Trigeminal Neuralgia: Disease Characterization and Prediction of Response to Surgical Intervention.

Abstract

Purpose of review: Trigeminal neuralgia (TN) is a highly heterogeneous condition with a wide choice of successful treatment options. However, differences between subtypes are poorly understood and it remains unknown which patients will respond to different treatments. This review aims to summarize the current state of the TN field and explore the problem of predicting surgical outcomes.

Recent findings: Attempts have been made to standardize classification, outcome reporting, and treatment guidelines. Identification of risk factors for poor surgical treatment outcomes has led to the development of prognostic scoring systems to predict outcomes and guide management. Advances in imaging techniques including diffusion tensor imaging show promise in better understanding the underlying pathophysiology and predicting surgical outcomes. Progress is hampered by lack of understanding of the pathophysiology in TN and differences between disease subtypes. Further work needs to be done to create accurate prediction tools for widespread use, including use of advanced imaging and computing tools, multicenter collaboration, and prospective validation.