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Related Experiment Videos

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

Marat Fudim1,2, Vanessa Van Empel3, Tobias Zehnder4

  • 1Department of Medicine, Duke University Medical Center Heart Center, Durham, NC, USA.

ESC Heart Failure
|April 9, 2026
PubMed
Summary

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This summary is machine-generated.

Machine learning models accurately predict mortality and heart failure hospitalizations in patients with heart failure with preserved ejection fraction (HFpEF). These models outperform existing scores, aiding personalized care and clinical trial recruitment.

Area of Science:

  • Cardiology
  • Medical Informatics
  • Machine Learning

Background:

  • Heart failure with preserved ejection fraction (HFpEF) is a complex condition with significant morbidity and mortality.
  • Accurate risk stratification is crucial for effective patient management and the development of targeted therapies.

Purpose of the Study:

  • To develop and validate machine learning-based prognostic models for predicting all-cause mortality and heart failure (HF) hospitalization in patients with HFpEF.
  • To compare the performance of these novel models against established risk scores like PREDICT-HFpEF and MAGGIC.

Main Methods:

  • The CONFIDENT study utilized data from 1208 HFpEF patients across three European and US centers, with a follow-up of at least two years.
  • Machine learning models were developed using routinely collected electronic health records, lab tests, echocardiography, and electrocardiography data.
Keywords:
HFpEFheart failuremachine learningmortalityreal world datarisk stratification

Related Experiment Videos

  • Model performance was assessed using the C-index and validated in an external cohort, comparing it against PREDICT-HFpEF and MAGGIC scores.

    • Main Results:

    • The machine learning model for all-cause mortality demonstrated good discrimination (C-index: 0.72 in validation cohort), outperforming the PREDICT-HFpEF score.
    • The prognostic model for HF hospitalization also showed superior performance compared to existing risk scores, including MAGGIC + natriuretic peptide.
    • The models achieved reliable predictive accuracy using readily available clinical data.

    Conclusions:

    • The developed machine learning models provide reliable risk prediction for mortality and HF hospitalization in HFpEF patients.
    • These models have the potential to enhance personalized patient care and optimize recruitment strategies for clinical trials in HFpEF.
    • Routine data integration into machine learning models offers a promising avenue for improving outcomes in HFpEF management.