| Autor: |
Kikkert LHJ; University of Groningen, University Medical Center Groningen, Center for Human Movement Sciences, Groningen, The Netherlands.; Univ. Grenoble Alpes, EA AGEIS, Grenoble, France.; MC Slotervaart Hospital, Department of Geriatric Medicine, Amsterdam, The Netherlands., de Groot MH; MC Slotervaart Hospital, Department of Geriatric Medicine, Amsterdam, The Netherlands.; The Hague University of Applied Sciences, Faculty of Health, Nutrition & Sport, The Hague, The Netherlands., van Campen JP; MC Slotervaart Hospital, Department of Geriatric Medicine, Amsterdam, The Netherlands., Beijnen JH; MC Slotervaart Hospital, Department of Pharmacy and Pharmacology, Amsterdam, The Netherlands.; Utrecht University, Science Faculty, Department of Pharmaceutical Sciences, Utrecht, The Netherlands., Hortobágyi T; University of Groningen, University Medical Center Groningen, Center for Human Movement Sciences, Groningen, The Netherlands., Vuillerme N; Univ. Grenoble Alpes, EA AGEIS, Grenoble, France.; Institut Universitaire de France, Paris, France., Lamoth CCJ; University of Groningen, University Medical Center Groningen, Center for Human Movement Sciences, Groningen, The Netherlands. |
| Abstrakt: |
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 fall risk factors among geriatric patients to identify fallers with highest sensitivity and specificity with a focus on gait performance. Patients (n = 61, age = 79; 41% fallers) underwent extensive screening in three categories: (1) patient characteristics (e.g., handgrip strength, medication use, osteoporosis-related factors) (2) cognitive function (global cognition, memory, executive function), and (3) gait performance (speed-related and dynamic outcomes assessed by tri-axial trunk accelerometry). Falls were registered prospectively (mean follow-up 8.6 months) and one year retrospectively. Principal Component Analysis (PCA) on 11 gait variables was performed to determine underlying gait properties. Three fall-classification models were then built using Partial Least Squares-Discriminant Analysis (PLS-DA), with separate and combined analyses of the fall risk factors. PCA identified 'pace', 'variability', and 'coordination' as key properties of gait. The best PLS-DA model produced a fall classification accuracy of AUC = 0.93. The specificity of the model using patient characteristics was 60% but reached 80% when cognitive and gait outcomes were added. The inclusion of cognition and gait dynamics in fall classification models reduced misclassification. We therefore recommend assessing geriatric patients' fall risk using a multi-factorial approach that incorporates patient characteristics, cognition, and gait dynamics. |
