Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools

Arina, Pietro; Kaczorek, Maciej R.; Hofmaenner, Daniel A.; Pisciotta, Walter; Refinetti, Patricia; Singer, Mervyn; Mazomenos, Evangelos B.; Whittle, John

doi:10.1097/aln.0000000000004764

Cited by 13 publications

(1 citation statement)

References 147 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While the performance of the published models was high (mean AUC 0.91), we identified several key limitations in the recently published models. Unfortunately, these shortcomings are like those identified in other fields such as oncology [28] and anesthesiology [73]. First, the concern relates to the high risk of bias most notably in the statistical analysis section, which can undermine the validity of the models.…”

Section: Discussionmentioning

confidence: 99%

A Systematic Review of Machine Learning-based Prognostic Models for Acute Pancreatitis: Towards Improving Methods and Reporting Quality

Critelli,

Hassan,

Lahooti

et al. 2024

Preprint

View full text Add to dashboard Cite

BackgroundAn 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.MethodsUsing 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.ResultsThe 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),.DiscussionThere 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.FundingnoneRegistrationResearch Registry (reviewregistry1727)

show abstract

Section: Discussionmentioning

confidence: 99%