...

Application of predictive modelling techniques in industry: from food design up to risk assessment.

Science.gov (United States)

Membré, Jeanne-Marie; Lambert, Ronald J W

2008-11-30

In this communication, examples of applications of predictive microbiology in industrial contexts (i.e. Nestlé and Unilever) are presented which cover a range of applications in food safety from formulation and process design to consumer safety risk assessment. A tailor-made, private expert system, developed to support safe product/process design assessment is introduced as an example of how predictive models can be deployed for use by non-experts. Its use in conjunction with other tools and software available in the public domain is discussed. Specific applications of predictive microbiology techniques are presented relating to investigations of either growth or limits to growth with respect to product formulation or process conditions. An example of a probabilistic exposure assessment model for chilled food application is provided and its potential added value as a food safety management tool in an industrial context is weighed against its disadvantages. The role of predictive microbiology in the suite of tools available to food industry and some of its advantages and constraints are discussed.

Lipoprotein metabolism indicators improve cardiovascular risk prediction.

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Daniël B van Schalkwijk

Full Text Available BACKGROUND: Cardiovascular disease risk increases when lipoprotein metabolism is dysfunctional. We have developed a computational model able to derive indicators of lipoprotein production, lipolysis, and uptake processes from a single lipoprotein profile measurement. This is the first study to investigate whether lipoprotein metabolism indicators can improve cardiovascular risk prediction and therapy management. METHODS AND RESULTS: We calculated lipoprotein metabolism indicators for 1981 subjects (145 cases, 1836 controls from the Framingham Heart Study offspring cohort in which NMR lipoprotein profiles were measured. We applied a statistical learning algorithm using a support vector machine to select conventional risk factors and lipoprotein metabolism indicators that contributed to predicting risk for general cardiovascular disease. Risk prediction was quantified by the change in the Area-Under-the-ROC-Curve (ΔAUC and by risk reclassification (Net Reclassification Improvement (NRI and Integrated Discrimination Improvement (IDI. Two VLDL lipoprotein metabolism indicators (VLDLE and VLDLH improved cardiovascular risk prediction. We added these indicators to a multivariate model with the best performing conventional risk markers. Our method significantly improved both CVD prediction and risk reclassification. CONCLUSIONS: Two calculated VLDL metabolism indicators significantly improved cardiovascular risk prediction. These indicators may help to reduce prescription of unnecessary cholesterol-lowering medication, reducing costs and possible side-effects. For clinical application, further validation is required.

Using Predictive Modelling to Identify Students at Risk of Poor University Outcomes

Science.gov (United States)

Jia, Pengfei; Maloney, Tim

2015-01-01

Predictive modelling is used to identify students at risk of failing their first-year courses and not returning to university in the second year. Our aim is twofold. Firstly, we want to understand the factors that lead to poor first-year experiences at university. Secondly, we want to develop simple, low-cost tools that would allow universities to…

Evaluation of Malnutrition Risk after Liver Transplantation Using the Nutritional Screening Tools.

Science.gov (United States)

Lim, Hee-Sook; Kim, Hyung-Chul; Park, Yoon-Hyung; Kim, Soon-Kyung

2015-10-01

Malnutrition is a common problem in patients with end-stage liver disease requiring liver transplantation. The aim of this study was to evaluate nutritional status by using nutritional screening tools [Nutritional Risk Screening (NRS) 2002, Malnutrition Universal Screening Tool (MUST) and Subjective Global Assessment (SGA)] in patients before and after liver transplantation. We analyzed medical record, blood test, nutrient intake and malnutrition rate just before transplantation and at discharge, and at 3, 6, 12 months after transplantation respectively. Initially 33 patients enrolled as study subjects and finally 28 patients completed the study. Nutrients intake such as energy, fiber, calcium, potassium, vitamin C, and folate were insufficient at 12 months after transplantation. The rates of malnutrition before transplantation were very high, reported at 81.8% for the NRS 2002, 87.9% for the MUST, and 84.8% for the SGA. By 12 months after operation, malnutrition rates reported at NRS, MUST and SGA had decreased to 6.1%, 10.7%, and 10.7%, respectively. Sensitivity was 87.1% for the NRS 2002, 82.0% for the MUST, and 92.0% for the SGA. Of these screening tools the SGA was the highest sensitive tool that predict the risk of mortality in malnutrition patients who received transplantation. Further studies on nutritional status of patients and proper tools for nutrition intervention are needed to provide adequate nutritional care for patients.

RISK COMMUNICATION IN ACTION: THE TOOLS OF MESSAGE MAPPING

Science.gov (United States)

Risk Communication in Action: The Tools of Message Mapping, is a workbook designed to guide risk communicators in crisis situations. The first part of this workbook will review general guidelines for risk communication. The second part will focus on one of the most robust tools o...

Osteoporosis risk prediction for bone mineral density assessment of postmenopausal women using machine learning.

Science.gov (United States)

Yoo, Tae Keun; Kim, Sung Kean; Kim, Deok Won; Choi, Joon Yul; Lee, Wan Hyung; Oh, Ein; Park, Eun-Cheol

2013-11-01

A number of clinical decision tools for osteoporosis risk assessment have been developed to select postmenopausal women for the measurement of bone mineral density. We developed and validated machine learning models with the aim of more accurately identifying the risk of osteoporosis in postmenopausal women compared to the ability of conventional clinical decision tools. We collected medical records from Korean postmenopausal women based on the Korea National Health and Nutrition Examination Surveys. The training data set was used to construct models based on popular machine learning algorithms such as support vector machines (SVM), random forests, artificial neural networks (ANN), and logistic regression (LR) based on simple surveys. The machine learning models were compared to four conventional clinical decision tools: osteoporosis self-assessment tool (OST), osteoporosis risk assessment instrument (ORAI), simple calculated osteoporosis risk estimation (SCORE), and osteoporosis index of risk (OSIRIS). SVM had significantly better area under the curve (AUC) of the receiver operating characteristic than ANN, LR, OST, ORAI, SCORE, and OSIRIS for the training set. SVM predicted osteoporosis risk with an AUC of 0.827, accuracy of 76.7%, sensitivity of 77.8%, and specificity of 76.0% at total hip, femoral neck, or lumbar spine for the testing set. The significant factors selected by SVM were age, height, weight, body mass index, duration of menopause, duration of breast feeding, estrogen therapy, hyperlipidemia, hypertension, osteoarthritis, and diabetes mellitus. Considering various predictors associated with low bone density, the machine learning methods may be effective tools for identifying postmenopausal women at high risk for osteoporosis.

Flight Experiment Verification of Shuttle Boundary Layer Transition Prediction Tool

Science.gov (United States)

Berry, Scott A.; Berger, Karen T.; Horvath, Thomas J.; Wood, William A.

2016-01-01

Boundary layer transition at hypersonic conditions is critical to the design of future high-speed aircraft and spacecraft. Accurate methods to predict transition would directly impact the aerothermodynamic environments used to size a hypersonic vehicle's thermal protection system. A transition prediction tool, based on wind tunnel derived discrete roughness correlations, was developed and implemented for the Space Shuttle return-to-flight program. This tool was also used to design a boundary layer transition flight experiment in order to assess correlation uncertainties, particularly with regard to high Mach-number transition and tunnel-to-flight scaling. A review is provided of the results obtained from the flight experiment in order to evaluate the transition prediction tool implemented for the Shuttle program.

Liquidity risk charges as a macroprudential tool

NARCIS (Netherlands)

Perotti, E.; Suarez, J.

2009-01-01

Liquidity risk charges were proposed in February 2009 as a new macro-prudential tool to discourage systemic risk creation by banks. A new CEPR Policy Insight refines this proposal in order to clarify challenging issues surrounding the implementation of liquidity risk charges.

Risk analysis tools for force protection and infrastructure/asset protection

International Nuclear Information System (INIS)

Jaeger, C.D.; Duggan, R.A.; Paulus, W.K.

1998-01-01

The Security Systems and Technology Center at Sandia National Laboratories has for many years been involved in the development and use of vulnerability assessment and risk analysis tools. In particular, two of these tools, ASSESS and JTS, have been used extensively for Department of Energy facilities. Increasingly, Sandia has been called upon to evaluate critical assets and infrastructures, support DoD force protection activities and assist in the protection of facilities from terrorist attacks using weapons of mass destruction. Sandia is involved in many different activities related to security and force protection and is expanding its capabilities by developing new risk analysis tools to support a variety of users. One tool, in the very early stages of development, is EnSURE, Engineered Surety Using the Risk Equation. EnSURE addresses all of the risk equation and integrates the many components into a single, tool-supported process to help determine the most cost-effective ways to reduce risk. This paper will briefly discuss some of these risk analysis tools within the EnSURE framework

A brief screening tool for assessing psychological trauma in clinical practice: development and validation of the New York PTSD Risk Score.

Science.gov (United States)

Boscarino, Joseph A; Kirchner, H Lester; Hoffman, Stuart N; Sartorius, Jennifer; Adams, Richard E; Figley, Charles R

2011-01-01

The objective was to develop a brief posttraumatic stress disorder (PTSD) screening instrument that is useful in clinical practice, similar to the Framingham Risk Score used in cardiovascular medicine. We used data collected in New York City after the World Trade Center disaster (WTCD) and other trauma data to develop a new PTSD prediction tool--the New York PTSD Risk Score. We used diagnostic test methods to examine different clinical domains, including PTSD symptoms, trauma exposures, sleep disturbances, suicidal thoughts, depression symptoms, demographic factors and other measures to assess different PTSD prediction models. Using receiver operating curve (ROC) and bootstrap methods, five prediction domains, including core PTSD symptoms, sleep disturbance, access to care status, depression symptoms and trauma history, and five demographic variables, including gender, age, education, race and ethnicity, were identified. For the best prediction model, the area under the ROC curve (AUC) was 0.880 for the Primary Care PTSD Screen alone (specificity=82.2%, sensitivity=93.7%). Adding care status, sleep disturbance, depression and trauma exposure increased the AUC to 0.943 (specificity=85.7%, sensitivity=93.1%), a significant ROC improvement (Pdevelopment and validation samples. The New York PTSD Risk Score is a multifactor prediction tool that includes the Primary Care PTSD Screen, depression symptoms, access to care, sleep disturbance, trauma history and demographic variables and appears to be effective in predicting PTSD among patients seen in healthcare settings. This prediction tool is simple to administer and appears to outperform other screening measures. Copyright © 2011 Elsevier Inc. All rights reserved.

The predictive validity of the HERO Scorecard in determining future health care cost and risk trends.

Science.gov (United States)

Goetzel, Ron Z; Henke, Rachel Mosher; Benevent, Richele; Tabrizi, Maryam J; Kent, Karen B; Smith, Kristyn J; Roemer, Enid Chung; Grossmeier, Jessica; Mason, Shawn T; Gold, Daniel B; Noeldner, Steven P; Anderson, David R

2014-02-01

To determine the ability of the Health Enhancement Research Organization (HERO) Scorecard to predict changes in health care expenditures. Individual employee health care insurance claims data for 33 organizations completing the HERO Scorecard from 2009 to 2011 were linked to employer responses to the Scorecard. Organizations were dichotomized into "high" versus "low" scoring groups and health care cost trends were compared. A secondary analysis examined the tool's ability to predict health risk trends. "High" scorers experienced significant reductions in inflation-adjusted health care costs (averaging an annual trend of -1.6% over 3 years) compared with "low" scorers whose cost trend remained stable. The risk analysis was inconclusive because of the small number of employers scoring "low." The HERO Scorecard predicts health care cost trends among employers. More research is needed to determine how well it predicts health risk trends for employees.

PREDICTING THE RISK OF LATENT TUBERCULOUS INFECTION IN THOSE SERVING THEIR SENTENCES IN THE PENITENTIARY SYSTEM

Directory of Open Access Journals (Sweden)

V. S. Borovitsky

2018-01-01

Full Text Available The objective of the study: to assess the prevalence of latent tuberculous infection (LTI and risk of its development in the inmates servicing their sentence in a penal colony of the Federal Penitentiary System, using the tools of statistics analysisSubjects and Methods. 232 persons in the age from 18 to 67 (27; 225-35 years old were examined Results. Exposure to a tuberculosis case in the past and duration of imprisonment are statistically confident risk factors of developing LTI The use of statistic tools allows predicting the chances of LTI 

A GIS-based tool for bioaccumulation risk analysis and its application to study polychlorinated biphenyls in the Great Lakes

Directory of Open Access Journals (Sweden)

Fernanda P. Maciel

2018-01-01

Full Text Available This paper presents a GIS-based tool named Arc-BEST (Bioaccumulation Evaluation Screening Tool to perform spatially distributed bioaccumulation risk analyses. Estimating bioaccumulation risk is important to help predict potentially adverse effects from contaminants on ecosystems and human health, which are key factors in the development of sound public policy. Arc-BEST is based on the BEST model in the U.S. Army Corps of Engineers BRAMS (Bioaccumulation Risk Assessment Modeling System software, released in 2012. It predicts concentration of concern contaminants in predators’ tissues from concentrations in organisms at the bottom of the food chain, and corresponding bioaccumulation factors. Additionally, it estimates carcinogenic and non-carcinogenic risks for humans that consume those species. The greatest contribution of Arc-BEST is that it enables the automated use of digital spatial data sets, which improves model creation speed, analysis and visualization of results, and comparison and cross-referencing with other geographic datasets. Furthermore, the model was improved to consider up to four trophic levels. The code is written in Python and is open-source. In this work Arc-BEST is used as part of a screening-level risk assessment process in order to identify hot spots where further studies and monitoring should be performed to ensure humans and ecosystems health. The tool is successfully applied to a case study in the Laurentian Great Lakes, where long-term effects of polychlorinated biphenyls (PCBs is performed, based on measured concentrations in zebra mussels (Dreissena polymorpha, and local bioaccumulation factors from previous studies. Zebra mussels have a great filtration capacity and high bioconcentration rates, increasing the bioavailability of contaminants for predator species. PCBs concentrations in different-level predators are predicted. Furthermore, health risks for humans that consume sport fish are estimated for various

Calibration plots for risk prediction models in the presence of competing risks

DEFF Research Database (Denmark)

Gerds, Thomas A; Andersen, Per K; Kattan, Michael W

2014-01-01

A predicted risk of 17% can be called reliable if it can be expected that the event will occur to about 17 of 100 patients who all received a predicted risk of 17%. Statistical models can predict the absolute risk of an event such as cardiovascular death in the presence of competing risks...... prediction model is well calibrated. The first is lack of independent validation data, the second is right censoring, and the third is that when the risk scale is continuous, the estimation problem is as difficult as density estimation. To deal with these problems, we propose to estimate calibration curves...

Fracture risk assessment in postmenopausal women with diabetes: comparison between DeFRA and FRAX tools.

Science.gov (United States)

Bonaccorsi, Gloria; Messina, Carmelo; Cervellati, Carlo; Maietti, Elisa; Medini, Matilde; Rossini, Maurizio; Massari, Leo; Greco, Pantaleo

2018-05-01

This study aimed to compare the performance of Fracture Risk Assessment Tool (FRAX) with that of Derived FRAX (DeFRA) in estimating fracture risk in a cohort of type-2 diabetes mellitus (T2DM) postmenopausal women. One hundred nineteen T2DM postmenopausal women and 118 consecutive healthy postmenopausal women were enrolled. Fracture risk was assessed with FRAX (adjusted or non- for trabecular bone score, TBS) and DeFRA. Bone mineral density (BMD) and TBS were evaluated by dual-energy X-ray absorptiometry (DXA). The outcome was the presence of vertebral/non-vertebral fragility fractures (FFs). T2DM women showed higher spinal BMD T-score (p < .05), but lower TBS (p < .05), than controls. Diabetic patients had higher prevalence of FFs compared to controls (p < .05), but no significant difference were found in the scores of any of the predictor tools. Differently, in the T2DM group, the scores of DeFRA, FRAX and adjusted-FRAX were significantly (p < .01 for all) higher in fractured compared with non-fractured women. DeFRA showed the best discriminative power among all fracture risk predictor tools (area under curves: DeFra: 0.89; adjusted FRAX: 0.80; non-adjusted FRAX: 0.73). In summary, all fracture risk assessment tools appeared to be effective in predicting bone fractures in T2DM postmenopausal women, with DeFRA showing a slightly better diagnostic accuracy.

Using exposure prediction tools to link exposure and dosimetry for risk-based decisions: A case study with phthalates

Science.gov (United States)

A few different exposure prediction tools were evaluated for use in the new in vitro-based safety assessment paradigm using di-2-ethylhexyl phthalate (DEHP) and dibutyl phthalate (DnBP) as case compounds. Daily intake of each phthalate was estimated using both high-throughput (HT...

Evaluation of the efficacy of nutritional screening tools to predict malnutrition in the elderly at a geriatric care hospital.

Science.gov (United States)

Baek, Myoung-Ha; Heo, Young-Ran

2015-12-01

Malnutrition in the elderly is a serious problem, prevalent in both hospitals and care homes. Due to the absence of a gold standard for malnutrition, herein we evaluate the efficacy of five nutritional screening tools developed or used for the elderly. Elected medical records of 141 elderly patients (86 men and 55 women, aged 73.5 ± 5.2 years) hospitalized at a geriatric care hospital were analyzed. Nutritional screening was performed using the following tools: Mini Nutrition Assessment (MNA), Mini Nutrition Assessment-Short Form (MNA-SF), Geriatric Nutritional Risk Index (GNRI), Malnutrition Universal Screening Tool (MUST) and Nutritional Risk Screening 2002 (NRS 2002). A combined index for malnutrition was also calculated as a reference tool. Each patient evaluated as malnourished to any degree or at risk of malnutrition according to at least four out of five of the aforementioned tools was categorized as malnourished in the combined index classification. According to the combined index, 44.0% of the patients were at risk of malnutrition to some degree. While the nutritional risk and/or malnutrition varied greatly depending on the tool applied, ranging from 36.2% (MUST) to 72.3% (MNA-SF). MUST showed good validity (sensitivity 80.6%, specificity 98.7%) and almost perfect agreement (k = 0.81) with the combined index. In contrast, MNA-SF showed poor validity (sensitivity 100%, specificity 49.4%) and only moderate agreement (k = 0.46) with the combined index. MNA-SF was found to overestimate the nutritional risk in the elderly. MUST appeared to be the most valid and useful screening tool to predict malnutrition in the elderly at a geriatric care hospital.

GAPIT: genome association and prediction integrated tool.

Science.gov (United States)

Lipka, Alexander E; Tian, Feng; Wang, Qishan; Peiffer, Jason; Li, Meng; Bradbury, Peter J; Gore, Michael A; Buckler, Edward S; Zhang, Zhiwu

2012-09-15

Software programs that conduct genome-wide association studies and genomic prediction and selection need to use methodologies that maximize statistical power, provide high prediction accuracy and run in a computationally efficient manner. We developed an R package called Genome Association and Prediction Integrated Tool (GAPIT) that implements advanced statistical methods including the compressed mixed linear model (CMLM) and CMLM-based genomic prediction and selection. The GAPIT package can handle large datasets in excess of 10 000 individuals and 1 million single-nucleotide polymorphisms with minimal computational time, while providing user-friendly access and concise tables and graphs to interpret results. http://www.maizegenetics.net/GAPIT. zhiwu.zhang@cornell.edu Supplementary data are available at Bioinformatics online.

Chapter 4. Predicting post-fire erosion and sedimentation risk on a landscape scale

Science.gov (United States)

MacDonald, L.H.; Sampson, R.; Brady, D.; Juarros, L.; Martin, Deborah

2000-01-01

Historic fire suppression efforts have increased the likelihood of large wildfires in much of the western U.S. Post-fire soil erosion and sedimentation risks are important concerns to resource managers. In this paper we develop and apply procedures to predict post-fire erosion and sedimentation risks on a pixel-, catchment-, and landscape-scale in central and western Colorado.Our model for predicting post-fire surface erosion risk is conceptually similar to the Revised Universal Soil Loss Equation (RUSLE). One key addition is the incorporation of a hydrophobicity risk index (HY-RISK) based on vegetation type, predicted fire severity, and soil texture. Post-fire surface erosion risk was assessed for each 90-m pixel by combining HYRISK, slope, soil erodibility, and a factor representing the likely increase in soil wetness due to removal of the vegetation. Sedimentation risk was a simple function of stream gradient. Composite surface erosion and sedimentation risk indices were calculated and compared across the 72 catchments in the study area.When evaluated on a catchment scale, two-thirds of the catchments had relatively little post-fire erosion risk. Steeper catchments with higher fuel loadings typically had the highest post-fire surface erosion risk. These were generally located along the major north-south mountain chains and, to a lesser extent, in west-central Colorado. Sedimentation risks were usually highest in the eastern part of the study area where a higher proportion of streams had lower gradients. While data to validate the predicted erosion and sedimentation risks are lacking, the results appear reasonable and are consistent with our limited field observations. The models and analytic procedures can be readily adapted to other locations and should provide useful tools for planning and management at both the catchment and landscape scale.

Risk Jyouhou Navi (risk information navigator). Web tool for fostering of risk literacy. Set of data

International Nuclear Information System (INIS)

Mitsui, Seiichiro

2003-06-01

In addition to the conventional public understanding activities, Risk communication study team of Japan Nuclear Cycle Development Institutes (JNC) Tokai Works has started practical studies to promote risk communication with its local communities. Since its establishment in 2001, Risk communication study team has conducted analyses of already available results of public attitude surveys, case studies of domestic and overseas risk communication activities, and development of risk communication tools. A web tool for fostering of risk literacy 'Risk Jyouhou Navi (risk information navigator in English)', was developed as a web content for the official home page of Techno Kouryuu Kan Ricotti (Techno Community Square Ricotti in English)'. The objectives of this content are to provide risk information for public and to provide an electronic platform for promoting risk communication with the local community. To develop 'Risk Jyouhou Navi', the following concepts were considered. 1) To create public interest in risks in daily lives and in global risks. 2) To provide risk knowledge and information. 3) To support risk communication activities in Techno community square ricotti. (author)

Validation of three tools for identifying painful new osteoporotic vertebral fractures in older Chinese men: bone mineral density, Osteoporosis Self-Assessment Tool for Asians, and fracture risk assessment tool.

Science.gov (United States)

Lin, JiSheng; Yang, Yong; Fei, Qi; Zhang, XiaoDong; Ma, Zhao; Wang, Qi; Li, JinJun; Li, Dong; Meng, Qian; Wang, BingQiang

2016-01-01

This cross-sectional study compared three tools for predicting painful new osteoporotic vertebral fractures (PNOVFs) in older Chinese men: bone mineral density (BMD), the Osteoporosis Self-Assessment Tool for Asians (OSTA), and the World Health Organization fracture risk assessment tool (FRAX) (without BMD). Men aged ≥50 years were apportioned to a group for men with fractures who had undergone percutaneous vertebroplasty (n=111), or a control group of healthy men (n=385). Fractures were verified on X-ray and magnetic resonance imaging. BMD T-scores were determined by dual energy X-ray absorptiometry. Diagnosis of osteoporosis was determined by a BMD T-score of ≤2.5 standard deviations below the average for a young adult at peak bone density at the femoral neck, total hip, or L1-L4. Demographic and clinical risk factor data were self-reported through a questionnaire. BMD, OSTA, and FRAX scores were assessed for identifying PNOVFs via receiver-operating characteristic (ROC) curves. Optimal cutoff points, sensitivity, specificity, and areas under the ROC curves (AUCs) were determined. Between the men with fractures and the control group, there were significant differences in BMD T-scores (at femoral neck, total hip, and L1-L4), and OSTA and FRAX scores. In those with fractures, only 53.15% satisfied the criteria for osteoporosis. Compared to BMD or OSTA, the FRAX score had the best predictive value for PNOVFs: the AUC of the FRAX score (cutoff =2.9%) was 0.738, and the sensitivity and specificity were 82% and 62%, respectively. FRAX may be a valuable tool for identifying PNOVFs in older Chinese men.

Emerging Tools to Estimate and to Predict Exposures to ...

Science.gov (United States)

The timely assessment of the human and ecological risk posed by thousands of existing and emerging commercial chemicals is a critical challenge facing EPA in its mission to protect public health and the environment The US EPA has been conducting research to enhance methods used to estimate and forecast exposures for tens of thousands of chemicals. This research is aimed at both assessing risks and supporting life cycle analysis, by developing new models and tools for high throughput exposure screening and prioritization, as well as databases that support these and other tools, especially regarding consumer products. The models and data address usage, and take advantage of quantitative structural activity relationships (QSARs) for both inherent chemical properties and function (why the chemical is a product ingredient). To make them more useful and widely available, the new tools, data and models are designed to be: • Flexible • Intraoperative • Modular (useful to more than one, stand-alone application) • Open (publicly available software) Presented at the Society for Risk Analysis Forum: Risk Governance for Key Enabling Technologies, Venice, Italy, March 1-3, 2017

Develop risk-based procurement management tools for SMEs

NARCIS (Netherlands)

Staal, Anne; Hagelaar, Geoffrey; Walhof, Gert; Holman, Richard

2016-01-01

This paper provides guidance for developing risk-based management tools to improve the procurement (purchasing) performance of SMEs. Extant academic literature only offers little support on developing such tools and does not consider the wide variety of SMEs. The paper defines a procurement tool for

A decision model to predict the risk of the first fall onset.

Science.gov (United States)

Deschamps, Thibault; Le Goff, Camille G; Berrut, Gilles; Cornu, Christophe; Mignardot, Jean-Baptiste

2016-08-01

Miscellaneous features from various domains are accepted to be associated with the risk of falling in the elderly. However, only few studies have focused on establishing clinical tools to predict the risk of the first fall onset. A model that would objectively and easily evaluate the risk of a first fall occurrence in the coming year still needs to be built. We developed a model based on machine learning, which might help the medical staff predict the risk of the first fall onset in a one-year time window. Overall, 426 older adults who had never fallen were assessed on 73 variables, comprising medical, social and physical outcomes, at t0. Each fall was recorded at a prospective 1-year follow-up. A decision tree was built on a randomly selected training subset of the cohort (80% of the full-set) and validated on an independent test set. 82 participants experienced a first fall during the follow-up. The machine learning process independently extracted 13 powerful parameters and built a model showing 89% of accuracy for the overall classification with 83%-82% of true positive fallers and 96%-61% of true negative non-fallers (training set vs. independent test set). This study provides a pilot tool that could easily help the gerontologists refine the evaluation of the risk of the first fall onset and prioritize the effective prevention strategies. The study also offers a transparent framework for future, related investigation that would validate the clinical relevance of the established model by independently testing its accuracy on larger cohort. Copyright © 2016 Elsevier Inc. All rights reserved.

Simple, standardized incorporation of genetic risk into non-genetic risk prediction tools for complex traits: coronary heart disease as an example

Directory of Open Access Journals (Sweden)

Benjamin A Goldstein

2014-08-01

Full Text Available Purpose: Genetic risk assessment is becoming an important component of clinical decision-making. Genetic Risk Scores (GRSs allow the composite assessment of genetic risk in complex traits. A technically and clinically pertinent question is how to most easily and effectively combine a GRS with an assessment of clinical risk derived from established non-genetic risk factors as well as to clearly present this information to patient and health care providers. Materials & Methods: We illustrate a means to combine a GRS with an independent assessment of clinical risk using a log-link function. We apply the method to the prediction of coronary heart disease (CHD in the Atherosclerosis Risk in Communities (ARIC cohort. We evaluate different constructions based on metrics of effect change, discrimination, and calibration.Results: The addition of a GRS to a clinical risk score (CRS improves both discrimination and calibration for CHD in ARIC. Results are similar regardless of whether external vs. internal coefficients are used for the CRS, risk factor SNPs are included in the GRS, or subjects with diabetes at baseline are excluded. We outline how to report the construction and the performance of a GRS using our method and illustrate a means to present genetic risk information to subjects and/or their health care provider. Conclusion: The proposed method facilitates the standardized incorporation of a GRS in risk assessment.

2012 AAPS National Biotech Conference Open Forum: a perspective on the current state of immunogenicity prediction and risk management.

Science.gov (United States)

Rajadhyaksha, Manoj; Subramanyam, Meena; Rup, Bonnie

2013-10-01

The immunogenicity profile of a biotherapeutic is determined by multiple product-, process- or manufacturing-, patient- and treatment-related factors and the bioanalytical methodology used to monitor for immunogenicity. This creates a complex situation that limits direct correlation of individual factors to observed immunogenicity rates. Therefore, mechanistic understanding of how these factors individually or in concert could influence the overall incidence and clinical risk of immunogenicity is crucial to provide the best benefit/risk profile for a given biotherapeutic in a given indication and to inform risk mitigation strategies. Advances in the field of immunogenicity have included development of best practices for monitoring anti-drug antibody development, categorization of risk factors contributing to immunogenicity, development of predictive tools, and development of effective strategies for risk management and mitigation. Thus, the opportunity to ask "where we are now and where we would like to go from here?" was the main driver for organizing an Open Forum on Improving Immunogenicity Risk Prediction and Management, conducted at the 2012 American Association of Pharmaceutical Scientists' (AAPS) National Biotechnology Conference in San Diego. The main objectives of the Forum include the following: to understand the nature of immunogenicity risk factors, to identify analytical tools used and animal models and management strategies needed to improve their predictive value, and finally to identify collaboration opportunities to improve the reliability of risk prediction, mitigation, and management. This meeting report provides the Forum participant's and author's perspectives on the barriers to advancing this field and recommendations for overcoming these barriers through collaborative efforts.

Virtual Beach: Decision Support Tools for Beach Pathogen Prediction

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The Virtual Beach Managers Tool (VB) is decision-making software developed to help local beach managers make decisions as to when beaches should be closed due to predicted high levels of water borne pathogens. The tool is being developed under the umbrella of EPA's Advanced Monit...

A two-question tool to assess the risk of repeated falls in the elderly.

Directory of Open Access Journals (Sweden)

Alejandro Rodríguez-Molinero

Full Text Available Older adults' perception of their own risk of fall has never been included into screening tools. The goal of this study was to evaluate the predictive validity of questions on subjects' self-perception of their own risk of fall.This prospective study was conducted on a probabilistic sample of 772 Spanish community-dwelling older adults, who were followed-up for a one year period. At a baseline visit, subjects were asked about their recent history of falls (question 1: "Have you fallen in the last 6 months?", as well as on their perception of their own risk of fall by using two questions (question 2: "Do you think you may fall in the next few months?" possible answers: yes/no; question 3: "What is the probability that you fall in the next few months?" possible answers: low/intermediate/high. The follow-up consisted of quarterly telephone calls, where the number of falls occurred in that period was recorded.A short questionnaire built with questions 1 and 3 showed 70% sensitivity (95% CI: 56%-84%, 72% specificity (95% CI: 68%-76% and 0.74 area under the ROC curve (95% CI: 0.66-0.82 for prediction of repeated falls in the subsequent year.The estimation of one's own risk of fall has predictive validity for the occurrence of repeated falls in older adults. A short questionnaire including a question on perception of one's own risk of fall and a question on the recent history of falls had good predictive validity.

A two-question tool to assess the risk of repeated falls in the elderly.

