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

Abstract

Aims 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 resultsThis 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. Conclusion 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: ClinicalTrials.gov Identifier: NCT02112227

Aetiological classification and prognosis in patients with heart failure with preserved ejection fraction

“…In line with previous studies, our data however indicate that prognosis of HF patients remains poor, 26 , 27 independent of LVEF. 12 , 28 Moreover, our data show that the prognosis is relatively independent of the underlying causes of HF when used as sole classifier. However, the clustering analysis indicated that the phenogroup enriched with idiopathic HFpEF was associated with a significantly better prognosis as compared with the two other phenogroups enriched with secondary HFpEF patients.…”

“… 4 , 6 , 7 , 17 The heterogeneous nature of HFpEF has been previously recognized in different cohorts 2 and in studies that used unsupervised machine learning techniques to propose classification of HFpEF patients (so‐called phenogroups) with distinct clinical characteristics, outcomes, and responses to drugs. 10 , 11 , 12 , 18 In our study, we initially used a simpler and more pragmatic classification of patients into 12 aetiological groups based on the medical assessment of prevailing cause of HFpEF. The aetiological classification was inspired by recent guidelines that recognize potential specific aetiologies underlying HFpEF or HFpEF‐like syndromes.…”

“…Their application using main baseline characteristics and co‐morbidities has suggested that HFpEF encompasses different phenogroups of patients who likely share similar pathophysiological profiles. 10 , 11 , 12 However, whether an aetiological classification could further improve this classification of HFpEF patients has not been clearly shown.…”

Clinical Phenogroups Are More Effective Than Left Ventricular Ejection Fraction Categories in Stratifying Heart Failure Outcomes

“…The performance of this phenotype-based classification scheme relative to validated risk models in HF 35,36 is unknown and the subject of further investigation. We have demonstrated in a prior work that simple clinical risk prediction indices can predict clinical outcomes with similar or better performance than complex and validated risk prediction scores in HF, 37,38 and it is possible that this simple phenotype-based classification scheme may also compare favourably.…”

Hierarchical management of chronic heart failure: a perspective based on the latent structure of comorbidities

“…Understanding the clustering of comorbidities in patients with HF and its impact on major adverse outcome events can lay the foundation for clinically personalized treatment programmes. 8 HF is currently classified by left ventricular ejection fraction (LVEF), which does not reflect the true nature of HF as a complex heterogeneous syndrome that includes cardiovascular and non‐cardiovascular factors related to its pathophysiology and prognosis, 9 , 10 , 11 especially when there may be a two‐way causality between HF and comorbidities, and there may be an interaction between LVEF and comorbidities.…”