肺癌患者术中低体温风险预测模型的构建及验证

Construction and validation of Intraoperative Hypothermia risk prediction model in lung cancer patients

目的:构建并验证肺癌患者术中低体温风险预测模型,为临床医护人员识别术中低体温高危人群提供参考。方法:选取四川省某三级甲等肿瘤专科医院2022年6月—11月的肺癌手术患者,按照7:3的比例随机分为训练集(770例)和验证集(330例)。使用R语言中Logistic回归、XGBoost、随机森林、支持向量机4种机器学习算法构建预测模型,并对其性能进行比较,得到最优的肺癌患者术中低体温预测模型算法,并在验证集患者中进行模型验证。结果:术中低体温发生率为53.2%。术中出血量、术中输液量、手术时间、麻醉时间、手术室温度、麻醉后核心体温、手术切除部位是肺癌患者术中低体温的影响因素。随机森林模型训练集和验证集的ROC曲线下面积均为0.968,其性能优于其他3种预测模型。结论:基于随机森林算法的模型是最优的肺癌患者术中低体温预测模型,有利于临床筛选术中低体温高危人群,可为医护人员早期采取有针对性的预防措施提供借鉴。

Objective:To construct and validate the risk prediction model of Intraoperative Hypothermia in lung cancer patients,and to provide a reference for clinical medical staff to identify the high-risk groups of Intraoperative Hypothermia in lung cancer patients.Methods:Patients with lung cancer from June to November 2022 in a tertiary grade A cancer hospital in Sichuan province were randomly divided into training set(770 cases)and validation set(330 cases).Four machine learning algorithms(Logistic regression,XGBoost,random forest,support vector machine)in Recovery Component were used to construct prediction models,and their performance was compared to obtain the best prediction model algorithm for Intraoperative Hypothermia in lung cancer patients.The model was verified in the validation set.Results:The incidence of Intraoperative Hypothermia was 53.2%.Intraoperative blood loss,intraoperative infusion volume,operation time,anesthesia time,operating room temperature,core body temperature after anesthesia and surgical resection site were the influencing factors of Intraoperative Hypothermia in patients with lung cancer.The AUC of the random forest model in the training set was 0.968,and the AUC of the validation set was 0.968,which was better than the other three prediction models.Conclusion:The model based on random forest algorithm is the optimal Intraoperative Hypothermia prediction model for lung cancer patients,which is conducive to clinical screening of high-risk groups,can provide reference for medical staff to take targeted preventive measures early.