儿童甲型流行性感冒并发危重症肺炎的个体化诊断预测模型构建及危险因素分析  

Construction of individualized diagnosis and prediction model for children with influenza A complicated with severe pneumonia and risk factor mining

作　　者：张蕾[1] 田利远[1] 李芹[1] 李清涛[1] 张曼[1] 王亚坤[1] ZHANG Lei;TIAN Li-yuan;LI Qin;LI Qing-tao;ZHANG Man;WANG Ya-kun(First Department of Respiratory, Children′s Hospital of Hebei Province, Shijiazhuang 050031, China)

机构地区：[1]河北省儿童医院呼吸一科,河北石家庄050031

出　　处：《河北医科大学学报》2021年第3期314-319,共6页Journal of Hebei Medical University

基　　金：河北省医学科学研究重点课题计划(20180638)。

摘　　要：目的采用机器学习技术构建儿童甲型流行性感冒并发危重症肺炎的个体化预测模型,充分挖掘高危因素,以及早开展精准预防治疗措施改善预后。方法收集儿童甲型流行性感冒肺炎患儿的临床数据;采用机器学习XGBoost算法,分别基于临床全指标和临床常用指标构建2套诊断预测模型,对比2个模型的诊断价值及相应用于预测指标的重要性排名。结果全指标预测模型的准确度、模型诊断结果与观察结果一致性判断指标Kappa值、ROC曲线下面积(area under curve,AUC)、敏感度、阳性预测值、阴性预测值均优于临床常用指标预测模型。基于全指标的诊断预测模型的主要预测指标按重要性排名为呼吸衰竭、咳喘程度(重度喘息)、总B细胞、单核细胞、发热时间(>15 d)、辅助T细胞(CD4);基于临床常用指标的诊断预测模型的主要预测指标按重要性排名为呼吸衰竭、胸部X线片(肺炎)、混合菌或病毒。全指标预测模型的各指标增益值(横坐标)明显优于临床常用指标预测模型的指标。结论基于儿童甲型流行性感冒并发肺炎患儿的临床资料,采用机器学习技术,以儿童甲型流行性感冒并发危重症肺炎为变量,以临床基础资料和生化、免疫、影像等全指标作为预测因子,构建的个体化诊断预测模型的诊断价值较高,在预测个体并发危重症肺炎概率的同时,针对性挖掘个体高危因素,从而早预防治疗儿童甲型流行性感冒并发危重症肺炎,改善预后。Objective To build an individualized prediction model for children with influenza A complicated with severe pneumoniausing machine learning technology,and to fully explore high-risk factors,in order to improve the prognosis of children by early implementation of accurate preventive measures and treatment methods.Methods The clinical data of children with influenza A infected pneumonia were collected.Two sets of diagnosis and prediction models were constructed based on full clinical indexes and commonly used clinical indexes by using machine learning XGBoost algorithm,and the diagnostic value of the two models and the importance ranking of the corresponding indexes for prediction were compared.Results According tothe accuracy of the full index prediction model,the model diagnosis results and consistency of the observation results,Kappa value,area under curve(AUC),sensitivity,positive predictive value and negative predictive value of full index prediction modelwere better than those of commonly used clinical indexes.Based on full index prediction model,the main predictors of the model according to importance rankingwere respiratory failure,severity of cough and asthma(severe wheezing),total B cells,monocytes,duration offever(>15 d)andhelper T cells(CD4).The main predictors of the diagnostic prediction model based on commonly used clinical indexesaccording to ranking importance were respiratory failure,chest X-ray(pneumonia),mixed bacteria or virus.The gain value(X axis)of each index of the full index prediction model was significantly better than that of indexes inthe commonly used clinicalindexprediction model.Conclusion Based on the clinical data of children with influenza A complicated with pneumonia,using machine learning technology,children with influenza A complicated with critical pneumonia serveas variables,and all indexessuch asbasic clinical data and biochemical,immune,and imaging indexes serveas predictors.The individualized diagnostic prediction model has a higher diagnostic value,which could predict th

关 键 词：流感 人 肺炎 个体化诊断预测模型 

分 类 号：R725.117[医药卫生—儿科]