Baseline predictors of progression of Parkinson’s disease in a sample of Egyptian patients: clinical and biochemical

Background Clinical progression of Parkinson’s disease (PD) is highly heterogeneous, and its predictors are generally lacking. Identifying predictors of early disease progression is important for patients’ management and follow-up. The current study aims to identify clinical, neuroimaging and biochemical baseline predictors of motor progression in patients with PD. Forty-five PD patients were assessed at baseline, 6 months and 1 year using MDS-UPDRS total and subscores, Hoehn and Yahr (H&Y), Schwab and England (S&E), International Physical Activity Questionnaire (IPAQ). Baseline New Freezing of Gait Questionnaire (NFOG-Q), Berg Balance Scale (BBS), Ten-Meter Walking Test (10-MWT), and Time Up and Go Test (TUG), Non-Motor Symptoms Scale (NMSS), Beck Depression Inventory (BDI), PD questionnaire 39 (PDQ-39), MRI brain, uric acid, lipid profile and glycated hemoglobin were performed. Results Significant worsening of MDS-UPDRS total, part III scores, H&Y, S&E and IPAQ (p < 0.001) was detected. One-year progression of H&Y and S&E were significantly correlated to disease duration (p = 0.014, p = 0.025, respectively). Progression of H&Y was correlated to baseline TUG (p = 0.035). S&E progression was correlated to baseline MDS-UPDRS total score (rho = 0.478, p = 0.001) and part III (rho = 0.350, p = 0.020), H&Y (rho = 0.401, p = 0.007), PIGD (rho = 0.591, p < 0.001), NFOG-Q (rho = 0.498, p = 0.001), and TUG (rho = 0.565, p = 0.001). Using linear regression, there was no predictors of clinical progression among the used baseline variables. Conclusion Despite the significant motor and physical activity progression over 1 year that was correlated to baseline motor and gait severity, but without predictive value, further similar and longitudinal studies are warranted to detect predictors of early progression and confirm findings.

Holocue: A Wearable Holographic Cueing Application for Alleviating Freezing of Gait in Parkinson's Disease

External visual cueing is a well-known means to target freezing of gait (FOG) in Parkinson's disease patients. Holocue is a wearable visual cueing application that allows the HoloLens 1 mixed-reality headset to present on-demand patient-tailored action-relevant 2D and 3D holographic visual cues in free-living environments. The aim of this study involving 24 Parkinson's disease patients with dopaminergic “ON state” FOG was two-fold. First, to explore unfamiliarity and habituation effects associated with wearing the HoloLens on FOG. Second, to evaluate the potential immediate effect of Holocue on alleviating FOG in the home environment. Three sessions were conducted to examine (1) the effect of wearing the unfamiliar HoloLens on FOG by comparing walking with and without the HoloLens, (2) habituation effects to wearing the HoloLens by comparing FOG while walking with HoloLens over sessions, and (3) the potential immediate effect of Holocue on FOG by comparing walking with HoloLens with and without Holocue. Wearing the HoloLens (without Holocue) did significantly increase the number and duration of FOG episodes, but this unfamiliarity effect disappeared with habituation over sessions. This not only emphasizes the need for sufficient habituation to unfamiliar devices, but also testifies to the need for research designs with appropriate control conditions when examining effects of unfamiliar wearable cueing devices. Holocue had overall no immediate effect on FOG, although objective and subjective benefits were observed for some individuals, most notably those with long and/or many FOG episodes. Our participants raised valuable opportunities to improve Holocue and confirmed our assumptions about current and anticipated future design choices, which supports ongoing Holocue development for and with end users.

Longitudinal Prediction of Freezing of Gait in Parkinson's Disease: A Prospective Cohort Study

Objective: Freezing of gait (FOG) is a disabling complication in Parkinson's disease (PD). Yet, studies on a validated model for the onset of FOG based on longitudinal observation are absent. This study aims to develop a risk prediction model to predict the probability of future onset of FOG from a multicenter cohort of Chinese patients with PD.Methods: A total of 350 patients with PD without FOG were prospectively monitored for ~2 years. Demographic and clinical data were investigated. The multivariable logistic regression analysis was conducted to develop a risk prediction model for FOG.Results: Overall, FOG was observed in 132 patients (37.70%) during the study period. At baseline, longer disease duration [odds ratio (OR) = 1.214, p = 0.008], higher total levodopa equivalent daily dose (LEDD) (OR = 1.440, p &lt; 0.001), and higher severity of depressive symptoms (OR = 1.907, p = 0.028) were the strongest predictors of future onset of FOG in the final multivariable model. The model performed well in the development dataset (with a C-statistic = 0.820, 95% CI: 0.771–0.865), showed acceptable discrimination and calibration in internal validation, and remained stable in 5-fold cross-validation.Conclusion: A new prediction model that quantifies the risk of future onset of FOG has been developed. It is based on clinical variables that are readily available in clinical practice and could serve as a small tool for risk counseling.