Only 50% of patients with new-onset epilepsy achieve seizure freedom with their first antiseizure medication (ASM). A growing body of data illustrates the complexity of predicting ASM response and tolerability, which is influenced by age, sex, and comorbidities. Randomized data with sufficient resolution for personalized medicine are unlikely to emerge. Two potential facilitators of ASM selection are big data using real-world retention rates or algorithms based on expert opinion. We asked how these methods compare in adult-onset focal epilepsy.
ASM retention rates were determined by cross-referencing data from comprehensive Swedish registers for 37 643 individuals, with identified comorbidities. Eight fictive cases were created and expert advice was collected from the algorithm Epipick. We compared Epipick suggestions in representative patient subgroups, and determined whether ranking based on retention rate reflected expert advice.
The Epipick algorithm suggested six ASM alternatives for younger patients and three ASM alternatives for older patients. In the real-world data, retention rates for the ASMs ranked as best options by Epipick were high; 65%–72% for young patients and 71%–84% for older patients. The lowest retention rate for Epipick suggestions was 42%–56% in younger cases, and 70%–80% in older cases. The ASM with the best retention rate was generally recommended by Epipick.
We found a large overlap between expert advice and real-world retention rates. Notably, Epipick did suggest some ASMs with more modest retention rates. Conversely, clearly inappropriate ASMs (not recommended by Epipick) had high retention rates in some cases, showing that decision systems should not rely indiscriminately on retention rates alone. In future clinical decision support systems, expert opinion and real-world retention rates could work synergistically.