Refining diagnostic boundaries and electroclinical profiles of Lennox-Gastaut syndrome through unsupervised clustering.

objectiveLennox-Gastaut syndrome (LGS) is a developmental and epileptic encephalopathy defined by polymorphic seizures, intellectual disability (ID), and characteristic electroencephalographic (EEG) patterns. The applicability and biological validity of current electroclinical criteria remain debated. This study evaluated the concordance between clinical and electroclinical definitions of LGS and applied unsupervised clustering to identify data-driven profiles within LGS.

methodsWe retrospectively analyzed patients clinically fulfilling LGS criteria, with longitudinal electroclinical documentation and at least one sleep EEG. Patients meeting complete electroclinical criteria were compared with those meeting clinical criteria alone. Additionally, exploratory unsupervised K-modes clustering and discriminant correspondence analysis were applied in adolescent and adult patients.

resultsWe included 105 patients (60.9% female), with a median age of 24 years (interquartile range = 16-35). Sixty-nine patients (65.7%) met complete electroclinical criteria, yet neurodevelopmental features, seizure types, magnetic resonance imaging (MRI) findings, and treatment refractoriness were comparable to the clinically defined subgroup. K-modes clustering (n = 97 patients) identified three data-driven electroclinical profiles. Cluster 1 comprised patients with the earliest seizure onset, profound intellectual and motor impairment, EEG background slowing, lower prevalence of generalized paroxysmal fast activity (GPFA), highest prevalence of structural etiologies, and lowest likelihood of meeting complete electroclinical criteria for LGS. Among clusters more frequently fulfilling complete electroclinical criteria, Cluster 2 represented an early onset profile with higher rates of ID, EEG background slowing, and high prevalence of polymorphic seizures. Cluster 3 included comparatively preserved patients, characterized by later onset, milder neurodevelopmental impairment, and higher frequency of normal MRI. Despite similar clinical severity, Cluster 1 patients had undergone fewer antiseizure medication trials. SIGNIFICANCE: Exploratory data-driven clustering identified clinically relevant electroclinical profiles that may help refine phenotypic stratification within clinically defined LGS. These included a severe GPFA-underrepresented profile and two GPFA-positive phenotypes differing in developmental severity, seizure profile, and age at onset.