• Anoop Mayampurath, L Nelson Sanchez-Pinto, Emma Hegermiller, Amarachi Erondu, Kyle Carey, Priti Jani, Robert Gibbons, Dana Edelson, and Matthew M Churpek.
• Department of Pediatrics, University of Chicago, Chicago, IL.
• Pediatr Crit Care Me. 2022 Jul 1; 23 (7): 514523514-523.

ObjectivesUnrecognized clinical deterioration during illness requiring hospitalization is associated with high risk of mortality and long-term morbidity among children. Our objective was to develop and externally validate machine learning algorithms using electronic health records for identifying ICU transfer within 12 hours indicative of a child's condition.DesignObservational cohort study.SettingTwo urban, tertiary-care, academic hospitals (sites 1 and 2).PatientsPediatric inpatients (age <18 yr).InterventionsNone.Measurement And Main ResultsOur primary outcome was direct ward to ICU transfer. Using age, vital signs, and laboratory results, we derived logistic regression with regularization, restricted cubic spline regression, random forest, and gradient boosted machine learning models. Among 50,830 admissions at site 1 and 88,970 admissions at site 2, 1,993 (3.92%) and 2,317 (2.60%) experienced the primary outcome, respectively. Site 1 data were split longitudinally into derivation (2009-2017) and validation (2018-2019), whereas site 2 constituted the external test cohort. Across both sites, the gradient boosted machine was the most accurate model and outperformed a modified version of the Bedside Pediatric Early Warning Score that only used physiologic variables in terms of discrimination ( C -statistic site 1: 0.84 vs 0.71, p < 0.001; site 2: 0.80 vs 0.74, p < 0.001), sensitivity, specificity, and number needed to alert.ConclusionsWe developed and externally validated a novel machine learning model that identifies ICU transfers in hospitalized children more accurately than current tools. Our model enables early detection of children at risk for deterioration, thereby creating opportunities for intervention and improvement in outcomes.Copyright © 2022 by the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies.

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