Churpek Matthew M, Carey Kyle A, Snyder Ashley, Winslow Christopher J, Gilbert Emily R, Shah Nirav S, Patterson Brian W, Afshar Majid, Weiss Alan, Amin Devendra N, Rhodes Deborah J, Edelson Dana P
medRxiv. 2024 Oct 3:2024.03.18.24304462. doi: 10.1101/2024.03.18.24304462.
Early detection of clinical deterioration using machine learning early warning scores may improve outcomes. However, most implemented scores were developed using logistic regression, only underwent retrospective validation, and were not tested in important subgroups. Our objective was to develop and prospectively validate a gradient boosted machine model (eCARTv5) for identifying clinical deterioration on the wards.
Multicenter retrospective and prospective observational study.
Inpatient admissions to the medical-surgical wards at seven hospitals in three health systems for model development (2006-2022) and at 21 hospitals from three health systems for retrospective (2009-2023) and prospective (2023-2024) external validation.
All adult patients hospitalized at each participating health system during the study years.
None MEASUREMENTS AND MAIN RESULTS: Predictor variables (demographics, vital signs, documentation, and laboratory values) were used in a gradient boosted trees algorithm to predict intensive care unit transfer or death in the next 24 hours. The developed model (eCART) was compared to the Modified Early Warning Score (MEWS) and the National Early Warning Score (NEWS) using the area under the receiver operating characteristic curve (AUROC). The development cohort included 901,491 admissions, the retrospective validation cohort included 1,769,461 admissions, and the prospective validation cohort included 205,946 admissions. In retrospective validation, eCART had the highest AUROC (0.835; 95%CI 0.834, 0.835), followed by NEWS (0.766 (95%CI 0.766, 0.767)), and MEWS (0.704 (95%CI 0.703, 0.704)). eCART's performance remained high (AUROC ≥0.80) across a range of patient demographics, clinical conditions, and during prospective validation.
We developed eCART, which performed better than the NEWS and MEWS retrospectively, prospectively, and across a range of subgroups. These results served as the foundation for Food and Drug Administration clearance for its use in identifying deterioration in hospitalized ward patients.
使用机器学习早期预警评分来早期发现临床病情恶化情况可能会改善治疗结果。然而，大多数已实施的评分是通过逻辑回归开发的，仅经过回顾性验证，且未在重要亚组中进行测试。我们的目标是开发并前瞻性验证一种梯度提升机器模型（eCARTv5），用于识别病房中的临床病情恶化情况。
多中心回顾性和前瞻性观察性研究。
在三个医疗系统的七家医院的内科 - 外科病房进行住院患者入组，用于模型开发（2006 - 2022年）；在三个医疗系统的21家医院进行回顾性（2009 - 2023年）和前瞻性（2023 - 2024年）外部验证。
研究期间在每个参与医疗系统住院的所有成年患者。
无
预测变量（人口统计学、生命体征、病历记录和实验室值）被用于梯度提升树算法中，以预测未来24小时内转入重症监护病房或死亡情况。使用受试者操作特征曲线下面积（AUROC）将开发的模型（eCART）与改良早期预警评分（MEWS）和国家早期预警评分（NEWS）进行比较。开发队列包括901,491例入院患者，回顾性验证队列包括1,769,461例入院患者，前瞻性验证队列包括205,946例入院患者。在回顾性验证中，eCART的AUROC最高（0.835；95%CI 0.834，0.835），其次是NEWS（0.766（95%CI 0.766，0.767））和MEWS（0.704（95%CI 0.703，0.704））。在一系列患者人口统计学、临床状况以及前瞻性验证期间，eCART的表现均保持较高水平（AUROC≥0.80）。
我们开发的eCART在回顾性、前瞻性以及一系列亚组中的表现均优于NEWS和MEWS。这些结果为美国食品药品监督管理局批准其用于识别住院病房患者的病情恶化奠定了基础。
