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Clinical Diagnostic Prediction Model For Mycoplasma pneumoniae Pneumonia in Children
Xiaohua Liu1, Hongfei Du2, Linyan Zhang1
1Department of Medical Laboratory, Xindu District People's Hospital of Chengdu, Chengdu, 610500, People's Republic of China.
Background:
The aim of this research is to ascertain the risk determinants associated with Mycoplasma pneumoniae pneumonia (MPP) in pediatric patients diagnosed with community-acquired pneumonia (CAP), as well as to construct predictive models to forecast the incidence of MPP.
Methods:
This study was conducted at Xindu District People's Hospital of Chengdu from August 2023 to March 2024. A total of 1030 children aged 0 to 14 years with CAP were enrolled and divided into MPP (n=414) and non-MPP (NMPP, n=616) groups based on diagnostic criteria including MP antibody and MP RNA. Data were collected within 24 hours of admission, including peripheral blood counts, inflammatory markers, and other biochemical parameters. The Logistic+Stepwise, Logistic+Lasso, Logistic+Elastic-net, and Logistic+Ridge were employed to identify risk factors, and were used for variable selection with penalization algorithms. Model performance was evaluated using C-index, sensitivity, specificity, accuracy, recall, and F1 score.
Results:
The results of prediction model showed that four models had good performance. The area under the ROC curve revealed good predictive ability (AUC > 0.8 in both Logistic model and Experience model), the results of calibration curves indicated a good consistency consistency. Logistic+Lasso model selected 9 key variables for further analysis.
Conclusion:
We have developed and validated a clinical prediction model in children with Mycoplasma pneumoniae pneumonia (MPP). The model identifies NEUT%, EOS%, HSCRP, ADA, Crea, Urea, HDL, P, and ESR as significant independent predictors. It demonstrated robust discriminative ability and good calibration, offering a practical tool for clinicians to stratify risk and guide early intervention in pediatric patients.
Insights
This study identified key risk factors for Mycoplasma pneumoniae pneumonia (MPP) in children with community-acquired pneumonia (CAP). A predictive model was developed to aid early diagnosis and intervention for pediatric MPP cases.
Area of Science:
- Pediatric Pulmonology
- Infectious Diseases
- Clinical Prediction Modeling
Background:
- Community-acquired pneumonia (CAP) in children can be caused by various pathogens, including Mycoplasma pneumoniae.
- Accurate and timely diagnosis of Mycoplasma pneumoniae pneumonia (MPP) is crucial for effective treatment in pediatric populations.
- Identifying risk determinants for MPP is essential for developing targeted prevention and management strategies.
Purpose of the Study:
- To determine the risk factors associated with Mycoplasma pneumoniae pneumonia (MPP) in pediatric patients diagnosed with community-acquired pneumonia (CAP).
- To develop and validate predictive models for forecasting the incidence of MPP in children.
- To provide clinicians with a tool for early risk stratification and intervention in pediatric MPP cases.
Main Methods:
- A cohort of 1030 children (0-14 years) with CAP was enrolled, divided into MPP (n=414) and non-MPP (NMPP, n=616) groups.
- Data including peripheral blood counts, inflammatory markers, and biochemical parameters were collected within 24 hours of admission.
- Four logistic regression models (Stepwise, Lasso, Elastic-net, Ridge) were employed for risk factor identification and variable selection using penalization algorithms.
Main Results:
- Four predictive models demonstrated good performance with Area Under the ROC Curve (AUC) > 0.8, indicating strong predictive ability.
- Calibration curves showed good consistency, validating the reliability of the models.
- The Logistic+Lasso model identified 9 key variables: NEUT%, EOS%, HSCRP, ADA, Crea, Urea, HDL, P, and ESR as significant independent predictors.
Conclusions:
- A validated clinical prediction model for pediatric Mycoplasma pneumoniae pneumonia (MPP) has been developed.
- The model effectively identifies key predictors, enabling robust risk stratification in children with CAP.
- This tool can assist clinicians in guiding early intervention strategies for pediatric MPP, improving patient outcomes.
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