Blood urea nitrogen, blood urea nitrogen to creatinine ratio and incident stroke: The Dongfeng-Tongji cohort

Multimodal data-driven, vertical visualization prediction model for early prediction of atherosclerotic cardiovascular disease in patients with new-onset hypertension

BACKGROUND

: Hypertension is an important contributing factor to atherosclerotic cardiovascular disease (ASCVD), and multiple risk factors, many of which are implicated in metabolic disorders, contribute to the cause of hypertension. Despite the promise of multimodal data-driven prediction model, no such prediction model was available to predict the risk of ASCVD in Chinese individuals with new-onset hypertension and no history of ASCVD.

METHODS

: A total of 514 patients were randomly allocated to training and verification cohorts (ratio, 7 : 3). We employed Boruta feature selection and conducted multivariate Cox regression analyses to identify variables associated with ASCVD in these patients, which were subsequently utilized for constructing the predictive model. The performance of prediction model was assessed in terms of discriminatory power (C-index), calibration (calibration curves), and clinical utility [decision curve analysis (DCA)].

RESULTS

: This model was derived from four clinical variables: 24-h SBP coefficient of variation, 24-h DBP coefficient of variation, urea nitrogen and the triglyceride-glucose (TyG) index. Bootstrapping with 500 iterations was conducted to adjust the C-indexes were C-index = 0.731, 95% confidence interval (CI) 0.620-0.794 and C-index: 0.799, 95% CI 0.677-0.892 in the training and verification cohorts, respectively. Calibration plots with 500 bootstrapping iterations exhibited a strong correlation between the predicted and observed occurrences of ASCVD in both the training and verification cohorts. DCA analysis confirmed the clinical utility of this prediction model. The constructed nomogram demonstrated significant additional prognostic utility for ASCVD, as evidenced by improvements in the C-index, net reclassification improvement, integrated discrimination improvement, and DCA compared with the overall ASCVD risk assessment.

CONCLUSION

The developed longitudinal prediction model based on multimodal data can effectively predict ASCVD risk in individuals with an initial diagnosis of hypertension.

TRIAL REGISTRATION

: The trial was registered in the Chinese Clinical Trial Registry (ChiCTR2300074392).

Association of hydration status and in-hospital mortality in critically ill patients with ischemic stroke: Data from the MIMIC-IV database

BACKGROUND

Hydration plays a critical role in the pathophysiological progression of ischemic stroke. However, the impact of extreme hydration on the mortality of critically ill patients with ischemic stroke remains unclear. Therefore, our objective was to evaluate the association between hydration, as indicated by the blood urea nitrogen to creatinine ratio (UCR), and in-hospital mortality in critically ill patients with ischemic stroke.

METHODS

Data from the Medical Information Mart for Intensive Care (MIMIC-IV) database were utilized. Patients with ischemic stroke admitted to the Intensive Care Unit (ICU) for the first time were identified. The exposure variable was the hydration state represented by the UCR. The study outcome measure was in-hospital mortality. The primary analytical approach involved multivariate Cox regression analysis. Kaplan-Meier curves were constructed, and subgroup analyses with interaction were performed.

RESULTS

A total of 1539 patients, with a mean age of 69.9 years, were included in the study. Kaplan-Meier curves illustrated that patients in higher UCR tertiles exhibited increased in-hospital mortality. Accordingly, the risk of in-hospital mortality significantly rose by 29 % with every 10 units increase in UCR. Subgroup analysis indicated a robust association between UCR and in-hospital mortality in each subgroup, with no statistically significant interactions observed.

CONCLUSION

Hydration status is significantly associated with in-hospital all-cause mortality in critically ill patients with ischemic stroke. This finding underscores the importance of closely monitoring critically ill patients for adequate hydration and implementing appropriate rehydration strategies.

