Department of Anesthesiology, Chi Mei Medical Center, Tainan, Taiwan.
Department of Medical Research, Chi Mei Medical Center, Tainan, Taiwan.
BMC Anesthesiol. 2022 Apr 23;22(1):116. doi: 10.1186/s12871-022-01648-y.
This study aims to develop a machine learning-based application in a real-world medical domain to assist anesthesiologists in assessing the risk of complications in patients after a hip surgery.
Data from adult patients who underwent hip repair surgery at Chi-Mei Medical Center and its 2 branch hospitals from January 1, 2013, to March 31, 2020, were analyzed. Patients with incomplete data were excluded. A total of 22 features were included in the algorithms, including demographics, comorbidities, and major preoperative laboratory data from the database. The primary outcome was a composite of adverse events (in-hospital mortality, acute myocardial infarction, stroke, respiratory, hepatic and renal failure, and sepsis). Secondary outcomes were intensive care unit (ICU) admission and prolonged length of stay (PLOS). The data obtained were imported into 7 machine learning algorithms to predict the risk of adverse outcomes. Seventy percent of the data were randomly selected for training, leaving 30% for testing. The performances of the models were evaluated by the area under the receiver operating characteristic curve (AUROC). The optimal algorithm with the highest AUROC was used to build a web-based application, then integrated into the hospital information system (HIS) for clinical use.
Data from 4,448 patients were analyzed; 102 (2.3%), 160 (3.6%), and 401 (9.0%) patients had primary composite adverse outcomes, ICU admission, and PLOS, respectively. Our optimal model had a superior performance (AUROC by DeLong test) than that of ASA-PS in predicting the primary composite outcomes (0.810 vs. 0.629, p < 0.01), ICU admission (0.835 vs. 0.692, p < 0.01), and PLOS (0.832 vs. 0.618, p < 0.01).
The hospital-specific machine learning model outperformed the ASA-PS in risk assessment. This web-based application gained high satisfaction from anesthesiologists after online use.
本研究旨在开发一个基于机器学习的应用程序，应用于真实的医学领域，以帮助麻醉师评估髋关节手术后患者发生并发症的风险。
分析了 2013 年 1 月 1 日至 2020 年 3 月 31 日期间在奇美医疗中心及其 2 家分院接受髋关节修复手术的成年患者的数据。排除数据不完整的患者。算法共纳入 22 个特征，包括数据库中的人口统计学、合并症和主要术前实验室数据。主要结局是不良事件的综合指标（院内死亡率、急性心肌梗死、中风、呼吸、肝和肾功能衰竭以及败血症）。次要结局为重症监护病房（ICU）入住和住院时间延长（PLOS）。将获得的数据导入 7 种机器学习算法中，以预测不良结局的风险。将 70%的数据随机抽取用于训练，30%的数据用于测试。通过接收者操作特征曲线下的面积（AUROC）评估模型的性能。使用具有最高 AUROC 的最佳算法构建基于网络的应用程序，然后将其集成到医院信息系统（HIS）中用于临床使用。
分析了 4448 例患者的数据；102 例（2.3%）、160 例（3.6%）和 401 例（9.0%）患者分别发生了主要复合不良结局、入住 ICU 和 PLOS。我们的最佳模型在预测主要复合结局（DeLong 检验的 AUROC 0.810 与 0.629，p<0.01）、入住 ICU（0.835 与 0.692，p<0.01）和 PLOS（0.832 与 0.618，p<0.01）方面的表现均优于 ASA-PS。
医院特定的机器学习模型在风险评估方面优于 ASA-PS。该基于网络的应用程序在在线使用后得到了麻醉师的高度满意度。
