Sepsis is a life-threatening organ dysfunction syndrome caused by a dysregulated host response to infection. Septic acute kidney injury (SAKI) is one of the most common complications of sepsis, and the occurrence of acute kidney injury (AKI) indicates that the patient's condition is critical with a poor prognosis. The traditional view holds that the main mechanism of SAKI is the reduction of renal blood flow, inadequate renal perfusion, inflammatory response, and microcirculatory dysfunction caused by sepsis, which subsequently leads to ischemia and necrosis of renal tubular cells. Recent research findings indicate that processes such as autophagy and other forms of programmed cell death play an increasingly important role. Autophagy is a programmed intracellular degradation process and is a form of programmed cell death. Cells degrade their cytoplasmic components via lysosomes, breaking down and recycling intracellular constituents to meet their metabolic needs, maintain intracellular homeostasis, and renew organelles. During SAKI, autophagy plays a crucial protective role through various mechanisms, including regulating inflammation and immune responses, clearing damaged organelles, and maintaining stability in the intracellular environment. In recent years, the role of autophagy in the pathogenesis and treatment of SAKI has received widespread attention. Research has confirmed that various intracellular signaling pathways and signaling molecules targeting autophagy [such as mammalian target of rapamycin (mTOR) signaling pathway, AMP-activated protein kinase (AMPK) signaling pathway, nuclear factor-κB (NF-κB) signaling pathway, and Sirtuins (SIRT), autophagy associated factor Beclin-1, and Toll-like receptor (TLR)] are involved in the development of SAKI. Due to the complex pathogenesis of SAKI, current treatment strategies include fluid management, infection control, maintenance of internal environment balance, and renal replacement therapy; however, the mortality remains high. In recent years, it has been found that autophagy plays a critical protective role in sepsis-mediated AKI. As a result, an increasing number of drugs are being developed to alleviate SAKI by regulating autophagy. This article reviews the latest advances in the role of autophagy in the pathogenesis and treatment of SAKI, with the aim of providing insights for the development of new drugs for SAKI patients.
Humans ; Acute Kidney Injury/etiology* ; Autophagy ; Sepsis/complications* ; Signal Transduction

OBJECTIVE: To assess the relationship between blood pressure reactivity index (BPRI) and in-hospital mortality risk in patients with sepsis-associated acute kidney injury (SA-AKI). METHODS: A retrospective cohort study was conducted to collect data from patients admitted to the intensive care unit (ICU) and clinically diagnosed with SA-AKI between 2008 and 2019 in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database in the United States. The collected data included demographic characteristics, comorbidities, vital signs, laboratory parameters, sequential organ failure assessment (SOFA) and simplified acute physiology scoreII(SAPSII) within 48 hours of SA-AKI diagnosis, stages of AKI, treatment regimens, mean BPRI during the first and second 24 hours (BPRI_0_24, BPRI_24_48), and outcome measures including primary outcome (in-hospital mortality) and secondary outcomes (ICU length of stay and total hospital length of stay). Variables with statistical significance in univariate analysis were included in LASSO regression analysis for variable selection, and the selected variables were subsequently incorporated into multivariate Logistic regression analysis to identify independent predictors associated with in-hospital mortality in SA-AKI patients. Restricted