Abstract

Background: Biomarker-driven prognostic models incorporating NT-proBNP and hs-cTnT in HFpEF are lacking. Aims: To generate a biomarker-driven prognostic tool for patients with chronic HFpEF enrolled in EMPEROR-Preserved. Methods: Multivariable Cox regression models were created for (i) the primary composite outcome of HF hospitalization or cardiovascular death (ii) all-cause death (iii) cardiovascular death and (iv) HF hospitalization. PARAGON-HF was used as a validation cohort. Results: NT-proBNP and hs-cTnT were the dominant predictors of the primary outcome, and in addition, a shorter time since last hospitalization, NYHA class III or IV, history of COPD, insulin-treated diabetes, low hemoglobin, and a longer time since HF diagnosis were key predictors (8 variables, all P<0.001). The consequent primary outcome risk score discriminated well (c-statistic=0.75) with patients in the top 10th of risk having an event rate >22x higher than those in the bottom 10th. A model for HF hospitalization alone had even better discrimination (c=0.79). Empagliflozin reduced the risk of cardiovascular death or hospitalization for heart failure in patients across all risk levels. NT-proBNP and hs-cTnT were also the dominant predictors of all-cause and cardiovascular mortality followed by history of COPD, low albumin, older age, LVEF ≥50%, NYHA class III or IV and insulin-treated diabetes (8 variables, all P<0.001). The mortality risk model had similar discrimination for all-cause and cardiovascular mortality (c-statistic=0.72 for both). External validation provided c-statistics of 0.71, 0.71, 0.72, and 0.72 for the primary outcome, HF hospitalization alone, all-cause death, and cardiovascular death, respectively. Conclusions: The combination of NT-proBNP and hs-cTnT along with a few readily available clinical variables provides effective risk discrimination both for morbidity and mortality in patients with HFpEF. A predictive toolkit facilitates the ready implementation of these risk models in routine clinical practice.