Citation

Kabeshova A, Launay CP, Gromov VA, Annweiler C, Fantino B, Beauchet O. J. Am. Med. Dir. Assoc. 2014; 16(4): 277-281.

Affiliation

Department of Neuroscience, Division of Geriatric Medicine, Angers University Hospital, Angers, France. Electronic address: olbeauchet@chu-angers.fr.

Copyright

(Copyright © 2014, Lippincott Williams and Wilkins)

DOI

10.1016/j.jamda.2014.09.013

PMID

25444572

Abstract

BACKGROUND: Identification of the risk of recurrent falls is complex in older adults. The aim of this study was to examine the efficiency of 3 artificial neural networks (ANNs: multilayer perceptron [MLP], modified MLP, and neuroevolution of augmenting topologies [NEAT]) for the classification of recurrent fallers and nonrecurrent fallers using a set of clinical characteristics corresponding to risk factors of falls measured among community-dwelling older adults.

METHODS: Based on a cross-sectional design, 3289 community-dwelling volunteers aged 65 and older were recruited. Age, gender, body mass index (BMI), number of drugs daily taken, use of psychoactive drugs, diphosphonate, calcium, vitamin D supplements and walking aid, fear of falling, distance vision score, Timed Up and Go (TUG) score, lower-limb proprioception, handgrip strength, depressive symptoms, cognitive disorders, and history of falls were recorded. Participants were separated into 2 groups based on the number of falls that occurred over the past year: 0 or 1 fall and 2 or more falls. In addition, total population was separated into training and testing subgroups for ANN analysis.

RESULTS: Among 3289 participants, 18.9% (n = 622) were recurrent fallers. NEAT, using 15 clinical characteristics (ie, use of walking aid, fear of falling, use of calcium, depression, use of vitamin D supplements, female, cognitive disorders, BMI <21 kg/m(2), number of drugs daily taken >4, vision score <8, use of psychoactive drugs, lower-limb proprioception score ≤5, TUG score >9 seconds, handgrip strength score ≤29 (N), and age ≥75 years), showed the best efficiency for identification of recurrent fallers, sensitivity (80.42%), specificity (92.54%), positive predictive value (84.38), negative predictive value (90.34), accuracy (88.39), and Cohen κ (0.74), compared with MLP and modified MLP.

CONCLUSIONS: NEAT, using a set of 15 clinical characteristics, was an efficient ANN for the identification of recurrent fallers in older community-dwellers.

Language: en