Characteristics, prognosis and treatment response in distinct phenogroups of heart failure with preserved ejection fraction.

Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome. We aimed to derive HFpEF phenotype-based groups based on clinical features using machine learning, and to compare clinical characteristics, outcomes and treatment response across the phenogroups. We applied model-based clustering to 11 clinical and laboratory variables collected in 970 HFpEF patients. An additional 290 HFpEF patients was enrolled as a validation cohort. During 5-year follow-up, all-cause mortality was used as the primary endpoints, and composite endpoints (all-cause mortality or HF hospitalization) were set as the secondary endpoint. We identified three phenogroups, for which significant differences in the age and gender, the prevalence of concomitant ischaemic heart disease, atrial fibrillation and type 2 diabetes mellitus, the burden of B-type natriuretic peptide level and HF symptoms. Patients with phenogroup 3 had higher all-cause mortality or composite endpoints, whereas patients in phenogroup 1 had less adverse events after 5-year follow-up. Moreover, it was indicated that beta-blockers or angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker (ACEI/ARB) use was associated with a lower risk of all-cause mortality or composite endpoints in phenogroup 3, instead of the other phenogroups. This HFpEF phenogroup classification, including its ability to stratify risk, was successfully replicated in a prospective validation cohort. Machine-learning based clustering strategy is used to identify three distinct phenogroups of HFpEF that are characterized by significant differences in comorbidity burden, underlying cardiac abnormalities, and long-term prognosis. Beta-blockers or ACEI/ARB therapy is associated with a lower risk of adverse events in specific phenogroup.

Gu J ，Pan JA ，Lin H ，Zhang JF ，Wang CQ ... -  《-》

Phenomapping of patients with heart failure with preserved ejection fraction using machine learning-based unsupervised cluster analysis.

To identify distinct phenotypic subgroups in a highly-dimensional, mixed-data cohort of individuals with heart failure (HF) with preserved ejection fraction (HFpEF) using unsupervised clustering analysis. The study included all Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) participants from the Americas (n = 1767). In the subset of participants with available echocardiographic data (derivation cohort, n = 654), we characterized three mutually exclusive phenogroups of HFpEF participants using penalized finite mixture model-based clustering analysis on 61 mixed-data phenotypic variables. Phenogroup 1 had higher burden of co-morbidities, natriuretic peptides, and abnormalities in left ventricular structure and function; phenogroup 2 had lower prevalence of cardiovascular and non-cardiac co-morbidities but higher burden of diastolic dysfunction; and phenogroup 3 had lower natriuretic peptide levels, intermediate co-morbidity burden, and the most favourable diastolic function profile. In adjusted Cox models, participants in phenogroup 1 (vs. phenogroup 3) had significantly higher risk for all adverse clinical events including the primary composite endpoint, all-cause mortality, and HF hospitalization. Phenogroup 2 (vs. phenogroup 3) was significantly associated with higher risk of HF hospitalization but a lower risk of atherosclerotic event (myocardial infarction, stroke, or cardiovascular death), and comparable risk of mortality. Similar patterns of association were also observed in the non-echocardiographic TOPCAT cohort (internal validation cohort, n = 1113) and an external cohort of patients with HFpEF [Phosphodiesterase-5 Inhibition to Improve Clinical Status and Exercise Capacity in Heart Failure with Preserved Ejection Fraction (RELAX) trial cohort, n = 198], with the highest risk of adverse outcome noted in phenogroup 1 participants. Machine learning-based cluster analysis can identify phenogroups of patients with HFpEF with distinct clinical characteristics and long-term outcomes.

Segar MW ，Patel KV ，Ayers C ，Basit M ，Tang WHW ，Willett D ，Berry J ，Grodin JL ，Pandey A ... -  《-》

Heart failure with preserved ejection fraction phenogroup classification using machine learning.

