BackgroundBioimpedance analysis (BIA) helps measuring the constituents of the body noninvasively. Prior studies suggest that BIA-guided fluid assessment helps to predict survival in dialysis patients. We aimed to evaluate the clinical usefulness of BIA for predicting the survival rate of hemodialysis patients in Korea.MethodsWe conducted a single-center retrospective study. All patients were diagnosed with end-stage renal disorder and started maintenance hemodialysis between June 2009 and April 2014. BIA was performed within the 1st week from the start of hemodialysis. The patients were classified into 2 groups based on volume status measured by the body composition monitor (BCM; Fresenius): an overhydrated group [OG; overhydration/extracellular water (OH/ECW) >15%] and a nonoverhydrated group (NOG; OH/ECW ≤15%).ResultsA total of 344 patients met the inclusion criteria. Of these, 252 patients (73.3%) were categorized into the OG and 92 patients (26.7%) into the NOG. Age- and sex-matching patients were selected with a rate of 2:1. Finally, 160 overhydrated patients and 80 nonoverhydrated patients were analyzed. Initial levels of hemoglobin and serum albumin were significantly lower in the OG. During follow-up, 43 patients from the OG and 7 patients from the NOG died (median follow-up duration, 24.0 months). The multivariate-adjusted all-cause mortality was significantly increased in the OG (odds ratio, 2.569; P = 0.033) and older patients (odds ratio, 1.072/y; P < 0.001). No significant difference of all-cause or disease-specific admission rate was observed between the 2 groups.ConclusionThe ratio of OH/ECW volume measured with body composition monitor is related to the overall survival of end-stage renal disorder patients who started maintenance hemodialysis.

Background: Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. This disease, which is quickly spreading worldwide, has high potential for infection and causes rapid progression of lung lesions, resulting in a high mortality rate. This study aimed to investigate the effects of SARS-CoV-2 infection on renal function in patients with COVID-19. Methods: From February 21 to April 24, 2020, 66 patients diagnosed with COVID-19 at Chungnam National University Hospital were analyzed; all patients underwent routine urinalysis and were tested for serum creatinine, urine protein to creatinine ratio (PCR), and urine albumin to creatinine ratio (ACR). Results: Acute kidney injury (AKI) occurred in 3 (4.5%) of the 66 patients, and 1 patient with AKI stage 3 underwent hemodialysis. Upon follow-up, all 3 patients recovered normal renal function. Compared with patients with mild COVID-19, AKI (n = 3) occurred in patients with severe COVID-19, of whom both urine PCR and ACR were markedly increased. Conclusion: The incidence of AKI was not high in COVID-19 patients. The lower mortality rate in SARS-CoV-2 infection compared with previous Middle East respiratory syndrome and SARS-CoV infections is thought to be associated with a low incidence of dysfunction in organs other than the lungs.

The presence of anti-HBs confers protection against HBV infection. We recommend monitoring for HBV infection after KT in HBsAg(-) anti-HBc(+) anti-HBs(-) patients, but not in HBsAg(-) anti-HBc(+) anti-HBs(+) patients.

Background and aims Knowledge of the proper dry weight plays a critical role in the efficiency of dialysis and the survival of hemodialysis patients. Recently, bioimpedance spectroscopy(BIS) has been widely used for set dry weight in hemodialysis patients. However, BIS is often misrepresented in clinical healthy weight. In this study, we tried to predict the clinically proper dry weight (DWCP) using machine learning for patient’s clinical information including BIS. We then analyze the factors that influence the prediction of the clinical dry weight. Methods As a retrospective, single center study, data of 1672 hemodialysis patients were reviewed. DWCP data were collected when the dry weight was measured using the BIS (DWBIS). The gap between the two (GapDW) was calculated and then grouped and analyzed based on gaps of 1 kg and 2 kg. Results Based on the gap between DWBIS and DWCP, 972, 303, and 384 patients were placed in groups with gaps of <1 kg, ≧1kg and <2 kg, and ≧2 kg, respectively. For less than 1 kg and 2 kg of GapDW, It can be seen that the average accuracies for the two groups are 83% and 72%, respectively, in usign XGBoost machine learning. As GapDW increases, it is more difficult to predict the target property. As GapDW increase, the mean values of hemoglobin, total protein, serum albumin, creatinine, phosphorus, potassium, and the fat tissue index tended to decrease. However, the height, total body water, extracellular water (ECW), and ECW to intracellular water ratio tended to increase. Conclusions Machine learning made it slightly easier to predict DWCP based on DWBIS under limited conditions and gave better insights into predicting DWCP. Malnutrition-related factors and ECW were important in reflecting the differences between DWBIS and DWCP.

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