Facial reconstruction

Search LJMU Research Online

Browse Repository | Browse E-Theses

Identification of patients who will not achieve seizure remission within 5 years on AEDs

Hughes, DM, Bonnett, LJ, Czanner, G, Komarek, A, Marson, AG and Garcia-Finana, M (2018) Identification of patients who will not achieve seizure remission within 5 years on AEDs. Neurology, 91 (22). ISSN 0028-3878

[img]
Preview
Text
Identification of patients who will not achieve seizure remission within 5 years on AEDs.pdf - Published Version
Available under License Creative Commons Attribution.

Download (565kB) | Preview

Abstract

Objective: To identify people with epilepsy who will not achieve a 12-month seizure remission within 5 vyears of starting treatment.
Methods: The Standard and New Antiepileptic Drug (SANAD) study is the largest prospective study in patients with epilepsy to date. We applied a recently developed multivariable approach to the SANAD dataset that takes into account not only baseline covariates describing a patient’s history before diagnosis but also follow-up data as predictor variables.
Results: Changes in number of seizures and treatment history were the most informative timedependent predictors and were associated with history of neurologic insult, epilepsy type, age at start of treatment, sex, and having a first-degree relative with epilepsy. Our model classified 95% of patients. Of those classified, 95% of patients observed not to achieve remission at 5 years were correctly classified (95% confidence interval [CI] 89.5%–100%), with 51% identified by 3 years and 90% within 4 years of follow-up. Ninety-seven percent (95% CI 93.3%–98.8%) of patients observed to achieve a remission within 5 years were correctly classified. Of those predicted not to achieve remission, 76% (95% CI 58.5%–88.2%) truly did not achieve remission (positive predictive value). The predictive model achieved similar accuracy levels via external validation in 2 independent United Kingdom–based datasets.
Conclusion: Our approach generates up-to-date predictions of the patient’s risk of not achieving seizure remission whenever new clinical information becomes available that could influence patient counseling and management decisions.

Item Type: Article
Uncontrolled Keywords: 1103 Clinical Sciences, 1109 Neurosciences, 1702 Cognitive Sciences
Subjects: R Medicine > R Medicine (General)
Divisions: Applied Mathematics (merged with Comp Sci 10 Aug 20)
Publisher: American Academy of Neurology
Related URLs:
Date Deposited: 04 Oct 2019 11:31
Last Modified: 04 Sep 2021 08:46
DOI or ID number: 10.1212/WNL.0000000000006564
URI: https://researchonline.ljmu.ac.uk/id/eprint/11471
View Item View Item