This study investigates ways to accurately predict chronic postsurgical pain (CPSP), a common, costly and often burdensome complication after surgery. Current prediction models lack precision, limiting the ability to prevent CPSP through targeted care. The research will validate a new electronic preoperative questionnaire, combined with information from electronic health records (EHRs), to measure functionally significant CPSP at 3 and 12 months post-surgery. Over six months, approximately 6,000 patients undergoing planned surgery at Health NZ – Waitematā in Auckland will participate. The questionnaire will assess known CPSP risk factors, including pre-existing pain, medication use, and psychological well-being. Demographic and medical history data will also be extracted from EHRs. Participants will complete follow-up 2 surveys to measure CPSP and its interference with daily functioning (CPSP-F). The study will determine CPSP and CPSP-F prevalence across a wide range of surgery types and use both traditional statistical methods and Machine Learning to test predictive model accuracy.
By identifying high-risk patients early, the findings aim to support timely, cost-effective, and targeted interventions, improving recovery outcomes, preventing chronic pain development, and optimizing healthcare resource allocation for those most likely to benefit with an existing Perioperative Medicine model of care.
Health New Zealand Waitematā-Te Whatu Ora, Auckland
Associate Professor Michal Kluger
Associate Professor David Rice
Dr Daniel Chiang
Dr Chen Kai Jin
Auckland University of Technology, New Zealand
Dr Nico Magni, Associate
Professor Mangor Pedersen
The project was awarded A$69,944 funding through the ANZCA research grants program for 2026.
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