TY - JOUR
T1 - Web-based decision support system for patient-tailored selection of antiseizure medication in adolescents and adults
T2 - An external validation study
AU - Hadady, Levente
AU - Klivényi, Péter
AU - Perucca, Emilio
AU - Rampp, Stefan
AU - Fabó, Dániel
AU - Bereczki, Csaba
AU - Rubboli, Guido
AU - Asadi-Pooya, Ali A.
AU - Sperling, Michael R.
AU - Beniczky, Sándor
N1 - Funding Information:
E.P. has received speaker's or consultancy fees from Arvelle, Biogen, Eisai, GW Pharma, Sanofi, Sun Pharma, UCB Pharma, Xenon Pharma, and Zogenix and publication royalties from Wiley and Elsevier. A.A.A.‐P. has received honoraria from Cobel Daruo, Tekaje, Sanofi, and Raymand Rad; royalties from Oxford University Press (book publication); and a grant from the National Institute for Medical Research Development. G.R. has received speakers honoraria from UCB, Eisai, and Biocodex and has acted as scientific consultant for Ology Medical Education. M.R.S. has received personal compensation for speaking from Neurology Live, Eisai, Medscape, Projects for Knowledge, and International Medical Press and publication royalties from Oxford University Press. He is an advisor for scientific publications for Neurelis and consults for Medtronic with payments to Thomas Jefferson University. M.R.S. has received research support from Eisai, Medtronic, Neurelis, SK Life Science, Takeda, Sunovion, Xenon, Cerevel, UCB Pharma, Eisai, and Engage Pharmaceuticals. S.B. serves as scientific consultant for Epihunter and received speaker's fees from Natus, Philips, Eisai, UCB Pharma, GW Pharma, and BIAL. None of the other authors has any conflict of interest to disclose.
Publisher Copyright:
© 2021 European Academy of Neurology
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - adolescent
KW - adult
KW - adverse effects
KW - antiepileptic drugs
KW - epilepsy
KW - neuropharmacology
U2 - 10.1111/ene.15168
DO - 10.1111/ene.15168
M3 - Journal article
C2 - 34741372
AN - SCOPUS:85119320671
SN - 1351-5101
VL - 29
SP - 382
EP - 389
JO - European Journal of Neurology
JF - European Journal of Neurology
IS - 2
ER -