Are competing-risk models superior to standard Cox models for predicting cardiovascular risk in older adults? Analysis of a whole-of-country primary prevention cohort aged ≥65 years.

Abstract

Both cardiovascular disease (CVD) risk and deaths from non-CVD causes, which may preclude a CVD event, increase with age. We evaluated whether accounting for the competing risk of non-CVD death improves the performance of CVD risk-prediction equations in older adults. All New Zealanders aged ≥65 years in 2012 without a prior CVD hospitalization were identified by anonymized linkage of eight routinely collected national health data sets. Sex-specific equations estimating the 5-year risk of a fatal or non-fatal CVD event were constructed using standard Cox and Fine-Gray (competing-risk) approaches. The pre-specified predictors were: age, ethnicity, deprivation level, diabetes, atrial fibrillation and baseline preventive pharmacotherapy. Model performance was evaluated by assessing calibration and discrimination in the overall cohort and in ethnic and age-specific subgroups. Among 360 443 people aged ≥65 years with 1 615 412 person-years of follow-up, 14.6% of men and 12.1% of women had a first CVD event, whereas 8.5% of men and 7.6% of women died from a non-CVD cause. Standard Cox models overestimated the mean predicted the 5-year CVD risk by ∼1% overall and by 5-6% in the highest risk deciles. The mean predicted CVD risk from the Fine-Gray models approximated the observed risk overall, although slight underestimation occurred in some subgroups. Discrimination was similar for both models. In a whole-of-country primary prevention cohort aged ≥65 years, standard Cox models overestimated the 5-year CVD risk whereas the Fine-Gray models were generally better calibrated. New CVD risk equations that take competing risks into account should be considered for older people.