OBJECTIVES: To evaluate the correlation of mean arterial oxygen tension (PaO₂) during the first 24 h following intensive care unit (ICU) admission with mortality in critically ill patients with acute kidney injury (AKI) and determine the optimal PaO₂ threshold for devising oxygen therapy strategies for these patients. METHODS: We collected the clinical data of ICU patients with AKI from the MIMIC-IV database. Based on the optimal first 24-h PaO₂ threshold determined by receiver operating characteristic (ROC) curve analysis and the Youden index maximization principle, we classified the patients into hyperoxia group (with PaO₂ ≥137.029 mmHg) and hypoxemia group (PaO₂<137.029 mm Hg). Multivariable logistic regression and propensity score matching were used to evaluate the correlation of first 24-h PaO₂ levels with in-hospital mortality of the patients. RESULTS: Among the 18 335 patients, 46.7% were in the hyperoxia group, who had an overall mortality rate of 16.9%. The optimal PaO₂ threshold (137.029 mm Hg) had a sensitivity of 78.3%, a specificity of 63.7%, and an AUC of 0.76 (95% CI: 0.74=0.78). Hyperoxia within the first 24 h after ICU admission was associated with a significantly lower in-hospital mortality (OR=0.78) and 90-day mortality (OR=0.77), particularly in stage 1 AKI patients. A non-linear relationship was identified between PaO₂ and mortality of the patients (P<0.001). Kaplan-Meier survival curves indicated a significantly increased 90-day survival rate in the patients in hyperoxia group (P<0.001), who also had shorter durations of mechanical ventilation, less vasopressor use, and shorter lengths of hospital/ICU stay. CONCLUSIONS Maintenance of a PaO₂ level ≥137.029 mmHg within 24 h after ICU admission may improve clinical outcomes of critically ill AKI patients, which underscores the importance of targeted oxygen delivery in ICU care.
Humans ; Acute Kidney Injury/blood* ; Male ; Female ; Middle Aged ; Intensive Care Units ; Aged ; Oxygen/blood* ; Hospital Mortality ; Partial Pressure ; Adult ; Databases, Factual

Renal ischemia-reperfusion injury (IRI) is a major contributor to acute kidney injury (AKI), leading to substantial morbidity and mortality. Spexin (SPX), a 14-amino acid endogenous peptide involved in metabolic regulation and immune modulation, has not yet been studied in the context of chronic treatment and renal IRI. This study evaluated the effects of exogenous SPX on renal function, histopathological changes, and molecular pathways in both IRI-induced injured and healthy kidneys. Twenty-eight male BALB/c mice were divided into four groups: control, SPX, IRI, and SPX+IRI. IRI was induced by 30 minutes of bilateral renal ischemia followed by 6 hours of reperfusion. Renal injury markers, histopathological changes, inflammatory mediators, apoptotic markers, and fibrosis-related proteins were analyzed. SPX significantly exacerbated IRI-induced kidney injury by activating the Wnt/β-catenin signaling pathway and promoting the upregulation of pro-inflammatory, pro-apoptotic, and pro-fibrotic mediators. It is noteworthy that SPX exerted more severe deleterious nephrotoxic effects in the healthy kidney compared to those observed in the IRI-induced injured kidney. These findings indicate that chronic treatment with SPX administration may have intrinsic pro-inflammatory, pro-apoptotic and fibrotic properties, raising concerns about its therapeutic potential. Further research is needed to clarify its physiological role and therapeutic implications in kidney diseases.
Animals ; Reperfusion Injury/chemically induced* ; Male ; Mice, Inbred BALB C ; Mice ; Acute Kidney Injury/metabolism* ; Kidney/blood supply* ; Peptide Hormones/adverse effects* ; Apoptosis/drug effects* ; Wnt Signaling Pathway/drug effects* ; Disease Models, Animal

