Seizure · 2025
A risk prediction model for seizure development in patients with brain metastases.
СтатьяНазвание и аннотация: Перевод в очереди
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Makkawi Seraj, Alqurashi Shatha, Banjar Toka, Alharbi Feras, Alkhiri Ahmed, Betar Manar, Ahmed Mohamed Eldigire, Aljafari Danya, Halawani Aisha, Mufti Hani
Аннотация
BACKGROUND: Limited data exists evaluating the incidence and risk factors for seizures in patients with brain metastases (BM). This study aimed to investigate seizure incidence patterns in relation to several radiological and clinical factors and develop a predictive model to stratify patients with BM according to their seizure risk.
METHOD: A single-center retrospective analysis was conducted at King Abdulaziz Medical City, Saudi Arabia, studying patients with BM between July 2016 and January 2023. Univariate regression analysis evaluated potential risk factors, and a predictive model was developed. The model's general performance was assessed using the Area Under the Receiver Operator Curve (AUC-ROC) and the Bayesian Information Criterion (BIC). Internal validation was done using the bootstrapping method.
RESULTS: Among 272 patients, epilepsy was diagnosed in 80 (29.4 %). The score model identified cortical involvement (OR 28.08 [95 % CI, 9.22- 85.48],p < 0.001), leptomeningeal metastases (OR 11.2 [95 % CI, 3.52-35.65],p < 0.001), and female gender (OR 2.67 [95 % CI, 1.16-6.17],p = 0. 0211) as key signifcant predictors of seizure development. The model achieved an AUC-ROC of 81.89 %.
CONCLUSION: Female gender, cortical involvement, and leptomeningeal metastases were identified as significant seizure predictors in BM patients. The derived model can help identify high-risk patients potentially benefiting from prophylactic anti-seizure medications. Further validation in larger cohorts is needed.
METHOD: A single-center retrospective analysis was conducted at King Abdulaziz Medical City, Saudi Arabia, studying patients with BM between July 2016 and January 2023. Univariate regression analysis evaluated potential risk factors, and a predictive model was developed. The model's general performance was assessed using the Area Under the Receiver Operator Curve (AUC-ROC) and the Bayesian Information Criterion (BIC). Internal validation was done using the bootstrapping method.
RESULTS: Among 272 patients, epilepsy was diagnosed in 80 (29.4 %). The score model identified cortical involvement (OR 28.08 [95 % CI, 9.22- 85.48],p < 0.001), leptomeningeal metastases (OR 11.2 [95 % CI, 3.52-35.65],p < 0.001), and female gender (OR 2.67 [95 % CI, 1.16-6.17],p = 0. 0211) as key signifcant predictors of seizure development. The model achieved an AUC-ROC of 81.89 %.
CONCLUSION: Female gender, cortical involvement, and leptomeningeal metastases were identified as significant seizure predictors in BM patients. The derived model can help identify high-risk patients potentially benefiting from prophylactic anti-seizure medications. Further validation in larger cohorts is needed.