Development and Validation of a Nomogram for Predicting Acute Kidney Injury in Septic Patients.

Purpose: Sepsis-associated acute kidney injury (S-AKI) is associated with increased morbidity and mortality. We aimed to develop a nomogram for predicting the risk of S-AKI patients. Patients and Methods: We collected data from septic patients admitted to the Provincial Hospital Affiliated with Shandong First Medical University from January 2019 to September 2022. Septic patients were divided into two groups based on the occurrence of AKI. A nomogram was developed by multiple logistic regression analyses. The performance of the nomogram was evaluated using C-statistics, calibration curves, and decision curve analysis (DCA). The validation cohort contained 70 patients between December 2022, and March 2023 in the same hospital. Results: 198 septic patients were enrolled in the training cohort. Multivariate logistic regression analysis showed that neutrophil gelatinase-associated lipocalin (NGAL), platelet-to-lymphocyte ratio (PLR), and vasopressor use were independent risk factors for S-AKI. A nomogram was developed based on these factors. C-statistics for the training and validation cohorts were respectively 0.873 (95% CI 0.825-0.921) and 0.826 (95% CI 0.727-0.924), indicating high prediction accuracy. The calibration curves showed good concordance. DCA revealed that the nomogram was of great clinical value. Conclusion: The nomogram presents early and effective prediction for the S-AKI patients, and provides optimal intervention to improve patient outcomes.

Uncovering the mechanisms of diosmin in treating obesity-related kidney injury based on network pharmacology, molecular docking, and in vitro validation.

Obesity increases the risk of kidney injury, involving various pathological events such as inflammation, insulin resistance, lipid metabolism disorders, and hemodynamic changes, making it a significant risk factor for the development and progression of chronic kidney disease. Diosmin, a natural flavonoid glycoside, exhibits anti-inflammatory, antioxidant, anti-lipid, and vasodilatory effects. However, whether diosmin has a protective effect on obesity-related kidney injury remains unclear. The molecular formula of diosmin was obtained, and diosmin and target genes related to obesity-related kidney injury were screened. The interaction between overlapping target genes was analyzed. GO functional enrichment and KEGG pathway enrichment analyses were performed on overlapping target genes. Molecular docking was employed to assess the binding strength between overlapping target genes. Palmitic acid-induced damage to HK-2 cells, which were then treated with diosmin. Subsequently, the expression levels of relevant mRNAs and proteins were measured. Network analysis identified 219 potential diosmin target genes, 6800 potential target genes related to obesity-related kidney injury, and 93 potential overlapping target genes. Protein-protein interaction networks and molecular docking results revealed that AKT1, TNF-α, SRC, EGFR, ESR1, CASP3, MMP9, PPAR-γ, GSK-3ß, and MMP2 were identified as key therapeutic targets, and they exhibited stable binding with diosmin. GO analysis indicated that these key targets may participate in inflammation, chemical stress, and protein phosphorylation. KEGG revealed that pathways in cancer, AGE-RAGE signaling pathway, PI3K-AKT signaling pathway, PPAR signaling pathway, and insulin resistance as crucial in treating obesity-related kidney injury. CCK-8 assay showed that diosmin significantly restored the viability of HK-2 cells affected by palmitic acid. Oil Red O staining demonstrated that diosmin significantly improved lipid deposition in HK-2 cells induced by palmitic acid. PCR results showed that diosmin inhibited the mRNA levels of AKT1, TNF-α, EGFR, ESR1, CASP3, MMP9, GSK-3ß, and MMP2 while promoting the mRNA level of PPAR-γ. Western blot analysis revealed that diosmin restored PPAR-γ protein expression, inhibited NF-kB p-p65 protein expression, and reduced TNF-α protein expression. Diosmin demonstrated multi-target and multi-pathway effects in the treatment of obesity-associated renal injury, with key targets including AKT1, TNF-α, EGFR, ESR1, CASP3, MMP9, PPAR-γ, GSK-3ß, and MMP2. The mechanism may be through the modulation of the PPAR-γ/NF-κB signaling pathway, which can attenuate inflammatory responses and protect the kidney.

Flavonoids intake and weight-adjusted waist index: insights from a cross-sectional study of NHANES.

