ORIGINAL RESEARCH article
Front. Pediatr.
Sec. Pediatric Pulmonology
Development and validation of an immune-based nomogram model for predicting severe adenovirus pneumonia in hospitalized children
1. Department of Respiratory Medicine, Capital Center for Children’s Health, Capital Medical University, Capital Institute of Pediatrics, Beijing, China
2. Center for Evidence-Based Medicine, Capital Center for Children's Health, Capital Medical University, Capital Institute of Pediatrics, Beijing, China
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Abstract
Background Human adenovirus (HAdV) is a significant cause of severe pneumonia in children that often causes sequelae. Immune disorders are linked to disease progression, but comprehensive immunological predictors for severe adenovirus pneumonia (SAP) remain unidentified. The purpose of this study was to explore multiple immunological indicators’ predictive value for SAP and developed an immune-based nomogram for early prediction in children. Methods We retrospectively analyzed children with adenovirus pneumonia hospitalized at Capital Center for Children's Health (Jan 2017–Jun 2025), stratified into mild and severe groups. Patients were randomly allocated at an 80:20 ratio into a training set and a cross-validation set for nomogram development and validation. R software was used for statistical analysis, with effect sizes as odds ratios (ORs) and 95% confidence intervals (CIs). Results Of 1220 cases included, 357 (29.3%) were classified as severe. In both the training and cross-validation sets, SAP patients were younger and had longer hospital stays (all P<0.001). After the adjustments for age and sex, logistic regression in the training set revealed seven significant factors associated with SAP occurrence in children: Mycoplasma pneumoniae infection (OR=1.372, 95% CI: 1.04–1.809, P<0.001); complement component 3 (C3) (OR=0.234, 95% CI: 0.132–0.417, P<0.001) and 4 (C4) (OR=0.075, 95% CI: 0.018–0.31, P<0.001); immunoglobulin G (IgG) (OR=1.049, 95% CI: 1.028–1.071, P<0.001); and the percentages of CD3+ [CD3+ (%)] (OR=0.965, 95% CI: 0.952–0.979, P<0.001), CD4+ [CD4+ (%)] (OR=0.950, 95% CI: 0.934–0.967, P<0.001), and CD19+ cells [CD19+ (%)] (OR=1.042, 95% CI: 1.028–1.057, P<0.001). Validation set confirmed C3, C4, IgG, CD3+(%), CD4+(%) and CD19+(%) as consistent predictors. Incorporating the significant factors improved model discrimination, increasing the area under the curve (AUC) from 0.627 to 0.836 in the training set and from 0.678 to 0.913 in the cross-validation set. The final nomogram model based on the significant factors demonstrated strong calibration and discrimination (C-index: 0.731 in the training set, 0.812 in the cross-validation set), supporting its potential for clinical risk stratification. Conclusion Seven independent SAP-related factors were identified and validated. The developed nomogram incorporating these factors exhibited favorable discrimination, calibration, and predictive accuracy across datasets, suggesting potential clinical utility for individualized SAP risk stratification in children.
Summary
Keywords
Children, Human adenovirus, immune, nomogram, prediction, Severe pneumonia
Received
08 November 2025
Accepted
20 February 2026
Copyright
© 2026 Wu, Ma, Niu and Ling. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Cao Ling
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