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 ›› 2026, Vol. 18 ›› Issue (1): 71-76.doi: 10.20274/j.cnki.issn.1674-3865.2026.01.014

• 临床研究 • 上一篇    下一篇

基于随机森林模型的小儿重症肺炎支原体肺炎风险因素分析的应用研究

孔森, 张亮(), 廖楠, 涂光   

  1. 344600 江西 抚州,黎川县人民医院儿科
  • 收稿日期:2025-07-29 修回日期:2025-09-29 出版日期:2026-02-25 上线日期:2026-02-25
  • 通讯作者: 张亮 E-mail:987928918@qq.com
  • 作者简介:孔森(1984-),男,主治医师。研究方向:小儿呼吸系统疾病的诊治
  • 基金资助:
    抚州市2024年度第一批市级社会发展指导性科技计划项目(抚科社字〔2024〕6号)

Application research on risk factor analysis of severe Mycoplasma pneumoniae pneumonia in children based on random forest model

Sen KONG, Liang ZHANG(), Nan LIAO, Guang TU   

  1. Lichuan People's Hospital, Fuzhou 344600, China
  • Received:2025-07-29 Revised:2025-09-29 Published:2026-02-25 Online:2026-02-25
  • Contact: Liang ZHANG E-mail:987928918@qq.com
  • Supported by:
    First Batch of 2024 Fuzhou Municipal Guiding Science and Technology Projects for Social Development

摘要:

目的 本研究旨在通过随机森林模型分析小儿重症肺炎支原体肺炎(MPP)的风险因素,为临床早期识别和干预提供依据。 方法 选择2024年1~12月黎川县人民医院收治住院的小儿MPP患者198例的临床资料,其中133例为非重症组,65例为重症组。对两组患者的临床特征进行比较,并采用单因素Logistic回归分析和随机森林模型评估各变量对重症MPP的影响。随机森林模型的构建包括特征重要性分析和受试者工作特征(ROC)曲线评估。 结果 在临床特征比较中,两组在年龄和性别分布上差异无统计学意义(P>0.05),重症组在发热病程、白细胞计数、C反应蛋白、红细胞沉降率、降钙素原、白细胞介素-6、乳酸脱氢酶、肌酸激酶同工酶等指标上均显著高于非重症组,差异有统计学意义(P<0.05)。同时,重症组中高热和喘息的发生率显著高于非重症组,差异有统计学意义(P<0.05)。单因素Logistic回归分析显示,高热、发热病程、喘息、白细胞计数、C反应蛋白、红细胞沉降率、降钙素原、白细胞介素-6、乳酸脱氢酶、肌酸激酶同工酶均与重症MPP风险有显著关联。随机森林模型分析显示,模型的错误率在决策树数目为45时最低,危险因素的重要性排序依次是发热病程、白细胞计数、年龄、是否高热、肌酸激酶同工酶、红细胞沉降率、C反应蛋白、红细胞计数、白细胞介素-6、乳酸脱氢酶、降钙素原、是否喘息、血小板计数、性别。ROC曲线分析显示,随机森林模型的ROC曲线下面积为0.882,灵敏度为75.68%,特异度为93.33%。 结论 本研究通过单因素Logistic回归分析和随机森林模型,识别了发热病程、高热、喘息、白细胞计数等变量作为小儿重症MPP的风险因素。这些结果为临床早期识别和干预重症MPP提供了重要参考,有助于改善患儿的预后。

关键词: 重症肺炎, 肺炎支原体肺炎, 风险因素, 随机森林模型, Logistic回归分析, 儿童

Abstract:

Objective To identify risk factors for severe pediatric Mycoplasma pneumoniae pneumonia using a random forest model and to provide evidence for clinical recognition and intervention. Methods Clinical data of 198 children hospitalized for Mycoplasma pneumoniae pneumonia in our hospital between January and December 2024 were collected; 133 were classified as non-severe and 65 as severe. Clinical characteristics were compared between the two groups. Univariate Logistic regression analysis and a random forest model were employed to evaluate the impact of each variable on severe Mycoplasma pneumoniae pneumonia. The construction of random forest model included feature importance analysis and ROC curve assessment. Results No statistically significant differences were observed in age or gender distribution between the groups(P>0.05). The severe group showed significantly higher levels of fever duration, WBC, CRP, ESR, PCT, IL-6, LDH, and CK-MB than the non-severe group(P<0.05). The incidence of high fever and wheezing was also significantly higher in the severe group(P<0.05). Univariate logistic regression indicated that high fever, fever duration, wheezing, WBC, CRP, ESR, PCT, IL-6, LDH, and CK-MB were significantly associated with the risk of severe Mycoplasma pneumoniae pneumonia. The random forest model analysis revealed the lowest error rate when the number of decision trees was 45. The descending order of risk factor importance was fever duration, WBC, age, presence of high fever, CK-MB, ESR, CRP, RBC, IL-6, LDH, PCT, presence of wheezing, PLT, and gender. ROC curve analysis demonstrated an AUC of 0.882 of the random forest model, with a sensitivity of 75.68% and a specificity of 93.33%. Conclusion Through univariate Logistic regression and the random forest model, this study identifies fever duration, high fever, wheezing, and WBC as key risk factors for severe pediatric Mycoplasma pneumoniae pneumonia. These findings offer valuable reference for early clinical identification and intervention of severe Mycoplasma pneumoniae pneumonia, helping to improve the outcomes of affected children.

Key words: Severe pneumonia, Mycoplasma pneumoniae pneumonia, Risk factors, Random forest model, Logistic regression analysis, Child

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