Web‐based decision support system for patient‐tailored selection of antiseizure medication in adolescents and adults: An external validation study

• Published in: European journal of neurology Vol. 29; no. 2; pp. 382 - 389
• Main Authors: Hadady, Levente, Klivényi, Péter, Perucca, Emilio, Rampp, Stefan, Fabó, Dániel, Bereczki, Csaba, Rubboli, Guido, Asadi‐Pooya, Ali A., Sperling, Michael R., Beniczky, Sándor
• Format: Journal Article
• Language: English
• Published: England John Wiley & Sons, Inc 01.02.2022
• Subjects: adolescent; Adolescents; adult; adverse effects; Algorithms; Allergies; antiepileptic drugs; Convulsions & seizures; Decision support systems; Drugs; Epilepsy; Health care; neuropharmacology; Patients; Retention; Seizures; Side effects; Survival analysis; Validation studies; Webs (structural); epilepsy; adverse effects; antiepileptic drugs; adolescent; adult; neuropharmacology
• ISSN: 1351-5101; 1468-1331; 1468-1331
• DOI: 10.1111/ene.15168

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. Epipick is an algorithm to aid in selecting patient‐tailored antiseizure medication (ASM), implemented into a decision support application, freely available at epipick.org. We validated the algorithm using an independent dataset of 425 patients; we compared the outcome of prescribing ASMs recommended by the algorithm as best options with ASMs ranked as less desirable. The ASMs recommended as best choices by the algorithm had significantly higher retention and seizure freedom rates, and a significantly lower rate of discontinuation due to adverse effects.