Science.gov (United States)

Rodríguez-Molinero, Alejandro; Gálvez-Barrón, César; Narvaiza, Leire; Miñarro, Antonio; Ruiz, Jorge; Valldosera, Esther; Gonzalo, Natalia; Ng, Thalia; Sanguino, María Jesús; Yuste, Antonio

2017-01-01

Older adults' perception of their own risk of fall has never been included into screening tools. The goal of this study was to evaluate the predictive validity of questions on subjects' self-perception of their own risk of fall. This prospective study was conducted on a probabilistic sample of 772 Spanish community-dwelling older adults, who were followed-up for a one year period. At a baseline visit, subjects were asked about their recent history of falls (question 1: "Have you fallen in the last 6 months?"), as well as on their perception of their own risk of fall by using two questions (question 2: "Do you think you may fall in the next few months?" possible answers: yes/no; question 3: "What is the probability that you fall in the next few months?" possible answers: low/intermediate/high). The follow-up consisted of quarterly telephone calls, where the number of falls occurred in that period was recorded. A short questionnaire built with questions 1 and 3 showed 70% sensitivity (95% CI: 56%-84%), 72% specificity (95% CI: 68%-76%) and 0.74 area under the ROC curve (95% CI: 0.66-0.82) for prediction of repeated falls in the subsequent year. The estimation of one's own risk of fall has predictive validity for the occurrence of repeated falls in older adults. A short questionnaire including a question on perception of one's own risk of fall and a question on the recent history of falls had good predictive validity.

Evaluation of fetal anthropometric measures to predict the risk for shoulder dystocia.

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Burkhardt, T; Schmidt, M; Kurmanavicius, J; Zimmermann, R; Schäffer, L

2014-01-01

To evaluate the quality of anthropometric measures to improve the prediction of shoulder dystocia by combining different sonographic biometric parameters. This was a retrospective cohort study of 12,794 vaginal deliveries with complete sonographic biometry data obtained within 7 days before delivery. Receiver-operating characteristics (ROC) curves of various combinations of the biometric parameters, namely, biparietal diameter (BPD), occipitofrontal diameter (OFD), head circumference, abdominal diameter (AD), abdominal circumference (AC) and femur length were analyzed. The influences of independent risk factors were calculated and their combination used in a predictive model. The incidence of shoulder dystocia was 1.14%. Different combinations of sonographic parameters showed comparable ROC curves without advantage for a particular combination. The difference between AD and BPD (AD - BPD) (area under the curve (AUC) = 0.704) revealed a significant increase in risk (odds ratio (OR) 7.6 (95% CI 4.2-13.9), sensitivity 8.2%, specificity 98.8%) at a suggested cut-off ≥ 2.6 cm. However, the positive predictive value (PPV) was low (7.5%). The AC as a single parameter (AUC = 0.732) with a cut-off ≥ 35 cm performed worse (OR 4.6 (95% CI 3.3-6.5), PPV 2.6%). BPD/OFD (a surrogate for fetal cranial shape) was not significantly different between those with and those without shoulder dystocia. The combination of estimated fetal weight, maternal diabetes, gender and AD - BPD provided a reasonable estimate of the individual risk. Sonographic fetal anthropometric measures appear not to be a useful tool to screen for the risk of shoulder dystocia due to a low PPV. However, AD - BPD appears to be a relevant risk factor. While risk stratification including different known risk factors may aid in counseling, shoulder dystocia cannot effectively be predicted. Copyright © 2013 ISUOG. Published by John Wiley & Sons Ltd.

Predictions of titanium alloy properties using thermodynamic modeling tools

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Zhang, F.; Xie, F.-Y.; Chen, S.-L.; Chang, Y. A.; Furrer, D.; Venkatesh, V.

2005-12-01

Thermodynamic modeling tools have become essential in understanding the effect of alloy chemistry on the final microstructure of a material. Implementation of such tools to improve titanium processing via parameter optimization has resulted in significant cost savings through the elimination of shop/laboratory trials and tests. In this study, a thermodynamic modeling tool developed at CompuTherm, LLC, is being used to predict β transus, phase proportions, phase chemistries, partitioning coefficients, and phase boundaries of multicomponent titanium alloys. This modeling tool includes Pandat, software for multicomponent phase equilibrium calculations, and PanTitanium, a thermodynamic database for titanium alloys. Model predictions are compared with experimental results for one α-β alloy (Ti-64) and two near-β alloys (Ti-17 and Ti-10-2-3). The alloying elements, especially the interstitial elements O, N, H, and C, have been shown to have a significant effect on the β transus temperature, and are discussed in more detail herein.

Clinical utility of polymorphisms in one-carbon metabolism for breast cancer risk prediction

Directory of Open Access Journals (Sweden)

Shaik Mohammad Naushad

2011-01-01

Full Text Available This study addresses the issues in translating the laboratory derived data obtained during discovery phase of research to a clinical setting using a breast cancer model. Laboratory-based risk assessment indi-cated that a family history of breast cancer, reduced folate carrier 1 (RFC1 G80A, thymidylate synthase (TYMS 5’-UTR 28bp tandem repeat, methylene tetrahydrofolate reductase (MTHFR C677T and catecholamine-O-methyl transferase (COMT genetic polymorphisms in one-carbon metabolic pathway increase the risk for breast cancer. Glutamate carboxypeptidase II (GCPII C1561T and cytosolic serine hydroxymethyl transferase (cSHMT C1420T polymorphisms were found to decrease breast cancer risk. In order to test the clinical validity of this information in the risk prediction of breast cancer, data was stratified based on number of protective alleles into four categories and in each category sensitivity and 1-specificity values were obtained based on the distribution of number of risk alleles in cases and controls. Receiver operating characteristic (ROC curves were plotted and the area under ROC curve (C was used as a measure of discriminatory ability between cases and controls. In subjects without any protective allele, aberrations in one-carbon metabolism showed perfect prediction (C=0.93 while the predictability was lost in subjects with one protective allele (C=0.60. However, predictability increased steadily with increasing number of protective alleles (C=0.63 for 2 protective alleles and C=0.71 for 3 protective alleles. The cut-off point for discrimination was >4 alleles in all predictable combinations. Models of this kind can serve as valuable tools in translational re-search, especially in identifying high-risk individuals and reducing the disease risk either by life style modification or by medical intervention.

Ability of different screening tools to predict positive effect on nutritional intervention among the elderly in primary health care

DEFF Research Database (Denmark)

Beck, Anne Marie; Beermann, Tina; Kjær, Stine

2013-01-01

Routine identification of nutritional risk screening is paramount as the first stage in nutritional treatment of the elderly. The major focus of former validation studies of screening tools has been on the ability to predict undernutrition. The aim of this study was to validate Mini Nutritional A...

Engineering a mobile health tool for resource-poor settings to assess and manage cardiovascular disease risk: SMARThealth study.

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Raghu, Arvind; Praveen, Devarsetty; Peiris, David; Tarassenko, Lionel; Clifford, Gari

2015-04-29

The incidence of chronic diseases in low- and middle-income countries is rapidly increasing both in urban and rural regions. A major challenge for health systems globally is to develop innovative solutions for the prevention and control of these diseases. This paper discusses the development and pilot testing of SMARTHealth, a mobile-based, point-of-care Clinical Decision Support (CDS) tool to assess and manage cardiovascular disease (CVD) risk in resource-constrained settings. Through pilot testing, the preliminary acceptability, utility, and efficiency of the CDS tool was obtained. The CDS tool was part of an mHealth system comprising a mobile application that consisted of an evidence-based risk prediction and management algorithm, and a server-side electronic medical record system. Through an agile development process and user-centred design approach, key features of the mobile application that fitted the requirements of the end users and environment were obtained. A comprehensive analytics framework facilitated a data-driven approach to investigate four areas, namely, system efficiency, end-user variability, manual data entry errors, and usefulness of point-of-care management recommendations to the healthcare worker. A four-point Likert scale was used at the end of every risk assessment to gauge ease-of-use of the system. The system was field-tested with eleven village healthcare workers and three Primary Health Centre doctors, who screened a total of 292 adults aged 40 years and above. 34% of participants screened by health workers were identified by the CDS tool to be high CVD risk and referred to a doctor. In-depth analysis of user interactions found the CDS tool feasible for use and easily integrable into the workflow of healthcare workers. Following completion of the pilot, further technical enhancements were implemented to improve uptake of the mHealth platform. It will then be evaluated for effectiveness and cost-effectiveness in a cluster randomized

PRmePRed: A protein arginine methylation prediction tool.

Directory of Open Access Journals (Sweden)

Pawan Kumar

Full Text Available Protein methylation is an important Post-Translational Modification (PTMs of proteins. Arginine methylation carries out and regulates several important biological functions, including gene regulation and signal transduction. Experimental identification of arginine methylation site is a daunting task as it is costly as well as time and labour intensive. Hence reliable prediction tools play an important task in rapid screening and identification of possible methylation sites in proteomes. Our preliminary assessment using the available prediction methods on collected data yielded unimpressive results. This motivated us to perform a comprehensive data analysis and appraisal of features relevant in the context of biological significance, that led to the development of a prediction tool PRmePRed with better performance. The PRmePRed perform reasonably well with an accuracy of 84.10%, 82.38% sensitivity, 83.77% specificity, and Matthew's correlation coefficient of 66.20% in 10-fold cross-validation. PRmePRed is freely available at http://bioinfo.icgeb.res.in/PRmePRed/.

Screening Tool for Early Postnatal Prediction of Retinopathy of Prematurity in Preterm Newborns (STEP-ROP).

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Ricard, Caroline A; Dammann, Christiane E L; Dammann, Olaf

2017-01-01

Retinopathy of prematurity (ROP) is a disorder of the preterm newborn characterized by neurovascular disruption in the immature retina that may cause visual impairment and blindness. To develop a clinical screening tool for early postnatal prediction of ROP in preterm newborns based on risk information available within the first 48 h of postnatal life. Using data submitted to the Vermont Oxford Network (VON) between 1995 and 2015, we created logistic regression models based on infants born <28 completed weeks gestational age. We developed a model with 60% of the data and identified birth weight, gestational age, respiratory distress syndrome, non-Hispanic ethnicity, and multiple gestation as predictors of ROP. We tested the model in the remaining 40%, performed tenfold cross-validation, and tested the score in ELGAN study data. Of the 1,052 newborns in the VON database, 627 recorded an ROP status. Forty percent had no ROP, 40% had mild ROP (stages 1 and 2), and 20% had severe ROP (stages 3-5). We created a weighted score to predict any ROP based on the multivariable regression model. A cutoff score of 5 had the best sensitivity (95%, 95% CI 93-97), while maintaining a strong positive predictive value (63%, 95% CI 57-68). When applied to the ELGAN data, sensitivity was lower (72%, 95% CI 69-75), but PPV was higher (80%, 95% CI 77-83). STEP-ROP is a promising screening tool. It is easy to calculate, does not rely on extensive postnatal data collection, and can be calculated early after birth. Early ROP screening may help physicians limit patient exposure to additional risk factors, and may be useful for risk stratification in clinical trials aimed at reducing ROP. © 2017 S. Karger AG, Basel.

Web tools for predictive toxicology model building.

Science.gov (United States)

Jeliazkova, Nina

2012-07-01

The development and use of web tools in chemistry has accumulated more than 15 years of history already. Powered by the advances in the Internet technologies, the current generation of web systems are starting to expand into areas, traditional for desktop applications. The web platforms integrate data storage, cheminformatics and data analysis tools. The ease of use and the collaborative potential of the web is compelling, despite the challenges. The topic of this review is a set of recently published web tools that facilitate predictive toxicology model building. The focus is on software platforms, offering web access to chemical structure-based methods, although some of the frameworks could also provide bioinformatics or hybrid data analysis functionalities. A number of historical and current developments are cited. In order to provide comparable assessment, the following characteristics are considered: support for workflows, descriptor calculations, visualization, modeling algorithms, data management and data sharing capabilities, availability of GUI or programmatic access and implementation details. The success of the Web is largely due to its highly decentralized, yet sufficiently interoperable model for information access. The expected future convergence between cheminformatics and bioinformatics databases provides new challenges toward management and analysis of large data sets. The web tools in predictive toxicology will likely continue to evolve toward the right mix of flexibility, performance, scalability, interoperability, sets of unique features offered, friendly user interfaces, programmatic access for advanced users, platform independence, results reproducibility, curation and crowdsourcing utilities, collaborative sharing and secure access.

Assessing Bleeding Risk in Patients Taking Anticoagulants

Science.gov (United States)

Shoeb, Marwa; Fang, Margaret C.

2013-01-01

Anticoagulant medications are commonly used for the prevention and treatment of thromboembolism. Although highly effective, they are also associated with significant bleeding risks. Numerous individual clinical factors have been linked to an increased risk of hemorrhage, including older age, anemia, and renal disease. To help quantify hemorrhage risk for individual patients, a number of clinical risk prediction tools have been developed. These risk prediction tools differ in how they were derived and how they identify and weight individual risk factors. At present, their ability to effective predict anticoagulant-associated hemorrhage remains modest. Use of risk prediction tools to estimate bleeding in clinical practice is most influential when applied to patients at the lower spectrum of thromboembolic risk, when the risk of hemorrhage will more strongly affect clinical decisions about anticoagulation. Using risk tools may also help counsel and inform patients about their potential risk for hemorrhage while on anticoagulants, and can identify patients who might benefit from more careful management of anticoagulation. PMID:23479259

Development of a Korean Fracture Risk Score (KFRS for Predicting Osteoporotic Fracture Risk: Analysis of Data from the Korean National Health Insurance Service.

Directory of Open Access Journals (Sweden)

Ha Young Kim

Full Text Available Asian-specific prediction models for estimating individual risk of osteoporotic fractures are rare. We developed a Korean fracture risk prediction model using clinical risk factors and assessed validity of the final model.A total of 718,306 Korean men and women aged 50-90 years were followed for 7 years in a national system-based cohort study. In total, 50% of the subjects were assigned randomly to the development dataset and 50% were assigned to the validation dataset. Clinical risk factors for osteoporotic fracture were assessed at the biennial health check. Data on osteoporotic fractures during the follow-up period were identified by ICD-10 codes and the nationwide database of the National Health Insurance Service (NHIS.During the follow-up period, 19,840 osteoporotic fractures were reported (4,889 in men and 14,951 in women in the development dataset. The assessment tool called the Korean Fracture Risk Score (KFRS is comprised of a set of nine variables, including age, body mass index, recent fragility fracture, current smoking, high alcohol intake, lack of regular exercise, recent use of oral glucocorticoid, rheumatoid arthritis, and other causes of secondary osteoporosis. The KFRS predicted osteoporotic fractures over the 7 years. This score was validated using an independent dataset. A close relationship with overall fracture rate was observed when we compared the mean predicted scores after applying the KFRS with the observed risks after 7 years within each 10th of predicted risk.We developed a Korean specific prediction model for osteoporotic fractures. The KFRS was able to predict risk of fracture in the primary population without bone mineral density testing and is therefore suitable for use in both clinical setting and self-assessment. The website is available at http://www.nhis.or.kr.

SitesIdentify: a protein functional site prediction tool

Directory of Open Access Journals (Sweden)

Doig Andrew J

2009-11-01

Full Text Available Abstract Background The rate of protein structures being deposited in the Protein Data Bank surpasses the capacity to experimentally characterise them and therefore computational methods to analyse these structures have become increasingly important. Identifying the region of the protein most likely to be involved in function is useful in order to gain information about its potential role. There are many available approaches to predict functional site, but many are not made available via a publicly-accessible application. Results Here we present a functional site prediction tool (SitesIdentify, based on combining sequence conservation information with geometry-based cleft identification, that is freely available via a web-server. We have shown that SitesIdentify compares favourably to other functional site prediction tools in a comparison of seven methods on a non-redundant set of 237 enzymes with annotated active sites. Conclusion SitesIdentify is able to produce comparable accuracy in predicting functional sites to its closest available counterpart, but in addition achieves improved accuracy for proteins with few characterised homologues. SitesIdentify is available via a webserver at http://www.manchester.ac.uk/bioinformatics/sitesidentify/

Popularity Prediction Tool for ATLAS Distributed Data Management

Science.gov (United States)

Beermann, T.; Maettig, P.; Stewart, G.; Lassnig, M.; Garonne, V.; Barisits, M.; Vigne, R.; Serfon, C.; Goossens, L.; Nairz, A.; Molfetas, A.; Atlas Collaboration

2014-06-01

This paper describes a popularity prediction tool for data-intensive data management systems, such as ATLAS distributed data management (DDM). It is fed by the DDM popularity system, which produces historical reports about ATLAS data usage, providing information about files, datasets, users and sites where data was accessed. The tool described in this contribution uses this historical information to make a prediction about the future popularity of data. It finds trends in the usage of data using a set of neural networks and a set of input parameters and predicts the number of accesses in the near term future. This information can then be used in a second step to improve the distribution of replicas at sites, taking into account the cost of creating new replicas (bandwidth and load on the storage system) compared to gain of having new ones (faster access of data for analysis). To evaluate the benefit of the redistribution a grid simulator is introduced that is able replay real workload on different data distributions. This article describes the popularity prediction method and the simulator that is used to evaluate the redistribution.

Popularity prediction tool for ATLAS distributed data management

International Nuclear Information System (INIS)

Beermann, T; Maettig, P; Stewart, G; Lassnig, M; Garonne, V; Barisits, M; Vigne, R; Serfon, C; Goossens, L; Nairz, A; Molfetas, A

2014-01-01

This paper describes a popularity prediction tool for data-intensive data management systems, such as ATLAS distributed data management (DDM). It is fed by the DDM popularity system, which produces historical reports about ATLAS data usage, providing information about files, datasets, users and sites where data was accessed. The tool described in this contribution uses this historical information to make a prediction about the future popularity of data. It finds trends in the usage of data using a set of neural networks and a set of input parameters and predicts the number of accesses in the near term future. This information can then be used in a second step to improve the distribution of replicas at sites, taking into account the cost of creating new replicas (bandwidth and load on the storage system) compared to gain of having new ones (faster access of data for analysis). To evaluate the benefit of the redistribution a grid simulator is introduced that is able replay real workload on different data distributions. This article describes the popularity prediction method and the simulator that is used to evaluate the redistribution.

Predictive validity of the Hendrich fall risk model II in an acute geriatric unit.

Science.gov (United States)

Ivziku, Dhurata; Matarese, Maria; Pedone, Claudio

2011-04-01

Falls are the most common adverse events reported in acute care hospitals, and older patients are the most likely to fall. The risk of falling cannot be completely eliminated, but it can be reduced through the implementation of a fall prevention program. A major evidence-based intervention to prevent falls has been the use of fall-risk assessment tools. Many tools have been increasingly developed in recent years, but most instruments have not been investigated regarding reliability, validity and clinical usefulness. This study intends to evaluate the predictive validity and inter-rater reliability of Hendrich fall risk model II (HFRM II) in order to identify older patients at risk of falling in geriatric units and recommend its use in clinical practice. A prospective descriptive design was used. The study was carried out in a geriatric acute care unit of an Italian University hospital. All over 65 years old patients consecutively admitted to a geriatric acute care unit of an Italian University hospital over 8-month period were enrolled. The patients enrolled were screened for the falls risk by nurses with the HFRM II within 24h of admission. The falls occurring during the patient's hospital stay were registered. Inter-rater reliability, area under the ROC curve, sensitivity, specificity, positive and negative predictive values and time for the administration were evaluated. 179 elderly patients were included. The inter-rater reliability was 0.87 (95% CI 0.71-1.00). The administration time was about 1min. The most frequently reported risk factors were depression, incontinence, vertigo. Sensitivity and specificity were respectively 86% and 43%. The optimal cut-off score for screening at risk patients was 5 with an area under the ROC curve of 0.72. The risk factors more strongly associated with falls were confusion and depression. As falls of older patients are a common problem in acute care settings it is necessary that the nurses use specific validate and reliable

Calibration plots for risk prediction models in the presence of competing risks.

Science.gov (United States)

Gerds, Thomas A; Andersen, Per K; Kattan, Michael W

2014-08-15

A predicted risk of 17% can be called reliable if it can be expected that the event will occur to about 17 of 100 patients who all received a predicted risk of 17%. Statistical models can predict the absolute risk of an event such as cardiovascular death in the presence of competing risks such as death due to other causes. For personalized medicine and patient counseling, it is necessary to check that the model is calibrated in the sense that it provides reliable predictions for all subjects. There are three often encountered practical problems when the aim is to display or test if a risk prediction model is well calibrated. The first is lack of independent validation data, the second is right censoring, and the third is that when the risk scale is continuous, the estimation problem is as difficult as density estimation. To deal with these problems, we propose to estimate calibration curves for competing risks models based on jackknife pseudo-values that are combined with a nearest neighborhood smoother and a cross-validation approach to deal with all three problems. Copyright © 2014 John Wiley & Sons, Ltd.

Screening for Malnutrition in Community Dwelling Older Japanese: Preliminary Development and Evaluation of the Japanese Nutritional Risk Screening Tool (NRST).

Science.gov (United States)

Htun, N C; Ishikawa-Takata, K; Kuroda, A; Tanaka, T; Kikutani, T; Obuchi, S P; Hirano, H; Iijima, K

2016-02-01

Early and effective screening for age-related malnutrition is an essential part of providing optimal nutritional care to older populations. This study was performed to evaluate the adaptation of the original SCREEN II questionnaire (Seniors in the Community: Risk Evaluation for Eating and Nutrition, version II) for use in Japan by examining its measurement properties and ability to predict nutritional risk and sarcopenia in community-dwelling older Japanese people. The ultimate objective of this preliminary validation study is to develop a license granted full Japanese version of the SCREEN II. The measurement properties and predictive validity of the NRST were examined in this cross-sectional study of 1921 community-dwelling older Japanese people. Assessments included medical history, and anthropometric and serum albumin measurements. Questions on dietary habits that corresponded to the original SCREEN II were applied to Nutritional Risk Screening Tool (NRST) scoring system. Nutritional risk was assessed by the Geriatric Nutrition Risk Index (GNRI) and the short form of the Mini-Nutritional Assessment (MNA-SF). Sarcopenia was diagnosed according to the criteria of the European Working Group on Sarcopenia in Older People. The nutritional risk prevalences determined by the GNRI and MNA-SF were 5.6% and 34.7%, respectively. The prevalence of sarcopenia was 13.3%. Mean NRST scores were significantly lower in the nutritionally at-risk than in the well-nourished groups. Concurrent validity analysis showed significant correlations between NRST scores and both nutritional risk parameters (GNRI or MNA-SF) and sarcopenia. The areas under the receiver operating characteristic curves (AUC) of NRST for the prediction of nutritional risk were 0.635 and 0.584 as assessed by GNRI and MNA-SF, respectively. AUCs for the prediction of sarcopenia were 0.602 (NRST), 0.655 (age-integrated NRST), and 0.676 (age and BMI-integrated NRST). These results indicate that the NRST is a

Development of an Automated Security Risk Assessment Methodology Tool for Critical Infrastructures.

Energy Technology Data Exchange (ETDEWEB)

Jaeger, Calvin Dell; Roehrig, Nathaniel S.; Torres, Teresa M.

2008-12-01

This document presents the security automated Risk Assessment Methodology (RAM) prototype tool developed by Sandia National Laboratories (SNL). This work leverages SNL's capabilities and skills in security risk analysis and the development of vulnerability assessment/risk assessment methodologies to develop an automated prototype security RAM tool for critical infrastructures (RAM-CITM). The prototype automated RAM tool provides a user-friendly, systematic, and comprehensive risk-based tool to assist CI sector and security professionals in assessing and managing security risk from malevolent threats. The current tool is structured on the basic RAM framework developed by SNL. It is envisioned that this prototype tool will be adapted to meet the requirements of different CI sectors and thereby provide additional capabilities.

Which screening tools can predict injury to the lower extremities in team sports?: a systematic review.

Science.gov (United States)

Dallinga, Joan M; Benjaminse, Anne; Lemmink, Koen A P M

2012-09-01

Injuries to lower extremities are common in team sports such as soccer, basketball, volleyball, football and field hockey. Considering personal grief, disabling consequences and high costs caused by injuries to lower extremities, the importance for the prevention of these injuries is evident. From this point of view it is important to know which screening tools can identify athletes who are at risk of injury to their lower extremities. The aim of this article is to determine the predictive values of anthropometric and/or physical screening tests for injuries to the leg, anterior cruciate ligament (ACL), knee, hamstring, groin and ankle in team sports. A systematic review was conducted in MEDLINE (1966 to September 2011), EMBASE (1989 to September 2011) and CINAHL (1982 to September 2011). Based on inclusion criteria defined a priori, titles, abstracts and full texts were analysed to find relevant studies. The analysis showed that different screening tools can be predictive for injuries to the knee, ACL, hamstring, groin and ankle. For injuries in general there is some support in the literature to suggest that general joint laxity is a predictive measure for leg injuries. The anterior right/left reach distance >4 cm and the composite reach distance injuries. Furthermore, an increasing age, a lower hamstring/quadriceps (H : Q) ratio and a decreased range of motion (ROM) of hip abduction may predict the occurrence of leg injuries. Hyperextension of the knee, side-to-side differences in anterior-posterior knee laxity and differences in knee abduction moment between both legs are suggested to be predictive tests for sustaining an ACL injury and height was a predictive screening tool for knee ligament injuries. There is some evidence that when age increases, the probability of sustaining a hamstring injury increases. Debate exists in the analysed literature regarding measurement of the flexibility of the hamstring as a predictive screening tool, as well as using the H

How many holes is too many? A prototype tool for estimating mosquito entry risk into damaged bed nets.

Science.gov (United States)

Sutcliffe, James; Ji, Xin; Yin, Shaoman

2017-08-01

Insecticide-treated bed nets (ITNs) have played an integral role in malaria reduction but how insecticide depletion and accumulating physical damage affect ITN performance is poorly understood. More accurate methods are needed to assess damage to bed nets so that they can be designed, deployed and replaced optimally. Video recordings of female Anopheles gambiae in near approach (1-½ cm) to occupied untreated rectangular bed nets in a laboratory study were used to quantify the amount of mosquito activity (appearances over time) around different parts of the net, the per-appearance probability of a mosquito coming close to holes of different sizes (hole encounter) and the per-encounter probability of mosquitoes passing through holes of different sizes (hole passage). Appearance frequency on different parts of the net reflected previously reported patterns: the area of the net under greatest mosquito pressure was the roof, followed by the bottom 30 cm of the sides, followed by the 30 cm area immediately above this, followed by the upper two-thirds of the sides. The ratio of activity in these areas was (respectively) 250:33:5:1. Per-appearance probability of hole encounter on all parts of the net was strongly predicted by a factor combining hole perimeter and area. Per-encounter probability of hole passage, in turn, was strongly predicted by hole width. For a given width, there was a 20% greater risk of passage through holes on the roof than holes on the sides. Appearance, encounter and passage predictors correspond to various mosquito behaviours that have previously been described and are combined into a prototype mosquito entry risk tool that predicts mosquito entry rates for nets with various amounts of damage. Scenarios that use the entry risk tool to test the recommendations of the WHOPES proportionate hole index (pHI) suggest that the pHI hole size categories and failure to account for hole location likely sometimes lead to incorrect conclusions about net

Development of a Breast Cancer Risk Prediction Model for Women in Nigeria.

Science.gov (United States)

Wang, Shengfeng; Ogundiran, Temidayo O; Ademola, Adeyinka; Oluwasola, Olayiwola A; Adeoye, Adewunmi O; Sofoluwe, Adenike; Morhason-Bello, Imran; Odedina, Stella O; Agwai, Imaria; Adebamowo, Clement; Obajimi, Millicent; Ojengbede, Oladosu; Olopade, Olufunmilayo I; Huo, Dezheng

2018-04-20

Risk prediction models have been widely used to identify women at higher risk of breast cancer. We aim to develop a model for absolute breast cancer risk prediction for Nigerian women. A total of 1,811 breast cancer cases and 2,225 controls from the Nigerian Breast Cancer Study (NBCS, 1998~2015) were included. Subjects were randomly divided into the training and validation sets. Incorporating local incidence rates, multivariable logistic regressions were used to develop the model. The NBCS model included age, age at menarche, parity, duration of breast feeding, family history of breast cancer, height, body mass index, benign breast diseases and alcohol consumption. The model developed in the training set performed well in the validation set. The discriminating accuracy of the NBCS model (area under ROC curve [AUC]=0.703, 95% confidence interval [CI]: 0.687-0.719) was better than the Black Women's Health Study (BWHS) model (AUC=0.605, 95% CI: 0.586-0.624), Gail model for White population (AUC=0.551, 95% CI: 0.531-0.571), and Gail model for Black population (AUC=0.545, 95% CI: 0.525-0.565). Compared to the BWHS, two Gail models, the net reclassification improvement of the NBCS model were 8.26%, 13.45% and 14.19%, respectively. We have developed a breast cancer risk prediction model specific to women in Nigeria, which provides a promising and indispensable tool to identify women in need of breast cancer early detection in SSA populations. Our model is the first breast cancer risk prediction model in Africa. It can be used to identify women at high-risk for breast cancer screening. Copyright ©2018, American Association for Cancer Research.

Tools and techniques for ageing predictions in nuclear reactors through condition monitoring

International Nuclear Information System (INIS)

Verma, R.M.P.

1994-01-01

To operate the nuclear reactors beyond their design predicted life is gaining importance because of huge replacement and decommissioning costs. But experience shows that nuclear plant safety and reliability may decline in the later years of plant life due to ageing degradation. Ageing of nuclear plant components, structures and systems, if unmitigated reduces their safety margins provided in the design and thus increases risks to public health and safety. These safety margins must be monitored throughout plant service life including any extended life. Condition monitoring of nuclear reactor components/equipment and systems can be done to study the effect of ageing, status of safety margins and effect of corrective and mitigating actions taken. The tools and techniques of condition monitoring are also important in failure trending, predictive maintenance, evaluation of scheduled maintenance, in mitigation of ageing, life extension and reliability studies. (author). 1 fig., 1 annexure

Predicting child maltreatment: A meta-analysis of the predictive validity of risk assessment instruments.

Science.gov (United States)

van der Put, Claudia E; Assink, Mark; Boekhout van Solinge, Noëlle F

2017-11-01

Risk assessment is crucial in preventing child maltreatment since it can identify high-risk cases in need of child protection intervention. Despite widespread use of risk assessment instruments in child welfare, it is unknown how well these instruments predict maltreatment and what instrument characteristics are associated with higher levels of predictive validity. Therefore, a multilevel meta-analysis was conducted to examine the predictive accuracy of (characteristics of) risk assessment instruments. A literature search yielded 30 independent studies (N=87,329) examining the predictive validity of 27 different risk assessment instruments. From these studies, 67 effect sizes could be extracted. Overall, a medium significant effect was found (AUC=0.681), indicating a moderate predictive accuracy. Moderator analyses revealed that onset of maltreatment can be better predicted than recurrence of maltreatment, which is a promising finding for early detection and prevention of child maltreatment. In addition, actuarial instruments were found to outperform clinical instruments. To bring risk and needs assessment in child welfare to a higher level, actuarial instruments should be further developed and strengthened by distinguishing risk assessment from needs assessment and by integrating risk assessment with case management. Copyright © 2017 Elsevier Ltd. All rights reserved.

Predicting Risk of Type 2 Diabetes Mellitus: A Population-Based Study

Directory of Open Access Journals (Sweden)

Mahmut Kilic

2016-04-01

Full Text Available Aim: One of the major risk factors that can cause death in the world is also type-2 diabetes mellitus (DM. Turkey does not have a vehicle in the society has been formulate predicting the risk of developing DM. The purpose of this study is to determine the level of DM risk in Turkish society using the Finnish Diabetes Risk Score (FINDRISC tool. Material and Method: This is a cross-sectional study. The data has been obtained from %u201Cbehavioral risk factors for chronic diseases study%u201D that was made in the province of Yozgat, in 2011. The study population included 825 subjects between 25 to 79 years old who had measured their blood sugar before, but who were not diagnosed DM. DM risk level was calculated using FINDRISC tool. The scale score is between 0-26, %u226515 points are considered high risk (risk ratio 1/3. In analyzing the data, t-test, ANOVA and chi-square test and binary logistic regression were used. Results: Of the subjects 10 years of DM risk score%u2019s mean was 8.8 ± 4.6. When FINDRISC score low / medium and high divided into 2 groups, the proportion of those in the high risk group is 11.5%. This rate is similar to the 10-year incidence of DM calculated (11-12.4% for Turkey. In this study, all of the factors taken into FINDRISC calculations were statistically significant (p 0.05. Discussion: FINDRISC used to be in the DM risk calculations of Turkish population. One out of every ten adults are at high risk of developing DM in 10 years. To avoid this problem urgently needs to be implemented by the various programs on an individual and societal level.

