Longitudinal Prediction of Freezing of Gait in Parkinson's Disease: A Prospective Cohort Study - Zhao, Jiahao; Wan, Ying; Song, Lu; Wu, Na; Zhang, Zien; Liu, Zhenguo; Gan, Jing (Frontiers in Neurology, 2022)
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 PD patients. Methods: A total of 350 PD patients without FOG were prospectively monitored for approximately two years. Demographic and clinical data were investigated. 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 (OR = 1.214, p =0.008), higher total levodopa equivalent daily dose (LEDD) (OR = 1.440, p <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 five-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.