cubic spline (RCS) analysis was employed to examine whether there was a linear relationship between BPRI within 48 hours and in-hospital mortality in SA-AKI patients. Basic prediction models were constructed based on the independent predictors identified through multivariate Logistic regression analysis, and receiver operator characteristic curve (ROC curve) was plotted to evaluate the predictive performance of each basic prediction model before and after incorporating BPRI. RESULTS: A total of 3 517 SA-AKI patients admitted to the ICU were included, of whom 826 died during hospitalization and 2 691 survived. The BPRI values within 48 hours of SA-AKI diagnosis were significantly lower in the death group compared with the survival group [BPRI_0_24: 4.53 (1.81, 8.11) vs. 17.39 (5.16, 52.43); BPRI_24_48: 4.76 (2.42, 12.44) vs. 32.23 (8.85, 85.52), all P < 0.05]. LASSO regression analysis identified 20 variables with non-zero coefficients that were included in the multivariate Logistic regression analysis. The results showed that respiratory rate, temperature, pulse oxygen saturation (SpO₂), white blood cell count (WBC), hematocrit (HCT), activated partial thromboplastin time (APTT), lactate, oxygenation index, SOFA score, fluid balance (FB), BPRI_0_24, and BPRI_24_48 were all independent predictors for in-hospital mortality in SA-AKI patients (all P < 0.05). RCS analysis revealed that both BPRI showed "L"-shaped non-linear relationships with the risk of in-hospital mortality in SA-AKI patients. When BPRI_0_24 ≤ 14.47 or BPRI_24_48 ≤ 24.21, the risk of in-hospital mortality in SA-AKI increased as BPRI values decreased. Three basic prediction models were constructed based on the identified independent predictors: Model 1 (physiological indicator model) included respiratory rate, temperature, SpO₂, and oxygenation index; Model 2 (laboratory indicator model) included WBC, HCT, APTT, and lactate; Model 3 (scoring indicator model) included SOFA score and FB. ROC curve analysis showed that the predictive performance of the basic models ranked from high to low as follows: Model 3, Model 2, and Model 1, with area under the curve (AUC) values of 0.755, 0.661, and 0.655, respectively. The incorporation of BPRI indicators resulted in significant improvement in the discriminative ability of each model (all P < 0.05), with AUC values increasing to 0.832 for Model 3+BPRI, 0.805 for Model 2+BPRI, and 0.808 for Model 1+BPRI. CONCLUSIONS BPRI is an independent predictor factor for in-hospital mortality in SA-AKI patients. Incorporating BPRI into the prediction model for in-hospital mortality risk in SA-AKI can significantly improve its predictive capability.
Humans ; Acute Kidney Injury/mortality* ; Sepsis/complications* ; Retrospective Studies ; Hospital Mortality ; Prognosis ; Blood Pressure ; Intensive Care Units ; Male ; Female ; Length of Stay ; Middle Aged ; Aged ; Adult ; Logistic Models

OBJECTIVE: To identify and validate novel biomarkers for the early diagnosis of sepsis-associated acute kidney injury (SA-AKI) and precise continuous renal replacement therapy (CRRT) using proteomics. METHODS: A prospective multicenter cohort study was conducted. Patients with sepsis admitted to five hospitals in Taizhou City of Zhejiang Province from April 2019 to December 2021 were continuously enrolled, based on the occurrence of acute kidney injury (AKI). Sepsis patients were divided into SA-AKI group and non-SA-AKI group, and healthy individuals who underwent physical examinations during the same period were used as control (NC group). Peripheral blood samples from participants were collected for protein mass spectrometry analysis. Differentially expressed proteins were identified, and functional enrichment analysis was conducted on these proteins. The