This study conducted data on 15,446 adults to explore the impact of flavonoids on weight-adjusted waist index (WWI). This was a nationwide cross-sectional study among US adults aged 20 years or older. Dietary intake of flavonoids was assessed through 24-h recall questionnaire. WWI was calculated by dividing waist circumference (WC) by the square root of weight. We utilized weighted generalized linear regression to evaluate the association between flavonoids intake and WWI, and restricted cubic splines (RCS) to explore potential non-linear relationships. Our findings indicated that individuals with lower WWI experienced a notable increase in their consumption of total flavonoids, flavanones, flavones, flavan-3-ols, and anthocyanidins intake (ß (95% CI); -0.05(-0.09, -0.01); -0.07(-0.13, 0.00); -0.07(-0.11, -0.02); -0.06(-0.11, 0.00); -0.13(-0.18, -0.08), respectively), with the exception of flavonols and isoflavones. Additionally, consumption of total flavonoids, flavonols, flavanones, isoflavones, and flavan-3-ols had a non-linear relationship with WWI (all P for non-linearity < 0.05). Furthermore, the effect of total flavonoids on WWI varied in race (P for interaction = 0.011), gender (P for interaction = 0.038), and poverty status (P for interaction = 0.002). These findings suggested that increase the intake of flavonoids might prevent abdominal obesity, but further prospective studies are requested before dietary recommendation.

Development and validation of a nomogram for predicting in-hospital death in cirrhotic patients with acute kidney injury.

BACKGROUND: The purpose of this study was to develop a nomogram for predicting in-hospital mortality in cirrhotic patients with acute kidney injury (AKI) in order to identify patients with a high risk of in-hospital death early. METHODS: This study collected data on cirrhotic patients with AKI from 2008 to 2019 using the Medical Information Mart for Intensive Care IV. Multivariate logistic regression was used to identify confounding factors related to in-hospital mortality, which were then integrated into the nomogram. The concordance index (C-Index) was used to evaluate the accuracy of the model predictions. The area under the curve (AUC) and decision curve analysis (DCA) was used to assess the predictive performance and clinical utility of the nomogram. RESULTS: The final study population included 886 cirrhotic patients with AKI, and 264 (29.8%) died in the hospital. After multivariate logistic regression, age, gender, cerebrovascular disease, heart rate, respiration rate, temperature, oxygen saturation, hemoglobin, blood urea nitrogen, serum creatinine, international normalized ratio, bilirubin, urine volume, and sequential organ failure assessment score were predictive factors of in-hospital mortality. In addition, the nomogram showed good accuracy in estimating the in-hospital mortality of patients. The calibration plots showed the best agreement with the actual presence of in-hospital mortality in patients. In addition, the AUC and DCA curves showed that the nomogram has good prediction accuracy and clinical value. CONCLUSIONS: We have created a prognostic nomogram for predicting in-hospital death in cirrhotic patients with AKI, which may facilitate timely intervention to improve prognosis in these patients.

Interpretable machine learning model for predicting acute kidney injury in critically ill patients.

BACKGROUND: This study aimed to create a method for promptly predicting acute kidney injury (AKI) in intensive care patients by applying interpretable, explainable artificial intelligence techniques. METHODS: Population data regarding intensive care patients were derived from the Medical Information Mart for Intensive Care IV database from 2008 to 2019. Machine learning (ML) techniques with six methods were created to construct the predicted models for AKI. The performance of each ML model was evaluated by comparing the areas under the curve (AUC). Local Interpretable Model-Agnostic Explanations (LIME) method and Shapley Additive exPlanation values were used to decipher the best model. RESULTS: According to inclusion and exclusion criteria, 53,150 severely sick individuals were included in the present study, of which 42,520 (80%) were assigned to the training group, and 10,630 (20%) were allocated to the validation group. Compared to the other five ML models, the eXtreme Gradient Boosting (XGBoost) model greatly predicted AKI following ICU admission, with an AUC of 0.816. The top four contributing variables of the XGBoost model were SOFA score, weight, mechanical ventilation, and the Simplified Acute Physiology Score II. An AKI and Non-AKI cases were predicted separately using the LIME algorithm. CONCLUSION: Overall, the constructed clinical feature-based ML models are excellent in predicting AKI in intensive care patients. It would be constructive for physicians to provide early support and timely intervention measures to intensive care patients at risk of AKI.

SGLT2 inhibition, high-density lipoprotein, and kidney function: a mendelian randomization study.

