%0 Journal Article %T Identification of novel pheno-groups in heart failure with preserved ejection fraction using machine learning %+ Karolinska Institutet [Stockholm] %+ Pfizer %+ Göteborgs Universitet (GU) %+ Northwestern University [Evanston] %+ Centre d'Investigation Clinique [Rennes] (CIC) %+ CHU Pontchaillou [Rennes] %+ Laboratoire Traitement du Signal et de l'Image (LTSI) %A Hedman, Åsa K %A Hage, Camilla %A Sharma, Anil %A Brosnan, Mary Julia %A Buckbinder, Leonard %A Gan, Li-Ming %A Shah, Sanjiv J %A Linde, Cecilia M %A Donal, Erwan %A Daubert, Jean-Claude %A Mälarstig, Anders %A Ziemek, Daniel %A Lund, Lars %Z Medtronic %Z 20150557, Hjärt-Lungfonden %Z 20140220, Stockholms Läns Landsting %Z 523-2014-2336, Vetenskapsrådet %< avec comité de lecture %@ 1355-6037 %J Heart %I BMJ Publishing Group %V 106 %N 5 %P 342-349 %8 2020-03 %D 2020 %R 10.1136/heartjnl-2019-315481 %M 31911501 %K ECG/electrocardiogram %K heart failure with preserved ejection fraction %Z Life Sciences [q-bio]/BioengineeringJournal articles %X Objective - Heart 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. Methods - We 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. Results - We identified six phenogroups, for which significant differences in the prevalence of concomitant atrial fibrillation (AF), anaemia and kidney disease were observed (p<0.05). Fifteen out of 86 plasma proteins differed between phenogroups (false discovery rate, FDR<0.05), including biomarkers of HF, AF and kidney function. The composite end point was significantly different between phenogroups (log-rank p<0.001), at short-term (100 days), mid-term (18 months) and longer-term follow-up (1000 days). Phenogroup 2 was older, with poorer diastolic and right ventricular function and higher burden of risk factors as AF (85%), hypertension (83%) and chronic obstructive pulmonary disease (30%). In this group a third experienced the primary outcome to 100 days, and two-thirds to 18 months (HR (95% CI) versus phenogroups 1, 3, 4, 5, 6: 1.5 (0.8-2.9); 5.7 (2.6-12.8); 2.9 (1.5-5.6); 2.7 (1.6-4.6); 2.1 (1.2-3.9)). Conclusions - Using machine learning we identified distinct HFpEF phenogroups with differential characteristics and outcomes, as well as differential levels of inflammatory and cardiovascular proteins. %G English %L hal-02440635 %U https://univ-rennes.hal.science/hal-02440635 %~ INSERM %~ UNIV-RENNES1 %~ LTSI %~ HL %~ CIC %~ CIC203 %~ STATS-UR1 %~ UR1-UFR-SVE %~ UR1-HAL %~ UR1-MATH-STIC %~ UR1-SDV %~ TEST-UR-CSS %~ UNIV-RENNES %~ UR1-MATH-NUM %~ UR1-BIO-SA