Peer-reviewed article
PCT-Anchored Machine Learning for Pre-Culture Identification of Gram-Negative Sepsis in Children: A Four-Site Study.
Tom Velez, Maya Dewan, Oluwakemi Badaki-Makun, Danielle Hirsch, Danielle Claire Mercurio et al. ·
Summary
Written by AI, not yet AICS's view · claude-haiku-4-5-20251001This is a technical medical study evaluating machine learning algorithms to detect gram-negative sepsis in children using procalcitonin and other biomarkers. The research analyzed 431 pediatric cases across four sites to develop a predictive model. While the work is medically rigorous, it focuses on diagnostic algorithm development rather than child health outcomes or well-being.
Key points
- A 3-feature machine learning model (PCT, platelet count, creatinine) achieved AUROC 0.884 for detecting gram-negative bacteremia with organ dysfunction
- PCT alone showed limited performance (AUROC 0.762) but improved when combined with other features
- The optimal model differed from adult sepsis models, relying on renal dysfunction rather than hemodynamics
- The study was retrospective and the authors note findings are hypothesis-generating and require prospective validation
Limitations
This retrospective diagnostic algorithm study does not evaluate an intervention for a defined child health problem or measure clinical outcomes. It cannot demonstrate whether using this model improves actual treatment decisions, patient safety, or child survival in practice.
Abstract
OBJECTIVE: Procalcitonin (PCT) has moderate accuracy for bacteremia detection but is infrequently used in pediatric emergency departments, partly due to poor sensitivity when applied as a standalone threshold and guideline recommendations against its isolated use. We evaluated whether a machine learning (ML) approach that contextualizes PCT with additional clinical features has the potential to improve detection of gram-negative bacteremic organ dysfunction in children prior to culture results. METHODS: We conducted a retrospective analysis of 431 pediatric encounters across four sites where blood culture and PCT were co-ordered. Blood culture results were classified as gram-negative bloodstream infection (BSI), gram-positive BSI, contaminant, or negative using a priority-based NLP algorithm applied to free-text result strings. The primary outcome was gram-negative BSI with concurrent organ dysfunction ascertained by Phoenix-8 criteria excluding the immunologic domain. We evaluated PCT alone, a four-feature adult sepsis-aligned benchmarking model, and candidate multi-feature combinations identified through systematic univariate screening, using Random Forest (RF) and penalized logistic regression (LASSO) as co-primary algorithms with nested repeated stratified cross-validation and permutation importance analysis. Model discrimination was compared using paired fold-level AUROC testing and calibration was assessed using Platt scaling. RESULTS: Among 431 encounters with a numeric PCT result, 20 (4.6%) met the primary outcome of gram-negative BSI with Phoenix-8 organ dysfunction. PCT alone achieved AUROC 0.762. A four-feature adult sepsis-aligned model achieved AUROC 0.862; respiratory rate and systolic blood pressure contributed negligibly. Systematic screening identified platelet count and creatinine as optimal co-features; the three-feature model (PCT, platelet count, creatinine) achieved AUROC 0.884 (BCa 95% CI 0.871-0.982), consistent across RF and LASSO (0.874), with Brier score 0.038 below the null model (0.044), and positive likelihood ratio 12.56 (number needed to assess of 2.6). CONCLUSIONS: PCT alone showed limited performance. Embedding PCT within a 3-feature ML model improved discrimination over PCT alone (delta AUROC 0.122, p<0.001), with consistent performance across RF and LASSO. Unlike adult models, pediatric prediction relied on renal dysfunction (creatinine) rather than hemodynamics. These findings are hypothesis-generating and require prospective validation in adequately powered cohorts before clinical implementation.