Gait dynamics to optimize fall risk assessment in geriatric patients admitted to an outpatient diagnostic clinic.

摘要: Fall prediction in geriatric patients remains challenging because the increased fall risk involves multiple, interrelated factors caused by natural aging and/or pathology. Therefore, we used a multi-factorial statistical approach to model categories of modifiable among identify fallers with highest sensitivity and specificity focus on gait performance. Patients (n = 61, age 79; 41% fallers) underwent extensive screening three categories: (1) patient characteristics (e.g., handgrip strength, medication use, osteoporosis-related factors) (2) cognitive function (global cognition, memory, executive function), (3) performance (speed-related dynamic outcomes assessed tri-axial trunk accelerometry). Falls were registered prospectively (mean follow-up 8.6 months) one year retrospectively. Principal Component Analysis (PCA) 11 variables was performed determine underlying properties. Three fall-classification models then built using Partial Least Squares-Discriminant (PLS-DA), separate combined analyses factors. PCA identified 'pace', 'variability', 'coordination' as key properties gait. The best PLS-DA produced classification accuracy AUC 0.93. 60% but reached 80% when added. inclusion cognition dynamics reduced misclassification. We therefore recommend assessing patients' that incorporates characteristics, dynamics.