VUNO, Seoul, Republic of Korea.
Division of Critical Care Medicine, Department of Hospital Medicine, Inha College of Medicine, Incheon, Republic of Korea.
Crit Care. 2023 Sep 5;27(1):346. doi: 10.1186/s13054-023-04609-0.
Retrospective studies have demonstrated that the deep learning-based cardiac arrest risk management system (DeepCARS™) is superior to the conventional methods in predicting in-hospital cardiac arrest (IHCA). This prospective study aimed to investigate the predictive accuracy of the DeepCARS™ for IHCA or unplanned intensive care unit transfer (UIT) among general ward patients, compared with that of conventional methods in real-world practice.
This prospective, multicenter cohort study was conducted at four teaching hospitals in South Korea. All adult patients admitted to general wards during the 3-month study period were included. The primary outcome was predictive accuracy for the occurrence of IHCA or UIT within 24 h of the alarm being triggered. Area under the receiver operating characteristic curve (AUROC) values were used to compare the DeepCARS™ with the modified early warning score (MEWS), national early warning Score (NEWS), and single-parameter track-and-trigger systems.
Among 55,083 patients, the incidence rates of IHCA and UIT were 0.90 and 6.44 per 1,000 admissions, respectively. In terms of the composite outcome, the AUROC for the DeepCARS™ was superior to those for the MEWS and NEWS (0.869 vs. 0.756/0.767). At the same sensitivity level of the cutoff values, the mean alarm counts per day per 1,000 beds were significantly reduced for the DeepCARS™, and the rate of appropriate alarms was higher when using the DeepCARS™ than when using conventional systems.
The DeepCARS™ predicts IHCA and UIT more accurately and efficiently than conventional methods. Thus, the DeepCARS™ may be an effective screening tool for detecting clinical deterioration in real-world clinical practice. Trial registration This study was registered at ClinicalTrials.gov ( NCT04951973 ) on June 30, 2021.
回顾性研究表明，基于深度学习的心脏骤停风险管理系统（DeepCARS™）在预测院内心脏骤停（IHCA）方面优于传统方法。本前瞻性研究旨在比较 DeepCARS™ 与常规方法在真实世界实践中的预测准确性，以预测普通病房患者发生 IHCA 或非计划性重症监护病房转科（UIT）的可能性。
这是一项在韩国四家教学医院进行的前瞻性、多中心队列研究。研究期间的 3 个月内，所有入住普通病房的成年患者均被纳入研究。主要结局是预测警报触发后 24 小时内发生 IHCA 或 UIT 的准确性。受试者工作特征曲线下面积（AUROC）值用于比较 DeepCARS™ 与改良早期预警评分（MEWS）、国家早期预警评分（NEWS）和单参数跟踪和触发系统。
在 55083 名患者中，IHCA 和 UIT 的发生率分别为每 1000 例住院患者 0.90 和 6.44 例。就复合结局而言，DeepCARS™ 的 AUROC 优于 MEWS 和 NEWS（0.869 比 0.756/0.767）。在相同的截断值灵敏度水平下，DeepCARS™ 的平均每日报警次数/千张床位显著减少，并且使用 DeepCARS™ 的适当报警率高于使用传统系统。
DeepCARS™ 比传统方法更准确、更有效地预测 IHCA 和 UIT。因此，DeepCARS™ 可能是一种有效的筛查工具，可用于在真实世界的临床实践中检测临床恶化。
本研究于 2021 年 6 月 30 日在 ClinicalTrials.gov（NCT04951973）注册。
