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

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 with CS. Methods: The authors retrospectively applied both classification systems at 6- to 12-hour intervals for the first 72 hours to patients from the multicenter CSWG (Cardiogenic Shock Working Group) registry. The primary outcome was in-hospital mortality. Results: A total of 7,716 CS patients were included (admission CSWG-SCAI stages A to E: n = 1,526, n = 1,602, n = 838, n = 2,445, and n = 1,305, respectively; phenotypes I to III: n = 2,963, n = 3,266, n = 1,487, respectively). Within 6 hours from admission, CSWG-SCAI stages and phenotypes changed in 78% and 77% of patients, respectively, then remained relatively unchanged throughout the first 72 hours. Combining ML-based phenotypes with CSWG-SCAI stages to subclassify patients improved risk stratification (mortality for stages C-I: 12%-14%, C-II: 22%-26%, D-I: 21%-23%, D-II: 31%-34%, and D-III: 37%-40%). Admission phenotypes II and III strongly increased the odds of CS progression from stages A-C to D-E or death within 72 hours of admission (phenotype II: OR: 1.2 [95% CI: 0.99-1.39]; P = 0.051; phenotype III: OR: 11.4 [95% CI: 3.50-36.95]; P < 0.0001). Conclusions: Most patients with CS reached phenotype I and stage D within 6 hours after admission. Combining ML-based phenotypes with CSWG-SCAI staging may facilitate the transition from typical treatment intensity–based approaches to mechanistic classification that reflects the heterogeneity within CS populations.