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Abstract Age-related macular degeneration (AMD) is a major cause of blindness in developed countries, and the number of affected patients is increasing worldwide. Intravitreal injections of anti-vascular endothelial growth factor (VEGF) are the standard therapy for neovascular AMD (nAMD), and optical coherence tomography (OCT) is a crucial tool for evaluating the anatomical condition of the macula. However, OCT has limitations in accurately predicting the degree of functional and morphological improvement following intravitreal injections. Artificial intelligence (AI) has been proposed as a tool for predicting the treatment response of nAMD based on OCT biomarkers. Our study focuses on the development and assessment of an AI model utilizing the DenseNet201 algorithm. The model aims to predict anatomical improvement based on OCT images before, and during anti-VEGF therapy. The training process involves two scenarios: (1) using only preinjection OCT images and (2) utilizing both OCT images before and during anti-VEGF therapy for model training. The outcomes of our investigation, involving 2068 images from a cohort of 517 Korean patients diagnosed with nAMD, indicate that the AI model we introduced surpassed the predictive performance of ophthalmologists. The model exhibited a sensitivity of 0.915, specificity of 0.426, and accuracy of 0.820. Notably, its predictive capabilities were further enhanced with the inclusion of additional OCT images taken after the first and second injections during the loading phase. The treatment prediction performance of the model was the highest when using all input modalities (before injection, and after the first and second injections) and concatenation-based fusion layers. This study highlights the potential of AI in assisting individualized and tailored nAMD treatment.

Abstract Patients with end-stage kidney disease (ESKD) frequently experience anemia, and maintaining hemoglobin (Hb) levels within a targeted range using erythropoiesis-stimulating agents (ESAs) is challenging. This study introduces a gated recurrent unit-attention-based module (GAM) for efficient anemia management among patients undergoing chronic dialysis and proposes a novel alert system for anticipating the need for red blood cell transfusions. Data on demographic characteristics, dialysis metrics, drug administration, laboratory tests, and transfusion history were retrospectively collected from patients undergoing hemodialysis at Kangwon National University Hospital between 2017 and 2022. After preprocessing, a final dataset of 252 patients was used for model training. Our model functions in two major phases: (1) Hb level prediction and ESA dose recommendation and (2) transfusion alert framework. The GAM model outperformed traditional machine learning algorithms, including linear regression, XGBoost, and multilayer perceptron, in predicting Hb levels (R-squared value = 0.60). The model also demonstrated a recommendation accuracy of 0.78 compared to that of clinical experts, indicating a high degree of concordance with the ESA dosing recommendations. Additionally, the model exhibited considerably high accuracy (0.99) for transfusion alarms. Thus, the GAM model holds promise for improving anemia management in patients with ESKD by optimizing ESA dosages and providing timely transfusion alerts.

Abstract Severe fever with thrombocytopenia syndrome (SFTS) is a tick-borne illness with a notable morality risk that is becoming increasingly prevalent in East Asia (14–36%). Increasing evidence indicates a more direct role of the SFTS virus in renal impairment. However, few studies have explored the risk factors for and clinical outcomes of AKI in patients with SFTS. Therefore, in this study, we aimed to investigate risk factors and outcomes associated with AKI in patients with SFTS. In this retrospective cohort study, we included the data of 53 patients who were diagnosed with SFTS virus infection at Kangwon National University Hospital between 2016 and 2020. We incorporated laboratory data and medical information including comorbidities, complications, and mortality. Baseline characteristics, clinical features, laboratory parameters, and mortality rates of the non-AKI and AKI groups were compared. Patient survival of non-AKI and AKI groups were compared using the Kaplan–Meier method. To identify the population with poor prognosis, Cox regression analysis was used to identify the independent risk factors for in-hospital mortality in patients with SFTS. Of the 53 individuals, 29 (54.7%) were male, with an average age of 66.5 years. Nine patients (15.1%) died of SFTS. Twenty-seven (50.9%) patients exhibited AKI; the average time interval from fever onset to AKI occurrence was 3.6 days. Notably, 24 (88.9%) patients developed AKI within the first week of fever onset. Patients in the AKI group exhibited a significantly higher prevalence of diabetes and were older than those in the non-AKI group. The mortality rate was notably higher (29.6%) in the AKI group than in the non-AKI group (3.8%). Within the AKI cohort, advanced stages (stages 2 and 3) showed a 50% mortality rate, which was significantly higher than the 17.6% mortality rate in patients with stage 1 AKI. Additionally, Kaplan–Meier curves revealed lower survival rates among patients with AKI than among those without AKI (P = 0.017). Cox regression analysis identified leukopenia and elevated serum creatinine levels as significant risk factors for mortality. AKI is a common complication associated with SFTS. Moreover, the mortality rate was significantly higher in the patients who developed AKI than in those who did not. Our findings underscore the pivotal role of AKI as a prognostic marker of disease severity in patients with SFTS.

