Date: Monday, June 20, 2016
Session Title: Parkinsonism, MSA, PSP (secondary and parkinsonism-plus)
Session Time: 12:30pm-2:00pm
Location: Exhibit Hall located in Hall B, Level 2
Objective: To (1) evaluate which factors are associated with increasing falls in Progressive Supranuclear Palsy (PSP), unrelated to freezing; and (2) subsequently develop a predictive model to identify those at risk of increasing falls using clinical parameters amenable to treatment, to develop a treatment plan to reduce the risk of future falls.
Background: Falls in PSP occur early and increase in frequency and severity over time, causing significant morbidity and mortality. Few studies have evaluated the factors contributing to these falls, or ways to predict or help prevent them. Moreover, no predictive models to identify patients at risk are currently available, and little research has investigated measures that could prevent future falls in PSP.
Methods: Using comprehensive clinical data, 339 PSP patients were divided into two groups based on falls within the previous month: Infrequent Fallers ("IF"; n = 118) with no or rare falls, and Frequent Fallers ("FF"; n = 221) who fell occasionally to multiple times a day. 38 clinical parameters were analyzed to determine their relationship with an increasing risk of falls. A multivariate logistic regression model and Neural Network model to identify FF or IF were developed and compared. Both models were reduced to include only clinical parameters amenable to intervention (postural stability/body sway, neck rigidity or dystonia, arising from a chair, sitting down, gait, eyelid dysfunction, and modified turning).
Results: 25 of 38 clinical parameters analyzed were significantly correlated with FF (Table 1).
|Dementia Rating Scale|
|Frontal Assessment Battery|
|Voluntary Upward Saccades||N||NS|
|Voluntary Downward saccades||N||NS|
|Voluntary Left saccades||N||0.0009|
|Voluntary Right saccades||N||0.0006|
|Neck rigidity or dystonia||Y||0.0067|
|Arising from chair||Y||<0.0001|
|Right leg agility||N||NS|
|Left leg agility||N||0.02|
|Arising from a chair||Y||<0.0001|
|Postural stability-Body sway||Y||0.0006|
|Body bradykinesia and hypokinesia||N||<0.0001|
|PSP Staging System: Gait/Stability||N||<0.0001|
|Modified Hoehn & Yahr||N||<0.0001|
|Arising from a Chair (UPDRS)||Eyelid Dysfunction (PSPRS)||Modified Turning||Prob (0)||Prob (1)||Likelihood of Fall||Risk Reduction|
Conclusions: Certain clinical parameters are associated with a higher frequency of falls, which help identify PSP patients at risk for increasing falls and improve our understanding of why these falls occur. The neural network model correctly identified FF with high accuracy, and warrants validation in a prospective study. The ability to create a treatment plan based on symptoms amenable to treatment could help reduce the high morbidity and mortality caused by falls in PSP.
CurePSP 2015 International Symposium.
To cite this abstract in AMA style:B.R. Bluett, I. Litvan, S. Cheng, J. Juncos, Y. Bordelon, D.E. Riley, D. Standaert, S.G. Reich, D.A. Hall, B. Kluger, D. Shprecher, C. Marras, J. Jankovic. Understanding, predicting, and preventing falls in progressive supranuclear palsy [abstract]. Mov Disord. 2016; 31 (suppl 2). https://www.mdsabstracts.org/abstract/understanding-predicting-and-preventing-falls-in-progressive-supranuclear-palsy/. Accessed December 7, 2023.
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MDS Abstracts - https://www.mdsabstracts.org/abstract/understanding-predicting-and-preventing-falls-in-progressive-supranuclear-palsy/