Hyunmi Choi1, Kamil Detyniecki2, Carl Bazil2, Suzanne Thornton2, Peter Crosta2, Hatem Tolba2, Manahil Muneeb2, Lawrence J Hirsch2, Erin L Heinzen2, Arjune Sen2, Chantal Depondt2, Piero Perucca2, Gary A Heiman2. 1. From the Department of Neurology (H.C., C.B., P.C., M.M.) and Institute for Genomic Medicine (E.L.H.), Columbia University Medical Center, New York, NY; Department of Neurology (K.D.), University of Miami, FL; Department of Statistics and Biostatistics (S.T.), Rutgers University, Piscataway, NJ; Department of Neurology (H.T., L.J.H.), Yale University, New Haven, CT; Nuffield Department of Clinical Neurosciences (A.S.), NIHR Biomedical Research Centre, University of Oxford, UK; Department of Neurology (C.D.), Free University of Brussels, Belgium; Department of Neuroscience (P.P.), Monash University; Departments of Medicine and Neurology (P.P.), The Royal Melbourne Hospital, The University of Melbourne; Department of Neurology (P.P.), Alfred Health, Melbourne, Australia; and Department of Genetics (G.A.H.), Human Genetics Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, NJ. hc323@columbia.edu. 2. From the Department of Neurology (H.C., C.B., P.C., M.M.) and Institute for Genomic Medicine (E.L.H.), Columbia University Medical Center, New York, NY; Department of Neurology (K.D.), University of Miami, FL; Department of Statistics and Biostatistics (S.T.), Rutgers University, Piscataway, NJ; Department of Neurology (H.T., L.J.H.), Yale University, New Haven, CT; Nuffield Department of Clinical Neurosciences (A.S.), NIHR Biomedical Research Centre, University of Oxford, UK; Department of Neurology (C.D.), Free University of Brussels, Belgium; Department of Neuroscience (P.P.), Monash University; Departments of Medicine and Neurology (P.P.), The Royal Melbourne Hospital, The University of Melbourne; Department of Neurology (P.P.), Alfred Health, Melbourne, Australia; and Department of Genetics (G.A.H.), Human Genetics Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, NJ.
Abstract
OBJECTIVE: To develop and validate a clinical prediction model for antiepileptic drug (AED)-resistant genetic generalized epilepsy (GGE). METHOD: We performed a case-control study of patients with and without drug-resistant GGE, nested within ongoing longitudinal observational studies of AED response at 2 tertiary epilepsy centers. Using a validation dataset, we tested the predictive performance of 3 candidate models, developed from a training dataset. We then tested the candidate models' predictive ability on an external testing dataset. RESULTS: Of 5,189 patients in the ongoing longitudinal study, 121 met criteria for AED-resistant GGE and 468 met criteria for AED-responsive GGE. There were 66 patients with GGE in the external dataset, of whom 17 were cases. Catamenial epilepsy, history of a psychiatric condition, and seizure types were strongly related with drug-resistant GGE case status. Compared to women without catamenial epilepsy, women with catamenial epilepsy had about a fourfold increased risk for AED resistance. The calibration of 3 models, assessing the agreement between observed outcomes and predictions, was adequate. Discriminative ability, as measured with area under the receiver operating characteristic curve (AUC), ranged from 0.58 to 0.65. CONCLUSION: Catamenial epilepsy, history of a psychiatric condition, and the seizure type combination of generalized tonic clonic, myoclonic, and absence seizures are negative prognostic factors of drug-resistant GGE. The AUC of 0.6 is not consistent with truly effective separation of the groups, suggesting other unmeasured variables may need to be considered in future studies to improve predictability.
OBJECTIVE: To develop and validate a clinical prediction model for antiepileptic drug (AED)-resistant genetic generalized epilepsy (GGE). METHOD: We performed a case-control study of patients with and without drug-resistant GGE, nested within ongoing longitudinal observational studies of AED response at 2 tertiary epilepsy centers. Using a validation dataset, we tested the predictive performance of 3 candidate models, developed from a training dataset. We then tested the candidate models' predictive ability on an external testing dataset. RESULTS: Of 5,189 patients in the ongoing longitudinal study, 121 met criteria for AED-resistant GGE and 468 met criteria for AED-responsive GGE. There were 66 patients with GGE in the external dataset, of whom 17 were cases. Catamenial epilepsy, history of a psychiatric condition, and seizure types were strongly related with drug-resistant GGE case status. Compared to women without catamenial epilepsy, women with catamenial epilepsy had about a fourfold increased risk for AED resistance. The calibration of 3 models, assessing the agreement between observed outcomes and predictions, was adequate. Discriminative ability, as measured with area under the receiver operating characteristic curve (AUC), ranged from 0.58 to 0.65. CONCLUSION: Catamenial epilepsy, history of a psychiatric condition, and the seizure type combination of generalized tonic clonic, myoclonic, and absence seizures are negative prognostic factors of drug-resistant GGE. The AUC of 0.6 is not consistent with truly effective separation of the groups, suggesting other unmeasured variables may need to be considered in future studies to improve predictability.
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Authors: Amy Shakeshaft; Naim Panjwani; Amber Collingwood; Holly Crudgington; Anna Hall; Danielle M Andrade; Christoph P Beier; Choong Yi Fong; Elena Gardella; Joanna Gesche; David A Greenberg; Khalid Hamandi; Jeanette Koht; Kheng Seang Lim; Rikke S Møller; Ching Ching Ng; Alessandro Orsini; Mark I Rees; Guido Rubboli; Kaja K Selmer; Pasquale Striano; Marte Syvertsen; Rhys H Thomas; Jana Zarubova; Mark P Richardson; Lisa J Strug; Deb K Pal Journal: Sci Rep Date: 2022-02-21 Impact factor: 4.379