Are neural networks the ultimate risk prediction models in patients at high risk of acute myocardial infarction?

2020 ◽  
Vol 27 (19) ◽  
pp. 2045-2046
Author(s):  
Marius Roman
Keyword(s):  
Myocardial Infarction ◽  
Neural Networks ◽  
Acute Myocardial Infarction ◽  
High Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Prediction Models

scholarly journals Acute Myocardial Infarction Readmission Risk Prediction Models

2018 ◽  
Vol 11 (1) ◽  
Author(s):  
Lauren N. Smith ◽  
Anil N. Makam ◽  
Douglas Darden ◽  
Helen Mayo ◽  
Sandeep R. Das ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Prediction Models ◽  
Readmission Risk

scholarly journals Risk Prediction Models for Liver Cancer in Adults: A Systematic Review and Meta-Analysis

2021 ◽  
Author(s):  
Maomao Cao ◽  
He Li ◽  
Dianqin Sun ◽  
Siyi He ◽  
Yadi Zheng ◽  
...  
Keyword(s):  
Systematic Review ◽  
High Risk ◽  
Liver Cancer ◽  
Risk Prediction ◽  
Prediction Models ◽  
Meta Analysis ◽  
Risk Of Bias ◽  
Risk Models ◽  
Risk Prediction Models ◽  
Prediction Risk

Abstract Background Prediction of liver cancer risk is beneficial to define high-risk population of liver cancer and guide clinical decisions. We aimed to review and critically appraise the quality of existing risk-prediction models for liver cancer. Methods This systematic review followed the guidelines of CHARMS (Checklist for Critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies) and Preferred Reporting Items for Systematic Reviews and Meta (PRISMA). We searched for PubMed, Embase, Web of Science, and the Cochrane Library from inception to July 2020. Prediction model Risk Of Bias Assessment Tool was used to assess the risk of bias of all potential articles. A narrative description and meta-analysis were conducted. Results After removal irrespective and duplicated citations, 20 risk prediction publications were finally included. Within the 20 studies, 15 studies performed model derivation and validation process, three publications only conducted developed procedure without validation and two articles were used to validate existing models. Discrimination was expressed as area under curve or C statistic, which was acceptable for most models, ranging from 0.64 to 0.96. Calibration of the predictions model were rarely assessed. All models were graded at high risk of bias. The risk bias of applicability in 13 studies was considered low. Conclusions This systematic review gives an overall review of the prediction risk models for liver cancer, pointing out several methodological issues in their development. No prediction risk models were recommended due to the high risk of bias.Systematic review registration: This systematic has been registered in PROSPERO (International Prospective Register of Systemic Review: CRD42020203244).

scholarly journals 1227Colorectal cancer risk prediction models incorporating lifestyle and biomarker data: Results from the EPIC cohort

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Krasimira Aleksandrova ◽  
Robin Reichmann ◽  
Mazda Jenab ◽  
Sabina Rinaldi ◽  
Rudolf Kaaks ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
High Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
Lifestyle Factors ◽  
Health Concern ◽  
Incidence Density ◽  
Processed Meat ◽  
Risk Prediction Models ◽  
Biomarker Data

Abstract Background Colorectal cancer represents a major public health concern and there is a worrying tendency of increasing incidence rates among younger people in the last decades. Risk stratification of high-risk individuals may aid targeted disease prevention. We therefore aimed to evaluate the predictive value of a wide range of lifestyle and biomarker variables using data within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Methods A range of lifestyle, anthropometric and dietary variables in 329,885 participants in the EPIC cohort were evaluated as potential predictors for risk of colorectal cancer over 10 years. Biomarker measurements of 41 parameters were available for 1,320 CRC cases and 1,320 controls selected using incidence density matching. Best sets of predictors were selected using elastic net regularization with bootstrapping. Random survival forest was applied as a novel technique to validate the set of selected predictors taking variable interactions into account. Results The results suggested a set of lifestyle factors including age, waist circumference, height, smoking, alcohol consumption, physical activity, vegetables, dairy products, processed meat, and sugar and confectionary that showed good discrimination (Harrell's C-index: 0.710) and excellent calibration. The analyses further revealed a set of biomarkers that increased the predictive performance beyond age, sex and lifestyle factors. Conclusions Risk prediction models based on lifestyle and biomarker data may prove useful in the identification of individuals at high risk for colorectal cancer. Key messages Risk prediction models incorporating lifestyle and biomarker data could contribute to developing strategies for targeted colorectal cancer prevention.

Review of Risk Prediction Approaches for Bipolar Episodes in the Perinatal Period

2017 ◽  
Vol 41 (S1) ◽  
pp. S113-S113
Author(s):  
M. Casanova Dias ◽  
I. Jones ◽  
A. Di Florio ◽  
L. Jones ◽  
N. Craddock
Keyword(s):  
Decision Making ◽  
Bipolar Disorder ◽  
High Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
Perinatal Period ◽  
Management Options ◽  
Risk Prediction Models ◽  
Risk Period ◽  
The Impact

IntroductionThe perinatal period is a high-risk period for the development of illness episodes in women with bipolar disorder. Relapse rates vary between 9 and 75% depending on the study. The overall risk of a severe episode is approximately 20%. The impact on women, the relationships with their babies and their families can be devastating. In the UK costs to society are £8.1 billion per year-cohort of births. The advice currently given to women is based of general risk rates. Women's needs of information for decision-making in the perinatal period are not being met.ObjectivesTo review the risk prediction approaches used for women with bipolar disorder in the perinatal period.AimsTo understand the existing risk prediction models and approaches used for prognosis of the risk of recurrence of bipolar disorder for women in the perinatal period.MethodsSystematic literature search of public medical electronic databases and grey literature on risk prediction for bipolar episodes in the perinatal period.ResultsWe will present the existing models and approaches used for risk prediction of illness episodes in the perinatal period.ConclusionsAwareness of existing risk prediction models for recurrence of bipolar disorder in the perinatal period will allow better informed risk-benefit analysis of treatment and management options.This person-centred approach will help women and clinicians in their decision-making at this crucial high-risk period.Disclosure of interestThe authors have not supplied their declaration of competing interest.

