Risk Prediction Model for Necrotizing Pneumonia in Children with Mycoplasma pneumoniae Pneumonia
1Second Department of Infectious Disease, Kunming Children's Hospital, Kunming, Yunnan, People's Republic of China.
Objective:
To analyze the predictive factors for necrotizing pneumonia (NP) in children with Mycoplasma pneumoniae pneumonia (MPP) and construct a prediction model.
Methods:
The clinical data with MPP at the Children's Hospital of Kunming Medical University from January 2014 to November 2022 were retrospectively analyzed. Eighty-four children with MPP who developed NP were divided into the necrotizing group, and 168 children who did not develop NP were divided into the non-necrotizing group by propensity-score matching. LASSO regression was used to select the optimal factors, and multivariate logistic regression analysis was used to establish a clinical prediction model. The receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the discrimination and calibration of the nomogram. Clinical decision curve analysis was used to evaluate the clinical predictive value.
Results:
LASSO regression analysis showed that bacterial co-infection, chest pain, LDH, CRP, duration of fever, and D-dimer were the influencing factors for NP in children with MPP (P < 0.05). The results of ROC analysis showed that the AUC of the prediction model established in this study for predicting necrotizing MPP was 0.870 (95% CI: 0.813-0.927, P < 0.001) in the training set and 0.843 (95% CI: 0.757-0.930, P < 0.001) in the validation set. The Bootstrap repeated sampling for 1000 times was used for internal validation, and the calibration curve showed that the model had good consistency. The Hosmer-Lemeshow test showed that the predicted probability of the model had a good fit with the actual probability in the training set and the validation set (P values of 0.366 and 0.667, respectively). The clinical decision curve showed that the model had good clinical application value.
Conclusion:
The prediction model based on bacterial co-infection, chest pain, LDH, CRP, fever duration, and D-dimer has a good predictive value for necrotizing MPP.
Insights
This study identifies key factors like bacterial co-infection and elevated CRP that predict necrotizing pneumonia (NP) in children with Mycoplasma pneumoniae pneumonia (MPP). A validated model aids in early detection of severe MPP cases.
Area of Science:
- Pediatric Infectious Diseases
- Respiratory Medicine
- Clinical Diagnostics
Background:
- Mycoplasma pneumoniae pneumonia (MPP) is a common childhood respiratory infection.
- Necrotizing pneumonia (NP) is a severe complication of MPP, necessitating early identification.
- Predictive factors for NP in pediatric MPP cases require further elucidation.
Purpose of the Study:
- To identify predictive factors for the development of necrotizing pneumonia (NP) in children diagnosed with Mycoplasma pneumoniae pneumonia (MPP).
- To construct and validate a clinical prediction model for necrotizing MPP.
Main Methods:
- Retrospective analysis of clinical data from children with MPP.
- Propensity-score matching to create necrotizing and non-necrotizing groups.
- LASSO and multivariate logistic regression for factor selection and model building.
- ROC curve, calibration curves, and decision curve analysis for model evaluation.
Main Results:
- Bacterial co-infection, chest pain, lactate dehydrogenase (LDH), C-reactive protein (CRP), fever duration, and D-dimer were significant predictors of NP (P < 0.05).
- The developed prediction model demonstrated strong predictive performance with an AUC of 0.870 in the training set and 0.843 in the validation set.
- The model exhibited good calibration and clinical utility, as confirmed by calibration curves and decision curve analysis.
Conclusions:
- A prediction model incorporating bacterial co-infection, chest pain, LDH, CRP, fever duration, and D-dimer effectively predicts necrotizing MPP in children.
- This model holds significant clinical value for early identification and management of severe MPP cases.
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