Improvements are needed in the adherence to the TRIPOD statement for clinical prediction models for patients with spinal pain or osteoarthritis: a meta-research study.

diagnosis machine learning prognosis reporting reporting guidelines

Journal

The journal of pain
ISSN: 1528-8447
Titre abrégé: J Pain
Pays: United States
ID NLM: 100898657

Informations de publication

Date de publication:
11 Jul 2024
Historique:
received: 30 01 2024
revised: 26 06 2024
accepted: 01 07 2024
medline: 14 7 2024
pubmed: 14 7 2024
entrez: 13 7 2024
Statut: aheadofprint

Résumé

This meta-research study aimed to evaluate the completeness of reporting of prediction model studies in patients with spinal pain or osteoarthritis (OA) in terms of adherence to the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) statement. We searched for prognostic and diagnostic prediction models in patients with spinal pain or OA in MEDLINE, Embase, Web of Science, and CINAHL. Using a standardized assessment form, we assessed the adherence to the TRIPOD of the included studies. Two independent reviewers performed the study selection and data extraction phases. We included 66 studies. Approximately 35% of the studies declared to have used the TRIPOD. The median adherence to the TRIPOD was 59% overall (IQR: 21.8), with the items of the methods and results sections having the worst reporting. Studies on neck pain had better adherence to the TRIPOD than studies on back pain and OA (medians of 76.5%, 59%, and 53%, respectively). External validation studies had the highest total adherence (median: 79.5%; IQR: 12.8) of all the study types. The median overall adherence was 4 points higher in studies that declared TRIPOD use than those that did not. Finally, we did not observe any improvement in adherence over the years. The adherence to the TRIPOD of prediction models in the spinal and OA fields is low, with the methods and results sections being the most poorly reported. Future studies on prediction models in spinal pain and OA should follow the TRIPOD to improve their reporting completeness. PERSPECTIVE: This article provides data about adherence to the TRIPOD statement in 66 prediction model studies for spinal pain or osteoarthritis. The adherence to the TRIPOD statement was found to be generally low (median adherence of 59%). This inadequate reporting may negatively impact the effective use of the models in clinical practice.

Identifiants

pubmed: 39002741
pii: S1526-5900(24)00565-0
doi: 10.1016/j.jpain.2024.104624
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

104624

Informations de copyright

Copyright © 2024. Published by Elsevier Inc.

Auteurs

Daniel Feller (D)

Provincial Agency for Health of the Autonomous Province of Trento, Trento, Italy; Centre of Higher Education for Health Sciences of Trento, Trento, Italy; Department of General Practice, Erasmus MC, University Medical Center, Rotterdam, the Netherlands. Electronic address: d.feller@erasmusmc.nl.

Roel Wingbermuhle (R)

Department of General Practice, Erasmus MC, University Medical Center, Rotterdam, the Netherlands; SOMT University of Physiotherapy, Amersfoort, the Netherlands.

Bjørnar Berg (B)

Centre for Intelligent Musculoskeletal Health, Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway.

Ørjan Nesse Vigdal (ØN)

Department of Rehabilitation Science and Health Technology, Faculty of Health Science, OsloMet - Oslo Metropolitan University, Oslo, Norway.

Tiziano Innocenti (T)

Department of Health Sciences, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, the Netherlands; GIMBE Foundation, Bologna, Italy.

Margreth Grotle (M)

Centre for Intelligent Musculoskeletal Health, Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway; Department of Research and Innovation, Division of Clinical Neuroscience, Oslo University Hospital.

Raymond Ostelo (R)

Department of Health Sciences, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, the Netherlands; Department of Epidemiology and Data Science, Amsterdam UMC, Vrije Universiteit & Amsterdam Movement Sciences, Musculoskeletal Health, the Netherlands.

Alessandro Chiarotto (A)

Department of General Practice, Erasmus MC, University Medical Center, Rotterdam, the Netherlands; Department of Health Sciences, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, the Netherlands.

Classifications MeSH