Anthropometric parameters of nutritional assessment as predictive factors of the MINI nutritional assessment (MNA) of hospitalized elderly patients
- First Online:
- Cite this article as:
- Leandro-Merhi, V.A. & Braga De Aquino, J.L. J Nutr Health Aging (2011) 15: 181. doi:10.1007/s12603-010-0116-8
- 227 Views
The objective of this study was to identify nutritional indicators that predict MNA (mini nutritional assessment) classification in hospitalized elderly patients.
This cross-sectional study assessed the nutritional status of 109 elderly patients at the beginning of their hospital stay with anthropometric and laboratory indicators and the MNA. Habitual energy intake (HEI) was also determined. The assessed nutritional indicators were investigated by univariate and multivariate logistic regression analysis to verify if they can predict MNA classification. The odds ratio (OR) and its respective confidence interval (CI) of 95% were also calculated, and the significance level was set at 5% (p<0.05).
The nutritional status of most patients (61.47%) was appropriate but 30.28% were at risk of malnourishment and 8.26% were malnourished. Statistical differences were found for those aged more than 70 years and for arm circumference, body mass index, calf circumference, triceps skinfold thickness and mid-arm muscle circumference. Initially, the predictive factors identified by univariate logistic regression were body mass index (BMI) (p=0.0001; OR=0.825), calf circumference (CC) (p=0.0026; OR=0.832), arm circumference (AC) (p<0.0001; OR=0.787), triceps skinfold thickness (TST) (p=0.0014; OR=0.920) and mid-arm muscle circumference (MAMC) (p=0.0003; OR=0.975); later, multiple logistic regression analyses revealed that first AC (p=0.0025; OR=0.731 (0.597–0.895)), then BMI (p=<0.0001; OR=10.909 (3.298–36.085)) and finally TST (p=0.0040; OR=0.924 (0.876–0.975)) and MAMC (p=0.0010; OR=0.976 (0.962–0.990)) were factors that predict MNA classification.
In the conditions of this study, first AC, then BMI and finally TST and MAMC together were capable of predicting MNA classification.