Predictive models for starting antiseizure medication withdrawal following epilepsy surgery in adults

Ferreira-Atuesta, Carolina, de Tisi, Jane, McEvoy, Andrew W, Miserocchi, Anna, Khoury, Jean, Yardi, Ruta, Vegh, Deborah T, Butler, James, Lee, Hamin J, Deli-Peri, Victoria, Yao, Yi, Wang, Feng-Peng, Zhang, Xiao-Bin, Shakhatreh, Lubna, Siriratnam, Pakeeran, Neal, Andrew, Sen, Arjune, Tristram, Maggie, Varghese, Elizabeth, Biney, Wendy, Gray, William P ORCID: https://orcid.org/0000-0001-7595-8887, Peralta, Ana Rita, Rainha-Campos, Alexandre, Gonçalves-Ferreira, António J C, Pimentel, José, Arias, Juan Fernando, Terman, Samuel, Terziev, Robert, Lamberink, Herm J, Braun, Kees P J, Otte, Willem M, Rugg-Gunn, Fergus J, Gonzalez, Walter, Bentes, Carla, Hamandi, Khalid, O'Brien, Terence J, Perucca, Piero, Yao, Chen, Burman, Richard J, Jehi, Lara, Duncan, John S, Sander, Josemir W, Koepp, Matthias and Galovic, Marian 2023. Predictive models for starting antiseizure medication withdrawal following epilepsy surgery in adults. Brain 146 (6) , 2389–2398. 10.1093/brain/awac437

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Abstract

More than half of adults with epilepsy undergoing resective epilepsy surgery achieve long-term seizure freedom and might consider withdrawing antiseizure medications. We aimed to identify predictors of seizure recurrence after starting postoperative antiseizure medication withdrawal and develop and validate predictive models. We performed an international multicentre observational cohort study in nine tertiary epilepsy referral centres. We included 850 adults who started antiseizure medication withdrawal following resective epilepsy surgery and were free of seizures other than focal non-motor aware seizures before starting antiseizure medication withdrawal. We developed a model predicting recurrent seizures, other than focal non-motor aware seizures, using Cox proportional hazards regression in a derivation cohort (n = 231). Independent predictors of seizure recurrence, other than focal non-motor aware seizures, following the start of antiseizure medication withdrawal were focal non-motor aware seizures after surgery and before withdrawal [adjusted hazard ratio (aHR) 5.5, 95% confidence interval (CI) 2.7–11.1], history of focal to bilateral tonic-clonic seizures before surgery (aHR 1.6, 95% CI 0.9–2.8), time from surgery to the start of antiseizure medication withdrawal (aHR 0.9, 95% CI 0.8–0.9) and number of antiseizure medications at time of surgery (aHR 1.2, 95% CI 0.9–1.6). Model discrimination showed a concordance statistic of 0.67 (95% CI 0.63–0.71) in the external validation cohorts (n = 500). A secondary model predicting recurrence of any seizures (including focal non-motor aware seizures) was developed and validated in a subgroup that did not have focal non-motor aware seizures before withdrawal (n = 639), showing a concordance statistic of 0.68 (95% CI 0.64–0.72). Calibration plots indicated high agreement of predicted and observed outcomes for both models. We show that simple algorithms, available as graphical nomograms and online tools (predictepilepsy.github.io), can provide probabilities of seizure outcomes after starting postoperative antiseizure medication withdrawal. These multicentre-validated models may assist clinicians when discussing antiseizure medication withdrawal after surgery with their patients.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Medicine
Publisher:	Oxford University Press
ISSN:	0006-8950
Date of First Compliant Deposit:	31 May 2024
Date of Acceptance:	6 November 2022
Last Modified:	14 Sep 2024 01:31
URI:	https://orca.cardiff.ac.uk/id/eprint/169337

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