BACKGROUND: Sodium-glucose cotransporter 2 (SGLT2) inhibition is recognized for its evident renoprotective benefits in diabetic renal disease. Recent data suggest that SGLT2 inhibition also slows down kidney disease progression and reduces the risk of acute kidney injury, regardless of whether the patient has diabetes or not, but the mechanism behind these observed effects remains elusive. The objective of this study is to utilize a mendelian randomization (MR) methodology to comprehensively examine the influence of metabolites in circulation regarding the impact of SGLT2 inhibition on kidney function. METHODS: We used a MR study to obtain associations between genetic proxies for SGLT2 inhibition and kidney function. We retrieved the most recent and comprehensive summary statistics from genome-wide association studies (GWAS) that have been previously published and involved kidney function parameters such as estimated glomerular filtration rate (eGFR), urine albumin-to-creatinine ratio (UACR), and albuminuria. Additionally, we included blood metabolite data from 249 biomarkers in the UK Biobank for a more comprehensive analysis. We performed MR analyses to explore the causal relationships between SGLT2 inhibition and kidney function and two-step MR to discover potential mediating metabolites. RESULTS: The study found that a decrease in HbA1c levels by one standard deviation, which is genetically expected to result in SGLT2 inhibition, was linked to a decreased likelihood of developing type 2 diabetes mellitus (T2DM) (odds ratio [OR] = 0.55 [95% CI 0.35, 0.85], P = 0.007). Meanwhile, SGLT2 inhibition also protects eGFR (ß = 0.05 [95% CI 0.03, 0.08], P = 2.45 × 10- 5) and decreased UACR (-0.18 [95% CI -0.33, -0.02], P = 0.025) and albuminuria (-1.07 [95% CI -1.58, -0.57], P = 3.60 × 10- 5). Furthermore, the study found that of the 249 metabolites present in the blood, only one metabolite, specifically the concentration of small high-density lipoprotein (HDL) particles, was significantly correlated with both SGLT2 inhibition and kidney function. This metabolite was found to play a crucial role in mediating the improvement of renal function through the use of SGLT2 inhibition (ß = 0.01 [95% CI 0.005, 0.018], P = 0.001), with a mediated proportion of 13.33% (95% CI [5.71%, 26.67%], P = 0.020). CONCLUSIONS: The findings of this investigation provide evidence in favor of a genetically anticipated biological linkage between the inhibition of SGLT2, the presence of circulating metabolites, and renal function. The findings demonstrate that the protective effect of SGLT2 inhibition on renal function is mostly mediated by HDL particle concentrations in circulating metabolites. These results offer significant theoretical support for both the preservation of renal function and a better comprehension of the mechanisms underlying SGLT2 inhibition.

Obesity and septic patient outcomes: Shaping the puzzle through age and sex perspectives.

BACKGROUND & AIMS: While obesity has been reported as a protective factor in septic patients, little is known about the potential modifying effects of age and sex. The objective of this study is to investigate age and sex-specific associations between obesity and the prognosis of septic patients. METHODS: A retrospective analysis was conducted on a cohort of 15,464 septic patients, categorized by body mass index (BMI) into four groups: underweight (<18.5 kg/m2, n = 483), normal (18.5-24.9 kg/m2, n = 4344), overweight (25-29.9 kg/m2, n = 4949) and obese (≥30 kg/m2, n = 5688). Multivariable logistic regression and inverse probability weighting were employed to robustly confirm the protective effect of a higher BMI on 28-day mortality, with normal weight serving as the reference category. Subgroup analyses based on age (young: 18-39, middle-aged: 40-64 and elderly: ≥65) and sex were performed. RESULTS: The findings demonstrate that high BMI independently confers a protective effect against 28-day mortality in septic patients. However, the relationship between BMI and 28-day mortality exhibits a non-linear trend, with a BMI of 34.5 kg/m2 displaying the lowest odds ratio. Notably, the survival benefits associated with a high BMI were not observed in the young group. Moreover, being underweight emerges as an independent risk factor for middle-aged and elderly female patients, while in males it is only a risk factor in the elderly group. Interestingly, being overweight and obese were identified as independent protective factors in middle-aged and elderly male patients, but not in females. CONCLUSIONS: The effect of BMI on mortality in septic patients varies according to age and sex. Elderly individuals with sepsis may derive more prognostic benefits from obesity.

Machine learning algorithm for predict the in-hospital mortality in critically ill patients with congestive heart failure combined with chronic kidney disease.

