Preoperative predictors of chronic pain after laparoendoscopic groin hernia repair: A Swedish Hernia Registry study

Chronic postoperative inguinal pain (CPIP) is a major adverse outcome of groin hernioplasty. Despite multiple CPIP studies, investigations involving unselected patients and sufficiently large cohorts to assess multiple predictors concurrently are still needed. This study evaluated the relative impact of preoperative predictors of CPIP after laparoendoscopic groin hernia repair. A secondary aim was to assess selection bias.

This population-based cohort study included unilateral laparoendoscopic repairs from a prospective CPIP project within the Swedish Hernia Registry, where all groin hernia repairs recorded between September 2012 and December 2018 were surveyed at 1 year postoperatively. Responses were analyzed using multivariable logistic regression to assess whether any of 15 predetermined preoperative candidate variables, including demographics, comorbidities, and hernia-related factors, were associated with CPIP.

Among 15 360 eligible patients, 10 525 (69%) responded, of whom 3 027 (29%) reported CPIP. Seven preoperative factors significantly associated with CPIP: • female sex (adjusted odds ratio [AOR] 1.15, 95% CI 1.03-1.28) • younger age (< 45 vs. ≥ 65 years: AOR 1.36, 95% CI 1.25-1.49) • BMI > 25 kg/m² (AOR 1.38, 95% CI 1.26-1.51) • ASA grade > 1 (AOR 1.23, 95% CI 1.12-1.36) • recurrent hernia (AOR 1.33, 95% CI 1.19-1.49) • femoral hernia (vs. medial and/or lateral; AOR 1.20, 95% CI 1.002-1.43) • small defects (AOR 1.18, 95% CI 1.06-1.30) Response rates varied significantly across most variables, particularly age; non-respondents were on average 7 years younger, suggesting selection bias.

The 7 identified preoperative predictors should be considered when designing and interpreting CPIP studies. Selection bias was present, but likely less pronounced than in previous CPIP surveys. Future research should incorporate preoperative pain assessment, improve response rates among younger patients, and conduct robust non-respondent analyses.