The PREVENT equations independently predicted major adverse cardiovascular events in patients with HFpEF, with the PREV-CVD score showing a significant association (HR 1.04; 95% CI 1.02-1.07).
Observational (n=280)
Yes
Do the PREVENT equations predict major adverse cardiovascular events in patients with HFpEF?
The PREVENT equations, originally designed for primary prevention, effectively predict major adverse cardiovascular events and heart failure hospitalizations in patients with HFpEF.
Effect estimate: HR 1.04 (95% CI 1.02-1.07)
p-value: p=0.00004
Abstract Background Predicting mortality and morbidity is crucial for risk stratification in patients with heart failure (HF). The American Heart Association has recently introduced the PREVENT (Predicting Risk of CVD EVENTs) equations, designed to estimate the risk of incidental cardiovascular disease (CVD), HF, or atherosclerotic cardiovascular disease (ASCVD). Although these equations were developed for primary prevention patients, they incorporate clinical factors that are relevant to HF development. Purpose This study investigated whether the PREVENT equations can predict clinical outcomes in patients with HF with preserved ejection fraction (HFpEF). Methods We enrolled 434 patients aged 30 to 79 years from the PURSUIT-HFpEF (Prospective, Multicenter, Observational Study of Patients with Heart Failure with Preserved Ejection Fraction) study. The PREVENT online calculator was used to determine the 10-year risks of cardiovascular disease (PREV-CVD), heart failure (PREV-HF), and atherosclerotic cardiovascular disease (PREV-ASCVD). These risks were then examined to see if they predicted major adverse cardiovascular events (MACE), a composite of death, HF hospitalisation (HHF), and stroke. Results Risk scores were calculated for 280 patients (64. 5%; mean age 75 years, 48. 6% female) after excluding those with missing or out-of-range factors. Over a median follow-up of 1384 days (IQR 882-1785), 125 patients experienced MACEs. Cox proportional hazard regression analysis selected PREVENT risk scores as independent predictors of MACEs (PREV-CVD: HR=1. 04 95% CI 1. 02 - 1. 07; PREV-HF: HR = 1. 03 1. 01 -1. 06, PREV-ASCVD: HR = 1. 07 1. 02 - 1. 12) and HHF (PREV-CVD: HR=1. 05, 1. 02 - 1. 08; PREV-HF: HR = 1. 05, 1. 02 -1. 08; PREV-ASCVD: HR = 1. 08 1. 02 - 1. 14). Kaplan-Meier curve analysis revealed significant differences in MACE rates among PREV-CVD quartiles (p=0. 00004 by log-rank test), with the lowest risk group (Q1) having significantly better outcomes (Figure). Similar results were observed for PREV-HF and PREV-ASCVD. All three scores demonstrated good predictive ability for MACEs and HHF at 1 and 3 years, as indicated by time-dependent ROC curve analysis (Table). Conclusions The PREVENT equations effectively predict MACE in patients with HFpEF. These findings support the potential application of PREVENT scores for risk stratification and personalised management strategies in this high-risk population. Kaplan-Meier curves for MACE AUC of time-dependent ROC curves
Iwakura et al. (Sat,) conducted a observational in Heart failure with preserved ejection fraction (HFpEF) (n=280). PREVENT equations was evaluated on Major adverse cardiovascular events (MACE), a composite of death, HF hospitalisation (HHF), and stroke (HR 1.04, 95% CI 1.02-1.07, p=0.00004). The PREVENT equations independently predicted major adverse cardiovascular events in patients with HFpEF, with the PREV-CVD score showing a significant association (HR 1.04; 95% CI 1.02-1.07).
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