BACKGROUND: The objective of this study was to develop and validate a machine learning (ML) model for predict in-hospital mortality among critically ill patients with congestive heart failure (CHF) combined with chronic kidney disease (CKD). METHODS: After employing least absolute shrinkage and selection operator regression for feature selection, six distinct methodologies were employed in the construction of the model. The selection of the optimal model was based on the area under the curve (AUC). Furthermore, the interpretation of the chosen model was facilitated through the utilization of SHapley Additive exPlanation (SHAP) values and the Local Interpretable Model-Agnostic Explanations (LIME) algorithm. RESULTS: This study collected data and enrolled 5041 patients on CHF combined with CKD from 2008 to 2019, utilizing the Medical Information Mart for Intensive Care Unit. After selection, 22 of the 47 variables collected post-intensive care unit admission were identified as mortality-associated and subsequently utilized in the development of ML models. Among the six models generated, the eXtreme Gradient Boosting (XGBoost) model demonstrated the highest AUC at 0.837. Notably, the SHAP values highlighted the sequential organ failure assessment score, age, simplified acute physiology score II, and urine output as the four most influential variables in the XGBoost model. In addition, the LIME algorithm explains the individualized predictions. CONCLUSIONS: In conclusion, our study accomplished the successful development and validation of ML models for predicting in-hospital mortality in critically ill patients with CHF combined with CKD. Notably, the XGBoost model emerged as the most efficacious among all the ML models employed.

Diosmin ameliorates renal fibrosis through inhibition of inflammation by regulating SIRT3-mediated NF-κB p65 nuclear translocation.

BACKGROUND: Renal fibrosis is considered an irreversible pathological process and the ultimate common pathway for the development of all types of chronic kidney diseases and renal failure. Diosmin is a natural flavonoid glycoside that has antioxidant, anti-inflammatory, and antifibrotic activities. However, whether Diosmin protects kidneys by inhibiting renal fibrosis is unknown. We aimed to investigate the role of Diosmin in renal interstitial fibrosis and to explore the underlying mechanisms. METHODS: The UUO mouse model was established and gavaged with Diosmin (50 mg/kg·d and 100 mg/kg·d) for 14 days. HE staining, Masson staining, immunohistochemistry, western blotting and PCR were used to assess renal tissue injury and fibrosis. Elisa kits were used to detect the expression levels of IL-1ß, IL-6, and TNF-α and the activity of SIRT3 in renal tissues. In addition, enrichment maps of RNA sequencing analyzed changes in signaling pathways. In vitro, human renal tubular epithelial cells (HK-2) were stimulated with TGF-ß1 and then treated with diosmin (75 µM). The protein and mRNA expression levels of SIRT3 were detected in the cells. In addition, 3-TYP (selective inhibitor of SIRT3) and SIRT3 small interfering RNA (siRNA) were used to reduce SIRT3 levels in HK-2. RESULTS: Diosmin attenuated UUO-induced renal fibrosis and TGF-ß1-induced HK-2 fibrosis. In addition, Diosmin reduced IL-1ß, IL-6, and TNF-α levels in kidney tissues and supernatants of HK-2 medium. Interestingly, Diosmin administration increased the enzymatic activity of SIRT3 in UUO kidneys. In addition, Diosmin significantly increased mRNA and protein expression of SIRT3 in vitro and in vivo. Inhibition of SIRT3 expression using 3-TYP or SIRT3 siRNA abolished the anti-inflammatory effects of diosmin in HK-2 cells. Enrichment map analysis by RNA sequencing indicates that the nuclear factor-kappa B (NF-κB) signaling pathway was inhibited in the Diosmin intervention group. Furthermore, we found that TGF-ß1 increased the nuclear expression of nuclear NF-κB p65 but had little significant effect on the total intracellular expression of NF-κB p65. Additionally, Diosmin reduced TGF-ß1-caused NF-κB p65 nuclear translocation. Knockdown of SIRT3 expression by SIRT3 siRNA increased the nuclear expression of NF-κB p65 and abolished the inhibition effect of Diosmin in NF-κB p65 expression. CONCLUSIONS: Diosmin reduces renal inflammation and fibrosis, which is contributed by inhibiting nuclear translocation of NF-κB P65 through activating SIRT3.

Global, regional and national burden of CKD in children and adolescents from 1990 to 2019.

