Novel Phenotyping for Acute Heart Failure-Unsupervised Machine Learning-Based Approach.
Szymon UrbanMikołaj BłaziakMaksym JuraGracjan IwanekAgata ZdanowiczMateusz GuzikArtur BorkowskiPiotr GajewskiJan BiegusAgnieszka SiennickaMaciej PondelPetr BerkaPiotr PonikowskiRobert ZymlińskiPublished in: Biomedicines (2022)
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.
Keyphrases
- machine learning
- acute heart failure
- end stage renal disease
- big data
- chronic kidney disease
- single cell
- newly diagnosed
- ejection fraction
- heart failure
- healthcare
- physical activity
- peritoneal dialysis
- study protocol
- prognostic factors
- cardiovascular disease
- randomized controlled trial
- rna seq
- coronary artery disease
- type diabetes
- clinical trial
- left ventricular
- risk factors
- patient reported outcomes
- cardiovascular events
- insulin resistance
- atrial fibrillation
- patient reported
- amino acid
- phase ii