Machine learning-based predictive model for the development of thrombolysis resistance in patients with acute ischemic stroke

BACKGROUND

The objective of this study was to establish a predictive model utilizing machine learning techniques to anticipate the likelihood of thrombolysis resistance (TR) in acute ischaemic stroke (AIS) patients undergoing recombinant tissue plasminogen activator (rt-PA) intravenous thrombolysis, given that nearly half of such patients exhibit poor clinical outcomes.

METHODS

Retrospective clinical data were collected from AIS patients who underwent intravenous thrombolysis with rt-PA at the First Affiliated Hospital of Bengbu Medical University. Thrombolysis resistance was defined as ([National Institutes of Health Stroke Scale (NIHSS) at admission - 24-hour NIHSS] × 100％/ NIHSS at admission) ≤ 30%. In this study, we developed five machine learning models: logistic regression (LR), extreme gradient boosting (XGBoost), support vector machine (SVM), the least absolute shrinkage and selection operator (LASSO), and random forest (RF). We assessed the model's performance by using receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA), and presented the results through a nomogram.

RESULTS

This study included a total of 218 patients with AIS who were treated with intravenous thrombolysis, 88 patients experienced TR. Among the five machine learning models, the LASSO model performed the best. The area under the curve (AUC) on the testing group was 0.765 (sensitivity: 0.767, specificity: 0.694, accuracy: 0.727). The apparent curve in the calibration curve was similar to the ideal curve, and DCA showed a positive net benefit. Key features associated with TR included NIHSS at admission, blood glucose, white blood cell count, neutrophil count, and blood urea nitrogen.

CONCLUSION

Machine learning methods with multiple clinical variables can help in early screening of patients at high risk of thrombolysis resistance, particularly in contexts where healthcare resources are limited.

Construction and validation of a predictive model of mortality of tuberculosis-destroyed lung patients requiring mechanical ventilation: A single-center retrospective cohort study

The mortality rate for intensive care unit tuberculosis-destroyed lung (TDL) patients requiring mechanical ventilation (MV) remains high. We conducted a retrospective analysis of adult TDL patients requiring MV who were admitted to the intensive care unit of a tertiary infectious disease hospital in Chengdu, Sichuan Province, China from January 2019 to March 2023. Univariate and multivariate COX regression analyses were conducted to determine independent patient prognostic risk factors that were used to construct a predictive model of patient mortality. A total of 331 patients were included, the median age was 63.0 (50.0-71.0) years, 262 (79.2%) were males and the mortality rate was 48.64% (161/331). Training and validation data sets were obtained from 245 and 86 patients, respectively. Analysis of the training data set revealed that body mass index <18.5 kg/m2, blood urea nitrogen ≥7.14 mmol/L and septic shock were independent risk factors for increased mortality of TDL patients requiring MV. These variables were then used to construct a risk-based model for predicting patient mortality. Area under curve, sensitivity, and specificity values obtained using the model for the training data set were 0.808, 79.17%, and 68.80%, respectively, and corresponding values obtained using the validation data set were 0.876, 95.12%, and 62.22%, respectively. Concurrent correction curve and decision curve analyses confirmed the high predictive ability of the model, indicating its potential to facilitate early identification and classification-based clinical management of high-risk TDL patients requiring MV.

A pilot clinical risk model to predict polymyxin-induced nephrotoxicity: a real-world, retrospective cohort study

OBJECTIVES

Polymyxin-induced nephrotoxicity (PIN) is a major safety concern and challenge in clinical practice, which limits the clinical use of polymyxins. This study aims to investigate the risk factors and to develop a scoring tool for the early prediction of PIN.

METHODS

Data on critically ill patients who received intravenous polymyxin B or colistin sulfate for over 24 h were collected. Logistic regression with the least absolute shrinkage and selection operator (LASSO) was used to identify variables that are associated with outcomes. The eXtreme Gradient Boosting (XGB) classifier algorithm was used to further visualize factors with significant differences. A prediction model for PIN was developed through binary logistic regression analysis and the model was assessed by temporal validation and external validation. Finally, a risk-scoring system was developed based on the prediction model.

