Association of estimated glomerular filtration rate with all-cause and cardiovascular mortality: the role of malnutrition-inflammation-cachexia syndrome

Background Previous studies have demonstrated that high estimated glomerular filtration rate (eGFR) is paradoxically associated with an increased risk of mortality, and the association becomes more predominant in older people. However, the role of malnutrition-inflammation-cachexia syndrome (MICS) in the association between eGFR and mortality has never been explored. Methods We conducted a community-based cohort study using data from the Taipei City Elderly Health Examination Database, collected during the period 2001-10. All participants aged >= 65 years were included and stratified by the absence or presence of MICS, which is defined as the presence of at least one of the following markers: body mass index < 22 kg/m(2), serum albumin < 3.0mg/dL, or Geriatric Nutritional Risk Index (GNRI) < 98. The study endpoints were all-cause and cardiovascular mortality. Results A total of 131 354 participants were identified and categorized according to the chronic kidney disease stage based on eGFR. Compared with the reference eGFR of 60-89 mL/min/1.73m(2), the overall and cardiovascular mortality risks were markedly high in the groups with eGFR of < 30 mL/min/1.73m(2) [overall: adjusted hazard ratio (aHR), 1.86; 95% confidence interval (CI), 1.72-2.00; cardiovascular: aHR, 1.87; 95% CI, 1.60-2.19] and >= 90 mL/min/1.73m(2) (overall: aHR, 1.23; 95% CI, 1.13-1.34; cardiovascular: aHR, 1.28; 95% CI, 1.06-1.54). In the absence of MICS, high eGFR was associated with lower mortality risk (aHR, 0.71; 95% CI, 0.62-0.80), and the U-shaped relationship disappeared. Subgroup analyses produced consistent results. Conclusions MICS could influence the association observed between high eGFR and mortality in older people, particularly in those with low body mass index, albumin level, GNRI, and very low serum creatinine level

Influence of Dialysis Membranes on Clinical Outcomes: From History to Innovation

Dialysis membranes were traditionally classified according to their material compositions (i.e., as cellulosic or synthetic) and on the basis of the new concept of the sieving coefficient (determined by the molecular weight retention onset and molecular weight cut-off). The advantages of synthetic polymer membranes over cellulose membranes are also described on the basis of their physical, chemical, and structural properties. Innovations of dialysis membrane in recent years include the development of medium cutoff membranes; graphene oxide membranes; mixed-matrix membranes; bioartificial kidneys; and membranes modified with vitamin E, lipoic acid, and neutrophil elastase inhibitors. The current state of research on these membranes, their effects on clinical outcomes, the advantages and disadvantages of their use, and their potential for clinical use are outlined and described

What Is Known and Unknown About Twice-Weekly Hemodialysis.

BackgroundThe 2006 Kidney Disease Outcomes Quality Initiative guidelines suggest twice-weekly or incremental hemodialysis for patients with substantial residual kidney function (RKF). However, in most affluent nations de novo and abrupt transition to thrice-weekly hemodialysis is routinely prescribed for all dialysis-naïve patients regardless of their RKF. We review historical developments in hemodialysis therapy initiation and revisit twice-weekly hemodialysis as an individualized, incremental treatment especially upon first transitioning to hemodialysis therapy.SummaryIn the 1960's, hemodialysis treatment was first offered as a life-sustaining treatment in the form of long sessions (≥10 hours) administered every 5 to 7 days. Twice- and then thrice-weekly treatment regimens were subsequently developed to prevent uremic symptoms on a long-term basis. The thrice-weekly regimen has since become the 'standard of care' despite a lack of comparative studies. Some clinical studies have shown benefits of high hemodialysis dose by more frequent or longer treatment times mainly among patients with limited or no RKF. Conversely, in selected patients with higher levels of RKF and particularly higher urine volume, incremental or twice-weekly hemodialysis may preserve RKF and vascular access longer without compromising clinical outcomes. Proposed criteria for twice-weekly hemodialysis include urine output >500 ml/day, limited interdialytic weight gain, smaller body size relative to RKF, and favorable nutritional status, quality of life, and comorbidity profile.Key messagesIncremental hemodialysis including twice-weekly regimens may be safe and cost-effective treatment regimens that provide better quality of life for incident dialysis patients who have substantial RKF. These proposed criteria may guide incremental hemodialysis frequency and warrant future randomized controlled trials

Artificial Intelligence for Risk Prediction of Rehospitalization with Acute Kidney Injury in Sepsis Survivors

