Predictive value of immune-related parameters in severe Mycoplasma pneumoniae pneumonia in children

Chaoyue Jiang1, Siwen Bao1, Weifeng Shen1

  • 1Department of Laboratory Medicine, The First Hospital of Jiaxing, Affiliated Hospital of Jiaxing University, Jiaxing, China.

Translational Pediatrics
|October 14, 2024
PubMed
Abstract

Insights

Early diagnosis of severe Mycoplasma pneumoniae pneumonia (MPP) in children is possible using immune-related parameters. A predictive model combining age, B cell ratio, and monocyte counts shows promise for identifying severe MPP cases.

Area of Science:

  • Pediatric Infectious Diseases
  • Immunology
  • Clinical Diagnostics

Background:

  • Mycoplasma pneumoniae pneumonia (MPP) severity correlates with the host's immune-inflammatory response.
  • Early diagnosis of severe MPP (sMPP) in children is crucial for timely intervention.
  • Immune-related parameters may offer predictive value for sMPP.

Purpose of the Study:

  • To explore the predictive value of immune-related parameters for diagnosing severe MPP in admitted children.
  • To identify independent risk factors associated with the development of sMPP.
  • To develop and evaluate a predictive model for sMPP.

Main Methods:

  • Database analysis of pediatric patients diagnosed with MPP between 2021-2023.
  • Examination of associations between complete blood cell count (CBC), lymphocyte subsets, and MPP severity.
  • Binary logistic regression and Receiver Operating Characteristic (ROC) curve analysis to identify risk factors and assess discriminant ability.

Main Results:

  • Elevated WBC, neutrophil, monocyte, platelet counts, and NLR were observed in the sMPP group.
  • Lower CD3+ T cell and CD3+CD8+ T cell ratios, and higher CD3-CD19+ B cell ratios were found in the sMPP group.
  • Age, CD3-CD19+%, and monocyte counts were identified as independent risk factors for sMPP, with an overall prediction model AUC of 0.715.

Conclusions:

  • A predictive model incorporating age, CD3-CD19+%, and monocyte counts shows potential for early sMPP diagnosis in children.
  • The model demonstrated higher predictive accuracy in younger children (≤5 years, AUC 0.823) compared to older children (>5 years, AUC 0.693).
  • This model can aid in the early identification of severe MPP cases requiring prompt medical attention.