Liu Tie-Hu, Liu Xiao-Xue, Tang Yang, Qi Fei, Liu Deng-Pin
Department of Pediatrics, Xiangya Changde Hospital, Changde, Hunan 415000, China.
Zhongguo Dang Dai Er Ke Za Zhi. 2024;26(9):946-953. doi: 10.7499/j.issn.1008-8830.2402008.
To explore the establishment of a risk prediction model for concurrent bronchiolitis obliterans (BO) in children with refractory pneumonia (RMPP).
A retrospective study included 116 RMPP children treated in the Department of Pediatrics of Xiangya Changde Hospital from June 2021 to December 2023. Eighty-one cases were allocated to the training set and thirty-five cases to the validation set based on a 7:3 ratio. Among them, 26 cases in the training set developed BO, while 55 did not. The multivariate logistic regression was used to select variable factors for constructing the BO risk prediction model. Nomograms were drawn, and the receiver operating characteristic (ROC) curve was used to assess the discriminative ability of the model, while calibration curves and Hosmer-Lemeshow tests evaluated the model's calibration.
Multivariate logistic regression analysis indicated that several factors were significantly associated with concurrent BO in RMPP children, including length of hospital stay, duration of fever, atelectasis, neutrophil percentage (NEUT%), peak lactate dehydrogenase (LDH), ferritin, peak C reactive protein (CRP), oxygenation index (PaO/FiO), ≥2/3 lung lobe consolidation, pleural effusion, bronchial mucous plugs, bronchial mucosal necrosis, and arterial oxygen partial pressure (PaO) (<0.05). ROC curve analysis for the training set indicated an area under the curve of 0.904 with 88% sensitivity and 83% specificity; the validation set showed an area under the curve of 0.823 with 76% sensitivity and 93% specificity. The Hosmer-Lemeshow test's Chi-square values for the training and validation sets were 2.17 and 1.92, respectively, with values of 0.221 and 0.196, respectively.
The risk prediction model for BO in RMPP children based on logistic regression has good performance. Variables such as length of hospital stay, duration of fever, atelectasis, peak LDH, peak CRP, NEUT%, ferritin, ≥2/3 lung lobe consolidation, pleural effusion, bronchial mucous plugs, bronchial mucosal necrosis, PaO/FiO, andPaO can be used as predictors.
探讨建立难治性肺炎（RMPP）患儿并发闭塞性细支气管炎（BO）的风险预测模型。
一项回顾性研究纳入了2021年6月至2023年12月在湘雅常德医院儿科治疗的116例RMPP患儿。根据7:3的比例，将81例分配到训练集，35例分配到验证集。其中，训练集中26例发生BO，55例未发生。采用多因素logistic回归筛选构建BO风险预测模型的变量因素。绘制列线图，采用受试者工作特征（ROC）曲线评估模型的判别能力，采用校准曲线和Hosmer-Lemeshow检验评估模型的校准情况。
多因素logistic回归分析表明，住院时间、发热持续时间、肺不张、中性粒细胞百分比（NEUT%）、乳酸脱氢酶峰值（LDH）、铁蛋白、C反应蛋白峰值（CRP）、氧合指数（PaO/FiO）、≥2/3肺叶实变、胸腔积液、支气管黏液栓、支气管黏膜坏死及动脉血氧分压（PaO）等因素与RMPP患儿并发BO显著相关（<0.05）。训练集的ROC曲线分析显示曲线下面积为0.904，灵敏度为88%，特异度为83%；验证集曲线下面积为0.823，灵敏度为76%，特异度为93%。训练集和验证集的Hosmer-Lemeshow检验的卡方值分别为2.17和1.92，P值分别为0.221和0.196。
基于logistic回归的RMPP患儿BO风险预测模型性能良好。住院时间、发热持续时间、肺不张、LDH峰值、CRP峰值、NEUT%、铁蛋白、≥2/3肺叶实变、胸腔积液、支气管黏液栓、支气管黏膜坏死、PaO/FiO及PaO等变量可作为预测指标。
