Department of Clinical Neurophysiology, Danish Epilepsy Centre (Member of the ERN EpiCARE), Dianalund, Denmark.
Department of Clinical Medicine, Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus University, Aarhus, Denmark.
Epilepsia. 2021 Jan;62(1):220-227. doi: 10.1111/epi.16763. Epub 2020 Dec 6.
Optimal choice of antiseizure medication (ASM) depends on seizure type, syndrome, age, gender, comorbidities and co-medications. There are no fixed rules on how to weigh these factors; choices are subjective and experience-driven. We investigated agreement among experts in selecting ASM as monotherapy and used their prevailing choices to validate a web-based decision-support application.
Twenty-four international experts, blinded to the app, selected the optimal ASM for 25 individual patient-cases covering a wide variation of seizure types and other factors influencing ASM selection. The app ranked ASMs in order of likely appropriateness for each case. In a second step, experts rated anonymously the choices of the app.
Of the 25 patient-cases (age 13-74 years), 13 were female, 18 (72%) had comorbidities, six (24%) were on contraceptives, and 13 (52%) had other co-medications. The median number of experts who selected the same ASM for a given case was 15 (62.5%) and interquartile range (IQR) 13-18 (54%-75%). Gwet's agreement coefficient among experts was 0.38 (95% confidence interval [CI] 0.32-0.44), corresponding to a "fair" agreement. Agreement between the app and the prevailing expert choice for each case was 0.48 (95% CI 0.29-0.67), corresponding to a "moderate" beyond chance agreement. The percent agreement between the highest ranked selections of the app and the expert selections was 73% (95% CI 64%-82%). Ninety-five percent of the experts considered that no incorrect or potentially harmful ASMs were ranked highest by the app, and most experts strongly agreed with the app's selections.
This app, now validated by experts, provides an objective, reproducible method for selecting ASM that accounts for relevant clinical features. It is freely available at: https://epipick.org.
抗癫痫药物（ASM）的最佳选择取决于发作类型、综合征、年龄、性别、合并症和合并用药。如何权衡这些因素没有固定的规则；选择是主观的，取决于经验。我们调查了专家在选择 ASM 作为单药治疗方面的一致性，并使用他们的主要选择来验证一个基于网络的决策支持应用程序。
24 名国际专家在不了解应用程序的情况下，为 25 名个体患者选择最佳 ASM，涵盖了广泛的发作类型和影响 ASM 选择的其他因素。应用程序按每个病例的合适程度对 ASM 进行排序。在第二步中，专家匿名对应用程序的选择进行评分。
在 25 名患者病例中（年龄 13-74 岁），13 名是女性，18 名（72%）有合并症，6 名（24%）正在服用避孕药，13 名（52%）有其他合并用药。中位数为给定病例选择相同 ASM 的专家人数为 15 名（62.5%），四分位距（IQR）为 13-18 名（54%-75%）。专家之间的 Gwet 一致性系数为 0.38（95%置信区间[CI] 0.32-0.44），对应于“公平”的一致性。应用程序与每个病例的主要专家选择之间的协议为 0.48（95%CI 0.29-0.67），对应于超出机会的“中度”协议。应用程序和专家选择的最高排名选择之间的百分比一致性为 73%（95%CI 64%-82%）。95%的专家认为应用程序排名最高的没有错误或潜在有害的 ASM，大多数专家强烈同意应用程序的选择。
这个应用程序现在已经通过专家验证，为考虑相关临床特征的 ASM 选择提供了一种客观、可重复的方法。它可在以下网址免费获取：https://epipick.org。
