Key result
K-means clustering of ECG and clinical data identifies three distinct risk groups for post-COVID-19 CV complications.
Why the study?
Does K-means clustering of multimodal clinical and ECG data identify distinct risk profiles for long-term cardiovascular complications in COVID-19 patients?
Does K-means clustering of multimodal clinical and ECG data identify distinct risk profiles for long-term cardiovascular complications in COVID-19 patients?
Answers are grounded in this paper and cite their sources.
Machine learning clustering of clinical and ECG data can successfully stratify COVID-19 survivors into distinct risk categories for long-term cardiovascular complications.
This study employs K-means clustering to analyze long-term cardiovascular complications in COVID-19 patients through ECG parameters, demographics, comorbidities, and hospitalization data. Three distinct clusters emerged: Cluster 0 (moderate heart rate variability/ICU admissions), Cluster 1 (lower variability/admissions), and Cluster 2 (higher variability/admissions, indicating elevated risk). Bootstrap validation confirmed model robustness, supported by high silhouette scores and consistent cluster labels. The novel integration of multimodal data with machine learning revealed hidden cardiovascular outcome patterns, demonstrating clinical utility for risk stratification. Findings underscore the value of clustering techniques in personalizing post-COVID care and optimizing resource allocation for high-risk survivors.
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Sadegh‐Zadeh et al. (2025) studied this question. K-means clustering identified three distinct risk groups for long-term cardiovascular complications post-COVID-19, with Cluster 2 showing highest risk based on ECG and clinical data.
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