Journal of interventional cardiology · 2022
Rapid Classification and Treatment Algorithm of Cardiogenic Shock Complicating Acute Coronary Syndromes: The SAVE ACS Classification.
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Panoulas Vasileios, Ilsley Charles
Аннотация
INTRODUCTION: We aimed to identify the independent "frontline" predictors of 30-day mortality in patients with acute coronary syndromes (ACS) and propose a rapid cardiogenic shock (CS) classification and management pathway.
MATERIALS AND METHODS: From 2011 to 2019, a total of 11439 incident ACS patients were treated in our institution. Forward conditional logistic regression analysis was performed to determine the "frontline" predictors of 30 day mortality. The C-statistic assessed the discriminatory power of the model. As a validation cohort, we used 431 incident ACS patients admitted from January 1, 2020, to July 20, 2020.
RESULTS: Independent predictors of 30-day mortality included age (OR 1.05; 95% CI 1.04 to 1.07, p < 0.001), intubation (OR 7.4; 95% CI 4.3 to 12.74, p < 0.001), LV systolic impairment (OR severe_vs_normal 1.98; 95% CI 1.14 to 3.42, p=0.015, OR moderate_vs_normal 1.84; 95% CI 1.09 to 3.1, p=0.022), serum lactate (OR 1.25; 95% CI 1.12 to 1.41, p < 0.001), base excess (OR 1.1; 95% CI 1.04 to 1.07, p < 0.001), and systolic blood pressure (OR 0.99; 95% CI 0.982 to 0.999, p=0.024). The model discrimination was excellent with an area under the curve (AUC) of 0.879 (0.851 to 0.908) (p < 0.001). Based on these predictors, we created the SAVE (SBP, Arterial blood gas, and left Ventricular Ejection fraction) ACS classification, which showed good discrimination for 30-day AUC 0.814 (0.782 to 0.845) and long-term mortality (p log-rank < 0.001). A similar AUC was demonstrated in the validation cohort (AUC 0.815).
CONCLUSIONS: In the current study, we introduce a rapid way of classifying CS using frontline parameters. The SAVE ACS classification could allow for future randomized studies to explore the benefit of mechanical circulatory support in different CS stages in ACS patients.
MATERIALS AND METHODS: From 2011 to 2019, a total of 11439 incident ACS patients were treated in our institution. Forward conditional logistic regression analysis was performed to determine the "frontline" predictors of 30 day mortality. The C-statistic assessed the discriminatory power of the model. As a validation cohort, we used 431 incident ACS patients admitted from January 1, 2020, to July 20, 2020.
RESULTS: Independent predictors of 30-day mortality included age (OR 1.05; 95% CI 1.04 to 1.07, p < 0.001), intubation (OR 7.4; 95% CI 4.3 to 12.74, p < 0.001), LV systolic impairment (OR severe_vs_normal 1.98; 95% CI 1.14 to 3.42, p=0.015, OR moderate_vs_normal 1.84; 95% CI 1.09 to 3.1, p=0.022), serum lactate (OR 1.25; 95% CI 1.12 to 1.41, p < 0.001), base excess (OR 1.1; 95% CI 1.04 to 1.07, p < 0.001), and systolic blood pressure (OR 0.99; 95% CI 0.982 to 0.999, p=0.024). The model discrimination was excellent with an area under the curve (AUC) of 0.879 (0.851 to 0.908) (p < 0.001). Based on these predictors, we created the SAVE (SBP, Arterial blood gas, and left Ventricular Ejection fraction) ACS classification, which showed good discrimination for 30-day AUC 0.814 (0.782 to 0.845) and long-term mortality (p log-rank < 0.001). A similar AUC was demonstrated in the validation cohort (AUC 0.815).
CONCLUSIONS: In the current study, we introduce a rapid way of classifying CS using frontline parameters. The SAVE ACS classification could allow for future randomized studies to explore the benefit of mechanical circulatory support in different CS stages in ACS patients.