Development and Retrospective Validation of a Cardiogenic Shock Staging and Triage Assessment Tool

Introduction: Cardiogenic shock (CS) remains a critical form of circulatory failure with high mortality. Early identification and risk stratification are essential. The Society for Cardiovascular Angiography and Interventions (SCAI) classification provides a set of clinical definitions for different stages of CS (A through E). While it guides clinical thinking, it does not offer a specific, quantifiable scoring system for assigning a patient to a stage. Based on this framework, we developed a novel Cardiogenic Shock Staging and Triage Assessment Tool (CS-STAT) to address this challenge. The CS-STAT is a scoring system that uses objective clinical, hemodynamic, and laboratory data to produce a numerical score. This study's purpose was to validate that the CS-STAT specific scoring method and is predictive of patient outcomes. We hypothesize that the CS-STAT score is a predictor of mortality.


Methods: A retrospective cohort study was conducted at a single community hospital on 61 patients with CS (ICD-10 code R57.0) between January-March 2025. We examined patients’ demographic and clinic data, including APACHE II and CardShock scores. Statistical analyses included Chi-Square, Pearson correlations, and both linear and logistic regression models.


Results: Chi-Square analysis showed a significant relationship between the SCAI stage derived from CS-STAT and patient mortality (p = .032). Mortality rates increased progressively from 45.0% in SCAI Stage C to 65.4% in Stage D, and 66.7% in Stage E. Pearson's correlations revealed significant associations between the CS-STAT score and both the APACHE II score (p = .000, r = .492) and CardShock score (p = .001, r = .420). Logistic regression confirmed the CS-STAT’s strong predictive capability, accurately classifying 83.6% of cases (sensitivity 83.3%, specificity 83.9%), and was a significant predictor of mortality (p = .028, odds ratio 1.437). Other significant predictors included age (p = .006) and cardiac surgery status (p = .021).


Conclusions: The CS-STAT demonstrates its potential for risk stratification and mortality prediction. This study’s findings, along with the observed strong, dose-dependent relationship between increasing SCAI stage and mortality, provide a robust scientific basis for a prospective validation of the CS-STAT in a real-time clinical setting.