P77. Prognosis of cognitive decline in Parkinsons disease: a combined marker of quantitative EEG and clinical variables improves prediction. Issue 8 (August 2018)

• Record Type: Journal Article
• Authors: Meyer, A.; Bogaarts, J.G.; Cozac, V.; Chaturvedi, M.; Handabaka, I.; Hatz, F.; Gschwandtner, U.; Fuhr, P.
• Is Part Of: Clinical neurophysiology. Volume 129:Issue 8(2018:Aug.)
• Page Start: e98
• Page End: e99
• Publication Date: 2018-08
• Subjects: Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2018.04.709
• Languages: English
• ISSNs: 1388-2457

Abstract

Abstract : Background: Models have been constructed to estimate individual risk for global cognitive impairment in Parkinson's disease (PD) using a small set of clinical predictor variables (age at disease onset, sex, education, MMSE, motor impairment, depression) (Liu et al., 2017 ). The prediction algorithm accurately forecast cognitive decline with a predefined cut-off score. Slowing of the electroencephalogram (EEG) is frequent in PD and as it is a predictive biomarker for dementia in PD (PDD), it is likely that adding information about EEG frequency might increase predictive accuracy of cognitive decline. Objective: The present study aims at (1) investigating whether quantitative EEG (qEEG) measures could identify differences between PD patients at high risk and PD patients at low risk of cognitive decline and at (2) analysing whether the inclusion of qEEG parameters improve predictive accuracy of cognitive decline within 3 years. Methods: In a total of 44 non-demented PD patients (disease duration: median = 2 years), a prediction algorithm for cognitive decline developed byLiu et al. (2017) was applied. At baseline, according to the defined cut-off score byLiu et al. (2017), n = 23 patients were identified at high risk and n = 21 patients at low risk of cognitive decline. Resting state EEG was recorded from 256 electrodes. Relative power spectra and median frequency (4–14 Hz) were compared between groups using ANOVA. Receiver-operator-characteristic (ROC) was used to demonstrate prediction of global cognitive decline after 3 years (dementia vs. non dementia) using clinical risk score only and in combination with qEEG variable. Results: At baseline after correction for multiple comparisons, differences in global theta power and theta power in all brain regions ( p < 0.05, most pronounced: temporal left p < 0.004) and global alpha2 power and alpha2 power in temporo-posterior regions ( p < 0.05) between groups were detected. After 3 years, 4 patients had progressed to dementia. Dementia was predicted by cognitive risk score with an area under the curve (AUC) of 71%. Prediction slightly increased when best predicting variable (theta temporal left) was added (AUC: 83%, p = 0.06). Conclusion: PD patients at high risk of cognitive decline are characterized by pronounced slowing as compared to PD patients at low risk. Even at a very short time span, cognitive risk scores are indicative of dementia in PD patients. Adding information about qEEG enhances prediction. Combined marker (qEEG and clinical-only risk score) may help to improve prediction of cognitive decline in PWD patients.