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中华实验和临床感染病杂志(电子版) ›› 2025, Vol. 19 ›› Issue (02) : 77 -83. doi: 10.3877/cma.j.issn.1674-1358.2025.02.003

论著

重度肺炎患儿感染后闭塞性细支气管炎列线图预测模型的构建及验证
赵艾红1, 唐为娟1,(), 傅俊建1, 徐湘1   
  1. 1. 224700 建湖县,扬州大学建湖临床医学院儿科
  • 收稿日期:2024-08-14 出版日期:2025-04-15
  • 通信作者: 唐为娟
  • 基金资助:
    江苏省妇幼健康科研项目(No. F202157)

Construction and validation of a line chart prediction model for post infectious bronchiolitis obliterans in children with severe pneumonia

Aihong Zhao1, Weijuan Tang1,(), Junjian Fu1, Xiang Xu1   

  1. 1. Department of Pediatrics, Jianhu Clinical Medical College, Yangzhou University, Jianhu 224700, China
  • Received:2024-08-14 Published:2025-04-15
  • Corresponding author: Weijuan Tang
引用本文:

赵艾红, 唐为娟, 傅俊建, 徐湘. 重度肺炎患儿感染后闭塞性细支气管炎列线图预测模型的构建及验证[J/OL]. 中华实验和临床感染病杂志(电子版), 2025, 19(02): 77-83.

Aihong Zhao, Weijuan Tang, Junjian Fu, Xiang Xu. Construction and validation of a line chart prediction model for post infectious bronchiolitis obliterans in children with severe pneumonia[J/OL]. Chinese Journal of Experimental and Clinical Infectious Diseases(Electronic Edition), 2025, 19(02): 77-83.

目的

分析重度肺炎患儿感染后闭塞性细支气管炎(PIBO)的影响因素,构建列线图预测模型并验证。

方法

收集扬州大学建湖临床医学院2022年1月至2023年12月收治的252例重度肺炎患儿病历资料。随访6个月,根据患儿是否发生PIBO分为PIBO组(192例)和非PIBO组(60例)。比较两组患儿的临床指标,筛选重度肺炎患儿发生PIBO的影响因素,进一步应用多因素Logistic回归分析筛选出发生PIBO的独立影响因素。构建列线图模型,通过受试者工作特征曲线(ROC)评估预测模型的区分度,通过决策曲线、校准曲线评估预测模型的临床实用性和校准度。

结果

252例重度肺炎患儿中发生PIBO者共60例,PIBO发生率为23.81%。PIBO组患儿月龄低于非PIBO组[(18.87 ± 6.72)月 vs. (28.30 ± 9.04)月:t = 8.648、P < 0.001)],热程长于非PIBO组[(13.77 ± 3.75)d vs. (9.92 ± 3.05)d:t = 8.057、P < 0.001)],LDH水平高于非PIBO组[(725.78 ± 98.72)U/L vs. (628.49 ± 88.35)U/L:t = 7.236、P < 0.001)],机械通气时间长于非PIBO组[(7.10 ± 2.33)d vs.(4.89 ± 0.97)d:t = 10.541、P < 0.001],差异均有统计学意义。多因素二元Logistic回归分析结果显示,月龄(OR = 0.836、95%CI:0.773~0.904)、热程(OR:1.548、95%CI:1.288~1.861)、LDH(OR = 1.014、95%CI:1.008~1.021)和机械通气时间(OR = 2.060、95%CI:1.496~2.836)均为重度肺炎患儿发生PIBO的独立影响因素(P均<0.001)。列线图预测模型ROC曲线下面积(AUC)为0.960(95%CI:0.937~0.984)。预测模型校准曲线提示模型预测概率与实际概率相近,校准度良好。预测模型决策曲线DCA位于None线及All线上方,提示在该范围内模型具有临床实用性。Hosmer-Lemeshow χ2检验预测模型的拟合度良好。

结论

月龄、热程、LDH和机械通气时间均为重度肺炎患儿发生PIBO的独立影响因素,根据以上独立影响因素构建的列线图预测模型可为早期识别和干预重度肺炎患儿PIBO提供实用工具。

Objective

To investigate the influence factors for post infectious bronchiolitis obliterans(PIBO) in children with severe pneumonia, and construct and validate a column chart prediction model.

Methods

The medical records of 252 children with severe pneumonia treated in Jianhu Clinical Medical College of Yangzhou University from January 2022 to December 2023 were collected. The children were followed up for 6 months and divided into PIBO group (192 cases) and non-PIBO group (60 cases)according to whether PIBO occurred. The clinical indicators of the two groups were compared to screen the influence factors for PIBO in severe pneumonia children, and independent influence factors were screened by multivariate Logistic regression analysis. A nomogram model was constructed, and the differentiation of the prediction model was evaluated by receiver operating characteristic curve (ROC). The clinical practicability and calibration degree of the prediction model were evaluated by decision curve and calibration curve.