OBJECTIVE: To investigate the predictive value of inflammatory indicator and serum cystatin C (Cys C) for the prognosis of patients with sepsis-associated acute kidney injury (SA-AKI). METHODS: A prospective observational study was conducted. Patients with SA-AKI admitted to the intensive care unit (ICU) of the General Hospital of Ningxia Medical University from January 2022 to December 2023 were selected as the study subjects. General patient data, sequential organ failure assessment (SOFA), acute physiology and chronic health evaluation II (APACHE II), inflammatory indicator, and serum Cys C levels were collected. The 28-day survival status of the patients was observed. A multivariate Logistic regression model was used to analyze the risk factors affecting the poor prognosis of SA-AKI patients. Receiver operator characteristic curve (ROC curve) was plotted to evaluate the predictive efficacy of each risk factor for the prognosis of SA-AKI patients. RESULTS: A total of 111 SA-AKI patients were included, with 65 patients (58.6%) in the survival group and 46 patients (41.4%) in the death group. The SOFA score, APACHE II score, interleukin-6 (IL-6), procalcitonin (PCT), hypersensitive C-reactive protein (hs-CRP), and serum Cys C levels in the death group were significantly higher than those in the survival group [SOFA score: 15.00 (14.00, 17.25) vs. 14.00 (11.00, 16.00), APACHE II score: 26.00 (23.75, 28.00) vs. 23.00 (18.50, 28.00), IL-6 (ng/L): 3 731.00±1 573.61 vs. 2 087.93±1 702.88, PCT (μg/L): 78.19±30.35 vs. 43.56±35.37, hs-CRP (mg/L): 266.50 (183.75, 326.75) vs. 210.00 (188.00, 273.00), serum Cys C (mg/L): 2.01±0.61 vs. 1.62±0.50, all P < 0.05]. Multivariate Logistic regression analysis showed that SOFA score [odds ratio (OR) = 1.273, 95% confidence interval (95%CI) was 1.012-1.600, P = 0.039], IL-6 (OR = 1.000, 95%CI was 1.000-1.001, P = 0.043), PCT (OR = 1.018, 95%CI was 1.002-1.035, P = 0.030), and Cys C (OR = 4.139, 95%CI was 1.727-9.919, P = 0.001) were independent risk factors affecting the 28-day prognosis of SA-AKI patients. ROC curve analysis showed that the area under the curve (AUC) of SOFA score, IL-6, PCT, and Cys C in predicting the 28-day prognosis of SA-AKI patients were 0.682 (95%CI was 0.582-0.782, P = 0.001), 0.753 (95%CI was 0.662-0.843, P < 0.001), 0.765 (95%CI was 0.677-0.854, P < 0.001), and 0.690 (95%CI was 0.583-0.798, P = 0.001), respectively. The combined predictive value of these four indicators for the prognosis of SA-AKI patients were superior to that of any single indicator, with an AUC of 0.847 (95%CI was 0.778-0.916, P < 0.001), a sensitivity of 95.7%, and a specificity of 56.9%. CONCLUSION The combination of SOFA score, IL-6, PCT, and Cys C provides a reliable predictive value for the prognosis of SA-AKI patients.
Humans ; Acute Kidney Injury/mortality* ; APACHE ; C-Reactive Protein ; Cystatin C/blood* ; Interleukin-6/blood* ; Logistic Models ; Predictive Value of Tests ; Procalcitonin/blood* ; Prognosis ; Prospective Studies ; Risk Factors ; ROC Curve ; Sepsis/mortality*

