Machine learning-based risk prediction of hypoxemia for outpatients undergoing sedation colonoscopy: a practical clinical tool

Hypoxemia as a common complication in colonoscopy under sedation and may result in serious consequences. Unfortunately, a hypoxemia prediction model for outpatient colonoscopy has not been developed. Consequently, the objective of our study was to develop a practical and accurate model to predict the risk of hypoxemia in outpatient colonoscopy under sedation. In this study, we included patients who received colonoscopy with anesthesia in Nanjing First Hospital from July to September 2021. Risk factors were selected through the least absolute shrinkage and selection operator (LASSO). Prediction models based on logistic regression (LR), random forest classifier (RFC), extreme gradient boosting (XGBoost), support vector machine (SVM), and stacking classifier (SCLF) model were implemented and assessed by standard metrics such as the area under the receiver operating characteristic curve (AUROC), sensitivity and specificity. Then choose the best model to develop an online tool for clinical use. We ultimately included 839 patients. After LASSO, body mass index (BMI) (coefficient = 0.36), obstructive sleep apnea-hypopnea syndrome (OSAHS) (coefficient = 1.32), basal oxygen saturation (coefficient = -0.14), and remifentanil dosage (coefficient = 0.04) were independent risk factors for hypoxemia. The XGBoost model with an AUROC of 0.913 showed the best performance among the five models. Our study selected the XGBoost as the first model especially for colonoscopy, with over 95% accuracy and excellent specificity. The XGBoost includes four variables that can be quickly obtained. Moreover, an online prediction practical tool has been provided, which helps screen high-risk outpatients with hypoxemia swiftly and conveniently. Colonoscopy under sedation is an effective technique for the inspection and treatment of alimentary canal diseases, but hypoxemia associated with this process cannot be ignored, since prolonged or severe hypoxemia may result in several serious consequences. We wanted to develop a practical and accurate model to predict the risk of hypoxemia for outpatient colonoscopy under sedation, which could help clinicians make more accurate and objective judgments to prevent patients from being harmed. A total of 839 patients were included in our study and we constructed five machine learning models and selected the best one, which demonstrated satisfactory performance. On this basis, a user-friendly data interface has been developed for convenient application. Clinicians can log in to this interface at any time and it will automatically calculate the patient’s risk of hypoxemia when entering patient information. This study offers evidence that machine learning algorithms can accurately predict the risk of hypoxemia for outpatient colonoscopy under sedation and the model we developed is a practical and interpretable tool that could be used as a clinical decision-making aid.

低氧血症（hypoxemia）是镇静下结肠镜检查的常见并发症，可引发严重不良后果。遗憾的是，目前尚无针对门诊结肠镜检查的低氧血症预测模型。因此，本研究旨在构建一款实用且精准的模型，用于预测镇静下门诊结肠镜检查患者的低氧血症风险。本研究纳入了2021年7月至9月于南京市第一医院接受麻醉结肠镜检查的患者。通过最小绝对收缩和选择算子（least absolute shrinkage and selection operator, LASSO）筛选危险因素。构建基于逻辑回归（logistic regression, LR）、随机森林分类器（random forest classifier, RFC）、极限梯度提升（extreme gradient boosting, XGBoost）、支持向量机（support vector machine, SVM）及堆叠分类器（stacking classifier, SCLF）的预测模型，并采用受试者工作特征曲线下面积（area under the receiver operating characteristic curve, AUROC）、灵敏度、特异度等标准指标进行评估。随后选取最优模型开发临床在线工具。本研究最终纳入839例患者。经LASSO筛选后，体重指数（body mass index, BMI，系数=0.36）、阻塞性睡眠呼吸暂停低通气综合征（obstructive sleep apnea-hypopnea syndrome, OSAHS，系数=1.32）、基础血氧饱和度（系数=-0.14）及瑞芬太尼剂量（系数=0.04）为低氧血症的独立危险因素。在5种模型中，AUROC为0.913的XGBoost模型表现最优。本研究选取XGBoost作为结肠镜检查专用预测模型，其准确率超95%，且具备优异的特异度。该XGBoost模型仅包含4项可快速获取的变量。此外，本研究还开发了一款实用的在线预测工具，可快速便捷地筛查出低氧血症高危门诊患者。镇静下结肠镜检查是消化道疾病诊疗的有效手段，但该操作相关的低氧血症不容忽视，因为持续或严重的低氧血症可引发多种严重不良后果。本研究旨在构建一款实用且精准的模型，用于预测镇静下门诊结肠镜检查患者的低氧血症风险，帮助临床医师做出更精准、客观的判断，避免患者受到伤害。本研究共纳入839例患者，构建了5种机器学习模型并筛选出最优模型，该模型表现令人满意。在此基础上，开发了一款操作便捷的用户界面，临床医师可随时登录该界面，输入患者信息后即可自动计算其低氧血症风险。本研究证实，机器学习算法可精准预测镇静下门诊结肠镜检查患者的低氧血症风险，本研究开发的模型是一款实用且可解释的工具，可作为临床决策辅助工具。