Improving cardiovascular risk prediction beyond pooled cohort equations: a prospective cohort of 304,356 participants

Abstract

AbstractBackgroundPooled Cohort Equations (PCE) are used to predict cardiovascular disease (CVD) risk. Inclusion of other variables may improve risk prediction.ObjectiveIdentify variables improving CVD risk prediction beyond recalibrated PCE.DesignProspective cohort study; sex-stratified Cox survival models with LASSO stability selection to predict CVD in non-overlapping subsets: variable selection (40%), model training (30%) and testing (30%).SettingUK population.ParticipantsUK Biobank: 121,724 and 182,632 healthy men and women, respectively, aged 38-73 years at baseline.MeasurementsPersonal/family medical history; lifestyle factors; genetic, biochemical, hematological, and metabolomic blood markers. Outcomes were incident hospitalization or mortality from CVD.ResultsThere were 11,899 (men) and 9,110 (women) incident CVD cases with median 12.1 years follow-up. Variables selected for both men and women were: age, albumin, antihypertensive medication, apolipoprotein B, atrial fibrillation, C-reactive protein, current smoker, cystatin C, family history of coronary artery disease, glycated hemoglobin, polygenic risk score (PRS) for CVD and systolic blood pressure. Also selected: apolipoprotein A1, lipoprotein(a), white blood cell count, deprivation index (men); triglycerides (women). C-statistics for recalibrated PCE were 0.67 [0.66-0.68] and 0.69 [0.68-0.70] in men and women, respectively, improving to 0.71 [0.70-0.72] and 0.72 [0.71-0.73] with LASSO stably selected variables. Categorical net reclassification improvement (7.5% risk threshold) versus PCE was 0.054 [0.038-0.070] (men) and 0.081 [0.063-0.099] (women). Addition of targeted metabolomic data to LASSO stability selection did not improve predictive accuracy.LimitationsAnalyses were done in a single population study and require external replication.ConclusionAdditional personal/family medical history, blood-based markers and genetic information improve CVD risk prediction beyond PCE.Funding sourceNational Institute for Health Research Academic Clinical Fellowship (JE); Medical Research Council studentship (BB); European Union H2020 (MC-H).