Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden.
Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden.
Epilepsia. 2022 Jun;63(6):1553-1562. doi: 10.1111/epi.17235. Epub 2022 Apr 3.
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.
仅有 50%的新发癫痫患者在使用首种抗癫痫药物（ASM）时实现无癫痫发作。越来越多的数据表明，预测 ASM 反应和耐受性的复杂性受到年龄、性别和合并症的影响。不太可能出现具有足够分辨率用于个体化医学的随机数据。两种潜在的 ASM 选择促进因素是基于真实世界保留率的大数据或基于专家意见的算法。我们询问了这些方法在成人起病局灶性癫痫中的比较情况。
通过交叉引用瑞典综合登记处 37643 名个体的资料确定 ASM 保留率，并确定了合并症。创建了 8 个虚构病例，并从 Epipick 算法中收集了专家意见。我们比较了代表患者亚组的 Epipick 建议，并确定基于保留率的排名是否反映了专家意见。
Epipick 算法为年轻患者建议了六种 ASM 替代药物，为老年患者建议了三种 ASM 替代药物。在真实世界数据中，Epipick 排名最佳的 ASM 保留率较高；年轻患者为 65%-72%，老年患者为 71%-84%。Epipick 建议的最低保留率在年轻病例中为 42%-56%，在老年病例中为 70%-80%。保留率最佳的 ASM 通常是 Epipick 推荐的。
我们发现专家意见和真实世界保留率之间有很大的重叠。值得注意的是，Epipick 确实建议了一些保留率较低的 ASM。相反，在某些情况下，明显不合适的 ASM（未被 Epipick 推荐）保留率较高，这表明决策系统不应不加区分地仅依赖保留率。在未来的临床决策支持系统中，专家意见和真实世界保留率可以协同工作。
