Browsing by Author "McCrory, Michael C."

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    The Pediatric Data Science and Analytics Subgroup of the Pediatric Acute Lung Injury and Sepsis Investigators Network: Use of Supervised Machine Learning Applications in Pediatric Critical Care Medicine Research
    (Wolters Kluwer, 2024) Heneghan, Julia A.; Walker, Sarah B.; Fawcett, Andrea; Bennett, Tellen D.; Dziorny, Adam C.; Sanchez-Pinto, L. Nelson; Farris, Reid W. D.; Winter, Meredith C.; Badke, Colleen; Martin, Blake; Brown, Stephanie R.; McCrory, Michael C.; Ness-Cochinwala, Manette; Rogerson, Colin; Baloglu, Orkun; Harwayne-Gidansky, Ilana; Hudkins, Matthew R.; Kamaleswaran, Rishikesan; Gangadharan, Sandeep; Tripathi, Sandeep; Mendonca, Eneida A.; Markovitz, Barry P.; Mayampurath, Anoop; Spaeder, Michael C.; Pediatric Data Science and Analytics (PEDAL) subgroup of the Pediatric Acute Lung Injury and Sepsis Investigators (PALISI) Network; Pediatrics, School of Medicine
    Objective: Perform a scoping review of supervised machine learning in pediatric critical care to identify published applications, methodologies, and implementation frequency to inform best practices for the development, validation, and reporting of predictive models in pediatric critical care. Design: Scoping review and expert opinion. Setting: We queried CINAHL Plus with Full Text (EBSCO), Cochrane Library (Wiley), Embase (Elsevier), Ovid Medline, and PubMed for articles published between 2000 and 2022 related to machine learning concepts and pediatric critical illness. Articles were excluded if the majority of patients were adults or neonates, if unsupervised machine learning was the primary methodology, or if information related to the development, validation, and/or implementation of the model was not reported. Article selection and data extraction were performed using dual review in the Covidence tool, with discrepancies resolved by consensus. Subjects: Articles reporting on the development, validation, or implementation of supervised machine learning models in the field of pediatric critical care medicine. Interventions: None. Measurements and main results: Of 5075 identified studies, 141 articles were included. Studies were primarily (57%) performed at a single site. The majority took place in the United States (70%). Most were retrospective observational cohort studies. More than three-quarters of the articles were published between 2018 and 2022. The most common algorithms included logistic regression and random forest. Predicted events were most commonly death, transfer to ICU, and sepsis. Only 14% of articles reported external validation, and only a single model was implemented at publication. Reporting of validation methods, performance assessments, and implementation varied widely. Follow-up with authors suggests that implementation remains uncommon after model publication. Conclusions: Publication of supervised machine learning models to address clinical challenges in pediatric critical care medicine has increased dramatically in the last 5 years. While these approaches have the potential to benefit children with critical illness, the literature demonstrates incomplete reporting, absence of external validation, and infrequent clinical implementation.