全身麻醉下肿瘤细胞减灭术联合腹腔热灌注化疗术患者术后肺部并发症的随机森林预测模型  

A random forest prediction model for postoperative pulmonary complications in patients undergoing tumor cell cytoreduc⁃tive surgery combined with intraperitoneal hyperthermic chemoperfusion under general anesthesia

作　　者：宋明雪 盛崴宣[1] 刘鹏飞[1] 缪慧慧 Song Mingxue;Sheng Weixuan;Liu Pengfei;Miao Huihui(Department of Anesthesiology,Beijing Shijitan Hospital,Capital Medical University,Beijing 100038,China)

机构地区：[1]首都医科大学附属北京世纪坛医院麻醉科,北京100038

出　　处：《国际麻醉学与复苏杂志》2024年第9期977-983,共7页International Journal of Anesthesiology and Resuscitation

摘　　要：目的分析行肿瘤细胞减灭术(CRS)联合腹腔热灌注化疗术(HIPEC)的患者术后肺部并发症(PPC)的危险因素,并构建预测模型。方法收集行CRS+HIPEC的298例患者围手术期信息[性别、年龄、美国麻醉医师协会(ASA)分级、手术时间、术中总入量、术中总出量、出血量、尿量、胶体液输注量、晶体液输注量、自体血回输量、红细胞输注量、血浆输注量、围手术期进行目标导向液体治疗(GDFT)时参考的每搏变异度(SVV)值]。根据患者术后有无PPC,将患者分为PPC组(106例)和非PPC组(192例)。采用逐步回归分析筛选PPC的特征变量并建立随机森林预测模型,计算随机森林预测模型的袋外误差率,分别在训练集和测试集上计算混淆矩阵及参数(包括准确度、Kappa值、灵敏度、特异度、精准度、召回率、F1⁃Score);绘制受试者操作特征曲线(ROC曲线)[并计算曲线下面积(AUC)及95%置信区间(CI)]、校准曲线,绘制自变量排序图和各特征变量的偏依赖图。结果与非PPC组比较,PPC组的手术时间较长(P<0.05),术中总入量、术中总出量、出血量、胶体液输注量、尿量和红细胞输注量均较多(均P<0.05),围手术期进行GDFT时参考的SVV值较低,差异有统计学意义(P<0.05)。逐步回归分析显示手术时间、出血量、红细胞输注量和围手术期进行GDFT时参考的SVV值为PPC的特征变量(P<0.05)。随机森林预测模型的袋外误差率为1.400%。训练集准确度1.000,测试集准确度0.952,说明模型整体预测准确性高。Kappa值训练集1.000,测试集为0.894,说明模型整体预测能力的一致性高。训练集的灵敏度为1.000,特异度为1.000,测试集的灵敏度为0.871,特异度为1.000,说明模型的整体区分度较好。训练集的精准度为1.000,召回率为1.000,F1⁃Score为1.000,测试集的精准度为1.000,召回率为0.871,F1⁃Score为0.931,说明模型对于阳性结果的预测能力高。训练集ROC曲线的AUC为1.000Objective To analyze the risk factors for postoperative pulmonary complications(PPC)in patients undergoing cy⁃toreductive surgery(CRS)combined with hyperthermic intraperitoneal chemotherapy(HIPEC)and to construct a prediction model.Methods Collect perioperative information of 298 patients undergoing CRS+HIPEC[including gender,age,American Society of Anes⁃thesiologists(ASA)classification,operation duration,total intraoperative infusion volume,total intraoperative output volume,blood loss,urine volume,colloid infusion volume,crystalloid infusion volume,autologous blood transfusion volume,red blood cell transfusion vol⁃ume,plasma transfusion volume,and stroke volume variation(SVV)value referred to during goal⁃directed fluid therapy(GDFT)during the perioperative period].According to the presence or absence of PPC after surgery,patients were divided into PPC group(106 cases)and non⁃PPC group(192 cases).Stepwise regression analysis was used to screen the characteristic variables of PPC and establish a ran⁃dom forest prediction model.The out⁃of⁃bag error rate of the random forest prediction model was calculated,and the confusion matrix and parameters(including accuracy,Kappa value,sensitivity,specificity,precision,recall,F1⁃Score)were calculated on the training set and test set,respectively.Receiver operating characteristic(ROC)curves were plotted[and the area under the curve(AUC)and 95%confi⁃dence interval(CI)were calculated],calibration curves were drawn,and the ranking of independent variables and partial dependence plots of each characteristic variable were plotted.Results Compared with the non⁃PPC group,the PPC group had a longer operation du⁃ration(P<0.05),increased total intraoperative infusion volume,total intraoperative output volume,blood loss,colloid infusion volume,urine volume,and red blood cell transfusion volume(all P<0.05),and a decreased SVV value during the perioperative period,with statistically sig⁃nificant differences(P<0.05).The stepwise regression analysis showed that ope

关 键 词：随机森林预测模型 目标导向液体治疗 肺部并发症 偏依赖图 混淆矩阵 

分 类 号：R614.2[医药卫生—麻醉学] R730.5[医药卫生—外科学]