Risk factors and the prediction model of necrotizing pneumonia in children with Mycoplasma pneumoniae pneumonia
10.3760/cma.j.cn101070-20240731-00482
- VernacularTitle:儿童肺炎支原体坏死性肺炎的危险因素及预测模型
- Author:
Juan LUO
1
;
Peng CHEN
;
Hongxi GUO
;
Juanjuan DING
Author Information
1. 华中科技大学同济医学院附属武汉儿童医院(武汉市妇幼保健院)综合内科病区，武汉 430015
- Publication Type:Journal Article
- Keywords:
Child;
Mycoplasma pneumoniae;
Necrotizing pneumonia;
Risk factor;
Nomogram model
- From:
Chinese Journal of Applied Clinical Pediatrics
2025;40(3):187-193
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To analyze the early risk factors of necrotizing pneumonia (NP) in children with Mycoplasma pneumoniae pneumonia (MPP) and construct a clinical prediction model.Methods:In this case-control study, the clinical data of MPP patients who were hospitalized at Wuhan Children′s Hospital, Tongji Medical College, Huazhong University of Science & Technology, from January 2021 to May 2024 were retrospectively analyzed.According to whether NP occurred, the children were divided into the NP group and the non-NP (NNP) group.A total of 62 and 124 children were included in the NP and NNP groups after nearest neighbor matching at a ratio of 1∶2 (with a caliper value of 0.02), respectively.LASSO regression was used to select the optimal factors, and the multivariate Logistic regression analysis was used to establish a clinical prediction model.Internal and external validation of the prediction model was then conducted.The receiver-operating characteristic (ROC) curve and calibration curve were used to evaluate the predictive ability and calibration of the prediction model.The clinical decision curve analysis (DCA) was used to evaluate its clinical predictive value.Results:The LASSO regression analysis showed that white blood cells (WBC), neutrophil percentage, C-reactive protein (CRP), procalcitonin, D-dimer, ferritin, fever duration, and lung consolidation were factors influencing the occurrence of NP in children with MPP ( P<0.05).The ROC analysis showed that the area under the curve (AUC) of the prediction model was 0.838 (95% CI: 0.765-0.911, P<0.001) in the training set, 0.834 (95% CI: 0.755-0.913, P<0.001) in the validation set, and 0.924 (95% CI: 0.902-0.981, P<0.001) in the external validation set.Bootstrap was used for repeated sampling for 1 000 times for internal validation, and the calibration curve showed that the model had good early consistency.The clinical DCA showed that the model had good clinical application value. Conclusions:WBC, CRP, D-dimer, ferritin, fever duration and lung consolidation have good value for the early prediction of MPNP in children.
