基于临床资料和一氧化氮相融合的毛细支气管炎发生反复喘息的预测模型构建及验证  

Construction and validation of a prediction model for recurrent wheezing in bronchiolitis based on the fusion of clinical data and nitric oxide

作　　者：吴绍霞[1] 沈广礼[2] 吴海霞[3] 刘继鹏 王洪洲[1] 朱美云 WU Shaoxia;SHEN Guangli;WU Haixia;LIU Jipeng;WANG Hongzhou;ZHU Meiyun(People′s Hospital of Linqu County,Weifang 262600,China)

机构地区：[1]山东省临朐县人民医院儿科,山东潍坊262600 [2]山东省临朐县人民医院内分泌科,山东潍坊262600 [3]山东省临朐县人民医院产科,山东潍坊262600 [4]山东省临朐县人民医院神经内科,山东潍坊262600

出　　处：《中国中西医结合儿科学》2024年第3期215-221,共7页Chinese Pediatrics of Integrated Traditional and Western Medicine

基　　金：2022年度山东省医药卫生科技发展计划项目(202206010142)。

摘　　要：目的构建关于毛细支气管炎后发生反复喘息的Nomogram预测模型,为临床反复喘息的患儿提供更科学的防治和临床决策指导。方法选取2022年1月至2022年12月于山东省临朐县人民医院儿科就诊并诊断毛细支气管炎的住院患儿作为研究对象,收集临床资料及呼出气一氧化氮(FeNO)等相关指标,出院后随访1年,根据随访期间是否发生反复喘息分为观察组(反复喘息)和对照组(未发生反复喘息)。将两组参数通过Lasso回归和单因素Logistic回归分析筛选出有意义的变量,纳入多因素Logistic回归分析,建立关于毛细支气管炎后发生反复喘息的Nomogram预测模型。采用Hosmer-Lemeshow拟合优度检验,计算C指数和受试者工作特征曲线下面积(AUC),评价模型的准确性。应用决策曲线评价列线图的临床应用价值。结果共85例患儿纳入本研究,其中观察组17例,对照组68例。在一般资料分析中发现,FeNO(P=0.048)和淋巴细胞计数(P=0.023)存在组间差异。分别纳入单因素Logistic回归分析和Lasso回归分析。单因素Logistic回归经过年龄和性别调整后,发现FeNO[OR=1.242,95%CI=(1.002,1.541),P=0.048]和淋巴细胞计数[OR=1.428,95%CI=(1.028,1.985),P=0.034],差异有统计学意义;Lasso回归分析,其最小均方误差的λ为0.042,对应模型的变量选择为FeNO和淋巴细胞计数。多因素Logistic回归方程建立预测毛细支气管炎反复喘息发生的Nomogram列线图模型后,Hosmer-Lemeshow检验χ^(2)=3.881,P=0.868,C指数为0.706,FeNO和淋巴细胞计数AUC分别为0.654和0.674,表明该评分模型工作效果良好。决策曲线分析发现模型=反复喘息-FeNO+淋巴细胞计数具有较好的临床应用价值。结论通过FeNO和淋巴细胞计数建立的关于毛细支气管炎后发生反复喘息的Nomogram预测模型具有较好的临床应用价值。Objective To construct a Nomogram prediction model for recurrent wheezing after bronchiolitis,providing more scientific prevention and clinical decision-making guidance for children with recurrent wheezing in clinical practice.Methods Hospitalized pediatric patients diagnosed with bronchiolitis at Linqu County People′s Hospital from January 2022 to December 2022 were selected as the study subjects.Clinical data and related indicators such as FeNO were collected.After discharge,patients were followed up for one year.According to whether recurrent wheezing occurred during the follow-up period,they were divided into an observation group(recurrent wheezing occurred)and a control group(no recurrent wheezing occurred).The two sets of parameters were screened for meaningful variables through Lasso regression and univariate logistic regression analysis,and multivariate logistic regression analysis was included to establish a Nomogram prediction model for recurrent wheezing after bronchiolitis.Use the Hosmer Lemeshow goodness of fit test to calculate the C-index and the area under the receiver operating characteristic curve(AUC)to evaluate the accuracy of the model.The clinical application value of the nomogram was assessed by using decision curves.Results A total of 85 pediatric patients were included in this study,17 in the observation group and 68 in the control group.In general data analysis,it was found that there were intergroup differences in FeNO(P=0.048)and lymphocyte count(P=0.023).Include single factor logistic regression analysis and Lasso regression analysis separately.After adjusting for age and gender,univariate logistic regression found that the differences in FeNO(OR=1.242,95%CI(1.002,1.541),P=0.048)and lymphocyte count(OR=1.428,95%CI(1.028,1.985),P=0.034)were statistically significant;Lasso regression analysis showed that theλof minimum mean square error was 0.042,and the corresponding model variables were FeNO and lymphocyte count.After establishing a Nomogram model for predicting recurrent wheezing

关 键 词：毛细支气管炎 呼出气一氧化氮 反复喘息 预测模型 儿童 

分 类 号：R725.6[医药卫生—儿科]