ArticleNeurosurgical review2025
Examination of variables linked to the recurrence of primary trigeminal neuralgia following percutaneous transluminal micro balloon compression.
Article in Neurosurgical review, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
To analyze the risk factors of recurrence after percutaneous puncture micro balloon compression (PBC) in patients with primary trigeminal neuralgia (TN) and establish a risk prediction model to analyze the factors associated with pain recurrence. The preoperative magnetic resonance imaging (MRI) data and clinical baseline data of 330 TN patients with PBC were analyzed retrospectively at Liaoning Provincial People’s Hospital’s Department of Neurosurgery II between January 2017 and December 2023.A Nomogram risk prediction model was constructed to investigate the factors affecting postoperative recurrence. Using the subject’s work characteristic curve, the model’s capacity to forecast TN recurrence after PBC was assessed, and the area under the model curve (AUC) was computed. The results of the logistic regression analysis revealed that age (OR = 1.0331, 95% CI: 0.0081–0.0579), presence or absence of a responsible vessel (OR = 2.0999, 95% CI: 0.1995–1.2970), type of responsible vessel (OR = 2.5988, 95% CI: 0.0532–1.9630), volume of the posterior cerebral fossa (OR = 1.0216, 95% CI: 0.0118–0.0316), and preoperative symptom duration (OR = 1.1782, 95% CI: 0.1116–0.2220) were the variables influencing PBC recurrence after surgery (all[Formula: see text] ), and the AUC of the model was 0.809. Calibrate plots showed that the model was well calibrated, and decision curve analysis (DCA) results showed a high level of clinical benefit from the model. Patient age, presence of responsible vessel, type of responsible vessel, posterior cranial fossa volume, and duration of preoperative symptoms were the relevant factors affecting postoperative recurrence of primary TN treated with PBC, and the nomogram risk prediction model had good predictive efficacy and clinical application value.
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