Comparison of bioelectrical impedance analysis and computed tomography for the assessment of muscle mass in patients with gastric cancer.

BACKGROUND: Low muscle mass was significantly correlated with poor clinical outcomes in cancer patients. This study aimed to compare the differences between bioelectrical impedance analysis (BIA) and computed tomography (CT) in measuring skeletal muscle mass and detecting low muscle mass in patients with gastric cancer (GC). METHOD: This cross-sectional study included a total of 302 consecutive patients diagnosed with GC at our institution from October 2021 to March 2023. CT images were analyzed at the L3 level to obtain the cross-sectional area of skeletal muscle, which was subsequently used for calculating whole-body skeletal muscle mass via the Shen equation and skeletal muscle tissue density. BIA was utilized to measure skeletal muscle mass using the manufacturer's proprietary algorithms. Skeletal muscle mass (kg) was divided by height squared (m2) to obtain skeletal muscle index (SMI, kg/m2). Pearson's correlation coefficient was performed to assess the correlation between SMI measured by BIA and CT. The agreement between the two methods was assessed using Bland-Altman analyses. The clinically acceptable agreement was defined as the 95% limits of agreement (LOA) for the percentage bias falling within ± 10%. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of BIA in identifying low muscle mass. RESULTS: A total of 59 patients (19.5%) were identified as having low muscle mass based on CT analysis, whereas only 19 patients (6.3%) met the criteria for low muscle mass according to BIA analysis. BIA-measured SMI showed a strong positive correlation with CT-measured SMI in all patients (r = 0.715, P < 0.001). With Bland-Altman analysis, there was a significant mean bias of 1.18 ± 0.96 kg/m2 (95% CI 1.07-1.29, P < 0.001) between SMI measured by BIA and CT. The 95% LOA for the percentage bias ranged from -7.98 to 33.92%, which exceeded the clinically acceptable range of ± 10%. A significant difference was observed in the mean bias of SMI measured by BIA and CT between patients with and without GLIM malnutrition (1.42 ± 0.91 kg/m2 versus 0.98 ± 0.96 kg/m2, P < 0.001). The cut-off values for BIA-measured SMI in identifying low muscle mass using CT as the reference were 10.11 kg/m2 for males and 8.71 kg/m2 for females (male: AUC = 0.840, 95% CI: 0.772-0.908; female: AUC = 0.721, 95% CI: 0.598-0.843). CONCLUSIONS: Despite a significant correlation, the values of skeletal muscle mass obtained BIA and CT cannot be used interchangeably. The BIA method may overestimate skeletal muscle mass in GC patients compared to CT, especially among those with GLIM malnutrition, leading to an underestimation of low muscle mass prevalence.

Rapid assessment of malnutrition based on GLIM diagnosis in Crohn's disease.

Background and aims: Malnutrition is strongly linked to adverse outcomes in patients with Crohn's disease (CD). In this study, our objective was to validate the Global Leadership Initiative on Malnutrition (GLIM) criteria and develop a fast and accurate diagnostic approach for identifying malnutrition in CD patients. Methods: This study assessed 177 CD patients from four general hospitals. The efficacy of the GLIM criteria for the diagnosis of CD malnutrition was compared. By analyzing the independent factors, a nomogram model was derived and internally validated to predict the diagnosis of malnutrition in patients with CD. Model performance was assessed using discrimination and calibration, decision curves, and net benefit analyses. Results: Compared with the SGA criteria, the GLIM criteria was consistent in sensitivity (88.89%) and specificity (78.43%) [AUC = 0.84; 95% Confidence Interval (CI): 0.77-0.89]. The Harvey-Bradshaw index(HBI) score (OR: 1.58; 95% CI: 1.15-2.18), body mass index (OR: 0.41; 95% CI: 0.27-0.64), and mid-upper arm circumference (OR: 0.68; 95% CI: 0.47-0.9) were independent factors associated with malnutrition. The nomogram was developed based on these indicators showing good discrimination in malnutrition diagnosis (AUC = 0.953; 95% CI: 0.922-0.984), with agreement after calibration curve and decision curve analysis. Conclusion: The GLIM criteria are appropriate for diagnosing malnutrition in CD patients. The HBI score may be used to diagnose malnutrition in patients with CD and become a possible selection for the GLIM etiologic criteria of inflammation. The HBM nomogram could be a simple, rapid, and efficient method for diagnosing malnutrition in CD patients.