Identification of novel pheno-groups in heart failure with preserved ejection fraction using machine learning. Issue 5 (7th January 2020)

• Record Type: Journal Article
• Authors: Hedman, Åsa K; Hage, Camilla; Sharma, Anil; Brosnan, Mary Julia; Buckbinder, Leonard; Gan, Li-Ming; Shah, Sanjiv J; Linde, Cecilia M; Donal, Erwan; Daubert, Jean-Claude; Mälarstig, Anders; Ziemek, Daniel; Lund, Lars
• Is Part Of: Heart. Volume 106:Issue 5(2020)
• Page Start: 342
• Page End: 349
• Publication Date: 2020-01-07
• Subjects: heart failure with preserved ejection fraction -- ECG/electrocardiogram; Heart -- Diseases -- Treatment -- Periodicals; Cardiology -- Periodicals; 616.12
• DOI: 10.1136/heartjnl-2019-315481
• Languages: English
• ISSNs: 1355-6037

Abstract

Abstract : 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.