Objective To establish a predictive risk model for acute kidney injury (AKI) in acute myocardial infarction (AMI) patients based on machine learning algorithm and compare with a traditional logistic regression model. Methods It was a retrospective study. The demographic data, laboratory examination, treatment regimen and medication of AMI patients from July 2011 to December 2016 in Beijing Anzhen Hospital, Capital Medical University were collected. The diagnostic criteria of AKI were based on the AKI diagnosis and treatment guidelines published by Kidney Diseases: Improving Global Outcomes in 2012. The selected AMI patients were randomly divided into training set (70%) and internal test set (30%) by simple random sampling. SelectFromModel and Lasso regression models were used to extract clinical parameters as predictors of AKI in AMI patients. Logistic regression model (model A) and machine learning algorithm (model B) were used to establish the risk prediction model of AKI in AMI patients. DeLong method was used to compare the area under the receiver-operating characteristic (ROC) curve (AUC) between model A and model B for selecting the best model. Results A total of 6 014 AMI patients were included in the study, with age of (58.4±11.7) years old and 3 414 males (80.5%). There were 674 patients (11.2%) with AKI. There were 4 252 patients (70.7%) in the training set and 1 762 patients (29.3%) in the test set. The selected twelve clinical parameters by the SelectFromModel and Lasso regression models included the number of myocardial infarctions, ST-segment elevation myocardial infarction, ventricular tachycardia, third degree atrioventricular block, decompensated heart failure at admission, admission serum creatinine, admission blood urea nitrogen, admission peak creatine kinase isoenzyme, diuretics, maximum daily dose of diuretics, days of diuretic use and statins. Logistic regression prediction model showed that AUC for the test set was 0.80 (95% CI 0.76-0.84). The machine learning algorithm model obtained AUC in the test set with 0.82 (95% CI 0.78-0.85).There was no significant difference in AUC between the two models (Z=0.858, P=0.363), and AUC of the machine learning algorithm predictive model was slightly higher than that of the traditional logistic regression model. Conclusions The prediction effect of AKI risk in AMI patients based on machine learning algorithm is similar to that of traditional logistic regression model, and the prediction accuracy of machine learning algorithm is better. The introduction of machine learning algorithm model may improve the ability to predict AKI risk.

Objective To construct the risk prediction nomogram model of acute kidney injury (AKI) with R language and traditional statistical methods based on the large sample clinical database, and verify the accuracy of the model. Methods It was a a retrospective case control study. The patients who met the diagnostic criteria of AKI in Tongji Hospital of Tongji University from January 1 to December 31, 2021 were screened in the clinical database, and the patients with monitored serum creatinine within 48 hours but without AKI were included as the control group. The demographic data, disease history, surgical history, medication history and laboratory test data were collected to screen the risk factors of AKI in clinic.Firstly, based on multivariate logistic regression analysis and forward stepwise logistic regression analysis, the selected risk factors were included to construct the nomogram model. At the same time, cross validation, bootstrap validation and randomly split sample validation were used for internal verification, and clinical data of patients in the sane hospital after one year (January to December, 2022) were collected for external verification. The receiver-operating characteristic curve was used to determine the discrimination of the model, and calibration curve and decision curve analysis were carried out to evaluate the accuracy and clinical net benefit, respectively. Results A total of 5 671 patients were enrolled in the study, with 1 884 AKI patients (33.2%) and 3 787 non-AKI patients (66.7%). Compared with non-AKI group, age, and proportions of surgical history, renal replacement therapy, hypertension, diabetes, cerebrovascular accident,chronic kidney disease, drug use histories and mortality in AKI group were all higher (all P<0.05). Multivariate logistic regression analysis showed that the independent influencing factors of AKI were surgical history, hypertension, cerebrovascular accident, diabetes, chronic kidney disease, diuretics, nitroglycerin, antidiuretic hormones, body temperature, serum creatinine, C-reactive protein, red blood cells, white blood cells, D-dimer, myoglobin, hemoglobin, blood urea nitrogen, brain natriuretic peptide, aspartate aminotransferase, alanine aminotransferase, triacylglycerol, lactate dehydrogenase, total bilirubin, activated partial thromboplastin time, blood uric acid and potassium ion (all P<0.05). Finally, the predictive factors in the nomogram were determined by forward stepwise logistic regression analysis, including chronic kidney disease, hypertension, myoglobin, serum creatinine and blood urea nitrogen, and the area under the curve of the prediction nomogram model was 0.926 [95% CI 0.918-0.933, P<0.001]. The calibration curve showed that the calibration effect of nomogram was good (P>0.05). The decision curve showed that when the risk threshold of nomogram model was more than 0.04, the model construction was useful in clinic. In addition, the area under the curve of receiver-operating characteristic curve predicted by nomograph model in external validation set was 0.876 (95% CI 0.865-0.886), which indicated that nomograph model had a high discrimination degree. Conclusion A nomogram model for predicting the occurrence of AKI is established successfully, which is helpful for clinicians to find high-risk AKI patients early, intervene in time and improve the prognosis.

