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.
Highlights
Sepsis is a high-impact condition that affects both adults and children
The original University of California San Francisco (UCSF) data collection did not impact patient safety, as all data were de-identified in accordance with the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule prior to commencement of this study and no individual patient data were linked prior to being de-identified
Encounters were removed if they were missing any of the required measurements to be used in training and prediction; while supplemental measurements (Glasgow Coma Score, white blood cell count, and platelet count) were passed to the training and testing routines, their presence was not required
Summary
Sepsis is a high-impact condition that affects both adults and children. In 2001, the total burden of sepsis-spectrum syndromes in the United States was estimated at $16.7 billion and 215,000 deaths annually [2]. In 2007, the mean, per-hospitalization cost of severe sepsis was estimated to be $47,126 [3], and a recent study assessed that sepsis is responsible for as many as 5.3 million deaths per year globally [4]. Pediatric sepsis in particular causes over 6,500 deaths annually in the United States, with an estimated $4.8 billion burden of care, at approximately $64,280 per hospitalization [5]. Detection of pediatric severe sepsis is necessary in order to optimize effective treatment, and new methods are needed to facilitate this early detection
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