RESULTS

Of 508 patients, 161 (31.6%) patients developed PIN. Polymyxin type, loading dose, septic shock, concomitant vasopressors and baseline blood urea nitrogen (BUN) level were identified as significant predictors of PIN. All validation exhibited great discrimination, with the AUC of 0.742 (95% CI: 0.696-0.787) for internal validation, of 0.708 (95% CI: 0.605-0.810) for temporal validation and of 0.874 (95% CI: 0.759-0.989) for external validation, respectively. A simple risk-scoring tool was developed with a total risk score ranging from -3 to 4, corresponding to a risk of PIN from 0.79% to 81.24%.

CONCLUSIONS

This study established a prediction model for PIN. Before using polymyxins, the simple risk-scoring tool can effectively identify patients at risk of developing PIN within a range of 7% to 65%.

Acute kidney injury with intravenous colistin sulfate compared with polymyxin B in critically ill patients: A real-world, retrospective cohort study

BACKGROUND

Polymyxins have re-emerged as a last-resort therapeutic option for infections caused by carbapenem-resistant gram-negative bacteria. Nephrotoxicity induced by polymyxins is a significant limitation of its use in the clinic. Polymyxin B and colistin sulfate are two widely used active formulations of polymyxins. However, there is a lack of studies conducting a comparative assessment of nephrotoxicity between the two formulations. This study aimed to compare the nephrotoxicity of polymyxin B and colistin sulfate in critically ill patients.

METHODS

We conducted a retrospective cohort study among critically ill patients who received intravenous polymyxin B or colistin sulfate for over 48 h from January 2017 to January 2024. The primary outcome was the incidence of acute kidney injury (AKI) associated with polymyxins, and the secondary outcome was 30-day all-cause mortality. Additionally, the risk factors of polymyxins-induced AKI and 30-day all-cause mortality were identified by Cox proportional hazard regression analysis.

RESULTS

A total of 473 patients were included in this study. The overall incidence of AKI was significantly higher in patients who received polymyxin B compared to those who received colistin sulfate in the unmatched cohort (20.8% vs. 9.0%, p = 0.002) and in the propensity score matching cohort (21.1% vs. 7.0%, p = 0.004), respectively. However, there was no significant difference in 30-day all-cause mortality between the two groups. Polymyxin type, septic shock, and concomitant use of vasopressors were identified as independent risk factors for polymyxin-induced AKI.

CONCLUSIONS

The prevalence of AKI was higher among patients who received polymyxin B compared to those treated with colistin sulfate. However, there was no significant difference in 30-day all-cause mortality between the two groups. Further prospective, multicenter studies with larger sample sizes are needed to validate these findings.

Relationship between admission blood urea nitrogen levels and postoperative length of stay in patients with hip fracture: A retrospective study

To investigate the relationship between admission blood urea nitrogen (BUN) levels and postoperative length of stay (LOS) in hip fracture (HF) patients. This retrospective study retrieved related data from the MIMIC-IV database, of which the laboratory variables were taken preoperatively. The patients were divided into 4 groups according to the BUN quartile levels. After exploring the nonlinear relationship between BUN and LOS by generalized additive model, their connection was further analyzed using the generalized linear models, quantile regression models, and interaction analysis. Receiver operating characteristic curve analysis and decision curve analysis were performed to evaluate its value in predicting first intensive care unit admission and in-hospital mortality. Totally 1274 patients with HF were enrolled in the study. There was a nonlinear relationship between BUN and LOS (P < .05). Besides, BUN was an independent predictor for LOS after adjusting different covariates in 3 models (P < .05). Age served as a significant interactor in this relationship (P < .05). Moreover, receiver operating characteristic curve and decision curve analysis revealed the predictive value of BUN for intensive care unit admission and in-hospital mortality in HF. Admission BUN level as a cost-effective and easy-to-collect biomarker is significantly related to LOS in patients with HF. It helps clinicians to identify potential high-risk populations and take effective preventions before surgery to reduce postoperative LOS.

