A Systematic Review of Machine Learning-based Prognostic Models for Acute Pancreatitis: Towards Improving Methods and Reporting Quality
Year: 2024
Type: preprint
Study design: Systematic Review
Abstract: Background An accurate prognostic tool is essential to aid clinical decision making (e.g., patient triage) and to advance personalized medicine. However, such prognostic tool is lacking for acute pancreatitis (AP). Increasingly machine learning (ML) techniques are being used to develop high-performing prognostic models in AP. However, methodologic and reporting quality has received little attention . High-quality reporting and study methodology are critical to model validity, reproducibility, and clinical implementation. In collaboration with content experts in ML methodology, we performed a systematic review critically appraising the quality of methodology and reporting of recently published ML AP prognostic models. Methods Using a validated search strategy, we identified ML AP studies from the databases MEDLINE, PubMed, and EMBASE published between January 2021 and December 2023. Eligibility criteria included all retrospective or prospective studies that developed or validated new or existing ML models in patients with AP that predicted an outcome following an episode of AP. Meta-analysis was considered if there was homogeneity in the study design and in the type of outcome predicted. For risk of bias (ROB) assessment, we used the Prediction Model Risk of Bias Assessment Tool (PROBAST). Quality of reporting was assessed using the Transparent Reporting of a Multivariable Prediction Model of Individual Prognosis or Diagnosis – Artificial Intelligence (TRIPOD+AI) statement that defines standards for 27 items that should be reported in publications using ML prognostic models. Results The search strategy identified 6480 publications of which 30 met the eligibility criteria. Studies originated from China (22), U.S (4), and other (4). All 30 studies developed a new ML model and none sought to validate an existing ML model, producing a total of 39 new ML models. AP severity (23/39) or mortality (6/39) were the most common outcomes predicted. The mean area-under-the-curve for all models and endpoints was 0.91 (SD 0.08). The ROB was high for at least one domain in all 39 models, particularly for the analysis domain (37/39 models). Steps were not taken to minimize over-optimistic model performance in 27/39 models. Due to heterogeneity in the study design and in how the outcomes were defined and determined, meta-analysis was not performed. Studies reported on only 15/27 items from TRIPOD+AI standards, with only 7/30 justifying sample size and 13/30 assessing data quality. Other reporting deficiencies included omissions regarding human-AI interaction (28/30), handling low-quality or incomplete data in practice (27/30), sharing analytical codes (25/30), study protocols (25/30) and reporting source data (19/30),. Discussion There are significant deficiencies in the methodology and reporting of recently published ML based prognostic models in AP patients. These undermine the validity, reproducibility and implementation of these prognostic models despite their promise of superior predictive accuracy. Funding none Registration Research Registry ( reviewregistry1727 )
Source: medRxiv
Authors: Brian Critelli, Amier Hassan, Ila Lahooti, Lydia Noh, Jun Sung Park, Kathleen Tong, Ali Lahooti, Nate Matzko, Jan Niklas Adams, Lukas Liss, Justin Quion, David Restrepo, Melica Nikahd, Stacey Culp, Adam Lacy-Hulbert, Cate Speake, James Buxbaum, Jason Bischof, Cemal Yazici, Anna Evans Phillips, Sophie Terp, Alexandra Weissman, Darwin Conwell, Phil Hart, Mitch Ramsey, Somashekar Krishna, Samuel Han, Erica Park, Raj Shah, Venkata Akshintala, John A Windsor, Nikhil K Mull, Georgios I Papachristou, Leo Anthony Celi, Peter J Lee
Institutions: Weill Cornell Medicine, The Ohio State University Wexner Medical Center, Ohio University, The Ohio State University, Northeast Ohio Medical University, RWTH Aachen University, Massachusetts Institute of Technology, Benaroya Research Institute, University of Southern California, University of Illinois Chicago, University of Pittsburgh, University of Pittsburgh Medical Center, University of Kentucky, Johns Hopkins University, Johns Hopkins Medicine, Johns Hopkins Hospital, University of Auckland, University of Pennsylvania Health System, University of Pennsylvania
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4400072078