Evaluating Mild Cognitive Dysfunction in Patients with Parkinson’s Disease in Clinical Practice in Taiwan

Our study aimed to examine the contribution of commonly used tools, including the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA), and develop a formula for conversion of these tests in the Chinese population. We also create a predictive model for the detection of Chinese patients’ mild cognitive impairment (MCI). We recruited 168 patients with Parkinson’s disease (PD) from 12 medical centres or teaching hospitals in Taiwan, and each participant received a comprehensive neuropsychological assessment. Logistic regression analysis was conducted to find predictors of MCI with the help of a generalized additive model. We found that patients with an MMSE > 25 or a MoCA > 21 were less likely to have MCI. The discrimination powers of the two tests used for detecting MCI were 0.902 and 0.868, respectively, as measured by the area under the receiver operating characteristic curve (ROC). The best predictive model suggested that patients with a higher MMSE score, delayed recall scores of the 12-item Word Recall Test ≥ 5.817, and no test decline in the visuospatial index were less likely to have MCI (ROC = 0.982). Our findings have clinical utility in MCI detection in Chinese PD and need a larger sample to confirm.