Score TO Predict SHOCK - STOP SHOCK

Acute Coronary Syndrome, Acute Myocardial Infarction, Cardiogenic Shock

cardiogenic shock, acute coronary syndrome, artificial intelligence, scoring system, machine learning, acute myocardial infarction

The goal of this international multicenter study is to develop a scoring system to identify the risk of developing cardiogenic shock (CS) in patients suffering from acute coronary syndrome (ACS) utilising artificial intelligence. Study hypothesis: A complex machine learning (ML) model utilising standard patient's admission data predicts the development of cardiogenic shock in patients suffering from acute myocardial infarction better than standard prediction models. Study objectives: The primary objective of this study is to further improve predictive parameters of #STOPSHOCK model for prediction of development of cardiogenic shock in patients suffering from acute myocardial infarction. The secondary objective of this study is to develop a new predictive model for the development of cardiogenic shock in patients suffering from acute myocardial infarction based on larger combined cohort of patients utilising advanced ML algorithms, continuous model performance monitoring and continual learning.

Background: Cardiogenic shock is a serious life-threatening condition affecting almost 10% of patients suffering from acute coronary syndrome (ACS). When untreated, it can rapidly progress to collapse of circulation and sudden death. Despite recent improvements in diagnostic and treatment options, mortality remains incredibly high, reaching nearly 50%. Currently available mechanical circulatory support devices can replace the function of the heart and/or lungs, thereby essentially eliminating the primary cause. However, cardiogenic shock is not only an isolated decrease in cardiac function but a rapidly progressing multiorgan dysfunction accompanied by severe cellular and metabolic abnormalities. The window for successful treatment is relatively narrow, and when missed, even the elimination of the underlying primary cause is not enough to reverse this vicious circle. The ability to identify high-risk patients prior to the development of shock would allow to take pre-emptive measures, such as the implantation of mechanical circulatory support, and thus prevent the development of shock leading to improved survival. Rationale: The AI-based scoring system could aid in identifying high-risk patients prior to the development of cardiogenic shock. This would allow taking pre-emptive measures, implanting mechanical circulatory support, and thus prevent the development of shock, leading to improved survival. For this purpose, a predictive scoring system STOP SHOCK (Score TO Predict SHOCK) was developed. This scoring system showed better prediction compared to standard models. STOP SHOCK was validated on an external cohort of patients with area under the curve (AUC) of 0.844 surpassing other externally validated cardiogenic shock (CS) models (e.g. ORBI score). Furthermore, this model is based on variables that are readily available at the first contact with patients and thus STOPSHOCK can be utilized in emergency room (ER) or ambulance even before catheterization.

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