Development and external validation of a dynamic risk score for early prediction of cardiogenic shock in cardiac intensive care units using machine learning.

IF 3.9 2区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS
Yuxuan Hu, Albert Lui, Mark Goldstein, Mukund Sudarshan, Andrea Tinsay, Cindy Tsui, Samuel D Maidman, John Medamana, Neil Jethani, Aahlad Puli, Vuthy Nguy, Yindalon Aphinyanaphongs, Nicholas Kiefer, Nathaniel R Smilowitz, James Horowitz, Tania Ahuja, Glenn I Fishman, Judith Hochman, Stuart Katz, Samuel Bernard, Rajesh Ranganath
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引用次数: 0

Abstract

Aims: Myocardial infarction and heart failure are major cardiovascular diseases that affect millions of people in the USA with morbidity and mortality being highest among patients who develop cardiogenic shock. Early recognition of cardiogenic shock allows prompt implementation of treatment measures. Our objective is to develop a new dynamic risk score, called CShock, to improve early detection of cardiogenic shock in the cardiac intensive care unit (ICU).

Methods and results: We developed and externally validated 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 the onset of cardiogenic shock. We prepared a cardiac ICU dataset using the Medical Information Mart for Intensive Care-III database by annotating with physician-adjudicated outcomes. This dataset which consisted of 1500 patients with 204 having cardiogenic/mixed shock was then used to train CShock. The features used to train the model for CShock included patient demographics, cardiac ICU admission diagnoses, routinely measured laboratory values and vital signs, and relevant features manually extracted from echocardiogram and left heart catheterization reports. We externally validated the risk model on the New York University (NYU) Langone Health cardiac ICU database which was also annotated with physician-adjudicated outcomes. The external validation cohort consisted of 131 patients with 25 patients experiencing cardiogenic/mixed shock. CShock achieved an area under the receiver operator characteristic curve (AUROC) of 0.821 (95% CI 0.792-0.850). CShock was externally validated in the more contemporary NYU cohort and achieved an AUROC of 0.800 (95% CI 0.717-0.884), demonstrating its generalizability in other cardiac ICUs. Having an elevated heart rate is most predictive of cardiogenic shock development based on Shapley values. The other top 10 predictors are having an admission diagnosis of myocardial infarction with ST-segment elevation, having an admission diagnosis of acute decompensated heart failure, Braden Scale, Glasgow Coma Scale, blood urea nitrogen, systolic blood pressure, serum chloride, serum sodium, and arterial blood pH.

Conclusion: The novel CShock score has the potential to provide automated detection and early warning for cardiogenic shock and improve the outcomes for millions of patients who suffer from myocardial infarction and heart failure.

利用机器学习开发和外部验证用于早期预测心脏重症监护病房心源性休克的动态风险评分。
背景: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,其中发生心源性休克的患者发病率和死亡率最高。及早识别心源性休克可以及时采取治疗措施。我们的目标是开发一种名为 CShock 的新型动态风险评分,以改善心脏重症监护病房(ICU)对心源性休克的早期检测:我们开发了基于深度学习的风险分层工具 CShock,并进行了外部验证,该工具适用于因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,可预测心源性休克的发生。我们利用 MIMIC-III 数据库准备了一个心脏重症监护室数据集,并标注了医生裁定的结果。该数据集由 1500 名患者组成,其中 204 名患者患有心源性/混合性休克,然后用于训练 CShock。用于训练 CShock 模型的特征包括患者人口统计学特征、心脏重症监护室入院诊断、常规测量的实验室值和生命体征,以及从超声心动图和左心导管检查报告中手动提取的相关特征。我们在纽约大学(NYU)朗格尼医疗中心心脏重症监护室数据库中对风险模型进行了外部验证,该数据库还注释了医生裁定的结果。外部验证队列由 131 名患者组成,其中 25 名患者经历了心源性/混合性休克:CShock的接收者操作特征曲线下面积(AUROC)为0.821(95% CI 0.792-0.850)。CShock在更现代的纽约大学队列中进行了外部验证,AUROC达到0.800(95% CI 0.717-0.884),证明了它在其他心脏重症监护病房中的通用性。根据 Shapley 值,心率升高最能预测心源性休克的发生。其他十大预测因素包括入院诊断为心肌梗死伴 ST 段抬高、入院诊断为急性失代偿性心力衰竭、布莱登量表、格拉斯哥昏迷量表、血尿素氮、收缩压、血清氯化物、血清钠和动脉血 pH 值:新型 CShock 评分可自动检测和预警心源性休克,改善数百万心肌梗死和心力衰竭患者的预后。
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来源期刊
CiteScore
8.50
自引率
4.90%
发文量
325
期刊介绍: The European Heart Journal - Acute Cardiovascular Care (EHJ-ACVC) offers a unique integrative approach by combining the expertise of the different sub specialties of cardiology, emergency and intensive care medicine in the management of patients with acute cardiovascular syndromes. Reading through the journal, cardiologists and all other healthcare professionals can access continuous updates that may help them to improve the quality of care and the outcome for patients with acute cardiovascular diseases.
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