Sepsis survivors have a higher risk of long-term complications. Acute kidney injury (AKI) may still be common among sepsis survivors after discharge from sepsis. Therefore, our study utilized an artificial-intelligence-based machine learning approach to predict future risks of rehospitalization with AKI between 1 January 2008 and 31 December 2018. We included a total of 23,761 patients aged ≥ 20 years who were admitted due to sepsis and survived to discharge. We adopted a machine learning method by using models based on logistic regression, random forest, extra tree classifier, gradient boosting decision tree (GBDT), extreme gradient boosting, and light gradient boosting machine (LGBM). The LGBM model exhibited the highest area under the receiver operating characteristic curves (AUCs) of 0.816 to predict rehospitalization with AKI in sepsis survivors and followed by the GBDT model with AUCs of 0.813. The top five most important features in the LGBM model were C-reactive protein, white blood cell counts, use of inotropes, blood urea nitrogen and use of diuretics. We established machine learning models for the prediction of the risk of rehospitalization with AKI in sepsis survivors, and the machine learning model may set the stage for the broader use of clinical features in healthcare

Artificial Intelligence for Risk Prediction of End-Stage Renal Disease in Sepsis Survivors with Chronic Kidney Disease

Sepsis may lead to kidney function decline in patients with chronic kidney disease (CKD), and the deleterious effect may persist in patients who survive sepsis. We used a machine learning approach to predict the risk of end-stage renal disease (ESRD) in sepsis survivors. A total of 11,661 sepsis survivors were identified from a single-center database of 112,628 CKD patients between 2010 and 2018. During a median follow-up of 3.5 years, a total of 1366 (11.7%) sepsis survivors developed ESRD after hospital discharge. We adopted the random forest, extra trees, extreme gradient boosting, light gradient boosting machine (LGBM), and gradient boosting decision tree (GBDT) algorithms to predict the risk of ESRD development among these patients. GBDT yielded the highest area under the receiver operating characteristic curve of 0.879, followed by LGBM (0.868), and extra trees (0.865). The GBDT model revealed the strong effect of estimated glomerular filtration rates <25 mL/min/1.73 m2 at discharge in predicting ESRD development. In addition, hemoglobin and proteinuria were also essential predictors. Based on a large-scale dataset, we established a machine learning model computing the risk for ESRD occurrence among sepsis survivors with CKD. External validation is required to evaluate the generalizability of this model

Characterization of aluminum gallium oxide films grown by pulsed laser deposition

Aluminum gallium oxide (AGO) films were prepared on conventional c-plane sapphire by pulsed laser deposition (PLD). In the current PLD-AGO studies, target composition or growth temperature is usually the main deposition variable, and the other growth conditions are fixed. This would make it difficult to fully understand the theory and characterization of AGO films. In this study, several growth parameters such as target composition, gas atmosphere, laser repetition frequency, growth pressure, and substrate temperature (Ts) were all modulated to realize and optimize the AGO growth. When the (AlxGa1-x)2O3 target with the Al content larger than 20 at% was used, a serious target poisoning phenomenon occurred, leading to the extremely unstable growth rate. In comparison to the AGO film grown with argon atmosphere, the higher transparency was reached in the film prepared with oxygen atmosphere due to the relative abundance of oxygen. Because of the homogeneous oxygen reduction, the AGO film with the higher crystal quality was obtained at a higher laser repetition frequency. With an increment of growth pressure, the Al content of AGO film was increased. The growth of AGO film at the higher Ts would cause the higher bandgap value, smoother surface, and growth rate degradation. Additionally, the crystal quality of AGO film can be also improved both by increasing the growth pressure and Ts. The better characterization can be reached in the AGO film grown using the (Al0.05Ga0.95)2O3 target with oxygen atmosphere at the working pressure of 2 × 10−1 Torr, the laser repetition frequency of 10 Hz, and the Ts of 800 °C. When the metal-semiconductor-metal photodetector fabricated with this AGO active layer, the best performance including the photocurrent of 7.56 × 10−8 A, dark current of 1.59 × 10–12 A, and photo/dark current ratio of 4.76 × 104 (@5 V and 240 nm) were achieved

Urinary Galectin-3 as a Novel Biomarker for the Prediction of Renal Fibrosis and Kidney Disease Progression

Plasma galectin-3 (Gal-3) is associated with organ fibrosis, but whether urinary Gal-3 is a potential biomarker of kidney disease progression has never been explored. Between 2018 and 2021, we prospectively enrolled 280 patients who underwent renal biopsy and were divided into three groups based on their urinary Gal-3 levels (<354.6, 354.6–510.7, and ≥510.8 pg/mL) to assess kidney disease progression (defined as ≥40% decline in the estimated glomerular filtration rate or end-stage renal disease) and renal histology findings. Patients in the highest urinary Gal-3 tertile had the lowest eGFRs and highest proteinuria levels. In multivariate Cox regression models, patients in the highest tertile had the highest risk of kidney disease progression (adjusted hazard ratio, 4.60; 95% confidence interval, 2.85–7.71) compared to those in the lowest tertile. Higher urinary Gal-3 levels were associated with more severe renal fibrosis. Intrarenal mRNA expression of LGALS3 (Gal-3-encoded gene) was most correlated with the renal stress biomarkers (IGFBP7 and TIMB2), renal function biomarkers (PTGDS) and fibrosis-associated genes (TGFB1). The urinary Gal-3 level may be useful for the identification of patients at high risk of kidney disease progression and renal fibrosis, and for the early initiation of treatments for these patients