Repurposing cardiovascular disease prediction models for cancer

BackgroundPopulation cancer screening detects the presence of early-stage disease rather than assessing future disease risk. We evaluated whether widely implemented cardiovascular disease (CVD) risk models can predict 10-year cancer risk and compared them with a less widely used cancer risk model (QCancer). MethodsWe evaluated four CVD prediction models: QRISK3, the Pooled Cohort Equations (PCE), SCORE2 and SCORE2-OP. All models were recalibrated using 20% of the UK Biobank (UKB) cohort and tested in the remainder, as well as in the Clinical Practice Research Datalink (CPRD). We gauged model performance using c-statistics for discrimination and evaluated the fidelity of calibration. We also identified the most influential risk factors in the QRISK3 model. FindingsIn the UKB test set, the c-statistics for incident CVD ranged from 0{middle dot}71 to 0{middle dot}74 (11,022 events). All CVD models achieved a c-statistic of 0{middle dot}63 for any cancer (23,010 events) and showed CVD-equivalent discrimination for gastro-oesophageal, liver and biliary tree, laryngeal, renal tract, and lung cancers (c-statistic range: 0{middle dot}70;0{middle dot}81). Overall, the discrimination of the CVD models was comparable that of the QCancer models (median difference in c-statistic: -0{middle dot}01 (95%CI -0{middle dot}03;0{middle dot}00). The recalibrated CVD models showed near-perfect calibration (median intercept 0{middle dot}01, Q1;Q3 -0{middle dot}05;0{middle dot}03 and slope 1{middle dot}00, Q1;Q3 0{middle dot}93;1{middle dot}15). Performance in CPRD (393,658 cancer events) was similar: the median c-statistic, calibration intercept, and slope were 0{middle dot}01 (95%CI 0{middle dot}00;0{middle dot}02), 0{middle dot}05 (95%CI 0{middle dot}02;0{middle dot}17), and 0{middle dot}04 (95%CI 0{middle dot}01;0{middle dot}15) higher, respectively, in CPRD than in UKB. After age, smoking status and systolic blood pressure were the most influential predictors of cancer risk. InterpretationWidely implemented CVD prediction models perform similarly to the QCancer models in the prediction of incident cancers. They may be used to inform cancer prevention and guide risk-stratified monitoring. The recalibrated models are available through an API. FundingHealth Data Research UK, British Heart Foundation and UK Research and Innovation.