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中国循证儿科杂志

中国循证儿科杂志 ›› 2023, Vol. 18 ›› Issue (3): 182-186.DOI: 10.3969/j.issn.1673-5501.2023.03.003

• 论著 • 上一篇    下一篇

肺炎支原体肺炎患儿伴气道黏液栓形成的预测模型

栾文君1,2,路素坤1,2,黄坤玲1,帅金凤1,吕文山1,牛波1,曹丽洁1,刘建华1   

  1. 1 河北省儿童医院石家庄，050031；2 共同第一作者
  • 收稿日期:2023-03-02 修回日期:2023-06-17 出版日期:2023-06-25 发布日期:2023-06-25
  • 通讯作者: 刘建华

Prediction model for mycoplasma pneumoniae pneumonia with airway mucus plug formation

LUAN Wenjun1,2, LU Sukun1,2, HUANG Kunling1, SHUAI Jinfeng1, LYU Wenshan1, NIU Bo1, CAO Lijie1, LIU Jianhua1#br#

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  1. 1 Hebei Provincial Children's Hospital, Shijiazhuang 050031, China; 2 Co-first authors
  • Received:2023-03-02 Revised:2023-06-17 Online:2023-06-25 Published:2023-06-25
  • Contact: LIU Jianhua, email: liuwjm@sina.com

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摘要： 背景：肺炎支原体肺炎（MPP）病情严重程度与气道内黏液栓形成有关，但目前气道内黏液栓主要依赖支气管镜检查。 目的：基于临床特征并结合血浆细胞因子，建立对MPP患儿气道黏液栓形成的列线图预测模型。 设计：病例对照研究。 方法：纳入确诊MPP且行支气管镜检查的患儿，分为黏液栓组和无黏液栓组。采集临床特征、炎症指标和细胞因子［血浆和肺泡灌洗液（BALF）］。通过单因素分析筛选两组中差异有统计学意义的临床特征、炎症指标和细胞因子危险因素；对炎症指标和细胞因子（血浆和BALF）行Spearman相关性分析，排除共线性问题；使用R语言RMS程序包建立基于多因素二分类Logistic逻辑回归的列线图模型，并绘制ROC曲线检验其预测效能。 主要结局指标：预测MPP患儿气道黏液栓形成的列线图模型的预测效能。 结果：263例MPP患儿纳入分析，男134例、女129例，年龄（7.0±0.2）岁，黏液栓组82例（31.2%）、无黏液栓组181例。①单因素分析显示，两组年龄，支气管镜检查前的最高体温，入院时听诊呼吸音低、胸部CT肺实变、影像学提示胸腔积液、肺外并发症、重症肺炎的比例，中性粒细胞百分比、CRP、PCT、D-二聚体、LDH、IgA、淋巴细胞百分比、ALT差异均有统计学意义。②细胞因子检测：两组血浆中IL-5、IL-6、IL-8、IFN-γ水平差异有统计学意义；BALF中IL-1β、IL-5、IL-8、IL-10、IFN-γ、TNF-α水平差异有统计学意义。③Spearman相关性分析显示，中性粒细胞百分比、CRP、PCT、LDH、D-二聚体、IgA、血浆IFN-γ、血浆IL-6、血浆IL-5、血浆IL-8均无共线性；血液炎症指标与BALF细胞因子存在一定相关性，其中血浆细胞因子IFN-γ、IL-6、IL-5、IL-8与BALF细胞对应因子亦呈正相关。④基于年龄、有胸腔积液、D-二聚体、血浆IFN-γ建立的MPP患儿气道黏液栓形成的列线图预测模型，AUC=0.817（95%CI：0.747~0.889），敏感度为79.0%，特异度为69.1%。 结论：利用年龄、胸腔积液、D-二聚体、血浆IFN-γ建立的列线图预测模型对MPP患儿气道黏液栓形成有较好的预测效能。

关键词: 肺炎支原体肺炎, 黏液栓, 儿童, 预测模型

Abstract: Background：The severity of mycoplasma pneumoniae pneumonia (MPP) is related to the formation of mucus plugs (MUP) in the airway, but the current MUP detection mainly depends on bronchoscopy. Objective：To establish a nomogram predictive model based on clinical features and plasma cytokines for airway MUP formation in MPP children. Design：Case-control study. Methods：Children who were diagnosed as MPP and underwent bronchoscopy were classified into MUP and non-MUP groups. Clinical features, inflammatory markers and cytokines in both plasma and BALF were collected. Univariate analysis was performed to identify statistically significant clinical features, inflammatory markers, and cytokine risk factors between the two groups. Spearman correlation analysis was conducted to assess the correlation between inflammatory markers, plasma cytokines and BALF cytokines and to exclude collinearity issues. A receiver operating characteristic (ROC) curve was generated to assess the predictive performance of a multivariable logistic regressionbased predictive model using the R package of RMS. Main outcome measures：Predictive efficacy of the nomogram model for predicting the probability of airway mucus plug formation in children with MPP. Results：A total of 263 children with MPP were included in the analysis, including 134 males and 129 females, with an average age of (7.0 ± 0.2) years. There were 82 (31.2%) cases in the MUP group and 181 cases in the non-MUP group. Univariate analysis showed significant differences between the two groups in age, maximum temperature before bronchoscopy, decreased breath sounds upon auscultation at admission, chest CT showing pulmonary consolidation, imaging suggesting pleural effusion, extrapulmonary complications, proportion of severe pneumonia, neutrophil percentage, CRP, PCT, D-dimer, LDH, IgA, lymphocyte percentage, and ALT. Cytokine analysis revealed significant differences between the two groups in levels of IL-5, IL-6, IL-8, and IFN-γ in plasma, as well as in levels of IL-1β, IL-5, IL-8, IL-10, IFN-γ, and TNF-α in BALF. Spearman correlation analysis showed there was no collinearity in neutrophil percentage, CRP, PCT, LDH, D-dimer, IgA, plasma IFN-γ, plasma IL-6, plasma IL-5, and plasma IL-8; there was a certain correlation between blood inflammatory markers and BALF cytokines; plasma cytokines (IFN-γ, IL-6, IL-5, and IL-8) were positively correlated with their corresponding BALF cytokines. The nomogram predictive model for the formation of airway mucus plugs in MPP patients, based on age, pleural effusion, D-dimer, and plasma IFN-γ levels, had an AUC of 0.817 (95% CI: 0.747-0.889) with the sensitivity of 79.0% and specificity of 69.1%. Conclusion：The nomogram predictive model based on age, pleural effusion, D-dimer, and plasma IFN-γ levels had a good predictive performance for the formation of airway mucus plugs in MPP patients.

Key words: Mycoplasma pneumoniae pneumonia, Mucus plug, Children, Predictive model