NNN01
Identifying Prospective Memory Deficits in MS with a Single Task from the Memory for Intentions Test: An IRT and ROC Analysis

Tuesday, October 26, 2021
Exhibit Hall (Rosen Shingle Creek)
Elizabeth S Gromisch, PhD , Mandell Center for Multiple Sclerosis, Mount Sinai Rehabilitation Hospital, Trinity Health Of New England, Hartford, CT, Department of Neurology, University of Connecticut School of Medicine, Farmington, CT, Departments of Rehabilitative Medicine and Medical Sciences, Frank H. Netter MD School of Medicine at Quinnipiac University, North Haven, CT
Aaron P Turner, Ph.D. ABPP (RP) , Department of Rehabilitation Medicine, University of Washington, Seattle, WA, Rehabilitation Care Service, VA Puget Sound Health Care System, Seattle, WA, Multiple Sclerosis Center of Excellence West, Veterans Affairs, Seattle, WA
Lindsay O Neto, MPH , Mandell Center for Multiple Sclerosis, Mount Sinai Rehabilitation Hospital, Trinity Health Of New England, Hartford, CT, Department of Rehabilitative Medicine, Frank H. Netter MD School of Medicine at Quinnipiac University, North Haven, CT
Sarah A Raskin, PhD , Neuroscience Program, Trinity College, Hartford, CT, Department of Psychology, Trinity College, Hartford, CT



Background: While many persons with multiple sclerosis (PwMS) have prospective memory (PM) issues, which can have an impact on everyday functioning, it is not routinely assessed. A single task that is sensitive and specific to PM deficits could be added to a screening battery, such as the abbreviated Minimal Assessment of Cognitive Function in MS (aMACFIMS), and thus improve detection and monitoring of these issues.

Objectives: To identify which of the eight tasks on the Memory for Intentions Test (MIST) has the best classification accuracy, sensitivity, and specificity for detecting PM deficits in PwMS.

Methods: Participants (n = 112) were PwMS who were part of a self-management study. PM was assessed with the MIST, with participants classified as impaired if they performed at the 1st percentile or below on the overall measure, per the test manual. Each task’s difficulty and discriminability were evaluated with item response theory (IRT) analyses with the R package MIRT. Five tasks with adequate difficulty and discriminability were further examined using receiver operating characteristic (ROC) analyses to determine their classification accuracies, sensitivities, and specificities. The tasks with the best classification accuracies were then compared in terms of their sensitivities and specificities using the R package DTComPair.

Results: Two tasks from the MIST had a classification accuracy of 90% and above in the current sample of PwMS: Trial 3 (100% sensitivity, 81% specificity) and Trial 4 (86% sensitivity, 90% specificity). These two tasks had comparable sensitivity (p = .317), with a trend towards Trial 4 having higher specificity (p = .061).

Conclusions: Trial 4 of the MIST, a verbal task with an event-based cue that requires participants to complete a pre-specified action after a 15 minute delay, has the optimal balance of sensitivity and specificity for detecting PM deficits in MS. Given the timing of its administration in the full MIST, it could potentially be added to the aMACFIMS. While Trial 3, a verbal task with an action-based cue and a two minute delay, also had strong classification accuracy, its ability to detect PM deficits in PwMS was likely influenced by the preceding cognitive load. The next steps will be to evaluate Trial 4’s utility as part of the aMACFIMS in terms of screening and monitoring PwMS’ PM deficits.