Objective To analyze the changes of diagnosis and treatment before and after renal biopsy in adult patients with acute kidney disease (AKD), and to explore the value of renal biopsy in the diagnosis and treatment of AKD. Methods It was a single-center retrospective observational study. The adult patients with AKD who underwent renal biopsy in the Department of Nephrology of the First Affiliated Hospital of Nanjing Medical University from January 1, 2017 to December 31, 2021 were enrolled. Demographic data, general clinical data, laboratory tests, and diagnosis and treatment data before and after renal biopsy were collected to analyze the concordance rate between clinical and pathological diagnoses, changes in treatment after renal biopsy, and bleeding complication. Results A total of 575 patients diagnosed with AKD by renal biopsy were included in this study, with age of 51 (36, 63) years old and 359 males (62.4%). Among them, there were 293 patients (51.0%) of acute kidney injury, 348 patients (60.5%) of hypertension and 124 patients (21.6%) of diabetes. The peak serum creatinine was 272 (190, 477) μmol/L. The hemoglobin was 106 (86, 126) g/L. The 24-hour urine protein was 2.15 (0.79, 4.82) g. There were 347 patients (60.3%) of acute glomerular diseases, 136 patients (23.7%) of acute interstitial nephritis, 47 patients (8.2%) of thrombotic microangiopathy, and 45 patients (7.8%) of acute tubular necrosis. The most common types of acute glomerular diseases were IgA nephropathy and anti-neutrophil cytoplasmic antibody-associated glomerulonephritis, accounting for 22.3% (128/575) and 12.2% (70/575), respectively. The clinical diagnoses before renal biopsy were consistent with the renal histopathological diagnoses in 454 patients, with an accuracy rate of 79.0%. Following the renal biopsy, the treatment plan involving glucocorticoids or immunosuppressants was adjusted in 394 patients (68.5%). Significant post-biopsy bleeding occurred in 15 patients (2.6%), with 12 patients requiring blood transfusion and 1 patient requiring surgical intervention. Conclusions Twenty-one clinical diagnoses do not match the pathological diagnoses in adult AKD patients, 68.5% of patients have changes in their treatment plans, and 2.6% of patients have significant hemorrhagic complications after renal biopsy. Clinicians need to carefully consider the benefits and risks and make individualized decisions about renal biopsy.

Objective To investigate the predictive value of serum uric acid/albumin ratio (sUAR) for acute kidney injury (AKI) after cardiac valve surgery. Methods The clinical data of adult patients undergoing cardiac valve surgery under cardiopulmonary bypass from January 2021 to December 2021 from the Heart Center of Henan Provincial People's Hospital were collected retrospectively, and the sUAR was calculated. All patients were divided into AKI group and non-AKI group according to whether AKI occurred within 7 days after cardiac valve surgery, and the differences of clinical data between the two groups were compared. Multivariate logistic regression model was used to analyze the independent correlation factors of AKI after cardiac valve surgery. The receiver operating characteristic (ROC) curve was used to evaluate the performance of relevant indicators. Results A total of 422 patients were enrolled, including 194 females (46.0%), 141 hypertension patients (33.4%) and 172 atrial fibrillation patients (40.8%). They were 57 (50, 65) years old. Their sUAR was 8.13 (6.57, 9.54) μmol/g, and hemoglobin was 135 (125, 145) g/L. There were 142 cases in AKI group and 280 cases in non-AKI group, and the incidence of AKI after cardiac valve surgery was 33.6%. Age, atrial fibrillation rate, baseline serum creatinine, N terminal pro B type natriuretic peptide, serum urea,serum uric acid, blood glucose and sUAR were higher in the AKI group than those in the non-AKI group (all P<0.05), and estimated glomerular filtration rate, lymphocyte count,hemoglobin and serum albumin were lower in the AKI group than those in the non-AKI group (all P<0.05). The median cardiopulmonary bypass time of patients in the AKI group was slightly longer than that in the non-AKI group, but the difference was not statistically significant [159 (125, 192) min vs. 151 (122, 193) min, Z=-0.797, P=0.426], and there were no statistically significant differences in other indicators between the two groups. The results of multivariate logistic regression analysis showed that sUAR (OR=1.467, 95% CI 1.308-1.645, P<0.001), age (OR=1.045, 95% CI 1.020-1.072, P<0.001), atrial fibrillation (OR=2.520, 95% CI 1.580-4.020, P<0.001), hemoglobin (OR=0.984, 95% CI 0.971-0.997, P=0.015) were the independent correlation factors. ROC curve analysis showed that the area under the curve (AUC) of sUAR predicting AKI after cardiac valve surgery was 0.710 (95% CI 0.659-0.760, P<0.001) with a sensitivity of 85.2% and specificity of 45.0% for the sUAR cut-off point of 7.28 μmol/g. The AUC for the diagnosis of AKI after cardiac valve surgery was 0.780 (95% CI 0.734-0.825,P<0.001) with a sensitivity of 72.5% and specificity of 71.8% for the combination of sUAR with age, hemoglobin and atrial fibrillation. Conclusions For patients undergoing cardiac valve surgery under cardiopulmonary bypass, preoperative high sUAR is an independent risk factor for postoperative AKI, and sUAR has a certain predictive value for postoperative AKI.