Abstract Neovascular age-related macular degeneration (nAMD) can result in blindness if left untreated, and patients often require repeated anti-vascular endothelial growth factor injections. Although, the treat-and-extend method is becoming popular to reduce vision loss attributed to recurrence, it may pose a risk of overtreatment. This study aimed to develop a deep learning model based on DenseNet201 to predict nAMD recurrence within 3 months after confirming dry-up 1 month following three loading injections in treatment-naïve patients. A dataset of 1076 spectral domain optical coherence tomography (OCT) images from 269 patients diagnosed with nAMD was used. The performance of the model was compared with that of 6 ophthalmologists, using 100 randomly selected samples. The DenseNet201-based model achieved 53.0% accuracy in predicting nAMD recurrence using a single pre-injection image and 60.2% accuracy after viewing all the images immediately after the 1st, 2nd, and 3rd injections. The model outperformed experienced ophthalmologists, with an average accuracy of 52.17% using a single pre-injection image and 53.3% after examining four images before and after three loading injections. In conclusion, the artificial intelligence model demonstrated a promising ability to predict nAMD recurrence using OCT images and outperformed experienced ophthalmologists. These findings suggest that deep learning models can assist in nAMD recurrence prediction, thus improving patient outcomes and optimizing treatment strategies.

A vision transformer (ViT)-based deep neural network was applied to classify the flavonoid glycoside isomers by analyzing electrospray ionization tandem mass spectrometry (ESI-MS/MS) spectra. Our model successfully classified the flavonoid isomers with various substitution patterns (3-O, 6-C, 7-O, 8-C, 4′-O) and multiple glycosides, achieving over 80% accuracy during training. In addition, the experimental spectra from flavonoid glycoside standards were acquired with different adducts, and our model showed robust performance regardless of the experimental conditions. As a result, the vision transformer-based computer vision model is promising for analyzing mass spectrometry data.

BackgroundAlthough previous research has made substantial progress in developing high-performance artificial intelligence (AI)–based computer-aided diagnosis (AI-CAD) systems in various medical domains, little attention has been paid to developing and evaluating AI-CAD system in ophthalmology, particularly for diagnosing retinal diseases using optical coherence tomography (OCT) images. ObjectiveThis diagnostic study aimed to determine the usefulness of a proposed AI-CAD system in assisting ophthalmologists with the diagnosis of central serous chorioretinopathy (CSC), which is known to be difficult to diagnose, using OCT images. MethodsFor the training and evaluation of the proposed deep learning model, 1693 OCT images were collected and annotated. The data set included 929 and 764 cases of acute and chronic CSC, respectively. In total, 66 ophthalmologists (2 groups: 36 retina and 30 nonretina specialists) participated in the observer performance test. To evaluate the deep learning algorithm used in the proposed AI-CAD system, the training, validation, and test sets were split in an 8:1:1 ratio. Further, 100 randomly sampled OCT images from the test set were used for the observer performance test, and the participants were instructed to select a CSC subtype for each of these images. Each image was provided under different conditions: (1) without AI assistance, (2) with AI assistance with a probability score, and (3) with AI assistance with a probability score and visual evidence heatmap. The sensitivity, specificity, and area under the receiver operating characteristic curve were used to measure the diagnostic performance of the model and ophthalmologists. ResultsThe proposed system achieved a high detection performance (99% of the area under the curve) for CSC, outperforming the 66 ophthalmologists who participated in the observer performance test. In both groups, ophthalmologists with the support of AI assistance with a probability score and visual evidence heatmap achieved the highest mean diagnostic performance compared with that of those subjected to other conditions (without AI assistance or with AI assistance with a probability score). Nonretina specialists achieved expert-level diagnostic performance with the support of the proposed AI-CAD system. ConclusionsOur proposed AI-CAD system improved the diagnosis of CSC by ophthalmologists, which may support decision-making regarding retinal disease detection and alleviate the workload of ophthalmologists.

Background and Objectives: Spinal anesthesia is widely used in various types of surgery. However, several complications can occur afterward. This study aimed to identify differences in the incidence of anesthesia-related complications according to the approach methods (midline versus paramedian) for landmark-based spinal anesthesia. Materials and Methods: We searched electronic databases, including PubMed, EMBASE, CENTRAL, Scopus, and Web of Science, for eligible randomized controlled trials. The primary outcome was post-dural puncture headache (PDPH) incidence, and secondary outcomes were low back pain (LBP) incidence and success rate in the first trial of spinal anesthesia. We estimated the odds ratio (OR) with 95% confidence intervals (CI) using a random-effects model. Results: In total, 2280 patients from 13 randomized controlled trials were included in the final analysis. The incidence rates of PDPH were 5.9% and 10.4% in the paramedian and midline approach groups, respectively. The pooled effect size revealed that the incidence of PDPH (OR: 0.43, 95% CI [0.22–0.83]; p = 0.01; I2 = 53%) and LBP (OR: 0.27, 95% CI [0.16–0.44]; p < 0.001; I2 = 16%) decreased, and the success rate in the first attempt was higher (OR: 2.30, 95% CI [1.36–3.87]; p = 0.002; I2 = 35%) with the paramedian than with the midline approach. Conclusions: Paramedian spinal anesthesia reduced PDPH and LBP and increased the success rate of the first attempt.

