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Clustering Based on Laboratory Data in Patients With Heart Failure Admitted to the Intensive Care Unit
Sepehr Nemati1, Babak Mohammadi2, Zahra Hooshanginezhad3
1Department of Cardiology, BooAli Hospital, Azad University of Medical Sciences, Tehran, Iran.
Journal of Clinical Laboratory Analysis
|October 5, 2024
Summary
Routine laboratory biomarkers identified two distinct heart failure patient phenotypes in the ICU. These phenotypes significantly impact mortality risk and can be distinguished by blood cell counts and electrolyte levels.
Area of Science:
- Cardiology
- Intensive Care Medicine
- Biomarker Discovery
Background:
- Heart failure (HF) presents a substantial healthcare burden.
- Identifying patient subgroups in intensive care units (ICUs) is crucial for tailored treatment.
- Routinely measured laboratory biomarkers can aid in subgroup identification.
Purpose of the Study:
- To identify distinct patient subgroups among those with heart failure admitted to the ICU.
- To characterize these subgroups using laboratory biomarkers.
- To assess the prognostic significance of identified subgroups.
Main Methods:
- Analysis of a large dataset (N=1176) of ICU-admitted heart failure patients.
- Clustering of patients based on laboratory biomarkers to identify phenotypes.
- Cluster profiling using binary logistic models for characterization.
Main Results:
- Two distinct patient clusters (N=679 and 497) were identified.
- Significant differences in mortality rates (7.4% vs. 21.9%) between clusters.
- Cluster 2 patients were older and had higher rates of chronic kidney disease; significant predictors included leukocyte count, MCV, RBC distribution width, HcT, lactic acid, BUN, potassium, magnesium, and sodium.
Conclusions:
- Two heart failure phenotypes were identified in ICU patients using laboratory data.
- These phenotypes hold prognostic importance regarding mortality.
- Differentiation is achievable through blood cell counts, kidney function, and serum electrolytes.
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