Moving towards a taxonomy of cognitive impairments in epilepsy: application of latent profile analysis to 1178 patients with temporal lobe epilepsy

Abstract

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 multicenter 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 1,178 participants met inclusion criteria for the study, which included a diagnosis of temporal lobe epilepsy and availability of comprehensive neuropsychological data. Models with 2-5 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 3-class solution was the optimal solution. This 3-class solution was comprised of 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 was comprised of a greater proportion of patients with mesial temporal sclerosis, longer disease duration, and higher number of antiseizure medications. The 4-Class and 5-Class solutions demonstrated the lowest probabilities of group membership. Analyses with imputed data demonstrated that the 4-Class solution was the optimal solution; however, there was weak agreement between the missing and imputed datasets for the 4-Class solutions (κ = .288, p < .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.

Validity of the MoCA as a cognitive screening tool in epilepsy: Are there implications for global care and research?

“…The overall impairment classification rate for the MoCA domains (40%) was somewhat similar to the IC‐CoDE (34%), providing some evidence that the domains in the MoCA are sensitive to broader cognitive impairments beyond memory. The ability of the MoCA to detect non‐amnestic cognitive changes increases its clinical utility in epilepsy, as the cognitive profiles observed are broader compared to many other neurological conditions 14,22,38,39,40 …”

Update in Progress: Cognitive Phenotypes in Temporal Lobe Epilepsy

“…In the study by Reyes et al, 4 7 epilepsy centers pooled their neuropsychological data to try to tackle this question. They use an analytical technique termed latent profile analysis (LPA) 5 which belongs to the family of person-centered statistical approaches and provides advantages over the techniques used to date; cluster analysis and using prespecified cutoffs for impairment (such as having scores 1 or 1.5 standard deviations below the mean).…”

Cognitive phenotypes in late-onset epilepsy: results from the atherosclerosis risk in communities study

“…Our group has shown that in young-to-middle aged adults, the phenotyping approach better captures the heterogeneity inherent both within and across epilepsy syndromes compared to analyzing individual scores in isolation (40, [49][50][51][52][53][54][55]. We have demonstrated that cognitive phenotypes are stable and robust across cohorts and are associated with distinct patterns of brain imaging abnormalities (49,51,56,57).…”