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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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Molloy MJ, Zackoff M, Gifford A, Hagedorn P, Tegtmeyer K, Britto MT, Dewan M. Usability Testing of Situation Awareness Clinical Decision Support in the Intensive Care Unit. Appl Clin Inform 2024; 15:327-334. [PMID: 38378044 PMCID: PMC11062760 DOI: 10.1055/a-2272-6184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 02/18/2024] [Indexed: 02/22/2024] Open
Abstract
OBJECTIVE Our objective was to evaluate the usability of an automated clinical decision support (CDS) tool previously implemented in the pediatric intensive care unit (PICU) to promote shared situation awareness among the medical team to prevent serious safety events within children's hospitals. METHODS We conducted a mixed-methods usability evaluation of a CDS tool in a PICU at a large, urban, quaternary, free-standing children's hospital in the Midwest. Quantitative assessment was done using the system usability scale (SUS), while qualitative assessment involved think-aloud usability testing. The SUS was scored according to survey guidelines. For think-aloud testing, task times were calculated, and means and standard deviations were determined, stratified by role. Qualitative feedback from participants and moderator observations were summarized. RESULTS Fifty-one PICU staff members, including physicians, advanced practice providers, nurses, and respiratory therapists, completed the SUS, while ten participants underwent think-aloud usability testing. The overall median usability score was 87.5 (interquartile range: 80-95), with over 96% rating the tool's usability as "good" or "excellent." Task completion times ranged from 2 to 92 seconds, with the quickest completion for reviewing high-risk criteria and the slowest for adding to high-risk criteria. Observations and participant responses from think-aloud testing highlighted positive aspects of learnability and clear display of complex information that is easily accessed, as well as opportunities for improvement in tool integration into clinical workflows. CONCLUSION The PICU Warning Tool demonstrates good usability in the critical care setting. This study demonstrates the value of postimplementation usability testing in identifying opportunities for continued improvement of CDS tools.
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Affiliation(s)
- Matthew J. Molloy
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States
- Division of Hospital Medicine, Cincinnati Children's Hospital, Cincinnati, Ohio, United States
- Division of Biomedical Informatics, Cincinnati Children's Hospital, Cincinnati, Ohio, United States
| | - Matthew Zackoff
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States
- Division of Critical Care, Cincinnati Children's Hospital, Cincinnati, Ohio, United States
| | | | - Philip Hagedorn
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States
- Division of Hospital Medicine, Cincinnati Children's Hospital, Cincinnati, Ohio, United States
- Division of Biomedical Informatics, Cincinnati Children's Hospital, Cincinnati, Ohio, United States
| | - Ken Tegtmeyer
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States
- Division of Critical Care, Cincinnati Children's Hospital, Cincinnati, Ohio, United States
| | - Maria T. Britto
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States
- James M. Anderson Center for Health Systems Excellence, Cincinnati Children's Hospital, Cincinnati, Ohio, United States
| | - Maya Dewan
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States
- Division of Biomedical Informatics, Cincinnati Children's Hospital, Cincinnati, Ohio, United States
- Division of Critical Care, Cincinnati Children's Hospital, Cincinnati, Ohio, United States
- James M. Anderson Center for Health Systems Excellence, Cincinnati Children's Hospital, Cincinnati, Ohio, United States
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Dewan M, Tegtmeyer K, Stalets EL. Through the Looking-Glass Door. Pediatr Crit Care Med 2023; 24:425-426. [PMID: 37140334 DOI: 10.1097/pcc.0000000000003227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Affiliation(s)
- Maya Dewan
- All authors: Cincinnati Children's Hospital Medical Center, Cincinnati, OH
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Liang D, Molloy MJ. Making tools that work for us: Improving clinical decision support. J Hosp Med 2023. [PMID: 37127944 DOI: 10.1002/jhm.13114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 04/18/2023] [Indexed: 05/03/2023]
Affiliation(s)
- Danni Liang
