A novel risk score incorporating frailty or congestion status predicted mortality in patients with heart failure with preserved ejection fraction.

Abstract Introduction In patients with heart failure (HF), frailty or congestion is strongly associated with clinical outcomes. However, existing risk prediction models for HF prognosis do not incorporate these important clinical factors. The estimated plasma volume status (ePVS) is an easy-to-calculate index to estimate plasma volume. which is associated with the prognosis in HF patients. Purpose This study intends to construct an inclusive risk prediction model for all-cause mortality in patients with HF with preserved ejection fraction (HFpEF), incorporating evaluations of frailty and congestion alongside traditional risk factors. Methods We analyzed data from 1212 patients (81±9 years, 55% female) registered in the PURSUIT-HFpEF (Prospective, Multicenter, Observational Study of Patients with Heart Failure with Preserved Ejection Fraction) study, a multicenter registry of patients hospitalized for acute decompensated HFpEF. We evaluated clinical factors including Clinical Frailty Scale (CFS) and blood test results collected before discharge. We calculated ePVS at discharge using the Duarte formula as ePVS (mL/g)=100×(1-hematocrit)/hemoglobin (g/dL). Patients were followed for median of 713 days (IQR 369-1088 days) to observe all-cause death. Patients were randomly assigned to the derivation and validation cohorts (2:1 ratio). Using the Bayesian Information Criterion (BIC), a stepwise model selection was executed on 51 clinical parameters, including CFS and ePVS, to identify independent predictors of mortality. A risk model for mortality was constructed based on these predictors and their coefficients. The accuracy of the risk score was validated using time-dependent ROC curves both in the derivation cohort (n=801) and in the validation cohort (n=411). Results During the follow-up period, all-cause death was observed in 315 patients (25.9%). Age, sex, CFS, NT-proBNP and ePVS were selected as independent predictors of all-cause death. The score shown in the Table was applied to each factor, and the sum of the scores was calculated as the risk score. The AUC of the risk score for predicting 1-year mortality in the derivation cohort was 0.79 (95% CI 0.74, 0.85). The optimal cut-off value of 6 provided a sensitivity and specificity of 74.9% and 74.4%, respectively. The AUC for 1-year mortality in the validation cohort was 0.70 (95% CI 0.63, 0.77). Conclusions The novel risk prediction model incorporating CFS and ePVS effectively predicted mortality in HFpEF. This model could be a valuable tool for risk stratification.Table