Neurophysiological and Clinical Effects of Laparoscopic Retroperitoneal Triple Neurectomy in Patients with Refractory Postherniorrhaphy Neuropathic Inguinodynia.

BACKGROUND: Chronic postherniorrhaphy inguinal pain (CPIP) is a complex, major health problem. In the absence of recurrence or meshoma, laparoscopic retroperitoneal triple neurectomy (LRTN) has emerged as an effective surgical treatment of CPIP.

METHODS: This prospective pilot study evaluated the neurophysiological and clinical effects of LRTN. Ten consecutive adult CPIP patients with unilateral predominantly neuropathic inguinodynia underwent three comprehensive quantitative sensory testing (QST) assessments (preoperative, immediate postoperative, and late postoperative). Pain severity, health-related function, and sleep quality were assessed over the course of a 6-month follow-up period.

RESULTS: QST revealed marked increases in mechanical, pressure, thermal, and pain thresholds in the areas with maximum pain prior to LRTN surgery for the immediate (P < 0.01; mean 160.9 minutes, range 103 to 255 minutes after extubation) and late postoperative (P < 0.05; mean 27.9 days, range 14 to 78 days after surgery) assessments compared to baseline. Wind-up phenomena were eliminated postoperatively. LRTN provided robust group-level improvements of all clinical measures. No preoperative QST variables were found to be predictive of surgical outcomes. The positive change in heat pain threshold (preoperative compared to late postoperative) showed significant positive correlations with improvements of pain scores and function.

CONCLUSIONS: LRTN may produce immediate, profound, and consistent positive effects across multiple mechanical, pressure, and thermal QST variables, and marked improvements of clinical outcomes in selected CPIP patients. These data contribute to the understanding of mechanisms involved in the success of LRTN. Large, high-powered studies are warranted to determine whether preoperative or repeated longitudinal QST may guide patient selection and predict effectiveness of LRTN.