Critical appraisal of machine learning prognostic models for acute pancreatitis: protocol for a systematic review

doi:10.1186/s41512-024-00169-1

. 2024 Apr 2;8(1):6.

doi: 10.1186/s41512-024-00169-1.

Critical appraisal of machine learning prognostic models for acute pancreatitis: protocol for a systematic review

Amier Hassan^#¹, Brian Critelli^#¹, Ila Lahooti², Ali Lahooti¹, Nate Matzko¹, Jan Niklas Adams³, Lukas Liss³, Justin Quion⁴, David Restrepo⁴, Melica Nikahd⁵, Stacey Culp⁵, Lydia Noh⁶, Kathleen Tong², Jun Sung Park², Venkata Akshintala⁷, John A Windsor⁸, Nikhil K Mull⁹, Georgios I Papachristou², Leo Anthony Celi^{8

10}, Peter J Lee¹¹

Affiliations

¹ Division of Gastroenterology and Hepatology, Weill Cornell Medical College, New York, USA.
² Division of Gastroenterology and Hepatology, Ohio State University Wexner Medical Center, Columbus, OH, USA.
³ Division of Process and Data Science, Rheinisch-Westfälische Technische Hochschule Aachen University, Aachen, Germany.
⁴ Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, USA.
⁵ Division of Bioinformatics, Ohio State University Wexner Medical Center, Columbus, USA.
⁶ Northeast Ohio Medical School, Rootstown, USA.
⁷ Division of Gastroenterology, Johns Hopkins Medical Center, Baltimore, USA.
⁸ Department of Surgery, University of Auckland, Auckland, New Zealand.
⁹ Division of Hospital Medicine and Penn Medicine Center for Evidence-based Practice, University of Pennsylvania, Philadelphia, USA.
¹⁰ Division of Critical Care, Beth Israel Medical Center, Boston, USA.
¹¹ Division of Gastroenterology and Hepatology, Ohio State University Wexner Medical Center, Columbus, OH, USA. Peter.Lee@osumc.edu.

^# Contributed equally.

PMID: 38561864
PMCID: PMC10986113
DOI: 10.1186/s41512-024-00169-1

Critical appraisal of machine learning prognostic models for acute pancreatitis: protocol for a systematic review

Amier Hassan et al. Diagn Progn Res. 2024.

. 2024 Apr 2;8(1):6.

doi: 10.1186/s41512-024-00169-1.

Authors

Affiliations

¹ Division of Gastroenterology and Hepatology, Weill Cornell Medical College, New York, USA.
² Division of Gastroenterology and Hepatology, Ohio State University Wexner Medical Center, Columbus, OH, USA.
³ Division of Process and Data Science, Rheinisch-Westfälische Technische Hochschule Aachen University, Aachen, Germany.
⁴ Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, USA.
⁵ Division of Bioinformatics, Ohio State University Wexner Medical Center, Columbus, USA.
⁶ Northeast Ohio Medical School, Rootstown, USA.
⁷ Division of Gastroenterology, Johns Hopkins Medical Center, Baltimore, USA.
⁸ Department of Surgery, University of Auckland, Auckland, New Zealand.
⁹ Division of Hospital Medicine and Penn Medicine Center for Evidence-based Practice, University of Pennsylvania, Philadelphia, USA.
¹⁰ Division of Critical Care, Beth Israel Medical Center, Boston, USA.
¹¹ Division of Gastroenterology and Hepatology, Ohio State University Wexner Medical Center, Columbus, OH, USA. Peter.Lee@osumc.edu.

^# Contributed equally.

PMID: 38561864
PMCID: PMC10986113
DOI: 10.1186/s41512-024-00169-1

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 .).

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Conflict of interest statement

The authors declare that they have no competing interests.

