Lead Researchers: Dr. Aline Talhouk and Dr. Paul Yong
Status: Ongoing
Up to 34% of individuals undergoing endometriosis surgery experience persistent pain, with 19-28% facing recurrent pain and a concerning 50% requiring reoperation within 5 years. Despite these challenging outcomes, reliable predictors for surgical success remain elusive, largely due to the lack of standardized data collection and the multifactorial nature of the disease. This project leverages machine learning to analyze complex patterns within the longitudinal prospective registry data from the Endometriosis and Pelvic Pain Interdisciplinary Cohort (EPPIC) at BC Women’s Centre for Pelvic Pain and Endometriosis. EPPIC’s comprehensive data encompasses patient history, pain, psycho-social factors, quality of life, physical exam pain mapping, specialized ultrasound results, surgical findings, and pathology. The goal is to identify robust predictors and develop tools for predicting pain-related outcomes after endometriosis surgery, including improvements in quality of life and chronic pelvic pain reduction. Spearheaded by Dwayne Tucker, a PhD Candidate, the project is a collaboration between the Uterine Health Research Laboratory (Dr. Aline Talhouk) and the Endometriosis and Pelvic Pain Laboratory (Dr. Paul Yong).