Novel Phenotyping for Acute Heart Failure—Unsupervised Machine Learning-Based Approach

Abstract

Acute heart failure (AHF) is a life-threatening, heterogeneous disease requiring urgent diagnosis and treatment. The clinical severity and medical procedures differ according to a complex interplay between the deterioration cause, underlying cardiac substrate, and comorbidities. This study aimed to analyze the natural phenotypic heterogeneity of the AHF population and evaluate the possibilities offered by clustering (unsupervised machine-learning technique) in a medical data assessment. We evaluated data from 381 AHF patients. Sixty-three clinical and biochemical features were assessed at the admission of the patients and were included in the analysis after the preprocessing. The K-medoids algorithm was implemented to create the clusters, and optimization, based on the Davies-Bouldin index, was used. The clustering was performed while blinded to the outcome. The outcome associations were evaluated using the Kaplan-Meier curves and Cox proportional-hazards regressions. The algorithm distinguished six clusters that differed significantly in 58 variables concerning i.e., etiology, clinical status, comorbidities, laboratory parameters and lifestyle factors. The clusters differed in terms of the one-year mortality (p = 0.002) and two-year mortality (p = 0.002). Using the clustering techniques, we extracted six phenotypes from AHF patients with distinct clinical characteristics and outcomes. Our results can be valuable for future trial constructions and customized treatment.

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“…The ML-based analysis is gaining popularity in cardiovascular research [ 19 ]. There were some magnificent attempts to implement ML in the HF population [ 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. Yagi tried to identify distinct phenotypes among AHF patients who experienced WRF [ 27 ].…”

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“…The overall in-hospital and 6-month mortality in the entire study sample were 152 (33%) and 180 (39%) respectively. The median of hospital stay was 11 [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] days. The in-hospital mortality from cluster 1 to cluster 3 was: 28% vs. 34% vs. 48%, p = 0.028, respectively, while 6-month mortality was: 34% vs. 41% vs. 52%, p = 0.056.…”

“…ML, particularly statistical clustering, is a technique designed to learn the inherent structure within a dataset [15]. Clustering is the unsupervised ML technique that segments the population into smaller subgroups, which are internally similar and distinct from the other ones.…”

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“…Acute HF represents a common yet complex presentation in emergency departments, requiring a high index of suspicion in order to differentiate it from other causes of suddenonset dyspnea, and also a prompt diagnosis of the underlying cardiovascular substrate, followed by adequate therapeutic management. Not only is the initial diagnosis essential, but also the risk stratification in the first hours after hospitalization, acute HF being characterized by high mortality rates (both in-hospital and after discharge) and frequent readmissions, with a generally poor prognosis [3][4][5].…”