Abundance profiles from metagenomic sequencing data synthesize information from billions of sequenced reads coming from thousands of microbial genomes. Analyzing and understanding these profiles can be a challenge since the data they represent are complex. Particularly challenging is their visualization, as existing techniques are inadequate when the taxa number is in the thousands. We present a technique, and accompanying software, for the visualization of metagenomic abundance profiles using a space-filling curve that transforms a profile into an interactive 2D image. We created Jasper, an easy to use tool for the visualization and exploration of metagenomic profiles from DNA sequencing data. It orders taxa using a space-filling Hilbert curve, and creates a “Microbiome Map”, where each position in the image represents the abundance of a single taxon from a reference collection. Jasper can order taxa in multiple ways, and the resulting microbiome maps can highlight “hot spots” of microbes that are dominant in taxonomic clades or biological conditions. We use Jasper to visualize samples from a variety of microbiome studies, and discuss ways in which microbiome maps can be an invaluable tool to visualize spatial, temporal, disease, and differential profiles. Our approach can create detailed microbiome maps involving hundreds of thousands of microbial reference genomes with the potential to unravel latent relationships (taxonomic, spatio-temporal, functional, and other) that could remain hidden using traditional visualization techniques. The maps can also be converted into animated movies that bring to life the dynamicity of microbiomes.

Abstract We aimed to evaluate the association between the prevalence of diabetic retinopathy (DR) and coffee consumption in a Korean population. This cross-sectional study was based on data from the 2008–2011 Korean National Health and Nutrition Survey. Among 37,753 survey participants, the data of 1350 subjects with type 2 diabetes who underwent DR examination were analyzed. DR was graded using the modified Airlie House classification system. Coffee consumption data were obtained through food frequency questionnaires and categorized into four groups: almost none,

Abstract Anti-dementia medications are widely prescribed to patients with Alzheimer’s dementia (AD) in South Korea. This study investigated the pattern of medical management in newly diagnosed patients with AD using a standardized data format—the Observational Medical Outcome Partnership Common Data Model from five hospitals. We examined the anti-dementia treatment patterns from datasets that comprise > 5 million patients during 2009–2019. The medication utility information was analyzed with respect to treatment trends and persistence across 11 years. Among the 8653 patients with newly diagnosed AD, donepezil was the most commonly prescribed anti-dementia medication (4218; 48.75%), followed by memantine (1565; 18.09%), rivastigmine (1777; 8.98%), and galantamine (494; 5.71%). The rising prescription trend during observation period was found only with donepezil. The treatment pathways for the three cholinesterase inhibitors combined with N-methyl-d-aspartate receptor antagonist were different according to the drugs (19.6%; donepezil; 28.1%; rivastigmine, and 17.2%; galantamine). A 12-month persistence analysis showed values of approximately 50% for donepezil and memantine and approximately 40% for rivastigmine and galantamine. There were differences in the prescribing pattern and persistence among anti-dementia medications from database using the Observational Medical Outcome Partnership Common Data Model on the Federated E-health Big Data for Evidence Renovation Network platform in Korea.

Abstract Central serous chorioretinopathy (CSC) is one of the most common macular diseases that can reduce the quality of life of patients. This study aimed to build a deep learning-based classification model using multiple spectral domain optical coherence tomography (SD-OCT) images together to diagnose CSC. Our proposed system contains two modules: single-image prediction (SIP) and a final decision (FD) classifier. A total of 7425 SD-OCT images from 297 participants (109 acute CSC, 106 chronic CSC, 82 normal) were included. In the fivefold cross validation test, our model showed an average accuracy of 94.2%. Compared to other end-to-end models, for example, a 3D convolutional neural network (CNN) model and a CNN-long short-term memory (CNN-LSTM) model, the proposed system showed more than 10% higher accuracy. In the experiments comparing the proposed model and ophthalmologists, our model showed higher accuracy than experts in distinguishing between acute, chronic, and normal cases. Our results show that an automated deep learning-based model could play a supplementary role alongside ophthalmologists in the diagnosis and management of CSC. In particular, the proposed model seems clinically applicable because it can classify CSCs using multiple OCT images simultaneously.