- Department of Pediatrics, Division of Pediatric Hospital Medicine, Stanford University School of Medicine, Palo Alto, California, USA
| | - Matthew J Molloy
- Department of Pediatrics, Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
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Leveraging Clinical Informatics and Data Science to Improve Care and Facilitate Research in Pediatric Acute Respiratory Distress Syndrome: From the Second Pediatric Acute Lung Injury Consensus Conference. Pediatr Crit Care Med 2023; 24:S1-S11. [PMID: 36661432 DOI: 10.1097/pcc.0000000000003155] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
OBJECTIVES The use of electronic algorithms, clinical decision support systems, and other clinical informatics interventions is increasing in critical care. Pediatric acute respiratory distress syndrome (PARDS) is a complex, dynamic condition associated with large amounts of clinical data and frequent decisions at the bedside. Novel data-driven technologies that can help screen, prompt, and support clinician decision-making could have a significant impact on patient outcomes. We sought to identify and summarize relevant evidence related to clinical informatics interventions in both PARDS and adult respiratory distress syndrome (ARDS), for the second Pediatric Acute Lung Injury Consensus Conference. DATA SOURCES MEDLINE (Ovid), Embase (Elsevier), and CINAHL Complete (EBSCOhost). STUDY SELECTION We included studies of pediatric or adult critically ill patients with or at risk of ARDS that examined automated screening tools, electronic algorithms, or clinical decision support systems. DATA EXTRACTION Title/abstract review, full text review, and data extraction using a standardized data extraction form. DATA SYNTHESIS The Grading of Recommendations Assessment, Development and Evaluation approach was used to identify and summarize evidence and develop recommendations. Twenty-six studies were identified for full text extraction to address the Patient/Intervention/Comparator/Outcome questions, and 14 were used for the recommendations/statements. Two clinical recommendations were generated, related to the use of electronic screening tools and automated monitoring of compliance with best practice guidelines. Two research statements were generated, related to the development of multicenter data collaborations and the design of generalizable algorithms and electronic tools. One policy statement was generated, related to the provision of material and human resources by healthcare organizations to empower clinicians to develop clinical informatics interventions to improve the care of patients with PARDS. CONCLUSIONS We present two clinical recommendations and three statements (two research one policy) for the use of electronic algorithms and clinical informatics tools for patients with PARDS based on a systematic review of the literature and expert consensus.
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The use of machine learning and artificial intelligence within pediatric critical care. Pediatr Res 2023; 93:405-412. [PMID: 36376506 PMCID: PMC9660024 DOI: 10.1038/s41390-022-02380-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 09/15/2022] [Accepted: 10/30/2022] [Indexed: 11/16/2022]
Abstract
The field of pediatric critical care has been hampered in the era of precision medicine by our inability to accurately define and subclassify disease phenotypes. This has been caused by heterogeneity across age groups that further challenges the ability to perform randomized controlled trials in pediatrics. One approach to overcome these inherent challenges include the use of machine learning algorithms that can assist in generating more meaningful interpretations from clinical data. This review summarizes machine learning and artificial intelligence techniques that are currently in use for clinical data modeling with relevance to pediatric critical care. Focus has been placed on the differences between techniques and the role of each in the clinical arena. The various forms of clinical decision support that utilize machine learning are also described. We review the applications and limitations of machine learning techniques to empower clinicians to make informed decisions at the bedside. IMPACT: Critical care units generate large amounts of under-utilized data that can be processed through artificial intelligence. This review summarizes the machine learning and artificial intelligence techniques currently being used to process clinical data. The review highlights the applications and limitations of these techniques within a clinical context to aid providers in making more informed decisions at the bedside.