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 explore the influencing factors of prognosis in patients with sepsis-induced acute kidney injury undergoing continuous renal replacement therapy (CRRT), and to construct a mortality risk prediction model. METHODS: A retrospective research method was adopted, patients with sepsis-induced acute kidney injury who received CRRT at Fuyang People's Hospital from February 2021 to September 2023 were included in this study. Collect general information, comorbidities, vital signs, laboratory indicators, disease severity scores, treatment status, length of stay in the intensive care unit (ICU), and 28-day prognosis were collected within 24 hours of patient enrollment. The Cox regression model was used to identify the factors influencing prognosis in patients with sepsis-induced acute kidney injury, and a nomogram model was developed to predict mortality in these patients. Receiver operator characteristic curve (ROC curve), calibration curve, and Hosmer-Lemeshow test were used to validate the predictive performance of the nomogram model. RESULTS: A total of 146 patients with sepsis-induced acute kidney injury were included, of which 98 survived and 48 died (with a mortality of 32.88%) after 28 days of treatment. The blood lactic acid, interleukin-6 (IL-6), serum cystatin C, acute physiology and chronic health evaluation II (APACHE II), sequential organ failure assessment (SOFA), and proportion of mechanical ventilation in the death group were significantly higher than those in the survival group. The ICU stay was significantly longer than that in the survival group, and the glomerular filtration rate was significantly lower than that in the survival group. Cox regression analysis showed that blood lactic acid [odds ratio (OR) = 2.992, 95% confidence interval (95%CI) was 1.023-8.754], IL-6 (OR = 3.522, 95%CI was 1.039-11.929), serum cystatin C (OR = 3.999, 95%CI was 1.367-11.699), mechanical ventilation (OR = 4.133, 95%CI was 1.413-12.092), APACHE II score (OR = 5.013, 95%CI was 1.713-14.667), SOFA score (OR = 3.404, 95%CI was 1.634-9.959) were risk factors for mortality in patients with sepsis-induced acute kidney injury (all P < 0.05), glomerular filtration rate (OR = 0.294, 95%CI was 0.101-0.860) was a protective factor for mortality in patients with sepsis-induced acute kidney injury (P < 0.05). The ROC curve showed that the column chart model has a sensitivity of 80.0% (95%CI was 69.1%-89.2%) and a specificity of 89.3% (95%CI was 83.1%-95.2%) in predicting 28-day mortality in patients with acute kidney injury caused by sepsis. CONCLUSIONS Blood lactic acid, IL-6, mechanical ventilation, APACHEII score, SOFA score, glomerular filtration rate, and serum cystatin C are associated with the risk of death in patients with sepsis-induced acute kidney injury. The nomogram model could help early identification of mortality risk in these patients.
Humans ; Acute Kidney Injury/diagnosis* ; Sepsis/therapy* ; Retrospective Studies ; Prognosis ; Continuous Renal Replacement Therapy/methods* ; Nomograms ; Intensive Care Units ; ROC Curve ; Interleukin-6/blood* ; Proportional Hazards Models ; Female ; Male ; Cystatin C/blood* ; Middle Aged ; Risk Factors ; Lactic Acid/blood*

