Observational studyESC heart failure2026
CONFIDENT-HFpEF: a machine learning-based risk stratification for mortality and hospitalization using multimodal real-world data.
Observational study in ESC heart failure, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Electrocardiographic Alterations Combined with Hematological, Biochemical, and Metabolic Profiles Predict Prognosis in Kawasaki Disease.Journal of cardiovascular development and disease · 2026Article
Corrections and comments
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Authors and funding
21 authors.
Funding
Abstract
introductionHeart 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.
methodsCONFIDENT is an observational, multi-cohort study across three centres in Europe and the USA. 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 the validated risk score and validated in an external cohort.
resultsA total of 1208 patients were included in the study. The mean age was 72 ± 12, and the mean body mass index was 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 = .006; 0.65, [95% CI 0.55-0.72], P-value < .001 and 0.67 [95% CI 0.59-0.73], P-value = .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.
conclusionCONFIDENT 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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