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This study aimed to compare health-related quality of life (HRQOL) over time in patients initiating hemodialysis (HD) or peritoneal dialysis (PD). A total of 989 incident patients starting HD or PD were included from a prospective nationwide cohort study. HRQOL was assessed 3, 12, and 24 months after the start of dialysis. The scores of questionnaires were adjusted for clinical and socioeconomic parameters. The adjusted three months scores of patients on PD showed better HRQOL in eight end-stage renal disease (ESRD), three physical component summary and one mental component summary domains compared with patients on HD. Both patients on HD and PD experienced significant decreases in different HRQOL domains over two years and the degree of changes in HRQOL over time was not different between dialysis modality. However, the scores of three (effects of kidney disease, burden of kidney disease, and dialysis staff encouragement, all P  < 0.05) and two (sexual function and dialysis staff encouragement, all P  < 0.05) ESRD domains were still higher in patients on PD compared with patients on HD at one and two years after initiation of dialysis, respectively. PD shows better HRQOL during the initial period after dialysis even after adjusting for clinical and socioeconomic characteristics, and the effect lasts up to two years. It was similar in terms of changes in HRQOL over time between HD and PD.

Immunoglobulin A nephropathy (IgAN) involves repeated events of gross haematuria with concurrent upper airway infections. The mucosal immune system, especially the tonsil, is considered the initial site of inflammation, although the role of the tonsillar microbiota has not been established in IgAN. In this study, we compared the tonsillar microbiota of patients with IgAN (n = 21) and other glomerular diseases (n = 36) as well as, healthy controls (n = 23) from three medical centres in Korea. The microbiota was analysed from tonsil swabs using the Illumina MiSeq system based on 16S rRNA gene. Tonsillar bacterial diversity was higher in IgAN than in other glomerular diseases, although it did not differ from that of healthy controls. Principal coordinates analysis revealed differences between the tonsillar microbiota of IgAN and both healthy and disease controls. The proportions of Rahnella , Ruminococcus_g2 , and Clostridium_g21 were significantly higher in patients with IgAN than in healthy controls (corrected p < 0.05). The relative abundances of several taxa were correlated with the estimated glomerular filtration rate, blood urea nitrogen, haemoglobin, and serum albumin levels. Based on our findings, tonsillar microbiota may be associated with clinical features and possible immunologic pathogenesis of IgAN.

An understanding of immunological mechanisms in kidney diseases has advanced using mouse kidneys. However, the profiling of immune cell subsets in human kidneys remains undetermined, particularly compared with mouse kidneys. Normal human kidneys were obtained from radically nephrectomised patients with urogenital malignancy (n = 15). Subsequently, human kidney immune cell subsets were analysed using multicolor flow cytometry and compared with subsets from C57BL/6 or BALB/c mice under specific pathogen-free conditions. Twenty kidney sections from healthy kidney donors or subjects without specific renal lesions were additionally analysed by immunohistochemistry. In human kidneys, 47% ± 12% (maximum 63%) of immune cells were CD3 + T cells. Kidney CD4 + and CD8 + T cells comprised 44% and 56% of total T cells. Of these, 47% ± 15% of T cells displayed an effector memory phenotype (CCR7 − CD45RA − CD69 − ), and 48% ± 19% were kidney-resident cells (CCR7 − CD45RA − CD69 + ). However, the proportions of human CD14 + and CD16 + myeloid cells were approximately 10% of total immune cells. A predominance of CD3 + T cells and a low proportion of CD14 + or CD68 + myeloid cells were also identified in healthy human kidney sections. In mouse kidneys, kidney-resident macrophages (CD11b low F4/80 high ) were the most predominant subset (up to 50%) but the proportion of CD3 + T cells was less than 20%. These results will be of use in studies in which mouse results are translated into human cases under homeostatic conditions or with disease.