OBJECTIVE: To investigate the protective effect and mechanism of tumor necrosis factor receptor-associated factor 6 (TRAF6) inhibitor C25-140 on acute kidney injury (AKI) induced by acute diquat (DQ) poisoning in mice. METHODS: A total of 80 SPF grade healthy male C57BL/6 mice were randomly divided into the normal control group, DQ model group, C25-140 intervention group, and C25-140 control group, with 20 mice in each group. The DQ poisoning mouse model was established by using one-time intraperitoneal injection of 1 mL of 40 mg/kg DQ solution. The normal control group and C25-140 control group were injected with an equal amount of pure water into the peritoneal cavity. After 4 hours of model establishment, the C25-140 intervention group and C25-140 control group were given intraperitoneal injection of C25-140 5 mg/kg. The normal control group and DQ model group were given equal amounts of pure water, once a day for 7 consecutive days. After 7 days, the mice were anesthetized, eye blood was collected, and renal tissue was collected after sacrifice. The pathological changes of renal tissue were observed under a light microscope and renal tissue structure and mitochondrial changes were observed under transmission electron microscopy. The levels of serum creatinine (SCr) and blood urea nitrogen (BUN) were measured. Enzyme-linked immunosorbent assay (ELISA) was used to measure the levels of serum interleukins (IL-6, IL-1β) and tumor necrosis factor-α (TNF-α). Western blotting was used to detect the protein expression levels of TRAF6, myeloid differentiation factor 88 (MyD88), and nuclear factor-κB (NF-κB) in renal tissue. Chemical method was used to determine the content of serum malondialdehyde (MDA) and superoxide dismutase (SOD). RESULTS: During the observation period, there were no abnormal behaviors in the normal control group mice. The DQ model group mice gradually showed symptoms such as mental fatigue, fluffy fur, reduced activity, and low food intake after being exposed to the toxin, and severe cases resulted in death. The above symptoms were alleviated in the C25-140 intervention group compared to the DQ model group. Under light microscopy, HE staining showed infiltration of inflammatory cells, glomerulosclerosis, proximal tubular dilation, and vacuolization in the DQ model group, while the inflammatory response was reduced in the C25-140 intervention group compared to the DQ model group. Under transmission electron microscopy, the DQ model group showed relatively high levels of mitochondrial damage, severe swelling, increased volume, matrix dissolution, ridge fracture and loss. The degree of mitochondrial damage in the C25-140 intervention group was reduced compared to the DQ model group. Compared with the normal control group, the levels of serum SCr, BUN, IL-6, IL-1β, TNF-α, and MDA in the DQ model group were significantly increased, while the serum SOD level was significantly decreased. Compared with the DQ model group, the levels of serum SCr, BUN, IL-6, IL-1β, TNF-α, and MDA in the C25-140 intervention group were significantly reduced [SCr (μmol/L): 59.07±13.11 vs. 83.61±20.13, BUN (mmol/L): 25.83±9.95 vs. 40.78±11.53, IL-6 (ng/L): 40.76±7.03 vs. 83.33±21.83, IL-1β (ng/L): 53.87±7.82 vs. 91.74±12.53, TNF-α (ng/L): 102.52±32.13 vs. 150.92±31.75, MDA (μmol/L): 3.57±1.06 vs. 5.75±1.83], and the serum SOD level was significantly increased (kU/g: 162.52±36.13 vs. 122.72±22.13), and the differences were statistically significant (all P < 0.01). Western blotting results showed that the protein expression levels of TRAF6, NF-κB, and MyD88 in the renal tissue of DQ model group mice were significantly higher than those in the normal control group. The expression levels of the above-mentioned proteins in the C25-140 intervention group of mice were significantly lower than those in the DQ model group (TRAF6/β-actin: 1.05±0.36 vs. 1.74±0.80, NF-κB/β-actin: 0.57±0.07 vs. 1.03±0.75, MyD88/β-actin: 0.58±0.07 vs. 1.03±0.33, all P < 0.05). CONCLUSIONS TRAF6 inhibitor C25-140 can alleviate AKI induced by DQ poisoning in mice by regulating the Toll-like receptor 4 (TLR4)/TRAF6/NF-κB signaling pathway and downregulating the levels of inflammatory cytokines IL-1β, IL-6, and TNF-α.
Animals ; Male ; Acute Kidney Injury/prevention & control* ; Mice ; Mice, Inbred C57BL ; Diquat ; TNF Receptor-Associated Factor 6/metabolism* ; Interleukin-6/blood* ; Kidney/pathology* ; NF-kappa B/metabolism* ; Peptide Fragments

