Dataset Information

Switching generic antiepileptic drug manufacturer not linked to seizures: A case-crossover study.

ABSTRACT:

Objective

With more antiepileptic drugs (AED) becoming available in generic form, we estimated the risk of seizure-related events associated with refilling generic AEDs and the effect of switching between different manufacturers of the same generic drug.

Methods

We designed a population-based case-crossover study using the Medicaid Analytic eXtract and a US commercial health insurance database. We identified 83,001 generic AED users who experienced a seizure-related hospital admission or emergency room visit between 2000 and 2013 and assessed whether they received a refill of the same AED from the same manufacturer or a different manufacturer. Patients served as their own controls and conditional logistic regression was used to compare exposure to a refill during the hazard period, defined as days 2-36 preceding the seizure-related event, to exposure during the control period, defined as days 51-85 preceding the seizure-related event.

Results

Generic AED refilling was associated with an 8% increase in the odds of seizure-related events (odds ratio [OR] 1.08; 95% confidence interval [CI] 1.06-1.11). The OR following a switch to a different manufacturer of the same AED was 1.09 (95% CI 1.03-1.15); however, after adjusting for the process of refilling, there was no association between switching and seizure-related hospital visits (OR 1.00; 95% CI 0.94-1.07).

Conclusions

Among patients on a generic AED, refilling the same AED was associated with an elevated risk of seizure-related event; however, there was no additional risk from switching during that refill to a different manufacturer. Generic AEDs available to US patients, with Food and Drug Administration-validated bioequivalence, appear to be safe clinical choices.

SUBMITTER: Kesselheim AS

PROVIDER: S-EPMC5089522 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:ObjectivesTo develop prognostic models for risk of a breakthrough seizure, risk of seizure recurrence after a breakthrough seizure, and likelihood of achieving 12-month remission following a breakthrough seizure. A breakthrough seizure is one that occurs following at least 12 months remission whilst on treatment.MethodsWe analysed data from the SANAD study. This long-term randomised trial compared treatments for participants with newly diagnosed epilepsy. Multivariable Cox models investigated how clinical factors affect the probability of each outcome. Best fitting multivariable models were produced with variable reduction by Akaike's Information Criterion. Risks associated with combinations of risk factors were calculated from each multivariable model.ResultsSignificant factors in the multivariable model for risk of a breakthrough seizure following 12-month remission were number of tonic-clonic seizures by achievement of 12-month remission, time taken to achieve 12-month remission, and neurological insult. Significant factors in the model for risk of seizure recurrence following a breakthrough seizure were total number of drugs attempted to achieve 12-month remission, time to achieve 12-month remission prior to breakthrough seizure, and breakthrough seizure treatment decision. Significant factors in the model for likelihood of achieving 12-month remission after a breakthrough seizure were gender, age at breakthrough seizure, time to achieve 12-month remission prior to breakthrough, and breakthrough seizure treatment decision.ConclusionsThis is the first analysis to consider risk of a breakthrough seizure and subsequent outcomes. The described models can be used to identify people most likely to have a breakthrough seizure, a seizure recurrence following a breakthrough seizure, and to achieve 12-month remission following a breakthrough seizure. The results suggest that focussing on achieving 12-month remission swiftly represents the best therapeutic aim to reduce the risk of a breakthrough seizure and subsequent negative outcomes. This will aid individual patient risk stratification and the design of future epilepsy trials.