Risk prediction in patients with heart failure with preserved ejection fraction: the LIFE-Preserved model

BACKGROUND AND AIMS: Heart failure (HF) with preserved ejection fraction (HFpEF) constitutes a heterogeneous disease with varying prognosis. Given the rising incidence of HFpEF, accurate risk prediction for these patients is needed to identify high-risk individuals, who may benefit the most from preventive treatments. The LIFE-Preserved model was developed and validated for the prediction of individual short-term and lifetime risk for HF hospitalization or cardiovascular (CV) death in patients with HFpEF. METHODS: LIFE-Preserved was derived in 20,332 patients aged 40-90 years with a left ventricular ejection fraction ≥50% from the Swedish HF Registry (SwedeHF). Cause- and sex-specific Cox models were derived to predict the risk of HF hospitalization or CV death using 14 routinely available predictors. Use of age as the timescale allowed for predictions beyond the maximum follow-up duration in the derivation data, adjusted for competing risks. External validation was performed in two trials (EMPEROR-Preserved, TOPCAT-Americas) and three registries (NHS England Secure Data Environment, Veterans Affairs, HF-Particles). Model performance was assessed by discrimination and calibration. RESULTS: During a median follow-up of 1.8 years (interquartile range 0.6-4.2, maximum 19 years), 9341 first HF hospitalizations or CV deaths (46%) were observed in SwedeHF. External validation included data from 28 062 patients with HFpEF (9930 [35%] first HF hospitalizations or CV deaths). Pooled C-statistics were 0.714 (95% confidence interval [CI] 0.652-0.775) in trials and 0.658 (95% CI 0.599-0.717) in registries, with adequate calibration in all external validation sources. Performance was similar in men and women. An interactive calculator of the LIFE-Preserved model has been made available here. CONCLUSIONS: The LIFE-Preserved model enables prediction of short-term and lifetime risk of HF hospitalization or CV death in patients with HFpEF. The model could serve as a tool to identify high-risk HFpEF patients, guiding clinical management and shared decision-making.