BACKGROUND: Chronic kidney disease(CKD) is one of the most prevalent non-communicable health concerns in children and adolescents worldwide; however, data on its incidence, prevalence, disability-adjusted life years (DALYs) and trends in the population are limited. We aimed to assess the global, regional and national trends in CKD burden in children and adolescents. METHODS: In this trend analysis based on the 2019 Global Diseases, Injuries, and Risk Factors Study, CKD incidence, prevalence and DALYs rates per 100 000 population for children and adolescents were reported at the global, regional and national levels, as well as the average annual percentage change (AAPC). These global trends were analyzed by age, sex, region and socio-demographic index (SDI). RESULTS: Globally, the overall incidence of CKD (all stages including kidney replacement therapy) in children and adolescents showed an increasing trend [AAPC 0.44 (95% confidence interval 0.36-0.52)] between 1990 and 2019. Similarly, the overall prevalence of CKD also showed an upward trend [AAPC 0.46 (0.42-0.51)]. However, the DALYs of CKD showed a continuous decreasing trend [AAPC -1.18 (-1.37 to -0.99)]. The population aged 15-19 years had the largest CKD incidence increase during this period. The largest increase in age-standardized incidence rate (ASIR) was in middle SDI countries [AAPC 0.56 (0.45-0.67)]. The relationship between the ASIR and SDI showed an inverse U-shaped correlation while the relationship between the age-standardized DALYs rate (ASDR) and SDI showed an inverse trend with SDI. Among adolescents (15-19 years), the ASIR continued to increase for five causes of CKD, owing to type 2 diabetes mellitus and hypertension. Most of the disease burden was concentrated in countries with a lower SDI. Andean Latin America and Central Latin America showed the largest increases in CKD ASIR between 1990 and 2019. CONCLUSION: The burden of CKD in children and adolescents has increased worldwide, especially in regions and countries with a lower SDI.

The Causal Effect of Obesity on the Risk of 15 Autoimmune Diseases: A Mendelian Randomization Study.

INTRODUCTION: Observational studies have shown that obesity is a risk factor for various autoimmune diseases. However, the causal relationship between obesity and autoimmune diseases is unclear. Mendelian randomization (MR) was used to investigate the causal effects of obesity on 15 autoimmune diseases. METHODS: MR analysis employed instrumental variables, specifically single-nucleotide polymorphisms associated with obesity measures such as body mass index (BMI), waist circumference, hip circumference, and waist-to-hip ratio. The study utilized UK Biobank and FinnGen data to estimate the causal relationship between obesity and autoimmune diseases. RESULTS: Genetically predicted BMI was associated with risk for five autoimmune diseases. The odds ratio per 1-SD increase in genetically predicted BMI, the OR was 1.28 (95% CI, 1.18-1.09; p < 0.001) for asthma, 1.37 (95% CI, 1.24-1.51; p < 0.001) for hypothyroidism, 1.52 (95% CI, 1.27-1.83; p < 0.001) for psoriasis, 1.22 (95% CI, 1.06-1.40; p = 0.005) for rheumatoid arthritis, and 1.55 (95% CI, 1.32-1.83; p < 0.001) for type 1 diabetes. However, after adjusting for genetic susceptibility to drinking and smoking, the correlation between BMI and rheumatoid arthritis was not statistically significant. Genetically predicted waist circumference, hip circumference, and waist and hip circumference were associated with 6, 6, and 1 autoimmune disease, respectively. CONCLUSION: This study suggests that obesity may be associated with an increased risk of several autoimmune diseases, such as asthma, hypothyroidism, psoriasis, rheumatoid arthritis, and type 1 diabetes.

Development and validation of a nomogram for predicting gram-negative bacterial infections in patients with peritoneal dialysis-associated peritonitis.

Background: This study aimed to develop a nomogram for predicting gram-negative bacterial (GNB) infections in patients with peritoneal dialysis-associated peritonitis (PDAP) to identify patients at high risk for GNB infections. Methods: In this investigation, hospitalization information was gathered retrospectively for patients with PDAP from January 2016 to December 2021. The concatenation of potential biomarkers obtained by univariate logistic regression, LASSO analysis, and RF algorithms into multivariate logistic regression was used to identify confounding factors related to GNB infections, which were then integrated into the nomogram. The concordance index (C-Index) was utilized to assess the precision of the model's predictions. The area under the curve (AUC) and decision curve analysis (DCA) was used to assess the predictive performance and clinical utility of the nomogram. Results: The final study population included 217 patients with PDAP, and 37 (17.1%) patients had gram-negative bacteria due to dialysate effluent culture. After multivariate logistic regression, age, procalcitonin, and hemoglobin were predictive factors of GNB infections. The C-index and bootstrap-corrected index of the nomogram for estimating GNB infections in patients were 0.821 and 0.814, respectively. The calibration plots showed good agreement between the predictions of the nomogram and the actual observation of GNB infections. The AUC of the receiver operating characteristic curve was 0.821, 95% CI: 0.747-0.896, which indicates that the model has good predictive accuracy. In addition, the DCA curve showed that the nomogram had a high clinical value in the range of 1%-94%, which further demonstrated that the nomogram could accurately predict GNB infection in patients with PDAP. Conclusions: We have created a new nomogram for predicting GNB infections in patients with PDAP. The nomogram model may improve the identification of GNB infections in patients with PDAP and contribute to timely intervention to improve patient prognosis.

