Objective: To investigate and evaluate behavioral predictors of conversion to Freezing of Gait in Parkinson’s Disease (PD).

Background: Freezing of Gait (FOG) has devastating consequences on fall risk and quality of life. However, a comprehensive understanding and evaluation of markers of FOG is lacking. Here we used a multidimensional approach to investigate predictors of FOG conversion.

Methods: We conducted a cohort study with a two year follow-up in sixty non-freezers when OFF-medication. Instrumented and clinical tests covering domains of disease severity, gait, cognition, affect, repeated limb movements, balance, turning and dual tasking were assessed once every year. Conversion was determined by the New Freezing of Gait Questionnaire. A multivariable prediction model was built considering 128 clinical and instrumented measures from the various domains. Preselection of variables was based on univariate logistic regression followed by a backward logistic regression. The discriminative ability of the model (AUC) was internally validated using the regular bootstrap [1,2] including all steps in the model building procedure.

Results: Twelve patients (20%) developed FOG during the study, six in each year. Data from the year before conversion was used for prediction. The model was able to predict one-year conversion with a bootstrap-corrected AUC of 0.813 (Adjusted R-square = 31.74%, Brier score = 0.13). The measures most often selected (~20% of the bootstrap samples) were response time variability to the auditory Stroop while turning, non-motor (part I) and axial motor (part III) of the MDS-UPDRS, frequency variability while toe tapping and relative phase error variability while finger tapping. Further analysis of the MDS-UPDRS sub-scores revealed that self-reported cognitive impairment, pain and fatigue contributed to worse non-motor scores while more rigidity, difficulty with rising from the chair and poor facial expression and spontaneous movement contributed to worse axial motor scores. Cognitive and balance measures were not found to be related to FOG conversion in this single-center cohort.

Conclusions: A model combining clinical and objective markers was able to predict FOG conversion in the following year with good discriminative ability. These findings indicate a breakdown in repetitive movement control as well as increased axial and non-motor burden prior to FOG development.

References: 1. Harrell, F. E., Lee, K. L., & Mark, D. B. (1996). Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statistics in medicine, 15(4), 361-387. 2. Steyerberg, E. W., Harrell, F. E., Borsboom, G. J., Eijkemans, M. J. C., Vergouwe, Y., & Habbema, J. D. F. (2001). Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. Journal of clinical epidemiology, 54(8), 774-781.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/motor-and-non-motor-features-predict-freezing-of-gait-in-parkinsons-disease/