Cluster analysis of a large dataset of patients with Lennox-Gastaut syndrome

Abstract

ObjectivesWe applied the Two-Step cluster analysis on a large cohort of patients with Lennox-Gastaut syndrome (LGS). We hypothesized that there are distinct clusters of patients with LGS based on their clinical characteristics. We also studied whether the seizure outcome differs between these patient clusters. MethodsThis was a longitudinal study of a prospectively developed database. All patients with a diagnosis of LGS were studied at the outpatient epilepsy clinic at Shiraz University of Medical Sciences, Shiraz, Iran, from 2008 until 2020. The Two-Step cluster analysis (Schwarz's Bayesian Criterion: BIC) was applied to the whole dataset considering the demographic data, seizure types, and EEG data. In the next step, the seizure outcome was compared between the clusters of patients. ResultsTwo hundred and fifty-eight patients were studied. The Two-Step cluster analysis showed that there were three clusters of homogeneous subgroups of patients with LGS, with a fair silhouette measure of cohesion and separation. The seizure outcomes were significantly different between the clusters. In cluster 1, three out of 35 patients (8.6%) were seizure-free; in cluster 2, 13 out of 34 patients (38.2%) were seizure-free; and in cluster 3, nine out of 49 patients (18.4%) were seizure-free (p = 0.009; df: 2). ConclusionThe Two-Step cluster analysis could identify three distinct clusters of patients with LGS; these data are helpful in providing appropriate prognosis and counselling for patients and their care-givers. Future studies should also investigate the effects of etiology in cluster analysis of patients with LGS.