Han Changho, Kim Hyun Il, Soh Sarah, Choi Ja Woo, Song Jong Wook, Yoon Dukyong
Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yongin, Republic of Korea.
Department of Anesthesiology and Pain Medicine, Anesthesia and Pain Research Institute Yonsei University College of Medicine, Seoul, Republic of Korea.
iScience. 2024 May 8;27(6):109932. doi: 10.1016/j.isci.2024.109932. eCollection 2024 Jun 21.
Early identification of patients at high risk of delirium is crucial for its prevention. Our study aimed to develop machine learning models to predict delirium after cardiac surgery using intraoperative biosignals and clinical data. We introduced a novel approach to extract relevant features from continuously measured intraoperative biosignals. These features reflect the patient's overall or baseline status, the extent of unfavorable conditions encountered intraoperatively, and beat-to-beat variability within the data. We developed a soft voting ensemble machine learning model using retrospective data from 1,912 patients. The model was then prospectively validated with data from 202 additional patients, achieving a high performance with an area under the receiver operating characteristic curve of 0.887 and an accuracy of 0.881. According to the SHapley Additive exPlanation method, several intraoperative biosignal features had high feature importance, suggesting that intraoperative patient management plays a crucial role in preventing delirium after cardiac surgery.
早期识别谵妄高危患者对预防谵妄至关重要。我们的研究旨在开发机器学习模型，利用术中生物信号和临床数据预测心脏手术后的谵妄。我们引入了一种新颖的方法，从连续测量的术中生物信号中提取相关特征。这些特征反映了患者的整体或基线状态、术中遇到的不利情况的程度以及数据中的逐搏变异性。我们使用来自1912名患者的回顾性数据开发了一个软投票集成机器学习模型。然后，该模型用另外202名患者的数据进行前瞻性验证，在受试者工作特征曲线下面积为0.887，准确率为0.881，表现出色。根据夏普利值解释方法，几个术中生物信号特征具有很高的特征重要性，这表明术中患者管理在预防心脏手术后谵妄中起着关键作用。
