Update in Progress: Cognitive Phenotypes in Temporal Lobe Epilepsy

In efforts to understand the cognitive heterogeneity within and across epilepsy syndromes, cognitive phenotyping has been proposed as a new taxonomy aimed at developing a harmonized approach to cognitive classification in epilepsy. Data- and clinically driven approaches have been previously used with variability in the phenotypes derived across studies. In our study, we utilize latent profile analysis to test several models of phenotypes in a large multicentre sample of patients with temporal lobe epilepsy and evaluate their demographic and clinical profiles. For the first time, we examine the added value of replacing missing data and examine factors that may be contributing to missingness. A sample of 1178 participants met the inclusion criteria for the study, which included a diagnosis of temporal lobe epilepsy and the availability of comprehensive neuropsychological data. Models with two to five classes were examined using latent profile analysis and the optimal model was selected based on fit indices, posterior probabilities and proportion of sample sizes. The models were also examined with imputed data to investigate the impact of missing data on model selection. Based on the fit indices, posterior probability and distinctiveness of the latent classes, a three-class solution was the optimal solution. This three-class solution comprised a group of patients with multidomain impairments, a group with impairments predominantly in language and a group with no impairments. Overall, the multidomain group demonstrated a worse clinical profile and comprised a greater proportion of patients with mesial temporal sclerosis, a longer disease duration and a higher number of anti-seizure medications. The four-class and five-class solutions demonstrated the lowest probabilities of a group membership. Analyses with imputed data demonstrated that the four-class solution was the optimal solution; however, there was a weak agreement between the missing and imputed data sets for the four-Class solutions (kappa = 0.288, P < 0.001). This study represents the first to use latent profile analysis to test and compare multiple models of cognitive phenotypes in temporal lobe epilepsy and to determine the impact of missing data on model fit. We found that the three-phenotype model was the most meaningful based on several fit indices and produced phenotypes with unique demographic and clinical profiles. Our findings demonstrate that latent profile analysis is a rigorous method to identify phenotypes in large, heterogeneous epilepsy samples. Furthermore, this study highlights the importance of examining the impact of missing data in phenotyping methods. Our latent profile analysis-derived phenotypes can inform future studies aimed at identifying cognitive phenotypes in other neurological disorders.