A dynamic risk score for early prediction of cardiogenic shock using machine learning
This addresses the critical need for early identification of cardiogenic shock in cardiac ICU patients to improve outcomes, representing a domain-specific incremental improvement over existing tools.
The researchers tackled the problem of early prediction of cardiogenic shock in cardiac ICU patients by developing a deep learning tool called CShock, which achieved an AUROC of 0.820, outperforming an existing risk score (AUROC 0.519) and showing generalizability with an AUROC of 0.800 in external validation.
Myocardial infarction and heart failure are major cardiovascular diseases that affect millions of people in the US. The morbidity and mortality are highest among patients who develop cardiogenic shock. Early recognition of cardiogenic shock is critical. Prompt implementation of treatment measures can prevent the deleterious spiral of ischemia, low blood pressure, and reduced cardiac output due to cardiogenic shock. However, early identification of cardiogenic shock has been challenging due to human providers' inability to process the enormous amount of data in the cardiac intensive care unit (ICU) and lack of an effective risk stratification tool. We developed a deep learning-based risk stratification tool, called CShock, for patients admitted into the cardiac ICU with acute decompensated heart failure and/or myocardial infarction to predict onset of cardiogenic shock. To develop and validate CShock, we annotated cardiac ICU datasets with physician adjudicated outcomes. CShock achieved an area under the receiver operator characteristic curve (AUROC) of 0.820, which substantially outperformed CardShock (AUROC 0.519), a well-established risk score for cardiogenic shock prognosis. CShock was externally validated in an independent patient cohort and achieved an AUROC of 0.800, demonstrating its generalizability in other cardiac ICUs.