Untangling Symptom Overlap in Multiple Sclerosis and Depression: An Adjusted Depression Score for Patient-Centered Care

• Activity Number: 162
• Type: Topic Contributed
• Date/Time: Monday, August 10, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Nathan Morris*
• Companies: Case Western Reserve University
• Keywords: multiple sclerosis ; depression; structural equation modeling ; electronic health records ; patient-reported outcomes

Abstract

While Multiple Sclerosis (MS) patients commonly experience depressive symptoms, clinicians cannot reliably distinguish the roles of MS and depression in fatigue, cognitive impairment and functional impairment. This can lead to inappropriate clinical decisions (medication selection, escalation, etc.) and to false-positive or false-negative inferences regarding treatment effectiveness. Structural equation modeling (SEM) methodology is a very suitable and flexible approach for analyses of overlapping symptoms in co-occurring conditions. SEM is a general, powerful multivariate technique combining factor analysis and regression or path analysis. In data analysis using an Electronic Health Records (EHR) database of 3507 MS patients retrospectively, we have used SEM methods to 1) show substantial overlap of depression (via PHQ-9 self-report survey) with other symptoms of MS patients; 2) adjust the PHQ-9 to isolate the depression latent dimension of the scale, for a more pure estimate of an MS patient's mood.