Abstract
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.
Abstract (translated)
心脏病和 heart failure 是影响美国数百万人的 major 心血管疾病。患者发展心脏病性休克的严重程度最高。及早识别心脏病性休克是至关重要的。及时采取治疗措施可以防止缺血、低血压和心脏病性休克引起的心脏输出减少的有益螺旋。然而，早期识别心脏病性休克由于人类医护人员无法处理心脏重症监护室(ICU)中巨大的数据量和缺乏有效的风险分层工具而具有挑战性。我们开发了基于深度学习的风险分层工具 CShock，用于 admission to the heart lung center with acute decompensated heart failure and/or myocardial infarction 的心脏病性休克预测。为了开发并验证 CShock，我们给心脏重症监护室的数据集加上医生判断的结果。CShock 在Receiver operator characteristic 曲线上的 area under the curve (AUROC) 达到了 0.820，大大超过了 CardShock (AUROC 0.519), CardShock 是心脏病性休克预测的一个确立的风险评分。CShock 在独立的患者群体上进行外部验证，并达到了 AUROC 0.800，这表明它可以在其他心脏重症监护中通用。
URL
https://arxiv.org/abs/2303.12888
