A Novel Presurgical Risk Prediction Model for Chronic Post-Surgical Pain in Adults Undergoing Surgical Procedures:...

Despite extensive research efforts into risk factors and management strategies, the prevalence of chronic post-surgical pain (CPSP) remains high. Additionally, the treatment of chronic pain is often complex, with outcomes frequently falling short of expectations. As chronic pain continues to be a significant global problem affecting medical, psychological and socioeconomic aspects, and with the growing number of surgeries performed, there is a pressing need for an early, comprehensive model to predict CPSP. Various models have been created in recent years to predict postsurgical pain. However, to date, no generalizable CPSP risk stratification model independent for type of surgery is extensively applied. This study aims to create a simple and effective model to predict presurgically the likelihood of CPSP occurring three months after elective surgery.

An observational, single center, pragmatic, pilot cohort study [PERISCOPE] in adult elective surgery patients was conducted at the Antwerp University Hospital, Belgium, between December 2022 and September 2023. More than 40 different types of surgeries in 11 disciplines were included. CPSP was defined as persistent pain in the surgical area, reported by the patient three months after surgery on a 11-level numeric rating scale. Biopsychosocial candidate variables, including health-related quality of life and psychosocial concerns, were identified based on clinical knowledge and literature review.

The final study population at our tertiary hospital included 415 patients of which 19.3% reported CPSP. Four predictors: preoperative pain intensity, education level, type of surgery and concerns about the planned surgery were identified leading to the best predictive model (P⁴-Prevoque™) in terms of area under the curve (0.81; 95% confidence interval [0.76, 0.87]) and significance.

The P⁴-Prevoque™ questionnaire is able to identify presurgically a major part of CPSP patients with a sensitivity of 74%, and specificity of 77%. Using four straightforward and readily available questions, the proposed user-friendly prediction model has the strength to be easily implemented in daily practice. Future research should focus on its further validation.

ClinicalTrials.gov NCT05526976, Ethics Committee B3002022000112.