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Heneghan JA, Walker SB, Fawcett A, Bennett TD, Dziorny AC, Sanchez-Pinto LN, Farris RW, Winter MC, Badke C, Martin B, Brown SR, McCrory MC, Ness-Cochinwala M, Rogerson C, Baloglu O, Harwayne-Gidansky I, Hudkins MR, Kamaleswaran R, Gangadharan S, Tripathi S, Mendonca EA, Markovitz BP, Mayampurath A, Spaeder MC. 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. Pediatr Crit Care Med 2024; 25:364-374. [PMID: 38059732 PMCID: PMC10994770 DOI: 10.1097/pcc.0000000000003425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/08/2023]
Abstract
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.
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Affiliation(s)
- Julia A. Heneghan
- Division of Pediatric Critical Care, University of Minnesota Masonic Children’s Hospital; Minneapolis, MN
| | - Sarah B. Walker
- Department of Pediatrics (Critical Care), Northwestern University Feinberg School of Medicine and Ann & Robert H. Lurie Children’s Hospital of Chicago; Chicago, IL
| | - Andrea Fawcett
- Department of Clinical and Organizational Development; Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL
| | - Tellen D. Bennett
- Departments of Biomedical Informatics and Pediatrics (Critical Care Medicine), University of Colorado School of Medicine; Aurora, CO
| | - Adam C. Dziorny
- Department of Pediatrics, University of Rochester; Rochester, NY
| | - L. Nelson Sanchez-Pinto
- Department of Pediatrics (Critical Care) and Preventive Medicine (Health & Biomedical Informatics), Northwestern University Feinberg School of Medicine and Ann & Robert H. Lurie Children’s Hospital of Chicago; Chicago, IL
| | - Reid W.D. Farris
- Department of Pediatrics, University of Washington and Seattle Children’s Hospital; Seattle, WA
| | - Meredith C. Winter
- Department of Anesthesiology Critical Care Medicine, Children’s Hospital Los Angeles and Keck School of Medicine, University of Southern California; Los Angeles, CA
| | - Colleen Badke
- Department of Pediatrics (Critical Care), Northwestern University Feinberg School of Medicine and Ann & Robert H. Lurie Children’s Hospital of Chicago; Chicago, IL
| | - Blake Martin
- Departments of Biomedical Informatics and Pediatrics (Critical Care Medicine), University of Colorado School of Medicine; Aurora, CO
| | - Stephanie R. Brown
- Section of Pediatric Critical Care, Oklahoma Children’s Hospital and Department of Pediatrics, University of Oklahoma Health Sciences Center, Oklahoma City, OK
| | - Michael C. McCrory
- Department of Anesthesiology, Wake Forest University School of Medicine; Winston Salem, NC
| | | | - Colin Rogerson
- Division of Critical Care, Department of Pediatrics, Indiana University; Indianapolis, IN
| | - Orkun Baloglu
- Pediatric Critical Care Medicine and Pediatric Cardiology, Cleveland Clinic Children’s Center for Artificial Intelligence (C4AI), Cleveland Clinic; Cleveland, OH
| | | | - Matthew R. Hudkins
- Division of Pediatric Critical Care, Department of Pediatrics, Oregon Health & Science University; Portland, OR
| | - Rishikesan Kamaleswaran
- Departments of Biomedical Informatics and Pediatrics, Emory University School of Medicine; Department of Biomedical Engineering, Georgia Institute of Technology; Atlanta, GA
| | - Sandeep Gangadharan
- Department of Pediatrics, Mount Sinai Icahn School of Medicine; New York, NY
| | - Sandeep Tripathi
- Department of Pediatrics. University of Illinois College of Medicine at Peoria/OSF HealthCare, Children’s Hospital of Illinois; Peoria, IL
| | - Eneida A. Mendonca
- Division of Biomedical Informatics, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center and University of Cincinnati; Cincinnati, OH
| | - Barry P. Markovitz
- Division of Pediatric Critical Care, Department of Pediatrics, University of Utah Spencer F Eccles School of Medicine, Intermountain Primary Children’s Hospital; Salt Lake City, UT
| | - Anoop Mayampurath
- Department of Biostatistics & Medical Informatics, University of Wisconsin-Madison; Madison, WI
| | - Michael C. Spaeder
- Department of Pediatrics, University of Virginia School of Medicine, Charlottesville, VA
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Li HY, Wang J, Wang T, Wang HS. Management of venous thromboembolism in pediatric patients: Quality assessment of clinical practice guidelines and variations in recommendations. Thromb Res 2023; 226:107-116. [PMID: 37141795 DOI: 10.1016/j.thromres.2023.04.022] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 04/06/2023] [Accepted: 04/25/2023] [Indexed: 05/06/2023]
Abstract
INTRODUCTION We performed a quality assessment and comparison of clinical practice guidelines (CPGs) for the prevention and treatment of venous thromboembolism (VTE) in pediatric patients and to provide a clinical reference. MATERIALS AND METHODS Electronic databases, guideline development organizations, and professional societies were searched to identify CPGs for VTE in pediatric patients between January 1, 2012, and April 7, 2022. The Appraisal of Guidelines Research & Evaluation (AGREE) II instrument was used to evaluate quality. Recommendations for preventing and treating VTE in pediatric patients were extracted via descriptive synthesis. RESULTS Six CPGs were included. The median scores (interquartile range [IQR]) for each AGREE II domain were as follows: scope and purpose, 88.89 % (IQR: 8.33 %); stakeholder involvement, 88.89 % (25 %); rigor of development, 67.71 % (24.47 %); clarity and presentation, 88.89 % (0 %); applicability, 50 % (42.71 %); and editorial independence, 66.67 % (50.00 %). In total, 268 key recommendations were extracted, and traditional anticoagulants (heparin and warfarin) remain the standard treatment. However, in recent years direct oral anticoagulants (DOACs) have shown similar efficacy and safety results for the treatment of VTE in children to those reported in adults; therefore, this practice is recommended in recent guidelines. CONCLUSIONS Variability exists in the development and reporting of CPGs for VTE in pediatric patients. There may be changes to the recommendations for the prevention and treatment of VTE in pediatrics in the future due to the efficacy of DOACs in children, and recommendations should be revised periodically as new evidence emerges.
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Affiliation(s)
- Hong-Yan Li
- Department of Pharmacy, Qindao University Medical College Affiliated Yantai Yuhuangding Hospital, Yantai, Shandong, China
| | - Jing Wang
- Department of Pharmacy, Qindao University Medical College Affiliated Yantai Yuhuangding Hospital, Yantai, Shandong, China
| | - Tao Wang
- Department of Vascular Surgery, Qindao University Medical College Affiliated Yantai Yuhuangding Hospital, Yantai, Shandong, China
| | - Hai-Shan Wang
- Department of Intensive Care Unit, Yantai YEDA Hospital, Yantai, Shandong, China.
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Wang YJ, Lei L, Huang Y. Factors associated with venous thromboembolism in the paediatric intensive care unit: A systematic review and meta‐analysis. Nurs Crit Care 2022. [DOI: 10.1111/nicc.12813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Ya Jing Wang
- West China School of Nursing, Sichuan University/Nursing Department, West China Second University Hospital Sichuan University Chengdu China
- Nursing Department West China Second University Hospital, Sichuan University Chengdu China
| | - Lei Lei
- Nursing Department West China Second University Hospital, Sichuan University Chengdu China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University) Ministry of Education Chengdu China
| | - Yan Huang
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University) Ministry of Education Chengdu China
- Department of pediatric Intensive Care Unit, Nursing Department, West China second University Sichuan University Chengdu China
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