Potential for improved retention rate by personalized antiseizure medication selection: A register-based analysis
Journal article, 2021

Objective: The first antiseizure medication (ASM) is ineffective or intolerable in 50% of epilepsy cases. Selection between more than 25 available ASMs is guided by epilepsy factors, but also age and comorbidities. Randomized evidence for particular patient subgroups is seldom available. We asked whether register data could be used for retention rate calculations based on demographics, comorbidities, and ASM history, and quantified the potential improvement in retention rates of the first ASM in several large epilepsy cohorts. We also describe retention rates in patients with epilepsy after traumatic brain injury and dementia, patient groups with little available evidence. Methods: We used medical, demographic, and drug prescription data from epilepsy cohorts from comprehensive Swedish registers, containing 6380 observations. By analyzing 381 840 prescriptions, we studied retention rates of first- and second-line ASMs for patients with epilepsy in multiple sclerosis (MS), brain infection, dementia, traumatic brain injury, or stroke. The rank of retention rates of ASMs was validated by comparison to published randomized control trials. We identified the optimal stratification for each brain disease, and quantified the potential improvement if all patients had received the optimal ASM. Results: Using optimal stratification for each brain disease, the potential improvement in retention rate (percentage points) was MS, 20%; brain infection, 21%; dementia, 14%; trauma, 21%; and stroke, 14%. In epilepsy after trauma, levetiracetam had the highest retention rate at 80% (95% confidence interval [CI] = 65–89), exceeding that of the most commonly prescribed ASM, carbamazepine (p =.04). In epilepsy after dementia, lamotrigine (77%, 95% CI = 68–84) and levetiracetam (74%, 95% CI = 68–79) had higher retention rates than carbamazepine (p =.006 and p =.01, respectively). Significance: We conclude that personalized ASM selection could improve retention rates and that national registers have potential as big data sources for personalized medicine in epilepsy.

epidemiology

personalized medicine

comorbidity

big data

Author

Samuel Håkansson

University of Gothenburg

Wallenberg Lab.

Sahlgrenska University Hospital

Markus Karlander

University of Gothenburg

Södra Älvsborg Hospital (SÄS)

David Larsson

University of Gothenburg

Wallenberg Lab.

Sahlgrenska University Hospital

Zamzam Mahamud

Sahlgrenska University Hospital

Wallenberg Lab.

University of Gothenburg

Sara Garcia-Ptacek

Karolinska Institutet

Karolinska University Hospital

Aleksej Zelezniak

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Science for Life Laboratory (SciLifeLab)

Johan Zelano

Sahlgrenska University Hospital

Wallenberg Lab.

University of Gothenburg

Epilepsia

0013-9580 (ISSN) 1528-1167 (eISSN)

Vol. 62 9 2123-2132

Subject Categories

Cardiac and Cardiovascular Systems

Social and Clinical Pharmacy

Neurology

DOI

10.1111/epi.16987

PubMed

34245010

More information

Latest update

9/16/2021