levels of target proteins were detected by enzyme linked immunosorbent assay (ELISA), and the predictive value of target protein for SA-AKI were evaluated by receiver operator characteristic curve (ROC curve). Additionally, sepsis patients and healthy individuals were selected from one hospital to externally verify the expression level of the target protein and its predictive value for SA-AKI, as well as the accuracy of CRRT treatment. RESULTS: A total of 37 patients with sepsis (including 19 with AKI and 18 without AKI) and 31 healthy individuals were enrolled for proteomic analysis. Seven proteins were identified with significantly differential expression between the SA-AKI group and non-SA-AKI group: namely cystatin C (CST3), β ₂-microglobulin (β ₂M), insulin-like growth factor-binding protein 4 (IGFBP4), complement factor I (CFI), complement factor D (CFD), CD59, and glycoprotein prostaglandin D2 synthase (PTGDS). Functional enrichment analysis revealed that these proteins were involved in immune response, complement activation, coagulation cascade, and neutrophil degranulation. ELISA results demonstrated specific expression of each target protein in the SA-AKI group. Additionally, 65 patients with sepsis (38 with AKI and 27 without AKI) and 20 healthy individuals were selected for external validation of the 7 target proteins. ELISA results showed that there were statistically significant differences in the expression levels of CST3, β ₂M, IGFBP4, CFD, and CD59 between the SA-AKI group and non-SA-AKI group. ROC curve analysis indicated that the area under the curve (AUC) values of CST3, β ₂M, IGFBP4, CFD, and CD59 for predicting SA-AKI were 0.788, 0.723, 0.723, 0.795, and 0.836, respectively, all exceeding 0.7. Further analysis of patients who underwent CRRT or not revealed that IGFBP4 had a good predictive value, with an AUC of 0.84. CONCLUSIONS Based on proteomic analysis, CST3, β ₂M, IGFBP4, CFD, and CD59 may serve as potential biomarkers for the diagnosis of SA-AKI, among which IGFBP4 might be a potential biomarker for predicting the need for CRRT in SA-AKI patients. However, further clinical validation is required.
Humans ; Sepsis/complications* ; Acute Kidney Injury/blood* ; Proteomics ; Prospective Studies ; Biomarkers/blood* ; Male ; Female ; beta 2-Microglobulin/blood* ; Middle Aged ; Cystatin C/blood* ; Aged

OBJECTIVE: To investigate the effects of liriodendrin on the intestinal flora and the ferroptosis signaling pathway in renal tissue of rats with sepsis-induced acute kidney injury (AKI). METHODS: Thirty male Sprague-Dawley (SD) rats were randomly divided into sham operation group (Sham group), sepsis model induced by cecal ligation and puncture group (CLP group), and liriodendrin intervention group (CLP+LIR group), with 10 rats in each group. The CLP+LIR group was given 0.2 mL of 100 mg/kg liriodendrin by gavage 2 hours before modeling; Sham group and CLP group were given the same volume of normal saline by gavage. The samples were collected after anesthesia 24 hours after modeling. The pathological changes of renal tissue were observed by hematoxylin-eosin (HE) staining. The levels of inflammatory factors such as tumor necrosis factor-α (TNF-α), interleukins (IL-1β, IL-6) were detected by enzyme linked immunosorbent assay (ELISA). The levels of renal function indicators such as creatinine (Cr), and urea nitrogen (UREA) in peripheral blood, and the content of malondialdehyde (MDA) and Fe²⁺ in renal tissue were detected. Western blotting was used to detect the expressions of nuclear factor E2-related factor 2 (Nrf2), glutathione peroxidase 4 (GPX4) and heme oxygenase-1 (HO-1) in renal tissues. The changes of intestinal flora were detected by 16S rDNA high-throughput sequencing. RESULTS: Compared with the