Implementation of a scalable, web-based, automated clinical decision support risk-prediction tool for chronic kidney disease using C-CDA and application programming interfaces.

Science.gov (United States)

Samal, Lipika; D'Amore, John D; Bates, David W; Wright, Adam

2017-11-01

Clinical decision support tools for risk prediction are readily available, but typically require workflow interruptions and manual data entry so are rarely used. Due to new data interoperability standards for electronic health records (EHRs), other options are available. As a clinical case study, we sought to build a scalable, web-based system that would automate calculation of kidney failure risk and display clinical decision support to users in primary care practices. We developed a single-page application, web server, database, and application programming interface to calculate and display kidney failure risk. Data were extracted from the EHR using the Consolidated Clinical Document Architecture interoperability standard for Continuity of Care Documents (CCDs). EHR users were presented with a noninterruptive alert on the patient's summary screen and a hyperlink to details and recommendations provided through a web application. Clinic schedules and CCDs were retrieved using existing application programming interfaces to the EHR, and we provided a clinical decision support hyperlink to the EHR as a service. We debugged a series of terminology and technical issues. The application was validated with data from 255 patients and subsequently deployed to 10 primary care clinics where, over the course of 1 year, 569 533 CCD documents were processed. We validated the use of interoperable documents and open-source components to develop a low-cost tool for automated clinical decision support. Since Consolidated Clinical Document Architecture-based data extraction extends to any certified EHR, this demonstrates a successful modular approach to clinical decision support. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association.

A novel risk score to predict cardiovascular disease risk in national populations (Globorisk)

DEFF Research Database (Denmark)

Hajifathalian, Kaveh; Ueda, Peter; Lu, Yuan

2015-01-01

BACKGROUND: Treatment of cardiovascular risk factors based on disease risk depends on valid risk prediction equations. We aimed to develop, and apply in example countries, a risk prediction equation for cardiovascular disease (consisting here of coronary heart disease and stroke) that can be reca...

Popularity Prediction Tool for ATLAS Distributed Data Management

CERN Document Server

Beermann, T; The ATLAS collaboration; Stewart, G; Lassnig, M; Garonne, V; Barisits, M; Vigne, R; Serfon, C; Goossens, L; Nairz, A; Molfetas, A

2013-01-01

This paper describes a popularity prediction tool for data-intensive data management systems, such as ATLAS distributed data management (DDM). It is fed by the DDM popularity system, which produces historical reports about ATLAS data usage, providing information about files, datasets, users and sites where data was accessed. The tool described in this contribution uses this historical information to make a prediction about the future popularity of data. It finds trends in the usage of data using a set of neural networks and a set of input parameters and predicts the number of accesses in the near term future. This information can then be used in a second step to improve the distribution of replicas at sites, taking into account the cost of creating new replicas (bandwidth and load on the storage system) compared to gain of having new ones (faster access of data for analysis). To evaluate the benefit of the redistribution a grid simulator is introduced that is able replay real workload on different data distri...

Popularity Prediction Tool for ATLAS Distributed Data Management

CERN Document Server

Beermann, T; The ATLAS collaboration; Stewart, G; Lassnig, M; Garonne, V; Barisits, M; Vigne, R; Serfon, C; Goossens, L; Nairz, A; Molfetas, A

2014-01-01

This paper describes a popularity prediction tool for data-intensive data management systems, such as ATLAS distributed data management (DDM). It is fed by the DDM popularity system, which produces historical reports about ATLAS data usage, providing information about files, datasets, users and sites where data was accessed. The tool described in this contribution uses this historical information to make a prediction about the future popularity of data. It finds trends in the usage of data using a set of neural networks and a set of input parameters and predicts the number of accesses in the near term future. This information can then be used in a second step to improve the distribution of replicas at sites, taking into account the cost of creating new replicas (bandwidth and load on the storage system) compared to gain of having new ones (faster access of data for analysis). To evaluate the benefit of the redistribution a grid simulator is introduced that is able replay real workload on different data distri...

Climate risk screening tools and their application: A guide to the guidance

Energy Technology Data Exchange (ETDEWEB)

Traerup, S.; Olhoff, A.

2011-07-01

Climate risk screening is an integral part of efforts to ascertain current and future vulnerabilities and risks related to climate change. It is a prerequisite for identifying and designing adaptation measures, and an important element in the process of integrating, or mainstreaming, climate change adaptation into development project, planning and policy processes. There is an increasing demand and attention among national stakeholders in developing countries to take into account potential implications of climate variability and change for planning and prioritizing of development strategies and activities. Subsequently, there is a need for user friendly guidance on climate risk screening tools and their potentials for application that targets developing country stakeholders. This need is amplified by the sheer volume of climate change mainstreaming guidance documents and risk screening and assessment tools available and currently under development. Against this background, this paper sets out to provide potential users in developing countries, including project and programme developers and managers, with an informational entry point to climate risk screening tools. The emphasis in this report is on providing: 1) An overview of available climate risk screening and assessment tools along with indications of the tools available and relevant for specific purposes and contexts (Section 3). 2) Examples of application of climate risk screening and assessment tools along with links to further information (Section 4). Before turning to the respective sections on available climate risk screening tools and examples of their application, a delimitation of the tools included in this paper is included in Section 2. This section also provides a brief overview of how climate screening and related tools fit into decision making steps at various planning and decision making levels in conjunction with an outline of overall considerations to make when choosing a tool. The paper is

Modifiable risk factors predicting major depressive disorder at four year follow-up: a decision tree approach.

Science.gov (United States)

Batterham, Philip J; Christensen, Helen; Mackinnon, Andrew J

2009-11-22

Relative to physical health conditions such as cardiovascular disease, little is known about risk factors that predict the prevalence of depression. The present study investigates the expected effects of a reduction of these risks over time, using the decision tree method favoured in assessing cardiovascular disease risk. The PATH through Life cohort was used for the study, comprising 2,105 20-24 year olds, 2,323 40-44 year olds and 2,177 60-64 year olds sampled from the community in the Canberra region, Australia. A decision tree methodology was used to predict the presence of major depressive disorder after four years of follow-up. The decision tree was compared with a logistic regression analysis using ROC curves. The decision tree was found to distinguish and delineate a wide range of risk profiles. Previous depressive symptoms were most highly predictive of depression after four years, however, modifiable risk factors such as substance use and employment status played significant roles in assessing the risk of depression. The decision tree was found to have better sensitivity and specificity than a logistic regression using identical predictors. The decision tree method was useful in assessing the risk of major depressive disorder over four years. Application of the model to the development of a predictive tool for tailored interventions is discussed.

Modifiable risk factors predicting major depressive disorder at four year follow-up: a decision tree approach

Directory of Open Access Journals (Sweden)

Christensen Helen

2009-11-01

Full Text Available Abstract Background Relative to physical health conditions such as cardiovascular disease, little is known about risk factors that predict the prevalence of depression. The present study investigates the expected effects of a reduction of these risks over time, using the decision tree method favoured in assessing cardiovascular disease risk. Methods The PATH through Life cohort was used for the study, comprising 2,105 20-24 year olds, 2,323 40-44 year olds and 2,177 60-64 year olds sampled from the community in the Canberra region, Australia. A decision tree methodology was used to predict the presence of major depressive disorder after four years of follow-up. The decision tree was compared with a logistic regression analysis using ROC curves. Results The decision tree was found to distinguish and delineate a wide range of risk profiles. Previous depressive symptoms were most highly predictive of depression after four years, however, modifiable risk factors such as substance use and employment status played significant roles in assessing the risk of depression. The decision tree was found to have better sensitivity and specificity than a logistic regression using identical predictors. Conclusion The decision tree method was useful in assessing the risk of major depressive disorder over four years. Application of the model to the development of a predictive tool for tailored interventions is discussed.

Engineering and Ingenuity, Tools and Technologies (1)

DEFF Research Database (Denmark)

Jensen, Jørgen Juncher; Ravn, Erik Sonne; Guarin, Luis

2007-01-01

Risk-based ship design demands advanced tools to predict the safety performance of a given design. Such tools have been developed or refined in the SAFEDOR project covering • Fast and accurate flooding prediction • Probabilistic assessment of the strength of ship structures • Probabilistic...... assessment of intact stability • Prevention of collision and grounding events • Prevention of fire and explosion events Various procedures have been used to derive the tools: Bayesian network, artificial neural networks, CFD calculations, non-linear time domain calculations and reliability models...... with tools for fast and reliable evaluation of various risks associated with failure of the ship or its subsystems and able to evaluate the effect of various risk-control options. Examples will be given within prevention of collision, grounding and fire events....

ePORT, NASA's Computer Database Program for System Safety Risk Management Oversight (Electronic Project Online Risk Tool)

Science.gov (United States)

Johnson, Paul W.

2008-01-01

ePORT (electronic Project Online Risk Tool) provides a systematic approach to using an electronic database program to manage a program/project risk management processes. This presentation will briefly cover the standard risk management procedures, then thoroughly cover NASA's Risk Management tool called ePORT. This electronic Project Online Risk Tool (ePORT) is a web-based risk management program that provides a common framework to capture and manage risks, independent of a programs/projects size and budget. It is used to thoroughly cover the risk management paradigm providing standardized evaluation criterion for common management reporting, ePORT improves Product Line, Center and Corporate Management insight, simplifies program/project manager reporting, and maintains an archive of data for historical reference.

Prediction models : the right tool for the right problem

NARCIS (Netherlands)

Kappen, Teus H.; Peelen, Linda M.

2016-01-01

PURPOSE OF REVIEW: Perioperative prediction models can help to improve personalized patient care by providing individual risk predictions to both patients and providers. However, the scientific literature on prediction model development and validation can be quite technical and challenging to

Developmental dyslexia: predicting individual risk.

Science.gov (United States)

Thompson, Paul A; Hulme, Charles; Nash, Hannah M; Gooch, Debbie; Hayiou-Thomas, Emma; Snowling, Margaret J

2015-09-01

Causal theories of dyslexia suggest that it is a heritable disorder, which is the outcome of multiple risk factors. However, whether early screening for dyslexia is viable is not yet known. The study followed children at high risk of dyslexia from preschool through the early primary years assessing them from age 3 years and 6 months (T1) at approximately annual intervals on tasks tapping cognitive, language, and executive-motor skills. The children were recruited to three groups: children at family risk of dyslexia, children with concerns regarding speech, and language development at 3;06 years and controls considered to be typically developing. At 8 years, children were classified as 'dyslexic' or not. Logistic regression models were used to predict the individual risk of dyslexia and to investigate how risk factors accumulate to predict poor literacy outcomes. Family-risk status was a stronger predictor of dyslexia at 8 years than low language in preschool. Additional predictors in the preschool years include letter knowledge, phonological awareness, rapid automatized naming, and executive skills. At the time of school entry, language skills become significant predictors, and motor skills add a small but significant increase to the prediction probability. We present classification accuracy using different probability cutoffs for logistic regression models and ROC curves to highlight the accumulation of risk factors at the individual level. Dyslexia is the outcome of multiple risk factors and children with language difficulties at school entry are at high risk. Family history of dyslexia is a predictor of literacy outcome from the preschool years. However, screening does not reach an acceptable clinical level until close to school entry when letter knowledge, phonological awareness, and RAN, rather than family risk, together provide good sensitivity and specificity as a screening battery. © 2015 The Authors. Journal of Child Psychology and Psychiatry published by

The predictive ability of the STarT Back Tool was limited in people with chronic low back pain: a prospective cohort study

Directory of Open Access Journals (Sweden)

Michelle Kendell

2018-04-01

Full Text Available Questions: In people with chronic non-specific low back pain (LBP, what is the predictive and discriminative validity of the STarT Back Tool (SBT for pain intensity, self-reported LBP-related disability, and global self-perceived change at 1-year follow-up? What is the profile of the SBT risk subgroups with respect to demographic variables, pain intensity, self-reported LBP-related disability, and psychological measures? Design: Prospective cohort study. Participants: A total of 290 adults with dominant axial LBP of ≥ 3 months’ duration recruited from the general community, and private physiotherapy, psychology, and pain-management clinics in Western Australia. Outcome measures: The 1-year follow-up measures were pain intensity, LBP-related disability, and global self-perceived change. Results: Outcomes were collected on 264 participants. The SBT categorised 82 participants (28% as low risk, 116 (40% as medium risk, and 92 (32% as high risk. The risk subgroups differed significantly (p < 0.05 on baseline pain, disability, and psychological scores. The SBT’s predictive ability was strongest for disability: RR was 2.30 (95% CI 1.28 to 4.10 in the medium-risk group and 2.86 (95% CI 1.60 to 5.11 in the high-risk group. The SBT’s predictive ability was weaker for pain: RR was 1.25 (95% CI 1.04 to 1.51 in the medium-risk group and 1.26 (95% CI 1.03 to 1.52 in the high-risk group. For the SBT total score, the AUC was 0.71 (95% CI 0.64 to 0.77 for disability and 0.63 (95% CI 0.55 to 0.71 for pain. Conclusion: This was the first large study to investigate the SBT in a population exclusively with chronic LBP. The SBT provided an acceptable indication of 1-year disability, had poor predictive and discriminative ability for future pain, and was unable to predict or discriminate global perceived change. In this cohort with chronic non-specific LBP, the SBT’s predictive and discriminative abilities were restricted to disability at 1�

Estimation of the Cardiovascular Risk Using World Health Organization/International Society of Hypertension (WHO/ISH Risk Prediction Charts in a Rural Population of South India

Directory of Open Access Journals (Sweden)

Arun Gangadhar Ghorpade

2015-08-01

Full Text Available Background World Health Organization/International Society of Hypertension (WHO/ISH charts have been employed to predict the risk of cardiovascular outcome in heterogeneous settings. The aim of this research is to assess the prevalence of Cardiovascular Disease (CVD risk factors and to estimate the cardiovascular risk among adults aged >40 years, utilizing the risk charts alone, and by the addition of other parameters. Methods A cross-sectional study was performed in two of the villages availing health services of a medical college. Overall 570 subjects completed the assessment. The desired information was obtained using a pretested questionnaire and participants were also subjected to anthropometric measurements and laboratory investigations. The WHO/ISH risk prediction charts for the South-East Asian region was used to assess the cardiovascular risk among the study participants. Results The study covered 570 adults aged above 40 years. The mean age of the subjects was 54.2 (±11.1 years and 53.3% subjects were women. Seventeen percent of the participants had moderate to high risk for the occurrence of cardiovascular events by using WHO/ISH risk prediction charts. In addition, CVD risk factors like smoking, alcohol, low High-Density Lipoprotein (HDL cholesterol were found in 32%, 53%, 56.3%, and 61.5% study participants, respectively. Conclusion Categorizing people as low (20% risk is one of the crucial steps to mitigate the magnitude of cardiovascular fatal/non-fatal outcome. This cross-sectional study indicates that there is a high burden of CVD risk in the rural Pondicherry as assessed by WHO/ISH risk prediction charts. Use of WHO/ISH charts is easy and inexpensive screening tool in predicting the cardiovascular event.

An Integrative Review of Pediatric Fall Risk Assessment Tools.

Science.gov (United States)

DiGerolamo, Kimberly; Davis, Katherine Finn

Patient fall prevention begins with accurate risk assessment. However, sustained improvements in prevention and quality of care include use of validated fall risk assessment tools (FRATs). The goal of FRATs is to identify patients at highest risk. Adult FRATs are often borrowed from to create tools for pediatric patients. Though factors associated with pediatric falls in the hospital setting are similar to those in adults, such as mobility, medication use, and cognitive impairment, adult FRATs and the factors associated with them do not adequately assess risk in children. Articles were limited to English language, ages 0-21years, and publish date 2006-2015. The search yielded 22 articles. Ten were excluded as the population was primarily adult or lacked discussion of a FRAT. Critical appraisal and findings were synthesized using the Johns Hopkins Nursing evidence appraisal system. Twelve articles relevant to fall prevention in the pediatric hospital setting that discussed fall risk assessment and use of a FRAT were reviewed. Comparison between and accuracy of FRATs is challenged when different classifications, definitions, risk stratification, and inclusion criteria are used. Though there are several pediatric FRATs published in the literature, none have been found to be reliable and valid across institutions and diverse populations. This integrative review highlights the importance of choosing a FRAT based on an institution's identified risk factors and validating the tool for one's own patient population as well as using the tool in conjunction with nursing clinical judgment to guide interventions. Copyright © 2017 Elsevier Inc. All rights reserved.

Predicting the onset of hazardous alcohol drinking in primary care: development and validation of a simple risk algorithm.

Science.gov (United States)

Bellón, Juan Ángel; de Dios Luna, Juan; King, Michael; Nazareth, Irwin; Motrico, Emma; GildeGómez-Barragán, María Josefa; Torres-González, Francisco; Montón-Franco, Carmen; Sánchez-Celaya, Marta; Díaz-Barreiros, Miguel Ángel; Vicens, Catalina; Moreno-Peral, Patricia

2017-04-01

Little is known about the risk of progressing to hazardous alcohol use in abstinent or low-risk drinkers. To develop and validate a simple brief risk algorithm for the onset of hazardous alcohol drinking (HAD) over 12 months for use in primary care. Prospective cohort study in 32 health centres from six Spanish provinces, with evaluations at baseline, 6 months, and 12 months. Forty-one risk factors were measured and multilevel logistic regression and inverse probability weighting were used to build the risk algorithm. The outcome was new occurrence of HAD during the study, as measured by the AUDIT. From the lists of 174 GPs, 3954 adult abstinent or low-risk drinkers were recruited. The 'predictAL-10' risk algorithm included just nine variables (10 questions): province, sex, age, cigarette consumption, perception of financial strain, having ever received treatment for an alcohol problem, childhood sexual abuse, AUDIT-C, and interaction AUDIT-C*Age. The c-index was 0.886 (95% CI = 0.854 to 0.918). The optimal cutoff had a sensitivity of 0.83 and specificity of 0.80. Excluding childhood sexual abuse from the model (the 'predictAL-9'), the c-index was 0.880 (95% CI = 0.847 to 0.913), sensitivity 0.79, and specificity 0.81. There was no statistically significant difference between the c-indexes of predictAL-10 and predictAL-9. The predictAL-10/9 is a simple and internally valid risk algorithm to predict the onset of hazardous alcohol drinking over 12 months in primary care attendees; it is a brief tool that is potentially useful for primary prevention of hazardous alcohol drinking. © British Journal of General Practice 2017.

Cardiovascular risk prediction in the Netherlands

NARCIS (Netherlands)

Dis, van S.J.

2011-01-01

Background: In clinical practice, Systematic COronary Risk Evaluation (SCORE) risk prediction functions and charts are used to identify persons at high risk for cardiovascular diseases (CVD), who are considered eligible for drug treatment of elevated blood pressure and serum cholesterol. These

Screening strategies and predictive diagnostic tools for the development of new-onset diabetes mellitus after transplantation: an overview

Directory of Open Access Journals (Sweden)

Pham PT

2012-10-01

Full Text Available Phuong-Thu T Pham,1 Kari L Edling,2 Harini A Chakkera,3 Phuong-Chi T Pham,4 Phuong-Mai T Pham51Department of Medicine, Nephrology Division, Kidney Transplant Program, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; 2Department of Medicine, Division of Endocrinology, Diabetes and Hypertension, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; 3Department of Medicine, Nephrology Division Kidney Transplant Program, Mayo Clinic Hospital, Phoenix, AZ, USA; 4Department of Medicine, Nephrology Division, UCLA-Olive View Medical Center, Sylmar, CA, USA; 5Department of Medicine, Greater Los Angeles, Veterans Administration Health Care System, CA, USAAbstract: New-onset diabetes mellitus after transplantation (NODAT is a serious and common complication following solid organ transplantation. NODAT has been reported in 2% to 53% of all solid organ transplants. Kidney transplant recipients who develop NODAT have variably been reported to be at increased risk of fatal and nonfatal cardiovascular events and other adverse outcomes including infection, reduced patient survival, graft rejection, and accelerated graft loss compared with those who do not develop diabetes. Limited clinical studies in liver, heart, and lung transplants similarly suggested that NODAT has an adverse impact on patient and graft outcomes. Early detection and management of NODAT must, therefore, be integrated into the treatment of transplant recipients. Studies investigating the best screening or predictive tool for identifying patients at risk for developing NODAT early after transplantation, however, are lacking. We review the clinical predictive values of fasting plasma glucose, oral glucose tolerance test, and A1C in assessing the risk for NODAT development and as a screening tool. Simple diabetes prediction models that incorporate clinical and/or metabolic risk factors (such as age, body mass index, hypertriglyceridemia, or metabolic syndrome are also

Risk based service life prediction of underground cast iron pipes subjected to corrosion

International Nuclear Information System (INIS)

Li, C.Q.; Mahmoodian, M.

2013-01-01

Aging and deterioration of underground cast iron pipes is inevitable after their long time in service, with corrosion being the most predominant mechanism for pipe failures. Although considerable research has been undertaken in the past few decades, more is on the effects of corrosion on structural capacity of pipes than that on the prediction of their service life. This paper presents a methodology to quantitatively assess the risk of pipe collapse and predict its remaining service life using a time-dependent reliability theory. The concept of stress intensity in fracture mechanics is employed to establish the failure criterion of pipe collapse. An empirical model is derived for maximum pit growth of corrosion from the available data based on mathematical regressions. An example is provided to illustrate the application of the proposed method. It is found in the paper that the risk of pipe collapse increases with an increase in the diameter of the pipe for both external and internal corrosion. It is also found that the tougher the pipe is, the smaller the risk of its collapse. The paper concludes that a time-dependent reliability method is a very useful tool to predict the risk of pipe collapse and its remaining service life. The proposed method can help the water industry develop rehabilitation or replacement strategy for existing pipe networks with a view for better management of the pipe asset

How well does the Post-fire Erosion Risk Management Tool (ERMiT) really work?

Science.gov (United States)

Robichaud, Peter; Elliot, William; Lewis, Sarah; Miller, Mary Ellen

2016-04-01

The decision of where, when, and how to apply the most effective postfire erosion mitigation treatments requires land managers to assess the risk of damaging runoff and erosion events occurring after a fire. The Erosion Risk Management Tool (ERMiT) was developed to assist post fire assessment teams identify high erosion risk areas and effectiveness of various mitigation treatments to reduce that risk. ERMiT is a web-based application that uses the Water Erosion Prediction Project (WEPP) technology to estimate erosion, in probabilistic terms, on burned and recovering forest, range, and chaparral lands with and without the application of mitigation treatments. User inputs are processed by ERMiT to combine rain event variability with spatial and temporal variabilities of hillslope burn severity and soil properties which are then used as WEPP inputs. Since 2007, the model has been used in making hundreds of land management decisions in the US and elsewhere. We use eight published field study sites in the Western US to compare ERMiT predictions to observed hillslope erosion rates. Most sites experience only a few rainfall events that produced runoff and sediment except for a California site with a Mediterranean climate. When hillslope erosion occurred, significant correlations occurred between the observed hillslope erosion and those predicted by ERMiT. Significant correlation occurred for most mitigation treatments as well as the five recovery years. These model validation results suggest reasonable estimates of probabilistic post-fire hillslope sediment delivery when compared to observation.

Development, Validation and Deployment of a Real Time 30 Day Hospital Readmission Risk Assessment Tool in the Maine Healthcare Information Exchange.

Science.gov (United States)

Hao, Shiying; Wang, Yue; Jin, Bo; Shin, Andrew Young; Zhu, Chunqing; Huang, Min; Zheng, Le; Luo, Jin; Hu, Zhongkai; Fu, Changlin; Dai, Dorothy; Wang, Yicheng; Culver, Devore S; Alfreds, Shaun T; Rogow, Todd; Stearns, Frank; Sylvester, Karl G; Widen, Eric; Ling, Xuefeng B

2015-01-01

Identifying patients at risk of a 30-day readmission can help providers design interventions, and provide targeted care to improve clinical effectiveness. This study developed a risk model to predict a 30-day inpatient hospital readmission for patients in Maine, across all payers, all diseases and all demographic groups. Our objective was to develop a model to determine the risk for inpatient hospital readmission within 30 days post discharge. All patients within the Maine Health Information Exchange (HIE) system were included. The model was retrospectively developed on inpatient encounters between January 1, 2012 to December 31, 2012 from 24 randomly chosen hospitals, and then prospectively validated on inpatient encounters from January 1, 2013 to December 31, 2013 using all HIE patients. A risk assessment tool partitioned the entire HIE population into subgroups that corresponded to probability of hospital readmission as determined by a corresponding positive predictive value (PPV). An overall model c-statistic of 0.72 was achieved. The total 30-day readmission rates in low (score of 0-30), intermediate (score of 30-70) and high (score of 70-100) risk groupings were 8.67%, 24.10% and 74.10%, respectively. A time to event analysis revealed the higher risk groups readmitted to a hospital earlier than the lower risk groups. Six high-risk patient subgroup patterns were revealed through unsupervised clustering. Our model was successfully integrated into the statewide HIE to identify patient readmission risk upon admission and daily during hospitalization or for 30 days subsequently, providing daily risk score updates. The risk model was validated as an effective tool for predicting 30-day readmissions for patients across all payer, disease and demographic groups within the Maine HIE. Exposing the key clinical, demographic and utilization profiles driving each patient's risk of readmission score may be useful to providers in developing individualized post discharge

Risk prediction model: Statistical and artificial neural network approach

Science.gov (United States)

Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

2017-04-01

Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.

The predictive ability of the STarT Back Tool was limited in people with chronic low back pain: a prospective cohort study.

Science.gov (United States)

Kendell, Michelle; Beales, Darren; O'Sullivan, Peter; Rabey, Martin; Hill, Jonathan; Smith, Anne

2018-04-01

In people with chronic non-specific low back pain (LBP), what is the predictive and discriminative validity of the STarT Back Tool (SBT) for pain intensity, self-reported LBP-related disability, and global self-perceived change at 1-year follow-up? What is the profile of the SBT risk subgroups with respect to demographic variables, pain intensity, self-reported LBP-related disability, and psychological measures? Prospective cohort study. A total of 290 adults with dominant axial LBP of≥3months' duration recruited from the general community, and private physiotherapy, psychology, and pain-management clinics in Western Australia. The 1-year follow-up measures were pain intensity, LBP-related disability, and global self-perceived change. Outcomes were collected on 264 participants. The SBT categorised 82 participants (28%) as low risk, 116 (40%) as medium risk, and 92 (32%) as high risk. The risk subgroups differed significantly (ppredictive ability was strongest for disability: RR was 2.30 (95% CI 1.28 to 4.10) in the medium-risk group and 2.86 (95% CI 1.60 to 5.11) in the high-risk group. The SBT's predictive ability was weaker for pain: RR was 1.25 (95% CI 1.04 to 1.51) in the medium-risk group and 1.26 (95% CI 1.03 to 1.52) in the high-risk group. For the SBT total score, the AUC was 0.71 (95% CI 0.64 to 0.77) for disability and 0.63 (95% CI 0.55 to 0.71) for pain. This was the first large study to investigate the SBT in a population exclusively with chronic LBP. The SBT provided an acceptable indication of 1-year disability, had poor predictive and discriminative ability for future pain, and was unable to predict or discriminate global perceived change. In this cohort with chronic non-specific LBP, the SBT's predictive and discriminative abilities were restricted to disability at 1year. [Kendell M, Beales D, O'Sullivan P, Rabey M, Hill J, Smith A (2018) The predictive ability of the STarT Back Tool was limited in people with chronic low back pain: a prospective

Fall Risk Score at the Time of Discharge Predicts Readmission Following Total Joint Arthroplasty.

Science.gov (United States)

Ravi, Bheeshma; Nan, Zhang; Schwartz, Adam J; Clarke, Henry D

2017-07-01

Readmission among Medicare recipients is a leading driver of healthcare expenditure. To date, most predictive tools are too coarse for direct clinical application. Our objective in this study is to determine if a pre-existing tool to identify patients at increased risk for inpatient falls, the Hendrich Fall Risk Score, could be used to accurately identify Medicare patients at increased risk for readmission following arthroplasty, regardless of whether the readmission was due to a fall. This study is a retrospective cohort study. We identified 2437 Medicare patients who underwent a primary elective total joint arthroplasty (TJA) of the hip or knee for osteoarthritis between 2011 and 2014. The Hendrich Fall Risk score was recorded for each patient preoperatively and postoperatively. Our main outcome measure was hospital readmission within 30 days of discharge. Of 2437 eligible TJA recipients, there were 226 (9.3%) patients who had a score ≥6. These patients were more likely to have an unplanned readmission (unadjusted odds ratio 2.84, 95% confidence interval 1.70-4.76, P 3 days (49.6% vs 36.6%, P = .0001), and were less likely to be sent home after discharge (20.8% vs 35.8%, P fall risk score after TJA is strongly associated with unplanned readmission. Application of this tool will allow hospitals to identify these patients and plan their discharge. Copyright © 2017 Elsevier Inc. All rights reserved.

Variability in Predictions from Online Tools: A Demonstration Using Internet-Based Melanoma Predictors.

Science.gov (United States)

Zabor, Emily C; Coit, Daniel; Gershenwald, Jeffrey E; McMasters, Kelly M; Michaelson, James S; Stromberg, Arnold J; Panageas, Katherine S

2018-02-22

Prognostic models are increasingly being made available online, where they can be publicly accessed by both patients and clinicians. These online tools are an important resource for patients to better understand their prognosis and for clinicians to make informed decisions about treatment and follow-up. The goal of this analysis was to highlight the possible variability in multiple online prognostic tools in a single disease. To demonstrate the variability in survival predictions across online prognostic tools, we applied a single validation dataset to three online melanoma prognostic tools. Data on melanoma patients treated at Memorial Sloan Kettering Cancer Center between 2000 and 2014 were retrospectively collected. Calibration was assessed using calibration plots and discrimination was assessed using the C-index. In this demonstration project, we found important differences across the three models that led to variability in individual patients' predicted survival across the tools, especially in the lower range of predictions. In a validation test using a single-institution data set, calibration and discrimination varied across the three models. This study underscores the potential variability both within and across online tools, and highlights the importance of using methodological rigor when developing a prognostic model that will be made publicly available online. The results also reinforce that careful development and thoughtful interpretation, including understanding a given tool's limitations, are required in order for online prognostic tools that provide survival predictions to be a useful resource for both patients and clinicians.

Prediction of health risks from accidents: A comprehensive assessment methodology

International Nuclear Information System (INIS)

MacFarlane, D.R.; Yuan, Y.C.

