Abstract
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 < 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.
Highlights
Freezing of gait (FOG) is a dramatic gait difficulty defined as “a brief, episodic absence or marked reduction of forward progression of the feet despite the intention to walk [1].” It is a disabling symptom that increases the probability of falls, fractures and contributes to immobility, loss of independence, reducing the quality of life of patients with Parkinson’s disease (PD) [2]
Neuroimaging data, and medication status [4,5,6,7,8,9,10,11,12]. These risk factors were rarely used to predict the onset of FOG in clinical practice [13]
A FOG risk model is an example of a prognostic model [13]. Such models should ideally be developed by taking a large cohort of patients with PD without FOG, measuring baseline risk factors, and following the cohort for a sufficiently long time to see who develops FOG [14]
Summary
Freezing of gait (FOG) is a dramatic gait difficulty defined as “a brief, episodic absence or marked reduction of forward progression of the feet despite the intention to walk [1].” It is a disabling symptom that increases the probability of falls, fractures and contributes to immobility, loss of independence, reducing the quality of life of patients with Parkinson’s disease (PD) [2]. Several longitudinal follow-up studies reported risk factors of future FOG onset including demographic parameters, motor symptoms, non-motor symptoms, laboratory parameters, Risk Prediction Model of FOG neuroimaging data, and medication status [4,5,6,7,8,9,10,11,12]. This study aimed to develop a risk prediction model incorporating various elements available through clinical investigations to predict the probability of future onset of FOG in patients with PD. Such a model would be a useful tool for clinicians diagnosing FOG and making therapeutic decisions
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