Rapid Classification and Treatment Algorithm of Cardiogenic Shock Complicating Acute Coronary Syndromes: The SAVE ACS
Vasileios Panoulas1,2, Charles Ilsley1
1Department of Cardiology, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, Harefield, UK.
Insights
This study identified key predictors of 30-day mortality in acute coronary syndromes (ACS) patients. A new SAVE ACS classification using these frontline parameters aids in rapid cardiogenic shock (CS) assessment and management.
Area of Science:
- Cardiology
- Critical Care Medicine
- Health Outcomes Research
Background:
- Acute coronary syndromes (ACS) are a leading cause of mortality.
- Identifying early predictors of mortality in ACS is crucial for timely intervention.
- Cardiogenic shock (CS) complicates ACS and requires rapid risk stratification.
Purpose of the Study:
- To identify independent "frontline" predictors of 30-day mortality in ACS patients.
- To develop a rapid classification system for CS in ACS.
- To propose a management pathway for CS in ACS.
Main Methods:
- Analysis of 11,439 ACS patients treated between 2011-2019.
- Forward conditional logistic regression to identify mortality predictors.
- Validation using a separate cohort of 431 ACS patients (2020).
Main Results:
- Key predictors of 30-day mortality included age, intubation, LV systolic impairment, serum lactate, base excess, and systolic blood pressure.
- The developed SAVE (SBP, Arterial blood gas, and left Ventricular Ejection fraction) ACS classification demonstrated excellent discrimination (AUC 0.879).
- The SAVE classification showed good discrimination for 30-day (AUC 0.814) and long-term mortality, validated in an independent cohort (AUC 0.815).
Conclusions:
- A rapid method for classifying CS in ACS using frontline parameters has been developed.
- The SAVE ACS classification can facilitate future randomized trials on mechanical circulatory support for CS.
- This classification system aids in stratifying CS risk and guiding management in ACS patients.
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.
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