Sham group, the CLP group showed significantly enlarged glomeruli, noticeable renal interstitial edema, disorganized kidney tissue, and significantly increased pathological scores. The contents of TNF-α, IL-1β, IL-6, Cr, and UREA in peripheral blood and the levels of MDA and Fe²⁺ in renal tissue were significantly increased. The protein expressions of Nrf2, GPX4, and HO-1 in renal tissue were significantly down-regulated. The species richness of intestinal flora decreased significantly, and the relative abundances of pathogenic bacteria such as Morganella, Citrobacter, Proteus, Klebsiella, Shigella, Aggregatibacter, and Enterococcus increased significantly, while the relative abundances of beneficial bacteria such as Butyricimonas, Veillonella, Prevotella, Lactobacillus, Bifidobacterium, and Ruminococcus decreased significantly. Compared with the CLP group, CLP+LIR group could significantly reduce the pathological damage of renal tissue, the pathological score significantly decreased (1.80±0.84 vs. 4.20±1.30, P < 0.05), and improve the composition of intestinal flora, reduce the relative abundances of pathogenic bacteria such as Proteus, Klebsiella, Shigella, Aggregatibacter, and Enterococcus, and significantly increase the relative abundances of Lactobacillus, Bifidobacterium, and Ruminococcus, significantly reduce the contents of TNF-α, IL-1β, IL-6, Cr, and UREA in peripheral blood and the levels of MDA and Fe²⁺ in renal tissue [blood TNF-α (ng/L): 191.31±7.23 vs. 254.90±47.89, blood IL-1β (ng/L): 11.15±4.04 vs. 23.06±1.67, blood IL-6 (ng/L): 163.20±17.83 vs. 267.69±20.92, blood Cr (μmol/L): 24.14±4.25 vs. 41.17±5.43, blood UREA (mmol/L): 4.59±0.90 vs. 8.01±1.07, renal MDA (μmol/g): 9.67±0.46 vs. 16.05±0.88, renal Fe²⁺ (mg/g): 0.71±0.07 vs. 0.93±0.04, all P < 0.05], and increase the protein expressions of Nrf2, GPX4, and HO-1 (Nrf2/GAPDH: 1.21±0.01 vs. 0.39±0.01, GPX4/GAPDH: 0.74±0.04 vs. 0.48±0.04, HO-1/GAPDH: 0.91±0.01 vs. 0.41±0.02, all P < 0.05). CONCLUSIONS Liriodendrin has an obvious protective effect on sepsis-induced AKI. The mechanism may involve regulating the intestinal flora, increasing the activation of the Nrf2/HO-1/GPX4 signaling pathway in renal tissue, and reducing ferroptosis.
Animals ; Acute Kidney Injury/microbiology* ; Rats, Sprague-Dawley ; Sepsis/complications* ; Male ; Ferroptosis/drug effects* ; Gastrointestinal Microbiome/drug effects* ; Rats ; Signal Transduction ; Kidney/metabolism* ; Tumor Necrosis Factor-alpha/metabolism*

Humans ; Retrospective Studies ; Cardiopulmonary Bypass/adverse effects* ; Coronary Artery Bypass/adverse effects* ; Coronary Artery Disease/surgery* ; Acute Kidney Injury/etiology* ; Postoperative Complications ; Risk Factors ; Treatment Outcome

OBJECTIVE: To investigate the predictive value of pulse infusion index (PPI) in the short-term prognosis of patients with sepsis-induced acute kidney injury (AKI). METHODS: A retrospective cohort study was conducted. The clinical data of patients with sepsis-induced AKI admitted to intensive care unit (ICU) of the First Affiliated Hospital of Soochow University from July 2021 to December 2022 were enrolled. The basic information of the patients were collect, including age, gender, site of infection, underlying disease, mean arterial pressure (MAP) and heart rate (HR) at admission, as well as the use of mechanical ventilation and vasoactive drugs, and norepinephrine (NE) dosage. Laboratory indicators, sequential organ failure assessment (SOFA) score and PPI within 24 hours of admission were also recorded, and the patient's prognosis during ICU hospitalization was also recorded. The differences in clinical data between the patients of two groups with different prognosis were compared. Spearman correlation method was used to analyze the