The TRIPOD extension for reporting of prediction model studies using patient-level data from electronic healthcare records and meta-analysis




Oral session: Innovative solutions to challenges of evidence production (5)


Thursday 24 October 2019 - 16:00 to 17:30


All authors in correct order:

Debray T1, Collins G2, Riley R3, Reitsma H1, Moons K1
1 Julius Center for Health Sciences and Primary Care, The Netherlands
2 University of Oxford, UK
3 Keele University, UK
Presenting author and contact person

Presenting author:

Karel Moons

Contact person:

Abstract text
Background: the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Statement is a guideline to improve the reporting of studies developing, validating, or updating a prediction model. The guideline has been adopted by numerous journals and is widely used by authors to prepare their manuscript for publication. With the increased availability of large datasets (or 'big data') from electronic healthcare records and individual participant data meta-analysis, authors face novel challenges for conducting and reporting their prediction modeling research, as evidenced by related reporting guidelines such as PRISMA-IPD, STROBE and RECORD.

Objectives: to propose an extension for the TRIPOD statement to improve the conduct and reporting of prediction model studies using big and combined data sets.

Methods: we formed a steering committee in 2016 to discuss which TRIPOD items needed revision, and whether additional items were needed. We subsequently developed a formal extension and evaluated the proposed modifications through two Delphi surveys in February and March 2019. Hereto, we invited 77 experts in prediction model research from various countries including the Netherlands, the UK, and the USA.

Results: the original TRIPOD statement describes 37 reporting items, divided in 22 topics. Our extension modifies 18 of these items, and includes nine entirely new items. For each item, we discuss good reporting practice and provide recent examples. In this talk, we will present the TRIPOD extension and the recommendations from the Delphi panel.

Conclusions: our TRIPOD extension provides additional reporting guidance for the retrieval, evaluation, harmonization and analysis of multiple data sources when developing or validating a prediction model. We hope that it will provide a valuable reference when performing prediction model research using large, clustered, datasets, and when evaluating the resulting manuscripts.

Patient or healthcare consumer involvement: healthcare consumers were involved in the Delphi surveys to evalute the proposed modifications of the TRIPOD checklist.