基于随机森林模型的抗肿瘤化疗患者经外周静脉植入中心静脉导管置管后导管相关感染及影响因素

Catheter-associated infection and influencing factors in anti-tumor chemotherapy treated patients after indwelling peripherally inserted central ca-theter: analysis based on random forest model

目的 基于随机森林模型分析化学治疗患者经外周静脉置入中心静脉导管(PICC)置管后导管相关感染的影响因素。方法 选取接受化学治疗并留置PICC的400例肿瘤患者,采用计算机产生随机数法将就诊患者以3∶1的比例分为训练集(300例)和测试集(100例)。根据感染发生情况将训练集患者分为无感染组和感染组,比较两组的临床资料,采用多因素logistic回归模型及随机森林的集成分类算法分析患者PICC置管后出现导管相关感染的影响因素,并对比二者的预测效能。结果 训练集300例化学治疗患者中,32例患者出现导管相关感染(10.67%),与无感染组比较,感染组患者单次置管穿刺次数更多,PICC留置时间更长,导管移动比例、合并糖尿病比例及换药频次更高,白细胞计数(WBC)水平及免疫功能更低(均P<0.05)。PICC留置时间、导管移动情况、合并糖尿病情况、换药频次、WBC及免疫功能均为患者PICC置管后导管相关感染的独立影响因素(均P<0.05)。随机森林模型显示不同影响因素的重要程度排序结果依次为:PICC留置时间、导管移动情况、合并糖尿病情况、WBC、换药频次及免疫功能。随机森林模型的集成分类算法预测化学治疗患者发生导管相关感染的受试者工作特征(ROC)曲线下面积(AUC)为0.872,与logistic回归模型(AUC=0.791)相比预测效能更优。结论 PICC留置时间、导管移动情况、合并糖尿病情况、换药频次、WBC水平及免疫功能是化学治疗患者发生导管相关感染的独立影响因素,随机森林模型的集成分类算法可用于对化学治疗患者发生导管相关感染的预测分析,其预测性能优于logistic回归模型。

Objective To analyze the influencing factors for catheter-associated infection(CAI)in chemotherapy treated patients after indwelling peripherally inserted central catheter(PICC)based on a random forest model.Methods 400 tumor patients who received chemotherapy and PICC were selected and divided into the training set(n=300)and the test set(n=100)in a 3∶1 ratio through computer-generated random number.Patients in the training set were subdivided into the non-infection group and the infection group based on the occurrence of infection.Clinical data from two groups of patients were compared.Influencing factors for the occurrence of CAI after PICC were analyzed with multivariate logistic regression model and the integrated classification algorithm of random forest model,and the predictive performance of the two methods was compared.Results Among 300 chemotherapy treated patients in the training set,32 cases(10.67%)experienced CAI.Compared with the non-infection group,patients in the infection group had more single punctures for catheterization,longer PICC retention time,larger proportion of catheter movement,larger proportion of complication with diabetes,higher frequency of dressing changes,lower white blood cell count and immune function(all P<0.05).PICC retention time,catheter movement,complication with diabetes,dressing change frequency,white blood cell(WBC)and immune function were independent influencing factors for CAI after PICC(all P<0.05).The random forest model showed that ranking by the importance of different influencing factors was as following:PICC retention time,catheter movement,complication with diabetes,WBC,dressing change frequency and immune function.The integrated classification algorithm of random forest model for predicting the occurrence of CAI in chemotherapy treated patients showed that the area under the receiver operating characteristic(ROC)curve(AUC)was 0.872,which had better prediction performance compared with the logistic regression model(AUC=0.791).Conclusion PICC retention time,catheter movement,complicated with diabetes,dressing change frequency,WBC level and immune function are independent influencing factors for CAI in chemotherapy treated patients.The integrated classification algorithm of random forest model can be used to predict CAI in chemotherapy treated patients,and its prediction performance is better than that of the logistic regression model.