Abstract Neovascular age-related macular degeneration (nAMD) is among the main causes of visual impairment worldwide. We built a deep learning model to distinguish the subtypes of nAMD using spectral domain optical coherence tomography (SD-OCT) images. Data from SD-OCT images of nAMD (polypoidal choroidal vasculopathy, retinal angiomatous proliferation, and typical nAMD) and normal healthy patients were analyzed using a convolutional neural network (CNN). The model was trained and validated based on 4749 SD-OCT images from 347 patients and 50 healthy controls. To adopt an accurate and robust image classification architecture, we evaluated three well-known CNN structures (VGG-16, VGG-19, and ResNet) and two customized classification layers (fully connected layer with dropout vs. global average pooling). Following the test set performance, the model with the highest classification accuracy was used. Transfer learning and data augmentation were applied to improve the robustness and accuracy of the model. Our proposed model showed an accuracy of 87.4% on the test data (920 images), scoring higher than ten ophthalmologists, for the same data. Additionally, the part that our model judged to be important in classification was confirmed through Grad-CAM images, and consequently, it has a similar judgment criteria to that of ophthalmologists. Thus, we believe that our model can be used as an auxiliary tool in clinical practice.

Abstract Central serous chorioretinopathy (CSC) is the fourth most common retinopathy and can reduce quality of life. CSC is assessed using optical coherence tomography (OCT), but deep learning systems have not been used to classify CSC subtypes. This study aimed to build a deep learning system model to distinguish CSC subtypes using a convolutional neural network (CNN). We enrolled 435 patients with CSC from a single tertiary center between January 2015 and January 2020. Data from spectral domain OCT (SD-OCT) images of the patients were analyzed using a deep CNN. Five-fold cross-validation was employed to evaluate the model’s ability to discriminate acute, non-resolving, inactive, and chronic atrophic CSC. We compared the performances of the proposed model, Resnet-50, Inception-V3, and eight ophthalmologists. Overall, 3209 SD-OCT images were included. The proposed model showed an average cross-validation accuracy of 70.0% (95% confidence interval [CI], 0.676–0.718) and the highest test accuracy was 73.5%. Additional evaluation in an independent set of 104 patients demonstrated the reliable performance of the proposed model (accuracy: 76.8%). Our model could classify CSC subtypes with high accuracy. Thus, automated deep learning systems could be useful in the classification and management of CSC.

Myopic choroidal neovascularization (mCNV) is a common cause of vision loss in patients with pathological myopia. However, predicting the visual prognosis of patients with mCNV remains challenging. This study aimed to develop an artificial intelligence (AI) model to predict visual acuity (VA) in patients with mCNV. This study included 279 patients with mCNV at baseline; patient data were collected, including optical coherence tomography (OCT) images, VA, and demographic information. Two models were developed: one comprising horizontal/vertical OCT images (H/V cuts) and the second comprising 25 volume scan images. The coefficient of determination (R2) and root mean square error (RMSE) were computed to evaluate the performance of the trained network. The models achieved high performance in predicting VA after 1 (R2 = 0.911, RMSE = 0.151), 2 (R2 = 0.894, RMSE = 0.254), and 3 (R2 = 0.891, RMSE = 0.227) years. Using multiple-volume scanning, OCT images enhanced the performance of the models relative to using only H/V cuts. This study proposes AI models to predict VA in patients with mCNV. The models achieved high performance by incorporating the baseline VA, OCT images, and post-injection data. This model could assist in predicting the visual prognosis and evaluating treatment outcomes in patients with mCNV undergoing intravitreal anti-vascular endothelial growth factor therapy.

Abstract This cross-sectional study aimed to build a deep learning model for detecting neovascular age-related macular degeneration (AMD) and to distinguish retinal angiomatous proliferation (RAP) from polypoidal choroidal vasculopathy (PCV) using a convolutional neural network (CNN). Patients from a single tertiary center were enrolled from January 2014 to January 2020. Spectral-domain optical coherence tomography (SD-OCT) images of patients with RAP or PCV and a control group were analyzed with a deep CNN. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUROC) were used to evaluate the model’s ability to distinguish RAP from PCV. The performances of the new model, the VGG-16, Resnet-50, Inception, and eight ophthalmologists were compared. A total of 3951 SD-OCT images from 314 participants (229 AMD, 85 normal controls) were analyzed. In distinguishing the PCV and RAP cases, the proposed model showed an accuracy, sensitivity, and specificity of 89.1%, 89.4%, and 88.8%, respectively, with an AUROC of 95.3% (95% CI 0.727–0.852). The proposed model showed better diagnostic performance than VGG-16, Resnet-50, and Inception-V3 and comparable performance with the eight ophthalmologists. The novel model performed well when distinguishing between PCV and RAP. Thus, automated deep learning systems may support ophthalmologists in distinguishing RAP from PCV.

Abstract This study aimed to compare the surgical outcomes of pars plana vitrectomy (PPV) with and without air tamponade in patients with idiopathic epiretinal membrane (iERM). We prospectively enrolled 145 patients with iERM who underwent a 25-gauge transconjunctival sutureless PPV. Patients were assigned to either the air tamponade (air) group (79 eyes) or balanced salt solution (BSS; no tamponade) group (66 eyes). The central macular thickness (CMT), peripapillary retinal nerve fiber layer (pRNFL) thickness, and best-corrected visual acuity (BCVA) were compared for two years. At baseline, there were no significant differences between the two groups. CMT and BCVA were not significantly different between the groups for 2 years. However, the air group had a significantly lower thickness in the superior temporal pRNFL sector at 1 month (p = 0.01) and in the inferior temporal and superior temporal pRNFL sectors at 3 months (p = 0.02 for both). There were no significant differences between both groups in all the pRNFL sectors from 6 months to 2 years. The outcomes of PPV with air tamponade and that with no tamponade appear to be equivalent. This shows that air tamponade may not be an imperative procedure for iERM surgery and has no additional benefit.

