A Risk Stratification Model for Early Cognitive Impairment After Diagnosis of Parkinson’s Disease

Abstract

Cognitive decline is very common in patients with Parkinson’s disease (PD) as longitudinal studies have shown that given enough time almost all patients will eventually end up with a diagnosis of Mild Cognitive Impairment (MCI) or dementia. Certain patients however are found to be more susceptible to early cognitive impairment soon after the diagnosis of PD. In this study, baseline evaluation outcomes from newly diagnosed patients from the Parkinson’s Progression Markers Initiative are evaluated to identify risk factors for early cognitive impairment using machine learning techniques. Applying an extensive search in the available feature space, consisting of more than 400 baseline features, to isolate the most informative predictors, the proposed methodology can discriminate patients having a diagnosis of MCI or dementia within the first 5 years of PD from those with normal cognition with an accuracy of 80.38%. Older age along with non-motor symptoms including cognitive and memory dysfunction, sleep problems, daytime sleepiness, smell dysfunction, mood impairment and anxiety at baseline are strong determinants of early MCI and dementia.