An AI-based patient-specific clinical decision support system for OA patients choosing surgery or not: study protocol for a single-centre, parallel-group, non-inferiority randomised controlled trial

Nanna Kastrup*, Helene H. Bjerregaard, Mogens Laursen, Jan B. Valentin, Søren P. Johnsen, Cathrine E. Jensen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

3 Citations (Scopus)
29 Downloads (Pure)

Abstract

Background: Osteoarthritis (OA) affects 20% of the adult Danish population, and the financial burden to society amounts to DKK 4.6 billion annually. Research suggests that up to 75% of surgical patients could have postponed an operation and managed with physical training. ERVIN.2 is an artificial intelligence (AI)-based clinical support system that addresses this problem by enhancing patient involvement in decisions concerning surgical knee and hip replacement. However, the clinical outcomes and cost-effectiveness of using such a system are scantily documented. Objective: The primary objective is to investigate whether the usual care is non-inferior to ERVIN.2 supported care. The second objective is to determine if ERVIN.2 enhances clinical decision support and whether ERVIN.2 supported care is cost-effective. Methods: This study used a single-centre, non-inferiority, randomised controlled in a two-arm parallel-group design. The study will be reported in compliance with CONSORT guidelines. The control group receives the usual care. As an add-on, the intervention group have access to baseline scores and predicted Oxford hip/knee scores and HRQoL for both the surgical and the non-surgical trajectory. A cost-utility analysis will be conducted alongside the trial using a hospital perspective, a 1-year time horizon and effects estimated using EQ-5D-3L. Results will be presented as cost per QALY gain. Discussion: This study will bring knowledge about whether ERVIN.2 enhances clinical decision support, clinical effects, and cost-effectiveness of the AI system. The study design will not allow for the blinding of surgeons. Trial registration: ClinicalTrials.gov NCT04332055. Registered on 2 April 2020.

Original languageEnglish
Article number24
JournalTrials
Volume24
ISSN1745-6215
DOIs
Publication statusPublished - 12 Jan 2023

Bibliographical note

© 2023. The Author(s).

Keywords

  • Artificial intelligence
  • Clinical decision support system
  • Cost-effectiveness
  • Machine learning
  • Osteoarthritis
  • Patient-reported outcomes
  • Randomised controlled trial
  • Total hip replacement
  • Total knee replacement
  • Knee Joint/surgery
  • Humans
  • Artificial Intelligence
  • Decision Support Systems, Clinical
  • Randomized Controlled Trials as Topic
  • Cost-Benefit Analysis
  • Adult

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