correlation between PPI and SOFA score. Binary multivariate Logistic regression analysis was used to screen independent risk factors for death during ICU hospitalization in sepsis patients with AKI. Receiver operator characteristic curve (ROC curve) was plotted to evaluate the predictive value of PPI for the short-term prognosis of patients with sepsis-induced AKI. RESULTS: A total of 102 patients with sepsis-induced AKI were enrolled, of which 70 patients in the survival group and 32 patients in the death group, with ICU mortality of 31.4. Compared with the survival group, SOFA score, HR, procalcitonin (PCT), serum creatinine (SCr), and NE dosage in the death group were significantly increased [SOFA score: 11.22±2.48 vs. 8.56±2.01, HR (bpm): 103.80±12.47 vs. 97.41±9.73, PCT (μg/L): 9.22 (5.24, 17.84) vs. 6.19 (3.86, 7.71), SCr (μmol/L): 163.2 (104.7, 307.9) vs. 125.5 (89.3, 221.0), Lac (mmol/L): 2.81 (1.95, 4.22) vs. 2.13 (1.74, 2.89), NE usage (μg×kg^-1×min^-1): 0.7 (0.4, 1.1) vs. 0.5 (0.2, 0.6), all P < 0.05], while PPI was significantly lower than that in survival group [0.83 (0.42, 1.55) vs. 1.70 (1.14, 2.20), P < 0.01]. Spearman correlation analysis showed that based on SOFA score, PPI was closely related to the severity of patients with sepsis-induced AKI (r = -0.328, P < 0.05). Binary multivariate Logistic regression analysis showed that PPI [odds ratio (OR) = 0.590, 95% confidence interval (95%CI) was 0.361-0.966, P = 0.002], SOFA score (OR = 1.406, 95%CI was 1.280-1.545, P < 0.001), PCT (OR = 2.061, 95%CI was 1.267-3.350, P = 0.006) were independent risk factors of the short-term prognosis of patients with sepsis-induced AKI. ROC curve analysis showed that the area under the ROC curve (AUC) of PPI for death during ICU hospitalization in patients with sepsis-induced AKI was 0.779 (95%CI was 0.686-0.855, P < 0.001), which superior to PCT (AUC = 0.677, 95%CI was 0.577-0.766, P = 0.004), and similar to SOFA score (AUC = 0.794, 95%CI was 0.703-0.868, P < 0.001). When the cut-off value of PPI was 0.72, the sensitivity was 50.0%, and the specificity was 97.1%. CONCLUSIONS PPI has a good predictive value for the short-term prognosis of patients with sepsis-induced AKI during ICU hospitalization.
Humans ; Heart Rate ; Retrospective Studies ; ROC Curve ; Sepsis/complications* ; Prognosis ; Procalcitonin ; Acute Kidney Injury/etiology* ; Intensive Care Units

OBJECTIVES: Abdominal aortic aneurysm is a pathological condition in which the abdominal aorta is dilated beyond 3.0 cm. The surgical options include open surgical repair (OSR) and endovascular aneurysm repair (EVAR). Prediction of acute kidney injury (AKI) after OSR is helpful for decision-making during the postoperative phase. To find a more efficient method for making a prediction, this study aims to perform tests on the efficacy of different machine learning models. METHODS: Perioperative data of 80 OSR patients were retrospectively collected from January 2009 to December 2021 at Xiangya Hospital, Central South University. The vascular surgeon performed the surgical operation. Four commonly used machine learning classification models (logistic regression, linear kernel support vector machine, Gaussian kernel support vector machine, and random forest) were chosen to predict AKI. The efficacy of the models was validated by five-fold cross-validation. RESULTS: AKI was identified in 33 patients. Five-fold cross-validation showed that among the 4 classification models, random forest was the most precise model for predicting AKI, with an area under the curve of 0.90±0.12. CONCLUSIONS Machine learning models can precisely predict AKI during early stages after surgery, which allows vascular surgeons to address complications earlier and may help improve the clinical outcomes of OSR.
