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

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...

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Published in:Biomedicines 2022-06, Vol.10 (7), p.1514
Main Authors: Urban, Szymon, Błaziak, Mikołaj, Jura, Maksym, Iwanek, Gracjan, Zdanowicz, Agata, Guzik, Mateusz, Borkowski, Artur, Gajewski, Piotr, Biegus, Jan, Siennicka, Agnieszka, Pondel, Maciej, Berka, Petr, Ponikowski, Piotr, Zymliński, Robert
Format: Article
Language:English
Subjects:
acute heart failure
Algorithms
Blood pressure
Cluster analysis
Clustering
Comorbidity
Congestive heart failure
Creatinine
Diabetes
Ejection fraction
Etiology
Heart failure
Hypertension
Learning algorithms
Machine learning
Patients
Phenotypes
Phenotyping
Population
Similarity measures
Statistical analysis
Variance analysis
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Summary: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). 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.
ISSN:2227-9059
2227-9059
DOI:10.3390/biomedicines10071514