Risk prediction of carbapenem-resistant Pseudomonas aeruginosa infection in children
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Updated: Jun 18, 2026
Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model
Published on: April 6, 2021
Lin Qin1, Xiaoling Wei1,2,3, Min Xue2,3
1Department of Health Data Application and Management, Children's Hospital Affiliated to Shandong University, Jinan, Shandong, China.
Insights
Carbapenem-resistant Pseudomonas aeruginosa (CRPA) infections in children are linked to longer hospital stays and prior antibiotic use. An XGBoost model effectively predicts CRPA risk, aiding early intervention for pediatric patients.
Area of Science:
- Pediatric Infectious Diseases
- Antimicrobial Resistance
- Computational Biology
Background:
- Pseudomonas aeruginosa (PA) is a significant pathogen in pediatric infections.
- Carbapenem resistance in PA poses a growing threat, necessitating better risk assessment.
- Understanding risk factors for carbapenem-resistant PA (CRPA) in children is crucial for effective management.
Purpose of the Study:
- To investigate the distribution, drug resistance patterns, and risk factors of CRPA in pediatric patients.
- To develop and validate a predictive model for CRPA infection in children.
- To identify key variables for early risk stratification of CRPA.
Main Methods:
- Retrospective cohort study including derivation (1397 children) and validation (431 children) groups.
- XGBoost model construction for CRPA prediction using LASSO regression for feature selection.
- Internal and temporal validation of the model; SHAP analysis for variable importance interpretation.
Main Results:
- CRPA was identified in 220 out of 1397 PA infections (15.8%).
- CRPA patients were younger, had longer hospital stays, and higher ICU/NICU prevalence.
- The XGBoost model achieved an AUC of 0.848 (internal) and 0.715 (temporal validation).
- Key predictors included length of hospital stay, CD8+ T cells, ventilator use, and prior carbapenem exposure.
Conclusions:
- CRPA infection in children is associated with prolonged hospitalization, immune status, invasive procedures, and carbapenem exposure.
- The developed XGBoost model shows promising generalizability for identifying children at high risk of CRPA.
- Early identification via the predictive model can facilitate timely clinical interventions.
Objective:
This study aimed to investigate the infection distribution, drug resistance, and risk factors associated with carbapenem-resistant Pseudomonas aeruginosa (PA) in children, and to construct a risk prediction model.
Methods:
Two retrospective cohorts were established based on the Children's Hospital Affiliated to Shandong University. The derivation cohort included 1397 children with Pseudomonas aeruginosa infection hospitalized from January 2020 to December 2024, divided into the carbapenem-resistant PA (CRPA) group and the carbapenem-sensitive PA (CSPA) group based on antimicrobial susceptibility testing. LASSO regression was used for feature selection, and an XGBoost model was constructed to predict CRPA infection, with internal validation performed using a randomly split test set. Subsequently, an independent temporal validation cohort of 431 children (January 2025 to March 2026) was used to evaluate model generalizability. SHapley Additive exPlanations (SHAP) was employed to interpret variable importance.
Results:
Among the 1397 PA isolates, 1177 were CSPA and 220 were CRPA. Children in the CRPA group were younger, had longer hospital stays, and were more prevalent in the NICU and PICU than those in the CSPA group. Among CRPA isolates, 88.2% met multidrug-resistant criteria and 46.4% met extensively drug-resistant criteria, while maintaining high sensitivity to amikacin (95%). The XGBoost model, incorporating 10 key variables, achieved an AUC of 0.848 (95% CI, 0.783-0.912) on the internal test set and 0.715 (95% CI, 0.622-0.807) on the temporal validation cohort. SHAP interpretability analysis showed that length of hospital stay, CD8+ T cells, ventilator use, and pre-infection carbapenem exposure were important risk factors for CRPA infection.
Conclusion:
CRPA infection in children is closely associated with prolonged hospitalization, immune dysregulation, invasive procedures, and prior carbapenem exposure. The XGBoost prediction model demonstrated good discrimination in internal validation, and its performance remained stable on temporal validation, suggesting potential utility in identifying high-risk children for early clinical intervention.
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