Objective To investigate interleukin-37 (IL?37) expression in patients with diabetic kidney disease (DKD), and to assess the regulation of exogenous IL?37 on CD8⁺ T cell function in DKD patients. Methods A cross-section study was carried out. Twenty healthy controls, thirty-six patients with diabetes mellitus type 2 (T2DM), and forty-seven DKD patients were enrolled in the study. Peripheral blood was collected. Plasma and peripheral blood mononuclear cells were isolated. IL?37 and soluble IL-1 receptor 8 (IL-1R8) levels in the plasma were measured by enzyme-linked immunosorbent assay (ELISA). IL-18 receptor α chain (IL-18Rα), IL-1R8 and immune checkpoint molecules levels in CD8⁺ T cells were measured by flow cytometry. CD8⁺ T cells were purified, and were stimulated with recombinant IL?37. CD8⁺ T cells were co-cultured with HEK293 cells in either direct contact or indirect contact manner. Levels of perforin, granzyme B, interferon-γ (IFN-γ) and tumor necrosis factor-α (TNF-α) were measured by ELISA. The proportion of target cell death was assessed by measuring lactate dehydrogenase level. Results Plasma IL?37 levels in DKD patients [(63.42±23.30) ng/L] were significant lower than those in healthy controls [(143.02±50.67) ng/L] and T2DM patients [(87.88±40.62) ng/L] (t=8.848, P<0.001; t=3.456, P<0.001). Plasma IL?37 level had good predictive values for T2DM in health individuals and for DKD in T2DM patients [the area under the curve was 0.797 (95% CI 0.676-0.917, P<0.001) and 0.691 (95% CI 0.576-0.807, P=0.003), respectively]. Plasma IL?37 level was negatively correlated with urea nitrogen (r=-0.313, P=0.032) and creatinine (r=-0.477, P<0.001), and positively correlated with estimated glomerular filtration rate (eGFR) (r_s =0.478, P<0.001) in DKD patients. IL-1R8⁺ CD8⁺ cell proportion in DKD patients (33.60%±9.47%) was significantly higher compared to healthy controls (16.29%±5.97%) and T2DM patients (17.13%±4.85%) (t=7.545, 9.516, both P<0.001), but did not correlate with fast blood glucose, urea nitrogen, creatinine, or eGFR (all P>0.05). There were no statistical differences of IL-18Rα⁺ CD8⁺ cell proportion, soluble IL-1R8 level, or immune checkpoint molecule proportion in CD8⁺ T cells among healthy controls, T2DM patients, and DKD patients (all P>0.05). Perforin and granzyme B secretions by CD8⁺ T cells were significantly elevated in DKD patients compared with healthy controls [(108.78±12.42) ng/L vs. (94.60±10.07) ng/L, t=3.096, P=0.005; (261.34±48.79) ng/L vs. (166.28±30.80) ng/L, t=3.387, P=0.002] and T2DM patients [(108.78±12.42) ng/L vs. (92.58±14.71) ng/L, t=3.263, P=0.003; (261.34±48.79) ng/L vs. (170.66±39.24) ng/L, t=2.627, P=0.014]. There were no significant differences of either IFN-γ or TNF-α secretions by CD8⁺ T cells among healthy controls, T2DM patients, and DKD patients (all P>0.05). In direct contact co-culture manner, CD8⁺ T cell-induced HEK293 cell death was down- regulated (13.03%±4.97% vs. 17.88%±5.19%, t=2.235, P=0.037). The levels of perforin [(222.02±25.79) ng/L vs. (294.30±25.58) ng/L, t=6.603, P<0.001], granzyme B [(416.27±90.24) ng/L vs. (524.71±115.53) ng/L, t=2.454, P=0.023], IFN-γ [(23.66±4.20) ng/L vs. (35.18±8.51) ng/L, t=4.026, P<0.001] and TNF-α [(1.62±0.29) μg/L vs. (2.09±0.57) μg/L, t=2.302, P=0.034] were also reduced as well. In indirect contact co-culture manner, there were no significant differences of CD8⁺ T cell-induced HEK293 cell death, perforin, or granzyme B levels between no stimulation and IL?37 stimulation (all P>0.05). IFN-γ and TNF-α levels in the supernatants were reduced in response to IL?37 stimulation [(23.56±6.24) ng/L vs. (32.56±9.90) ng/L, t=2.550, P=0.019; (1.41±0.31) μg/L vs. (2.10±0.44) μg/L, t=4.011, P<0.001]. Conclusion IL?37 level is reduced in DKD patients.Exogenous IL?37 suppresses the cytotoxicity of CD8⁺ T cells in DKD patients.

