Li Xiaoying, Zhao Lihua, Cui Xiaojian, Xu Yongsheng, Zhang Tongqiang, Guo Wei, Ning Jing
Department of Pulmonology, Tianjin Key Laboratory of Birth Defects for Prevention and Treatment, Tianjin Children's Hospital (Children's Hospital of Tianjin University, Tianjin Pediatric Research Institute, Tianjin, 300134, China.
Department of Clinical Lab, Tianjin Key Laboratory of Birth Defects for Prevention and Treatment, Tianjin Children's Hospital (Children's Hospital of Tianjin University, Tianjin Pediatric Research Institute, Tianjin, 300134, China.
Ital J Pediatr. 2025 May 21;51(1):150. doi: 10.1186/s13052-025-02006-7.
The early prediction of pulmonary necrosis in children with severe pneumonia improves patient prognosis and prevents complications. The aim of this study was to establish a linear model for predicting necrotizing pneumonia (NP) caused by Mycoplasma pneumoniae (MP) infection and to investigate the risk factors for lung necrosis in children with refractory Mycoplasma pneumoniae pneumonia (RMPP).
A total of 536 children with RMPP were enrolled, including 95 with NP and 441 with nonnecrotizing pneumonia (NNP). A prediction model was built on 375 cases and validated on 161 cases, which were divided by random sampling in R software. Multivariate logistic regression was performed to determine optimal predictors and to establish a nomogram for predicting NP. The performance of the nomogram was evaluated by the area under the characteristic curve (AUC), calibration ability and decision curve analysis (DCA).
There were 315 (84.0%) NNP patients and 60 (16.0%) NP patients in the training group (n = 375) and 126 (78.3%) NNP patients and 35 NP patients (21.7%) in the validation group (n = 161). Multivariate logistic regression analysis identified 4 independent predictors that were used to construct a nomogram for predicting NP in children with RMPP, namely, fever duration (AOR = 1.475; 95% CI 1.296-1.678; P < 0.001), WBC count (AOR = 1.149; 95% CI 1.073-1.231; P < 0.001), IL-6 concentration (AOR = 1.007; 95% CI 1.002-1.013; P = 0.007) and D-dimer concentration (AOR = 1.361; 95% CI 1.121-1.652; P = 0.002). The area under the curve (AUC) of the nomogram was 0.899 (95% CI, 0.850-0.947) in the training set and 0.920 (95% CI, 0.874-0.966) in the validation set, indicating a good fit. The calibration plot and Hosmer‒Lemeshow test indicated that the predicted probability had good consistency with the actual probability in the training (P = 0.439) and validation (P = 0.526) groups. The DCA curve demonstrated a significantly better net fit in the model.
We developed and validated a nomogram model for predicting RMPP-associated NP in its early clinical stages based on fever duration, WBC count, IL-6 and D-dimer concentration. This four-risk factor model may assist physicians in predicting NP induced by RMPP.
对重症肺炎儿童的肺坏死进行早期预测可改善患者预后并预防并发症。本研究的目的是建立一个预测肺炎支原体（MP）感染所致坏死性肺炎（NP）的线性模型，并探讨难治性肺炎支原体肺炎（RMPP）患儿发生肺坏死的危险因素。
共纳入536例RMPP患儿，其中95例为NP，441例为非坏死性肺炎（NNP）。在R软件中通过随机抽样将375例作为构建预测模型的病例，161例作为验证病例。采用多因素logistic回归确定最佳预测指标并建立预测NP的列线图。通过特征曲线下面积（AUC）、校准能力和决策曲线分析（DCA）评估列线图的性能。
训练组（n = 375）中有315例（84.0%）NNP患者和60例（16.0%）NP患者，验证组（n = 161）中有126例（78.3%）NNP患者和35例（21.7%）NP患者。多因素logistic回归分析确定了4个独立预测指标，用于构建预测RMPP患儿NP的列线图，即发热持续时间（比值比[AOR]=1.475；95%置信区间[CI]1.296 - 1.678；P < 0.001）、白细胞计数（AOR = 1.149；95% CI 1.073 - 1.231；P < 0.001）、白细胞介素-6浓度（AOR = 1.007；95% CI 1.002 - 1.013；P = 0.007）和D-二聚体浓度（AOR = 1.361；95% CI 1.121 - 1.652；P = 0.002）。列线图在训练集的曲线下面积（AUC）为0.899（95% CI，0.850 - 0.947），在验证集为0.920（95% CI，0.874 - 0.966），表明拟合良好。校准图和Hosmer-Lemeshow检验表明，预测概率与训练组（P = 0.439）和验证组（P = 0.526）的实际概率具有良好的一致性。DCA曲线显示该模型的净拟合明显更好。
我们基于发热持续时间、白细胞计数、白细胞介素-6和D-二聚体浓度，开发并验证了一种用于在临床早期预测RMPP相关NP的列线图模型。这个四危险因素模型可能有助于医生预测RMPP所致NP。
