Identifying patients who will not reachieve remission after breakthrough seizures. (22nd March 2019)

• Record Type: Journal Article
• Authors: Hughes, David M.; Bonnett, Laura J.; Marson, Anthony G.; García‐Fiñana, Marta
• Is Part Of: Epilepsia. Volume 60:issue 4(2019)
• Page Start: 774
• Page End: 782
• Publication Date: 2019-03-22
• Subjects: breakthrough -- dynamic classification -- epilepsy -- focal -- generalized -- remission; Epilepsy -- Periodicals; 616.853
• DOI: 10.1111/epi.14697
• Languages: English
• ISSNs: 0013-9580

Abstract

Summary: Objective: We aim to identify people with epilepsy who are unlikely to reachieve a 12‐month remission within 2 years after experiencing a breakthrough seizure following an initial 12‐month remission. Methods: We apply a novel longitudinal discriminant approach to data from the Standard and New Antiepileptic Drugs study to dynamically predict the risk of a patient not achieving a second remission after a breakthrough seizure by combining both baseline covariates (collected at the time of breakthrough seizure) and follow‐up data. Results: The model classifies 83% of patients. Of these, 73% of patients (95% confidence interval [CI] = 58%‐88%) who did not achieve a second remission were correctly identified (sensitivity), and 84% of patients (95% CI = 69%‐96%) who achieved a second remission were correctly identified (specificity). The area under the curve from our model was 87% (95% CI = 80%‐94%). Patients who did not achieve a second remission were correctly identified on average after 10 months of observation postbreakthrough. Occurrence of seizures after breakthrough and the number of seizures experienced were the most informative longitudinal variables. These longitudinal profiles were influenced by the following baseline covariates: age at breakthrough seizure, presence of neurological insult, and number of antiepileptic drugs required to achieve first remission. Significance: Using longitudinal data gathered during patient follow‐up allows more accurate predictions than using baseline covariates in a standard Cox model. The model developed in this paper is a useful first step in developing a tool for identifying patients who develop drug resistance after an initial remission.