Introduction Chronic postsurgical pain (CPSP) after hip arthroplasty is a major complication that affects patients' long-term quality of life. However, reliable tools for the individualised prediction of CPSP risk after hip arthroplasty are lacking.
This study aims to develop and validate a nomogram model to predict CPSP risk in patients undergoing hip arthroplasty. Methods and analysis This prospective observational cohort study will consecutively recruit 300 patients undergoing primary hip arthroplasty at the Department of Orthopaedics and Joints, Nanping First Hospital Affiliated with Fujian Medical University.
The primary outcome is CPSP assessed at 3 months postoperatively (Visual Analogue Scale score ≥4). Candidate predictor variables have been identified based on literature review and clinical expertise, and include demographics, comorbidities, preoperative pain, psychological status and surgical and perioperative management.
The dataset will be randomly split into development and internal validation sets in a 7:3 ratio. We will employ Least Absolute Shrinkage and Selection Operator regression to select variables and will use multivariable logistic regression to build the final prediction model.
Internal validation will be performed using bootstrap resampling (1000 repetitions).
BMJ Open published a clinical update in Research Highlights on 07 Apr 2026.
The item focuses on Analysis of risk factors and development of a predictive nomogram for chronic postsurgical pain after hip arthroplasty: a study protocol in a Chinese tertiary hospital.
Review the original article for the full source wording and details.