Predictive Value of SII and SIRI for Refractory Mycoplasma Pneumoniae Pneumonia in Children

Clinical Laboratory
|November 12, 2025
PubMed
Abstract

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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