Machine learning algorithms assist early evaluation of enteral nutrition in ICU patients.

Background: This study applied machine learning (ML) algorithms to construct a model for predicting EN initiation for patients in the intensive care unit (ICU) and identifying populations in need of EN at an early stage. Methods: This study collected patient information from the Medical Information Mart for Intensive Care IV database. All patients enrolled were split randomly into a training set and a validation set. Six ML models were established to evaluate the initiation of EN, and the best model was determined according to the area under curve (AUC) and accuracy. The best model was interpreted using the Local Interpretable Model-Agnostic Explanations (LIME) algorithm and SHapley Additive exPlanation (SHAP) values. Results: A total of 53,150 patients participated in the study. They were divided into a training set (42,520, 80%) and a validation set (10,630, 20%). In the validation set, XGBoost had the optimal prediction performance with an AUC of 0.895. The SHAP values revealed that sepsis, sequential organ failure assessment score, and acute kidney injury were the three most important factors affecting EN initiation. The individualized forecasts were displayed using the LIME algorithm. Conclusion: The XGBoost model was established and validated for early prediction of EN initiation in ICU patients.

Analysis of the potential biological mechanisms of diosmin against renal fibrosis based on network pharmacology and molecular docking approach.

BACKGROUND: Interstitial fibrosis is involved in the progression of various chronic kidney diseases and renal failure. Diosmin is a naturally occurring flavonoid glycoside that has antioxidant, anti-inflammatory, and antifibrotic activities. However, whether diosmin protects kidneys by inhibiting renal fibrosis is unknown. METHODS: The molecular formula of diosmin was obtained, targets related to diosmin and renal fibrosis were screened, and interactions among overlapping genes were analyzed. Overlapping genes were used for gene function and KEGG pathway enrichment analysis. TGF-ß1 was used to induce fibrosis in HK-2 cells, and diosmin treatment was administered. The expression levels of relevant mRNA were then detected. RESULTS: Network analysis identified 295 potential target genes for diosmin, 6828 for renal fibrosis, and 150 hub genes. Protein-protein interaction network results showed that CASP3, SRC, ANXA5, MMP9, HSP90AA1, IGF1, RHOA, ESR1, EGFR, and CDC42 were identified as key therapeutic targets. GO analysis revealed that these key targets may be involved in the negative regulation of apoptosis and protein phosphorylation. KEGG indicated that pathways in cancer, MAPK signaling pathway, Ras signaling pathway, PI3K-Akt signaling pathway, and HIF-1 signaling pathway were key pathways for renal fibrosis treatment. Molecular docking results showed that CASP3, ANXA5, MMP9, and HSP90AA1 stably bind to diosmin. Diosmin treatment inhibited the protein and mRNA levels of CASP3, MMP9, ANXA5, and HSP90AA1. Network pharmacology analysis and experimental results suggest that diosmin ameliorates renal fibrosis by decreasing the expression of CASP3, ANXA5, MMP9, and HSP90AA1. CONCLUSIONS: Diosmin has a potential multi-component, multi-target, and multi-pathway molecular mechanism of action in the treatment of renal fibrosis. CASP3, MMP9, ANXA5, and HSP90AA1 might be the most important direct targets of diosmin.

Dialysis vintage is associated with a high prevalence and severity of unpleasant symptoms in patients on hemodialysis.

