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Clinical Characteristics of Adenovirus Pneumonia in Children

Huifen Xu1, Wei Chen2, Qinrui Lai2

  • 1Department of Pharmacy, The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310053, China.

Pathogens (Basel, Switzerland)
|November 27, 2025
PubMed

Insights

Early detection of severe adenoviral pneumonia (SAP) is crucial. A study found that the CRP-to-prealbumin ratio (CPAR) and prealbumin levels are key indicators for predicting SAP risk in children.

Area of Science:

  • Pediatrics
  • Infectious Diseases
  • Biomarkers

Background:

  • Severe adenoviral pneumonia (SAP) poses a significant threat to patient health.
  • Early identification of SAP is essential for timely intervention and improved outcomes.
  • Existing diagnostic methods may not be sufficient for rapid and accurate SAP detection.

Purpose of the Study:

  • To identify effective indicators for the early detection of severe adenoviral pneumonia (SAP).
  • To develop and evaluate a predictive model for SAP using clinical laboratory indicators.
  • To compare clinical and laboratory features of patients with SAP and non-severe adenoviral pneumonia (NSAP).

Main Methods:

  • Retrospective analysis of 428 patients with adenoviral pneumonia (March 2022-January 2023).
  • Comparison of demographic, clinical, and laboratory data between SAP and NSAP groups.
  • Development of a random forest predictive model using identified indicators.

Main Results:

  • SAP was more prevalent in children aged 3-6 years and associated with polymicrobial coinfections.
  • Patients with SAP showed significantly higher prealbumin (PA) and elevated CRP-to-prealbumin ratio (CPAR), but lower C-reactive protein (CRP) levels.
  • The random forest model achieved an AUC of 0.699, with 84.5% accuracy and 91.5% precision for SAP prediction.

Conclusions:

  • The CRP-to-prealbumin ratio (CPAR), prealbumin (PA), and CRP are valuable indicators for assessing SAP risk.
  • Clinical laboratory indicators can be effectively utilized to construct a random forest-based predictive model for early-stage SAP detection.
  • This predictive model offers a promising approach for improving the early diagnosis and management of severe adenoviral pneumonia.

Keywords:
adenoviral upper respiratory infectionchildrenearly predictionrandom forestsevere pneumonia

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