新生儿坏死性小肠结肠炎相关因素研究  

A study of factors associated with neonatal necrotizing enterocolitis

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作  者:杨启越 张新华[2,3] 贾晓云 周浩[1,2] 康娅楠 王星雨 白丽霞 Yang Qiyue;Zhang Xinhua;Jia Xiaoyun;Zhou Hao;Kang Yanan;Wang Xingyu;Bai Lixia(Department of Epidemiology,School of Public Health,Shanxi Medical University,Taiyuan 030000,China;Children's Hospital Affiliated to Shanxi Medical University,Taiyuan 030000,China;Shanxi Provincial Children's Hospital(Shanxi Provincial Maternity and Child Health Center),Taiyuan 030000,China;Cadre Health Care Department,the Second Hospital of Shanxi Medical University,Taiyuan 030000,China)

机构地区:[1]山西医科大学公共卫生学院流行病学教研室,太原030000 [2]山西医科大学附属儿童医院,太原030000 [3]山西省儿童医院(山西省妇幼保健院),太原030000 [4]山西医科大学第二医院干部保健科,太原030000

出  处:《中华流行病学杂志》2025年第3期492-498,共7页Chinese Journal of Epidemiology

基  金:山西省基础研究计划(202403021211168);国家临床重点专科(建设)项目。

摘  要:目的通过构建和比较9种回归模型,探讨新生儿坏死性小肠结肠炎(NEC)的相关影响因素。方法将2020-2022年在山西省儿童医院(山西省妇幼保健院)新生儿内科、新生儿外科、新生儿重症监护病房住院的所有NEC患儿作为病例组,并根据纳入排除标准收集同期住院患儿作为对照组,对收集到的NEC数据利用Boruta算法进行特征筛选,构建logistic回归、多决策树梯度提升、高效梯度单边采样、随机森林、决策树、梯度提升决策树(GBDT)、神经网络、支持向量机及K近邻模型,通过比较后选出最优模型并进行Shap可解释分析。结果通过筛选确定了13个关键因素纳入9种回归模型构建。经过严格的比较分析,GBDT模型在稳定性方面表现优于其他8种回归模型。在验证集中,GBDT模型的受试者工作特征曲线下面积为0.958,准确度为0.925,灵敏度和特异度分别为0.827和0.950,对GBDT模型进行Shap可解释分析后显示,贫血、无创呼吸机、降钙素原、早产、低出生体重儿等会增加NEC的发生风险,而母乳喂养和益生菌会降低NEC的发生风险。结论本研究通过GBDT模型识别了NEC的危险及保护因素,为NEC防治提供科学依据。Objective To explore the related risk factors of neonatal necrotizing enterocolitis(NEC)by constructing and comparing nine regression models.Methods All NEC patients admitted to the neonatal internal medicine department,neonatal surgery department,and neonatal intensive care unit of Shanxi Provincial Children's Hospital(Shanxi Provincial Maternity and Child Health Center)from 2020 to 2022 were included as the case group.A control group consisted of children admitted during the same period based on the inclusion and exclusion criteria.The NEC data collected were used for feature selection by using the Boruta algorithm.Logistic regression,multi-decision tree gradient boosting,efficient gradient one-sided sampling,random forest,decision tree,gradient boosting decision tree(GBDT),neural network,support vector machine,and K-nearest neighbor models were constructed.The optimal model was selected through rigorous comparison and Shap explainable analysis was performed on the GBDT model.Results Thirteen key factors were identified through screening for nine regression models construction.After strict comparison and analysis,the GBDT model showed higher stability compared with other eight regression models.In the validation set,the area under the receiver operating characteristic curve of the GBDT model was 0.958,with an accuracy of 0.925,and sensitivity and specificity of 0.827 and 0.950,respectively.Shap explainable analysis on the GBDT model revealed that suffering from anemia,non-invasive ventilator use,procalcitonin use,premature birth,and low birth weight increased the risk for NEC,while breastfeeding and probiotics decreased the risk for NEC.Conclusion This study identified the risk factors and protective factors for NEC by using the GBDT model,which provided evidnce for the prevention and treatment of NEC.

关 键 词:Boruta算法 回归模型 新生儿坏死性小肠结肠炎 相关因素 

分 类 号:R72[医药卫生—儿科]

 

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