1992-01-01

We have developed two computer programs to predict radiation risks to individuals and/or the collective population from exposures to accidental releases of radioactive materials. When used together, these two codes provide a consistent, comprehensive tool to estimate not only the risks to specific individuals but also the distribution of risks in the exposed population and the total number of individuals within a specific level of risk. Prompt and latent fatalities are estimated for the exposed population, and from these, the risk to an average individual can be derived. Uncertainty in weather conditions is considered by estimating both the ''median'' and the ''maximum'' population doses based on the frequency distribution of wind speeds and stabilities for a given site. The importance of including all dispersible particles (particles smaller than about 100 μm) for dose and health risk analyses from nonfiltered releases for receptor locations within about 10 km from a release has been investigated. The dose contribution of the large particles (> 10 μm) has been shown to be substantially greater than those from the small particles for the dose receptors in various release and exposure conditions. These conditions include, particularly, elevated releases, strong wind weather, and exposure pathways associated with ground-deposited material over extended periods of time

WPPT, a tool for on-line wind power prediction

Energy Technology Data Exchange (ETDEWEB)

Skov Nielsen, T. [Dept. of Mathematical Modelling (IMM-DTU), Kgs. Lyngby (Denmark); Madsen, H. [Dept. of Mathematical Modelling (IMM-DTU) Kgs. Lyngby (Denmark); Toefting, J. [Elsam, Fredericia (Denmark)

2004-07-01

This paper dsecribes VPPT (Wind Power Prediction Tool), an application for assessing the future available wind power up to 36 hours ahead in time. WPPT has been installed in the Eltra/Elsam central dispatch center since October 1997. The paper describes the prediction model used, the actual implementation of WPPT as well as the experience gained by the operators in the dispatch center (au)

Evaluation of the white finger risk prediction model in ISO 5349 suggests need for prospective studies.

Science.gov (United States)

Gemne, G; Lundström, R

1996-05-01

The risk prediction model for white fingers in Annex A of ISO 5349 is not likely to offer protection from all tools and all work processes. It is also probable that some work place changes it has initiated are either redundant or lack the intended effect. The main reasons for these shortcomings are the following. The often demonstrated disagreement between predicted and observed white fingers occurrence may be related to the fact that the model is based on latency data. This leads to an overestimation, to an unknown extent, of true group risks. A possible healthy worker effect, resulting in underestimation, has not been considered, and uncertainty because of recall bias is connected with using latency as effect variable in a slowly developing disorder like white fingers. The diagnostic criteria for white fingers have varied over the years, causing a possible inclusion of circulatory disturbances other than those induced by vibration. Among insufficiently clarified matters unrelated to vibration are variations in individual susceptibility and other host factors that modify vibration effects, uncertainty concerning daily or total effective exposure, and the fact that variation in work methods and processes as well as ergonomic factors other than vibration tend to make different groups incomparable form the viewpoint of risk of injury. Lack of sufficient data on vibration measurements and employment durations add to the uncertainty, as do variations in tool conditions (grinder wheels, etc) and inherent difficulties in measurement. Finally, the ISO 5349 frequency-weighting curve only relates to acute sensory effects rather than chronic effects on vascular functions like white fingers, and directional difference in sensitivity has not been incorporated in the curve. Data on exposure-response relationships are needed from prospective studies that monitor the dose of exposure to special vibration types and all relevant environmental agents, employ diagnostics with good

The use of current risk analysis tools evaluated towards preventing external domino accidents

NARCIS (Netherlands)

Reniers, Genserik L L; Dullaert, W.; Ale, B. J.M.; Soudan, K.

Risk analysis is an essential tool for company safety policy. Risk analysis consists of identifying and evaluating all possible risks. The efficiency of risk analysis tools depends on the rigueur of identifying and evaluating all possible risks. The diversity in risk analysis procedures is such that

Chemical Risk Assessment Screening Tool of a Global Chemical Company

Directory of Open Access Journals (Sweden)

Evelyn Tjoe-Nij

2018-03-01

Full Text Available Background: This paper describes a simple-to-use and reliable screening tool called Critical Task Exposure Screening (CTES, developed by a chemical company. The tool assesses if the exposure to a chemical for a task is likely to be within acceptable levels. Methods: CTES is a Microsoft Excel tool, where the inhalation risk score is calculated by relating the exposure estimate to the corresponding occupational exposure limit (OEL or occupational exposure band (OEB. The inhalation exposure is estimated for tasks by preassigned ART1.5 activity classes and modifying factors. Results: CTES requires few inputs. The toxicological data, including OELs, OEBs, and vapor pressure are read from a database. Once the substance is selected, the user specifies its concentration and then chooses the task description and its duration. CTES has three outputs that may trigger follow-up: (1 inhalation risk score; (2 identification of the skin hazard with the skin warnings for local and systemic adverse effects; and (3 status for carcinogenic, mutagenic, or reprotoxic effects. Conclusion: The tool provides an effective way to rapidly screen low-concern tasks, and quickly identifies certain tasks involving substances that will need further review with, nevertheless, the appropriate conservatism. This tool shows that the higher-tier ART1.5 inhalation exposure assessment model can be included effectively in a screening tool. After 2 years of worldwide extensive use within the company, CTES is well perceived by the users, including the shop floor management, and it fulfills its target of screening tool. Keywords: occupational exposure, risk assessment, risk management

RISK REDUCTION WITH A FUZZY EXPERT EXPLORATION TOOL

Energy Technology Data Exchange (ETDEWEB)

Robert S. Balch; Ron Broadhead

2005-03-01

Incomplete or sparse data such as geologic or formation characteristics introduce a high level of risk for oil exploration and development projects. ''Expert'' systems developed and used in several disciplines and industries have demonstrated beneficial results when working with sparse data. State-of-the-art expert exploration tools, relying on a database, and computer maps generated by neural networks and user inputs, have been developed through the use of ''fuzzy'' logic, a mathematical treatment of imprecise or non-explicit parameters and values. Oil prospecting risk has been reduced with the use of these properly verified and validated ''Fuzzy Expert Exploration (FEE) Tools.'' Through the course of this project, FEE Tools and supporting software were developed for two producing formations in southeast New Mexico. Tools of this type can be beneficial in many regions of the U.S. by enabling risk reduction in oil and gas prospecting as well as decreased prospecting and development costs. In today's oil industry environment, many smaller exploration companies lack the resources of a pool of expert exploration personnel. Downsizing, volatile oil prices, and scarcity of domestic exploration funds have also affected larger companies, and will, with time, affect the end users of oil industry products in the U.S. as reserves are depleted. The FEE Tools benefit a diverse group in the U.S., allowing a more efficient use of scarce funds, and potentially reducing dependence on foreign oil and providing lower product prices for consumers.

HostPhinder: A Phage Host Prediction Tool

Directory of Open Access Journals (Sweden)

Julia Villarroel

2016-05-01

Full Text Available The current dramatic increase of antibiotic resistant bacteria has revitalised the interest in bacteriophages as alternative antibacterial treatment. Meanwhile, the development of bioinformatics methods for analysing genomic data places high-throughput approaches for phage characterization within reach. Here, we present HostPhinder, a tool aimed at predicting the bacterial host of phages by examining the phage genome sequence. Using a reference database of 2196 phages with known hosts, HostPhinder predicts the host species of a query phage as the host of the most genomically similar reference phages. As a measure of genomic similarity the number of co-occurring k-mers (DNA sequences of length k is used. Using an independent evaluation set, HostPhinder was able to correctly predict host genus and species for 81% and 74% of the phages respectively, giving predictions for more phages than BLAST and significantly outperforming BLAST on phages for which both had predictions. HostPhinder predictions on phage draft genomes from the INTESTI phage cocktail corresponded well with the advertised targets of the cocktail. Our study indicates that for most phages genomic similarity correlates well with related bacterial hosts. HostPhinder is available as an interactive web service [1] and as a stand alone download from the Docker registry [2].

Validation of the MARS (Medical Admission Risk System): A combined physiological and laboratory risk prediction tool for 5- to 7-day in-hospital mortality.

Science.gov (United States)

Ohman, Malin Charlotta; Atkins, Tara E Holm; Cooksley, Tim; Brabrand, Mikkel

2018-03-10

The MARS (Medical Admission Risk System) uses 11 physiological and laboratory data and had promising results in its derivation study for predicting 5 and 7 day mortality. To perform an external independent validation of the MARS score. An unplanned secondary cohort study. Patients admitted to the medical admission unit (MAU) at The Hospital of South West Jutland were included from 2 October 2008 until 19 February 2009 and 23 February 2010 until 26 May 2010 were analysed. Validation of the MARS score using 5 and 7 day mortality was the primary endpoint. 5858 patients were included in the study. 2923 (49.9%) patients were women with a median age of 65 years (15-107). The MARS score had an AUROC of 0.858 (95% CI: 0.831-0.884) for 5-day mortality and 0.844 (0.818-0.870) for 7 day mortality with poor calibration for both outcomes. The MARS score had excellent discriminatory power but poor calibration in predicting both 5 and 7-day mortality. The development of accurate combination physiological/laboratory data risk scores has the potential to improve the recognition of at risk patients.

Use of a functional movement screening tool to determine injury risk in female collegiate athletes.

Science.gov (United States)

Chorba, Rita S; Chorba, David J; Bouillon, Lucinda E; Overmyer, Corey A; Landis, James A

2010-06-01

Athletes often utilize compensatory movement strategies to achieve high performance. However, these inefficient movement strategies may reinforce poor biomechanical movement patterns during typical activities, resulting in injury. This study sought to determine if compensatory movement patterns predispose female collegiate athletes to injury, and if a functional movement screening (FMS™) tool can be used to predict injuries in this population. Scores on the FMS™, comprised of seven movement tests, were calculated for 38 NCAA Division II female collegiate athletes before the start of their respective fall and winter sport seasons (soccer, volleyball, and basketball). Seven athletes reported a previous history of anterior cruciate ligament reconstruction (ACLR). Injuries sustained while participating in sport activities were recorded throughout the seasons. The mean FMS™ score and standard deviation for all subjects was 14.3±1.77 (maximum score of 21). Eighteen injuries (17 lower extremity, 1 lower back) were recorded during this study. A score of 14/21 or less was significantly associated with injury (P=0.0496). Sixty-nine percent of athletes scoring 14 or less sustained an injury. Odds ratios were 3.85 with inclusion of all subjects, and 4.58 with exclusion of ACLR subjects. Sensitivity and specificity were 0.58 and 0.74 for all subjects, respectively. A significant correlation was found between low-scoring athletes and injury (P=0.0214, r=0.76). A score of 14 or less on the FMS™ tool resulted in a 4-fold increase in risk of lower extremity injury in female collegiate athletes participating in fall and winter sports. The screening tool was able to predict injury in female athletes without a history of major musculoskeletal injury such as ACLR. Compensatory fundamental movement patterns can increase the risk of injury in female collegiate athletes, and can be identified by using a functional movement screening tool.

The Predictive Accuracy of PREDICT : A Personalized Decision-Making Tool for Southeast Asian Women With Breast Cancer

NARCIS (Netherlands)

Wong, Hoong-Seam; Subramaniam, Shridevi; Alias, Zarifah; Taib, Nur Aishah; Ho, Gwo-Fuang; Ng, Char-Hong; Yip, Cheng-Har; Verkooijen, Helena M.; Hartman, Mikael; Bhoo Pathy, N

Web-based prognostication tools may provide a simple and economically feasible option to aid prognostication and selection of chemotherapy in early breast cancers. We validated PREDICT, a free online breast cancer prognostication and treatment benefit tool, in a resource-limited setting. All 1480

Atomic Oxygen Erosion Yield Predictive Tool for Spacecraft Polymers in Low Earth Orbit

Science.gov (United States)

Bank, Bruce A.; de Groh, Kim K.; Backus, Jane A.

2008-01-01

A predictive tool was developed to estimate the low Earth orbit (LEO) atomic oxygen erosion yield of polymers based on the results of the Polymer Erosion and Contamination Experiment (PEACE) Polymers experiment flown as part of the Materials International Space Station Experiment 2 (MISSE 2). The MISSE 2 PEACE experiment accurately measured the erosion yield of a wide variety of polymers and pyrolytic graphite. The 40 different materials tested were selected specifically to represent a variety of polymers used in space as well as a wide variety of polymer chemical structures. The resulting erosion yield data was used to develop a predictive tool which utilizes chemical structure and physical properties of polymers that can be measured in ground laboratory testing to predict the in-space atomic oxygen erosion yield of a polymer. The properties include chemical structure, bonding information, density and ash content. The resulting predictive tool has a correlation coefficient of 0.914 when compared with actual MISSE 2 space data for 38 polymers and pyrolytic graphite. The intent of the predictive tool is to be able to make estimates of atomic oxygen erosion yields for new polymers without requiring expensive and time consumptive in-space testing.

Risk Prediction Model for Severe Postoperative Complication in Bariatric Surgery.

Science.gov (United States)

Stenberg, Erik; Cao, Yang; Szabo, Eva; Näslund, Erik; Näslund, Ingmar; Ottosson, Johan

2018-01-12

Factors associated with risk for adverse outcome are important considerations in the preoperative assessment of patients for bariatric surgery. As yet, prediction models based on preoperative risk factors have not been able to predict adverse outcome sufficiently. This study aimed to identify preoperative risk factors and to construct a risk prediction model based on these. Patients who underwent a bariatric surgical procedure in Sweden between 2010 and 2014 were identified from the Scandinavian Obesity Surgery Registry (SOReg). Associations between preoperative potential risk factors and severe postoperative complications were analysed using a logistic regression model. A multivariate model for risk prediction was created and validated in the SOReg for patients who underwent bariatric surgery in Sweden, 2015. Revision surgery (standardized OR 1.19, 95% confidence interval (CI) 1.14-0.24, p prediction model. Despite high specificity, the sensitivity of the model was low. Revision surgery, high age, low BMI, large waist circumference, and dyspepsia/GERD were associated with an increased risk for severe postoperative complication. The prediction model based on these factors, however, had a sensitivity that was too low to predict risk in the individual patient case.

Risk based decision tool for space exploration missions

Science.gov (United States)

Meshkat, Leila; Cornford, Steve; Moran, Terrence

2003-01-01

This paper presents an approach and corresponding tool to assess and analyze the risks involved in a mission during the pre-phase A design process. This approach is based on creating a risk template for each subsystem expert involved in the mission design process and defining appropriate interactions between the templates.

Developing prediction equations and a mobile phone application to identify infants at risk of obesity.

Science.gov (United States)

Santorelli, Gillian; Petherick, Emily S; Wright, John; Wilson, Brad; Samiei, Haider; Cameron, Noël; Johnson, William

2013-01-01

Advancements in knowledge of obesity aetiology and mobile phone technology have created the opportunity to develop an electronic tool to predict an infant's risk of childhood obesity. The study aims were to develop and validate equations for the prediction of childhood obesity and integrate them into a mobile phone application (App). Anthropometry and childhood obesity risk data were obtained for 1868 UK-born White or South Asian infants in the Born in Bradford cohort. Logistic regression was used to develop prediction equations (at 6 ± 1.5, 9 ± 1.5 and 12 ± 1.5 months) for risk of childhood obesity (BMI at 2 years >91(st) centile and weight gain from 0-2 years >1 centile band) incorporating sex, birth weight, and weight gain as predictors. The discrimination accuracy of the equations was assessed by the area under the curve (AUC); internal validity by comparing area under the curve to those obtained in bootstrapped samples; and external validity by applying the equations to an external sample. An App was built to incorporate six final equations (two at each age, one of which included maternal BMI). The equations had good discrimination (AUCs 86-91%), with the addition of maternal BMI marginally improving prediction. The AUCs in the bootstrapped and external validation samples were similar to those obtained in the development sample. The App is user-friendly, requires a minimum amount of information, and provides a risk assessment of low, medium, or high accompanied by advice and website links to government recommendations. Prediction equations for risk of childhood obesity have been developed and incorporated into a novel App, thereby providing proof of concept that childhood obesity prediction research can be integrated with advancements in technology.

Qualitative risk assessment as a remediation management tool

International Nuclear Information System (INIS)

Knutson, D.E.

1991-01-01

The technique used to complete this thesis utilizes existing NRC and EPA guidance on health-based risk to qualitatively prioritize preliminary assessments and provide a tool for the direction and management of remediation activities. This method is intended as a decision making tool to aid in prioritizing the remediation effort and manage the remedial investigation and feasibility study (RI/FS) process. It is not a replacement for the RI/FS. The methodology for qualitative risk assessment utilizes data gathered in preliminary assessments and calculates the health-based hazards and consequences from contaminants found at each individual location. The health-based qualitative risk indicated that number-sign 6 fuel oil, carbon tetrachloride, depleted uranium, and enriched uranium were the contaminants of major concern, in that order. Plutonium ranked approximately sixth in the contaminant of concern priority. 38 refs., 1 fig., 9 tabs

Quantitative Risk reduction estimation Tool For Control Systems, Suggested Approach and Research Needs

Energy Technology Data Exchange (ETDEWEB)

Miles McQueen; Wayne Boyer; Mark Flynn; Sam Alessi

2006-03-01

For the past year we have applied a variety of risk assessment technologies to evaluate the risk to critical infrastructure from cyber attacks on control systems. More recently, we identified the need for a stand alone control system risk reduction estimation tool to provide owners and operators of control systems with a more useable, reliable, and credible method for managing the risks from cyber attack. Risk is defined as the probability of a successful attack times the value of the resulting loss, typically measured in lives and dollars. Qualitative and ad hoc techniques for measuring risk do not provide sufficient support for cost benefit analyses associated with cyber security mitigation actions. To address the need for better quantitative risk reduction models we surveyed previous quantitative risk assessment research; evaluated currently available tools; developed new quantitative techniques [17] [18]; implemented a prototype analysis tool to demonstrate how such a tool might be used; used the prototype to test a variety of underlying risk calculational engines (e.g. attack tree, attack graph); and identified technical and research needs. We concluded that significant gaps still exist and difficult research problems remain for quantitatively assessing the risk to control system components and networks, but that a useable quantitative risk reduction estimation tool is not beyond reach.

Predictive Validity of the HKT-R Risk Assessment Tool: Two and 5-Year Violent Recidivism in a Nationwide Sample of Dutch Forensic Psychiatric Patients.

Science.gov (United States)

Bogaerts, Stefan; Spreen, Marinus; Ter Horst, Paul; Gerlsma, Coby

2018-06-01

This study has examined the predictive validity of the Historical Clinical Future [ Historisch Klinisch Toekomst] Revised risk assessment scheme in a cohort of 347 forensic psychiatric patients, which were discharged between 2004 and 2008 from any of 12 highly secure forensic centers in the Netherlands. Predictive validity was measured 2 and 5 years after release. Official reconviction data obtained from the Dutch Ministry of Security and Justice were used as outcome measures. Violent reoffending within 2 and 5 years after discharge was assessed. With regard to violent reoffending, results indicated that the predictive validity of the Historical domain was modest for 2 (area under the curve [AUC] = .75) and 5 (AUC = .74) years. The predictive validity of the Clinical domain was marginal for 2 (admission: AUC = .62; discharge: AUC = .63) and 5 (admission: AUC = .69; discharge: AUC = .62) years after release. The predictive validity of the Future domain was modest (AUC = .71) for 2 years and low for 5 (AUC = .58) years. The total score of the instrument was modest for 2 years (AUC = .78) and marginal for 5 (AUC = .68) years. Finally, the Final Risk Judgment was modest for 2 years (AUC = .78) and marginal for 5 (AUC = .63) years time at risk. It is concluded that this risk assessment instrument appears to be a satisfactory instrument for risk assessment.

DR2DI: a powerful computational tool for predicting novel drug-disease associations

Science.gov (United States)

Lu, Lu; Yu, Hua

2018-05-01

Finding the new related candidate diseases for known drugs provides an effective method for fast-speed and low-risk drug development. However, experimental identification of drug-disease associations is expensive and time-consuming. This motivates the need for developing in silico computational methods that can infer true drug-disease pairs with high confidence. In this study, we presented a novel and powerful computational tool, DR2DI, for accurately uncovering the potential associations between drugs and diseases using high-dimensional and heterogeneous omics data as information sources. Based on a unified and extended similarity kernel framework, DR2DI inferred the unknown relationships between drugs and diseases using Regularized Kernel Classifier. Importantly, DR2DI employed a semi-supervised and global learning algorithm which can be applied to uncover the diseases (drugs) associated with known and novel drugs (diseases). In silico global validation experiments showed that DR2DI significantly outperforms recent two approaches for predicting drug-disease associations. Detailed case studies further demonstrated that the therapeutic indications and side effects of drugs predicted by DR2DI could be validated by existing database records and literature, suggesting that DR2DI can be served as a useful bioinformatic tool for identifying the potential drug-disease associations and guiding drug repositioning. Our software and comparison codes are freely available at https://github.com/huayu1111/DR2DI.

DR2DI: a powerful computational tool for predicting novel drug-disease associations

Science.gov (United States)

Lu, Lu; Yu, Hua

2018-04-01

Finding the new related candidate diseases for known drugs provides an effective method for fast-speed and low-risk drug development. However, experimental identification of drug-disease associations is expensive and time-consuming. This motivates the need for developing in silico computational methods that can infer true drug-disease pairs with high confidence. In this study, we presented a novel and powerful computational tool, DR2DI, for accurately uncovering the potential associations between drugs and diseases using high-dimensional and heterogeneous omics data as information sources. Based on a unified and extended similarity kernel framework, DR2DI inferred the unknown relationships between drugs and diseases using Regularized Kernel Classifier. Importantly, DR2DI employed a semi-supervised and global learning algorithm which can be applied to uncover the diseases (drugs) associated with known and novel drugs (diseases). In silico global validation experiments showed that DR2DI significantly outperforms recent two approaches for predicting drug-disease associations. Detailed case studies further demonstrated that the therapeutic indications and side effects of drugs predicted by DR2DI could be validated by existing database records and literature, suggesting that DR2DI can be served as a useful bioinformatic tool for identifying the potential drug-disease associations and guiding drug repositioning. Our software and comparison codes are freely available at https://github.com/huayu1111/DR2DI.

The ACTA PORT-score for predicting perioperative risk of blood transfusion for adult cardiac surgery.

Science.gov (United States)

Klein, A A; Collier, T; Yeates, J; Miles, L F; Fletcher, S N; Evans, C; Richards, T

2017-09-01

A simple and accurate scoring system to predict risk of transfusion for patients undergoing cardiac surgery is lacking. We identified independent risk factors associated with transfusion by performing univariate analysis, followed by logistic regression. We then simplified the score to an integer-based system and tested it using the area under the receiver operator characteristic (AUC) statistic with a Hosmer-Lemeshow goodness-of-fit test. Finally, the scoring system was applied to the external validation dataset and the same statistical methods applied to test the accuracy of the ACTA-PORT score. Several factors were independently associated with risk of transfusion, including age, sex, body surface area, logistic EuroSCORE, preoperative haemoglobin and creatinine, and type of surgery. In our primary dataset, the score accurately predicted risk of perioperative transfusion in cardiac surgery patients with an AUC of 0.76. The external validation confirmed accuracy of the scoring method with an AUC of 0.84 and good agreement across all scores, with a minor tendency to under-estimate transfusion risk in very high-risk patients. The ACTA-PORT score is a reliable, validated tool for predicting risk of transfusion for patients undergoing cardiac surgery. This and other scores can be used in research studies for risk adjustment when assessing outcomes, and might also be incorporated into a Patient Blood Management programme. © The Author 2017. Published by Oxford University Press on behalf of the British Journal of Anaesthesia. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Development of a risk prediction model for Barrett's esophagus in an Australian population.

Science.gov (United States)

Ireland, C J; Fielder, A L; Thompson, S K; Laws, T A; Watson, D I; Esterman, A

2017-11-01

Esophageal adenocarcinoma has poor 5-year survival rates. Increased survival might be achieved with earlier treatment, but requires earlier identification of the precursor, Barrett's esophagus. Population screening is not cost effective, this may be improved by targeted screening directed at individuals more likely to have Barrett's esophagus. To develop a risk prediction tool for Barrett's esophagus, this study compared individuals with Barrett's esophagus against population controls. Participants completed a questionnaire comprising 35 questions addressing medical history, symptom history, lifestyle factors, anthropomorphic measures, and demographic details. Statistical analysis addressed differences between cases and controls, and entailed initial variable selection, checking of model assumptions, and establishing calibration and discrimination. The area under the curve (AUC) was used to assess overall accuracy. One hundred and twenty individuals with Barrett's esophagus and 235 population controls completed the questionnaire. Significant differences were identified for age, gender, reflux history, family reflux history, history of hypertension, alcoholic drinks per week, and body mass index. These were used to develop a risk prediction model. The AUC was 0.82 (95% CI 0.78-0.87). Good calibration between predicted and observed risk was noted (Hosmer-Lemeshow test P = 0.67). At the point minimizing false positives and false negatives, the model achieved a sensitivity of 84.96% and a specificity of 66%. A well-calibrated risk prediction model with good discrimination has been developed to identify patients with Barrett's esophagus. The model needs to be externally validated before consideration for clinical practice. © The Authors 2017. Published by Oxford University Press on behalf of International Society for Diseases of the Esophagus. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Comparison of four modeling tools for the prediction of potential distribution for non-indigenous weeds in the United States

Science.gov (United States)

Magarey, Roger; Newton, Leslie; Hong, Seung C.; Takeuchi, Yu; Christie, Dave; Jarnevich, Catherine S.; Kohl, Lisa; Damus, Martin; Higgins, Steven I.; Miller, Leah; Castro, Karen; West, Amanda; Hastings, John; Cook, Gericke; Kartesz, John; Koop, Anthony

2018-01-01

This study compares four models for predicting the potential distribution of non-indigenous weed species in the conterminous U.S. The comparison focused on evaluating modeling tools and protocols as currently used for weed risk assessment or for predicting the potential distribution of invasive weeds. We used six weed species (three highly invasive and three less invasive non-indigenous species) that have been established in the U.S. for more than 75 years. The experiment involved providing non-U. S. location data to users familiar with one of the four evaluated techniques, who then developed predictive models that were applied to the United States without knowing the identity of the species or its U.S. distribution. We compared a simple GIS climate matching technique known as Proto3, a simple climate matching tool CLIMEX Match Climates, the correlative model MaxEnt, and a process model known as the Thornley Transport Resistance (TTR) model. Two experienced users ran each modeling tool except TTR, which had one user. Models were trained with global species distribution data excluding any U.S. data, and then were evaluated using the current known U.S. distribution. The influence of weed species identity and modeling tool on prevalence and sensitivity effects was compared using a generalized linear mixed model. Each modeling tool itself had a low statistical significance, while weed species alone accounted for 69.1 and 48.5% of the variance for prevalence and sensitivity, respectively. These results suggest that simple modeling tools might perform as well as complex ones in the case of predicting potential distribution for a weed not yet present in the United States. Considerations of model accuracy should also be balanced with those of reproducibility and ease of use. More important than the choice of modeling tool is the construction of robust protocols and testing both new and experienced users under blind test conditions that approximate operational conditions.

Feasibility and predictive performance of the Hendrich Fall Risk Model II in a rehabilitation department: a prospective study.

Science.gov (United States)

Campanini, Isabella; Mastrangelo, Stefano; Bargellini, Annalisa; Bassoli, Agnese; Bosi, Gabriele; Lombardi, Francesco; Tolomelli, Stefano; Lusuardi, Mirco; Merlo, Andrea

2018-01-11

Falls are a common adverse event in both elderly inpatients and patients admitted to rehabilitation units. The Hendrich Fall Risk Model II (HIIFRM) has been already tested in all hospital wards with high fall rates, with the exception of the rehabilitation setting. This study's aim is to address the feasibility and predictive performances of HIIFRM in a hospital rehabilitation department. A 6 months prospective study in a Italian rehabilitation department with patients from orthopaedic, pulmonary, and neurological rehabilitation wards. All admitted patients were enrolled and assessed within 24 h of admission by means of the HIIFRM. The occurrence of falls was checked and recorded daily. HIIFRM feasibility was assessed as the percentage of successful administrations at admission. HIIFRM predictive performance was determined in terms of area under the Receiver Operating Characteristic (ROC) curve (AUC), best cutoff, sensitivity, specificity, positive and negative predictive values, along with their asymptotic 95% confidence intervals (95% CI). One hundred ninety-one patents were admitted. HIIFRM was feasible in 147 cases (77%), 11 of which suffered a fall (7.5%). Failures in administration were mainly due to bedridden patients (e.g. minimally conscious state, vegetative state). AUC was 0.779(0.685-0.873). The original HIIFRM cutoff of 5 led to a sensitivity of 100% with a mere specificity of 49%(40-57%), thus suggesting using higher cutoffs. Moreover, the median score for non-fallers at rehabilitation units was higher than that reported in literature for geriatric non fallers. The best trade-off between sensitivity and specificity was obtained by using a cutoff of 8. This lead to sensitivity = 73%(46-99%), specificity = 72%(65-80%), positive predictive value = 17% and negative predictive value = 97%. These results support the use of the HIIFRM as a predictive tool. The HIIFRM showed satisfactory feasibility and predictive performances in

Aqueduct: an interactive tool to empower global water risk assessment

Science.gov (United States)

Reig, Paul; Gassert, Francis

2013-04-01

The Aqueduct Water Risk Atlas (Aqueduct) is a publicly available, global database and interactive tool that maps indicators of water related risks for decision makers worldwide. Aqueduct makes use of the latest geo-statistical modeling techniques to compute a composite index and translate the most recently available hydrological data into practical information on water related risks for companies, investors, and governments alike. Twelve global indicators are grouped into a Water Risk Framework designed in response to the growing concerns from private sector actors around water scarcity, water quality, climate change, and increasing demand for freshwater. The Aqueduct framework includes indicators of water stress, variability in supply, storage, flood, drought, groundwater, water quality and social conflict, addressing both spatial and temporal variation in water hazards. It organizes indicators into three categories of risk that bring together multiple dimensions of water related risk into comprehensive aggregated scores, which allow for dynamic weighting to capture users' unique exposure to water hazards. All information is compiled into an online, open access platform, from which decision-makers can view indicators, scores, and maps, conduct global risk assessments, and export data and shape files for further analysis. Companies can use this tool to evaluate their exposure to water risks across operations and supply chains, investors to assess water-related risks in their portfolio, and public-sector actors to better understand water security. Additionally, the open nature of the data and maps allow other organizations to build off of this effort with new research, for example in the areas of water-energy or water-food relationships. This presentation will showcase the Aqueduct Water Risk Atlas online tool and the features and functionalities it offers, as well as explain how it can be used for both private and public sector applications. The session will

Falls risk assessment begins with hello: lessons learned from the use of one home health agency's fall risk tool.

Science.gov (United States)

Flemming, Patricia J; Ramsay, Katherine

2012-10-01

Identifying older adults at risk for falls is a challenge all home healthcare agencies (HHAs) face. The process of assessing for falls risk begins with the initial home visit. One HHA affiliated with an academic medical center describes its experience in development and use of a Falls Risk Assessment (FRA) tool over a 10-year period. The FRA tool has been modified since initial development to clarify elements of the tool based on research and to reflect changes in the Outcome and Assessment Information Set (OASIS) document. The primary purpose of this article is to share a validated falls risk assessment tool to facilitate identification of fall-related risk factors in the homebound population. A secondary purpose is to share lessons learned by the HHA during the 10 years using the FRA.

Risk prediction model for knee pain in the Nottingham community: a Bayesian modelling approach.

Science.gov (United States)

Fernandes, G S; Bhattacharya, A; McWilliams, D F; Ingham, S L; Doherty, M; Zhang, W

2017-03-20

Twenty-five percent of the British population over the age of 50 years experiences knee pain. Knee pain can limit physical ability and cause distress and bears significant socioeconomic costs. The objectives of this study were to develop and validate the first risk prediction model for incident knee pain in the Nottingham community and validate this internally within the Nottingham cohort and externally within the Osteoarthritis Initiative (OAI) cohort. A total of 1822 participants from the Nottingham community who were at risk for knee pain were followed for 12 years. Of this cohort, two-thirds (n = 1203) were used to develop the risk prediction model, and one-third (n = 619) were used to validate the model. Incident knee pain was defined as pain on most days for at least 1 month in the past 12 months. Predictors were age, sex, body mass index, pain elsewhere, prior knee injury and knee alignment. A Bayesian logistic regression model was used to determine the probability of an OR >1. The Hosmer-Lemeshow χ 2 statistic (HLS) was used for calibration, and ROC curve analysis was used for discrimination. The OAI cohort from the United States was also used to examine the performance of the model. A risk prediction model for knee pain incidence was developed using a Bayesian approach. The model had good calibration, with an HLS of 7.17 (p = 0.52) and moderate discriminative ability (ROC 0.70) in the community. Individual scenarios are given using the model. However, the model had poor calibration (HLS 5866.28, p prediction model for knee pain, regardless of underlying structural changes of knee osteoarthritis, in the community using a Bayesian modelling approach. The model appears to work well in a community-based population but not in individuals with a higher risk for knee osteoarthritis, and it may provide a convenient tool for use in primary care to predict the risk of knee pain in the general population.

