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.
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