Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea.
Department of Anesthesiology and Pain Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.
Stud Health Technol Inform. 2024 Aug 22;316:286-290. doi: 10.3233/SHTI240400.
Early identification of patients at high risk of cardiac surgery-associated acute kidney injury (CSA-AKI) is crucial for its prevention. We aimed to leverage perioperative clinical and intraoperative biosignal data to develop machine learning models that predict CSA-AKI. We introduced a novel approach for extracting relevant features from high-resolution intraoperative biosignals to reflect the patient's baseline status, the extent of unfavorable conditions encountered intraoperatively, and data variability. We developed XGBoost models from 2,003 patients across three consecutive perioperative phases using: 1) only preoperative, 2) pre- and intraoperative, and 3) pre-, intra-, and postoperative variables. The predictive performance progressively improved throughout the three consecutive perioperative phases (e.g., AUROC of 0.767 to 0.797 and 0.840), all surpassing the Thakar Score's performance. According to the SHAP method, intraoperative perfusion pressure was most important in the prediction, highlighting the importance of intraoperative patient management and the use of high-resolution biosignal data in predictive modeling to analyze hemodynamic fluctuations during surgery. Early postoperative biomarkers were also important predictors, highlighting the importance of intensified monitoring early after surgery.
早期识别心脏手术相关急性肾损伤（CSA-AKI）高危患者对于预防该病至关重要。我们旨在利用围手术期临床和术中生物信号数据开发机器学习模型来预测 CSA-AKI。我们引入了一种从高分辨率术中生物信号中提取相关特征的新方法，以反映患者的基线状态、术中遇到的不利条件的程度和数据变异性。我们使用以下三种方法从连续三个围手术期阶段的 2003 名患者中开发了 XGBoost 模型：1）仅术前，2）术前和术中，3）术前、术中及术后变量。预测性能在连续三个围手术期阶段逐步提高（例如，AUROC 从 0.767 提高到 0.797 和 0.840），均超过了 Thakar 评分的性能。根据 SHAP 方法，术中灌注压在预测中最为重要，突出了术中患者管理和使用高分辨率生物信号数据进行预测建模以分析手术期间血流动力学波动的重要性。术后早期生物标志物也是重要的预测因素，突出了术后早期加强监测的重要性。