Heart failure (HF) with preserved ejection fraction (HFpEF) is a complex syndrome with a poor prognosis. Phenotyping is required to identify subtype-dependent treatment strategies. Phenotypes of Japanese HFpEF patients are not fully elucidated, whose obesity is much less than Western patients. This study aimed to reveal model-based phenomapping using unsupervised machine learning (ML) for HFpEF in Japanese patients. We studied 365 patients with HFpEF (left ventricular ejection fraction >50%) as a derivation cohort from the Nara Registry and Analyses for Heart Failure (NARA-HF), which registered patients with hospitalization by acute decompensated HF. We used unsupervised ML with a variational Bayesian-Gaussian mixture model (VBGMM) with common clinical variables. We also performed hierarchical clustering on the derivation cohort. We adopted 230 patients in the Japanese Heart Failure Syndrome with Preserved Ejection Fraction Registry as the validation cohort for VBGMM. The primary endpoint was defined as all-cause death and HF readmission within 5 years. Supervised ML was performed on the composite cohort of derivation and validation. The optimal number of clusters was three because of the probable distribution of VBGMM and the minimum Bayesian information criterion, and we stratified HFpEF into three phenogroups. Phenogroup 1 (n = 125) was older (mean age 78.9 ± 9.1 years) and predominantly male (57.6%), with the worst kidney function (mean estimated glomerular filtration rate 28.5 ± 9.7 mL/min/1.73 m2 ) and a high incidence of atherosclerotic factor. Phenogroup 2 (n = 200) had older individuals (mean age 78.8 ± 9.7 years), the lowest body mass index (BMI; 22.78 ± 3.94), and the highest incidence of women (57.5%) and atrial fibrillation (56.5%). Phenogroup 3 (n = 40) was the youngest (mean age 63.5 ± 11.2) and predominantly male (63.5 ± 11.2), with the highest BMI (27.46 ± 5.85) and a high incidence of left ventricular hypertrophy. We characterized these three phenogroups as atherosclerosis and chronic kidney disease, atrial fibrillation, and younger and left ventricular hypertrophy groups, respectively. At the primary endpoint, Phenogroup 1 demonstrated the worst prognosis (Phenogroups 1-3: 72.0% vs. 58.5% vs. 45%, P = 0.0036). We also successfully classified a derivation cohort into three similar phenogroups using VBGMM. Hierarchical and supervised clustering successfully showed the reproducibility of the three phenogroups. ML could successfully stratify Japanese HFpEF patients into three phenogroups (atherosclerosis and chronic kidney disease, atrial fibrillation, and younger and left ventricular hypertrophy groups).

Kyodo A ，Kanaoka K ，Keshi A ，Nogi M ，Nogi K ，Ishihara S ，Kamon D ，Hashimoto Y ，Nakada Y ，Ueda T ，Seno A ，Nishida T ，Onoue K ，Soeda T ，Kawakami R ，Watanabe M ，Nagai T ，Anzai T ，Saito Y ... -  《ESC Heart Failure》

Clinical Phenogroups in Heart Failure With Preserved Ejection Fraction: Detailed Phenotypes, Prognosis, and Response to Spironolactone.

This study sought to assess if clinical phenogroups differ in comprehensive biomarker profiles, cardiac and arterial structure/function, and responses to spironolactone therapy. Previous studies identified distinct subgroups (phenogroups) of patients with heart failure with preserved ejection fraction (HFpEF). Among TOPCAT (Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist Trial) participants, we performed latent-class analysis to identify HFpEF phenogroups based on standard clinical features and assessed differences in multiple biomarkers measured from frozen plasma; cardiac and arterial structure/function measured with echocardiography and arterial tonometry; prognosis; and response to spironolactone. Three HFpEF phenogroups were identified. Phenogroup 1 (n = 1,214) exhibited younger age, higher prevalence of smoking, preserved functional class, and the least evidence of left ventricular (LV) hypertrophy and arterial stiffness. Phenogroup 2 (n = 1,329) was older, with normotrophic concentric LV remodeling, atrial fibrillation, left atrial enlargement, large-artery stiffening, and biomarkers of innate immunity and vascular calcification. Phenogroup 3 (n = 899) demonstrated more functional impairment, obesity, diabetes, chronic kidney disease, concentric LV hypertrophy, high renin, and biomarkers of tumor necrosis factor-alpha-mediated inflammation, liver fibrosis, and tissue remodeling. Compared with phenogroup 1, phenogroup 3 exhibited the highest risk of the primary endpoint of cardiovascular death, heart failure hospitalization, or aborted cardiac arrest (hazard ratio [HR]: 3.44; 95% confidence interval [CI]: 2.79 to 4.24); phenogroups 2 and 3 demonstrated similar all-cause mortality (phenotype 2 HR: 2.36; 95% CI: 1.89 to 2.95; phenotype 3 HR: 2.26, 95% CI: 1.77 to 2.87). Spironolactone randomized therapy was associated with a more pronounced reduction in the risk of the primary endpoint in phenogroup 3 (HR: 0.75; 95% CI: 0.59 to 0.95; p for interaction = 0.016). Results were similar after excluding participants from Eastern Europe. We identified important differences in circulating biomarkers, cardiac/arterial characteristics, prognosis, and response to spironolactone across clinical HFpEF phenogroups. These findings suggest distinct underlying mechanisms across clinically identifiable phenogroups of HFpEF that may benefit from different targeted interventions.

Cohen JB ，Schrauben SJ ，Zhao L ，Basso MD ，Cvijic ME ，Li Z ，Yarde M ，Wang Z ，Bhattacharya PT ，Chirinos DA ，Prenner S ，Zamani P ，Seiffert DA ，Car BD ，Gordon DA ，Margulies K ，Cappola T ，Chirinos JA ... -  《-》

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

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

Hedman ÅK ，Hage C ，Sharma A ，Brosnan MJ ，Buckbinder L ，Gan LM ，Shah SJ ，Linde CM ，Donal E ，Daubert JC ，Mälarstig A ，Ziemek D ，Lund L ... -  《-》