Abstract The purpose is to evaluate the effects of multiple intravitreal ranibizumab (IVR) and aflibercept (IVA) injections on peripapillary retinal nerve fiber layer (RNFL) thickness in patients with exudative age-related macular degeneration (AMD). This retrospective, observational, consecutive case series study enrolled patients newly diagnosed with monocular exudative AMD from January 2014 to October 2019 who were administered IVR or IVA injections. Normal fellow eyes were included as controls. Medical records and spectral domain optical coherence tomography results were reviewed at baseline and at 3, 6, and 12 months after injection. No statistically significant differences in peripapillary RNFL thickness and intraocular pressure were observed between the treated and fellow eyes in the two groups. The global RNFL thicknesses for the treated eyes decreased significantly after 12 months compared with baseline, but no significant difference was observed in any of the six examined sectors (temporal, superior temporal, superior nasal, nasal, inferior nasal, and inferior temporal). At 12 months, the central macular thickness of the treated eyes decreased significantly. Multiple IVR and IVA injections are apparently safe considering peripapillary RNFL damage in patients with exudative AMD. The decreased RNFL thickness of the global sector was presumably due to anatomical improvement of macular lesions.

Abstract Backgrounds Glomerular diseases, a set of debilitating and complex disease entities, are related to mortality and morbidity. To gain insight into pathophysiology and novel treatment targets of glomerular disease, various types of biospecimens linked to deep clinical phenotyping including clinical information, digital pathology, and well-defined outcomes are required. We provide the rationale and design of the KOrea Renal biobank NEtwoRk System TOward Next-generation analysis (KORNERSTONE). Methods The KORNERSTONE, which has been initiated by Korea Centres for Disease Control and Prevention, is designed as a multi-centre, prospective cohort study and biobank for glomerular diseases. Clinical data, questionnaires will be collected at the time of kidney biopsy and subsequently every 1 year after kidney biopsy. All of the clinical data will be extracted from the electrical health record and automatically uploaded to the web-based database. High-quality digital pathologies are obtained and connected in the database. Various types of biospecimens are collected at baseline and during follow-up: serum, urine, buffy coat, stool, glomerular complementary DNA (cDNA), tubulointerstitial cDNA. All data and biospecimens are processed and stored in a standardised manner. The primary outcomes are mortality and end-stage renal disease. The secondary outcomes will be deterioration renal function, remission of proteinuria, cardiovascular events and quality of life. Discussion Ethical approval has been obtained from the institutional review board of each participating centre and ethics oversight committee. The KORNERSTONE is designed to deliver pioneer insights into glomerular diseases. The study design allows comprehensive, integrated and high-quality data collection on baseline laboratory findings, clinical outcomes including administrative data and digital pathologic images. This may provide various biospecimens and information to many researchers, establish the rationale for future more individualised treatment strategies for glomerular diseases. Trial registration NCT03929887 .

Background As an anesthesia induced during cesarean section, spinal anesthesia is preferred over general and epidural anesthesia. This study aimed to review the trend of anesthetic methods for cesarean section based on data obtained from the Korean Health Insurance Review and Assessment Service from 2013 to 2018. Methods The anesthetic methods were analyzed in 753,285 parturients who underwent a cesarean section in Korea from 2013 to 2018. We determined the association between each anesthetic method and hospital type and maternal and fetal factors. We also evaluated whether the anesthetic method was associated with the parturients’ length of hospital stay. Results General anesthesia, spinal anesthesia, and epidural anesthesia were induced in 28.8%, 47.7%, and 23.6% of parturients from 2013 to 2018, respectively. Trend analyses showed that spinal anesthesia increased from 40.0% in 2013 to 53.7% in 2018. The opposite trend applied to general anesthesia, decreasing from 37.1% in 2013 to 22.2% in 2018. The factors that were significantly associated with the anesthetic method were parturient’s parity, emergency condition, gestational age, and fetal weight. The type of hospital, parturient’s age, and multiple birth were also associated with the anesthetic methods. There was a strong association between general anesthesia and hospital stay longer than 7 days. Conclusions Spinal anesthesia is currently the main anesthetic method used for cesarean delivery, and the rate of spinal anesthesia is gradually increasing in Korea.