Development and validation of outcome prediction model for reperfusion therapy in acute ischemic stroke using nomogram and machine learning

OBJECTIVE

To develop logistic regression nomogram and machine learning (ML)-based models to predict 3-month unfavorable functional outcome for acute ischemic stroke (AIS) patients undergoing reperfusion therapy.

METHODS

Patients undergoing reperfusion therapy (intravenous thrombolysis and/or endovascular treatment) were prospectively recruited. Unfavorable outcome was defined as 3-month modified Rankin Scale (mRS) score 3-6. The independent risk factors associated with unfavorable outcome were obtained by regression analysis and included in the prediction model. The performance of nomogram was assessed by the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). ML models were compared with nomogram using AUC; the generalizability of all models was ascertained in an external cohort.

RESULTS

A total of 505 patients were enrolled, with 256 in the model construction, and 249 in the external validation. Five variables were identified as prognostic factors: baseline NIHSS, D-dimer level, random blood glucose (RBG), blood urea nitrogen (BUN), and systolic blood pressure (SBP) before reperfusion. The AUC values of nomogram were 0.865, 0.818, and 0.779 in the training set, test set, and external validation, respectively. The calibration curve and DCA indicated appreciable reliability and good net benefits. The best three ML models were extra trees (ET), CatBoost, and random forest (RF) models; all of them showed favorable discrimination in the training cohort, and confirmed in the test and external sets.

CONCLUSION

Baseline NIHSS, D-dimer, RBG, BUN, and SBP before reperfusion were independent predictors for 3-month unfavorable outcome after reperfusion therapy in AIS patients. Both nomogram and ML models showed good discrimination and generalizability.

Explainable machine learning for predicting neurological outcome in hemorrhagic and ischemic stroke patients in critical care

Aim

The objective of this study is to develop accurate machine learning (ML) models for predicting the neurological status at hospital discharge of critically ill patients with hemorrhagic and ischemic stroke and identify the risk factors associated with the neurological outcome of stroke, thereby providing healthcare professionals with enhanced clinical decision-making guidance.

Materials and methods

Data of stroke patients were extracted from the eICU Collaborative Research Database (eICU-CRD) for training and testing sets and the Medical Information Mart for Intensive Care IV (MIMIC IV) database for external validation. Four machine learning models, namely gradient boosting classifier (GBC), logistic regression (LR), multi-layer perceptron (MLP), and random forest (RF), were used for prediction of neurological outcome. Furthermore, shapley additive explanations (SHAP) algorithm was applied to explain models visually.

Results

A total of 1,216 hemorrhagic stroke patients and 954 ischemic stroke patients from eICU-CRD and 921 hemorrhagic stroke patients 902 ischemic stroke patients from MIMIC IV were included in this study. In the hemorrhagic stroke cohort, the LR model achieved the highest area under curve (AUC) of 0.887 in the test cohort, while in the ischemic stroke cohort, the RF model demonstrated the best performance with an AUC of 0.867 in the test cohort. Further analysis of risk factors was conducted using SHAP analysis and the results of this study were converted into an online prediction tool.

Conclusion

ML models are reliable tools for predicting hemorrhagic and ischemic stroke neurological outcome and have the potential to improve critical care of stroke patients. The summarized risk factors obtained from SHAP enable a more nuanced understanding of the reasoning behind prediction outcomes and the optimization of the treatment strategy.

Association of blood urea nitrogen with all-cause and cardiovascular mortality in hyperlipidemia: NHANES 1999-2018

OBJECTIVE

Although blood urea nitrogen (BUN) has a crucial impact on many diseases, its effect on outcomes in patients with hyperlipidemia remains unknown. The study aimed to investigate the relationships between BUN levels and all-cause and cardiovascular disease (CVD) mortality in individuals with hyperlipidemia.

METHODS

This analysis comprised 28,122 subjects with hyperlipidemia from the National Health and Nutrition Examination Survey (NHANES) spanning 1999 to 2018. The risk of BUN on mortality was evaluated using weighted Cox regression models. Additionally, to illustrate the dose-response association, the restricted cubic spline (RCS) was used.

