A Qualitative Approach to Extract Diagnostic Patterns of Cognitive Impairment in Parkinson’s Disease

Background: Parkinson’s disease (PD) is the second-most diagnosed age-related neurodegenerative disorder globally. PD pathology causes dysregulation of motor movement and for many, mild or minor cognitive impairment (PD-MCI). The most recommended global screening exam to detect PD-MCI is the Montreal Cognitive Assessment© (MoCA). Traditionally, the MoCA is scored according to guidelines and compared against a standardized cutoff, but clinical professionals additionally draw upon their observations of the patient’s performance to determine the score. To better understand how clinicians use the MoCA in real-world clinical settings, we employed the qualitative descriptive approach to identify performance patterns professionals utilize to assess the cognitive health of a person with PD. Methods: We curated retrospective data from nine people with PD-MCI to PD-Dementia. Each patient had one completed MoCA exam and one neuropsychological report containing health data. The assessments were organized into three groups of three and used in semi-structured interviews with six clinical professionals to gather at minimum two clinical opinions for each. Results: Three coders distilled, based on consensus, three clinically meaningful patterns from the interviews composed of features emphasized as vital by the interviewees for determining a person’s cognitive health. The derived features were from a patient’s performance on sections of the MoCA exam, sociodemographic and health data from the neuropsychological report, and dependent relationships between the assessments. Conclusions: Our study leveraged the popular MoCA exam to explore its real-world clinical use. Extracting these patterns clinicians recognized provides deeper insights into how they interpret cognitive health creating a blueprint for future efforts to tailor the exam for detecting cognitive impairment in people with PD.