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Clinical phenogroups are more effective than left ventricular ejection fraction categories in stratifying heart failure outcomes


Clinical phenogroups are more effective than left ventricular ejection fraction categories in stratifying heart failure outcomes Thumbnail



Heart failure (HF) guidelines place patients into 3 discrete groups according to left ventricular ejection fraction (LVEF): reduced (<40%), mid-range (40–49%), and preserved LVEF (=50%). We assessed whether clinical phenogroups offer better prognostication than LVEF.

Methods and results
This was a sub-study of the Patient-Centered Care Transitions in HF trial. We analysed baseline characteristics of hospitalized patients in whom LVEF was recorded. We used unsupervised machine learning to identify clinical phenogroups and, thereafter, determined associations between phenogroups and outcomes. Primary outcome was the composite of all-cause death or rehospitalization at 6 and 12 months. Secondary outcome was the composite cardiovascular death or HF rehospitalization at 6 and 12 months. Cluster analysis of 1693 patients revealed six discrete phenogroups, each characterized by a predominant comorbidity: coronary heart disease, valvular heart disease, atrial fibrillation (AF), sleep apnoea, chronic obstructive pulmonary disease (COPD), or few comorbidities. Phenogroups were LVEF independent, with each phenogroup encompassing a wide range of LVEFs. For the primary composite outcome at 6 months, the hazard ratios (HRs) for phenogroups ranged from 1.25 [95% confidence interval (CI) 1.00–1.58 for AF] to 2.04 (95% CI 1.62–2.57 for COPD) (log-rank P < 0.001); and at 12 months, the HRs for phenogroups ranged from 1.15 (95% CI 0.94–1.41 for AF) to 1.87 (95% 1.52–3.20 for COPD) (P < 0.002). LVEF-based classifications did not separate patients into different risk categories for the primary outcomes at 6 months (P = 0.69) and 12 months (P = 0.30). Phenogroups also stratified risk of the secondary composite outcome at 6 and 12 months more effectively than LVEF.

Among patients hospitalized for HF, clinical phenotypes generated by unsupervised machine learning provided greater prognostic information for a composite of clinical endpoints at 6 and 12 months compared with LVEF-based categories.

Trial Registration: Identifier: NCT02112227

Acceptance Date May 2, 2021
Publication Date May 24, 2021
Journal ESC Heart Failure
Print ISSN 2055-5822
Publisher Wiley Open Access
Keywords Heart failure, Ejection fraction, Comorbidities, Characteristics, HFpEF, HFmrEF, Phenogroups, Prognosis, Mortality, Survival, Aetiology, Machine Learning, Clinical Studies, Risk Factors
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