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.

我们开发了一种基于深度学习的风险分层工具 CShock，以预测急性失代偿性心力衰竭和 / 或心肌梗死入住心脏重症监护病房的患者的心源性休克的发作，并在外部验证中发现其具有泛化能力。