Humans ; Aortic Aneurysm, Abdominal/complications* ; Endovascular Procedures/methods* ; Retrospective Studies ; Blood Vessel Prosthesis Implantation/adverse effects* ; Acute Kidney Injury/etiology* ; Machine Learning ; Treatment Outcome ; Postoperative Complications/etiology* ; Risk Factors

The risk of developing perioperative acute kidney injury (AKI) in elderly patients increases with age. The combined involvement of aging kidneys, coexisting multiple underlying chronic diseases, and increased exposure to potential renal stressors and nephrotoxic drugs or invasive procedures constitute susceptibility factors for AKI in elderly patients. The perioperative AKI in elderly patients undergoing noncardiac surgery has its own specific population characteristics, so it is necessary to further explore the characteristics of AKI in elderly patients in terms of epidemiology, clinical diagnosis, risk factors, and preventive and curative measures to provide meaningful clinical advice to improve prognosis, accelerate recovery, and reduce medical burden in elderly patients. Since AKI has the fastest-growing incidence in older patients and is associated with a worse prognosis, early detection, early diagnosis, and prevention of AKI are important for elderly patients in the perioperative period. Large, multicenter, randomized controlled clinical studies in elderly non-cardiac surgery patients with AKI can be conducted in the future, with the aim of providing the evidence to reduce of the incidence of AKI and to improve the prognosis of patients.
Humans ; Aged ; Acute Kidney Injury/prevention & control* ; Kidney ; Risk Factors ; Prognosis ; Incidence ; Postoperative Complications/prevention & control*

OBJECTIVE: To establish a prediction model of acute kidney injury (AKI) in moderate and severe burn patients, so as to provide basic research evidence for early identification of burn-related AKI. METHODS: Patients who were admitted to the department of plastic burn surgery of the Affiliated Hospital of Southwest Medical University from November 2018 to January 2021 were selected, and their clinical characteristics, laboratory examinations and other indicators were recorded. Multivariate Logistic regression analysis was used to screen out the risk factors of AKI related to moderate and severe burns, and R software was used to establish the nomogram of moderate and severe burn patients complicated with AKI. The Bootstrap method model was used for internal verification by repeating sample for 1 000 times. Consistency index and calibration curve were used to evaluate the accuracy of the model, and the receiver operator characteristic curve (ROC curve) and the area under the curve (AUC) were used to evaluate the prediction efficiency, decision curve analysis (DCA) was used to evaluate the clinical utility of the model. RESULTS: A total of 186 patients with moderate and severe burn were included, among which 54 patients suffered from AKI, and the incidence rate was 29.03%. Multivariate Logistic regression analysis showed that the total burn surface area [TBSA; odds ratio (OR) = 1.072, 95% confidence interval (95%CI) was 1.031-1.115, P = 0.001], estimated glomerular filtration rate (eGFR; OR = 0.960, 95%CI was 0.931-0.990, P = 0.010), neutrophil (NEU; OR = 1.190, 95%CI was 1.021-1.386, P = 0.026), neutrophil/lymphocyte ratio (NLR; OR = 0.867, 95%CI was 0.770-0.977, P = 0.019), D-dimer (OR = 4.603, 95%CI was 1.792-11.822, P = 0.002) were the risk factors for patients with moderate and severe burn complicated with AKI. Taking the above indexes as predictive factors, a nomogram prediction model was established, the ROC curve was plotted with AUC of 0.998 (95%CI was 0.988-1.000). Optimum threshold of ROC curve was -0.862, the sensitivity was 98.0% and the specificity was 98.2%, and the consistency index was 0.998 (95%CI was 0.988-1.000). The calibration curve showed that the prognostic nomogram model was accurate, DCA showed that most patients can benefit from this model. CONCLUSIONS The burned patients with higher TBSA, NEU, NLR, D-dimer and lower eGFR tend to suffer from AKI. The nomogram based on the above five risk factors has high accuracy and clinical value, which can be used as a predictive tool to evaluate the risk of AKI in moderate and severe burn patients.
Humans ; Prognosis ; Nomograms ; Retrospective Studies ; Burns/complications* ; Acute Kidney Injury/etiology* ; ROC Curve

Humans ; Child ; COVID-19/complications* ; Brain Diseases ; Myositis/complications* ; Kidney ; Liver Failure, Acute/complications* ; Acute Kidney Injury/complications*