Development and Validation of a Preprocedural Risk Score to Predict Access Site Complications After Peripheral Vascular Interventions Based on the Vascular Quality Initiative Database

Directory of Open Access Journals (Sweden)

Daniel Ortiz

2016-01-01

Full Text Available Purpose: Access site complications following peripheral vascular intervention (PVI are associated with prolonged hospitalization and increased mortality. Prediction of access site complication risk may optimize PVI care; however, there is no tool designed for this. We aimed to create a clinical scoring tool to stratify patients according to their risk of developing access site complications after PVI. Methods: The Society for Vascular Surgery’s Vascular Quality Initiative database yielded 27,997 patients who had undergone PVI at 131 North American centers. Clinically and statistically significant preprocedural risk factors associated with in-hospital, post-PVI access site complications were included in a multivariate logistic regression model, with access site complications as the outcome variable. A predictive model was developed with a random sample of 19,683 (70% PVI procedures and validated in 8,314 (30%. Results: Access site complications occurred in 939 (3.4% patients. The risk tool predictors are female gender, age > 70 years, white race, bedridden ambulatory status, insulin-treated diabetes mellitus, prior minor amputation, procedural indication of claudication, and nonfemoral arterial access site (model c-statistic = 0.638. Of these predictors, insulin-treated diabetes mellitus and prior minor amputation were protective of access site complications. The discriminatory power of the risk model was confirmed by the validation dataset (c-statistic = 0.6139. Higher risk scores correlated with increased frequency of access site complications: 1.9% for low risk, 3.4% for moderate risk and 5.1% for high risk. Conclusions: The proposed clinical risk score based on eight preprocedural characteristics is a tool to stratify patients at risk for post-PVI access site complications. The risk score may assist physicians in identifying patients at risk for access site complications and selection of patients who may benefit from bleeding avoidance

A summary risk score for the prediction of Alzheimer disease in elderly persons.

Science.gov (United States)

Reitz, Christiane; Tang, Ming-Xin; Schupf, Nicole; Manly, Jennifer J; Mayeux, Richard; Luchsinger, José A

2010-07-01

To develop a simple summary risk score for the prediction of Alzheimer disease in elderly persons based on their vascular risk profiles. A longitudinal, community-based study. New York, New York. Patients One thousand fifty-one Medicare recipients aged 65 years or older and residing in New York who were free of dementia or cognitive impairment at baseline. We separately explored the associations of several vascular risk factors with late-onset Alzheimer disease (LOAD) using Cox proportional hazards models to identify factors that would contribute to the risk score. Then we estimated the score values of each factor based on their beta coefficients and created the LOAD vascular risk score by summing these individual scores. Risk factors contributing to the risk score were age, sex, education, ethnicity, APOE epsilon4 genotype, history of diabetes, hypertension or smoking, high-density lipoprotein levels, and waist to hip ratio. The resulting risk score predicted dementia well. According to the vascular risk score quintiles, the risk to develop probable LOAD was 1.0 for persons with a score of 0 to 14 and increased 3.7-fold for persons with a score of 15 to 18, 3.6-fold for persons with a score of 19 to 22, 12.6-fold for persons with a score of 23 to 28, and 20.5-fold for persons with a score higher than 28. While additional studies in other populations are needed to validate and further develop the score, our study suggests that this vascular risk score could be a valuable tool to identify elderly individuals who might be at risk of LOAD. This risk score could be used to identify persons at risk of LOAD, but can also be used to adjust for confounders in epidemiologic studies.

Predicting tool life in turning operations using neural networks and image processing

Science.gov (United States)

Mikołajczyk, T.; Nowicki, K.; Bustillo, A.; Yu Pimenov, D.

2018-05-01

A two-step method is presented for the automatic prediction of tool life in turning operations. First, experimental data are collected for three cutting edges under the same constant processing conditions. In these experiments, the parameter of tool wear, VB, is measured with conventional methods and the same parameter is estimated using Neural Wear, a customized software package that combines flank wear image recognition and Artificial Neural Networks (ANNs). Second, an ANN model of tool life is trained with the data collected from the first two cutting edges and the subsequent model is evaluated on two different subsets for the third cutting edge: the first subset is obtained from the direct measurement of tool wear and the second is obtained from the Neural Wear software that estimates tool wear using edge images. Although the complete-automated solution, Neural Wear software for tool wear recognition plus the ANN model of tool life prediction, presented a slightly higher error than the direct measurements, it was within the same range and can meet all industrial requirements. These results confirm that the combination of image recognition software and ANN modelling could potentially be developed into a useful industrial tool for low-cost estimation of tool life in turning operations.

A risk assessment tool for contaminated sites in low-permeability fractured media

DEFF Research Database (Denmark)

Chambon, Julie Claire Claudia; Binning, Philip John; Jørgensen, Peter R.

2011-01-01

A risk assessment tool for contaminated sites in low-permeability fractured media is developed, based on simple transient and steady-state analytical solutions. The discrete fracture (DF) tool, which explicitly accounts for the transport along fractures, covers different source geometries...... and history (including secondary sources) and can be applied to a wide range of compounds. The tool successfully simulates published data from short duration column and field experiments. The use for risk assessment is illustrated by three typical risk assessment case studies, involving pesticides...

2B-Alert Web: An Open-Access Tool for Predicting the Effects of Sleep/Wake Schedules and Caffeine Consumption on Neurobehavioral Performance.

Science.gov (United States)

Reifman, Jaques; Kumar, Kamal; Wesensten, Nancy J; Tountas, Nikolaos A; Balkin, Thomas J; Ramakrishnan, Sridhar

2016-12-01

Computational tools that predict the effects of daily sleep/wake amounts on neurobehavioral performance are critical components of fatigue management systems, allowing for the identification of periods during which individuals are at increased risk for performance errors. However, none of the existing computational tools is publicly available, and the commercially available tools do not account for the beneficial effects of caffeine on performance, limiting their practical utility. Here, we introduce 2B-Alert Web, an open-access tool for predicting neurobehavioral performance, which accounts for the effects of sleep/wake schedules, time of day, and caffeine consumption, while incorporating the latest scientific findings in sleep restriction, sleep extension, and recovery sleep. We combined our validated Unified Model of Performance and our validated caffeine model to form a single, integrated modeling framework instantiated as a Web-enabled tool. 2B-Alert Web allows users to input daily sleep/wake schedules and caffeine consumption (dosage and time) to obtain group-average predictions of neurobehavioral performance based on psychomotor vigilance tasks. 2B-Alert Web is accessible at: https://2b-alert-web.bhsai.org. The 2B-Alert Web tool allows users to obtain predictions for mean response time, mean reciprocal response time, and number of lapses. The graphing tool allows for simultaneous display of up to seven different sleep/wake and caffeine schedules. The schedules and corresponding predicted outputs can be saved as a Microsoft Excel file; the corresponding plots can be saved as an image file. The schedules and predictions are erased when the user logs off, thereby maintaining privacy and confidentiality. The publicly accessible 2B-Alert Web tool is available for operators, schedulers, and neurobehavioral scientists as well as the general public to determine the impact of any given sleep/wake schedule, caffeine consumption, and time of day on performance of a

Global Variance Risk Premium and Forex Return Predictability

OpenAIRE

Aloosh, Arash

2014-01-01

In a long-run risk model with stochastic volatility and frictionless markets, I express expected forex returns as a function of consumption growth variances and stock variance risk premiums (VRPs)—the difference between the risk-neutral and statistical expectations of market return variation. This provides a motivation for using the forward-looking information available in stock market volatility indices to predict forex returns. Empirically, I find that stock VRPs predict forex returns at a ...

Hedging tools and cross market applications

International Nuclear Information System (INIS)

Schlenker, C.

1997-01-01

The nature of the basic tools of the market - put, call, and swap - their various combinations and how they are used in various market transactions were explained. The role and effect of the use of these tools on prices, price fluctuations and risks were outlined. Predictions for the future for producers (reduced use of price optimization and its replacement by price diversification strategies, realignment of risk management tools to be in accord with the changed marketing techniques), and for end users (focus on managing short term fluctuations in input costs, short-term price fixing, more frequent utilization of traditional option strategies and spread options), were summarized. For the electricity market in particular, the prominence of gas-fired generation units as options on the spread between gas and electricity, providing opportunities for outperformance options was predicted

Does specific psychopathology predict development of psychosis in ultra high-risk (UHR) patients?

Science.gov (United States)

Thompson, Andrew; Nelson, Barnaby; Bruxner, Annie; O'Connor, Karen; Mossaheb, Nilufar; Simmons, Magenta B; Yung, Alison

2013-04-01

Studies have attempted to identify additional risk factors within the group identified as 'ultra high risk' (UHR) for developing psychotic disorders in order to characterise those at highest risk. However, these studies have often neglected clinical symptom types as additional risk factors. We aimed to investigate the relationship between baseline clinical psychotic or psychotic-like symptoms and the subsequent transition to a psychotic disorder in a UHR sample. A retrospective 'case-control' methodology was used. We identified all individuals from a UHR clinic who had subsequently developed a psychotic disorder (cases) and compared these to a random sample of individuals from the clinic who did not become psychotic within the sampling time frame (controls). The sample consisted of 120 patients (60 cases, 60 controls). An audit tool was used to identify clinical symptoms reported at entry to the clinic (baseline) using the clinical file. Diagnosis at transition was assessed using the Operational Criteria for Psychotic Illness (OPCRIT) computer program. The relationship between transition to a psychotic disorder and baseline symptoms was explored using survival analysis. Presence of thought disorder, any delusions and elevated mood significantly predicted transition to a psychotic disorder. When other symptoms were adjusted for, only the presence of elevated mood significantly predicted subsequent transition (hazard ratio 2.69, p = 0.002). Thought disorder was a predictor of transition to a schizophrenia-like psychotic disorder (hazard ratio 3.69, p = 0.008). Few individual clinical symptoms appear to be predictive of transition to a psychotic disorder in the UHR group. Clinicians should be cautious about the use of clinical profile alone in such individuals when determining who is at highest risk.

Tools for Predicting Cleaning Efficiency in the LHC

CERN Document Server

Assmann, R W; Brugger, M; Hayes, M; Jeanneret, J B; Kain, V; Kaltchev, D I; Schmidt, F

2003-01-01

The computer codes SIXTRACK and DIMAD have been upgraded to include realistic models of proton scattering in collimator jaws, mechanical aperture restrictions, and time-dependent fields. These new tools complement long-existing simplified linear tracking programs used up to now for tracking with collimators. Scattering routines from STRUCT and K2 have been compared with one another and the results have been cross-checked to the FLUKA Monte Carlo package. A systematic error is assigned to the predictions of cleaning efficiency. Now, predictions of the cleaning efficiency are possible with a full LHC model, including chromatic effects, linear and nonlinear errors, beam-beam kicks and associated diffusion, and time-dependent fields. The beam loss can be predicted around the ring, both for regular and irregular beam losses. Examples are presented.

Screening for substance abuse risk in cancer patients using the Opioid Risk Tool and urine drug screen.

Science.gov (United States)

Barclay, Joshua S; Owens, Justine E; Blackhall, Leslie J

2014-07-01

The use of opioids for management of cancer-related pain has increased significantly and has been associated with a substantial rise in rates of substance abuse and diversion. There is a paucity of data not only on the prevalence of substance abuse in cancer patients, but also for issues of drug use and diversion in family caregivers. This study aimed to evaluate the frequency of risk factors for substance abuse and diversion, and abnormal urine drug screens in cancer patients receiving palliative care. A retrospective chart review was performed for patients with cancer who were seen in the University of Virginia Palliative Care Clinic during the month of September 2012. We evaluated Opioid Risk Tool variables and total scores, insurance status, and urine drug screen results. Of the 114 cancer patients seen in September 2012, the mean Opioid Risk Tool score was 3.79, with 43% of patients defined as medium to high risk. Age (16-45 years old, 23%) and a personal history of alcohol (23%) or illicit drugs (21%) were the most common risk factors identified. We obtained a urine drug screen on 40% of patients, noting abnormal findings in 45.65%. Opioids are an effective treatment for cancer-related pain, yet substantial risk for substance abuse exits in the cancer population. Screening tools, such as the Opioid Risk Tool, should be used as part of a complete patient assessment to balance risk with appropriate relief of suffering.

Users' experiences of an emergency department patient admission predictive tool: A qualitative evaluation.

Science.gov (United States)

Jessup, Melanie; Crilly, Julia; Boyle, Justin; Wallis, Marianne; Lind, James; Green, David; Fitzgerald, Gerard

2016-09-01

Emergency department overcrowding is an increasing issue impacting patients, staff and quality of care, resulting in poor patient and system outcomes. In order to facilitate better management of emergency department resources, a patient admission predictive tool was developed and implemented. Evaluation of the tool's accuracy and efficacy was complemented with a qualitative component that explicated the experiences of users and its impact upon their management strategies, and is the focus of this article. Semi-structured interviews were conducted with 15 pertinent users, including bed managers, after-hours managers, specialty department heads, nurse unit managers and hospital executives. Analysis realised dynamics of accuracy, facilitating communication and enabling group decision-making Users generally welcomed the enhanced potential to predict and plan following the incorporation of the patient admission predictive tool into their daily and weekly decision-making processes. They offered astute feedback with regard to their responses when faced with issues of capacity and communication. Participants reported an growing confidence in making informed decisions in a cultural context that is continually moving from reactive to proactive. This information will inform further patient admission predictive tool development specifically and implementation processes generally. © The Author(s) 2015.

Cognitive mapping tools: review and risk management needs.

Science.gov (United States)

Wood, Matthew D; Bostrom, Ann; Bridges, Todd; Linkov, Igor

2012-08-01

Risk managers are increasingly interested in incorporating stakeholder beliefs and other human factors into the planning process. Effective risk assessment and management requires understanding perceptions and beliefs of involved stakeholders, and how these beliefs give rise to actions that influence risk management decisions. Formal analyses of risk manager and stakeholder cognitions represent an important first step. Techniques for diagramming stakeholder mental models provide one tool for risk managers to better understand stakeholder beliefs and perceptions concerning risk, and to leverage this new understanding in developing risk management strategies. This article reviews three methodologies for assessing and diagramming stakeholder mental models--decision-analysis-based mental modeling, concept mapping, and semantic web analysis--and assesses them with regard to their ability to address risk manager needs. © 2012 Society for Risk Analysis.

Scientific reporting is suboptimal for aspects that characterize genetic risk prediction studies: a review of published articles based on the Genetic RIsk Prediction Studies statement.

Science.gov (United States)

Iglesias, Adriana I; Mihaescu, Raluca; Ioannidis, John P A; Khoury, Muin J; Little, Julian; van Duijn, Cornelia M; Janssens, A Cecile J W

2014-05-01

Our main objective was to raise awareness of the areas that need improvements in the reporting of genetic risk prediction articles for future publications, based on the Genetic RIsk Prediction Studies (GRIPS) statement. We evaluated studies that developed or validated a prediction model based on multiple DNA variants, using empirical data, and were published in 2010. A data extraction form based on the 25 items of the GRIPS statement was created and piloted. Forty-two studies met our inclusion criteria. Overall, more than half of the evaluated items (34 of 62) were reported in at least 85% of included articles. Seventy-seven percentage of the articles were identified as genetic risk prediction studies through title assessment, but only 31% used the keywords recommended by GRIPS in the title or abstract. Seventy-four percentage mentioned which allele was the risk variant. Overall, only 10% of the articles reported all essential items needed to perform external validation of the risk model. Completeness of reporting in genetic risk prediction studies is adequate for general elements of study design but is suboptimal for several aspects that characterize genetic risk prediction studies such as description of the model construction. Improvements in the transparency of reporting of these aspects would facilitate the identification, replication, and application of genetic risk prediction models. Copyright © 2014 Elsevier Inc. All rights reserved.

iPat: intelligent prediction and association tool for genomic research.

Science.gov (United States)

Chen, Chunpeng James; Zhang, Zhiwu

2018-06-01

The ultimate goal of genomic research is to effectively predict phenotypes from genotypes so that medical management can improve human health and molecular breeding can increase agricultural production. Genomic prediction or selection (GS) plays a complementary role to genome-wide association studies (GWAS), which is the primary method to identify genes underlying phenotypes. Unfortunately, most computing tools cannot perform data analyses for both GWAS and GS. Furthermore, the majority of these tools are executed through a command-line interface (CLI), which requires programming skills. Non-programmers struggle to use them efficiently because of the steep learning curves and zero tolerance for data formats and mistakes when inputting keywords and parameters. To address these problems, this study developed a software package, named the Intelligent Prediction and Association Tool (iPat), with a user-friendly graphical user interface. With iPat, GWAS or GS can be performed using a pointing device to simply drag and/or click on graphical elements to specify input data files, choose input parameters and select analytical models. Models available to users include those implemented in third party CLI packages such as GAPIT, PLINK, FarmCPU, BLINK, rrBLUP and BGLR. Users can choose any data format and conduct analyses with any of these packages. File conversions are automatically conducted for specified input data and selected packages. A GWAS-assisted genomic prediction method was implemented to perform genomic prediction using any GWAS method such as FarmCPU. iPat was written in Java for adaptation to multiple operating systems including Windows, Mac and Linux. The iPat executable file, user manual, tutorials and example datasets are freely available at http://zzlab.net/iPat. zhiwu.zhang@wsu.edu.

Prediction Of Abrasive And Diffusive Tool Wear Mechanisms In Machining

Science.gov (United States)

Rizzuti, S.; Umbrello, D.

2011-01-01

Tool wear prediction is regarded as very important task in order to maximize tool performance, minimize cutting costs and improve the quality of workpiece in cutting. In this research work, an experimental campaign was carried out at the varying of cutting conditions with the aim to measure both crater and flank tool wear, during machining of an AISI 1045 with an uncoated carbide tool P40. Parallel a FEM-based analysis was developed in order to study the tool wear mechanisms, taking also into account the influence of the cutting conditions and the temperature reached on the tool surfaces. The results show that, when the temperature of the tool rake surface is lower than the activation temperature of the diffusive phenomenon, the wear rate can be estimated applying an abrasive model. In contrast, in the tool area where the temperature is higher than the diffusive activation temperature, the wear rate can be evaluated applying a diffusive model. Finally, for a temperature ranges within the above cited values an adopted abrasive-diffusive wear model furnished the possibility to correctly evaluate the tool wear phenomena.

Implications of Nine Risk Prediction Models for Selecting Ever-Smokers for Computed Tomography Lung Cancer Screening.

Science.gov (United States)

Katki, Hormuzd A; Kovalchik, Stephanie A; Petito, Lucia C; Cheung, Li C; Jacobs, Eric; Jemal, Ahmedin; Berg, Christine D; Chaturvedi, Anil K

2018-05-15

Lung cancer screening guidelines recommend using individualized risk models to refer ever-smokers for screening. However, different models select different screening populations. The performance of each model in selecting ever-smokers for screening is unknown. To compare the U.S. screening populations selected by 9 lung cancer risk models (the Bach model; the Spitz model; the Liverpool Lung Project [LLP] model; the LLP Incidence Risk Model [LLPi]; the Hoggart model; the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Model 2012 [PLCOM2012]; the Pittsburgh Predictor; the Lung Cancer Risk Assessment Tool [LCRAT]; and the Lung Cancer Death Risk Assessment Tool [LCDRAT]) and to examine their predictive performance in 2 cohorts. Population-based prospective studies. United States. Models selected U.S. screening populations by using data from the National Health Interview Survey from 2010 to 2012. Model performance was evaluated using data from 337 388 ever-smokers in the National Institutes of Health-AARP Diet and Health Study and 72 338 ever-smokers in the CPS-II (Cancer Prevention Study II) Nutrition Survey cohort. Model calibration (ratio of model-predicted to observed cases [expected-observed ratio]) and discrimination (area under the curve [AUC]). At a 5-year risk threshold of 2.0%, the models chose U.S. screening populations ranging from 7.6 million to 26 million ever-smokers. These disagreements occurred because, in both validation cohorts, 4 models (the Bach model, PLCOM2012, LCRAT, and LCDRAT) were well-calibrated (expected-observed ratio range, 0.92 to 1.12) and had higher AUCs (range, 0.75 to 0.79) than 5 models that generally overestimated risk (expected-observed ratio range, 0.83 to 3.69) and had lower AUCs (range, 0.62 to 0.75). The 4 best-performing models also had the highest sensitivity at a fixed specificity (and vice versa) and similar discrimination at a fixed risk threshold. These models showed better agreement on size of the

Strategic Risk Assessment: A Decision Tool for Complex Decisions

Energy Technology Data Exchange (ETDEWEB)

Pollard, Simon; Duarte-Davidson, Raquel; Yearsley, Roger [Environment Agency, London (United Kingdom). National Centre for Risk Analysis and Options Appraisal; Kemp, Ray; Crawford, Mark [Galson Sciences Limited, Oakham (United Kingdom)

2001-07-01

Reporting on the state of the environment often requires policy makers and regulators to prioritise a range of diverse environmental issues for the purpose of directing future action on environmental protection and improvement. Information on environmental issues to inform this type of analysis can be disparate, it may be too voluminous or even absent. Data on a range of issues are rarely presented in a common format that allows easy comparison. Nevertheless, strategic judgements are required on the significance of impacts from various environmental pressures and on the inherent uncertainties. Prioritising issues forces a discussion among stakeholders of the relative significance of 'environmental harm' from pressures acting on various receptors in the environment. Discussions of this sort rapidly evolve into a discourse on risks and values. In an attempt to help systematise these discussions and provide practical tools for the analysis of environmental risks at a strategic level, the Environment Agency of England and Wales has initiated developmental research on strategic risk assessment. The tools developed under this research use the concept of 'environmental harm' as a common currency, viewed from technical, social and economic perspectives, to analyse impacts from a range of environmental pressures. Critical to an informed debate is an understanding and analysis both of the various characteristics of harm (spatial and temporal extent, reversibility, latency, etc.) and of the social response to the actual or potential environmental harm. Recent developments in this approach allow a presentation of the analysis in a structured fashion so as to better inform risk management decisions. Here, we present recent developments in the strategic risk assessment research tool, as tested by case studies from state of the environment reporting and the analysis of a regional environmental plan. We discuss its relative advantages and limitations and its

Strategic Risk Assessment: A Decision Tool for Complex Decisions

International Nuclear Information System (INIS)

Pollard, Simon; Duarte-Davidson, Raquel; Yearsley, Roger

2001-01-01

Reporting on the state of the environment often requires policy makers and regulators to prioritise a range of diverse environmental issues for the purpose of directing future action on environmental protection and improvement. Information on environmental issues to inform this type of analysis can be disparate, it may be too voluminous or even absent. Data on a range of issues are rarely presented in a common format that allows easy comparison. Nevertheless, strategic judgements are required on the significance of impacts from various environmental pressures and on the inherent uncertainties. Prioritising issues forces a discussion among stakeholders of the relative significance of 'environmental harm' from pressures acting on various receptors in the environment. Discussions of this sort rapidly evolve into a discourse on risks and values. In an attempt to help systematise these discussions and provide practical tools for the analysis of environmental risks at a strategic level, the Environment Agency of England and Wales has initiated developmental research on strategic risk assessment. The tools developed under this research use the concept of 'environmental harm' as a common currency, viewed from technical, social and economic perspectives, to analyse impacts from a range of environmental pressures. Critical to an informed debate is an understanding and analysis both of the various characteristics of harm (spatial and temporal extent, reversibility, latency, etc.) and of the social response to the actual or potential environmental harm. Recent developments in this approach allow a presentation of the analysis in a structured fashion so as to better inform risk management decisions. Here, we present recent developments in the strategic risk assessment research tool, as tested by case studies from state of the environment reporting and the analysis of a regional environmental plan. We discuss its relative advantages and limitations and its wider potential role

Development of METAL-ACTIVE SITE and ZINCCLUSTER tool to predict active site pockets.

Science.gov (United States)

Ajitha, M; Sundar, K; Arul Mugilan, S; Arumugam, S

2018-03-01

The advent of whole genome sequencing leads to increasing number of proteins with known amino acid sequences. Despite many efforts, the number of proteins with resolved three dimensional structures is still low. One of the challenging tasks the structural biologists face is the prediction of the interaction of metal ion with any protein for which the structure is unknown. Based on the information available in Protein Data Bank, a site (METALACTIVE INTERACTION) has been generated which displays information for significant high preferential and low-preferential combination of endogenous ligands for 49 metal ions. User can also gain information about the residues present in the first and second coordination sphere as it plays a major role in maintaining the structure and function of metalloproteins in biological system. In this paper, a novel computational tool (ZINCCLUSTER) is developed, which can predict the zinc metal binding sites of proteins even if only the primary sequence is known. The purpose of this tool is to predict the active site cluster of an uncharacterized protein based on its primary sequence or a 3D structure. The tool can predict amino acids interacting with a metal or vice versa. This tool is based on the occurrence of significant triplets and it is tested to have higher prediction accuracy when compared to that of other available techniques. © 2017 Wiley Periodicals, Inc.

Drug response prediction in high-risk multiple myeloma

DEFF Research Database (Denmark)

Vangsted, A J; Helm-Petersen, S; Cowland, J B

2018-01-01

from high-risk patients by GEP70 at diagnosis from Total Therapy 2 and 3A to predict the response by the DRP score of drugs used in the treatment of myeloma patients. The DRP score stratified patients further. High-risk myeloma with a predicted sensitivity to melphalan by the DRP score had a prolonged...

A utility/cost analysis of breast cancer risk prediction algorithms

Science.gov (United States)

Abbey, Craig K.; Wu, Yirong; Burnside, Elizabeth S.; Wunderlich, Adam; Samuelson, Frank W.; Boone, John M.

2016-03-01

Breast cancer risk prediction algorithms are used to identify subpopulations that are at increased risk for developing breast cancer. They can be based on many different sources of data such as demographics, relatives with cancer, gene expression, and various phenotypic features such as breast density. Women who are identified as high risk may undergo a more extensive (and expensive) screening process that includes MRI or ultrasound imaging in addition to the standard full-field digital mammography (FFDM) exam. Given that there are many ways that risk prediction may be accomplished, it is of interest to evaluate them in terms of expected cost, which includes the costs of diagnostic outcomes. In this work we perform an expected-cost analysis of risk prediction algorithms that is based on a published model that includes the costs associated with diagnostic outcomes (true-positive, false-positive, etc.). We assume the existence of a standard screening method and an enhanced screening method with higher scan cost, higher sensitivity, and lower specificity. We then assess expected cost of using a risk prediction algorithm to determine who gets the enhanced screening method under the strong assumption that risk and diagnostic performance are independent. We find that if risk prediction leads to a high enough positive predictive value, it will be cost-effective regardless of the size of the subpopulation. Furthermore, in terms of the hit-rate and false-alarm rate of the of the risk prediction algorithm, iso-cost contours are lines with slope determined by properties of the available diagnostic systems for screening.

Combining Results from Distinct MicroRNA Target Prediction Tools Enhances the Performance of Analyses

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Arthur C. Oliveira

2017-05-01

Full Text Available Target prediction is generally the first step toward recognition of bona fide microRNA (miRNA-target interactions in living cells. Several target prediction tools are now available, which use distinct criteria and stringency to provide the best set of candidate targets for a single miRNA or a subset of miRNAs. However, there are many false-negative predictions, and consensus about the optimum strategy to select and use the output information provided by the target prediction tools is lacking. We compared the performance of four tools cited in literature—TargetScan (TS, miRanda-mirSVR (MR, Pita, and RNA22 (R22, and we determined the most effective approach for analyzing target prediction data (individual, union, or intersection. For this purpose, we calculated the sensitivity, specificity, precision, and correlation of these approaches using 10 miRNAs (miR-1-3p, miR-17-5p, miR-21-5p, miR-24-3p, miR-29a-3p, miR-34a-5p, miR-124-3p, miR-125b-5p, miR-145-5p, and miR-155-5p and 1,400 genes (700 validated and 700 non-validated as targets of these miRNAs. The four tools provided a subset of high-quality predictions and returned few false-positive predictions; however, they could not identify several known true targets. We demonstrate that union of TS/MR and TS/MR/R22 enhanced the quality of in silico prediction analysis of miRNA targets. We conclude that the union rather than the intersection of the aforementioned tools is the best strategy for maximizing performance while minimizing the loss of time and resources in subsequent in vivo and in vitro experiments for functional validation of miRNA-target interactions.

The use of machine learning and nonlinear statistical tools for ADME prediction.

Science.gov (United States)

Sakiyama, Yojiro

2009-02-01

Absorption, distribution, metabolism and excretion (ADME)-related failure of drug candidates is a major issue for the pharmaceutical industry today. Prediction of ADME by in silico tools has now become an inevitable paradigm to reduce cost and enhance efficiency in pharmaceutical research. Recently, machine learning as well as nonlinear statistical tools has been widely applied to predict routine ADME end points. To achieve accurate and reliable predictions, it would be a prerequisite to understand the concepts, mechanisms and limitations of these tools. Here, we have devised a small synthetic nonlinear data set to help understand the mechanism of machine learning by 2D-visualisation. We applied six new machine learning methods to four different data sets. The methods include Naive Bayes classifier, classification and regression tree, random forest, Gaussian process, support vector machine and k nearest neighbour. The results demonstrated that ensemble learning and kernel machine displayed greater accuracy of prediction than classical methods irrespective of the data set size. The importance of interaction with the engineering field is also addressed. The results described here provide insights into the mechanism of machine learning, which will enable appropriate usage in the future.

Is monocyte HLA-DR expression monitoring a useful tool to predict the risk of secondary infection?

Science.gov (United States)

Lukaszewicz, A-C; Faivre, V; Payen, D

2010-09-01

Downregulation of the immune response is common among Intensive Care Unit (ICU) patients after an acute inflammatory injury, whether it was septic or not. Such a modification could be seen as an adaptation to attenuate the effects of the inflammatory storm on tissues, but it exposes the subject to the risk of nosocomial infection and impairs recovery processes. The intensity of immunity downregulation is difficult to characterize, since clinical presentation is silent and non-specific, which urges the use of tools for immune monitoring. This review focuses on the use of monocyte HLA-DR expression to detect immune hyporesponsiveness and to select the appropriate immunomodulating therapy, as well as the efficiency of this technique in controlling secondary infections.

Application of structural reliability and risk assessment to life prediction and life extension decision making

International Nuclear Information System (INIS)

Meyer, T.A.; Balkey, K.R.; Bishop, B.A.

1987-01-01

There can be numerous uncertainties involved in performing component life assessments. In addition, sufficient data may be unavailable to make a useful life prediction. Structural Reliability and Risk Assessment (SRRA) is primarily an analytical methodology or tool that quantifies the impact of uncertainties on the structural life of plant components and can address the lack of data in component life prediction. As a prelude to discussing the technical aspects of SRRA, a brief review of general component life prediction methods is first made so as to better develop an understanding of the role of SRRA in such evaluations. SRRA is then presented as it is applied in component life evaluations with example applications being discussed for both nuclear and non-nuclear components

Predictive tool of energy performance of cold storage in agrifood industries: The Portuguese case study

International Nuclear Information System (INIS)

Nunes, José; Neves, Diogo; Gaspar, Pedro D.; Silva, Pedro D.; Andrade, Luís P.

