CONFIDENT-HFpEF: A Machine Learning-Based Risk Stratification for Mortality and Hospitalization Using Multimodal Real-World Data

Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous condition with high morbidity and mortality. Accurate risk stratification is important for advancing drug development and improving clinical care.

CONFIDENT is an observational, multi-cohort study across three centers in Europe and the US. Patients with HFpEF, according to the HFA-PEFF criteria with ≥ 2 years of follow-up, were included from 2013 to 2022. Data include electronic health records, lab tests, echocardiography, and electrocardiography. We developed machine learning-based prognostic models to predict all-cause mortality and heart failure (HF) hospitalization.

Model performance was compared to validated risk score and validated in an external cohort. A total of 1208 patients were included in the study. The mean age was 72±12 and the mean BMI 32±9 kg/m2. The 2-year risk of HF hospitalization and all-cause mortality ranged from 13 to 44% and 9 to 19%, respectively. The all-cause mortality prognostic model achieved fair discrimination with a C-index of 0.68 [95% CI 0.62-0.74], and 0.71 [95% CI 0.64-0.78] in the training cohorts, and a good discrimination of 0.72 [95% CI 0.65-0.78] in the validation cohort, but performed better than the PREDICT-HFpEF score.

CONFIDENT prognostic models for all-cause mortality and HF hospitalization using routinely collected variables can reliably predict outcomes and potentially facilitate personalized care and trial recruitment strategies in HFpEF.