CSWG-SCAI Stages Combined With Machine Learning-Based Phenotypes for Serial Risk Stratification in Cardiogenic Shock.

doi:10.1016/j.jchf.2025.102611

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Article

CSWG-SCAI Stages Combined With Machine Learning-Based Phenotypes for Serial Risk Stratification in Cardiogenic Shock.

JACC. Heart failure - 1 Oct 2025

Zweck Elric, Ton Van-Khue, Kanwar Manreet, Li Song, Li Borui, Sinha Shashank S, Hernandez-Montfort Jaime, Garan A Reshad, Abraham Jacob, Blumer Vanessa, Kataria Rachna, Polzin Amin, Burkhoff Daniel, Kapur Navin K

Abstract excerpt

BACKGROUND: Cardiogenic shock (CS) severity can be defined by the SCAI (Society for Cardiovascular Angiography and Interventions) stages (A to E), or machine learning-based phenotypes (I: noncongested, II: cardiorenal, III: cardiometabolic). OBJECTIVES: This study aims to evaluate sequential applicability and prognostic relevance of combining SCAI stages and ML-based phenotypes for risk stratification of patients...

Read the complete abstract on PubMed

Topics

Humans
Shock, Cardiogenic
Machine Learning
Male
Female
Risk Assessment

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Retrospective Studies

Hospital Mortality

Aged

Phenotype

Registries

Prognosis

Middle Aged

Severity of Illness Index

Source

Open in PubMed

Identifiers

PMID: 40840206
DOI: 10.1016/j.jchf.2025.102611

Genes

SCAI
CS

Provenance

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