Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2025
Trigeminal reflex testing abnormalities as a predictive model for distinguishing classical and idiopathic trigeminal neuralgia.
СтатьяНазвание и аннотация: Перевод в очереди
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De Stefano Gianfranco, Mollica Cristina, Leone Caterina, Galosi Eleonora, Di Pietro Giuseppe, Falco Pietro, Esposito Nicoletta, Litewczuk Daniel, Evangelisti Enrico, Caramia Francesca, Truini Andrea, Di Stefano Giulia
Аннотация
OBJECTIVE: Primary trigeminal neuralgia (TN) is a representative neuropathic facial pain condition classified into classical (associated with neurovascular compression), and idiopathic (unknown etiology). Differentiating between classical and idiopathic TN based on clinical and neurophysiological findings remains challenging. In this clinical and neurophysiological study, we aimed to identify predictive clinical and neurophysiological variables that may distinguish between the two types of TN.
METHODS: We retrospectively analyzed clinical records and neurophysiological data from 114 patients with primary TN (84 classical TN, 30 idiopathic TN). We implemented a logistic regression model to identify predictive variables for classical and idiopathic TN.
RESULTS: The logistic regression model showed that a trigeminal reflex latency asymmetry longer than 0.5 ms between the affected and unaffected sides was predictive of classical TN (p < 0.05). Additionally, combined involvement of the second and third trigeminal divisions was predictive of idiopathic TN (p < 0.05).
CONCLUSIONS: Our findings suggesting that latency asymmetry in trigeminal reflexes differentiate between classical and idiopathic TN probably reflects the association of classical TN with neurovascular compression, while idiopathic TN may involve other factors affecting trigeminal nerve fibers.
SIGNIFICANCE: Our results enhance our understanding of pathophysiology of TN and could improve clinical differentiation between its types.
METHODS: We retrospectively analyzed clinical records and neurophysiological data from 114 patients with primary TN (84 classical TN, 30 idiopathic TN). We implemented a logistic regression model to identify predictive variables for classical and idiopathic TN.
RESULTS: The logistic regression model showed that a trigeminal reflex latency asymmetry longer than 0.5 ms between the affected and unaffected sides was predictive of classical TN (p < 0.05). Additionally, combined involvement of the second and third trigeminal divisions was predictive of idiopathic TN (p < 0.05).
CONCLUSIONS: Our findings suggesting that latency asymmetry in trigeminal reflexes differentiate between classical and idiopathic TN probably reflects the association of classical TN with neurovascular compression, while idiopathic TN may involve other factors affecting trigeminal nerve fibers.
SIGNIFICANCE: Our results enhance our understanding of pathophysiology of TN and could improve clinical differentiation between its types.