Predictive factors for post operative seizures following meningioma resection in patients without preoperative seizures: a multicenter retrospective analysis.

PURPOSE: Meningiomas are the most common primary brain tumor and represent 35% of all intracranial neoplasms. However, in the early post-operative period approximate 3-5% of patients experience an acute symptomatic seizure. Establishing risk factors for postoperative seizures will identify those patients without preoperative seizures at greatest risk of postoperative seizures and may guide antiseizure medications (ASMs) management. METHODS: Adult seizure naïve patients who underwent primary resection of a World Health Organization (WHO) Grade 1-3 meningioma at the three Mayo Clinic Campuses between 2012-2022 were retrospectively reviewed. Multivariate regression analyses were used to identify radiological, surgical, and management features with the development of new-onset seizures in patients undergoing meningioma resection. RESULTS: Of 113 seizure naïve patients undergoing meningioma resection 11 (9.7%) experienced a new-onset post-operative seizure. Tumor volume ≥ 25 cm3 (Odds Ratio (OR) 5.223, 95% Confidence Interval (CI) 1.546 - 17.650, p = 0.008) and cerebral convexity meningiomas (OR 4.742, 95% CI 1.255 - 14.336, p = 0.016) were most associated with new onset postoperative seizures in multivariate analysis. ASMs and corticosteroid therapies did not display a significant difference among those with and without a new onset postoperative seizure. CONCLUSION: In the current study, a larger tumor volume (≥ 25 cm3) and/or convexity meningiomas predicted the development of new onset post-operative seizures. Those who present with these factors should be counseled for their increased risk of new onset post-operative seizures and may benefit from prophylactic ASMs therapy.

Supervised machine learning to validate a novel scoring system for the prediction of disease remission of functional pituitary adenomas following transsphenoidal surgery.

Functional pituitary adenomas (FPAs) are associated with hormonal hypersecretion resulting in systemic endocrinopathies and increased mortality. The heterogenous composition of the FPA population has made modeling predictive factors of postoperative disease remission a challenge. Here, we aim to define a novel scoring system predictive of disease remission following transsphenoidal surgery (TSS) for FPAs and validate our process using supervised machine learning (SML). 392 patients with FPAs treated at one of the three Mayo Clinic campuses were retrospectively reviewed. Variables found significant on multivariate analysis were incorporated into our novel Pit-SCHEME score. The Pit-SCHEME score with a cut-off value ≥ 6 achieved a sensitivity of 86% and positive likelihood ratio of 2.88. In SML models, without the Pit-SCHEME score, the k-nearest neighbor (KNN) model achieved the highest accuracy at 75.6%. An increase in model sensitivity was achieved with inclusion of the Pit-SCHEME score with the linear discriminant analysis (LDA) model achieving an accuracy of 86.9%, which suggests the Pit-SCHEME score is the variable of most importance for prediction of postoperative disease remission. Ultimately, these results support the potential clinical utility of the Pit-SCHEME score and its prospective future for aiding in the perioperative decision making in patients with FPAs.