Background Although arteriovenous fistula (AVF) is the preferred vascular access for hemodialysis (HD), the association between vascular access types and quality of life is not well-known. We investigated the relationships between HD vascular access types and all-cause mortality, health-related quality of life (HRQOL) and the degree of depression in a large prospective cohort. Methods A total of 1461 patients who newly initiated HD were included. The initial vascular access types were classified into AVF, arteriovenous graft (AVG), and central venous catheter (CVC). The primary outcomes were all-cause mortality and HRQOL and depression. The secondary outcome was all-cause hospitalization. Kidney Disease Quality of Life Short Form 36 (KDQOL-36) and Beck’s depression inventory (BDI) scores were measured to assess HRQOL and depression. Results Among 1461 patients, we identified 314 patients who started HD via AVF, 76 via AVG, and 1071 via CVC. In the survival analysis, patients with AVF showed significantly better survival compared with patients with other accesses (p

Chronic kidney disease (CKD)-associated uremia aggravates—and is aggravated by—gut dysbiosis. However, the correlation between CKD severity and gut microbiota and/or their uremic metabolites is unclear. We enrolled 103 CKD patients with stage 1 to 5 and 46 healthy controls. We analyzed patients’ gut microbiota by MiSeq system and measured the serum concentrations of four uremic metabolites (p-cresyl sulfate, indoxyl sulfate, p-cresyl glucuronide, and trimethylamine N-oxide) by liquid chromatography–tandem mass spectrometry. Serum concentrations of the uremic metabolites increased with kidney function deterioration. Gut microbial diversity did not differ among the examined patient and control groups. In moderate or higher stage CKD groups, Oscillibacter showed positive interactions with other microbiota, and the proportions of Oscillibacter were positively correlated with those of the uremic metabolites. The gut microbiota, particularly Oscillibacter, was predicted to contribute to pyruvate metabolism which increased with CKD progression. Relative abundance of Oscillibacter was significantly associated with both serum uremic metabolite levels and kidney function. Predicted functional analysis suggested that kidney-function-associated changes in the contribution of Oscillibacter to pyruvate metabolism in CKD may greatly affect the gut environment according to kidney function, resulting in dysbiosis concomitant with uremic toxin production. The gut microbiota could be associated with uremia progression in CKD. These results may provide basis for further metagenomics analysis of kidney diseases.

Background: Aquaporin-11 (AQP11) is a novel member of the aquaporin family. Disruption of the murine Aqp11 gene causes severe proximal tubular injury and renal failure. The rs2276415 (G>A) single-nucleotide polymorphism in the human AQP11 gene results in glycine to serine substitution in a functionally important domain. In this study, the role of the genetic predispositions of AQP11 rs2276415 (G>A) on renal allograft outcomes was evaluated. Methods: A total of 198 pairs of donors and recipients were enrolled in this study. Long-term graft survival was traced and clinical parameters that could have influenced graft outcome were collected through the electronic medical record system. Results: The genotype distribution and allele frequency of rs2276415 polymorphism were not different between donors and recipients. Despite similar allele frequencies between donors and recipients, the minor allele rs2276415 (GA+AA) of AQP11 from the donors, but not from the recipients, had a harmful effect on the graft survival compared with the wild-type donor (GG; P=0.029). This association was significant after adjusting for several risk factors including age, sex, human leukocyte antigen mismatch, donor type, hypertension, and diabetes mellitus (P=0.032). Conclusion: A donor-derived, not recipient-derived, genetic AQP11 polymorphism has different effects on graft outcome. Thus, the genetic influence from donors should be carefully considered for proper management of allografts after kidney transplantation.

PURPOSE:We aimed to investigate the prevalence of diabetic retinopathy (DR) in patients with diabetic foot ulcer (DFU) and elucidate the association between DR and DFU severities and their shared risk factors. METHODS:A retrospective review was conducted on DFU patients who underwent ophthalmic and vascular examinations within 6 months; 100 type 2 diabetic patients with DFU were included. The medical records of 2496 type 2 diabetic patients without DFU served as control data. DR prevalence and severity were assessed in DFU patients. DFU patients were compared with the control group regarding each clinical variable. Additionally, DFU patients were divided into two groups according to DR severity and compared. RESULTS:Out of 100 DFU patients, 90 patients (90%) had DR and 55 (55%) had proliferative DR (PDR). There was no significant association between DR and DFU severities (R = 0.034, p = 0.734). A multivariable analysis comparing type 2 diabetic patients with and without DFUs showed that the presence of DR [OR, 226.12; 95% confidence interval (CI), 58.07-880.49; p < 0.001] and proliferative DR [OR, 306.27; 95% CI, 64.35-1457.80; p < 0.001), higher HbA1c (%, OR, 1.97, 95% CI, 1.46-2.67; p < 0.001), higher serum creatinine (mg/dL, OR, 1.62, 95% CI, 1.06-2.50; p = 0.027), older age (years, OR, 1.12; 95% CI, 1.06-1.17; p < 0.001), higher pulse pressure (mmHg, OR, 1.03; 95% CI, 1.00-1.06; p = 0.025), lower cholesterol (mg/dL, OR, 0.94; 95% CI, 0.92-0.97; p < 0.001), lower BMI (kg/m2, OR, 0.87, 95% CI, 0.75-1.00; p = 0.044) and lower hematocrit (%, OR, 0.80, 95% CI, 0.74-0.87; p < 0.001) were associated with DFUs. In a subgroup analysis of DFU patients, the PDR group had a longer duration of diabetes mellitus, higher serum BUN, and higher serum creatinine than the non-PDR group. In the multivariable analysis, only higher serum creatinine was associated with PDR in DFU patients (OR, 1.37; 95% CI, 1.05-1.78; p = 0.021). CONCLUSIONS:Diabetic retinopathy is prevalent in patients with DFU and about half of DFU patients had PDR. No significant association was found in terms of the severity of these two diabetic complications. To prevent blindness, patients with DFU, and especially those with high serum creatinine, should undergo retinal examinations for timely PDR diagnosis and management.

