Web-based decision support system for patient-tailored selection of antiseizure medication in adolescents and adults: An external validation study
Hadady L, Klivenyi P, Perucca E, Rampp S, Fabo D, Bereczki C, Rubboli G, Asadi-Pooya AA, Sperling MR, Beniczky S (2021)
Publication Type: Journal article
Publication year: 2021
Journal
DOI: 10.1111/ene.15168
Abstract
Background and purpose: Antiseizure medications (ASMs) should be tailored to individual characteristics, including seizure type, age, sex, comorbidities, comedications, drug allergies, and childbearing potential. We previously developed a web-based algorithm for patient-tailored ASM selection to assist health care professionals in prescribing medication using a decision support application (https://epipick.org). In this validation study, we used an independent dataset to assess whether ASMs recommended by the algorithm are associated with better outcomes than ASMs considered less desirable by the algorithm. Methods: Four hundred twenty-five consecutive patients with newly diagnosed epilepsy were followed for at least 1 year after starting an ASM chosen by their physician. Patient characteristics were fed into the algorithm, blinded to the physician's ASM choices and outcome. The algorithm recommended ASMs, ranked in hierarchical groups, with Group 1 ASMs labeled as the best option for that patient. We evaluated retention rates, seizure freedom rates, and adverse effects leading to treatment discontinuation. Survival analysis contrasted outcomes between patients who received favored drugs and those who received lower ranked drugs. Propensity score matching corrected for possible imbalances between the groups. Results: Antiseizure medications classified by the algorithm as best options had a higher retention rate (79.4% vs. 67.2%, p = 0.005), higher seizure freedom rate (76.0% vs. 61.6%, p = 0.002), and lower rate of discontinuation due to adverse effects (12.0% vs. 29.2%, p < 0.001) than ASMs ranked as less desirable by the algorithm. Conclusions: Use of the freely available decision support system is associated with improved outcomes. This drug selection application can provide valuable assistance to health care professionals prescribing medication for individuals with epilepsy.
Authors with CRIS profile
Involved external institutions
University of Szeged / József Attila Tudományegyetem Szeged
Hungary (HU)
Università degli Studi di Pavia
Italy (IT)
University of Copenhagen
Denmark (DK)
Shiraz University of Medical Sciences / دانشگاه علوم پزشکی شیراز
Iran, Islamic Republic of (IR)
Thomas Jefferson University
United States (USA) (US)
Aarhus University Hospital / Aarhus Universitetshospital
Denmark (DK)
Hungary (HU)
Università degli Studi di Pavia
Italy (IT)
University of Copenhagen
Denmark (DK)
Shiraz University of Medical Sciences / دانشگاه علوم پزشکی شیراز
Iran, Islamic Republic of (IR)
Thomas Jefferson University
United States (USA) (US)
Aarhus University Hospital / Aarhus Universitetshospital
Denmark (DK)
How to cite
APA:
Hadady, L., Klivenyi, P., Perucca, E., Rampp, S., Fabo, D., Bereczki, C.,... Beniczky, S. (2021). Web-based decision support system for patient-tailored selection of antiseizure medication in adolescents and adults: An external validation study. European Journal of Neurology. https://dx.doi.org/10.1111/ene.15168
MLA:
Hadady, Levente, et al. "Web-based decision support system for patient-tailored selection of antiseizure medication in adolescents and adults: An external validation study." European Journal of Neurology (2021).
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