Machine learning approach to identify phenotypes in patients with ischaemic heart failure with reduced ejection fraction
Résumé
Aims Patients experiencing ischaemic heart failure with reduced ejection fraction (HFrEF) represent a diverse group. We hypothesize that machine learning clustering can help separate distinctive patient phenotypes, paving the way for personalized management. Methods and results A total of 8591 ischaemic HFrEF patients pooled from the EPHESUS and CAPRICORN trials (64 ± 12 years; 28% women) were included in this analysis. Clusters were identified using both clinical and biological variables. Association between clusters and the composite of (i) heart failure hospitalization or all‐cause death, (ii) cardiovascular (CV) hospitalization or all‐cause death, and (iii) major adverse CV events was assessed. The derived algorithm was applied in the COMMANDER‐HF trial ( n = 5022) for external validation. Five clinical distinctive clusters were identified: Cluster 1 ( n = 2161) with the older patients, higher prevalence of atrial fibrillation and previous CV events; Cluster 2 ( n = 1376) with the higher prevalence of older hypertensive women and smoking habit; Cluster 3 ( n = 1157) with the higher prevalence of diabetes and peripheral artery disease; Cluster 4 ( n = 2073) with relatively younger patients, mostly men and with the higher left ventricular ejection fraction; Cluster 5 ( n = 1824) with the younger patients and lower CV events burden. Cluster membership was efficiently predicted by a random forest algorithm. Clusters were significantly associated with outcomes in derivation and validation datasets, with Cluster 1 having the highest risk, and Cluster 4 the lowest. Mineralocorticoid receptor antagonist benefit on CV hospitalization or all‐cause death was magnified in clusters with the lowest risk of events (Clusters 2 and 4). Conclusion Clustering reveals distinct risk subgroups in the heterogeneous array of ischaemic HFrEF patients. This classification, accessible online, could enhance future outcome predictions for ischaemic HFrEF cases.
Mots clés
AF
atrial fibrillation ARB
angiotensin receptor blocker BMI
body mass index CCB
calcium channel blocker CV
cardiovascular CVD
cardiovascular death CVH
cardiovascular hospitalization DBP
diastolic blood pressure eGFR
estimated glomerular filtration rate HFH
heart failure hospitalization LVEF
left ventricular ejection fraction MACE
major adverse cardiovascular events MI
myocardial infarction PAD
peripheral artery disease PP
pulse pressure SBP
systolic blood pressure WBC
white blood cell Machine learning Clustering HFrEF Clinical outcomes Ischaemic heart disease
atrial fibrillation
ARB
angiotensin receptor blocker
BMI
body mass index
CCB
calcium channel blocker
CV
cardiovascular
CVD
cardiovascular death
CVH
cardiovascular hospitalization
DBP
diastolic blood pressure
eGFR
estimated glomerular filtration rate
HFH
heart failure hospitalization
LVEF
left ventricular ejection fraction
MACE
major adverse cardiovascular events
MI
myocardial infarction
PAD
peripheral artery disease
PP
pulse pressure
SBP
systolic blood pressure
WBC
white blood cell Machine learning
Clustering
HFrEF
Clinical outcomes
Ischaemic heart disease
Domaines
Sciences du Vivant [q-bio]
Fichier principal
European J of Heart Fail - 2024 - Monzo - Machine learning approach to identify phenotypes in patients with ischaemic heart.pdf (11.71 Mo)
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ejhf3547-sup-0001-supinfo.pdf (1.34 Mo)
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