Comparison of Bioelectrical Impedance Vector Analysis (BIVA) to 7-point Subjective Global Assessment for the diagnosis of malnutrition

Author:

Sugizaki Clara S. A.1ORCID,Queiroz Nayara P.1ORCID,Silva Débora M.1ORCID,Freitas Ana T. V. S.1ORCID,Costa Nara A.1ORCID,Peixoto Maria R. G.1ORCID

Affiliation:

1. Universidade Federal de Goiás, Brasil

Abstract

Abstract Introduction: Bioelectrical impedance vector analysis (BIVA) is a non-invasive and low-cost strategy. The methods used to assess malnutrition in patients undergoing HD are still a challenge. The aim of the present study was to compare BIVA to 7-Point Subjective Global Assessment (7-point SGA) to identify malnutrition. We also investigated the sensitivity and specificity of the previously proposed cutoffs point for BIVA parameters. Methods: Patients of both sexes, over 20 years of age, on HD treatment were included. Anthropometric parameters, laboratory data, and bioelectrical impedance analysis (BIA) were evaluated. Values of resistance (R) and reactance (Xc) obtained by mono-frequency BIA were normalized to body height (H) to generate a graph of the bioimpedance vector with the BIVA software. The analysis of the area under the receiver operating curve ROC (AUC) was performed. Results: Among the included 104 patients, the mean age was 51.70 (±15.10) years, and 52% were male. The BIVA had a sensitivity of 35% for diagnosing malnutrition. The specificity of BIVA for identifying the well-nourished patients was 85.7%. The diagnostic accuracy between the BIVA and 7-point SGA was AUC=0.604; 95%CI 0.490-0.726, higher than the previously established cutoff values (AUC=0.514; 95%CI: 0.369-0.631). The 95% confidence ellipses did not overlap (p<0.05). Conclusion: Our study showed low accuracy of BIVA for diagnosing malnutrition using a 7-point SGA as a reference standard. However, it is a complementary method for assessing nutritional status as it provides data on cellularity and hydration, which are important aspects for the HD population.

Publisher

FapUNIFESP (SciELO)

Subject

General Medicine

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