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

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.