Development and Use of the UK Railway Network’s Safety Risk Model

2004 ◽  
pp. 2275-2282
Author(s):  
Colin Dennis ◽  
Kajal Somaiya
Keyword(s):  
Risk Model ◽  
Safety Risk ◽  
The Uk

scholarly journals Development and external validation of 1- and 2-year mortality prediction models in cystic fibrosis

2019 ◽  
Vol 54 (3) ◽  
pp. 1900224 ◽  
Author(s):  
Sanja Stanojevic ◽  
Jenna Sykes ◽  
Anne L. Stephenson ◽  
Shawn D. Aaron ◽  
George A. Whitmore
Keyword(s):  
Cystic Fibrosis ◽  
Health Status ◽  
Prediction Models ◽  
Risk Model ◽  
External Validation ◽  
Registry Data ◽  
Receiver Operating Curve ◽  
Clinical Tool ◽  
Risk Of Death ◽  
The Uk

IntroductionWe aimed to develop a clinical tool for predicting 1- and 2-year risk of death for patients with cystic fibrosis (CF). The model considers patients' overall health status as well as risk of intermittent shock events in calculating the risk of death.MethodsCanadian CF Registry data from 1982 to 2015 were used to develop a predictive risk model using threshold regression. A 2-year risk of death estimated conditional probability of surviving the second year given survival for the first year. UK CF Registry data from 2007 to 2013 were used to externally validate the model.ResultsThe combined effect of CF chronic health status and CF intermittent shock risk provided a simple clinical scoring tool for assessing 1-year and 2-year risk of death for an individual CF patient. At a threshold risk of death of ≥20%, the 1-year model had a sensitivity of 74% and specificity of 96%. The area under the receiver operating curve (AUC) for the 2-year mortality model was significantly greater than the AUC for a model that predicted survival based on forced expiratory volume in 1 s <30% predicted (AUC 0.95 versus 0.68 respectively, p<0.001). The Canadian-derived model validated well with the UK data and correctly identified 79% of deaths and 95% of survivors in a single year in the UK.ConclusionsThe prediction models provide an accurate risk of death over a 1- and 2-year time horizon. The models performed equally well when validated in an independent UK CF population.

A clinical prediction model to assess risk for pancreatic cancer among patients with new-onset diabetes.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e15780-e15780
Author(s):  
Ben Boursi ◽  
Brian S. Finkelman ◽  
Bruce J. Giantonio ◽  
Kevin Haynes ◽  
Anil K. Rustgi ◽  
...  
Keyword(s):  
Prediction Model ◽  
Risk Model ◽  
Ductal Adenocarcinoma ◽  
P Value ◽  
Diabetes Diagnosis ◽  
Roc Curve Analysis ◽  
Onset Diabetes ◽  
Definitive Screening ◽  
The Uk ◽  
New Onset

e15780 Background: Approximately 50% of all patients with pancreatic ductal adenocarcinoma (PDA) develop diabetes mellitus (DM) prior to cancer diagnosis. Targeted screening for PDA among those with new-onset diabetes may allow earlier diagnosis. We sought to develop and validate a PDA risk prediction model to identify high-risk individuals among those with new-onset diabetes. Methods: We conducted a retrospective cohort study in a population representative database from the UK. Individuals with incident diabetes after the age of 35 and ≥3 years of follow-up after DM diagnosis were eligible for inclusion. Candidate predictors consisted of epidemiological and clinical characteristics available at the time of diabetes diagnosis. Variables with p-value<0.25 in the univariable analyses were further evaluated using backward stepwise approach. Model discrimination was assessed using ROC curve analysis. Calibration was evaluated using the Hosmer–Lemeshow test. Results were internally validated using a bootstrapping procedure. Results: The study included 109,385 patients with new-onset diabetes. Among them, 390 (0.4%) were diagnosed with PDA within 3 years. The final model (AUC 0.82, 95% CI: 0.75-0.89) included age, BMI, BMI change, smoking, HbA1C, cholesterol, hemoglobin, creatinine and alkaline phosphatase, and use of PPI and anti-diabetic medication. Bootstrapping validation showed negligible optimism. If the predicted risk threshold for definitive PDA screening was set at 1% over 3 years, only 6.19% of the new-onset diabetes population would undergo definitive screening, and the corresponding sensitivity, specificity and positive predictive value would be 44.7%, 94.0%, and 2.6% respectively. Conclusions: A risk model based on widely available clinical parameters can help target PDA screening in patients with new-onset diabetes.

