aceEnhanced Early Risk Stratification in Cardiogenic Shock Using Machine Learning: A Hierarchical Clustering Analysis
Background Cardiogenic shock (CS) exhibits marked clinical heterogeneity, limiting the performance of conventional risk stratification tools like the Society for Cardiovascular Angiography and Interventions (SCAI) staging system. Machine learning (ML) has identified reproducible CS phenotypes that improve risk prediction, but their temporal consistency across the progression of shock remains incompletely described. Objectives The purpose of this study was to evaluate the temporal evolution of ML-derived CS pheno…
Clustering of clinical variables found three stable patient phenotypes in cardiogenic shock that separated mortality risk within each SCAI stage during the first three ICU days.
The study was a retrospective registry analysis, and the authors state that prospective validation is required before clinical implementation.
Evidence
- Peer-reviewedJACC: Advances2026-10-13
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Truvace Impact Record TRV-2026-2037, v1: “Enhanced Early Risk Stratification in Cardiogenic Shock Using Machine Learning: A Hierarchical Clustering Analysis.” Truvace, 2026-10-20. /record/TRV-2026-2037 (accessed at citation time). sha256 e760b9bccaa86995…
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