Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Big Data and Artificial Intelligence Center, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Anaesth Crit Care Pain Med. 2024 Dec;43(6):101424. doi: 10.1016/j.accpm.2024.101424. Epub 2024 Sep 13.
Postoperative pulmonary complications (PPCs) contribute to high mortality rates and impose significant financial burdens. In this study, a machine learning-based prediction model was developed to identify patients at high risk of developing PPCs following laparoscopic hepatectomy.
Data were collected from 1022 adult patients who underwent laparoscopic hepatectomy at two centres between January 2015 and February 2021. The dataset was divided into a development set and a temporal external validation set based on the year of surgery. A total of 42 factors were extracted for pre-modelling, including the implementation status of Enhanced Recovery after Surgery (ERAS). Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) method. Model performance was assessed using the area under the receiver operating characteristic curve (AUC). The model with the best performance was externally validated using temporal data.
The incidence of PPCs was 8.7%. Lambda.1se was selected as the optimal lambda for LASSO feature selection. For implementation of ERAS, serum gamma-glutamyl transferase levels, malignant tumour presence, total bilirubin levels, and age-adjusted Charleston Comorbidities Index were the selected factors. Seven models were developed. Among them, logistic regression demonstrated the best performance, with an AUC of 0.745 in the internal validation set and 0.680 in the temporal external validation set.
Based on the most recent definition, a machine learning model was employed to predict the risk of PPCs following laparoscopic hepatectomy. Logistic regression was identified as the best-performing model. ERAS implementation was associated with a reduction in the number of PPCs.
术后肺部并发症（PPCs）导致高死亡率，并带来巨大的经济负担。本研究建立了一种基于机器学习的预测模型，以识别接受腹腔镜肝切除术的患者发生 PPCs 的风险。
从 2015 年 1 月至 2021 年 2 月在两个中心接受腹腔镜肝切除术的 1022 名成年患者中收集数据。数据集根据手术年份分为开发集和时间外部验证集。共提取了 42 个因素进行预建模，包括增强术后康复（ERAS）的实施情况。使用最小绝对收缩和选择算子（LASSO）方法进行特征选择。使用接收者操作特征曲线下的面积（AUC）评估模型性能。使用时间数据对表现最佳的模型进行外部验证。
PPCs 的发生率为 8.7%。LASSO 特征选择的最佳 lambda 为 lambda.1se。对于 ERAS 的实施，谷氨酰转移酶水平、恶性肿瘤存在、总胆红素水平和年龄调整Charleston 合并症指数是选定的因素。开发了 7 种模型。其中，逻辑回归在内部验证集中表现最佳，AUC 为 0.745，在时间外部验证集中为 0.680。
根据最新定义，采用机器学习模型预测腹腔镜肝切除术后 PPCs 的风险。逻辑回归被确定为表现最佳的模型。ERAS 的实施与 PPCs 数量的减少相关。
