Key result
CONFIDENT machine learning model outperforms PREDICT-HFpEF score in predicting all-cause mortality.
Why the study?
Heart failure with preserved ejection fraction is heterogeneous with high morbidity and mortality, making accurate risk stratification important for advancing drug development and clinical care.
Does the CONFIDENT machine learning model improve prediction of all-cause mortality and HF hospitalization compared to existing risk scores in patients with HFpEF?
Observational (n=1,208)
Yes
Does the CONFIDENT machine learning model improve prediction of all-cause mortality and HF hospitalization compared to existing risk scores in patients with HFpEF?
Effect estimate: C-index 0.72 (95% CI 0.65-0.78)
Absolute Event Rate: 0.72% vs 0.67%
p-value: p=0.036
A machine learning-based prognostic model using multimodal real-world data outperformed existing risk scores in predicting mortality and HF hospitalization in patients with HFpEF.
Aims 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. Methods and Results 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 (C-index: 0.66 [95% CI 0.54-0.72], p-value = 0.006; 0.65, [95% CI 0.55-0.72], p-value < 0.001 and 0.67 [95% CI 0.59-0.73], p-value = 0.036, respectively). Similar results were observed when compared to the Meta-Analysis Global Group In Chronic Heart Failure Risk Score (MAGGIC). The HF hospitalization model also outperformed both comparators, including MAGGIC + natriuretic peptide. Conclusion 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.
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Fudim et al. (2026) conducted an observational in Heart failure with preserved ejection fraction (HFpEF) (n=1,208). CONFIDENT machine learning-based prognostic models vs. PREDICT-HFpEF score and MAGGIC score was evaluated on All-cause mortality (C-index 0.72, 95% CI 0.65-0.78, p=0.036). The CONFIDENT machine learning prognostic model for all-cause mortality outperformed the PREDICT-HFpEF score in the validation cohort (C-index 0.72 vs 0.67; P=0.036).
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