The paper reported a case of brachial artery ligation treatment of arteriovenous graft infection with arteriovenous graft exposure and bleeding. Based on the experience of vascular access center and the review of relevant literature, the causes and treatment options of this complication were analyzed, and the feasibility and safety of brachial artery ligation were elaborated for the treatment of this complication, to provide references for clinical diagnosis and treatment.

IgA nephropathy (IgAN) is currently the most common primary glomerulonephritis worldwide, with 20%-40% of patients progressing to end-stage renal disease within 20 years of diagnosis. At present, the pathogenesis of IgAN is not clear, and clinical treatment is mainly to control the progression, without specific treatment plan. A series of studies on galactose-deficient IgA1 (Gd-IgA1) suggest that the pathogenesis of IgAN involves multiple links. This review summarizes the research progress on the pathogenesis of IgAN, covering the structure characteristics of IgA1, Gd-IgA1 antibodies and Gd-IgA1 immune complexes in IgAN patients, the deposition of Gd-IgA1 immune complexes in the kidneys, kidney damage following the deposition of Gd-IgA1 immune complexes, the role of complement in IgAN, the genomics of IgAN, and mucosal immunity in IgAN, providing clues and insights for further research and clinical treatment.

With the prolongation of peritoneal dialysis time, the peritoneum probably confronts structural and functional deterioration due to multiple factors, which will affect the efficiency of peritoneal dialysis. Clinically effective measures to protect peritoneal function are still lacking. This article reviewed studies in the last decade on protection of peritoneal function, which included strategies on dialysis prescription, medicine treatments for protection of peritoneal function, and non-medicine treatments such as far-infrared therapy and stem cell transplantation, to provide guidances for subsequent researches.

Sepsis-associated acute kidney injury (SA-AKI) is defined as the presence of acute kidney injury (AKI) in the context of sepsis. In the setting of genetic susceptibility, sepsis can lead to SA-AKI through various mechanisms. Based on differences in pathophysiological mechanisms, SA-AKI is categorized into different "endotypes" and manifests as distinct "subtypes". The combination of biomarkers and predictive models has the potential to early identify high-risk AKI patients and elucidate SA-AKI "endotypes". Volume resuscitation and blood purification are optimized strategies for SA-AKI treatment. Furthermore, clinical research on SA-AKI in children is promising.

Hyperkalemia is one of the common ion metabolism disorders in clinical practice. Hyperkalemia is defined as serum potassium higher than 5.0 mmol/L according to the guidelines at home and abroad. Acute severe hyperkalemia can cause serious consequences, such as flaccid paralysis, fatal arrhythmia, and even cardiac arrest. The use of renin-angiotensin- aldosterone system inhibitors, β-blockers and diuretics, low-sodium and high-potassium diets, and the presence of related comorbidities increase the occurrence of hyperkalemia. Hyperkalemia risk exist in all clinical departments, but there is a lack of a standardization in the management of multi- department cooperation in hospital. Therefore, a number of domestic nephrology and cardiology department experts have discussed a management model for multi-department cooperation in hyperkalemia, formulating the management standard on hospital evaluation, early warning, diagnosis and treatment, and process. This can promote each department to more effectively participate in nosocomial hyperkalemia diagnosis and treatment, as well as the long-term management of chronic hyperkalemia, improving the quality of hyperkalemia management in hospital.