Predicting progression from coronary artery disease to heart failure in community cohorts

Abstract Background Approximately 10% of people develop heart failure (HF) within 5 years after an acute coronary event. Once ischaemic damage has occurred, the heart undergoes remodeling, which can lead to subclinical dysfunction and HF. However, remodeling varies considerably between patients and is difficult to predict. Purpose To assess whether adding genetic information to clinical factors improves the accuracy of predicting incident non-fatal/fatal HF within 5 years in people with coronary artery disease (CAD). Methods In the UK Biobank (UKB) [1], we developed a clinical risk model (predictors age, sex, ethnicity, body mass index, social deprivation, smoking, personal medical history, time since most recent CAD admission, lipids, HbA1C, creatinine, medications). Separately, we performed a genome-wide association meta-analysis of 13,360 (6,315 + 7,045) cases and 58,126 (37,245 + 20,881) non-cases from the UKB and deCODE [2] cohorts, relating common genetic variants to non-fatal/fatal HF or cardiomyopathy in people with CAD. For the current analysis of the UKB cohort, we derived a polygenic risk score using summary statistics from deCODE alone. We then compared risk estimates and assessed reclassification of 5-year risk using the clinical risk model with and without polygenic risk score, in UKB. Risk groups were <5%, 5-9.9%, 10-14.9%, ≥15%. Results Among people with CAD in the 5 years prior to assessment in UKB (n=8,880, mean age 61y, 27% women) or deCODE (n=10,113, mean age 66y, 34% women), 377 (4%) and 915 (9%) developed non-fatal/fatal HF over 5 years follow-up, respectively. In the UKB cohort, median 5-year risk with the clinical model was 3.2% (IQR 2-5.4%, C-statistic 0.72, 95% CI 0.70–0.74, Table 1) and plots of predicted versus observed risk showed very good calibration across deciles. The polygenic risk score was predictive of non-fatal/fatal HF in the UKB (hazard ratio 1.03, 95% CI 1.00 to 1.05 per standard deviation) with significance decreasing after adjusting for the prognostic index of the clinical risk model (hazard ratio 1.02, 95% CI 0.99 to 1.05 per standard deviation). Median 5-year risk by the clinical+polygenic model was 3.0% (IQR 1.9-5.0%). Net reclassification with the addition of the polygenic score improved accuracy of risk prediction for 3.6% of non-cases but deteriorated accuracy of risk prediction for 6% of cases. Overall net reclassification was deterioration of -0.025. Conclusion Our data suggest that common genetic variants may be associated with progression from CAD to HF, but that adding genetic data did not improve the accuracy of predicting progression from CAD to HF beyond established clinical predictors in the UKB cohort. Confirmation of these findings in deCODE and other large cohorts is warranted.