scholarly journals A genetic model of ivabradine recapitulates results from randomized clinical trials

2020 ◽  
Author(s):  
Marc-André Legault ◽  
Johanna Sandoval ◽  
Sylvie Provost ◽  
Amina Barhdadi ◽  
Louis-Philippe Lemieux Perreault ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Heart Failure ◽  
Atrial Fibrillation ◽  
Heart Rate ◽  
Drug Target ◽  
Drug Targets ◽  
Risk Model ◽  
Cardiovascular Death ◽  
Increased Risk ◽  
The Uk

ABSTRACTBackgroundNaturally occurring human genetic variants provide a valuable tool to identify drug targets and guide drug prioritization and clinical trial design. Ivabradine is a heart rate lowering drug with protective effects on heart failure despite increasing the risk of atrial fibrillation. In patients with coronary artery disease without heart failure, the drug does not protect against major cardiovascular adverse events prompting questions about the ability of genetics to have predicted those effects. This study evaluates the effect of a mutation in HCN4, ivabradine’s drug target, on safety and efficacy endpoints.MethodsWe used genetic association testing and Mendelian randomization to predict the effect of ivabradine and heart rate lowering on cardiovascular outcomes.ResultsUsing data from the UK Biobank and large GWAS consortia, we evaluated the effect of a heart rate-reducing genetic variant at the HCN4 locus encoding ivabradine’s drug target. These genetic association analyses showed increases in risk for atrial fibrillation (OR 1.09, 95% CI: 1.06-1.13, P=9.3 ×10−9) in the UK Biobank. In a cause-specific competing risk model to account for the increased risk of atrial fibrillation, the HCN4 variant reduced incident heart failure in participants that did not develop atrial fibrillation (HR 0.90, 95% CI: 0.83-0.98, P=0.013). In contrast, the same heart rate reducing HCN4 variant did not prevent a composite endpoint of myocardial infarction or cardiovascular death (OR 0.99, 95% CI: 0.93-1.04, P=0.61).ConclusionGenetic modelling of ivabradine recapitulates its benefits in heart failure, promotion of atrial fibrillation, and neutral effect on myocardial infarction.CONDENSED ABSTRACTThe effects of drugs can sometimes be predicted from the effects of mutations in genes encoding drug targets. We tested the effect of a heart rate reducing allele at the HCN4 locus encoding ivabradine’s drug target and found results coherent with the SHIFT and SIGNIFY clinical trials of ivabradine. The genetic variant increased the risk of atrial fibrillation and cardioembolic stroke and protected against heart failure in a competing risk model accounting for the increased risk of atrial fibrillation. The variant had a neutral effect on a composite of myocardial infarction and cardiovascular death.

scholarly journals Assessment of Workers’ Personal Vulnerability to COVID-19 Using “COVID-AGE”

2020 ◽  
Author(s):  
David Coggon ◽  
Peter Croft ◽  
Paul Cullinan ◽  
Anthony Williams
Keyword(s):  
Risk Factor ◽  
Risk Model ◽  
White Male ◽  
Summary Measure ◽  
Occupational Risks ◽  
Individual Vulnerability ◽  
Personal Vulnerability ◽  
The Uk ◽  
The Impact ◽  
New Evidence

