Predicting Outcomes in Patients with Trigeminal Neuralgia: Pre-Operative FIESTA MRI and Microvascular Decompression

Kurt J Niesner1, Jonathan Forbes, MD1, Calvin Cooper, MD1, Peter Konrad, MD, PhD1, Joseph Neimat, MD1

1Nashville, TN United States

Keywords: trigeminal neuralgia, pain, microvascular decompression, outcome, Imaging

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     Trigeminal neuralgia (TN) is a disorder characterized by intense facial pain in areas innervated by the trigeminal nerve. Reports indicate that 80-90% of cases are caused by microvascular compression (MC). Recent improvements in MRI resolution have provided visualization of potential compressing vessels.
     We evaluate the efficacy of pre-operative MRI in predicting post-operative outcome.
     We collected all available MRIs of patients that underwent a microvascular decompression (MVD) surgery in the past 5 to 10 years at a large academic hospital.
     We limited our sample to patients with a pre-operative MRI utilizing fast imaging with steady-state acquisition (FIESTA). An experienced neurosurgeon retrospectively reviewed the MRIs of each patient while blinded to patient identity, symptomatology, and outcome. The reviewer classified the degree and locus of trigeminal compression bilaterally. These results were compared to retrospective data on post-operative outcome. A Fisher''s Exact Test for binomial proportion was used to evaluate the results.
     We reviewed 47 patients that received high resolution MRI imaging prior to MVD surgery. Of these, 17 were found on review to have significant compression at the root entry zone of the symptomatic nerve. Fifteen of these patients experienced complete pain relief versus complete relief in 19/30 that had no such evidence of compression (Fisher''s Exact Test p=0.09).
     This is a retrospective
     These data suggest that pre-operative MRI may predict success in patients undergoing MVD surgery.
     Our future study will explore the value of hi-resolution MRI in predicting long-term outcome for various treatments of TN using a larger sample size.


Project Roles:

K. Niesner (), J. Forbes (), C. Cooper (), P. Konrad (), J. Neimat ()