Calf circumference predicts sarcopenia in maintenance hemodialysis.

BACKGROUND: Early recognition of sarcopenia in hemodialysis (HD) patients will be of great importance in preventing adverse outcomes and improving the quality of life in these patients. The main goal of this study was to evaluate the diagnostic accuracy of calf circumference (CC) measurement in detecting sarcopenia among CKD patients undergoing maintenance HD. METHODS: This cross-sectional study included 52 patients (53 ± 17 years) who were currently in a maintenance HD. Muscle strength was evaluated using handgrip strength, and smooth muscle mass index (SMI) was assessed through bioelectrical impedance analysis, and CC was measured at the widest part of the calf with the foot pressed against a hard surface. RESULTS: Out of the total patient population, sarcopenia was identified in 32.7% (n = 17). The two groups were comparable in terms of age, weight, and height, but the median body mass index of sarcopenic group was statistically lower than nonsarcopenic group (21.6 kg/m2 [18.9-24.6] vs 24.7 kg/m2 [21.4-27.3]. The sarcopenic group had a substantially smaller CC than the nonsarcopenic group (30.0 cm [26.5-32.0] vs 31.5 cm [30.3-34.8], P = 0.013; respectively). In the regression analysis, CC was found to be independently associated with post-HD SMI (P < 0.001; odds ratio, 1.938; and 95% CI, 1.020-2.856). CC was positively but weakly correlated with handgrip strength, however strong and significant correlation was found between CC and SMI (r = 0.277, P = 0.047 and r = 0.733, P < 0.001; respectively). A CC of 31 cm or less predicted confirmed sarcopenia in HD patients (area under the curve = 0.709, P = 0.006, specificity = 0.556, sensitivity = 0.765). CONCLUSIONS: Low muscle mass and potentially low muscle strength in HD patients can be accurately identified through the use of CC measurements. Specifically, a CC measurement below 31 cm has found to be a useful indicator to identify individuals on maintenance HD.

The Role of Muscle Ultrasonography to Diagnose Malnutrition and Sarcopenia in Maintenance Hemodialysis.

BACKGROUND: Sarcopenia and malnutrition are commonly seen and fundamental indicators of prognosis and are directly associated with increased mortality in maintenance hemodialysis (MHD) patients. We aimed to reveal the frequency of malnutrition and sarcopenia in patients undergoing MHD and investigate the role of muscle ultrasound (US) parameters to predict sarcopenia and malnutrition. METHODS: A total of 45 patients who have undertaken MHD for more than 6 months three times a week were evaluated for the present study. The US measurement was performed on gastrocnemius medialis, rectus femoris (RF), and abdominal muscles, including rectus abdominis, external oblique abdominalis, internal oblique abdominalis, and transversus abdominis. Nutritional status of the participants was assessed by mini-nutritional assessment short-form (MNA-SF). RESULTS: The prevalence of probable and confirmed sarcopenia was 51.1% (n = 23) and 35.6% (n = 16), respectively. The malnutrition and risk of malnutrition were observed in 31.1% of the whole study population (n = 14). All muscle US measurements were lower in the sarcopenic group; however, the difference is meaningful only for RF cross-sectional area (CSA) (P = .046). The malnourished group had substantially lower muscle thickness and CSA, except for the gastrocnemius muscle thickness. The value of RFCSA to predict sarcopenia and malnutrition was observed as 4.61 cm2, respectively (P < .05). RFCSA was independently associated with sarcopenia (odds ratio: 0.37; 95% confidence interval: 0.17-0.79; P = .011) and malnutrition (odds ratio: 0.45; 95% confidence interval: 0.23-0.87; P = .017). CONCLUSION: RFCSA may be a useful and simple predictor for predicting patients undergoing hemodialysis who are at risk of sarcopenia and malnutrition.

Independent predictors of in-hospital mortality and the need for intensive care in hospitalized non-critical COVID-19 patients: a prospective cohort study.

One of the most helpful strategies to deal with ongoing coronavirus pandemics is to use some prudence when treating patients infected with SARS-CoV-2. We aimed to evaluate the clinical, demographic, and laboratory parameters that might have predictive value for in-hospital mortality and the need for intensive care and build a model based on them. This study was a prospective, observational, single-center study including non-critical patients admitted to COVID-19 wards. Besides classical clinic-demographic features, basic laboratory parameters obtained on admission were tested, and then new models for each outcome were developed built on the most significant variables. Receiver operating characteristics (ROC) analyses were performed by calculating each model's probability. A total of 368 non-critical hospitalized patients were recruited, the need for ICU care was observed in 70 patients (19%). The total number of patients who died in either ICU or wards was 39 (10.6%). The first two models (based on clinical features and demographics) were developed to predict ICU and death, respectively; older age, male sex, active cancer, and low baseline saturation were noted to be independent predictors. The area under the curve values of the first two models were noted 0.878 and 0.882 (p < .001; confidence interval [CI] 95% [0.837-0.919], p < .001; CI 95% [0.844-0.922]). Following two models, the third and fourth were based on laboratory parameters with clinic-demographic features. Initial lower sodium and lower albumin levels were determined as independent factors in predicting the need for ICU care; higher blood urea nitrogen and lower albumin were independent factors in predicting in-hospital mortality. The area under the curve values of the third and fourth model was noted 0.938 and 0.929, respectively (p < .001; CI 95% [0.912-0.965], p < .001; CI 95% [0.895-962]). By integrating the widely available blood tests results with simple clinic demographic data, non-critical patients can be stratified according to their risk level. Such stratification is essential to filter the patients' non-critical underlying diseases and conditions that can obfuscate the physician's predictive capacity.