Erythropoiesis-stimulating agent (ESA) responsiveness has been reported to be associated with increased mortality in hemodialysis (HD) patients. ESA requirement to obtain the same hemoglobin (Hb) level is different between HD and peritoneal dialysis (PD) patients. In this study, we investigated the impact of ESA responsiveness on mortality between both HD and PD patients. Prevalent HD and PD patients were selected from the Clinical Research Center registry for end-stage renal disease, a prospective cohort study in Korea. ESA responsiveness was estimated using an erythropoietin resistant index (ERI) (U/kg/week/g/dL). Patients were divided into three groups by tertiles of ERI. ESA responsiveness was also assessed based on a combination of ESA dosage and hemoglobin (Hb) levels. The primary outcome was all-cause mortality. A total of 1,594 HD and 876 PD patients were included. The median ESA dose and ERI were lower in PD patients compared with HD patients (ESA dose: 4000 U/week vs 6000 U/week, respectively. P<0.001, ERI: 7.0 vs 10.4 U/kg/week/g/dl, respectively. P<0.001). The median follow-up period was 40 months. In HD patients, the highest ERI tertile was significantly associated with higher risk for all-cause mortality (HR 1.96, 95% CI, 1.07 to 3.59, P = 0.029). HD patients with high-dose ESA and low Hb levels (ESA hypo-responsiveness) had a significantly higher risk of all-cause mortality (HR 2.24, 95% CI, 1.16 to 4.31, P = 0.016). In PD patients, there was no significant difference in all-cause mortality among the ERI groups (P = 0.247, log-rank test). ESA hypo-responsiveness was not associated with all-cause mortality (HR = 1.75, 95% CI, 0.58 to 5.28, P = 0.319). Our data showed that ESA hypo-responsiveness was associated with an increased risk of all-cause mortality in HD patients. However, in PD patients, ESA hypo-responsiveness was not related to all-cause mortality. These finding suggest the different prognostic value of ESA responsiveness between HD and PD patients.

Hypotension after starting continuous renal replacement therapy (CRRT) is associated with worse outcomes compared with normotension, but it is difficult to predict because several factors have interactive and complex effects on the risk. The present study applied machine learning algorithms to develop models to predict hypotension after initiating CRRT. Among 2349 adult patients who started CRRT due to acute kidney injury, 70% and 30% were randomly assigned into the training and testing sets, respectively. Hypotension was defined as a reduction in mean arterial pressure (MAP) ≥ 20 mmHg from the initial value within 6 h. The area under the receiver operating characteristic curves (AUROCs) in machine learning models, such as support vector machine (SVM), deep neural network (DNN), light gradient boosting machine (LGBM), and extreme gradient boosting machine (XGB) were compared with those in disease-severity scores such as the Sequential Organ Failure Assessment and Acute Physiology and Chronic Health Evaluation II. The XGB model showed the highest AUROC (0.828 [0.796–0.861]), and the DNN and LGBM models followed with AUROCs of 0.822 (0.789–0.856) and 0.813 (0.780–0.847), respectively; all machine learning AUROC values were higher than those obtained from disease-severity scores (AUROCs < 0.6). Although other definitions of hypotension were used such as a reduction of MAP ≥ 30 mmHg or a reduction occurring within 1 h, the AUROCs of machine learning models were higher than those of disease-severity scores. Machine learning models successfully predict hypotension after starting CRRT and can serve as the basis of systems to predict hypotension before starting CRRT.

Serum alkaline phosphatase (ALP) levels have been reported to be associated with all-cause and cardiovascular mortality in peritoneal dialysis (PD) patients. However, it is unclear whether serum ALP levels predict infection-related clinical outcomes in PD patients. The aim of this study was to determine the relationships between serum ALP levels, infection-related mortality and hospitalization in PD patients. PD patients from the Clinical Research Center registry for end-stage renal disease, a multicenter prospective observational cohort study in Korea, were included in the present study. Patients were categorized into three groups by serum ALP tertiles as follows: Tertile 1, ALP <78 U/L; Tertile 2, ALP = 78-155 U/L; Tertile 3, ALP >155 U/L. Tertile 1 was used as the reference category. The primary outcomes were infection-related mortality and hospitalization. A total of 1,455 PD patients were included. The median follow-up period was 32 months. The most common cause of infection-related mortality and hospitalization was PD-related peritonitis. Multivariate Cox regression analyses showed that patients in the highest tertiles of serum ALP levels were at higher risk of infection-related mortality (HR 2.29, 95% CI, 1.42-5.21, P = 0.008) after adjustment for clinical variables. Higher tertiles of serum ALP levels were associated with higher risk of infection-related hospitalization (Tertile 2: HR 1.56, 95% CI, 1.18-2.19, P = 0.009, tertile 3: HR 1.34, 95% CI, 1.03-2.62, P = 0.031). Our data showed that elevated serum ALP levels were independently associated with a higher risk of infection-related mortality and hospitalization in PD patients.

