Should we use both clinical and mobility measures to identify fallers in Parkinson's disease?. (January 2023)

• Record Type: Journal Article
• Authors: Vitorio, Rodrigo; Mancini, Martina; Carlson-Kuhta, Patricia; Horak, Fay B.; Shah, Vrutangkumar V.
• Is Part Of: Parkinsonism & related disorders. Volume 106(2023)
• Publication Date: 2023-01
• Subjects: Fall -- Gait -- Balance -- Parkinson's disease; Parkinson's disease -- Periodicals; Movement disorders -- Periodicals; Movement Disorders -- Periodicals; Nerve Degeneration -- Periodicals; Nervous System Diseases -- Periodicals; Parkinson Disease -- Periodicals; Tremor -- Periodicals; Parkinson, Maladie de -- Périodiques; Parkinson's disease; 616.833
• DOI: 10.1016/j.parkreldis.2022.105235
• Languages: English
• ISSNs: 1353-8020

Abstract

Abstract: Background: Although much is known about the multifactorial nature of falls in Parkinson's disease (PD), optimal classification of fallers remains unclear. Objective: To identify clinical (demographic, motor, cognitive and patient-reported) and objective mobility (balance and gait) measures that best discriminate fallers from non-fallers in PD. Methods: People with mild-to-moderate idiopathic PD were classified as fallers (at least one fall; n = 54) or non-fallers (n = 90) based on previous six months falls. Clinical characteristics included demographic, motor and cognitive status and patient-reported outcomes. Mobility (balance and gait) characteristics were derived from body-worn, inertial sensors while performing walking and standing tasks. To investigate the combinations of (up to four) measures that best discriminate fallers from non-fallers in each scenario (i.e., clinical-only, mobility-only and combined clinical + mobility models), we applied logistic regression employing a 'best subsets selection strategy' with a 5-fold cross validation, and calculated the area under the curve (AUC). Results: The highest AUCs for the clinical-only, mobility-only and clinical + mobility models were 0.89, 0.88, and 0.94, respectively. The most consistently selected measures in the top-10 ranked models were freezing of gait status (8x), the root mean square of anterior-posterior trunk acceleration while standing on a foam with eyes open (5x), gait double support duration (4x) and the postural instability and gait disorders score from the MDS UPDRS (4x). Conclusions: Findings highlight the importance of considering multiple aspects of clinical as well as objective balance and gait characteristics for the classification of fallers and non-fallers in PD. Highlights: Three models were explored to investigate which balance and gait measures that best discriminate PD fallers from non-fallers. Logistic regression was used with a 'best subsets selection strategy' and 5-fold cross validation to test the model performance. The highest AUCs for the clinical-only, mobility-only and clinical + mobility models were 0.89, 0.88, and 0.94, respectively. Findings highlight the importance of considering both clinica and objective balance/gait measures for the classification of fallers in PD.