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| Logistic回归和机器学习模型预测胸腔镜肺部分切除术患者单肺通气期间低SpO2的比较 |
| Comparison of logistic regression and machine learning models predicting low SpO2 during one-lung ventilation in patients undergoing thoracoscopic partial pulmonary resection |
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| DOI：10.12089/jca.2024.10.003 |
| 中文关键词: 机器学习 胸腔镜肺部分切除术 单肺通气 年龄 体重指数 血糖 |
| 英文关键词: Machine learning Thoracoscopic partial pulmonary resection One-lung ventilation Age Body mass index Blood glucose |
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| 中文摘要: |
目的：比较Logistic回归和机器学习模型对胸腔镜肺部分切除术（TPPR）患者单肺通气（OLV）期间发生低SpO2的预测效能，并探讨低SpO2的危险因素。
方法：选择2022年8月1日至2023年4月30日行单侧TPPR患者127例，男61例，女66例，年龄18～80岁，ASA Ⅰ—Ⅲ级。根据术中OLV期间是否出现SpO2<90%将患者分为两组：低SpO2组（n=21）和正常SpO2组（n=106）。收集患者围术期相关数据，采用Logistic回归构建预测模型，与采用随机森林（RF）、极限梯度提升（XGBoost）、决策树（DT）、逻辑回归（LogR）、支持向量机（SVM）5种机器学习模型构建的预测模型进行比较，绘制受试者工作特征（ROC）曲线，计算曲线下面积（AUC）评价预测模型的效能。采用沙普利加和解释法（SHAP）解释输出的最佳模型，确定TPPR患者OLV期间低SpO2的危险因素。
结果：多因素Logistic回归分析显示，年龄增大（OR=1.087，95%CI 1.006～1.175，P=0.036）、BMI升高（OR=1.299，95%CI 1.050～1.608，P=0.016）、术前血糖浓度升高（OR=2.028，95%CI 1.378～2.983，P＜0.001）、RV/TLC%Pred降低（OR=0.936，95%CI 0.892～0.983，P=0.008）是OLV期间低SpO2独立危险因素，预测模型为Logit（p）=-10.098+0.08×年龄+0.231×BMI+0.633×血糖-0.059×RV/TLC%Pred，该模型AUC为0.873（95%CI 0.803～0.943，P＜0.001）。经过网格搜索与五折交叉验证结合优化机器学习模型参数，模型训练效果良好。ROC曲线分析结果显示，RF的AUC为0.921（95%CI 0.840～0.979），XGBoost的AUC为0.940（95%CI 0.812～0.981），DT的AUC为0.919（95%CI 0.828～0.982），LogR的AUC为0.892（95%CI 0.831～0.980），SVM的AUC为0.922（95%CI 0.832～0.982），XGBoost预测的AUC最高，且高于传统的Logistic回归预测模型。经SHAP方法处理后，XGBoost输出模型中最重要的危险因素是年龄增大、BMI和术前血糖浓度升高。
结论：年龄增大、BMI和术前血糖浓度升高是TPPR患者OLV期间低SpO2的危险因素，机器学习模型XGBoost预测OLV期间低SpO2发生的效能优于传统的Logistic回归，能分析变量与结局间的复杂关系，更精准地个体化预测OLV期间低SpO2的发生风险。 |
| 英文摘要: |
Objective: To compare the predictive effects of logistic regression and machine learning models on occurrence of low peripheral oxygen saturation (SpO2) during one-lung ventilation (OLV) in patients undergoing thoracoscopic partial pulmonary resection (TPPR), and to explore risk factors of low SpO2.
Methods: A total of 127 patients undergoing unilateral TPPR from August 1, 2022 to April 30, 2023 were enrolled, 61 males and 66 females, aged 18-80 years, ASA physical status Ⅰ-Ⅲ. Based on whether intraoperative SpO2 during OLV was less than 90%, the patients were divided into two groups: low SpO2 group (n = 21) and normal SpO2 group (n = 106). Perioperative data were collected and a predictive model was constructed using logistic regression. This model was compared with predictive models constructed using five machine learning models, including random forest (RF), extreme gradient boosting (XGBoost), decision tree (DT), logistic regression (LogR), and support vector machine (SVM). The receiver operating characteristic (ROC) curve was plotted, and the performance of the predictive models were evaluated by the area under the curve (AUC). The best output model was interpreted using Shapley additive explanations (SHAP) to identify the risk factors of low SpO2 during OLV in patients undergoing TPPR.
Results: Multivariate logistic regression analysis showed that increased age (OR = 1.087, 95% CI 1.006-1.175, P = 0.036), increased BMI (OR = 1.299, 95% CI 1.050-1.608, P = 0.016), increased preoperative blood glucose (OR = 2.028, 95% CI 1.378-2.983, P < 0.001), and decreased RV/TLC%Pred(OR = 0.936, 95% CI 0.892-0.983, P = 0.008) were independent risk factors of low SpO2 during OLV. The predictive model was Logit(p) = -10.098 + 0.08 × age + 0.231 × BMI + 0.633 × blood glucose - 0.059 × RV/TLC%Pred, with an AUC of 0.873 (95% CI 0.803-0.943, P < 0.001). After optimizing parameters of machine learning models using grid search combined with five-fold cross-validation, the model training results were satisfactory. ROC curve analysis showed that the AUC for RF was 0.921 (95% CI 0.840-0.979), XGBoost was 0.940 (95% CI 0.812-0.981), DT was 0.919 (95% CI 0.828-0.982), LogR was 0.892 (95% CI 0.831-0.980), and SVM was 0.922 (95% CI 0.832-0.982). XGBoost had the highest AUC, surpassing the logistic regression model. SHAP analysis indicated that the most important risk factors in the XGBoost output model were increased age, BMI, and preoperative blood glucose concentration.
Conclusion: Increased age, BMI, and preoperative blood glucose concentration are significant risk factors for low SpO2 during OLV in patients undergoing TPPR. The XGBoost machine learning model outperformed traditional logistic regression in predicting the occurrence of low SpO2 during OLV. XGBoost can analyze more complex relationships between variables and outcomes and provide more accurate individualized predictions of the risk of low SpO2 during OLV. |
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