ArticlePediatric research2025
Early prediction of antibiotic need and bacteremia risk in non-immunocompromised pediatric emergency patients using machine learning.
Article in Pediatric research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic Performance of Machine Learning Models for Predicting Bloodstream Infections in Large Cohorts: A Systematic Review and Meta-analysis.Infectious diseases and therapy · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
backgroundTimely identification of serious bacterial infections in children presenting to emergency departments is critical, especially among non-immunocompromised children, where early symptoms can be nonspecific. Although many children receive empiric antibiotic treatment based on clinical suspicion, true bloodstream infection is relatively uncommon, and unnecessary antibiotics can contribute to adverse effects and antimicrobial resistance.
methodsTo support individualized decision-making, we developed and evaluated a two-part machine learning framework using retrospective electronic health record data from 5706 pediatric patients aged 3 months to 17 years across six emergency departments. The first model predicted clinical deterioration-defined as admission to intensive care, use of vasopressors, mechanical ventilation, or in-hospital death-among children in whom antibiotics were initially withheld. The second model predicted the likelihood of bacteremia among those who received early empiric antibiotics. Both models were built using XGBoost and evaluated through cross-validation.
resultsPerformance was strong, with high area under the curve values and negative predictive values above 96%. Predictive features included supplemental oxygen use, fever, low oxygen saturation, age, and abnormal laboratory values.
conclusionsThis dual-model framework offers interpretable, evidence-based support for early treatment decisions and could improve both patient safety and antibiotic stewardship in pediatric emergency care. IMPACT: This study introduces a dual machine learning framework that informs early antibiotic decisions in non-immunocompromised pediatric emergency patients. It adds a novel two-model approach: one to predict deterioration when antibiotics are initially withheld, and another to predict bacteremia in those treated. Unlike prior tools, it uses harmonized multi-center EHR data and SHAP-based explain ability to support bedside clinical use. The impact lies in enhancing antibiotic stewardship and patient safety by identifying who may benefit from early antibiotics and who may safely avoid them.
Identifiers
41387552What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.