RESULTS

During the observation period, 4276 participant deaths were recorded, of which 1206 were due to CVD. Compared to patients with hyperlipidemia in the third BUN quintile, the hazard ratios (HRs) for all-cause mortality were 1.26 (95% CIs: 1.09, 1.45) and 1.22 (95% CIs: 1.09, 1.37) for patients in the first and fifth quintiles of BUN, respectively. The HRs for CVD mortality among patients in the fifth quintile of BUN were 1.48 (95% CIs: 1.14, 1.93). BUN levels were found to have a U-shaped association with all-cause mortality and a linear association with CVD mortality using restricted triple spline analysis.

CONCLUSIONS

This study revealed that both low and high BUN levels in patients with hyperlipidemia are associated with heightened all-cause mortality. Furthermore, elevated BUN levels are also associated with increased CVD mortality. The findings indicate that patients with hyperlipidemia may face an elevated risk of death if they have abnormal BUN levels.

The comparison of the antidiabetic effects of exenatide, empagliflozin, quercetin, and combination of the drugs in type 2 diabetic rats

BACKGROUND

Type 2 diabetes, a metabolic disease that involves extended treatment, is rapidly increasing in humans and animals worldwide.

OBJECTIVES

This study aimed to compare monotherapy and combined therapy of exenatide, empagliflozin, and quercetin in 67 Wistar Albino male rats.

METHODS

The animals were divided into the following seven groups: healthy control, diabetes control, diabetes + sham, diabetes + exenatide (10 μg/kg), diabetes + empagliflozin (50 mg/kg), diabetes + quercetin (50 mg/kg), and diabetes + combination treatment. The treatments were continued for 8 weeks.

RESULTS

At the end of the experiment, glucose and HbA1c levels decreased with all monotherapy treatments and the combination treatments, while insulin levels increased with exenatide and combined treatments. Adiponectin levels increased with empagliflozin, quercetin, and combined treatments, while leptin levels decreased only with combined treatments. All monotherapies caused an increase in total antioxidant levels. Exenatide and quercetin treatments reduced low-density lipoprotein (LDL) levels; therewithal, exenatide and combined treatments increased high-density lipoprotein (HDL) levels. Triglyceride levels decreased in all treatment groups. The homeostatic model assessment for insulin resistance (HOMA-IR) level decreased with the combined treatment; on the contrary, the homeostatic model assessment for β-cell activity (HOMA-β) level increased with empagliflozin, exenatide, and combined treatments.

CONCLUSION

In conclusion, the antidiabetic effects of exenatide were more pronounced than empagliflozin and quercetin, however, the combined treatment had better antidiabetic and antihyperlipidemic effects than monotherapies. Quercetin could be a supportive or food supplement antidiabetic agent. The exenatide treatment can be recommended for monotherapy in type 2 patients, and the combination of empagliflozin, exenatide, and quercetin may be effective in diabetic patients who need combined therapy.

The long-term effects of blood urea nitrogen levels on cardiovascular disease and all-cause mortality in diabetes: a prospective cohort study

BACKGROUND

The long-term effects of blood urea nitrogen(BUN) in patients with diabetes remain unknown. Current studies reporting the target BUN level in patients with diabetes are also limited. Hence, this prospective study aimed to explore the relationship of BUN with all-cause and cardiovascular mortalities in patients with diabetes.

METHODS

In total, 10,507 participants with diabetes from the National Health and Nutrition Examination Survey (1999-2018) were enrolled. The causes and numbers of deaths were determined based on the National Death Index mortality data from the date of NHANES interview until follow-up (December 31, 2019). Multivariate Cox proportional hazard regression models were used to calculate the hazard ratios (HRs) and 95% confidence interval (CIs) of mortality.

