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Current Gerontology and Geriatrics Research
Volume 2017, Article ID 8703503, 8 pages
https://doi.org/10.1155/2017/8703503
Research Article

Anthropometric Measures and Frailty Prediction in the Elderly: An Easy-to-Use Tool

1Graduate Program in Biomedical Gerontology, Institute of Geriatrics and Gerontology (IGG), Pontifical Catholic University of Rio Grande do Sul (PUCRS), Av. Ipiranga 6681, Prédio 81, 7 Andar, Sala 703, 90619-900 Porto Alegre, RS, Brazil
2Postgraduate Program in Epidemiology and Postgraduate Program in Cardiovascular Sciences, Federal University of Rio Grande do Sul (UFRGS), Av. Bento Gonçalves 9500, Prédio 43-111, Agronomia, 91509-900 Porto Alegre, RS, Brazil
3Department of Statistics, Federal University of Rio Grande do Sul (UFRGS), Av. Bento Gonçalves 9500, Prédio 43-111, Agronomia, 91509-900 Porto Alegre, RS, Brazil

Correspondence should be addressed to Carla Helena Augustin Schwanke; rb.srcup@eknawhcs

Received 13 August 2017; Accepted 1 November 2017; Published 20 November 2017

Academic Editor: Fulvio Lauretani

Copyright © 2017 Vera Elizabeth Closs et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Purpose. Anthropometry is a useful tool for assessing some risk factors for frailty. Thus, the aim of this study was to verify the discriminatory performance of anthropometric measures in identifying frailty in the elderly and to create an easy-to-use tool. Methods. Cross-sectional study: a subset from the Multidimensional Study of the Elderly in the Family Health Strategy (EMI-SUS) evaluating 538 older adults. Individuals were classified using the Fried Phenotype criteria, and 26 anthropometric measures were obtained. The predictive ability of anthropometric measures in identifying frailty was identified through logistic regression and an artificial neural network. The accuracy of the final models was assessed with an ROC curve. Results. The final model comprised the following predictors: weight, waist circumference, bicipital skinfold, sagittal abdominal diameter, and age. The final neural network models presented a higher ROC curve of 0.78 (CI 95% 0.74–0.82) () than the logistic regression model, with an ROC curve of 0.71 (CI 95% 0.66–0.77) (). Conclusion. The neural network model provides a reliable tool for identifying prefrailty/frailty in the elderly, with the advantage of being easy to apply in the primary health care. It may help to provide timely interventions to ameliorate the risk of adverse events.