Peer-Reviewed Publication
ESC Heart Fail2026April 7, 2026Journal Article
CONFIDENT-HFpEF: A Machine Learning-Based Risk Stratification for Mortality and Hospitalization Using Multimodal Real-World Data.
Marat Fudim1,2, Vanessa Van Empel3, Tobias Zehnder4, Benoit Sauty4, Christian Esposito4, Félix Balazard4, Imke Mayer4, Mohammad Hallal4, Nicolas Loiseau4, Jerremy Weerts3, Manesh Patel1,2, Suresh Balu5,6, Bradley Hintze5,6, Francisco Torres4, Mariann Micsinai7, Marzia Rigolli7, Paul Kessler7, Maxime Touzot4, Lars H Lund8, Aruna Pradhan7, Javed Butler9
1Department of Medicine, Duke University Medical Center Heart Center, Durham, NC, USA.
2Duke Clinical Research Institute, Durham, NC, USA.
3Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM) , Maastricht University Medical Centre (MUMC+), Maastricht, The Netherlands.
4Owkin France, Paris, France.
5Duke Institute for Health Innovation, NC, USA.
6Duke University, School of Medicine, NC, USA.
7Bristol-Myers Squibb, Princeton, NJ, US.
8Division of Cardiology, Department of Medicine, Karolinska Institutet, Stockholm, Sweden.
9Baylor Scott & White Research Institute, Dallas, TX, USA; Department of Medicine, University of Mississippi, Jackson, MS, USA.
Abstract
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 wi…
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