Abstract 032: Is Bigger Data Better? Predicting Readmissions in Acute Myocardial Infarction on Admission versus Discharge With Electronic Health Record Data

2017 ◽  
Vol 10 (suppl_3) ◽  
Author(s):  
Oanh K Nguyen ◽  
Anil N Makam ◽  
Christopher Clark ◽  
Song Zhang ◽  
Sandeep R Das ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Electronic Health Record ◽  
Risk Prediction ◽  
Prediction Models ◽  
Predictive Ability ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Electronic Health ◽  
Using Data

Background: Readmissions after hospitalization for acute myocardial infarction (AMI) are common, but the few available risk prediction models have poor predictive ability. Including more data from hospitalization may improve risk prediction. Objectives: To assess if an AMI-specific electronic health record (EHR) readmission risk prediction model derived and validated from data through the entire hospital course (‘full stay’ model) outperforms a model using data available only from the first day of hospitalization (‘first day’ model). Methods: EHR data from AMI hospitalizations from 6 diverse hospitals in north Texas from 2009-2010 were used to derive a model predicting all-cause non-elective 30-day readmissions which was then validated using five-fold cross-validation. Results: Of 826 consecutive index AMI admissions, 13% were followed by a 30-day readmission. History of diabetes (AOR 2.41, 95% CI 1.37-4.24), SBP <100 mmHg on admission (AOR 2.18, 95% CI 1.68-2.82), elevated Cr (≥2 mg/dL) on admission (AOR 2.56, 95% CI 2.52-6.08), elevated BNP on admission (AOR 6.36, 95% CI 1.65-24.47) and lack of PCI within 24 hours of admission (AOR 1.31, 95% CI 1.02-1.69) were significant predictors of readmission. Our ‘first-day’ AMI readmissions model based on these predictors had good discrimination ( Table ). Adding three other variables from the hospital course - use of IV diuretics (AOR 1.58, 95% CI 1.07-2.31), anemia (hematocrit ≤ 33%) on discharge (AOR 2.04, 95% CI 1.20-3.46), and discharge to post-acute care (AOR 1.50, 95% CI 0.90-2.50) - improved discrimination of the ‘full stay’ AMI model but only modestly improved net reclassification and calibration. Conclusions: A ‘full-stay’ AMI-specific EHR readmission model modestly outperformed a ‘first-day’ EHR model, a multi-condition EHR model, and the CMS AMI model. Surprisingly, incorporating more hospitalization data improved discrimination of the full-stay AMI model but did not meaningfully improve reclassification compared to the first-day model. Readmissions in AMI may be accurately predicted on the first day of hospitalization; waiting until later in hospitalization does not markedly improve risk prediction.

Abstract 026: Development and Validation of an Electronic Health Record Model for Predicting 30-Day Readmissions in Acute Myocardial Infarction: the AMI READMITS Score

2017 ◽  
Vol 10 (suppl_3) ◽  
Author(s):  
Oanh K Nguyen ◽  
Anil N Makam ◽  
Christopher Clark ◽  
Song Zhang ◽  
Sandeep R Das ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
High Risk ◽  
Electronic Health Record ◽  
Real Time ◽  
Risk Prediction ◽  
Cross Validation ◽  
Low Risk ◽  
Health Record ◽  
Predicted Risk

Background: Readmissions after hospitalization for acute myocardial infarction (AMI) are common, but the few available risk prediction models have poor predictive ability and are not readily usable in real-time. Objectives: To develop and validate an AMI readmission risk prediction model from electronic health record (EHR) data available on the first day of hospitalization, and to compare model performance to the Centers for Medicare and Medicaid Services (CMS) AMI model and a validated multi-condition EHR model. Methods: EHR data from AMI readmissions from 6 diverse hospitals in north Texas from 2009-2010 were used to derive a model predicting all-cause non-elective 30-day readmissions to any of 75 hospitals in the region, which was then validated using five-fold cross-validation. Results: Of 826 consecutive index AMI admissions, 13% were followed by a 30-day readmission. The AMI READMITS score included seven predictors, all ascertainable within the first 24 hours of hospitalization ( Table 1A ). The AMI READMITS score was strongly associated with 30-day readmission in our cross-validation cohort: ≤13 points = extremely low risk (bottom quintile, mean predicted risk 3%); 14-15 points = low risk (4 th quintile, predicted risk 7%); 16-17 points = moderate risk (3 rd quintile, predicted risk 11%); 18-19 points = high risk (2 nd quintile, predicted risk 16%); and ≥20 points = extremely high risk (top quintile, predicted risk 35%). The READMITS score had good discrimination with comparable performance to the CMS model in our cohort; it had improved discrimination, reclassification, and calibration compared to a multi-condition EHR model ( Table 1B ). Conclusions: The AMI READMITS score accurately stratifies patients hospitalized with AMI into groups at varying risk of 30-day readmission. Unlike claims-based models which require data not available until after discharge, READMITS is parsimonious, easy to implement, and leverages actionable real-time data available from the EHR within the first 24 hours of hospitalization to enable early prospective identification of high-risk AMI patients for targeted readmissions reduction interventions.

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