Chronic kidney disease (CKD) is usually diagnosed using the estimated glomerular filtration rate (eGFR) or kidney damage markers. The urine dipstick test is a widely used screening tool for albuminuria, a CKD marker. Although the urine albumin:creatinine ratio (ACR) has advantages over the dipstick test in sensitivity and quantification of levels, the two methods have not been compared in the general population. A total of 20,759 adults with urinalysis data in the Korea National Health and Nutrition Examination Survey 2011-2014 were examined. CKD risk categories were created using a combination of eGFR and albuminuria. Albuminuria was defined using an ACR cutoff of 30 mg/g or 300 mg/g and a urine dipstick cutoff of trace or 1+. The EQ-5D index was used for the health outcome. Prevalence estimates of ACR ≥30 mg/g and >300 mg/g vs dipstick ≥trace and ≥1+ in adults aged ≥20 years were 7.2% and 0.9% vs 9.1% and 1.2%, respectively. For ACR ≥30 mg/g detection, the sensitivity, specificity, and positive/negative predictive values of dipstick ≥trace were 43.6%, 93.6%, 34.6%, and 95.5%, respectively. When risk categories created based on dipstick cutoffs were compared with those based on ACR cutoffs, 10.4% of the total population was reclassified to different risk categories, with only 3.9% reclassified to the same CKD category. Akaike information criterion values were lower, and non-fatal disease burdens of CKD were larger, in models predicting EQ-5D index using ACR-based categories compared to those using dipstick-based categories, even after adjusting for confounders. In conclusion, the urine dipstick test had poor sensitivity and high false-discovery rates for ACR ≥30 mg/g detection, and classified a large number of individuals into different CKD risk categories compared with ACR-based categories. Therefore, ACR assessments in CKD screening appear beneficial for a more accurate prediction of worse quality of life.

The Global Burden of Disease 2010 and the WHO Global Health Estimates of years lived with disability (YLDs) uses disability-weights obtained from lay health-state descriptions, which cannot fully reflect different disease manifestations, according to severity, treatment, and environment. The aim of this study was to provide population-representative YLDs of noncommunicable diseases and injuries using a prevalence-based approach, with the disability weight measured in subjects with specific diseases or injuries. We included a total of 44969 adults, who completed the EQ-5D questionnaire as participation in the Korea National Health and Nutrition Examination Survey 2007-2014. We estimated the prevalence of each of 40 conditions identified from the noncommunicable diseases and injuries in the WHO list. Modified condition-specific disability-weight was determined from the adjusted mean difference of the EQ-5D index between the condition and reference groups. Condition-specific YLDs were calculated as the condition's prevalence multiplied by the condition's disability-weight. All-cause YLDs, estimated as "number of population × (1 - mean score of EQ-5D)" were 2165 thousands in 39044 thousand adults aged ≥20. The combined YLDs for all 40 conditions accounted for 67.6% of all-cause YLDs, and were 1604, 2126, 8749, and 12847 per 100000 young (age 20-59) males, young females, old (age ≥60) males, and old females, respectively. Back pain/osteoarthritis YLDs were exceptionally large (442/40, 864/146, 2037/836, and 4644/3039 per 100000 young males, young females, old males, and old females, respectively). Back pain, osteoarthritis, depression, diabetes, periodontitis, and stroke accounted for 22.3%, 9.1%, 4.6%, 3.3%, 3.2%, and 2.9% of all-cause YLDs, respectively. In conclusion, this estimation of YLDs using prevalence rates and disability-weights measured in a population-representative survey may form the basis for population-level strategies to prevent age-related worsening of disability.

A 48-year-old woman with autosomal dominant polycystic kidney disease (ADPKD) presented with generalized edema and arthralgia. She showed evidences of acute glomerulonephritis including nephrotic-ranged proteinuria. Because her serologic test results were consistent with those for systemic lupus erythematosus (SLE), we performed laparoscopic renal biopsy that confirmed World Health Organization (WHO) class IV lupus nephritis. She was treated with steroids and intravenous cyclophosphamide pulse therapy and eventually started hemodialysis 8 years after the lupus nephritis was diagnosed. To our knowledge, this is the first case wherein a patient with ADPKD underwent a laparoscopic biopsy for diagnosing lupus nephritis.