2014-01-01

Highlights: • A predictive tool for assessment of the energy performance in agrifood industries that use cold storage is developed. • The correlations used by the predictive tool result from the greatest number of data sets collected to date in Portugal. • Strong relationships between raw material, energy consumption and volume of cold stores were established. • Case studies were analyzed that demonstrate the applicability of the tool. • The tool results are useful in the decision-making process of practice measures for the improvement of energy efficiency. - Abstract: Food processing and conservation represent decisive factors for the sustainability of the planet given the significant growth of the world population in the last decades. Therefore, the cooling process during the manufacture and/or storage of food products has been subject of study and improvement in order to ensure the food supply with good quality and safety. A predictive tool for assessment of the energy performance in agrifood industries that use cold storage is developed in order to contribute to the improvement of the energy efficiency of this industry. The predictive tool is based on a set of characteristic correlated parameters: amount of raw material annually processed, annual energy consumption and volume of cold rooms. Case studies of application of the predictive tool consider industries in the meat sector, specifically slaughterhouses. The results obtained help on the decision-making of practice measures for improvement of the energy efficiency in this industry

DengueTools: innovative tools and strategies for the surveillance and control of dengue.

Science.gov (United States)

Wilder-Smith, Annelies; Renhorn, Karl-Erik; Tissera, Hasitha; Abu Bakar, Sazaly; Alphey, Luke; Kittayapong, Pattamaporn; Lindsay, Steve; Logan, James; Hatz, Christoph; Reiter, Paul; Rocklöv, Joacim; Byass, Peter; Louis, Valérie R; Tozan, Yesim; Massad, Eduardo; Tenorio, Antonio; Lagneau, Christophe; L'Ambert, Grégory; Brooks, David; Wegerdt, Johannah; Gubler, Duane

2012-01-01

Dengue fever is a mosquito-borne viral disease estimated to cause about 230 million infections worldwide every year, of which 25,000 are fatal. Global incidence has risen rapidly in recent decades with some 3.6 billion people, over half of the world's population, now at risk, mainly in urban centres of the tropics and subtropics. Demographic and societal changes, in particular urbanization, globalization, and increased international travel, are major contributors to the rise in incidence and geographic expansion of dengue infections. Major research gaps continue to hamper the control of dengue. The European Commission launched a call under the 7th Framework Programme with the title of 'Comprehensive control of Dengue fever under changing climatic conditions'. Fourteen partners from several countries in Europe, Asia, and South America formed a consortium named 'DengueTools' to respond to the call to achieve better diagnosis, surveillance, prevention, and predictive models and improve our understanding of the spread of dengue to previously uninfected regions (including Europe) in the context of globalization and climate change.The consortium comprises 12 work packages to address a set of research questions in three areas:Research area 1: Develop a comprehensive early warning and surveillance system that has predictive capability for epidemic dengue and benefits from novel tools for laboratory diagnosis and vector monitoring.Research area 2: Develop novel strategies to prevent dengue in children.Research area 3: Understand and predict the risk of global spread of dengue, in particular the risk of introduction and establishment in Europe, within the context of parameters of vectorial capacity, global mobility, and climate change.In this paper, we report on the rationale and specific study objectives of 'DengueTools'. DengueTools is funded under the Health theme of the Seventh Framework Programme of the European Community, Grant Agreement Number: 282589 Dengue Tools.

Prediction Model of Collapse Risk Based on Information Entropy and Distance Discriminant Analysis Method

Directory of Open Access Journals (Sweden)

Hujun He

2017-01-01

Full Text Available The prediction and risk classification of collapse is an important issue in the process of highway construction in mountainous regions. Based on the principles of information entropy and Mahalanobis distance discriminant analysis, we have produced a collapse hazard prediction model. We used the entropy measure method to reduce the influence indexes of the collapse activity and extracted the nine main indexes affecting collapse activity as the discriminant factors of the distance discriminant analysis model (i.e., slope shape, aspect, gradient, and height, along with exposure of the structural face, stratum lithology, relationship between weakness face and free face, vegetation cover rate, and degree of rock weathering. We employ postearthquake collapse data in relation to construction of the Yingxiu-Wolong highway, Hanchuan County, China, as training samples for analysis. The results were analyzed using the back substitution estimation method, showing high accuracy and no errors, and were the same as the prediction result of uncertainty measure. Results show that the classification model based on information entropy and distance discriminant analysis achieves the purpose of index optimization and has excellent performance, high prediction accuracy, and a zero false-positive rate. The model can be used as a tool for future evaluation of collapse risk.

Analysis and Prediction of Micromilling Stability with Variable Tool Geometry

Directory of Open Access Journals (Sweden)

Ziyang Cao

2014-11-01

Full Text Available Micromilling can fabricate miniaturized components using micro-end mill at high rotational speeds. The analysis of machining stability in micromilling plays an important role in characterizing the cutting process, estimating the tool life, and optimizing the process. A numerical analysis and experimental method are presented to investigate the chatter stability in micro-end milling process with variable milling tool geometry. The schematic model of micromilling process is constructed and the calculation formula to predict cutting force and displacements is derived. This is followed by a detailed numerical analysis on micromilling forces between helical ball and square end mills through time domain and frequency domain method and the results are compared. Furthermore, a detailed time domain simulation for micro end milling with straight teeth and helical teeth end mill is conducted based on the machine-tool system frequency response function obtained through modal experiment. The forces and displacements are predicted and the simulation result between variable cutter geometry is deeply compared. The simulation results have important significance for the actual milling process.

Comparison of the Nosocomial Pneumonia Mortality Prediction (NPMP) model with standard mortality prediction tools.

Science.gov (United States)

Srinivasan, M; Shetty, N; Gadekari, S; Thunga, G; Rao, K; Kunhikatta, V

2017-07-01

Severity or mortality prediction of nosocomial pneumonia could aid in the effective triage of patients and assisting physicians. To compare various severity assessment scoring systems for predicting intensive care unit (ICU) mortality in nosocomial pneumonia patients. A prospective cohort study was conducted in a tertiary care university-affiliated hospital in Manipal, India. One hundred patients with nosocomial pneumonia, admitted in the ICUs who developed pneumonia after >48h of admission, were included. The Nosocomial Pneumonia Mortality Prediction (NPMP) model, developed in our hospital, was compared with Acute Physiology and Chronic Health Evaluation II (APACHE II), Mortality Probability Model II (MPM 72  II), Simplified Acute Physiology Score II (SAPS II), Multiple Organ Dysfunction Score (MODS), Sequential Organ Failure Assessment (SOFA), Clinical Pulmonary Infection Score (CPIS), Ventilator-Associated Pneumonia Predisposition, Insult, Response, Organ dysfunction (VAP-PIRO). Data and clinical variables were collected on the day of pneumonia diagnosis. The outcome for the study was ICU mortality. The sensitivity and specificity of the various scoring systems was analysed by plotting receiver operating characteristic (ROC) curves and computing the area under the curve for each of the mortality predicting tools. NPMP, APACHE II, SAPS II, MPM 72  II, SOFA, and VAP-PIRO were found to have similar and acceptable discrimination power as assessed by the area under the ROC curve. The AUC values for the above scores ranged from 0.735 to 0.762. CPIS and MODS showed least discrimination. NPMP is a specific tool to predict mortality in nosocomial pneumonia and is comparable to other standard scores. Copyright © 2017 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved.

PROSPER: an integrated feature-based tool for predicting protease substrate cleavage sites.

Directory of Open Access Journals (Sweden)

Jiangning Song

Full Text Available The ability to catalytically cleave protein substrates after synthesis is fundamental for all forms of life. Accordingly, site-specific proteolysis is one of the most important post-translational modifications. The key to understanding the physiological role of a protease is to identify its natural substrate(s. Knowledge of the substrate specificity of a protease can dramatically improve our ability to predict its target protein substrates, but this information must be utilized in an effective manner in order to efficiently identify protein substrates by in silico approaches. To address this problem, we present PROSPER, an integrated feature-based server for in silico identification of protease substrates and their cleavage sites for twenty-four different proteases. PROSPER utilizes established specificity information for these proteases (derived from the MEROPS database with a machine learning approach to predict protease cleavage sites by using different, but complementary sequence and structure characteristics. Features used by PROSPER include local amino acid sequence profile, predicted secondary structure, solvent accessibility and predicted native disorder. Thus, for proteases with known amino acid specificity, PROSPER provides a convenient, pre-prepared tool for use in identifying protein substrates for the enzymes. Systematic prediction analysis for the twenty-four proteases thus far included in the database revealed that the features we have included in the tool strongly improve performance in terms of cleavage site prediction, as evidenced by their contribution to performance improvement in terms of identifying known cleavage sites in substrates for these enzymes. In comparison with two state-of-the-art prediction tools, PoPS and SitePrediction, PROSPER achieves greater accuracy and coverage. To our knowledge, PROSPER is the first comprehensive server capable of predicting cleavage sites of multiple proteases within a single substrate

An integrated risk assessment tool for team-based periodontal disease management.

Science.gov (United States)

Thyvalikakath, Thankam P; Padman, Rema; Gupta, Sugandh

2013-01-01

Mounting evidence suggests a potential association of periodontal disease with systemic diseases such as diabetes, cardiovascular disease, cancer and stroke. The objective of this study is to develop an integrated risk assessment tool that displays a patients' risk for periodontal disease in the context of their systemic disease, social habits and oral health. Such a tool will be used by not just dental professionals but also by care providers who participate in the team-based care for chronic disease management. Displaying relationships between risk factors and its influence on the patient's general health could be a powerful educational and disease management tool for patients and clinicians. It may also improve the coordination of care provided by the provider-members of a chronic care team.

Investigation on Cardiovascular Risk Prediction Using Physiological Parameters

Directory of Open Access Journals (Sweden)

Wan-Hua Lin

2013-01-01

Full Text Available Cardiovascular disease (CVD is the leading cause of death worldwide. Early prediction of CVD is urgently important for timely prevention and treatment. Incorporation or modification of new risk factors that have an additional independent prognostic value of existing prediction models is widely used for improving the performance of the prediction models. This paper is to investigate the physiological parameters that are used as risk factors for the prediction of cardiovascular events, as well as summarizing the current status on the medical devices for physiological tests and discuss the potential implications for promoting CVD prevention and treatment in the future. The results show that measures extracted from blood pressure, electrocardiogram, arterial stiffness, ankle-brachial blood pressure index (ABI, and blood glucose carry valuable information for the prediction of both long-term and near-term cardiovascular risk. However, the predictive values should be further validated by more comprehensive measures. Meanwhile, advancing unobtrusive technologies and wireless communication technologies allow on-site detection of the physiological information remotely in an out-of-hospital setting in real-time. In addition with computer modeling technologies and information fusion. It may allow for personalized, quantitative, and real-time assessment of sudden CVD events.

Young Children’s Risk-Taking: Mothers’ Authoritarian Parenting Predicts Risk-Taking by Daughters but Not Sons

Directory of Open Access Journals (Sweden)

Erin E. Wood

2017-01-01

Full Text Available We investigated how mothers’ parenting behaviors and personal characteristics were related to risk-taking by young children. We tested contrasting predictions from evolutionary and social role theories with the former predicting higher risk-taking by boys compared to girls and the latter predicting that mothers would influence children’s gender role development with risk-taking occurring more in children parented with higher levels of harshness (i.e., authoritarian parenting style. In our study, mothers reported their own gender roles and parenting styles as well as their children’s risk-taking and activities related to gender roles. The results were only partially consistent with the two theories, as the amount of risk-taking by sons and daughters did not differ significantly and risk-taking by daughters, but not sons, was positively related to mothers’ use of the authoritarian parenting style and the girls’ engagement in masculine activities. Risk-taking by sons was not predicted by any combination of mother-related variables. Overall, mothers who were higher in femininity used more authoritative and less authoritarian parenting styles. Theoretical implications as well as implications for predicting and reducing children’s risk-taking are discussed.

Risk monitor-a tool for computer aided risk assessment for NPPs

International Nuclear Information System (INIS)

Vinod, Gopika; Saraf, R.K.; Babar, A.K.; Kushwaha, H.S.; Hadap, Nikhil

2001-01-01

Considerable changes occur in components status and system design and subsequent operation due to changes in plant configuration and their operating procedures. These changes are organised because some components are randomly down and other can be planned for test, maintenance and repair. This results in a fluctuation of risk level over operating time, which is termed as risk profile. Probabilistic Safety Assessment (PSA) is an analytical technique for assessing the risk by integrating diverse aspects of design and operation of a Nuclear Power Plant. Risk can be defined as the product of the probability of an accident and the consequences from that accident. Reactor Safety Division of BARC has developed PC based tool, which can assess the risk profile. This package can be used to optimise the operation in Nuclear Power Plants with respect to a minimum risk level over the operating time, and is termed as Risk Monitor. Risk Monitor is user friendly and can re-evaluate core damage frequency for changes in component status, test interval, initiating event frequency etc. Plant restoration advice, when the plant is in high risk configuration, current status of all plant equipment, and equipment prioritization are also provided by the package. (author)

A systematic review of breast cancer incidence risk prediction models with meta-analysis of their performance.

Science.gov (United States)

Meads, Catherine; Ahmed, Ikhlaaq; Riley, Richard D

2012-04-01

A risk prediction model is a statistical tool for estimating the probability that a currently healthy individual with specific risk factors will develop a condition in the future such as breast cancer. Reliably accurate prediction models can inform future disease burdens, health policies and individual decisions. Breast cancer prediction models containing modifiable risk factors, such as alcohol consumption, BMI or weight, condom use, exogenous hormone use and physical activity, are of particular interest to women who might be considering how to reduce their risk of breast cancer and clinicians developing health policies to reduce population incidence rates. We performed a systematic review to identify and evaluate the performance of prediction models for breast cancer that contain modifiable factors. A protocol was developed and a sensitive search in databases including MEDLINE and EMBASE was conducted in June 2010. Extensive use was made of reference lists. Included were any articles proposing or validating a breast cancer prediction model in a general female population, with no language restrictions. Duplicate data extraction and quality assessment were conducted. Results were summarised qualitatively, and where possible meta-analysis of model performance statistics was undertaken. The systematic review found 17 breast cancer models, each containing a different but often overlapping set of modifiable and other risk factors, combined with an estimated baseline risk that was also often different. Quality of reporting was generally poor, with characteristics of included participants and fitted model results often missing. Only four models received independent validation in external data, most notably the 'Gail 2' model with 12 validations. None of the models demonstrated consistently outstanding ability to accurately discriminate between those who did and those who did not develop breast cancer. For example, random-effects meta-analyses of the performance of the

A risk assessment tool applied to the study of shale gas resources

Energy Technology Data Exchange (ETDEWEB)

Veiguela, Miguel [Mining, Energy and Materials Engineering School, University of Oviedo (Spain); Hurtado, Antonio; Eguilior, Sonsoles; Recreo, Fernando [Environment Department, CIEMAT, Madrid (Spain); Roqueñi, Nieves [Mining, Energy and Materials Engineering School, University of Oviedo (Spain); Loredo, Jorge, E-mail: jloredo@uniovi.es [Mining, Energy and Materials Engineering School, University of Oviedo (Spain)

2016-11-15

The implementation of a risk assessment tool with the capacity to evaluate the risks for health, safety and the environment (HSE) from extraction of non-conventional fossil fuel resources by the hydraulic fracturing (fracking) technique can be a useful tool to boost development and progress of the technology and winning public trust and acceptance of this. At the early project stages, the lack of data related the selection of non-conventional gas deposits makes it difficult the use of existing approaches to risk assessment of fluids injected into geologic formations. The qualitative risk assessment tool developed in this work is based on the approach that shale gas exploitation risk is dependent on both the geologic site and the technological aspects. It follows from the Oldenburg's ‘Screening and Ranking Framework (SRF)’ developed to evaluate potential geologic carbon dioxide (CO{sub 2}) storage sites. These two global characteristics: (1) characteristics centered on the natural aspects of the site and (2) characteristics centered on the technological aspects of the Project, have been evaluated through user input of Property values, which define Attributes, which define the Characteristics. In order to carry out an individual evaluation of each of the characteristics and the elements of the model, the tool has been implemented in a spreadsheet. The proposed model has been applied to a site with potential for the exploitation of shale gas in Asturias (northwestern Spain) with tree different technological options to test the approach. - Highlights: • The proposed methodology is a risk assessment useful tool for shale gas projects. • The tool is addressed to the early stages of decision making processes. • The risk assessment of a site is made through a qualitative estimation. • Different weights are assigned to each specific natural and technological property. • The uncertainty associated to the current knowledge is considered.

A risk assessment tool applied to the study of shale gas resources

International Nuclear Information System (INIS)

Veiguela, Miguel; Hurtado, Antonio; Eguilior, Sonsoles; Recreo, Fernando; Roqueñi, Nieves; Loredo, Jorge

2016-01-01

The implementation of a risk assessment tool with the capacity to evaluate the risks for health, safety and the environment (HSE) from extraction of non-conventional fossil fuel resources by the hydraulic fracturing (fracking) technique can be a useful tool to boost development and progress of the technology and winning public trust and acceptance of this. At the early project stages, the lack of data related the selection of non-conventional gas deposits makes it difficult the use of existing approaches to risk assessment of fluids injected into geologic formations. The qualitative risk assessment tool developed in this work is based on the approach that shale gas exploitation risk is dependent on both the geologic site and the technological aspects. It follows from the Oldenburg's ‘Screening and Ranking Framework (SRF)’ developed to evaluate potential geologic carbon dioxide (CO_2) storage sites. These two global characteristics: (1) characteristics centered on the natural aspects of the site and (2) characteristics centered on the technological aspects of the Project, have been evaluated through user input of Property values, which define Attributes, which define the Characteristics. In order to carry out an individual evaluation of each of the characteristics and the elements of the model, the tool has been implemented in a spreadsheet. The proposed model has been applied to a site with potential for the exploitation of shale gas in Asturias (northwestern Spain) with tree different technological options to test the approach. - Highlights: • The proposed methodology is a risk assessment useful tool for shale gas projects. • The tool is addressed to the early stages of decision making processes. • The risk assessment of a site is made through a qualitative estimation. • Different weights are assigned to each specific natural and technological property. • The uncertainty associated to the current knowledge is considered.

SU-D-BRB-01: A Predictive Planning Tool for Stereotactic Radiosurgery

Energy Technology Data Exchange (ETDEWEB)

Palefsky, S; Roper, J; Elder, E; Dhabaan, A [Winship Cancer Institute of Emory University, Atlanta, GA (United States)

2015-06-15

Purpose: To demonstrate the feasibility of a predictive planning tool which provides SRS planning guidance based on simple patient anatomical properties: PTV size, PTV shape and distance from critical structures. Methods: Ten framed SRS cases treated at Winship Cancer Institute of Emory University were analyzed to extract data on PTV size, sphericity (shape), and distance from critical structures such as the brainstem and optic chiasm. The cases consisted of five pairs. Each pair consisted of two cases with a similar diagnosis (such as pituitary adenoma or arteriovenous malformation) that were treated with different techniques: DCA, or IMRS. A Naive Bayes Classifier was trained on this data to establish the conditions under which each treatment modality was used. This model was validated by classifying ten other randomly-selected cases into DCA or IMRS classes, calculating the probability of each technique, and comparing results to the treated technique. Results: Of the ten cases used to validate the model, nine had their technique predicted correctly. The three cases treated with IMRS were all identified as such. Their probabilities of being treated with IMRS ranged between 59% and 100%. Six of the seven cases treated with DCA were correctly classified. These probabilities ranged between 51% and 95%. One case treated with DCA was incorrectly predicted to be an IMRS plan. The model’s confidence in this case was 91%. Conclusion: These findings indicate that a predictive planning tool based on simple patient anatomical properties can predict the SRS technique used for treatment. The algorithm operated with 90% accuracy. With further validation on larger patient populations, this tool may be used clinically to guide planners in choosing an appropriate treatment technique. The prediction algorithm could also be adapted to guide selection of treatment parameters such as treatment modality and number of fields for radiotherapy across anatomical sites.

SU-D-BRB-01: A Predictive Planning Tool for Stereotactic Radiosurgery

International Nuclear Information System (INIS)

Palefsky, S; Roper, J; Elder, E; Dhabaan, A

2015-01-01

Purpose: To demonstrate the feasibility of a predictive planning tool which provides SRS planning guidance based on simple patient anatomical properties: PTV size, PTV shape and distance from critical structures. Methods: Ten framed SRS cases treated at Winship Cancer Institute of Emory University were analyzed to extract data on PTV size, sphericity (shape), and distance from critical structures such as the brainstem and optic chiasm. The cases consisted of five pairs. Each pair consisted of two cases with a similar diagnosis (such as pituitary adenoma or arteriovenous malformation) that were treated with different techniques: DCA, or IMRS. A Naive Bayes Classifier was trained on this data to establish the conditions under which each treatment modality was used. This model was validated by classifying ten other randomly-selected cases into DCA or IMRS classes, calculating the probability of each technique, and comparing results to the treated technique. Results: Of the ten cases used to validate the model, nine had their technique predicted correctly. The three cases treated with IMRS were all identified as such. Their probabilities of being treated with IMRS ranged between 59% and 100%. Six of the seven cases treated with DCA were correctly classified. These probabilities ranged between 51% and 95%. One case treated with DCA was incorrectly predicted to be an IMRS plan. The model’s confidence in this case was 91%. Conclusion: These findings indicate that a predictive planning tool based on simple patient anatomical properties can predict the SRS technique used for treatment. The algorithm operated with 90% accuracy. With further validation on larger patient populations, this tool may be used clinically to guide planners in choosing an appropriate treatment technique. The prediction algorithm could also be adapted to guide selection of treatment parameters such as treatment modality and number of fields for radiotherapy across anatomical sites

Optical coherence tomography: a potential tool to predict premature rupture of fetal membranes.

Science.gov (United States)

Micili, Serap C; Valter, Markus; Oflaz, Hakan; Ozogul, Candan; Linder, Peter; Föckler, Nicole; Artmann, Gerhard M; Digel, Ilya; Artmann, Aysegul T

2013-04-01

A fundamental question addressed in this study was the feasibility of preterm birth prediction based on a noncontact investigation of fetal membranes in situ. Although the phenomena of preterm birth and the premature rupture of the fetal membrane are well known, currently, there are no diagnostic tools for their prediction. The aim of this study was to assess whether optical coherence tomography could be used for clinical investigations of high-risk pregnancies. The thickness of fetal membranes was measured in parallel by optical coherence tomography and histological techniques for the following types of birth: normal births, preterm births without premature ruptures and births at full term with premature rupture of membrane. Our study revealed that the membrane thickness correlates with the birth type. Normal births membranes were statistically significantly thicker than those belonging to the other two groups. Thus, in spite of almost equal duration of gestation of the normal births and the births at full term with premature rupture, the corresponding membrane thicknesses differed. This difference is possibly related to previously reported water accumulation in the membranes. The optical coherence tomography results were encouraging, suggesting that this technology could be used in future to predict and distinguish between different kinds of births.

Promoting energy efficiency investments with risk management decision tools

International Nuclear Information System (INIS)

Jackson, Jerry

2010-01-01

This paper reviews current capital budgeting practices and their impact on energy efficiency investments. The prevalent use of short payback 'rule-of-thumb' requirements to screen efficiency projects for risk is shown to bias investment choices towards 'sure bet' investments bypassing many profitable efficiency investment options. A risk management investment strategy is presented as an alternative to risk avoidance practices applied with payback thresholds. The financial industry risk management tool Value-at-Risk is described and extended to provide an Energy-Budgets-at-Risk or EBaR risk management analysis to convey more accurate energy efficiency investment risk information. The paper concludes with recommendations to expand the use of Value-at-Risk-type energy efficiency analysis.

Prognostic risk estimates of patients with multiple sclerosis and their physicians: comparison to an online analytical risk counseling tool.

Directory of Open Access Journals (Sweden)

Christoph Heesen

Full Text Available BACKGROUND: Prognostic counseling in multiple sclerosis (MS is difficult because of the high variability of disease progression. Simultaneously, patients and physicians are increasingly confronted with making treatment decisions at an early stage, which requires taking individual prognoses into account to strike a good balance between benefits and harms of treatments. It is therefore important to understand how patients and physicians estimate prognostic risk, and whether and how these estimates can be improved. An online analytical processing (OLAP tool based on pooled data from placebo cohorts of clinical trials offers short-term prognostic estimates that can be used for individual risk counseling. OBJECTIVE: The aim of this study was to clarify if personalized prognostic information as presented by the OLAP tool is considered useful and meaningful by patients. Furthermore, we used the OLAP tool to evaluate patients' and physicians' risk estimates. Within this evaluation process we assessed short-time prognostic risk estimates of patients with MS (final n = 110 and their physicians (n = 6 and compared them with the estimates of OLAP. RESULTS: Patients rated the OLAP tool as understandable and acceptable, but to be only of moderate interest. It turned out that patients, physicians, and the OLAP tool ranked patients similarly regarding their risk of disease progression. Both patients' and physicians' estimates correlated most strongly with those disease covariates that the OLAP tool's estimates also correlated with most strongly. Exposure to the OLAP tool did not change patients' risk estimates. CONCLUSION: While the OLAP tool was rated understandable and acceptable, it was only of modest interest and did not change patients' prognostic estimates. The results suggest, however, that patients had some idea regarding their prognosis and which factors were most important in this regard. Future work with OLAP should assess long-term prognostic

Prognostic risk estimates of patients with multiple sclerosis and their physicians: comparison to an online analytical risk counseling tool.

Science.gov (United States)

Heesen, Christoph; Gaissmaier, Wolfgang; Nguyen, Franziska; Stellmann, Jan-Patrick; Kasper, Jürgen; Köpke, Sascha; Lederer, Christian; Neuhaus, Anneke; Daumer, Martin

2013-01-01

Prognostic counseling in multiple sclerosis (MS) is difficult because of the high variability of disease progression. Simultaneously, patients and physicians are increasingly confronted with making treatment decisions at an early stage, which requires taking individual prognoses into account to strike a good balance between benefits and harms of treatments. It is therefore important to understand how patients and physicians estimate prognostic risk, and whether and how these estimates can be improved. An online analytical processing (OLAP) tool based on pooled data from placebo cohorts of clinical trials offers short-term prognostic estimates that can be used for individual risk counseling. The aim of this study was to clarify if personalized prognostic information as presented by the OLAP tool is considered useful and meaningful by patients. Furthermore, we used the OLAP tool to evaluate patients' and physicians' risk estimates. Within this evaluation process we assessed short-time prognostic risk estimates of patients with MS (final n = 110) and their physicians (n = 6) and compared them with the estimates of OLAP. Patients rated the OLAP tool as understandable and acceptable, but to be only of moderate interest. It turned out that patients, physicians, and the OLAP tool ranked patients similarly regarding their risk of disease progression. Both patients' and physicians' estimates correlated most strongly with those disease covariates that the OLAP tool's estimates also correlated with most strongly. Exposure to the OLAP tool did not change patients' risk estimates. While the OLAP tool was rated understandable and acceptable, it was only of modest interest and did not change patients' prognostic estimates. The results suggest, however, that patients had some idea regarding their prognosis and which factors were most important in this regard. Future work with OLAP should assess long-term prognostic estimates and clarify its usefulness for patients and physicians

Comparison of seven fall risk assessment tools in community-dwelling Korean older women.

Science.gov (United States)

Kim, Taekyoung; Xiong, Shuping

2017-03-01

This study aimed to compare seven widely used fall risk assessment tools in terms of validity and practicality, and to provide a guideline for choosing appropriate fall risk assessment tools for elderly Koreans. Sixty community-dwelling Korean older women (30 fallers and 30 matched non-fallers) were evaluated. Performance measures of all tools were compared between the faller and non-faller groups through two sample t-tests. Receiver Operating Characteristic curves were generated with odds ratios for discriminant analysis. Results showed that four tools had significant discriminative power, and the shortened version of Falls Efficacy Scale (SFES) showed excellent discriminant validity, followed by Berg Balance Scale (BBS) with acceptable discriminant validity. The Mini Balance Evaluation System Test and Timed Up and Go, however, had limited discriminant validities. In terms of practicality, SFES was also excellent. These findings suggest that SFES is the most suitable tool for assessing the fall risks of community-dwelling Korean older women, followed by BBS. Practitioner Summary: There is no general guideline on which fall risk assessment tools are suitable for community-dwelling Korean older women. This study compared seven widely used assessment tools in terms of validity and practicality. Results suggested that the short Falls Efficacy Scale is the most suitable tool, followed by Berg Balance Scale.

Validating SPICES as a Screening Tool for Frailty Risks among Hospitalized Older Adults

Science.gov (United States)

Aronow, Harriet Udin; Borenstein, Jeff; Haus, Flora; Braunstein, Glenn D.; Bolton, Linda Burnes

2014-01-01

Older patients are vulnerable to adverse hospital events related to frailty. SPICES, a common screening protocol to identify risk factors in older patients, alerts nurses to initiate care plans to reduce the probability of patient harm. However, there is little published validating the association between SPICES and measures of frailty and adverse outcomes. This paper used data from a prospective cohort study on frailty among 174 older adult inpatients to validate SPICES. Almost all patients met one or more SPICES criteria. The sum of SPICES was significantly correlated with age and other well-validated assessments for vulnerability, comorbid conditions, and depression. Individuals meeting two or more SPICES criteria had a risk of adverse hospital events three times greater than individuals with either no or one criterion. Results suggest that as a screening tool used within 24 hours of admission, SPICES is both valid and predictive of adverse events. PMID:24876954

Development of risk assessment tool for foundry workers.

Science.gov (United States)

Mohan, G Madhan; Prasad, P S S; Mokkapati, Anil Kumar; Venkataraman, G

2008-01-01

Occupational ill-health and work-related disorders are predominant in manufacturing industries due to the inevitable presence of manual work even after several waves of industrial automation and technological advancements. Ergonomic risk factors and musculoskeletal disorders like low-back symptoms have been noted amongst foundry workers. The purpose of this study was to formulate and develop a Physical Effort Index to assess risk factor. The questionnaire tool applicable to foundry environment has been designed and validated. The data recorded through survey across the foundries has been subjected to regression analysis to correlate between proposed physical effort index and the standard Borg's Ratings of Perceived Exertion (RPE) scale. The physical efforts of sixty seven workers in various foundry shop floors were assessed subjectively. The 'Job factors' and 'Work environment' were the two major parameters considered in assessing the worker discomfort level at workplace. A relation between Borg's RPE scale and the above two parameters were arrived at, through regression analysis. The study demonstrates the prevalence of risk factors amongst foundry workers and the effectiveness of the proposed index in estimating the risk factor levels. RELEVANCE TO THE INDUSTRY: The proposed tool will assist foundry supervisors and managers to assess the risk factors and helps in better understanding of the workplace to avoid work-related disorders, ensuring better output.

Development and validation of a risk model for prediction of hazardous alcohol consumption in general practice attendees: the predictAL study.

