ArticleItalian journal of pediatrics2025
Development and validation of a nomogram for predicting necrotizing pneumonia in children with refractory Mycoplasma pneumoniae pneumonia.
Article in Italian journal of pediatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Clinical factors associated with extrapulmonary complications in children with macrolide-resistantTranslational pediatrics · 2026Article
- Safety and long-term outcomes of surgical intervention in pediatrics with necrotizing pneumonia: a retrospective cohort study.European journal of pediatrics · 2026Article
- Early Prediction of Necrotizing Pneumonia in Children with Mycoplasma Pneumoniae Pneumonia: Development and Temporal Validation of a Clinical Model.Children (Basel, Switzerland) · 2026Article
- Clinical analysis of Mycoplasma pneumoniae necrotizing pneumonia in children.BMC infectious diseases · 2026Article
- Development and validation of a predictive nomogram for early detection of necrotizing pneumonia in children with refractoryFrontiers in pediatrics · 2026Article
- Exploring Clinical Characteristics and Risk Factors ofJournal of inflammation research · 2026Article
- Non-linear associations between inflammatory biomarkers andFrontiers in pediatrics · 2026Article
- Clinical Prediction of Necrotizing Pneumonia in Children With Mycoplasma pneumoniae: A Model Based on Early Response to Therapy.Canadian respiratory journal · 2026Article
- Development and validation of a nomogram for differentiating bacterial from non-bacterial necrotizing pneumonia in children: a retrospective cohort study.Frontiers in medicine · 2026Article
- Immunopathogenesis, molecular phenotyping, and host-directed interventions for severe pediatricFrontiers in cellular and infection microbiology · 2026Review
- A machine learning-based predictive model for multilobar pulmonary consolidation induced by macrolide-resistantMicrobiology spectrum · 2025Article
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7 authors.
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Abstract
backgroundThe 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).
methodsA 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).
resultsThere 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.
conclusionsWe 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.
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