Critical appraisal of machine learning prognostic models for acute pancreatitis: protocol for a systematic review
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41512_2024_Article_169.pdf
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Author(s)
Hassan, Amier
Critelli, Brian
Lahooti, Ila
Lahooti, Ali
Matzko, Nate
Adams, Jan Niklas
Liss, Lukas
Quion, Justin
Restrepo, David
Nikahd, Melica
Date Issued
April 2, 2024
Journal
Diagnostic and Prognostic Research
Publisher
Springer Science and Business Media LLC
Citation
Diagnostic and Prognostic Research. 2024 Apr 02;8(1):6
Version
Final published version
Abstract
Acute pancreatitis (AP) is an acute inflammatory disorder that is common, costly, and is increasing in incidence worldwide with over 300,000 hospitalizations occurring yearly in the United States alone. As its course and outcomes vary widely, a critical knowledge gap in the field has been a lack of accurate prognostic tools to forecast AP patients’ outcomes. Despite several published studies in the last three decades, the predictive performance of published prognostic models has been found to be suboptimal. Recently, non-regression machine learning models (ML) have garnered intense interest in medicine for their potential for better predictive performance. Each year, an increasing number of AP models are being published. However, their methodologic quality relating to transparent reporting and risk of bias in study design has never been systematically appraised. Therefore, through collaboration between a group of clinicians and data scientists with appropriate content expertise, we will perform a systematic review of papers published between January 2021 and December 2023 containing artificial intelligence prognostic models in AP. To systematically assess these studies, the authors will leverage the CHARMS checklist, PROBAST tool for risk of bias assessment, and the most current version of the TRIPOD-AI. (Research Registry (
http://www.reviewregistry1727
.).
Subjects
Applied Mathematics
General Mathematics
MIT Department
Harvard--MIT Program in Health Sciences and Technology. Laboratory for Computational Physiology
Terms of Use
Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1186/s41512-024-00169-1
