62 Prediction of Mild Cognitive Impairment Conversion Using Cox Model in Parkinson’s Disease

Objective:Mild cognitive impairment (MCI) in Parkinson’s disease (PD) is a critical state to consider. In fact, PD patients with MCI are more likely to develop dementia than the general population. Thus, identifying the risk factors for developing MCI in patients with PD could help with disease prevention. We aim to use the Cox regression model to identify the variables involved in the development of MCI in healthy controls (HC) and in a PD cohort.Participants and Methods:The Parkinson’s Progressive Markers Initiative (PPMI) database was used to analyze data from 166 HC and 365 patients with PD. They were analyzed longitudinally, at baseline and at 3-year follow up. Both HC and PD were further divided in 2 groups based on the presence or absence of MCI. Conversion to MCI was defined as the first detection of MCI. For all participants, we extracted the (1) Neuropsychiatric symptoms (anxiety, impulsive-compulsive disorders and sleep impairment), (2) 3T MRI-based data (cortical and subcortical brain volumes based on the Desikan atlas, using FreeSurfer 7.1.1) and (3) genetic markers (MAPT and APOE £4 genes). We used Python 3.9 to perform three Cox proportional hazard models (PD-HC, HC only and PD only) and to model the risk of conversion to MCI, attributable to neuropsychiatric symptoms and cortical brain parameters. We included as covariates: age, sex, education, and disease duration (for the PD group). Hazard ratios (HRs) along with their 95% confidence intervals (CIs) are reported.Results:When including both HC and PD in the model, Cox regression analyses showed that age of onset, diagnosis, the State-Trait Anxiety Inventory (STAI) and sleep impairment are variables that are associated with a greater risk of conversion to MCI (p