AbstractDecisions on fitness for employment that entails a risk of contracting Covid-19 require an assessment of the worker’s personal vulnerability should infection occur. Using recently published UK data, we have developed a risk model that provides estimates of personal vulnerability to Covid-19 according to sex, age, ethnicity, and various comorbidities. Vulnerability from each risk factor is quantified in terms of its equivalence to added years of age. Addition of the impact from each risk factor to an individual’s true age generates their “Covid-age”, a summary measure representing the age of a healthy UK white male with equivalent vulnerability. We discuss important limitations of the model, including current scientific uncertainties and limitations on generalisability beyond the UK setting and its use beyond informing assessments of individual vulnerability in the workplace. As new evidence becomes available, some of these limitations can be addressed. The model does not remove the need for clinical judgement or for other important considerations when managing occupational risks from Covid-19.

scholarly journals Modeling the Formation Mechanism of Food Safety Risk in Catering O2O Distribution Link

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ting Wu ◽  
Yilei Pei ◽  
Dandan Li ◽  
Peng Su
Keyword(s):  
Food Safety ◽  
System Dynamics ◽  
Formation Mechanism ◽  
Evaluation System ◽  
Risk Model ◽  
Model Simulation ◽  
Risk Index ◽  
Flow Diagram ◽  
Safety Risk ◽  
Food Safety Risk

This paper aims to solve the problem of food safety in catering O2O distribution link. We applied the system dynamics method to model the formation mechanism of food safety risk in the distribution link. The results of our experiment include identifying the risk factors that may be faced by food safety in the distribution link from five perspectives: O2O catering enterprise’s own risk, logistics distribution team’s distribution risk, O2O catering platform supervision risk, user-supervision risk, and government department supervision risk, and establishing a risk index evaluation system based on the Analytic Hierarchy Process. With the help of the system dynamics model, the corresponding risk formation mechanism system model flow diagram is established, and the model simulation analysis is carried out. Through this research, we concluded that we can use the risk model to understand the risks faced by different subjects so as to make targeted countermeasures.

An international risk prediction algorithm for the onset of generalized anxiety and panic syndromes in general practice attendees: predictA

2011 ◽  
Vol 41 (8) ◽  
pp. 1625-1639 ◽  
Author(s):  
M. King ◽  
C. Bottomley ◽  
J. A. Bellón-Saameño ◽  
F. Torres-Gonzalez ◽  
I. Švab ◽  
...  
Keyword(s):  
The Netherlands ◽  
Risk Model ◽  
Short Form ◽  
Current Status ◽  
Prediction Algorithm ◽  
Stepwise Logistic Regression ◽  
Generalized Anxiety ◽  
Risk Algorithm ◽  
Patient Health ◽  
The Uk

BackgroundThere are no risk models for the prediction of anxiety that may help in prevention. We aimed to develop a risk algorithm for the onset of generalized anxiety and panic syndromes.MethodFamily practice attendees were recruited between April 2003 and February 2005 and followed over 24 months in the UK, Spain, Portugal and Slovenia (Europe4 countries) and over 6 months in The Netherlands, Estonia and Chile. Our main outcome was generalized anxiety and panic syndromes as measured by the Patient Health Questionnaire. We entered 38 variables into a risk model using stepwise logistic regression in Europe4 data, corrected for over-fitting and tested it in The Netherlands, Estonia and Chile.ResultsThere were 4905 attendees in Europe4, 1094 in Estonia, 1221 in The Netherlands and 2825 in Chile. In the algorithm four variables were fixed characteristics (sex, age, lifetime depression screen, family history of psychological difficulties); three current status (Short Form 12 physical health subscale and mental health subscale scores, and unsupported difficulties in paid and/or unpaid work); one concerned country; and one time of follow-up. The overall C-index in Europe4 was 0.752 [95% confidence interval (CI) 0.724–0.780]. The effect size for difference in predicted log odds between developing and not developing anxiety was 0.972 (95% CI 0.837–1.107). The validation of predictA resulted in C-indices of 0.731 (95% CI 0.654–0.809) in Estonia, 0.811 (95% CI 0.736–0.886) in The Netherlands and 0.707 (95% CI 0.671–0.742) in Chile.ConclusionsPredictA accurately predicts the risk of anxiety syndromes. The algorithm is strikingly similar to the predictD algorithm for major depression, suggesting considerable overlap in the concepts of anxiety and depression.

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