Herein, we aim to assess mortality risk prediction in peritoneal dialysis patients using machine-learning algorithms for proper prognosis prediction. A total of 1,730 peritoneal dialysis patients in the CRC for ESRD prospective cohort from 2008 to 2014 were enrolled in this study. Classification algorithms were used for prediction of N-year mortality including neural network. The survival hazard ratio was presented by machine-learning algorithms using survival statistics and was compared to conventional algorithms. A survival-tree algorithm presented the most accurate prediction model and outperformed a conventional method such as Cox regression (concordance index 0.769 vs 0.745). Among various survival decision-tree models, the modified Charlson Comorbidity index (mCCI) was selected as the best predictor of mortality. If peritoneal dialysis patients with high mCCI (>4) were aged ≥70.5 years old, the survival hazard ratio was predicted as 4.61 compared to the overall study population. Among the various algorithm using longitudinal data, the AUC value of logistic regression was augmented at 0.804. In addition, the deep neural network significantly improved performance to 0.841. We propose machine learning-based final model, mCCI and age were interrelated as notable risk factors for mortality in Korean peritoneal dialysis patients.

Glaucoma shares common risk factors with chronic kidney disease (CKD) but previous cross-sectional studies have demonstrated discrepancies in the risk of glaucoma in CKD patients. This study enrolled kidney transplantation recipients (KTRs) (n = 10,955), end stage renal disease (ESRD) patients (n = 10,955) and healthy controls (n = 10,955) from National Health Insurance Service database of the Republic of Korea. A Cox proportional hazard regression model was used to calculate the hazard ratios (HR) for primary open-angle glaucoma (POAG) and primary angle-closure glaucoma (PACG) incidences. The incidence of POAG was higher in ESRD patients (3.36/1,000 person-years, P  < 0.0001) and KTRs (3.22 /1,000 person-years, P  < 0.0001), than in healthy controls (1.20/1,000 person-years). However, POAG risk showed no significant increase in either ESRD patients ( P  = 0.07) or KTRs ( P  = 0.08) when adjusted for the confounding factors. The incidence of PACG was significantly higher in ESRD patients (0.41/1,000 person-years) than in healthy controls (0.14/1,000 person-years, P  = 0.008). The PACG incidence was significantly lower in KTRs than in ESRD patients (HR = 0.35, P  = 0.015). In conclusion, this nationwide cohort study demonstrated that kidney transplantation can reduce the risk of PACG but not POAG in ESRD patients.

The precise prediction of acute kidney injury (AKI) after nephrectomy for renal cell carcinoma (RCC) is an important issue because of its relationship with subsequent kidney dysfunction and high mortality. Herein we addressed whether machine learning (ML) algorithms could predict postoperative AKI risk better than conventional logistic regression (LR) models. A total of 4104 RCC patients who had undergone unilateral nephrectomy from January 2003 to December 2017 were reviewed. ML models such as support vector machine, random forest, extreme gradient boosting, and light gradient boosting machine (LightGBM) were developed, and their performance based on the area under the receiver operating characteristic curve, accuracy, and F1 score was compared with that of the LR-based scoring model. Postoperative AKI developed in 1167 patients (28.4%). All the ML models had higher performance index values than the LR-based scoring model. Among them, the LightGBM model had the highest value of 0.810 (0.783–0.837). The decision curve analysis demonstrated a greater net benefit of the ML models than the LR-based scoring model over all the ranges of threshold probabilities. The application of ML algorithms improves the predictability of AKI after nephrectomy for RCC, and these models perform better than conventional LR-based models.

It has been known that retinal vein occlusion (RVO) is associated with chronic kidney disease, especially end-stage renal disease (ESRD). However, little is known about the effect of kidney transplantation (KT) on RVO incidence in ESRD patients. This study aimed to compare the incidence of RVO in KT recipients (n = 10,498), matched ESRD patients (n = 10,498), and healthy controls (HCs, n = 10,498), using a long-term population-based cohort. The incidence of RVO was 2.74, 5.68, and 1.02 per 1000 patient-years, for the KT group, the ESRD group, and the HCs group, respectively. Adjusted hazard ratios for RVO development compared to the HCs group, were 1.53 and 3.21, in the KT group and the ESRD group, respectively. In the KT group, multivariable regression analysis indicated that an age over 50, a Charlson Comorbidity Index score over 4, and a history of desensitization therapy were associated with an increased risk of RVO. In summary, KT recipients have a lower risk for development of RVO than ESRD patients treated with dialysis. However, the risk is still higher compared to healthy people who have normal kidney functions.