Background: The burden of physical and emotional symptoms caused by somatic illness is present in most dialysis patients. However, it's unclear how symptom burden varies among patients with different dialysis vintages. We sought to examine differences in the prevalence and severity of unpleasant symptoms in hemodialysis patients with diverse dialysis vintage cohorts.Methods: This cross-sectional study included patients on maintenance hemodialysis at the Second Hospital of Anhui Medical University. We used the Dialysis Symptom Index (DSI) to determine the associated unpleasant symptoms, which is a validated survey to assess symptom burden/severity (higher scores indicate more severe symptoms), over June 2022 - September 2022.Results: We studied 146 patients: 35 (24%) had a dialysis vintage of ≤12 months (group 1) and 111 (76%) had a dialysis vintage of >12 months (group 2). Concerning Group 1 patients, the prevalence and severity of unpleasant symptoms were significantly higher in Group 2, the most common individual symptoms included feeling tired or lack of energy and trouble falling asleep (i.e., 75-85% of patients in each group), with dialysis vintage being an independent influencing factor (adjusted OR, 0.19; 95% CI, 0.16 to 0.23). Lower hemoglobin levels, iron stores, and dialysis adequacy levels are correlated with longer dialysis vintage.Conclusion: We observed a high prevalence of unpleasant symptoms and symptom clusters in a diverse dialysis vintages hemodialysis cohort. Further studies are needed to accurately and routinely define the symptom burden of chronic patients with chronic kidney disease (CKD).

Machine learning algorithm to predict the in-hospital mortality in critically ill patients with chronic kidney disease.

BACKGROUND: This study aimed to establish and validate a machine learning (ML) model for predicting in-hospital mortality in critically ill patients with chronic kidney disease (CKD). METHODS: This study collected data on CKD patients from 2008 to 2019 using the Medical Information Mart for Intensive Care IV. Six ML approaches were used to build the model. Accuracy and area under the curve (AUC) were used to choose the best model. In addition, the best model was interpreted using SHapley Additive exPlanations (SHAP) values. RESULTS: There were 8527 CKD patients eligible for participation; the median age was 75.1 (interquartile range: 65.0-83.5) years, and 61.7% (5259/8527) were male. We developed six ML models with clinical variables as input factors. Among the six models developed, the eXtreme Gradient Boosting (XGBoost) model had the highest AUC, at 0.860. According to the SHAP values, the sequential organ failure assessment score, urine output, respiratory rate, and simplified acute physiology score II were the four most influential variables in the XGBoost model. CONCLUSIONS: In conclusion, we successfully developed and validated ML models for predicting mortality in critically ill patients with CKD. Among all ML models, the XGBoost model is the most effective ML model that can help clinicians accurately manage and implement early interventions, which may reduce mortality in critically ill CKD patients with a high risk of death.

Association between serum uric acid levels and clinical outcomes in patients with acute kidney injury.

BACKGROUND: The effects of serum uric acid (SUA) on clinical outcomes in patients with acute kidney injury (AKI) are unclear. The aim of this study was to investigate the association of SUA levels with clinical outcomes of AKI patients. METHODS: The data of AKI patients hospitalized in the Affiliated Hospital of Qingdao University were retrospectively reviewed. Multivariable logistic regression was utilized to assess the association between SUA levels and the clinical outcomes of AKI patients. Receiver operating characteristic (ROC) analysis was applied to assess the predictive ability of SUA levels for in-hospital mortality in patients with AKI. RESULTS: A total of 4,646 AKI patients were eligible for study inclusion. In multivariable analysis, after adjustment for various confounding factors in the fully adjusted model, a higher SUA level was found to be associated with increased in-hospital mortality of AKI patients with an odds ratio (OR) of 1.72 (95% CI, 1.21-2.33, p = 0.005) for the SUA level >5.1-6.9 mg/dl group and 2.75 (95% CI, 1.78-4.26, p < 0.001) for the SUA level >6.9 mg/dl group compared with the reference group (SUA ≤3.6 mg/dl). In the ROC analysis, the area under the curve (AUC) of SUA was 0.65 with a sensitivity of 51% and a specificity of 73%. CONCLUSIONS: An elevated SUA level is associated with an increased risk of in-hospital mortality in patients with AKI, and it appears to be an independent prognostic marker for these patients.

Machine learning algorithm to predict mortality in critically ill patients with sepsis-associated acute kidney injury.