References

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1. Xiao AY, Tan ML, Wu LM, Asrani VM, Windsor JA, Yadav D, Petrov MS. Global incidence and mortality of pancreatic diseases: a systematic review, meta-analysis, and meta-regression of population-based cohort studies. Lancet Gastroenterol Hepatol. 2016;1(1):45–55. doi: 10.1016/S2468-1253(16)30004-8. - DOI - PubMed
1. Abu-El-Haija M, Gukovskaya AS, Andersen DK, Gardner TB, Hegyi P, Pandol SJ, Papachristou GI, Saluja AK, Singh VK, Uc A, et al. Accelerating the drug delivery pipeline for acute and chronic pancreatitis: summary of the working group on drug development and trials in acute pancreatitis at the National Institute of Diabetes and Digestive and Kidney Diseases Workshop. Pancreas. 2018;47(10):1185–92. doi: 10.1097/MPA.0000000000001175. - DOI - PMC - PubMed

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[1] Peery AF, Crockett SD, Murphy CC, Jensen ET, Kim HP, Egberg MD, Lund JL, Moon AM, Pate V, Barnes EL, et al. Burden and cost of gastrointestinal, liver, and pancreatic diseases in the United States: update 2021. Gastroenterology. 2022;162(2):621–644. doi: 10.1053/j.gastro.2021.10.017. - DOI - PMC - PubMed

[2] Peery AF, Crockett SD, Murphy CC, Jensen ET, Kim HP, Egberg MD, Lund JL, Moon AM, Pate V, Barnes EL, et al. Burden and cost of gastrointestinal, liver, and pancreatic diseases in the United States: update 2021. Gastroenterology. 2022;162(2):621–644. doi: 10.1053/j.gastro.2021.10.017. - DOI - PMC - PubMed

[3] Suzuki M, Sai JK, Shimizu T. Acute pancreatitis in children and adolescents. World J Gastrointest Pathophysiol. 2014;5(4):416–26. doi: 10.4291/wjgp.v5.i4.416.PMID:25400985;PMCID:PMC4231506. - DOI - PMC - PubMed

[4] Suzuki M, Sai JK, Shimizu T. Acute pancreatitis in children and adolescents. World J Gastrointest Pathophysiol. 2014;5(4):416–26. doi: 10.4291/wjgp.v5.i4.416.PMID:25400985;PMCID:PMC4231506. - DOI - PMC - PubMed

[5] Petrov MS, Yadav D. Global epidemiology and holistic prevention of pancreatitis. Nat Rev Gastroenterol Hepatol. 2019;16(3):175–184. doi: 10.1038/s41575-018-0087-5. - DOI - PMC - PubMed

[6] Petrov MS, Yadav D. Global epidemiology and holistic prevention of pancreatitis. Nat Rev Gastroenterol Hepatol. 2019;16(3):175–184. doi: 10.1038/s41575-018-0087-5. - DOI - PMC - PubMed

[7] Xiao AY, Tan ML, Wu LM, Asrani VM, Windsor JA, Yadav D, Petrov MS. Global incidence and mortality of pancreatic diseases: a systematic review, meta-analysis, and meta-regression of population-based cohort studies. Lancet Gastroenterol Hepatol. 2016;1(1):45–55. doi: 10.1016/S2468-1253(16)30004-8. - DOI - PubMed

[8] Xiao AY, Tan ML, Wu LM, Asrani VM, Windsor JA, Yadav D, Petrov MS. Global incidence and mortality of pancreatic diseases: a systematic review, meta-analysis, and meta-regression of population-based cohort studies. Lancet Gastroenterol Hepatol. 2016;1(1):45–55. doi: 10.1016/S2468-1253(16)30004-8. - DOI - PubMed

[9] Abu-El-Haija M, Gukovskaya AS, Andersen DK, Gardner TB, Hegyi P, Pandol SJ, Papachristou GI, Saluja AK, Singh VK, Uc A, et al. Accelerating the drug delivery pipeline for acute and chronic pancreatitis: summary of the working group on drug development and trials in acute pancreatitis at the National Institute of Diabetes and Digestive and Kidney Diseases Workshop. Pancreas. 2018;47(10):1185–92. doi: 10.1097/MPA.0000000000001175. - DOI - PMC - PubMed

[10] Abu-El-Haija M, Gukovskaya AS, Andersen DK, Gardner TB, Hegyi P, Pandol SJ, Papachristou GI, Saluja AK, Singh VK, Uc A, et al. Accelerating the drug delivery pipeline for acute and chronic pancreatitis: summary of the working group on drug development and trials in acute pancreatitis at the National Institute of Diabetes and Digestive and Kidney Diseases Workshop. Pancreas. 2018;47(10):1185–92. doi: 10.1097/MPA.0000000000001175. - DOI - PMC - PubMed

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Critical appraisal of machine learning prognostic models for acute pancreatitis: protocol for a systematic review

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Critical appraisal of machine learning prognostic models for acute pancreatitis: protocol for a systematic review

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Abstract

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