Rezaee, M. | bioRxiv Science

bioRxiv2019

Prediction performance of a cardiovascular risk assessment tool using Stanford EHR data repository

BackgroundStratification of individuals for their risk to develop cardiovascular diseases can be used for effective prevention and intervention. A significant amount of information for risk assessment can be obtained through repurposing electronic health records (EHR). The objective of this study is to derive and assess the performance of prediction models for cardiovascular outcomes by using EHR-derived data.\n\nMethodsWe used the Stanford Medicine Research Data Repository (STARR) data from 2000-2017, containing over 2.1 million patients. A subset of 762,372 individuals with complete International Classification of Diseases (ICD) data was used to fit Cox proportional hazard models for prediction of six cardiovascular-related diseases and type 2 diabetes.\n\nResultsThe derived prediction models indicated consistent high discrimination performance (C-index) for all diseases examined: coronary artery disease (0.85), hypertension (0.82), type 2 diabetes (0.77), stroke (0.76), atrial fibrillation (0.82) and abdominal aortic aneurysm (0.77). Lower prediction abilities were observed for deep vein thrombosis (0.67). These results were consistent across age groups and maintained good prediction abilities among individuals with pre-existing diabetes or hypertension. Assessment of model calibration is ongoing.\n\nConclusionsWe proposed new prediction models for the seven diseases using ICD codes derived from EHR data. EHR data can be used for health risk assessment, but challenges related to data quality and model generalizability and calibration remain to be solved.

bioinformatics↗

bioRxiv2019

Development and validation of a next-gen health stratification engine to determine risk for multiple cardiovascular diseases

Cardiometabolic diseases (CMD) impose greater impact on every aspect of health care than any other disease group. Accurate and in-time risk assessment of individuals for their propensity to develop CMD events is one of the most critical paths in preventing these conditions. The principal objective of the present study is to report the development, and validation of a next generation risk engine to predict CMD. UK Biobank population data was used to derive predictive models for six CMD. Missing data were imputed using imputation algorithms. Cox proportional hazard models were used to estimate annual absolute risk and relative risk of different risk factors for these conditions. In addition to conventional risk factors, the applied model included socioeconomic data, lifestyle factors and comorbidities as predictors of outcomes. In total, 416,936 individuals were included in the analysis. The derived prediction models achieved consistent and moderate-to-high discrimination performance (C-index) for all diseases: coronary artery disease (0.79), hypertension (0.82), type 2 diabetes mellitus (0.87), stroke (0.79), deep vein thrombosis (0.75), and abdominal aortic aneurysm (0.90). These results were consistent across age groups (37-73 years) and showed similar predictive abilities amongst those with pre-existing diabetes or hypertension. Calibration of risk scores showed that there was moderate overestimation of CMD-related conditions only in the highest decile of risk scores for all models. In summary, the newly developed algorithms, based on Cox proportional models, resulted in high disclination and good calibration for several CMD. The integrations of these algorithms on a single platform may have direct clinical impact.

bioinformatics↗