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

Samuel Håkansson^1,2,3, Johan Zelano^1,2,3

¹Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden.

|March 24, 2022

Summary

Choosing antiseizure medications (ASMs) for epilepsy is complex. Expert opinion algorithms and real-world retention data largely agree on effective ASMs, suggesting a synergistic approach for personalized epilepsy treatment.

Keywords:

antiseizure medication clinical decision support system pharmacotherapy

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Area of Science:

Neurology
Pharmacology
Data Science

Background:

Only 50% of epilepsy patients achieve seizure freedom with the first antiseizure medication (ASM).
Predicting ASM response is complex, influenced by patient factors like age, sex, and comorbidities.
Personalized medicine requires high-resolution data, which is often unavailable.

Purpose of the Study:

To compare the effectiveness of big data (real-world retention rates) versus expert opinion algorithms for selecting ASMs in adult-onset focal epilepsy.
To assess the alignment between expert-derived recommendations and actual patient outcomes.

Main Methods:

Real-world ASM retention rates were analyzed from Swedish registers for 37,643 individuals.
An expert opinion algorithm (Epipick) provided ASM suggestions for eight fictive cases.
Epipick's recommendations were compared against retention rates in patient subgroups.

Main Results:

Epipick suggested 6 ASMs for younger and 3 for older patients.
ASMs recommended by Epipick showed high retention rates (65%-84%).
Some ASMs with modest retention rates were suggested by Epipick, and some inappropriate ASMs had high retention rates.

Conclusions:

Expert opinion and real-world retention data show significant overlap in ASM recommendations for epilepsy.
Decision support systems should integrate both expert opinion and real-world retention data for optimal ASM selection.
Relying solely on retention rates may lead to suboptimal ASM choices; a combined approach is recommended.