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

• Published in: PloS one Vol. 12; no. 6; p. e0178615
• Main Authors: Kikkert, Lisette H J, de Groot, Maartje H, van Campen, Jos P, Beijnen, Jos H, Hortobágyi, Tibor, Vuillerme, Nicolas, Lamoth, Claudine C J
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 02-06-2017; Public Library of Science (PLoS)
• Subjects: Accelerometers; Accidental falls; Accuracy; Age; Aged; Aged, 80 and over; Aging; Aging (natural); Alzheimer's disease; Alzheimers disease; Ambulatory Care Facilities; Analysis; Biocompatibility; Biology and Life Sciences; Biomedical materials; Categories; Classification; Clinical medicine; Cognition; Cognition & reasoning; Cognitive ability; Demographics; Diagnostic systems; Discriminant analysis; Drugs; Dynamic tests; Dynamics; Executive function; Falls; Female; Gait; Geriatrics; Gerontology; Health risks; Hospitals; Humans; Least squares method; Male; Mathematical models; Medical diagnosis; Medicine and Health Sciences; Memory; Older people; Osteoporosis; Pathology; Patient Admission; Patients; Physical Sciences; Polypharmacy; Posture; Principal Component Analysis; Principal components analysis; Prospective Studies; Research and Analysis Methods; Risk Assessment; Risk factors; Screening; Sensitivity; Strength; Studies; Variability; Walking; France; Netherlands
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0178615

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.