Development and validation of a hypoxemia prediction model in middle-aged and elderly outpatients undergoing painless gastroscopy

作者：Leilei Zheng, Xin‐Yan Wu, Wei Gu, Rui Wang, Jing Wang, Hongying He, Zhao Wang, Bin Yi, Yi Zhang · 发表于：Scientific Reports · 年份：2025 · DOI：10.1038/s41598-025-02540-8 · 被引用次数：7 · 研究领域：Obstructive Sleep Apnea Research、Respiratory Support and Mechanisms、Neuroscience of respiration and sleep

Hypoxemia is a common complication associated with anesthesia in painless gastroscopy. With the aging of the social population, the number of cases of hypoxemia among middle-aged and elderly patients is increasing. However, tools for predicting hypoxemia in middle-aged and elderly patients are lacking. In this study, we investigated the risk factors for hypoxemia in middle-aged and elderly outpatients undergoing painless gastroscopy based on machine learning and constructed a risk prediction model. In this retrospective study, we included the data on 1,348 outpatients undergoing painless gastroscopy. In total, 26 characteristic variables, including demographic information, past medical history, and clinical data of the patients were included, and BorutaShap was used for feature selection. Five machine learning algorithm models, including logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGB), and light gradient boosting machine (LightGBM), were selected. The best models were selected based on the area under the receiver operating characteristic curve (AUROC). Model feature importance was explained and analyzed using Shapley Additive Explanations (SHAP). The endpoint event of this study was considered to be hypoxemia during the procedure, defined as at least one occurrence of pulse oxygen saturation below 90% without probe misalignment or interference from the beginning of anesthesia induction to the end of painless gastrosc...