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Randolph AG, Bembea MM, Cheifetz IM, Curley MAQ, Flori HR, Khemani RG, Kudchadkar SR, Nishisaki A, Watson RS, Tucci M, Lacroix J, Thompson AE, Thomas NJ. Pediatric Acute Lung Injury and Sepsis Investigators (PALISI): Evolution of an Investigator-Initiated Research Network. Pediatr Crit Care Med 2022; 23:1056-1066. [PMID: 36454002 PMCID: PMC9747245 DOI: 10.1097/pcc.0000000000003100] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
The Pediatric Acute Lung Injury and Sepsis Investigators (PALISI) Network originated over 20 years ago to foster research to optimize the care of critically ill infants and children. Over this period, PALISI has seen two major evolutions: formalization of our network infrastructure and a broadening of our clinical research focus. First, the network is unique in that its activities and meetings are funded by subscriptions from members who now comprise a multidisciplinary group of investigators from over 90 PICUs all over the United States (US) and Canada, with collaborations across the globe. In 2020, the network converted into a standalone, nonprofit organizational structure (501c3), making the PALISI Network formally independent of academic and clinical institutions or professional societies. Such an approach allows us to invest in infrastructure and future initiatives with broader opportunities for fund raising. Second, our research investigations have expanded beyond the original focus on sepsis and acute lung injury, to incorporate the whole field of pediatric critical care, for example, efficient liberation from mechanical ventilator support, prudent use of blood products, improved safety of intubation practices, optimal sedation practices and glucose control, and pandemic research on influenza and COVID-19. Our network approach in each field follows, where necessary, the full spectrum of clinical and translational research, including: immunobiology studies for understanding basic pathologic mechanisms; surveys to explore contemporary clinical practice; consensus conferences to establish agreement about literature evidence; observational prevalence and incidence studies to measure scale of a clinical issue or question; case control studies as preliminary best evidence for design of definitive prospective studies; and, randomized controlled trials for informing clinical care. As a research network, PALISI and its related subgroups have published over 350 peer-reviewed publications from 2002 through September 2022.
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Affiliation(s)
- Adrienne G Randolph
- Division of Critical Care Medicine, Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, MA
- Departments of Anaesthesia and Pediatrics, Harvard Medical School, Boston, MA
| | - Melania M Bembea
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Ira M Cheifetz
- Division of Pediatric Critical Care Medicine, Rainbow Babies and Children's Hospital, Cleveland, OH
| | - Martha A Q Curley
- Department of Family and Community Health, School of Nursing, University of Pennsylvania, Philadelphia, PA
- Research Institute, Children's Hospital of Philadelphia, Philadelphia, PA
| | - Heidi R Flori
- Division of Critical Care Medicine, University of Michigan C.S. Mott Children's Hospital, Ann Arbor, MI
| | - Robinder G Khemani
- Division of Critical Care Medicine, Department of Anesthesia, Children's Hospital Los Angeles, Los Angeles, CA
| | - Sapna R Kudchadkar
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Akira Nishisaki
- Division of Critical Care Medicine, Department of Anesthesia, Children's Hospital of Philadelphia, Philadelphia, PA
| | - R Scott Watson
- Division of Critical Care Medicine, Department of Pediatrics, Seattle Children's Hospital, Seattle, WA
| | - Marisa Tucci
- Division of Critical Care Medicine, CHU Sainte-Justine, Montreal, QC, Canada
| | - Jacques Lacroix
- Division of Critical Care Medicine, CHU Sainte-Justine, Montreal, QC, Canada
| | - Ann E Thompson
- Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA
| | - Neal J Thomas
- Division of Critical Care Medicine, Penn State Milton S. Hershey Medical Center, Hershey, PA
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Molloy M, Hagedorn P, Dewan M. Why Does Current Clinical Decision Support Frequently Fail to Support Clinical Decisions? Pediatr Crit Care Med 2022; 23:670-672. [PMID: 36165945 PMCID: PMC9523478 DOI: 10.1097/pcc.0000000000003000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Matthew Molloy
- Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, Ohio
- Division of Hospital Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
| | - Philip Hagedorn
- Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, Ohio
- Division of Hospital Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
| | - Maya Dewan
- Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, Ohio
- Division of Critical Care Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
- James M. Anderson Center for Health Systems Excellence, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
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