The Cardiogenic Shock Staging and Triage Assessment Tool (CS-STAT) accurately classified 83.6% of cases and significantly predicted mortality (OR 1.437) in patients with cardiogenic shock.
Does the CS-STAT scoring system accurately predict mortality and stratify risk in patients with cardiogenic shock?
The novel CS-STAT scoring system provides a quantifiable method to assign SCAI shock stages and strongly predicts mortality in patients with cardiogenic shock.
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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.
Stone et al. (Sun,) reported a other. The Cardiogenic Shock Staging and Triage Assessment Tool (CS-STAT) accurately classified 83.6% of cases and significantly predicted mortality (OR 1.437) in patients with cardiogenic shock.