Science.gov (United States)

King, Michael; Marston, Louise; Švab, Igor; Maaroos, Heidi-Ingrid; Geerlings, Mirjam I; Xavier, Miguel; Benjamin, Vicente; Torres-Gonzalez, Francisco; Bellon-Saameno, Juan Angel; Rotar, Danica; Aluoja, Anu; Saldivia, Sandra; Correa, Bernardo; Nazareth, Irwin

2011-01-01

Little is known about the risk of progression to hazardous alcohol use in people currently drinking at safe limits. We aimed to develop a prediction model (predictAL) for the development of hazardous drinking in safe drinkers. A prospective cohort study of adult general practice attendees in six European countries and Chile followed up over 6 months. We recruited 10,045 attendees between April 2003 to February 2005. 6193 European and 2462 Chilean attendees recorded AUDIT scores below 8 in men and 5 in women at recruitment and were used in modelling risk. 38 risk factors were measured to construct a risk model for the development of hazardous drinking using stepwise logistic regression. The model was corrected for over fitting and tested in an external population. The main outcome was hazardous drinking defined by an AUDIT score ≥8 in men and ≥5 in women. 69.0% of attendees were recruited, of whom 89.5% participated again after six months. The risk factors in the final predictAL model were sex, age, country, baseline AUDIT score, panic syndrome and lifetime alcohol problem. The predictAL model's average c-index across all six European countries was 0.839 (95% CI 0.805, 0.873). The Hedge's g effect size for the difference in log odds of predicted probability between safe drinkers in Europe who subsequently developed hazardous alcohol use and those who did not was 1.38 (95% CI 1.25, 1.51). External validation of the algorithm in Chilean safe drinkers resulted in a c-index of 0.781 (95% CI 0.717, 0.846) and Hedge's g of 0.68 (95% CI 0.57, 0.78). The predictAL risk model for development of hazardous consumption in safe drinkers compares favourably with risk algorithms for disorders in other medical settings and can be a useful first step in prevention of alcohol misuse.

Development and validation of a risk model for prediction of hazardous alcohol consumption in general practice attendees: the predictAL study.

Directory of Open Access Journals (Sweden)

Michael King

Full Text Available Little is known about the risk of progression to hazardous alcohol use in people currently drinking at safe limits. We aimed to develop a prediction model (predictAL for the development of hazardous drinking in safe drinkers.A prospective cohort study of adult general practice attendees in six European countries and Chile followed up over 6 months. We recruited 10,045 attendees between April 2003 to February 2005. 6193 European and 2462 Chilean attendees recorded AUDIT scores below 8 in men and 5 in women at recruitment and were used in modelling risk. 38 risk factors were measured to construct a risk model for the development of hazardous drinking using stepwise logistic regression. The model was corrected for over fitting and tested in an external population. The main outcome was hazardous drinking defined by an AUDIT score ≥8 in men and ≥5 in women.69.0% of attendees were recruited, of whom 89.5% participated again after six months. The risk factors in the final predictAL model were sex, age, country, baseline AUDIT score, panic syndrome and lifetime alcohol problem. The predictAL model's average c-index across all six European countries was 0.839 (95% CI 0.805, 0.873. The Hedge's g effect size for the difference in log odds of predicted probability between safe drinkers in Europe who subsequently developed hazardous alcohol use and those who did not was 1.38 (95% CI 1.25, 1.51. External validation of the algorithm in Chilean safe drinkers resulted in a c-index of 0.781 (95% CI 0.717, 0.846 and Hedge's g of 0.68 (95% CI 0.57, 0.78.The predictAL risk model for development of hazardous consumption in safe drinkers compares favourably with risk algorithms for disorders in other medical settings and can be a useful first step in prevention of alcohol misuse.

Software Tool Implementing the Fuzzy AHP Method in Ecological Risk Assessment

Directory of Open Access Journals (Sweden)

Radionovs Andrejs

2017-12-01

Full Text Available Due to the increased spread of invasive animals and plants in the territory of Latvia, the necessity of ecological risk assessment related to such kind of spread has grown lately. In cases with sufficient statistical data, the risk assessment may be successfully performed on the basis of statistical methods. The amount of statistical data in the context of spread of invasive animals and plants is pretty poor; therefore, the only method of ecological risk assessment remains subjective judgements of experts. The present paper proposes using a programming tool for ecological risk analysis elaborated by the authors. With the help of this programming tool the method of Fuzzy Analytical Hierarchical Process is implemented. The elements of the pairwise comparison matrix are allowed to be expressed by triangular and trapezoidal fuzzy sets. The presented tool makes it possible to design the fuzzy pair-wise comparison matrix and process the results in a user-friendly way.

The Sydney Triage to Admission Risk Tool (START): A prospective validation study.

Science.gov (United States)

Ebker-White, Anja A; Bein, Kendall J; Dinh, Michael M

2018-02-08

The present study aims to prospectively validate the Sydney Triage to Admission Risk Tool (START) to predict ED disposition. This was a prospective validation study at two metropolitan EDs in Sydney, Australia. Consecutive triage encounters were observed by a trained researcher and START scores calculated. The primary outcome was patient disposition (discharge or inpatient admission) from the ED. Multivariable logistic regression was used to estimate area under curve of receiver operator characteristic (AUC ROC) for START scores as well as START score in combination with other variables such as frailty, general practitioner referral, overcrowding and major medical comorbidities. There were 894 patients analysed during the study period. The START score when applied to the data had AUC ROC of 0.80 (95% CI 0.77-0.83). The inclusion of other clinical variables identified at triage did not improve the overall performance of the model with an AUC ROC of 0.81 (95% CI 0.78-0.84) in the present study. The overall performance of the START tool with respect to model discrimination and accuracy has been prospectively validated. Further clinical trials are required to test the clinical effectiveness of the tool in improving patient flow and overall ED performance. © 2018 Australasian College for Emergency Medicine and Australasian Society for Emergency Medicine.

A systematic review on popularity, application and characteristics of protein secondary structure prediction tools.

Science.gov (United States)

Kashani-Amin, Elaheh; Tabatabaei-Malazy, Ozra; Sakhteman, Amirhossein; Larijani, Bagher; Ebrahim-Habibi, Azadeh

2018-02-27

Prediction of proteins' secondary structure is one of the major steps in the generation of homology models. These models provide structural information which is used to design suitable ligands for potential medicinal targets. However, selecting a proper tool between multiple secondary structure prediction (SSP) options is challenging. The current study is an insight onto currently favored methods and tools, within various contexts. A systematic review was performed for a comprehensive access to recent (2013-2016) studies which used or recommended protein SSP tools. Three databases, Web of Science, PubMed and Scopus were systematically searched and 99 out of 209 studies were finally found eligible to extract data. Four categories of applications for 59 retrieved SSP tools were: (I) prediction of structural features of a given sequence, (II) evaluation of a method, (III) providing input for a new SSP method and (IV) integrating a SSP tool as a component for a program. PSIPRED was found to be the most popular tool in all four categories. JPred and tools utilizing PHD (Profile network from HeiDelberg) method occupied second and third places of popularity in categories I and II. JPred was only found in the two first categories, while PHD was present in three fields. This study provides a comprehensive insight about the recent usage of SSP tools which could be helpful for selecting a proper tool's choice. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Application of the PredictAD Software Tool to Predict Progression in Patients with Mild Cognitive Impairment

DEFF Research Database (Denmark)

Simonsen, Anja H; Mattila, Jussi; Hejl, Anne-Mette

2012-01-01

of incremental data presentation using the software tool. A 5th phase was done with all available patient data presented on paper charts. Classifications by the clinical raters were compared to the clinical diagnoses made by the Alzheimer's Disease Neuroimaging Initiative investigators. Results: A statistical...... significant trend (p classification accuracy (from 62.6 to 70.0%) was found when using the PredictAD tool during the stepwise procedure. When the same data were presented on paper, classification accuracy of the raters dropped significantly from 70.0 to 63.2%. Conclusion: Best...... classification accuracy was achieved by the clinical raters when using the tool for decision support, suggesting that the tool can add value in diagnostic classification when large amounts of heterogeneous data are presented....

Risk analysis as a decision tool

International Nuclear Information System (INIS)

Yadigaroglu, G.; Chakraborty, S.

1985-01-01

From 1983 - 1985 a lecture series entitled ''Risk-benefit analysis'' was held at the Swiss Federal Institute of Technology (ETH), Zurich, in cooperation with the Central Department for the Safety of Nuclear Installations of the Swiss Federal Agency of Energy Economy. In that setting the value of risk-oriented evaluation models as a decision tool in safety questions was discussed on a broad basis. Experts of international reputation from the Federal Republic of Germany, France, Canada, the United States and Switzerland have contributed to report in this joint volume on the uses of such models. Following an introductory synopsis on risk analysis and risk assessment the book deals with practical examples in the fields of medicine, nuclear power, chemistry, transport and civil engineering. Particular attention is paid to the dialogue between analysts and decision makers taking into account the economic-technical aspects and social values. The recent chemical disaster in the Indian city of Bhopal again signals the necessity of such analyses. All the lectures were recorded individually. (orig./HP) [de

RDNAnalyzer: A tool for DNA secondary structure prediction and sequence analysis.

Science.gov (United States)

Afzal, Muhammad; Shahid, Ahmad Ali; Shehzadi, Abida; Nadeem, Shahid; Husnain, Tayyab

2012-01-01

RDNAnalyzer is an innovative computer based tool designed for DNA secondary structure prediction and sequence analysis. It can randomly generate the DNA sequence or user can upload the sequences of their own interest in RAW format. It uses and extends the Nussinov dynamic programming algorithm and has various application for the sequence analysis. It predicts the DNA secondary structure and base pairings. It also provides the tools for routinely performed sequence analysis by the biological scientists such as DNA replication, reverse compliment generation, transcription, translation, sequence specific information as total number of nucleotide bases, ATGC base contents along with their respective percentages and sequence cleaner. RDNAnalyzer is a unique tool developed in Microsoft Visual Studio 2008 using Microsoft Visual C# and Windows Presentation Foundation and provides user friendly environment for sequence analysis. It is freely available. http://www.cemb.edu.pk/sw.html RDNAnalyzer - Random DNA Analyser, GUI - Graphical user interface, XAML - Extensible Application Markup Language.

Malnutrition risk in hospitalized children: use of 3 screening tools in a large European population.

Science.gov (United States)

Chourdakis, Michael; Hecht, Christina; Gerasimidis, Konstantinos; Joosten, Koen Fm; Karagiozoglou-Lampoudi, Thomais; Koetse, Harma A; Ksiazyk, Janusz; Lazea, Cecilia; Shamir, Raanan; Szajewska, Hania; Koletzko, Berthold; Hulst, Jessie M

2016-05-01

Several malnutrition screening tools have been advocated for use in pediatric inpatients. We evaluated how 3 popular pediatric nutrition screening tools [i.e., the Pediatric Yorkhill Malnutrition Score (PYMS), the Screening Tool for the Assessment of Malnutrition in Pediatrics (STAMP), and the Screening Tool for Risk of Impaired Nutritional Status and Growth (STRONGKIDS)] compared with and were related to anthropometric measures, body composition, and clinical variables in patients who were admitted to tertiary hospitals across Europe. The 3 screening tools were applied in 2567 inpatients at 14 hospitals across 12 European countries. The classification of patients into different nutritional risk groups was compared between tools and related to anthropometric measures and clinical variables [e.g., length of hospital stay (LOS) and infection rates]. A similar rate of completion of the screening tools for each tool was achieved (PYMS: 86%; STAMP: 84%; and STRONGKIDS: 81%). Risk classification differed markedly by tool, with an overall agreement of 41% between tools. Children categorized as high risk (PYMS: 25%; STAMP: 23%; and STRONGKIDS: 10%) had a longer LOS than that of children at low risk (1.4, 1.4, and 1.8 d longer, respectively; P malnutrition risk varied across the pediatric tools used. A considerable portion of children with subnormal anthropometric measures were not identified with all of the tools. The data obtained do not allow recommending the use of any of these screening tools for clinical practice. This study was registered at clinicaltrials.gov as NCT01132742. © 2016 American Society for Nutrition.

The development of a practical tool for risk assessment of manual work – the HAT-tool

NARCIS (Netherlands)

Kraker, H. de; Douwes, M.

2008-01-01

For the Dutch Ministry of Social Affairs and Employment we developed a tool to assess the risks of developing complaints of the arm, neck or shoulders during manual work. The tool was developed for every type of organization and is easy to use, does not require measurements other than time and can

A random forest based risk model for reliable and accurate prediction of receipt of transfusion in patients undergoing percutaneous coronary intervention.

Directory of Open Access Journals (Sweden)

Hitinder S Gurm

Full Text Available BACKGROUND: Transfusion is a common complication of Percutaneous Coronary Intervention (PCI and is associated with adverse short and long term outcomes. There is no risk model for identifying patients most likely to receive transfusion after PCI. The objective of our study was to develop and validate a tool for predicting receipt of blood transfusion in patients undergoing contemporary PCI. METHODS: Random forest models were developed utilizing 45 pre-procedural clinical and laboratory variables to estimate the receipt of transfusion in patients undergoing PCI. The most influential variables were selected for inclusion in an abbreviated model. Model performance estimating transfusion was evaluated in an independent validation dataset using area under the ROC curve (AUC, with net reclassification improvement (NRI used to compare full and reduced model prediction after grouping in low, intermediate, and high risk categories. The impact of procedural anticoagulation on observed versus predicted transfusion rates were assessed for the different risk categories. RESULTS: Our study cohort was comprised of 103,294 PCI procedures performed at 46 hospitals between July 2009 through December 2012 in Michigan of which 72,328 (70% were randomly selected for training the models, and 30,966 (30% for validation. The models demonstrated excellent calibration and discrimination (AUC: full model  = 0.888 (95% CI 0.877-0.899, reduced model AUC = 0.880 (95% CI, 0.868-0.892, p for difference 0.003, NRI = 2.77%, p = 0.007. Procedural anticoagulation and radial access significantly influenced transfusion rates in the intermediate and high risk patients but no clinically relevant impact was noted in low risk patients, who made up 70% of the total cohort. CONCLUSIONS: The risk of transfusion among patients undergoing PCI can be reliably calculated using a novel easy to use computational tool (https://bmc2.org/calculators/transfusion. This risk prediction

Augmenting Predictive Modeling Tools with Clinical Insights for Care Coordination Program Design and Implementation.

Science.gov (United States)

Johnson, Tracy L; Brewer, Daniel; Estacio, Raymond; Vlasimsky, Tara; Durfee, Michael J; Thompson, Kathy R; Everhart, Rachel M; Rinehart, Deborath J; Batal, Holly

2015-01-01

The Center for Medicare and Medicaid Innovation (CMMI) awarded Denver Health's (DH) integrated, safety net health care system $19.8 million to implement a "population health" approach into the delivery of primary care. This major practice transformation builds on the Patient Centered Medical Home (PCMH) and Wagner's Chronic Care Model (CCM) to achieve the "Triple Aim": improved health for populations, care to individuals, and lower per capita costs. This paper presents a case study of how DH integrated published predictive models and front-line clinical judgment to implement a clinically actionable, risk stratification of patients. This population segmentation approach was used to deploy enhanced care team staff resources and to tailor care-management services to patient need, especially for patients at high risk of avoidable hospitalization. Developing, implementing, and gaining clinical acceptance of the Health Information Technology (HIT) solution for patient risk stratification was a major grant objective. In addition to describing the Information Technology (IT) solution itself, we focus on the leadership and organizational processes that facilitated its multidisciplinary development and ongoing iterative refinement, including the following: team composition, target population definition, algorithm rule development, performance assessment, and clinical-workflow optimization. We provide examples of how dynamic business intelligence tools facilitated clinical accessibility for program design decisions by enabling real-time data views from a population perspective down to patient-specific variables. We conclude that population segmentation approaches that integrate clinical perspectives with predictive modeling results can better identify high opportunity patients amenable to medical home-based, enhanced care team interventions.

Musite, a tool for global prediction of general and kinase-specific phosphorylation sites.

Science.gov (United States)

Gao, Jianjiong; Thelen, Jay J; Dunker, A Keith; Xu, Dong

2010-12-01

Reversible protein phosphorylation is one of the most pervasive post-translational modifications, regulating diverse cellular processes in various organisms. High throughput experimental studies using mass spectrometry have identified many phosphorylation sites, primarily from eukaryotes. However, the vast majority of phosphorylation sites remain undiscovered, even in well studied systems. Because mass spectrometry-based experimental approaches for identifying phosphorylation events are costly, time-consuming, and biased toward abundant proteins and proteotypic peptides, in silico prediction of phosphorylation sites is potentially a useful alternative strategy for whole proteome annotation. Because of various limitations, current phosphorylation site prediction tools were not well designed for comprehensive assessment of proteomes. Here, we present a novel software tool, Musite, specifically designed for large scale predictions of both general and kinase-specific phosphorylation sites. We collected phosphoproteomics data in multiple organisms from several reliable sources and used them to train prediction models by a comprehensive machine-learning approach that integrates local sequence similarities to known phosphorylation sites, protein disorder scores, and amino acid frequencies. Application of Musite on several proteomes yielded tens of thousands of phosphorylation site predictions at a high stringency level. Cross-validation tests show that Musite achieves some improvement over existing tools in predicting general phosphorylation sites, and it is at least comparable with those for predicting kinase-specific phosphorylation sites. In Musite V1.0, we have trained general prediction models for six organisms and kinase-specific prediction models for 13 kinases or kinase families. Although the current pretrained models were not correlated with any particular cellular conditions, Musite provides a unique functionality for training customized prediction models

Bigger Data, Collaborative Tools and the Future of Predictive Drug Discovery

Science.gov (United States)

Clark, Alex M.; Swamidass, S. Joshua; Litterman, Nadia; Williams, Antony J.

2014-01-01

Over the past decade we have seen a growth in the provision of chemistry data and cheminformatics tools as either free websites or software as a service (SaaS) commercial offerings. These have transformed how we find molecule-related data and use such tools in our research. There have also been efforts to improve collaboration between researchers either openly or through secure transactions using commercial tools. A major challenge in the future will be how such databases and software approaches handle larger amounts of data as it accumulates from high throughput screening and enables the user to draw insights, enable predictions and move projects forward. We now discuss how information from some drug discovery datasets can be made more accessible and how privacy of data should not overwhelm the desire to share it at an appropriate time with collaborators. We also discuss additional software tools that could be made available and provide our thoughts on the future of predictive drug discovery in this age of big data. We use some examples from our own research on neglected diseases, collaborations, mobile apps and algorithm development to illustrate these ideas. PMID:24943138

The Economic Value of Predicting Bond Risk Premia

DEFF Research Database (Denmark)

Sarno, Lucio; Schneider, Paul; Wagner, Christian

the expectations hypothesis (EH) out-ofsample: the forecasts do not add economic value compared to using the average historical excess return as an EH-consistent estimate of constant risk premia. We show that in general statistical signicance does not necessarily translate into economic signicance because EH...... deviations mainly matter at short horizons and standard predictability metrics are not compatible with common measures of economic value. Overall, the EH remains the benchmark for investment decisions and should be considered an economic prior in models of bond risk premia.......This paper studies whether the evident statistical predictability of bond risk premia translates into economic gains for bond investors. We show that ane term structure models (ATSMs) estimated by jointly tting yields and bond excess returns capture this predictive information otherwise hidden...

BARC-risk monitor- a tool for operational safety assessment in nuclear power plants

International Nuclear Information System (INIS)

Vinod, Gopika; Saraf, R.K.; Babar, A.K.; Hadap, Nikhil

2000-12-01

Probabilistic safety assessment has become a key tool as on today to identify and understand nuclear power plant vulnerabilities. As a result of the availability of these PSA studies, there is a desire to use them to enhance plant safety and to operate the nuclear stations in the most efficient manner. Risk monitor is a PC based tool, which computes the real time safety level and assists plant personnel to manage day-to-day activities. Risk monitor is a PC based user friendly software tool used for modification and re-analysis of a nuclear power plant. Operation of risk monitor is based on PSA methods for assisting in day to day applications. Risk monitoring programs can assess the risk profile and are used to optimise the operation of nuclear power plants with respect to a minimum risk level over the operating time. This report presents the background activities of risk monitor, its application areas and also gives the status of such tools in international scenarios. The software is based on the PSA model of Kaiga generating station and would be applicable to similar design configuration. (author)

Risk adjusted surgical audit in gynaecological oncology: P-POSSUM does not predict outcome.

Science.gov (United States)

Das, N; Talaat, A S; Naik, R; Lopes, A D; Godfrey, K A; Hatem, M H; Edmondson, R J

2006-12-01

To assess the Physiological and Operative Severity Score for the enumeration of mortality and morbidity (POSSUM) and its validity for use in gynaecological oncology surgery. All patients undergoing gynaecological oncology surgery at the Northern Gynaecological Oncology Centre (NGOC) Gateshead, UK over a period of 12months (2002-2003) were assessed prospectively. Mortality and morbidity predictions using the Portsmouth modification of the POSSUM algorithm (P-POSSUM) were compared to the actual outcomes. Performance of the model was also evaluated using the Hosmer and Lemeshow Chi square statistic (testing the goodness of fit). During this period 468 patients were assessed. The P-POSSUM appeared to over predict mortality rates for our patients. It predicted a 7% mortality rate for our patients compared to an observed rate of 2% (35 predicted deaths in comparison to 10 observed deaths), a difference that was statistically significant (H&L chi(2)=542.9, d.f. 8, prisk of mortality for gynaecological oncology patients undergoing surgery. The P-POSSUM algorithm will require further adjustments prior to adoption for gynaecological cancer surgery as a risk adjusted surgical audit tool.

Predictive Risk Modelling to Prevent Child Maltreatment and Other Adverse Outcomes for Service Users: Inside the 'Black Box' of Machine Learning.

Science.gov (United States)

Gillingham, Philip

2016-06-01

Recent developments in digital technology have facilitated the recording and retrieval of administrative data from multiple sources about children and their families. Combined with new ways to mine such data using algorithms which can 'learn', it has been claimed that it is possible to develop tools that can predict which individual children within a population are most likely to be maltreated. The proposed benefit is that interventions can then be targeted to the most vulnerable children and their families to prevent maltreatment from occurring. As expertise in predictive modelling increases, the approach may also be applied in other areas of social work to predict and prevent adverse outcomes for vulnerable service users. In this article, a glimpse inside the 'black box' of predictive tools is provided to demonstrate how their development for use in social work may not be straightforward, given the nature of the data recorded about service users and service activity. The development of predictive risk modelling (PRM) in New Zealand is focused on as an example as it may be the first such tool to be applied as part of ongoing reforms to child protection services.

Predicting infection risk of airborne foot-and-mouth disease.

Science.gov (United States)

Schley, David; Burgin, Laura; Gloster, John

2009-05-06

Foot-and-mouth disease is a highly contagious disease of cloven-hoofed animals, the control and eradication of which is of significant worldwide socio-economic importance. The virus may spread by direct contact between animals or via fomites as well as through airborne transmission, with the latter being the most difficult to control. Here, we consider the risk of infection to flocks or herds from airborne virus emitted from a known infected premises. We show that airborne infection can be predicted quickly and with a good degree of accuracy, provided that the source of virus emission has been determined and reliable geo-referenced herd data are available. A simple model provides a reliable tool for estimating risk from known sources and for prioritizing surveillance and detection efforts. The issue of data information management systems was highlighted as a lesson to be learned from the official inquiry into the UK 2007 foot-and-mouth outbreak: results here suggest that the efficacy of disease control measures could be markedly improved through an accurate livestock database incorporating flock/herd size and location, which would enable tactical as well as strategic modelling.

Influence of Genotype on Warfarin Maintenance Dose Predictions Produced Using a Bayesian Dose Individualization Tool.

Science.gov (United States)

Saffian, Shamin M; Duffull, Stephen B; Roberts, Rebecca L; Tait, Robert C; Black, Leanne; Lund, Kirstin A; Thomson, Alison H; Wright, Daniel F B

2016-12-01

A previously established Bayesian dosing tool for warfarin was found to produce biased maintenance dose predictions. In this study, we aimed (1) to determine whether the biased warfarin dose predictions previously observed could be replicated in a new cohort of patients from 2 different clinical settings, (2) to explore the influence of CYP2C9 and VKORC1 genotype on predictive performance of the Bayesian dosing tool, and (3) to determine whether the previous population used to develop the kinetic-pharmacodynamic model underpinning the Bayesian dosing tool was sufficiently different from the test (posterior) population to account for the biased dose predictions. The warfarin maintenance doses for 140 patients were predicted using the dosing tool and compared with the observed maintenance dose. The impact of genotype was assessed by predicting maintenance doses with prior parameter values known to be altered by genetic variability (eg, EC50 for VKORC1 genotype). The prior population was evaluated by fitting the published kinetic-pharmacodynamic model, which underpins the Bayesian tool, to the observed data using NONMEM and comparing the model parameter estimates with published values. The Bayesian tool produced positively biased dose predictions in the new cohort of patients (mean prediction error [95% confidence interval]; 0.32 mg/d [0.14-0.5]). The bias was only observed in patients requiring ≥7 mg/d. The direction and magnitude of the observed bias was not influenced by genotype. The prior model provided a good fit to our data, which suggests that the bias was not caused by different prior and posterior populations. Maintenance doses for patients requiring ≥7 mg/d were overpredicted. The bias was not due to the influence of genotype nor was it related to differences between the prior and posterior populations. There is a need for a more mechanistic model that captures warfarin dose-response relationship at higher warfarin doses.

Standardised risk analysis as a communication tool

International Nuclear Information System (INIS)

Pluess, Ch.; Montanarini, M.; Bernauer, M.

1998-01-01

Full text of publication follows: several European countries require a risk analysis for the production, storage or transport a dangerous goods. This requirement imposes considerable administrative effort for some sectors of the industry. In order to minimize the effort of such studies, a generic risk analysis for an industrial sector proved to help. Standardised procedures can consequently be derived for efficient performance of the risk investigations. This procedure was successfully established in Switzerland for natural gas transmission lines and fossil fuel storage plants. The development process of the generic risk analysis involved an intense discussion between industry and authorities about methodology of assessment and the criteria of acceptance. This process finally led to scientific consistent modelling tools for risk analysis and to an improved communication from the industry to the authorities and the public. As a recent example, the Holland-Italy natural gas transmission pipeline is demonstrated, where this method was successfully employed. Although this pipeline traverses densely populated areas in Switzerland, using this established communication method, the risk problems could be solved without delaying the planning process. (authors)

Risk avoidance in sympatric large carnivores: reactive or predictive?

Science.gov (United States)

Broekhuis, Femke; Cozzi, Gabriele; Valeix, Marion; McNutt, John W; Macdonald, David W

2013-09-01

1. Risks of predation or interference competition are major factors shaping the distribution of species. An animal's response to risk can either be reactive, to an immediate risk, or predictive, based on preceding risk or past experiences. The manner in which animals respond to risk is key in understanding avoidance, and hence coexistence, between interacting species. 2. We investigated whether cheetahs (Acinonyx jubatus), known to be affected by predation and competition by lions (Panthera leo) and spotted hyaenas (Crocuta crocuta), respond reactively or predictively to the risks posed by these larger carnivores. 3. We used simultaneous spatial data from Global Positioning System (GPS) radiocollars deployed on all known social groups of cheetahs, lions and spotted hyaenas within a 2700 km(2) study area on the periphery of the Okavango Delta in northern Botswana. The response to risk of encountering lions and spotted hyaenas was explored on three levels: short-term or immediate risk, calculated as the distance to the nearest (contemporaneous) lion or spotted hyaena, long-term risk, calculated as the likelihood of encountering lions and spotted hyaenas based on their cumulative distributions over a 6-month period and habitat-associated risk, quantified by the habitat used by each of the three species. 4. We showed that space and habitat use by cheetahs was similar to that of lions and, to a lesser extent, spotted hyaenas. However, cheetahs avoided immediate risks by positioning themselves further from lions and spotted hyaenas than predicted by a random distribution. 5. Our results suggest that cheetah spatial distribution is a hierarchical process, first driven by resource acquisition and thereafter fine-tuned by predator avoidance; thus suggesting a reactive, rather than a predictive, response to risk. © 2013 The Authors. Journal of Animal Ecology © 2013 British Ecological Society.

Young Children’s Risk-Taking: Mothers’ Authoritarian Parenting Predicts Risk-Taking by Daughters but Not Sons

OpenAIRE

Wood, Erin E.; Kennison, Shelia M.

2017-01-01

We investigated how mothers’ parenting behaviors and personal characteristics were related to risk-taking by young children. We tested contrasting predictions from evolutionary and social role theories with the former predicting higher risk-taking by boys compared to girls and the latter predicting that mothers would influence children’s gender role development with risk-taking occurring more in children parented with higher levels of harshness (i.e., authoritarian parenting style). In our st...

A Risk Prediction Score for Kidney Failure or Mortality in Rhabdomyolysis

Science.gov (United States)

McMahon, Gearoid M.; Zeng, Xiaoxi; Waikar, Sushrut S.

2016-01-01

IMPORTANCE Rhabdomyolysis ranges in severity from asymptomatic elevations in creatine phosphokinase levels to a life-threatening disorder characterized by severe acute kidney injury requiring hemodialysis or continuous renal replacement therapy (RRT). OBJECTIVE To develop a risk prediction tool to identify patients at greatest risk of RRT or in-hospital mortality. DESIGN, SETTING, AND PARTICIPANTS Retrospective cohort study of 2371 patients admitted between January 1, 2000, and March 31, 2011, to 2 large teaching hospitals in Boston, Massachusetts, with creatine phosphokinase levels in excess of 5000 U/L within 3 days of admission. The derivation cohort consisted of 1397 patients from Massachusetts General Hospital, and the validation cohort comprised 974 patients from Brigham and Women’s Hospital. MAIN OUTCOMES AND MEASURES The composite of RRT or in-hospital mortality. RESULTS The causes and outcomes of rhabdomyolysis were similar between the derivation and validation cohorts. In total, the composite outcome occurred in 19.0% of patients (8.0% required RRT and 14.1% died during hospitalization). The highest rates of the composite outcome were from compartment syndrome (41.2%), sepsis (39.3%), and following cardiac arrest (58.5%). The lowest rates were from myositis (1.7%), exercise (3.2%), and seizures (6.0%). The independent predictors of the composite outcome were age, female sex, cause of rhabdomyolysis, and values of initial creatinine, creatine phosphokinase, phosphate, calcium, and bicarbonate. We developed a risk-prediction score from these variables in the derivation cohort and subsequently applied it in the validation cohort. The C statistic for the prediction model was 0.82 (95% CI, 0.80–0.85) in the derivation cohort and 0.83 (0.80–0.86) in the validation cohort. The Hosmer-Lemeshow P values were .14 and .28, respectively. In the validation cohort, among the patients with the lowest risk score (10), 61.2% died or needed RRT. CONCLUSIONS AND

Experimental and Mathematical Modeling for Prediction of Tool Wear on the Machining of Aluminium 6061 Alloy by High Speed Steel Tools

Directory of Open Access Journals (Sweden)

Okokpujie Imhade Princess

2017-12-01

Full Text Available In recent machining operation, tool life is one of the most demanding tasks in production process, especially in the automotive industry. The aim of this paper is to study tool wear on HSS in end milling of aluminium 6061 alloy. The experiments were carried out to investigate tool wear with the machined parameters and to developed mathematical model using response surface methodology. The various machining parameters selected for the experiment are spindle speed (N, feed rate (f, axial depth of cut (a and radial depth of cut (r. The experiment was designed using central composite design (CCD in which 31 samples were run on SIEG 3/10/0010 CNC end milling machine. After each experiment the cutting tool was measured using scanning electron microscope (SEM. The obtained optimum machining parameter combination are spindle speed of 2500 rpm, feed rate of 200 mm/min, axial depth of cut of 20 mm, and radial depth of cut 1.0mm was found out to achieved the minimum tool wear as 0.213 mm. The mathematical model developed predicted the tool wear with 99.7% which is within the acceptable accuracy range for tool wear prediction.

Experimental and Mathematical Modeling for Prediction of Tool Wear on the Machining of Aluminium 6061 Alloy by High Speed Steel Tools

Science.gov (United States)

Okokpujie, Imhade Princess; Ikumapayi, Omolayo M.; Okonkwo, Ugochukwu C.; Salawu, Enesi Y.; Afolalu, Sunday A.; Dirisu, Joseph O.; Nwoke, Obinna N.; Ajayi, Oluseyi O.

