Pediatric Severe Sepsis Prediction Using Machine Learning

TLDR

Early detection of pediatric severe sepsis is essential for effective treatment, yet current methods are inadequate. This study evaluates whether a machine‑learning algorithm using EHR data can predict severe sepsis onset in children. The authors retrospectively analyzed de‑identified EHR data from 9,486 pediatric inpatients and emergency encounters (ages 2–17) at UCSF between 2011 and 2016 to develop the algorithm. The algorithm achieved an AUROC of 0.916 at onset and 0.718 four hours before onset, outperforming PELOD‑2 and SIRS, indicating high‑performance detection that could enable earlier sepsis recognition and treatment.

Abstract

Background: Early detection of pediatric severe sepsis is necessary in order to optimize effective treatment, and new methods are needed to facilitate this early detection. Objective: Can a machine-learning based prediction algorithm using electronic healthcare record (EHR) data predict severe sepsis onset in pediatric populations? Methods: EHR data were collected from a retrospective set of de-identified pediatric inpatient and emergency encounters for patients between 2-17 years of age, drawn from the University of California San Francisco (UCSF) Medical Center, with encounter dates between June 2011 and March 2016. Results: Pediatric patients (n = 9,486) were identified and 101 (1.06%) were labeled with severe sepsis following the pediatric severe sepsis definition of Goldstein et al. (1). In 4-fold cross-validation evaluations, the machine learning algorithm achieved an AUROC of 0.916 for discrimination between severe sepsis and control pediatric patients at the time of onset and AUROC of 0.718 at 4 h before onset. The prediction algorithm significantly outperformed the Pediatric Logistic Organ Dysfunction score (PELOD-2) (p < 0.05) and pediatric Systemic Inflammatory Response Syndrome (SIRS) (p < 0.05) in the prediction of severe sepsis 4 h before onset using cross-validation and pairwise t-tests. Conclusion: This machine learning algorithm has the potential to deliver high-performance severe sepsis detection and prediction through automated monitoring of EHR data for pediatric inpatients, which may enable earlier sepsis recognition and treatment initiation.