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Published on: February 24, 2023
Predictive Value of Traditional and Novel Composite Inflammatory Indicators for Severe and Refractory Mycoplasma
Guo Zhen Fan1, Yu Hui Zhu1, Li Xin Hu1
1Department of Pediatrics, The Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China.
Insights
This study developed predictive models using inflammatory indicators to identify severe Mycoplasma pneumoniae pneumonia (SMPP) and refractory MPP (RMPP) in children. These models aid in precise risk stratification for better clinical treatment and antibiotic selection.
Area of Science:
- Pediatric Pulmonology
- Infectious Diseases
- Biomarker Research
Background:
- Mycoplasma pneumoniae pneumonia (MPP) is a common childhood respiratory infection.
- Distinguishing severe (SMPP) and refractory (RMPP) forms of MPP is crucial for appropriate management.
- Current diagnostic tools may not sufficiently predict disease severity.
Purpose of the Study:
- To evaluate the predictive value of single and composite inflammatory indicators for SMPP and RMPP in children.
- To develop and validate robust prediction models for SMPP and RMPP.
- To assess the clinical applicability of these models for risk stratification.
Main Methods:
- Retrospective enrollment of 1791 children with MPP.
- Phased modeling strategy including univariate, LASSO, and multivariate logistic regression.
- Model performance evaluation using ROC curves (AUC), calibration curves, Hosmer-Lemeshow test, bootstrap validation, and DCA.
Main Results:
- The SMPP prediction model, incorporating nine factors (including Hb, LMR, NPR, SII), achieved an AUC of 0.803.
- The RMPP prediction model, using seven factors (including Hb, CRP, LMR, NPR), demonstrated an AUC of 0.889.
- Model validation confirmed robustness and clinical applicability for risk stratification.
Conclusions:
- Integrated inflammatory indicators provide precise risk stratification for pediatric MPP.
- Developed models offer significant implications for clinical treatment planning and antibiotic selection in MPP.
- This multi-parameter approach enhances the management of severe and refractory pediatric MPP.
Objective:
This study aimed to comprehensively evaluate the predictive efficacy of traditional single inflammatory indicators and novel composite inflammatory indicators (CLR, LMR, NLR, NPR, PIV, PLR, SII, SIRI) for severe Mycoplasma pneumoniae pneumonia (SMPP) and refractory MPP (RMPP) in children.
Methods:
This study retrospectively enrolled 1791 children with MPP and collected their case data. A phased modeling strategy (univariate analysis, LASSO regression, multivariate logistic regression) was employed to construct prediction models. Model performance was evaluated using area under the curve (AUC) from receiver operating characteristic (ROC) curves, calibration curves with the Hosmer-Lemeshow test, bootstrap resampling with 1000 repetitions, and decision curve analysis (DCA).
Results:
The cohort included 512 SMPP, 269 RMPP, and 1180 general MPP cases; mentiontly, 170 children met both SMPP and RMPP criteria. The SMPP prediction model identified nine independent risk factors (Hb, PLT, D-D, FIB, LMR, NPR, SII, duration of cough and fever), achieving an AUC of 0.803. The RMPP model identified seven factors (Hb, CRP, FIB, LMR, NPR, duration of cough and fever) with an AUC of 0.889. The calibration curves, Hosmer-Lemeshow test, bootstrap internal validation, and DCA curve together confirmed the robustness and clinical applicability of the models.
Conclusion:
This multi-parameter integration strategy enables precise MPP risk stratification, holding significant implications for clinical treatment planning and antibiotic selection.
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