This study aimed to establish and validate a machine learning (ML) model for predicting in-hospital mortality in patients with sepsis-associated acute kidney injury (SA-AKI). This study collected data on SA-AKI patients from 2008 to 2019 using the Medical Information Mart for Intensive Care IV. After employing Lasso regression for feature selection, six ML approaches were used to build the model. The optimal model was chosen based on precision and area under curve (AUC). In addition, the best model was interpreted using SHapley Additive exPlanations (SHAP) values and Local Interpretable Model-Agnostic Explanations (LIME) algorithms. There were 8129 sepsis patients eligible for participation; the median age was 68.7 (interquartile range: 57.2-79.6) years, and 57.9% (4708/8129) were male. After selection, 24 of the 44 clinical characteristics gathered after intensive care unit admission remained linked with prognosis and were utilized developing ML models. Among the six models developed, the eXtreme Gradient Boosting (XGBoost) model had the highest AUC, at 0.794. According to the SHAP values, the sequential organ failure assessment score, respiration, simplified acute physiology score II, and age were the four most influential variables in the XGBoost model. Individualized forecasts were clarified using the LIME algorithm. We built and verified ML models that excel in early mortality risk prediction in SA-AKI and the XGBoost model performed best.

[Risk factors of hypoxemia in patients admitted to intensive care unit after surgery].

OBJECTIVE: To investigate the risk factors of postoperative hypoxemia in patients admitted to intensive care unit (ICU) for resuscitation. METHODS: Clinical data of 220 postoperative patients admitted to the ICU for resuscitation in Shandong Provincial Hospital Affiliated to Shandong University from June to August 2020 were collected and retrospectively analyzed. According to their oxygenation index within 30 minutes after admission to ICU, they were divided into hypoxemia group (oxygenation index ≤ 300 mmHg, 1 mmHg ≈ 0.133 kPa) and non-hypoxemia group (oxygenation index > 300 mmHg). Baseline data and perioperative indicators were compared between the two groups, and risk factors for early postoperative hypoxemia were analyzed. The improvement of oxygenation index of patients with hypoxemia in next morning after admission to ICU was observed, and the factors related to the improvement of hypoxemia were analyzed. RESULTS: The incidence of hypoxemia was 36.8% (81/220) in the cohort. The majority cases of hypoxemia were from general surgery department, accounting for 42.0% (34/81). The incidence rate of hypoxemia from orthopaedic was the highest at 53.3% (16/30). Univariate analysis showed that body mass index (BMI), intraoperative hypoxemia, minimally invasive surgery were all risk factors of postoperative hypoxemia (test values were -2.566, 12.352 and 0.033; P values were 0.010, 0.000 and 0.019, respectively). Multivariate analysis showed that intraoperative hypoxemia and BMI were independent risk factors for postoperative hypoxemia [intraoperative hypoxemia: odds ratio (OR) = 3.602, 95% confidence interval (95%CI) was 1.143-3.817, P = 0.001; BMI: OR = 1.119, 95%CI was 1.026-1.208, P = 0.036]. The improvement rate of hypoxemia next morning after admission to ICU was 63.0% (51/81). Pulmonary dysfunction was the independent risk factor for the improvement of hypoxemia (OR = 0.200, 95%CI was 0.052-0.763, P = 0.019). CONCLUSIONS: Hypoxemia might occur early after surgery. Intraoperative hypoxemia and BMI were independent risk factors for postoperative hypoxemia.

Associations between early thiamine administration and clinical outcomes in critically ill patients with acute kidney injury.

The effects of early thiamine use on clinical outcomes in critically ill patients with acute kidney injury (AKI) are unclear. The purpose of this study was to investigate the associations between early thiamine administration and clinical outcomes in critically ill patients with AKI. The data of critically ill patients with AKI within 48 h after ICU admission were extracted from the Medical Information Mart for Intensive Care III (MIMIC III) database. PSM was used to match patients early receiving thiamine treatment to those not early receiving thiamine treatment. The association between early thiamine use and in-hospital mortality due to AKI was determined using a logistic regression model. A total of 15 066 AKI patients were eligible for study inclusion. After propensity score matching (PSM), 734 pairs of patients who did and did not receive thiamine treatment in the early stage were established. Early thiamine use was associated with lower in-hospital mortality (OR 0·65; 95 % CI 0·49, 0·87; P < 0·001) and 90-d mortality (OR 0·58; 95 % CI 0·45, 0·74; P < 0·001), and it was also associated with the recovery of renal function (OR 1·26; 95 % CI 1·17, 1·36; P < 0·001). In the subgroup analysis, early thiamine administration was associated with lower in-hospital mortality in patients with stages 1 to 2 AKI. Early thiamine use was associated with improved short-term survival in critically ill patients with AKI. It was possible beneficial role in patients with stages 1 to 2 AKI according to the Kidney Disease: Improving Global Outcomes criteria.