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

Abstract

24Cardiometabolic diseases (CMD) impose greater impact on every aspect of health care 25 than any other disease group. Accurate and in-time risk assessment of individuals for their 26 propensity to develop CMD events is one of the most critical paths in preventing these 27 conditions. The principal objective of the present study is to report the development, and 28 validation of a next generation risk engine to predict CMD. UK Biobank population data was 29 used to derive predictive models for six CMD. Missing data were imputed using imputation 30 algorithms. Cox proportional hazard models were used to estimate annual absolute risk and 31 relative risk of different risk factors for these conditions. In addition to conventional risk 32 factors, the applied model included socioeconomic data, lifestyle factors and comorbidities as 33 predictors of outcomes. In total, 416,936 individuals were included in the analysis. The 34 derived prediction models achieved consistent and moderate-to-high discrimination 35performance (C-index) for all diseases: coronary artery disease (0.79), hypertension (0.82), 36 type 2 diabetes mellitus (0.87), stroke (0.79), deep vein thrombosis (0.75), and abdominal 37 aortic aneurysm (0.90). These results were consistent across age groups (37-73 years) and 38 showed similar predictive abilities amongst those with pre-existing diabetes or hypertension. 39Calibration of risk scores showed that there was moderate overestimation of CMD-related 40 conditions only in the highest decile of risk scores for all models. In summary, the newly 41 developed algorithms, based on Cox proportional models, resulted in high disclination and 42 good calibration for several CMD. The integrations of these algorithms on a single platform 43 may have direct clinical impact. 44 45

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

“…The models derived in this paper focus on the same set of diseases that have been the focus of a companion paper developed on a large epidemiological cohort study (UK Biobank) [24]. Despite different data sources, we observed comparable prediction abilities across the studies.…”

Major Adverse Cardiovascular Event Risk Prediction in Asian Patients After Myocardial Infarction: A Novel, Dynamic, Machine-learning Approach

“…Different risk prediction scores have been developed in the past, which can be broadly classified into conventional regression analysis, such as Cox proportional hazard model, and ML techniques that have been adopted more recently. 10,26 Although the ML or deep learning models are more complex and predictive, they often lack clear physiological interpretability.…”