Identification of novel pheno-groups in heart failure with preserved ejection fraction using machine learning

• Published in: Heart (British Cardiac Society) Vol. 106; no. 5; pp. 342 - 349
• Main 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
• Format: Journal Article
• Language: English
• Published: England BMJ Publishing Group Ltd and British Cardiovascular Society 01.03.2020; BMJ Publishing Group LTD; BMJ Publishing Group
• Subjects: association; Bioengineering; Biomarkers; Blood pressure; Body mass index; Cardiac arrhythmia; Cardiology and Cardiovascular Disease; Cardiovascular disease; Cardiovascular System & Cardiology; Chronic obstructive pulmonary disease; Diabetes; dysfunction; ECG/electrocardiogram; Ejection fraction; Genotype & phenotype; Heart failure; Heart failure and cardiomyopathies; heart failure with preserved ejection fraction; Kardiologi och kardiovaskulära sjukdomar; Kidney diseases; Laboratories; Life Sciences; Machine learning; Medical prognosis; outcomes; Patients; predicting survival; Proteins; Proteomics; risk score; subgroups; therapies; Variables; Velocity; Sweden; ECG/electrocardiogram; heart failure with preserved ejection fraction
• ISSN: 1355-6037; 1468-201X; 1468-201X
• DOI: 10.1136/heartjnl-2019-315481

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 (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)).ConclusionsUsing machine learning we identified distinct HFpEF phenogroups with differential characteristics and outcomes, as well as differential levels of inflammatory and cardiovascular proteins.