PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
March 21, 2026Perioperative Medicine0 citationsOpen Access

Development of risk prediction model for chronic pain after knee replacement surgery: protocol for an individual patient data meta-analysis

BSBehnam SadeghiradMKMalahat KhaliliJBJason W. Busse

Key Points

  • The aim is to develop a risk prediction model for chronic pain following total knee arthroplasty using individual patient data.
  • Conduct a systematic review of prospective studies on chronic pain post-TKA.
  • Establish an international repository for individual patient data related to CPSP.
  • Include studies that assess perioperative risk factors and measure knee pain long-term.
  • Use a one-stage approach for individual patient data meta-analysis.
  • Chronic post-surgical pain affects about one in four patients after TKA.
  • Identification of prognostic factors will enhance the understanding of chronic pain risk.
  • Development of an online calculator for risk assessment will support clinical decisions.

Abstract

Chronic post-surgical pain (CPSP) impacts approximately one in four patients following total knee arthroplasty (TKA) and is associated with reduced function and quality of life. We will conduct a systematic review of prospective studies to identify eligible data and establish an international repository of individual patient data (IPD) on prognostic factors for chronic pain after TKA. This repository will be then used to develop and validate a prediction model for CPSP following TKA. We will identify eligible studies through a search of MEDLINE, CINAHL, EMBASE, and Cochrane CENTRAL from January 2005 to August 2025. We will include prospective studies that: (1) enrolled adults undergoing elective TKA, (2) assessed perioperative risk factors for CPSP, and (3) measured knee pain longitudinally at least 3 months post-surgery. Pairs of reviewers will independently screen titles and abstracts of retrieved citations and review the full texts of potentially eligible studies. We will reach out to principal investigators or authors of eligible studies to notify them of our initiative and request to receive their IPD into a secured repository, based on a data sharing agreement. We will use a one-stage approach for IPD meta-analysis of factors associated with CPSP following TKA, and development of a risk prediction model. We will use anonymized de-identified data for our IPD meta-analysis. This protocol was reviewed and approved by the Hamilton Integrated Research Ethics Board (HiREB). We will develop an online calculator to support our risk assessment model for research and clinical use. This IPD meta-analysis will facilitate the development of a robust prognostic model to guide clinical decisions or enrolment in interventional studies, with the ultimate goal of identifying pathways to effective CPSP prevention strategies after TKA. CRD42024591329.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Sadeghirad et al. (2026) studied this question.

synapsesocial.com/papers/69be38006e48c4981c6780cahttps://doi.org/10.1186/s13741-026-00672-7
Ask AI
Helpful
Bookmark
Share
View Full Paper