STOP SHOCK - Score to predict shock

2021-05 ~

Abstract: Cardiogenic shock is a serious life-threatening condition affecting almost 10% of patients suffering from the acute coronary syndrome. 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 preemptive measures and thus prevent the development of shock. This study aims to develop a predictive model for cardiogenic shock based on machine learning algorithms and compare its predictive power with existing scoring systems on a large population of patients. Nearly 4,000 patients suffering from acute coronary syndrome from the MIMIC database were analyzed in detail and included in this study. This cohort will be analyzed using appropriate statistical and machine learning methods. In particular, we will select appropriate predictors using methods known to optimize feature choices, such as feature selection or dimensionality reduction. The selected features then enter the classification algorithm. Here we will test the performance of more well-known methods, such as multivariate logistic regression, random forest, naive Bayes, and Gaussian Processes classifier. Finally, the STOP SHOCK scoring system will be subsequently validated on an external cohort of patients.

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