Cardiogenic shock is a serious life-threatening condition affecting almost 10% of patients suffering from acute coronary syndrome. When untreated, it can rapidly progress causing collapse of circulation and sudden death. Despite contemporary 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 underlying primary cause is not enough to reverse this vicious circle.
Our approach is to develop a predictive model for cardiogenic shock based on machine learning algorithms capable of identifying high risk patients prior to development of shock. Such tool would allow to take preemptive measures and thus prevent the development of shock.
- To develop a predictive model for cardiogenic shock based on machine learning algorithms.
- To compare its predictive power with existing scoring systems on a large population of patients.