Big data analysis of ASM retention rates and expert ASM algorithm: A comparative study

Abstract

Objective: 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.

Methods: 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.

Results: 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.

Significance: 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.

Keywords: antiseizure medication; clinical decision support system; pharmacotherapy.

FIGURE 1

Study concept. Of all individuals with new‐onset epilepsy (n = 94 321), at total of 37 643 were selected based on cross‐referenced registers: the National Patient Register (NPR) provided medical variables, the Cause of Death Register (CDR) provided information on survival, and the Drug Register (DR) provided information on antiseizure medication (ASM) prescriptions. The ASM tracking was based on (1) retention‐rate calculations using prescription (P) interval (double arrow) to detect treatment stop (S), and (2) selecting subgroups for estimates

FIGURE 2

Retention rates of the Epipick suggestion with the highest (gray area) and lowest (dashed) retention rate (large areas better). If all antiseizure medications (ASMs) with any number of individuals were included (A) some Epipick suggestions had low retention rates, but this was not seen if precision was increased by requiring 50 users (B). PTE = posttraumatic epilepsy, PSE = poststroke epilepsy

FIGURE 3

Evaluation of whether retention rates identify expert choice. Top row (larger area better): If all antiseizure medications (ASMs) with any number of users were included, the ASM with the highest retention rate was recommended by Epipick in six of eight cases (A). Performance of retention rates was improved if 50 users were required; then the ASM with the highest retention rate was recommended by Epipick in all cases (B). Lower row (points closer to center better): With any number of users, some potentially inappropriate ASMs not recommended by Epipick had the highest retention rates (C). With >50 users, “inappropriate” ASMs ranked three or lower in all cases (D)