Evaluation of Multi-Frequency Bioelectrical Impedance Analysis against Dual-Energy X-ray Absorptiometry for Estimation of Low Muscle Mass in Older Hospitalized Patients

• Published in: Journal of clinical medicine 2023-12, Vol.13 (1), p.196
• Main Authors: Nielsen, Rikke Lundsgaard, Andersen, Aino Leegaard, Kallemose, Thomas, Damgaard, Morten, Bornæs, Olivia, Juul-Larsen, Helle Gybel, Strejby Christensen, Louise Westberg, Jawad, Baker Nawfal, Andersen, Ove, Rasmussen, Henrik Højgaard, Munk, Tina, Lund, Trine Meldgaard, Houlind, Morten Baltzer
• Format: Article
• Language: English
• Subjects: Aged patients; Body composition; Bone densitometry; Dehydration (Physiology); Diagnosis; Hospital patients; Hospitalization; Hospitals; Impedance, Bioelectric; Malnutrition; Medical examination; Medical research; Medicine, Experimental; Mortality; Muscle strength; Muscles; Musculoskeletal system; Nutrition; Older people; Patients; Sarcopenia; Software
• ISSN: 2077-0383; 2077-0383
• DOI: 10.3390/jcm13010196

The accuracy of multi-frequency (MF) bioelectrical impedance analysis (BIA) to estimate low muscle mass in older hospitalized patients remains unclear. This study aimed to describe the ability of MF-BIA to identify low muscle mass as proposed by The Global Leadership Initiative on Malnutrition (GLIM) and The European Working Group on Sarcopenia in Older People (EWGSOP-2) and examine the association between muscle mass, dehydration, malnutrition, and poor appetite in older hospitalized patients. In this prospective exploratory cohort study, low muscle mass was estimated with MF-BIA against dual-energy X-ray absorptiometry (DXA) in 42 older hospitalized adults (≥65 years). The primary variable for muscle mass was appendicular skeletal muscle mass (ASM), and secondary variables were appendicular skeletal muscle mass index (ASMI) and fat-free mass index (FFMI). Cut-off values for low muscle mass were based on recommendations by GLIM and EWGSOP-2. MF-BIA was evaluated against DXA on the ability to estimate absolute values of muscle mass by mean bias, limits of agreement (LOA), and accuracy (5% and 10% levels). Agreement between MF-BIA and DXA to identify low muscle mass was evaluated with sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). The association between muscle mass, dehydration, malnutrition, and poor appetite was visually examined with boxplots. MF-BIA overestimated absolute values of ASM with a mean bias of 0.63 kg (CI: -0.20:1.46, LOA: -4.61:5.87). Agreement between MF-BIA and DXA measures of ASM showed a sensitivity of 86%, specificity of 94%, PPV of 75% and NPV of 97%. Boxplots indicate that ASM is lower in patients with malnutrition. This was not observed in patients with poor appetite. We observed a tendency toward higher ASM in patients with dehydration. Estimation of absolute ASM values with MF-BIA should be interpreted with caution, but MF-BIA might identify low muscle mass in older hospitalized patients.