1. Identification of novel pheno-groups in heart failure with preserved ejection fraction using machine learning
- Author
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Anders Mälarstig, Cecilia Linde, Åsa K Hedman, Anil Sharma, Leonard Buckbinder, Sanjiv J. Shah, Erwan Donal, Camilla Hage, Jean-Claude Daubert, M J Brosnan, Li-Ming Gan, Lars Lund, Daniel Ziemek, Karolinska Institutet [Stockholm], Pfizer, Göteborgs Universitet (GU), Northwestern University [Evanston], Centre d'Investigation Clinique [Rennes] (CIC), Université de Rennes (UR)-Hôpital Pontchaillou-Institut National de la Santé et de la Recherche Médicale (INSERM), CHU Pontchaillou [Rennes], Laboratoire Traitement du Signal et de l'Image (LTSI), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM), Medtronic, 20150557, Hjärt-Lungfonden, 20140220, Stockholms Läns Landsting, 523-2014-2336, Vetenskapsrådet, Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Hôpital Pontchaillou-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Santé et de la Recherche Médicale (INSERM), Jonchère, Laurent, Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Rennes 1 (UR1), and Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)
- Subjects
Male ,heart failure with preserved ejection fraction ,030204 cardiovascular system & hematology ,Machine learning ,computer.software_genre ,Cohort Studies ,Machine Learning ,03 medical and health sciences ,0302 clinical medicine ,medicine ,Humans ,ECG/electrocardiogram ,030212 general & internal medicine ,Aged ,Aged, 80 and over ,Heart Failure ,[SDV.IB] Life Sciences [q-bio]/Bioengineering ,End point ,business.industry ,Stroke Volume ,Atrial fibrillation ,medicine.disease ,3. Good health ,Phenotype ,Echocardiography ,Concomitant ,Heart failure ,Female ,[SDV.IB]Life Sciences [q-bio]/Bioengineering ,Artificial intelligence ,Cardiology and Cardiovascular Medicine ,business ,Heart failure with preserved ejection fraction ,computer ,Kidney disease ,Cohort study - Abstract
ObjectiveHeart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome. We aimed to derive HFpEF phenotype-based groups ('phenogroups') based on clinical and echocardiogram data using machine learning, and to compare clinical characteristics, proteomics and outcomes across the phenogroups.MethodsWe applied model-based clustering to 32 echocardiogram and 11 clinical and laboratory variables collected in stable condition from 320 HFpEF outpatients in the Karolinska-Rennes cohort study (56% female, median 78 years (IQR: 71–83)). Baseline proteomics and the composite end point of all-cause mortality or heart failure (HF) hospitalisation were used in secondary analyses.ResultsWe identified six phenogroups, for which significant differences in the prevalence of concomitant atrial fibrillation (AF), anaemia and kidney disease were observed (pConclusionsUsing machine learning we identified distinct HFpEF phenogroups with differential characteristics and outcomes, as well as differential levels of inflammatory and cardiovascular proteins.
- Published
- 2020