Adherence to TRIPOD+AI guideline: An updated reporting assessment tool

OBJECTIVES: Incomplete reporting of research limits its usefulness and contributes to research waste. Numerous reporting guidelines have been developed to support complete and accurate reporting of healthcare research studies. Completeness of reporting can be measured by evaluating the adherence to reporting guidelines. However, assessing adherence to a reporting guideline often lacks uniformity. In 2019, we developed a reporting adherence tool for the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement. With recent advances in regression and artificial intelligence (AI)/machine learning (ML) based methods, TRIPOD+AI (www.tripod-statment.org) was developed to replace the TRIPOD statement. The aim of this study was to develop an updated adherence tool for TRIPOD+AI.

STUDY DESIGN AND SETTING: Based on the TRIPOD+AI full reporting guideline, including the accompanying Explanation and Elaboration light, and TRIPOD+AI for Abstracts, we updated and expanded the original TRIPOD adherence tool and refined the adherence elements and their scoring rules through discussions within the author team and a pilot test.

RESULTS: The updated tool comprises of 37 main items and 136 adherence elements and includes several automated scoring rules. We developed separate TRIPOD+AI adherence tools for model development, model evaluation, and for studies describing both in a single paper .

CONCLUSION: A uniform approach to assessing reporting adherence of TRIPOD+AI allows for comparisons across various fields, monitor reporting over time, and incentivizes primary study authors to comply.

PLAIN LANGUAGE SUMMARY: Plain language summary: Accurate and complete reporting is crucial in biomedical research to ensure findings can be effectively used. To support researchers in reporting their findings well, reporting guidelines have been developed for different study types. One such guideline is TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis), which focuses on research studies about medical prediction tools. In 2024, TRIPOD was updated to TRIPOD+AI to address the increasing use of artificial intelligence and machine learning in prediction model studies. In 2019, we developed a scoring system to evaluate how well research papers on prediction tools adhered to the TRIPOD guideline, resulting in a reporting completeness score. This score allows for easier comparison of reporting completeness across various medical fields, and to monitor improvement in reporting over time. With the introduction of TRIPOD+AI, an update of the scoring system was required to align with the new reporting recommendations. We achieved this by reviewing our previous scoring system and incorporating the new items from TRIPOD+AI to better suit studies involving AI. We believe that this system will facilitate comparisons of prediction model reporting completeness across different fields and encourage improved reporting practices.