2017-12-01

In recent machining operation, tool life is one of the most demanding tasks in production process, especially in the automotive industry. The aim of this paper is to study tool wear on HSS in end milling of aluminium 6061 alloy. The experiments were carried out to investigate tool wear with the machined parameters and to developed mathematical model using response surface methodology. The various machining parameters selected for the experiment are spindle speed (N), feed rate (f), axial depth of cut (a) and radial depth of cut (r). The experiment was designed using central composite design (CCD) in which 31 samples were run on SIEG 3/10/0010 CNC end milling machine. After each experiment the cutting tool was measured using scanning electron microscope (SEM). The obtained optimum machining parameter combination are spindle speed of 2500 rpm, feed rate of 200 mm/min, axial depth of cut of 20 mm, and radial depth of cut 1.0mm was found out to achieved the minimum tool wear as 0.213 mm. The mathematical model developed predicted the tool wear with 99.7% which is within the acceptable accuracy range for tool wear prediction.

Screening for malnutrition among nursing home residents - a comparative analysis of the mini nutritional assessment, the nutritional risk screening, and the malnutrition universal screening tool.

Science.gov (United States)

Diekmann, R; Winning, K; Uter, W; Kaiser, M J; Sieber, C C; Volkert, D; Bauer, J M

2013-04-01

The European Society for Clinical Nutrition and Metabolism (ESPEN) has recommended the Mini Nutritional Assessment (MNA®), the Nutritional Risk Screening 2002 (NRS), and the Malnutrition Universal Screening Tool (MUST) for nutritional screening in various settings and age groups. While in recent years all three tools have been applied to nursing home residents, there is still no consensus on the most appropriate screening tool in this specific setting. The present study aims at comparing the MNA, the NRS, and the MUST with regard to applicability, categorization of nutritional status, and predictive value in the nursing home setting. MNA, NRS, and MUST were performed on 200 residents from two municipal nursing homes in Nuremberg, Germany. Follow-up data on infection, hospitalization, and mortality were collected after six and again after twelve months. Among 200 residents (mean age 85.5 ± 7.8 years) the MNA could be completed in 188 (94.0%) and the NRS and MUST in 198 (99.0%) residents. The prevalence of 'malnutrition' according to the MNA was 15.4%. The prevalence of 'risk of malnutrition' (NRS) and 'high risk of malnutrition' (MUST), respectively, was 8.6% for both tools. The individual categorization of nutritional status showed poor agreement between NRS and MUST on the one hand and MNA on the other. For all tools a significant association between nutritional status and mortality was demonstrated during follow-up as classification in 'malnourished', respectively 'high risk of malnutrition' or 'nutritional risk', was significantly associated with increased hazard ratios. However, the MNA showed the best predictive value for survival among well-nourished residents. The evaluation of nutritional status in nursing home residents by MNA, NRS, and MUST shows significant differences. This observation may be of clinical relevance as nutritional intervention is usually based on screening results. As the items of the MNA reflect particularities of the nursing home

Development and validation of the FRAGIRE tool for assessment an older person's risk for frailty.

Science.gov (United States)

Vernerey, Dewi; Anota, Amelie; Vandel, Pierre; Paget-Bailly, Sophie; Dion, Michele; Bailly, Vanessa; Bonin, Marie; Pozet, Astrid; Foubert, Audrey; Benetkiewicz, Magdalena; Manckoundia, Patrick; Bonnetain, Franck

2016-11-17

Frailty is highly prevalent in elderly people. While significant progress has been made to understand its pathogenesis process, few validated questionnaire exist to assess the multidimensional concept of frailty and to detect people frail or at risk to become frail. The objectives of this study were to construct and validate a new frailty-screening instrument named Frailty Groupe Iso-Ressource Evaluation (FRAGIRE) that accurately predicts the risk for frailty in older adults. A prospective multicenter recruitment of the elderly patients was undertaken in France. The subjects were classified into financially-helped group (FH, with financial assistance) and non-financially helped group (NFH, without any financial assistance), considering FH subjects are more frail than the NFH group and thus representing an acceptable surrogate population for frailty. Psychometric properties of the FRAGIRE grid were assessed including discrimination between the FH and NFH groups. Items reduction was made according to statistical analyses and experts' point of view. The association between items response and tests with "help requested status" was assessed in univariate and multivariate unconditional logistic regression analyses and a prognostic score to become frail was finally proposed for each subject. Between May 2013 and July 2013, 385 subjects were included: 338 (88%) in the FH group and 47 (12%) in the NFH group. The initial FRAGIRE grid included 65 items. After conducting the item selection, the final grid of the FRAGIRE was reduced to 19 items. The final grid showed fair discrimination ability to predict frailty (area under the curve (AUC) = 0.85) and good calibration (Hosmer-Lemeshow P-value = 0.580), reflecting a good agreement between the prediction by the final model and actual observation. The Cronbach's alpha for the developed tool scored as high as 0.69 (95% Confidence Interval: 0.64 to 0.74). The final prognostic score was excellent, with an AUC of 0

Development of Next Generation Multiphase Pipe Flow Prediction Tools

Energy Technology Data Exchange (ETDEWEB)

Cem Sarica; Holden Zhang

2006-05-31

The developments of oil and gas fields in deep waters (5000 ft and more) will become more common in the future. It is inevitable that production systems will operate under multiphase flow conditions (simultaneous flow of gas, oil and water possibly along with sand, hydrates, and waxes). Multiphase flow prediction tools are essential for every phase of hydrocarbon recovery from design to operation. Recovery from deep-waters poses special challenges and requires accurate multiphase flow predictive tools for several applications, including the design and diagnostics of the production systems, separation of phases in horizontal wells, and multiphase separation (topside, seabed or bottom-hole). It is crucial for any multiphase separation technique, either at topside, seabed or bottom-hole, to know inlet conditions such as flow rates, flow patterns, and volume fractions of gas, oil and water coming into the separation devices. Therefore, the development of a new generation of multiphase flow predictive tools is needed. The overall objective of the proposed study is to develop a unified model for gas-oil-water three-phase flow in wells, flow lines, and pipelines to predict flow characteristics such as flow patterns, phase distributions, and pressure gradient encountered during petroleum production at different flow conditions (pipe diameter and inclination, fluid properties and flow rates). In the current multiphase modeling approach, flow pattern and flow behavior (pressure gradient and phase fractions) prediction modeling are separated. Thus, different models based on different physics are employed, causing inaccuracies and discontinuities. Moreover, oil and water are treated as a pseudo single phase, ignoring the distinct characteristics of both oil and water, and often resulting in inaccurate design that leads to operational problems. In this study, a new model is being developed through a theoretical and experimental study employing a revolutionary approach. The

Prospective validation of a predictive model that identifies homeless people at risk of re-presentation to the emergency department.

Science.gov (United States)

Moore, Gaye; Hepworth, Graham; Weiland, Tracey; Manias, Elizabeth; Gerdtz, Marie Frances; Kelaher, Margaret; Dunt, David

2012-02-01

To prospectively evaluate the accuracy of a predictive model to identify homeless people at risk of representation to an emergency department. A prospective cohort analysis utilised one month of data from a Principal Referral Hospital in Melbourne, Australia. All visits involving people classified as homeless were included, excluding those who died. Homelessness was defined as living on the streets, in crisis accommodation, in boarding houses or residing in unstable housing. Rates of re-presentation, defined as the total number of visits to the same emergency department within 28 days of discharge from hospital, were measured. Performance of the risk screening tool was assessed by calculating sensitivity, specificity, positive and negative predictive values and likelihood ratios. Over the study period (April 1, 2009 to April 30, 2009), 3298 presentations from 2888 individuals were recorded. The homeless population accounted for 10% (n=327) of all visits and 7% (n=211) of all patients. A total of 90 (43%) homeless people re-presented to the emergency department. The predictive model included nine variables and achieved 98% (CI, 0.92-0.99) sensitivity and 66% (CI, 0.57-0.74) specificity. The positive predictive value was 68% and the negative predictive value was 98%. The positive likelihood ratio 2.9 (CI, 2.2-3.7) and the negative likelihood ratio was 0.03 (CI, 0.01-0.13). The high emergency department re-presentation rate for people who were homeless identifies unresolved psychosocial health needs. The emergency department remains a vital access point for homeless people, particularly after hours. The risk screening tool is key to identify medical and social aspects of a homeless patient's presentation to assist early identification and referral. Copyright © 2012 College of Emergency Nursing Australasia Ltd. Published by Elsevier Ltd. All rights reserved.

Development of a CME-associated geomagnetic storm intensity prediction tool

Science.gov (United States)

Wu, C. C.; DeHart, J. M.

2015-12-01

From 1995 to 2012, the Wind spacecraft recorded 168 magnetic cloud (MC) events. Among those events, 79 were found to have upstream shock waves and their source locations on the Sun were identified. Using a recipe of interplanetary magnetic field (IMF) Bz initial turning direction after shock (Wu et al., 1996, GRL), it is found that the north-south polarity of 66 (83.5%) out of the 79 events were accurately predicted. These events were tested and further analyzed, reaffirming that the Bz intial turning direction was accurate. The results also indicate that 37 of the 79 MCs originate from the north (of the Sun) averaged a Dst_min of -119 nT, whereas 42 of the MCs originating from the south (of the Sun) averaged -89 nT. In an effort to provide this research to others, a website was built that incorporated various tools and pictures to predict the intensity of the geomagnetic storms. The tool is capable of predicting geomagnetic storms with different ranges of Dst_min (from no-storm to gigantic storms). This work was supported by Naval Research Lab HBCU/MI Internship program and Chief of Naval Research.

Surging Seas Risk Finder: A Tool for Local-Scale Flood Risk Assessments in Coastal Cities

Science.gov (United States)

Kulp, S. A.; Strauss, B.

2015-12-01

Local decision makers in coastal cities require accurate, accessible, and thorough assessments of flood exposure risk within their individual municipality, in their efforts to mitigate against damage due to future sea level rise. To fill this need, we have developed Climate Central's Surging Seas Risk Finder, an interactive data toolkit which presents our sea level rise and storm surge analysis for every coastal town, city, county, and state within the USA. Using this tool, policy makers can easily zoom in on their local place of interest to receive a detailed flood risk assessment, which synthesizes a wide range of features including total population, socially vulnerable population, housing, property value, road miles, power plants, schools, hospitals, and many other critical facilities. Risk Finder can also be used to identify specific points of interest in danger of exposure at different flood levels. Additionally, this tool provides localized storm surge probabilities and sea level rise projections at tidal gauges along the coast, so that users can quickly understand the risk of flooding in their area over the coming decades.

Risk management: A tool for improving nuclear power plant performance

International Nuclear Information System (INIS)

2001-04-01

This technical document on risk management as a tool for improving nuclear power plant (NPP) operations is part of an ongoing project on management of NPP operations in a competitive environment. The overall objective of this project is to assist the management of operating organizations and NPPs in identifying and implementing appropriate measures to remain competitive in a rapidly changing business environment. Other reports developed through this project have identified overall strategies and techniques that NPP operating organization managers can use to succeed in more competitive energy markets. For example, in IAEA-TECDOC-1123, Strategies for Competitive Nuclear Power Plants, one of the most important strategies identified was integrated risk management. This publication provides a recommended structure for risk management along with examples of how NPP operating organizations are using this tool to help them integrate safety, operational and economic related risks in a changing business environment

Predicting Young Adults Binge Drinking in Nightlife Scenes: An Evaluation of the D-ARIANNA Risk Estimation Model.

Science.gov (United States)

Crocamo, Cristina; Bartoli, Francesco; Montomoli, Cristina; Carrà, Giuseppe

2018-05-25

Binge drinking (BD) among young people has significant public health implications. Thus, there is the need to target users most at risk. We estimated the discriminative accuracy of an innovative model nested in a recently developed e-Health app (Digital-Alcohol RIsk Alertness Notifying Network for Adolescents and young adults [D-ARIANNA]) for BD in young people, examining its performance to predict short-term BD episodes. We consecutively recruited young adults in pubs, discos, or live music events. Participants self-administered the app D-ARIANNA, which incorporates an evidence-based risk estimation model for the dependent variable BD. They were re-evaluated after 2 weeks using a single-item BD behavior as reference. We estimated D-ARIANNA discriminative ability through measures of sensitivity and specificity, and also likelihood ratios. ROC curve analyses were carried out, exploring variability of discriminative ability across subgroups. The analyses included 507 subjects, of whom 18% reported at least 1 BD episode at follow-up. The majority of these had been identified as at high/moderate or high risk (65%) at induction. Higher scores from the D-ARIANNA risk estimation model reflected an increase in the likelihood of BD. Additional risk factors such as high pocket money availability and alcohol expectancies influence the predictive ability of the model. The D-ARIANNA model showed an appreciable, though modest, predictive ability for subsequent BD episodes. Post-hoc model showed slightly better predictive properties. Using up-to-date technology, D-ARIANNA appears an innovative and promising screening tool for BD among young people. Long-term impact remains to be established, and also the role of additional social and environmental factors.

Validation of mathematical models for the prediction of organs-at-risk dosimetric metrics in high-dose-rate gynecologic interstitial brachytherapy

Energy Technology Data Exchange (ETDEWEB)

Damato, Antonio L.; Viswanathan, Akila N.; Cormack, Robert A. [Dana-Farber Cancer Institute and Brigham and Women' s Hospital, Boston, Massachusetts 02115 (United States)

2013-10-15

Purpose: Given the complicated nature of an interstitial gynecologic brachytherapy treatment plan, the use of a quantitative tool to evaluate the quality of the achieved metrics compared to clinical practice would be advantageous. For this purpose, predictive mathematical models to predict the D{sub 2cc} of rectum and bladder in interstitial gynecologic brachytherapy are discussed and validated.Methods: Previous plans were used to establish the relationship between D2cc and the overlapping volume of the organ at risk with the targeted area (C0) or a 1-cm expansion of the target area (C1). Three mathematical models were evaluated: D{sub 2cc}=α*C{sub 1}+β (LIN); D{sub 2cc}=α– exp(–β*C{sub 0}) (EXP); and a mixed approach (MIX), where both C{sub 0} and C{sub 1} were inputs of the model. The parameters of the models were optimized on a training set of patient data, and the predictive error of each model (predicted D{sub 2cc}− real D{sub 2cc}) was calculated on a validation set of patient data. The data of 20 patients were used to perform a K-fold cross validation analysis, with K = 2, 4, 6, 8, 10, and 20.Results: MIX was associated with the smallest mean prediction error <6.4% for an 18-patient training set; LIN had an error <8.5%; EXP had an error <8.3%. Best case scenario analysis shows that an error ≤5% can be achieved for a ten-patient training set with MIX, an error ≤7.4% for LIN, and an error ≤6.9% for EXP. The error decreases with the increase in training set size, with the most marked decrease observed for MIX.Conclusions: The MIX model can predict the D{sub 2cc} of the organs at risk with an error lower than 5% with a training set of ten patients or greater. The model can be used in the development of quality assurance tools to identify treatment plans with suboptimal sparing of the organs at risk. It can also be used to improve preplanning and in the development of real-time intraoperative planning tools.

Risk Management and Viability of Public Organizations. Development of a Risk Measurement Tool: The Case of Greece

Directory of Open Access Journals (Sweden)

Iordanis Eleftheriadis

2017-03-01

Full Text Available Purpose: This paper provides an important contribution towards the development of a valid, reliable and cost-effective instrument that reduces operational and economic risk levels in public sector organizations. Design/methodology/approach: A quantitative methodology based on the collection of primary data via a questionnaire has been adopted in this research. Findings: The research results showed that the measurement tool selected, applied, presented and proposed is comprised of three (3 scales. The reliability analysis proved that all three scales are reliable; therefore, they are suitable for use as a risk measurement instrument. Research limitations/implications: The study's academic contribution is the application and testing of the aforementioned measurement instruments, which can now be utilised by researchers in the field of risk management, to further advance the study of risk management in public organizations in Greece. On the empirical level, the implementation of these three measurement instruments can assist public organizations in Greece via an easy and fast assessment of economic and operational risks. Originality/value: This tool can help public organizations gain insight into the level of risk they face at any given point in time in order plan their actions accordingly. At the same time, central state administration will have the necessary tools to monitor and support the organizations it evaluates.

Predictive analytics for supply chain collaboration, risk management ...

African Journals Online (AJOL)

kirstam

management, and (2) supply chain risk management predicted financial .... overhead costs, delivery of ever-increasing customer value, flexibility with superior ... risk exposure, relationship longevity, trust and communication are considered as.

Venous Thromboembolism – Risk Assessment Tool and Thromboprophylaxis Policy: A National Survey

LENUS (Irish Health Repository)

Khan, MI

2017-01-01

Venous Thromboembolic (VTE) events in hospitalised patients are associated with significant mortality and morbidity and a major economic burden on the health service. It is well established in the literature that active implementation of a mandatory risk assessment tool and thromboprophylaxis policy reduces the incidence of hospital associated thrombosis (HAT). This study examines the utilization of a VTE risk assessment tool and thromboprophylaxis (TP) policy in Irish hospitals that manage acute admissions. A national survey was distributed to forty acute hospitals throughout Ireland. The response rate was 78% (31\\/40). The results showed that only 26% (n=8\\/31) of acute hospitals in Ireland have a local implemented TP policy. Six (75%) of these eight had a risk assessment tool in conjunction with the TP policy. All respondents who did not report to have a TP policy and risk assessment tool agreed that they should implement VTE prevention policy at their hospital. Based on the data from this survey and evidence from the effectiveness of the VTE prevention programme introduced in the United Kingdom, there is a need for a national risk assessment and thromboprophylaxis policy in Ireland. This change in practice would have the potential to prevent or reduce the morbidity and mortality associated with hospital acquired thrombosis

Risk assessment: tools, techniques, and their applications

National Research Council Canada - National Science Library

Ostrom, Lee T; Wilhelmsen, Cheryl A

2012-01-01

.... The central task of the risk assessor is predicting the success of a project. This includes isolating the entire spectrum of adverse events that can derail a project or threaten the health and safety of individuals, organizations, and the environment...

Linking spring phenology with mechanistic models of host movement to predict disease transmission risk

Science.gov (United States)

Merkle, Jerod A.; Cross, Paul C.; Scurlock, Brandon M.; Cole, Eric K.; Courtemanch, Alyson B.; Dewey, Sarah R.; Kauffman, Matthew J.

2018-01-01

Disease models typically focus on temporal dynamics of infection, while often neglecting environmental processes that determine host movement. In many systems, however, temporal disease dynamics may be slow compared to the scale at which environmental conditions alter host space-use and accelerate disease transmission.Using a mechanistic movement modelling approach, we made space-use predictions of a mobile host (elk [Cervus Canadensis] carrying the bacterial disease brucellosis) under environmental conditions that change daily and annually (e.g., plant phenology, snow depth), and we used these predictions to infer how spring phenology influences the risk of brucellosis transmission from elk (through aborted foetuses) to livestock in the Greater Yellowstone Ecosystem.Using data from 288 female elk monitored with GPS collars, we fit step selection functions (SSFs) during the spring abortion season and then implemented a master equation approach to translate SSFs into predictions of daily elk distribution for five plausible winter weather scenarios (from a heavy snow, to an extreme winter drought year). We predicted abortion events by combining elk distributions with empirical estimates of daily abortion rates, spatially varying elk seroprevelance and elk population counts.Our results reveal strong spatial variation in disease transmission risk at daily and annual scales that is strongly governed by variation in host movement in response to spring phenology. For example, in comparison with an average snow year, years with early snowmelt are predicted to have 64% of the abortions occurring on feedgrounds shift to occurring on mainly public lands, and to a lesser extent on private lands.Synthesis and applications. Linking mechanistic models of host movement with disease dynamics leads to a novel bridge between movement and disease ecology. Our analysis framework offers new avenues for predicting disease spread, while providing managers tools to proactively mitigate

The Climate-Agriculture-Modeling and Decision Tool (CAMDT) for Climate Risk Management in Agriculture

Science.gov (United States)

Ines, A. V. M.; Han, E.; Baethgen, W.

2017-12-01

Advances in seasonal climate forecasts (SCFs) during the past decades have brought great potential to improve agricultural climate risk managements associated with inter-annual climate variability. In spite of popular uses of crop simulation models in addressing climate risk problems, the models cannot readily take seasonal climate predictions issued in the format of tercile probabilities of most likely rainfall categories (i.e, below-, near- and above-normal). When a skillful SCF is linked with the crop simulation models, the informative climate information can be further translated into actionable agronomic terms and thus better support strategic and tactical decisions. In other words, crop modeling connected with a given SCF allows to simulate "what-if" scenarios with different crop choices or management practices and better inform the decision makers. In this paper, we present a decision support tool, called CAMDT (Climate Agriculture Modeling and Decision Tool), which seamlessly integrates probabilistic SCFs to DSSAT-CSM-Rice model to guide decision-makers in adopting appropriate crop and agricultural water management practices for given climatic conditions. The CAMDT has a functionality to disaggregate a probabilistic SCF into daily weather realizations (either a parametric or non-parametric disaggregation method) and to run DSSAT-CSM-Rice with the disaggregated weather realizations. The convenient graphical user-interface allows easy implementation of several "what-if" scenarios for non-technical users and visualize the results of the scenario runs. In addition, the CAMDT also translates crop model outputs to economic terms once the user provides expected crop price and cost. The CAMDT is a practical tool for real-world applications, specifically for agricultural climate risk management in the Bicol region, Philippines, having a great flexibility for being adapted to other crops or regions in the world. CAMDT GitHub: https://github.com/Agro-Climate/CAMDT

Predicting the risk of rheumatoid arthritis and its age of onset through modelling genetic risk variants with smoking.

Directory of Open Access Journals (Sweden)

Ian C Scott

Full Text Available The improved characterisation of risk factors for rheumatoid arthritis (RA suggests they could be combined to identify individuals at increased disease risks in whom preventive strategies may be evaluated. We aimed to develop an RA prediction model capable of generating clinically relevant predictive data and to determine if it better predicted younger onset RA (YORA. Our novel modelling approach combined odds ratios for 15 four-digit/10 two-digit HLA-DRB1 alleles, 31 single nucleotide polymorphisms (SNPs and ever-smoking status in males to determine risk using computer simulation and confidence interval based risk categorisation. Only males were evaluated in our models incorporating smoking as ever-smoking is a significant risk factor for RA in men but not women. We developed multiple models to evaluate each risk factor's impact on prediction. Each model's ability to discriminate anti-citrullinated protein antibody (ACPA-positive RA from controls was evaluated in two cohorts: Wellcome Trust Case Control Consortium (WTCCC: 1,516 cases; 1,647 controls; UK RA Genetics Group Consortium (UKRAGG: 2,623 cases; 1,500 controls. HLA and smoking provided strongest prediction with good discrimination evidenced by an HLA-smoking model area under the curve (AUC value of 0.813 in both WTCCC and UKRAGG. SNPs provided minimal prediction (AUC 0.660 WTCCC/0.617 UKRAGG. Whilst high individual risks were identified, with some cases having estimated lifetime risks of 86%, only a minority overall had substantially increased odds for RA. High risks from the HLA model were associated with YORA (P<0.0001; ever-smoking associated with older onset disease. This latter finding suggests smoking's impact on RA risk manifests later in life. Our modelling demonstrates that combining risk factors provides clinically informative RA prediction; additionally HLA and smoking status can be used to predict the risk of younger and older onset RA, respectively.

Bayesian network as a modelling tool for risk management in agriculture

DEFF Research Database (Denmark)

Rasmussen, Svend; Madsen, Anders Læsø; Lund, Mogens

The importance of risk management increases as farmers become more exposed to risk. But risk management is a difficult topic because income risk is the result of the complex interaction of multiple risk factors combined with the effect of an increasing array of possible risk management tools. In ......, and that it has the ability to link uncertainty from different external sources to budget figures and to quantify risk at the farm level....

Development and Validation of a Multidisciplinary Tool for Accurate and Efficient Rotorcraft Noise Prediction (MUTE)

Science.gov (United States)

Liu, Yi; Anusonti-Inthra, Phuriwat; Diskin, Boris

2011-01-01

A physics-based, systematically coupled, multidisciplinary prediction tool (MUTE) for rotorcraft noise was developed and validated with a wide range of flight configurations and conditions. MUTE is an aggregation of multidisciplinary computational tools that accurately and efficiently model the physics of the source of rotorcraft noise, and predict the noise at far-field observer locations. It uses systematic coupling approaches among multiple disciplines including Computational Fluid Dynamics (CFD), Computational Structural Dynamics (CSD), and high fidelity acoustics. Within MUTE, advanced high-order CFD tools are used around the rotor blade to predict the transonic flow (shock wave) effects, which generate the high-speed impulsive noise. Predictions of the blade-vortex interaction noise in low speed flight are also improved by using the Particle Vortex Transport Method (PVTM), which preserves the wake flow details required for blade/wake and fuselage/wake interactions. The accuracy of the source noise prediction is further improved by utilizing a coupling approach between CFD and CSD, so that the effects of key structural dynamics, elastic blade deformations, and trim solutions are correctly represented in the analysis. The blade loading information and/or the flow field parameters around the rotor blade predicted by the CFD/CSD coupling approach are used to predict the acoustic signatures at far-field observer locations with a high-fidelity noise propagation code (WOPWOP3). The predicted results from the MUTE tool for rotor blade aerodynamic loading and far-field acoustic signatures are compared and validated with a variation of experimental data sets, such as UH60-A data, DNW test data and HART II test data.

Pediatric Eating Assessment Tool-10 as an indicator to predict aspiration in children with esophageal atresia.

Science.gov (United States)

Soyer, Tutku; Yalcin, Sule; Arslan, Selen Serel; Demir, Numan; Tanyel, Feridun Cahit

2017-10-01

Airway aspiration is a common problem in children with esophageal atresia (EA). Pediatric Eating Assessment Tool-10 (pEAT-10) is a self-administered questionnaire to evaluate dysphagia symptoms in children. A prospective study was performed to evaluate the validity of pEAT-10 to predict aspiration in children with EA. Patients with EA were evaluated for age, sex, type of atresia, presence of associated anomalies, type of esophageal repair, time of definitive treatment, and the beginning of oral feeding. Penetration-aspiration score (PAS) was evaluated with videofluoroscopy (VFS) and parents were surveyed for pEAT-10, dysphagia score (DS) and functional oral intake scale (FOIS). PAS scores greater than 7 were considered as risk of aspiration. EAT-10 values greater than 3 were assessed as abnormal. Higher DS scores shows dysphagia whereas higher FOIS shows better feeding abilities. Forty patients were included. Children with PAS greater than 7 were assessed as PAS+ group, and scores less than 7 were constituted as PAS- group. Demographic features and results of surgical treatments showed no difference between groups (p>0.05). The median values of PAS, pEAT-10 and DS scores were significantly higher in PAS+ group when compared to PAS- group (p<0.05). The sensitivity and specificity of pEAT-10 to predict aspiration were 88% and 77%, and the positive and negative predictive values were 22% and 11%, respectively. Type-C cases had better pEAT-10 and FOIS scores with respect to type-A cases, and both scores were statistically more reliable in primary repair than delayed repair (p<0.05). Among the postoperative complications, only leakage had impact on DS, pEAT-10, PAS and FOIS scores (p<0.05). The pEAT-10 is a valid, simple and reliable tool to predict aspiration in children. Patients with higher pEAT-10 scores should undergo detailed evaluation of deglutitive functions and assessment of risks of aspiration to improve safer feeding strategies. Level II (Development of

Risk assessment as a management tool used to assess the effect of pesticide use in an irrigation system, situated in a semi-desert region

CSIR Research Space (South Africa)

Raschke, AM

1997-01-01

Full Text Available be used as a predictive tool to determine as accurately as possible from the data available if a complete scientific health risk assessment study is justified. The actual amounts of pesticides sold in the Vaalharts area by two major pesticide manufacturers...

AllerTool: a web server for predicting allergenicity and allergic cross-reactivity in proteins.

Science.gov (United States)

Zhang, Zong Hong; Koh, Judice L Y; Zhang, Guang Lan; Choo, Khar Heng; Tammi, Martti T; Tong, Joo Chuan

2007-02-15

Assessment of potential allergenicity and patterns of cross-reactivity is necessary whenever novel proteins are introduced into human food chain. Current bioinformatic methods in allergology focus mainly on the prediction of allergenic proteins, with no information on cross-reactivity patterns among known allergens. In this study, we present AllerTool, a web server with essential tools for the assessment of predicted as well as published cross-reactivity patterns of allergens. The analysis tools include graphical representation of allergen cross-reactivity information; a local sequence comparison tool that displays information of known cross-reactive allergens; a sequence similarity search tool for assessment of cross-reactivity in accordance to FAO/WHO Codex alimentarius guidelines; and a method based on support vector machine (SVM). A 10-fold cross-validation results showed that the area under the receiver operating curve (A(ROC)) of SVM models is 0.90 with 86.00% sensitivity (SE) at specificity (SP) of 86.00%. AllerTool is freely available at http://research.i2r.a-star.edu.sg/AllerTool/.

Integrating Risk Analyses and Tools at the DOE Hanford Site

International Nuclear Information System (INIS)

LOBER, R.W.

2002-01-01

Risk assessment and environmental impact analysis at the U.S. Department of Energy (DOE) Hanford Site in Washington State has made significant progress in refining the strategy for using risk analysis to support closing of several hundred waste sites plus 149 single-shell tanks at the Hanford Site. A Single-Shell Tank System Closure Work Plan outlines the current basis for closing the single-shell tank systems. An analogous site approach has been developed to address closure of aggregated groups of similar waste sites. Because of the complexity, decision time frames, proximity of non-tank farm waste sites to tank farms, scale, and regulatory considerations, various projects are providing integrated assessments to support risk analyses and decision-making. Projects and the tools that are being developed and applied at Hanford to support retrieval and cleanup decisions include: (1) Life Cycle Model (LCM) and Risk Receptor Model (RRM)--A site-level set of tools to support strategic analyses through scoping level risk management to assess different alternatives and options for tank closure. (2) Systems Assessment Capability for Integrated Groundwater Nadose Zone (SAC) and the Site-Wide Groundwater Model (SWGM)--A site-wide groundwater modeling system coupled with a risk-based uncertainty analysis of inventory, vadose zone, groundwater, and river interactions for evaluating cumulative impacts from individual and aggregate waste sites. (3) Retrieval Performance Evaluation (RPE)--A site-specific, risk-based methodology developed to evaluate performance of waste retrieval, leak detection and closure on a tank-specific basis as a function of past tank Leaks, potential leakage during retrieval operations, and remaining residual waste inventories following completion of retrieval operations. (4) Field Investigation Report (FIR)--A corrective action program to investigate the nature and extent of past tank leaks through characterization activities and assess future impacts to

Can machine-learning improve cardiovascular risk prediction using routine clinical data?

Science.gov (United States)

Kai, Joe; Garibaldi, Jonathan M.; Qureshi, Nadeem

2017-01-01

Background Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. Methods Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the ‘receiver operating curve’ (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins). Findings 24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723–0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739–0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755–0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755–0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759–0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm. Conclusions Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others

Chemical Risk Assessment Screening Tool of a Global Chemical Company

OpenAIRE

Evelyn Tjoe-Nij; Christophe Rochin; Nathalie Berne; Alessandro Sassi; Antoine Leplay

2018-01-01

Background: This paper describes a simple-to-use and reliable screening tool called Critical Task Exposure Screening (CTES), developed by a chemical company. The tool assesses if the exposure to a chemical for a task is likely to be within acceptable levels. Methods: CTES is a Microsoft Excel tool, where the inhalation risk score is calculated by relating the exposure estimate to the corresponding occupational exposure limit (OEL) or occupational exposure band (OEB). The inhalation exposure i...

Multi-Hazard Advanced Seismic Probabilistic Risk Assessment Tools and Applications