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Updated: Jan 11, 2026

An In vitro Model to Study Immune Responses of Human Peripheral Blood Mononuclear Cells to Human Respiratory Syncytial Virus Infection
Published on: December 10, 2013
Predictive Value of SII and SIRI for Refractory Mycoplasma Pneumoniae Pneumonia in Children
Background:
The purpose of this study was to analyze the clinical features of Mycoplasma pneumonia in children, especially refractory Mycoplasma pneumoniae pneumonia (RMPP), and to build a clinical prediction model to separate RMPP from general Mycoplasma pneumoniae pneumoniae (GMPP).
Methods:
A total of 234 children with MPP were enrolled in this study, out of which 152 were GMPP and 82 were RMPP. Independent predictors of RMPP were screened via univariate and multivariate logistic regression, analyzing clinical characteristics, laboratory indicators (including inflammatory markers like the neutrophil-to-lymphocyte ratio (NLR), systemic immuneinflammation index (SII), and systemic inflammation response index (SIRI)), and blood immune parameters. Based on these predictors, a clinical prediction model was constructed, and its predictive efficacy and clinical value were evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).
Results:
Compared to GMPP patients, those with RMPP had extended hospital stays and total fever days, a higher percentage of high-grade fever, a greater percentage of pulmonary atelectasis and unilateral lung lesions, and increased monocyte counts, platelet counts, CRP levels, SII, and SIRI. CRP, platelet count, monocyte count, SII, and SIRI were significantly associated with the risk of developing RMPP. CRP, monocyte count, and SII were independent predictors of RMPP, with CRP contributing the most, followed by SII. The constructed prediction model achieved an AUC of 0.82 (95% CI, 0.70 to 0.94) in the validation set. The DCA plot showed that the net benefit of the model was already greater than 0 at a high-risk threshold of approximately 0.1, and the smaller the threshold, the greater the net benefit in the range of 0.1 to 0.9.
Conclusions:
The constructed model accurately distinguishes RMPP from GMPP, with high predictive efficacy and clinical value.
Insights
This study developed a clinical prediction model to differentiate refractory Mycoplasma pneumoniae pneumonia (RMPP) from general cases in children. The model, utilizing inflammatory markers like CRP and SII, shows high accuracy in identifying RMPP.
Area of Science:
- Pediatric Pulmonology
- Infectious Diseases
- Clinical Prediction Modeling
Background:
- Mycoplasma pneumoniae pneumonia (MPP) is common in children.
- Refractory MPP (RMPP) presents unique clinical challenges.
- Distinguishing RMPP from general MPP (GMPP) is crucial for appropriate management.
Purpose of the Study:
- To analyze clinical features differentiating RMPP from GMPP in pediatric patients.
- To develop and validate a clinical prediction model for RMPP identification.
- To assess the predictive efficacy and clinical utility of the developed model.
Main Methods:
- Retrospective analysis of 234 children with MPP (152 GMPP, 82 RMPP).
- Screening of independent predictors using logistic regression, including clinical, laboratory, and immune parameters (CRP, SII, SIRI).
- Model construction and evaluation via ROC curve, calibration curve, and decision curve analysis (DCA).
Main Results:
- RMPP patients exhibited longer hospital stays, more fever days, higher fever grades, and increased pulmonary complications.
- Elevated monocyte counts, platelet counts, CRP, SII, and SIRI were observed in RMPP cases.
- CRP, monocyte count, and SII were independent predictors; the model achieved an AUC of 0.82 in the validation set.
Conclusions:
- The developed clinical prediction model accurately distinguishes RMPP from GMPP in children.
- The model demonstrates high predictive efficacy and significant clinical value for RMPP diagnosis.
- Key predictors include CRP, monocyte count, and SII, aiding in early RMPP identification.
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