Systematic metareview of prediction studies demonstrates stable trends in bias and low PROBAST inter-rater agreement

Langenhuijsen, Liselotte F.S.; Janse, Roemer J.; Venema, Esmée; Kent, David M.; Diepen, Merel van; Dekker, Friedo W.; Jong, Ype de

doi:10.1016/j.jclinepi.2023.04.012

Cited by 4 publications

(1 citation statement)

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“…Using PROBAST, we will systematically assess the applicability of published prognostic models in AP and their risk of bias. Given the concerns raised about low inter-rater agreement [ 20 ], we have conducted PROBAST rater training: this included weekly meetings with an AP content expert who has undergone appropriate PROBAST training by the PROBAST developers (PJL) to discuss every signaling question on the PROBAST domains with examples for 6 months. When ML content expertise is required to accurately complete PROBAST, the data scientists, led by ML methodology expert (LAC), will be consulted for a valid risk of bias assessment.…”

Section: Assessment Of Study Qualitymentioning

confidence: 99%

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

Hassan,

Critelli,

Lahooti

et al. 2024

Diagn Progn Res

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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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Section: Assessment Of Study Qualitymentioning

confidence: 99%