ISSN 1674-3865  CN 21-1569/R
主管：国家卫生健康委员会
主办：中国医师协会
　　　辽宁省基础医学研究所
　　　辽宁中医药大学附属医院
中国中西医结合儿科学

中国中西医结合儿科学 ›› 2024, Vol. 16 ›› Issue (3): 215-221.

• 临床论著 • 上一篇    下一篇

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

  

  • 出版日期:2024-06-25 上线日期:2024-08-26

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

  • Published:2024-06-25 Online:2024-08-26

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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χ2=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。 

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

Abstract: ObjectiveTo 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.MethodsHospitalized 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.ResultsA 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 the λ of 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 in bronchiolitis using a multiple factor logistic regression equation,the Hosmer Lemeshow test chi square value was 3.881,P=0.868,C-index was 0.706,and the AUC of FeNO and lymphocyte count was 0.654 and 0.674,respectively,indicating that the scoring model worked well.The decision curve analysis found that the model=repeated wheezing-FeNO+lymphocyte count had good clinical application value.ConclusionThe Nomogram prediction model for recurrent wheezing after bronchiolitis established through FeNO and lymphocyte count has good clinical application value.

Key words:

Bronchiolitis, Fractional exhaled nitric oxide(FeNO), Recurrent wheezing, Prediction model, Child