Levente Hadady1, Péter Klivényi1, Emilio Perucca2, Stefan Rampp3,4, Dániel Fabó1,5, Csaba Bereczki6, Guido Rubboli7, Ali A Asadi-Pooya8,9, Michael R Sperling9, Sándor Beniczky10,11. 1. Department of Neurology, Interdisciplinary Excellence Center, Faculty of Medicine, Albert Szent-Györgyi Clinical Center, University of Szeged, Szeged, Hungary. 2. Division of Clinical and Experimental Pharmacology, Department of Internal Medicine and Therapeutics, University of Pavia, Pavia, Italy. 3. Department of Neurosurgery, University Hospital Erlangen, Erlangen, Germany. 4. Department of Neurosurgery, University Hospital Halle (Saale), Halle (Saale), Germany. 5. Department of Neurology, National Institute of Clinical Neurosciences, Budapest, Hungary. 6. Department of Pediatrics, Interdisciplinary Excellence Center, Faculty of Medicine, Albert Szent-Györgyi Clinical Center, University of Szeged, Szeged, Hungary. 7. Department of Neurology, Danish Epilepsy Center (Member of the European Reference Network EpiCARE), Dianalund, University of Copenhagen, Copenhagen, Denmark. 8. Epilepsy Research Center, Shiraz University of Medical Sciences, Shiraz, Iran. 9. Department of Neurology, Jefferson Comprehensive Epilepsy Center, Thomas Jefferson University, Philadelphia, Pennsylvania, USA. 10. Department of Clinical Neurophysiology, Danish Epilepsy Center (Member of the European Reference Network EpiCARE), Aarhus University Hospital, Dianalund, Denmark. 11. Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
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.
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.