Background: In hemodialysis (HD) patients, traditional risk factors cannot explain all of the mortality and morbidity. This study was designed to investigate the effect of depression and health-related quality of life (HRQOL) on prognosis in maintenance HD patients. Methods: In February 2008, the Beck's Depression Inventory and the Kidney Dialysis Quality of Life—Short Form were utilized to measure depression and HRQOL. Until February 2011, the mortality, cardiovascular events, infection, and hospitalization were investigated, retrospectively. Results: Among the 166 patients, the 3-year cumulative survival rate was 88.8%, and the depression did not affect survival (depression vs. nondepression: 91.8% vs. 87.2%, P=0.437). The upper tertiles in physical component summary (PCS) were correlated with lower mortality (OR, 0.12; P=0.05) and fewer cardiovascular events (OR, 0.09; P=0.024) than the lower tertiles. The upper tertiles in kidney disease component summary (KDCS) were associated with less hospitalization than the lower tertiles (OR, 0.38; P=0.024). After adjusting for multiple variables including age, comorbidity index, and albumin, upper tertiles in PCS were correlated with fewer cardiovascular events than the lower tertiles (OR, 0.08; P=0.038). Conclusion: The cross-sectional survey of whether HD patients had depression was not significantly associated with mortality and morbidity. HRQOL was correlated with mortality, cardiovascular events and hospitalization.

Health-related quality of life (HRQOL) has recently become an important issue. It reportedly affects morbidity and mortality in patients with end-stage renal disease (ESRD). In this study, we investigated whether early referral and planned dialysis improve the HRQOL and depression of patients with ESRD.We prospectively enrolled newly diagnosed patients with ESRD, from 31 hospitals in Korea, who completed questionnaires at 3 months after dialysis. We also got follow-up survey at 1 year after dialysis. To measure HRQOL and depression, Kidney Disease Quality of Life Short Form 36 (KDQOL-36) and Beck's Depression Inventory (BDI) were utilized.A total of 643 patients were analyzed. Referral type did not affect either KDQOL-36 or BDI scores. However, the planned dialysis group showed significantly better scores in 4 of 5 KDQOL-36 domains than did the unplanned group at 3 months after dialysis and partly, the effect was sustained for 1 year after dialysis. The benefit of planned dialysis was significant after adjusting for age, sex, type of dialysis, marital status, educational attainment, occupation, modified Charlson comorbidity index, albumin, and hemoglobin levels. BDI scores were also lower which indicate less depressive mood in planned dialysis group than those in unplanned group both at 3 months and 1 year after dialysis.Not early referral but planned dialysis improved both the short- and long-term HRQOL and depression of patients with ESRD. Nephrologists should try to help patients to initiate dialysis in a planned manner.

Active glycemic control has been proven to delay the onset and slow the progression of diabetic retinopathy, nephropathy, and neuropathy in diabetic patients, but the optimal level is obscure in end-stage renal disease. In this study, we evaluated the effect of hemoglobin A1c (HbA1c) on mortality of diabetic patients on dialysis, focusing on age and dialysis type.Of 3,302 patients enrolled in the prospective cohort for end-stage renal disease in Korea between August 2008 and October 2013, 1,239 diabetic patients who had been diagnosed with diabetes or having HbA1c≥6.5% at the time of enrollment were analyzed. Age was categorized as

Hypokalemia has been suggested to be a risk factor in peritoneal dialysis (PD). Recently in US, a large peritoneal dialysis cohort study showed potassium abnormalities contribute disproportionately to the high death risk in PD patients. This issue was reviewed in Korean ESRD cohort comparing to hemodialysis (HD). Patients and methods: A nationwide prospective observational cohort study (CRC-ESRD) was performed between August 2008 and October 2013. Among them, patients had been selected who checked serum potassium level at least twice within 18 months. Overall mortality rate was evaluated as outcome. Patients were divided into following four groups based on time-averaged potassium level: K

The nature of cost-saving effects of early referral to a nephrologist in patients with chronic kidney disease (CKD) is not fully evaluated. We evaluated the health care costs before and after dialysis according to the referral time.A total of 879 patients who were newly diagnosed as having end-stage renal disease from August 2008 to June 2011 were prospectively enrolled. The early referral (ER) group was defined as patients who were referred to a nephrologist more than a year before dialysis and had visited a nephrology clinic 2 or more times. Patients whose referral time was less than a year were considered the late referral (LR) group. Information about medical costs was acquired from the claim data of the Korea Health Insurance Review and Assessment Service.The total medical costs during the first 12 months after the initiation of dialysis were not different between the 526 ER patients and the 353 LR patients. However, the costs of the ER patients during the first month were significantly lower than those of the LR patients (ER vs. LR: 3029 ± 2219 vs. 3438 ± 2821 US dollars [USD], P = 0.025). The total 12-month health care costs before the initiation of dialysis were significantly lower in the ER group (ER vs. LR: 6206 ± 5873 vs. 8610 ± 7820 USD, P