Short-Term Global Cardiovascular Disease Risk Prediction in Older Adults

Abstract

Background: Current prevention guidelines recommend using the Pooled Cohort Equation (PCE) for 10-year atherosclerotic cardiovascular disease (CVD) risk assessment. However, the PCE has serious limitations in older adults: it excludes heart failure (HF) hospitalization, estimates 10-year risk, which may not be the most relevant time frame, and is not indicated for individuals age >79 years.

Objectives: This study sought to determine whether adding biomarkers to PCE variables improves global CVD (coronary heart disease, stroke, and HF) risk prediction in older adults over a shorter time period.

Methods: Atherosclerosis Risk in Communities study participants without prevalent CVD including HF (n = 4,760; age 75.4 ± 5.1 years) were followed for incident global CVD events. Adding N-terminal pro-B-type natriuretic peptide, high-sensitivity cardiac troponin T, and high-sensitivity C-reactive protein to the PCE and a "lab model" with the biomarkers, age, race, and gender were assessed for prediction improvement. Area under the receiver operating characteristic curve (AUC) and net reclassification index (NRI) were calculated.

Results: Over median follow-up of ∼4 years, incident HF was the leading CVD event (n = 193 vs. 118 coronary heart disease and 81 stroke events). Compared to the PCE, each biomarker improved risk prediction. The largest improvement in risk prediction metrics was with the addition of all 3 biomarkers (ΔAUC 0.103; continuous NRI 0.484). The lab model also performed better than the PCE model (ΔAUC 0.091, continuous NRI 0.355).

Conclusions: Adding biomarkers to the PCE or a simpler "lab model" improves short-term global CVD risk prediction and may be useful to inform short-term preventive strategies in older adults.

Keywords: biomarkers; cardiovascular disease; elderly; heart failure; prevention; risk assessment.