广西医学2024，Vol.46Issue(1)：78-83,6.DOI:10.11675/j.issn.0253-4304.2024.01.13
建立梯度提升机模型预测RICU机械通气并发呼吸机相关性肺炎患者的短期预后
Establishment of the gradient boosting machine model for predicting short-term prognosis of patients with mechanical ventilation and concomitant ventilator-associated pneumonia in RICU
摘要
Abstract
Objective To establish the gradient boosting machine(GBM)model for predicting short-term prognosis of patients with mechanical ventilation and concomitant ventilator-associated pneumonia(VAP)in respiratory intensive care unit(RICU).Methods The clinical data of 350 patients with mechanical ventilation and concomitant VAP admitted to RICU were retrospectively analyzed,and they were divided into death group(n=110)or survival group(n=240)according to patients'follow-up outcome.The risk factors affecting their prognosis were screened.According to the ratio of 8:2,patients were randomly assigned to training set(280 cases)or validation set(70 cases).The R language 4.2.1 software was used to establish a model of GBM for predicting short-term prognosis in patients with mechanical ventilation and concomitant VAP in RICU,and prediction efficiency of the model was evaluated.Results Age,mechanical ventilation duration,C-reactive protein(CRP)level,serum procalcitonin level,Acute Physiology and Chronic Health Evaluation Ⅱ(APACHEⅡ)score,and Sequential Organ Failure Assessment(SOFA)score were the influencing factors for prognosis of patients with mechanical ventilation and concomitant VAP in RICU(P<0.05).Area under the curve(AUC)of receiver operating characteristic for the GBM model established based on the aforementioned influencing factors in the training set was 0.926(95%CI:0.894,0.958),the sensitivity was 85.4%,and the specificity was 86.2%;furthermore,AUC in the validation set was 0.880(95%CI:0.779,0.980),the sensitivity was 85.7%,and the specificity was 86.4%.The calibration curve revealed that the predicted probability of GBM model was basically consistent with the actual incidence rate.The decision curve analysis indicated that the threshold probabilities of the training set and the validation set were 0.10-0.98 and 0.10-0.80,respectively.Conclusions The GBM model established based on age,mechanical ventilation duration,CRP level,serum procalcitonin level,APACHEⅡ score and SOFA score exerts a favorable predictive value for short-term prognosis of patients with mechanical ventilation and concomitant VAP in RICU.关键词
呼吸机相关性肺炎/机械通气/呼吸重症监护病房/梯度提升机模型/危险因素/预后Key words
Ventilator-associated pneumonia/Mechanical ventilation/Respiratory intensive care unit/Gradient boosting machine model/Risk factors/Prognosis分类
医药卫生引用本文复制引用
黄小芬,夏良娥,赵世元,黄敏敏..建立梯度提升机模型预测RICU机械通气并发呼吸机相关性肺炎患者的短期预后[J].广西医学,2024,46(1):78-83,6.基金项目
崇左市科技计划项目(崇科攻2018028) （崇科攻2018028）
