The relationship between hyperuricemia, gout, and renal disease has been investigated for several years. From the beginning, kidney disease has been considered a complication of gout; however, the viewpoints changed, claiming that hypertension and elevated uric acid (UA) levels are caused by decreased urate excretion in patients with renal impairment. To date, several examples of evidence support the role of hyperuricemia in cardiovascular or renal diseases. Several mechanisms have been identified that explain the relationship between hyperuricemia and chronic kidney disease, including the crystal effect, renin–angiotensin–aldosterone system activation, nitric oxide synthesis inhibition, and intracellular oxidative stress stimulation, and urate-lowering therapy (ULT) has been proven to reduce renal disease progression in the past few years. In this comprehensive review, the source and physiology of UA are introduced, and the mechanisms that explain the reciprocal relationship between hyperuricemia and kidney disease are reviewed. Lastly, current evidence supporting the use of ULT to postpone renal disease progression in patients with hyperuricemia and gout are summarized.

Background: It has been a matter of much debate whether the co-administration of furosemide and albumin can achieve better diuresis and natriuresis than furosemide treatment alone. There is inconsistency in published trials regarding the effect of this combination therapy. We, therefore, conducted this meta-analysis to explore the efficacy of furosemide and albumin co-administration and the factors potentially influencing the diuretic effect of such co-administration. Methods: In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched the PubMed, Embase, Medline, and Cochrane databases. Prospective studies with adult populations which comparing the effect of furosemide and albumin co-administration with furosemide alone were included. The outcomes including diuretic effect and natriuresis effect measured by hourly urine output and hourly urine sodium excretion from both groups were extracted. Random effect model was applied for conducting meta-analysis. Subgroup analysis and sensitivity analysis were performed to explore potential sources of heterogeneity of treatment effects. Results: By including 13 studies with 422 participants, the meta-analysis revealed that furosemide with albumin co-administration increased urine output by 31.45 ml/hour and increased urine excretion by 1.76 mEq/hour in comparison to furosemide treatment alone. The diuretic effect of albumin and furosemide co-administration was better in participants with low baseline serum albumin levels (< 2.5 g/dL) and high prescribed albumin infusion doses (> 30 g), and the effect was more significant within 12 hours after administration. Diuretic effect of co-administration was better in those with baseline Cr > 1.2 mg/dL and natriuresis effect of co-administration was better in those with baseline eGFR < 60 ml/min/1.73m2. Conclusion: Co-administration of furosemide with albumin might enhance diuresis and natriuresis effects than furosemide treatment alone but with high heterogeneity in treatment response. According to the present meta-analysis, combination therapy might provide advantages compared to the furosemide therapy alone in patients with baseline albumin levels lower than 2.5 g/dL or in patients receiving higher albumin infusion doses or in patients with impaired renal function. Owing to high heterogeneity and limited enrolled participants, further parallel randomized controlled trials are warranted to examine our outcome. Registration: PROSEPRO ID: CRD42020211002; https://clinicaltrials.gov/.

Acute kidney disease (AKD) is the persistence of renal injury between days 8 and 90 after an initial acute kidney injury (AKI). In this study, we aimed to explore the incidence of AKD, the association between AKD, and patient outcomes after acute type A aortic dissection (type A AAD) surgery. We identified 696 participants who underwent type A AAD surgery. Patients were categorized into stages 1 to 3 or 0 (non-AKD) AKD groups. Outcomes included major adverse kidney events (MAKEs), respiratory failure, all-cause readmission, and ischemic stroke from day 91 after operation. A total of 376 (54%) participants developed AKI, and 135/376 (35.9%) developed AKD. Moreover, 34/320 (10.6%) patients without AKI still developed AKD. Overall, 169/696 (24.3%) participants developed AKD. Patients with stages 2 and 3 AKD are associated with persisted declined renal function within 1 year. AKD was associated with a higher risk of MAKEs (hazard ratio (HR): 2.52, 95% confidence interval (CI) 1.90–3.33) and all-cause readmission (HR: 2.86, 95% CI: 2.10–3.89). Development of AKD with or without AKI is associated with a higher risk of MAKEs and hospitalization after acute aortic dissection surgery. Higher-stage AKD is associated with a trend of persistent decline in kidney function.

The adverse impact of Coronavirus disease 2019 (COVID-19) on kidney function has been reported since the global pandemic. The burden of COVID-19 on kidney transplant recipients, however, has not been systematically analyzed. A systematic review and meta-analysis with a random-effect model was conducted to explore the rate of mortality, intensive care unit admission, invasive mechanical ventilation, acute kidney injury, kidney replacement therapy and graft loss in the adult kidney transplant population with COVID-19. Sensitivity analysis, subgroup analysis and meta-regression were also performed. Results: we demonstrated a pooled mortality rate of 21% (95% CI: 19−23%), an intensive care unit admission rate of 26% (95% CI: 22–31%), an invasive ventilation rate among those who required intensive care unit care of 72% (95% CI: 62–81%), an acute kidney injury rate of 44% (95% CI: 39–49%), a kidney replacement therapy rate of 12% (95% CI: 9–15%), and a graft loss rate of 8% (95% CI: 5–15%) in kidney transplant recipients with COVID-19. The meta-regression indicated that advancing age is associated with higher mortality; every increase in age by 10 years was associated with an increased mortality rate of 3.7%. Regional differences in outcome were also detected. Further studies focused on treatments and risk factor identification are needed.

Background: Urinary calprotectin is a novel biomarker that distinguishes between intrinsic or prerenal acute kidney injury (AKI) in different studies. However, these studies were based on different populations and different AKI criteria. We evaluated the diagnostic accuracy of urinary calprotectin and compared its diagnostic performance in different AKI criteria and study populations. Method: In accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched PubMed, Embase, and the Cochrane database up to September 2018. The diagnostic performance of urinary calprotectin (sensitivity, specificity, predictive ratio, and cutoff point) was extracted and evaluated. Result: This study included six studies with a total of 502 patients. The pooled sensitivity and specificity were 0.90 and 0.93, respectively. The pooled positive likelihood ratio (LR) was 15.15, and the negative LR was 0.11. The symmetric summary receiver operating characteristic (symmetric SROC) with pooled diagnostic accuracy was 0.9667. The relative diagnostic odds ratio (RDOC) of the adult to pediatric population and RDOCs of different acute kidney injury criteria showed no significant difference in their diagnostic accuracy. Conclusion: Urinary calprotectin is a good diagnostic tool for the discrimination of intrinsic and prerenal AKI under careful inspection after exclusion of urinary tract infection and urogenital malignancies. Its performance is not affected by different AKI criteria and adult or pediatric populations.

Acute kidney injury (AKI) is a common complication of hospitalization that greatly and negatively affects the short-term and long-term outcomes of patients. Current guidelines use serum creatinine level and urine output rate for defining AKI and as the staging criteria of AKI. However, because they are not sensitive or specific markers of AKI, clinicians find it difficult to predict the occurrence of AKI and prescribe timely treatment. Advances in computing technology have led to the recent use of machine learning and artificial intelligence in AKI prediction, recent research reported that by using electronic health records (EHR) the AKI prediction via machine-learning models can reach AUROC over 0.80, in some studies even reach 0.93. Our review begins with the background and history of the definition of AKI, and the evolution of AKI risk factors and prediction models is also appraised. Then, we summarize the current evidence regarding the application of e-alert systems and machine-learning models in AKI prediction.