OBJECTIVE: To investigate the association between the glucose-to-lymphocyte ratio (GLR) and prognosis of patients with sepsis-associated acute kidney injury (SA-AKI). METHODS: Based on the Medical Information Mart for Intensive Care-IV (MIMIC-IV), SA-AKI patients aged ≥ 18 years were selected. According to the tertiles of GLR, the patients were divided into GLR1 group (GLR ≤ 4.97×10^-9 mmol), GLR2 group (4.97×10^-9 mmol < GLR < 9.75×10^-9 mmol) and GLR3 group (GLR ≥ 9.75×10^-9 mmol). Patients with SA-AKI were divided into survival group and death group according to whether they survived 28 days after admission. The patient's gender, age, vital signs, laboratory test results, comorbidities, sequential organ failure assessment (SOFA), acute physiology score III (APS III) score and treatment measures were extracted from the database. Kaplan-Meier survival analysis was used to make the survival curves of patients with SA-AKI at 28 days, 90 days, 180 days and 1 year. Multivariate Logistic regression analysis model was used to explore the independent risk factors of 28-day mortality in patients with SA-AKI. Receiver operator characteristic curve (ROC curve) was used to analyze the predictive efficacy of GLR for the prognosis of patients with SA-AKI. RESULTS: A total of 1 524 patients with SA-AKI were included, with a median age of 68.28 (58.96, 77.24) years old, including 612 females (40.16%) and 912 males (59.84%). There were 507 patients in the GLR1 group, 509 patients in the GLR2 group and 508 patients in the GLR3 group. There were 1 181 patients in the 28-day survival group and 343 patients in the death group. Grouping according to GLR tertiles showed that with the increase of GLR, the 28-day, 90-day, 180-day and 1-year mortality of SA-AKI patients gradually increased (28-day mortality were 11.64%, 22.00%, 33.86%, respectively; 90-day mortality were 15.98%, 26.72%, 40.55%, respectively; 180-day mortality were 17.16%, 28.29% and 41.73%, and the 1-year mortality were 17.95%, 29.27% and 42.72%, respectively, all P < 0.01). According to 28-day survival status, the GLR of the death group was significantly higher than that of the survival group [×10^-9 mmol: 9.81 (5.75, 20.01) vs. 6.44 (3.64, 10.78), P < 0.01]. Multivariate Logistic regression analysis showed that GLR was an independent risk factor for 28-day mortality in patients with SA-AKI [when GLR was used as a continuous variable: odds ratio (OR) = 1.065, 95% confidence interval (95%CI) was 1.045-1.085, P < 0.001; when GLR was used as a categorical variable, compared with GLR1 group: GLR2 group OR = 1.782, 95%CI was 1.200-2.647, P = 0.004; GLR3 group OR = 2.727, 95%CI was 1.857-4.005, P < 0.001]. ROC curve analysis showed that the area under the ROC curve (AUC) of GLR for predicting 28-day mortality in patients with SA-AKI was 0.674, when the optimal cut-off value was 8.769×10^-9 mmol, the sensitivity was 57.1% and the specificity was 67.1%. The predictive performance was improved when GLR was combined with APS III score and SOFA score, and the AUC was 0.806, the sensitivity was 74.6% and the specificity was 71.4%. CONCLUSIONS GLR is an independent risk factor of 28-day mortality in patients with SA-AKI, and high GLR is associated with poor prognosis in patients with SA-AKI.
Male ; Female ; Humans ; Blood Glucose ; Glucose ; ROC Curve ; Prognosis ; Sepsis/diagnosis* ; Acute Kidney Injury ; Retrospective Studies ; 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

Objectives: The study aimed to identify whether pre-operative glycosylated hemoglobin level (HbA1c) and fasting blood sugar (FBS) can be used as markers for the development of acute kidney injury (AKI) in the immediate post-operative period of type 2 diabetic patients after elective abdominal surgery.

Methods: This retrospective cohort pilot study included seventy-four diabetic patients who underwent elective abdominal surgery from 2015 to 2018. HbA1c and FBS, demographic data, comorbidities, type and indication of surgery, and treatment history were correlated with the development of AKI using logistic regression analysis.

Results: In this cohort, 12% of subjects developed AKI. Univariate and multivariate logistic regression analysis, however, showed that neither HbA1c and FBS nor other studied factors were predictive for the occurrence of AKI (OR 2.55, p= 0.26 and OR 0.64, p= 0.72 respectively).

Conclusion: Pre-operative HbA1c and one-time FBS values in diabetic patients undergoing elective abdominal surgery procedures were not statistically predictive of AKI in the present data. However, the observed trend towards the risk of AKI among the elevated HbA1c subset of patients should drive further studies with a greater sample size and of a prospective nature looking at other metabolic factors contributing to AKI.