Development and multicenter validation of machine learning models for predicting postoperative pulmonary complications after neurosurgery

作者：Ming Xu, Wenhao Zhu, Siyu Hou, Hongzhi Xu, Jingwen Xia, Liyu Lin, 浩平 川副, Mingyu You, Jiafeng Wang, Zhi Xie, Xiaohong Wen, Ying‐Wei Wang · 发表于：Chinese Medical Journal · 年份：2025 · DOI：10.1097/cm9.0000000000003433 · 被引用次数：7 · 研究领域：Lung Cancer Diagnosis and Treatment、Enhanced Recovery After Surgery、Respiratory Support and Mechanisms

BACKGROUND: Postoperative pulmonary complications (PPCs) are major adverse events in neurosurgical patients. This study aimed to develop and validate machine learning models predicting PPCs after neurosurgery. METHODS: PPCs were defined according to the European Perioperative Clinical Outcome standards as occurring within 7 postoperative days. Data of cases meeting inclusion/exclusion criteria were extracted from the anesthesia information management system to create three datasets: The development (data of Huashan Hospital, Fudan University from 2018 to 2020), temporal validation (data of Huashan Hospital, Fudan University in 2021) and external validation (data of other three hospitals in 2023) datasets. Machine learning models of six algorithms were trained using either 35 retrievable and plausible features or the 11 features selected by Lasso regression. Temporal validation was conducted for all models and the 11-feature models were also externally validated. Independent risk factors were identified and feature importance in top models was analyzed. RESULTS: PPCs occurred in 712 of 7533 (9.5%), 258 of 2824 (9.1%), and 207 of 2300 (9.0%) patients in the development, temporal validation and external validation datasets, respectively. During cross-validation training, all models except Bayes demonstrated good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.840. In temporal validation of full-feature models, deep neural network (DNN) pe...