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

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) (P < 0.001) than the logistic regression model, with an ROC curve of 0.71 (CI 95% 0.66–0.77) (P < 0.001). 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.

Correlation Between Mini Nutritional Assessment and Anthropometric Measurements Among Community-Dwelling Elderly Individuals in Rural Southern Thailand

“… 15 , 16 These measurements can be used to determine malnutrition status and the prognosis of chronic and acute diseases and to assess risk factors for frailty in the elderly. 17 , 18 …”

Development and validation of a predictive risk model for frailty in elderly patients with multimorbidity

“…The AUC was 0.736 (95% CI 0.719, 0.754), which is greater than the theoretical acceptability of 0.700, indicating that the model has good discrimination. The AUC values of existing communitybased frailty risk prediction models for the elderly ranged from 0.695 to 0.89, [11][12][13]29 with the Singapore model 13 having the best predictive performance with an AUC value of 0.89. The Hosmer-Lemeshow test χ 2 = 7.509 (P = 0.483 > 0.05) for the model in this study indicates that the model predicts frailty risk in good agreement with the actual observations, fits the data well, and the coefficient of determination R 2 = 0.196 indicates that the variance explained by the variables incorporated in the model accounts for 19.6% of the total variance.…”

“…Several scholars have researched the risk prediction model of frailty. These models include different predictors, such as age, education level, hemoglobin, white blood cells, biceps skin‐fold thickness and sagittal abdominal diameter 11–13 . At present, most prediction models target the elderly population in the community.…”

“…These models include different predictors, such as age, education level, hemoglobin, white blood cells, biceps skin-fold thickness and sagittal abdominal diameter. [11][12][13] At present, most prediction models target the elderly population in the community. In addition, certain predictors are not easy to obtain.…”

Prevalence of frailty syndrome in Brazil: a systematic review

“…Table 2 displays the characteristics of 16 studies (34%) in which other assessment tools were employed [45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60] . The majority of the included studies (57.4%) were published over the past five years [17][18][19]21,23,25,26,28,29,31,32,37,38,[40][41][42][43][44][45][46][47]49,51,53,[57][58][59] . The sample size ranged from 30 14 to 8556 participants 17 .…”