The memory assessment clinics scale for epilepsy (MAC-E): A brief measure of subjective cognitive complaints in epilepsy

Abstract

Objective: The aim of this study was to conduct item reduction of the Memory Assessment Clinics Self-Rating Scale (MAC-S) to create a briefer measure that can be used to quickly evaluate subjective memory complaints in patients with epilepsy. Method: A total of 1333 adults with focal epilepsy completed the original 49-item MAC-S. The sample was randomly split into three subsamples, and a series of analyses (i.e. exploratory factor analysis, confirmatory factor analysis, and item response theory analyses) was conducted to identify an alternative factor structure, with a reduced number of items. A panel of 5 neuropsychologists independently reviewed the final model to assess appropriateness of each individual item as well as the factor loadings and overall factor structure. Final factor titles were subsequently decided as a group. Results: Five factors were identified: Attention, Working Memory, Retrieval, Semantic Memory, and Episodic Memory. The length of the MAC-S was reduced from 49 to 30 items, with items being removed because they failed to load onto any of the factors substantially, or because of poor item discrimination or threshold levels. Conclusions: The Memory Assessment Clinics Scale for Epilepsy (MAC-E), is an updated, brief measure of subjective memory functioning that can be used to efficiently assess relevant, every-day memory abilities in patients with epilepsy within both clinical and research settings.

Keywords:

• Epilepsy
• memory
• self-report
• item response theory
• factor analysis

Acknowledgments

Permission to use the Memory Assessment Clinics Self-Rating Scale (MAC-S) in the present study was granted by the estate of Thomas H. Crook. Some of the study data were collected and managed using REDCap electronic data capture tools hosted at Cleveland Clinic (Harris et al., 2009, Harris et al., 2019). REDCap (Research Electronic Data Capture) is a secure, web-based software platform designed to support data capture for research studies, providing 1) an intuitive interface for validated data capture; 2) audit trails for tracking data manipulation and export procedures; 3) automated export procedures for seamless data downloads to common statistical packages; and 4) procedures for data integration and interoperability with external sources.

Disclosure statement

No potential conflict of interest was reported by the authors.