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

Area of Science:

• Neurology
• Pharmacology
• Data Science

Background:

• Half of epilepsy patients do not respond to their first antiseizure medication (ASM).
• ASM selection is complex, considering epilepsy type, age, and comorbidities, with limited subgroup evidence.
• Personalized treatment strategies are needed to improve ASM effectiveness.

Purpose of the Study:

• To assess if Swedish register data can calculate ASM retention rates stratified by demographics, comorbidities, and treatment history.
• To quantify the potential improvement in first-line ASM retention rates across various epilepsy cohorts.
• To describe ASM retention in specific patient groups, including those with traumatic brain injury and dementia.

Main Methods:

• Utilized comprehensive Swedish national health registers with 6380 epilepsy patient observations and 381,480 ASM prescriptions.
• Analyzed first- and second-line ASM retention rates in patients with epilepsy secondary to multiple sclerosis, brain infection, dementia, traumatic brain injury, or stroke.
• Validated ASM retention rate rankings against published randomized controlled trials and identified optimal stratification for each condition.

Main Results:

• Optimal stratification could improve ASM retention rates by 14-21% across different epilepsy etiologies (e.g., 20% for MS, 21% for brain infection, 14% for dementia, 21% for trauma, 14% for stroke).
• In epilepsy post-trauma, levetiracetam showed higher retention (80%) than carbamazepine.
• In epilepsy post-dementia, lamotrigine (77%) and levetiracetam (74%) demonstrated superior retention rates compared to carbamazepine.

Conclusions:

• Personalized selection of antiseizure medications holds significant potential to enhance patient retention rates.
• National health registers serve as valuable big data resources for advancing personalized medicine in epilepsy management.