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Updated: Jul 20, 2026
Experimental Model to Evaluate Resolution of Pneumonia
Published on: February 17, 2023
Blood-based inflammatory parameters and machine learning models for mortality prediction in critically ill pediatric
Shuai Hu1, Wanmin Xia1, Pei Tao1
1Department of Pediatric Respiratory, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Background:
Pneumonia remains a leading cause of morbidity and mortality in critically ill children, yet comprehensive prognostic biomarkers specifically validated in pediatric intensive care settings remain limited. Identifying readily available blood parameters that predict mortality risk could enhance early risk stratification and guide therapeutic interventions. This study aimed to evaluate the prognostic value of blood parameters for hospital mortality in critically ill pediatric pneumonia patients and develop machine learning (ML)-based predictive models.
Methods:
This retrospective cohort study analyzed pediatric pneumonia patients (aged 3 months to 18 years) admitted to intensive care units for ≥24 hours from the Pediatric Intensive Care (PIC) database version 1.1. Multivariate Cox regression analyses assessed associations between blood parameters and hospital mortality, adjusting for age, gender, bacterial infection status, and pathogen detection. Restricted cubic spline analyses examined dose-response relationships. Six ML algorithms incorporating significant blood parameters and clinical covariates were developed and validated using 7:3 train-test split.
Results:
A total of 606 children were included, with an overall hospital mortality of 14.4% (87/606). Multivariate Cox regression identified three significant prognostic factors: neutrophil percentage adjusted hazard ratio (HR): 1.017, 95% confidence interval (CI): 1.004-1.031, P=0.01], lymphocyte percentage (adjusted HR: 0.985, 95% CI: 0.970-0.999, P=0.04), and platelet-to-neutrophil ratio (PNR) (adjusted HR: 0.995, 95% CI: 0.989-1.000, P=0.04). Restricted cubic spline analyses revealed predominantly linear or U-shaped dose-response relationships. Among ML models, random forest demonstrated superior predictive performance with test area under the curve (AUC) of 0.877 (95% CI: 0.860-0.895), sensitivity of 80.9%, specificity of 79.1%, and accuracy of 79.5%. The model significantly outperformed individual blood parameters (neutrophil percentage AUC: 0.712; lymphocyte percentage AUC: 0.660; PNR AUC: 0.689), and decision curve analysis confirmed positive net benefit across threshold probabilities of 5-50%.
Conclusions:
Neutrophil percentage, lymphocyte percentage, and PNR are independent prognostic indicators for mortality in critically ill pediatric pneumonia. ML-based models incorporating these parameters show promise for early mortality risk stratification in pediatric intensive care settings.
