Diagnosis switching and outcomes in a cohort of patients with potential epilepsy with myoclonic-atonic seizures

Abstract

IntroductionThere is overlap in the electroclinical features of many childhood epilepsy syndromes, especially those presenting with multiple seizure types, such as epilepsy with myoclonic-atonic seizures (EMAS) and Lennox-Gastaut syndrome (LGS). This study aimed to determine the frequency of diagnosis switching and the factors influencing epilepsy syndrome diagnosis in a cohort of children with possible EMAS, as well as to explore the relationship between epilepsy syndrome diagnoses, key electroclinical features, and clinically relevant outcomes. MethodsThis is a cross-sectional retrospective chart review of children treated at the Children’s Hospital of Colorado with a potential diagnosis of EMAS. ResultsThere were 77 patients that met eligibility criteria, including 39% (n = 30) with an initial diagnosis of EMAS and 74% (n = 57) with a final diagnosis of EMAS. On average, for the 65% of patients who received more than one epilepsy diagnosis, the first, second, and third diagnoses were received within one year, three years, and ten years after epilepsy onset, respectively. Final diagnosis was significantly related to obtaining at least a six-month period of seizure freedom, p = 0.03. Classic LGS traits, including paroxysmal fast activity, slow spike-and-wave, and tonic seizures were present in 50% of the overall cohort, although a minority of these patients had a final diagnosis of LGS. However, the presence of more LGS traits was associated with a higher likelihood of ongoing seizures. Adjusted for age of epilepsy onset, seizure freedom was half as likely for every additional LGS trait observed (0.49[0.31, 0.77], p = 0.002). ConclusionCurrent epilepsy syndrome classification has reduced applicability due to overlapping features. This results in diagnosis switching and limited prognostic value for patients with an overlapping clinical phenotype. Future studies should attempt to stratify patients based not only on epilepsy syndrome diagnosis, but also on the presence of various electroclinical traits to more accurately predict outcome.