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.

翻译：心肌梗死和心力衰竭是影响美国数百万人的主要心血管疾病。心源性休克患者的发病率和死亡率最高。早期识别心源性休克至关重要。及时实施治疗措施可预防因心源性休克导致的缺血、低血压和心输出量减少的恶性循环。然而，由于临床医生难以处理心脏重症监护病房（ICU）中的海量数据，且缺乏有效的风险分层工具，心源性休克的早期识别一直具有挑战性。我们开发了一种基于深度学习的风险分层工具CShock，用于预测入住心脏ICU的急性失代偿性心力衰竭和/或心肌梗死患者发生心源性休克的风险。为开发和验证CShock，我们标注了带有医生判定结果的心脏ICU数据集。CShock实现了0.820的受试者工作特征曲线下面积（AUROC），显著优于心源性休克预后评估的成熟风险评分CardShock（AUROC 0.519）。CShock在独立患者队列中进行了外部验证，AUROC达到0.800，证明了其在其他心脏ICU中的泛化能力。