Results

Among the 252 children with severe pneumonia, a total of 60 cases occurred PIBO, with the incidence rate of 23.81% (60/252). The age of children in PIBO group [(18.87 ± 6.72) months]was lower than that of non-PIBO group [(28.30 ± 9.04) months](t = 8.648, P < 0.001); the duration of fever [(13.77 ±3.75) days]was longer than that of non-PIBO group [(9.92 ± 3.05) days](t = 8.057, P < 0.001); the level of LDH [(725.78 ± 98.72) U/L]was higher than that of non-PIBO group [(628.49 ± 88.35) U/L](t = 7.236, P <0.001); the duration of mechanical ventilation [(7.10 ± 2.33) days]was longer than that of non-PIBO group[(4.89 ± 0.97) days](t = 10.541, P < 0.001), all with significant differences. The results of multivariate binary Logistic regression analysis showed that age (OR = 0.836, 95%CI: 0.773-0.904), duration of fever (OR =1.548, 95%CI: 1.288-1.861), LDH (OR = 1.014, 95%CI: 1.008-1.021) and duration of mechanical ventilation (OR =2.060, 95%CI: 1.496-2.836) were all independent influence factors for the occurrence of PIBO in children with severe pneumonia (all P < 0.001). The area under the ROC curve (AUC) of the column chart prediction model was 0.960 (95%CI: 0.937-0.984). The calibration curve of the predictive model indicates that the predicted probability of the model was close to the actual probability, and the calibration degree was well.The decision curve DCA of the prediction model was located above the None and All lines, indicating that the model had clinical practicality within this range. The Hosmer Lemeshow Chi-square test showed well fit of the prediction model.

Conclusions

Age, duration of heat, LDH and duration of mechanical ventilation were all independent influence factors for PIBO in children with severe pneumonia. Constructing a column chart prediction model based on these independent influencing factors can provide a practical tool for early identification and intervention of PIBO in children with severe pneumonia.

表1 非PIBO组和PIBO组重度肺炎患儿的临床指标
指标 非PIBO组(192例) PIBO组(60例) 统计量 P
性别 [例(%)] χ 2= 0.194 0.659a
122(63.54) 40(66.67)
70(36.46) 20(33.33)
月龄(xˉ± s ,月) 28.30 ± 9.04 18.87 ± 6.72 t= 8.648 < 0.001
热程(xˉ± s ,d) 9.92 ± 3.05 13.77 ± 3.75 t= 8.057 < 0.001
呼吸困难 [例(%)] χ 2= 3.587 0.058a
108(56.25) 42(70.00)
84(43.75) 18(30.00)
喘息音 [例(%)] χ 2= 2.802 0.094a
125(65.10) 46(76.67)
67(34.90) 14(23.33)
湿啰音 [例(%)]
191(99.48) 60(100.00)
1(0.52) 0(0.00) 1.000b
低氧血症 [例(%)] χ 2= 3.242a 0.072
134(69.79) 49(81.67)
58(30.21) 11(18.33)
贫血 [例(%)] χ 2= 1.703a 0.192
20(10.42) 10(16.67)
172(89.58) 50(83.33)
CRP(xˉ± s ,mg/L) 32.45 ± 10.13 35.01 ± 12.17 t= 1.626 0.105
PCT(xˉ± s ,mg/L) 0.35 ± 0.11 0.38 ± 0.18 t= 1.561 0.120
WBC(xˉ± s ,× 109/L) 10.88 ± 2.18 11.56 ± 3.55 t= 1.789 0.075
PC(xˉ± s ,× 109/L) 295.45 ± 60.26 310.88 ± 100.85 t= 1.450 0.148
LDH(xˉ± s ,U/L) 628.49 ± 88.35 725.78 ± 98.72 t= 7.236 < 0.001
IgG(xˉ± s ,g/L) 710.45 ± 102.85 735.52 ± 121.45 t= 1.576 0.116
IgA(xˉ± s ,g/L) 70.45 ± 20.06 65.41 ± 13.38 t= 1.822 0.070
IgM(xˉ± s ,g/L) 115.45 ± 24.48 122.06 ± 32.19 t= 1.686 0.093
肺实变 [例(%)] χ 2= 0.718 0.397a
92(47.92) 25(41.67)
100(52.08) 35(58.33)
胸腔积液 [例(%)] χ 2= 1.232 0.267a
27(14.06) 12(20.00)
165(85.94) 48(80.00)
机械通气时间(xˉ± s ,d) 4.89 ± 0.97 7.10 ± 2.33 t= 10.541 < 0.001
使用糖皮质激素 [例(%)] χ 2= 1.378 0.240a
161(83.85) 54(90.00)
31(16.15) 6(10.00)
使用免疫球蛋白 [例(%)] χ 2= 2.158 0.142a
135(70.31) 48(80.00)
57(29.69) 12(20.00)
表2 重度肺炎患儿发生PIBO影响因素的多因素二元Logistic回归分析
图1 重度肺炎患儿PIBO列线图预测模型
图2 重度肺炎患儿PIBO列线图预测模型内部引导验证 注:A:重度肺炎患儿PIBO预测模型ROC曲线;B:重度肺炎患儿PIBO预测模型的校准曲线;C:重度肺炎患儿PIBO预测模型的决策曲线DCA
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