RESULTS

Of the adult participants with diabetes, 4963 (47.2%) were female. The median (interquartile range) BUN level of participants was 5 (3.93-6.43) mmol/L. After 86,601 person-years of follow-up, 2,441 deaths were documented. After adjusting for variables, the HRs of cardiovascular disease (CVD) and all-cause mortality in the highest BUN level group were 1.52 and 1.35, respectively, compared with those in the lowest BUN level group. With a one-unit increment in BUN levels, the HRs of all-cause and CVD mortality rates were 1.07 and 1.08, respectively. The results remained robust when several sensitivity and stratified analyses were performed. Moreover, BUN showed a nonlinear association with all-cause and CVD mortality. Their curves all showed that the inflection points were close to the BUN level of 5 mmol/L.

CONCLUSION

BUN had a nonlinear association with all-cause and CVD mortality in patients with diabetes. The inflection point was at 5 mmol/L.

The effect of chlorpyrifos oral exposure on the histomorphometric and kidney function in Wistar rat

BACKGROUND

Chlorpyrifos belongs to a broad-spectrum organophosphate insecticide that has high toxicity, is metabolized in the liver by the oxidation reaction, and can inhibit acetylcholinesterase activity. Acetylcholinesterase inhibition generates the reactive oxygen species and induces oxidative stress, which ultimately results in cellular damage like in the kidney. Examining blood urea nitrogen (BUN) levels, creatinine, and kidney histopathology is an appropriate indicator to assess the toxicity of chlorpyrifos to the degree of damage to cells and kidney tissue.

MATERIALS AND METHODS

This research used to determine the effect of duration of exposure to chlorpyrifos and dose-response relationships is important for early detection of the effects of chlorpyrifos toxicity on health. The research study was a true experimental (completely randomized design) consisting of 30 subjects divided into 5 groups. Controlled Group (K1) given 1 mg/kg BW Tween 20 and NaCl 0, 9% until the 56th day. The chlorpyrifos exposed group (P1, P2, P3, and P4) was given chlorpyrifos 5 mg/kg BW for 7, 14, 28, and 56 days. After the treatment, BUN and creatinine levels were measured, and microscopic changes in the kidney were analyzed. The results of BUN, creatinine, and kidney histopathologic were analyzed using the analysis of variance statistical test.

RESULTS

The data result showed that compared to the control group, there were significant increases of BUN and creatinine (P = 0.013 and P = 0.003). Histopathological examinations of kidney glomerulus diameter were also smaller compared to the control group (P = 0.00). All the data measurement indicates significant differences compared to the control group.

CONCLUSIONS

We concluded that sub-chronic oral exposure to chlorpyrifos at low doses can damage the kidneys and cause kidney failure.

Association between the blood urea nitrogen-to-creatinine ratio and 3-month outcomes in patients with acute ischemic stroke: a secondary analysis based on a prospective cohort study

Introduction

This study aimed to assess the correlation between the blood urea nitrogen (BUN)-to-creatinine (BUN/Cr) ratio and adverse outcomes (AOs) at 3 months in patients with acute ischemic stroke (AIS) in the Korean population.

Methods

This cohort study encompassed 1906 cases of AIS at a South Korean hospital from January 2010 to December 2016. To determine the linear correlation between the BUN/Cr ratio and AOs in AIS, a binary logistic regression model (BLRM) was employed. Additionally, generalized additive models and techniques for smooth curve fitting were utilized to reveal the nonlinear dynamics between the BUN/Cr ratio and AOs in patients with AIS.

Results

The prevalence of AOs was 28.65%, with a median BUN/Cr ratio of 18.96. Following adjustments for covariates, the BLRM disclosed that the association between the BUN/Cr ratio and the risk of AOs in patients with AIS did not attain statistical significance. Nevertheless, a nonlinear relationship surfaced, pinpointing an inflection point at 21.591. To the left of this inflection point, a 31.42% reduction in the risk of AOs was noted for every 1-unit surge in the Z score of the BUN/Cr ratio [odds ratio (OR) = 0.686, 95% confidence interval (CI): 0.519, 0.906, p = 0.008]. On the right side of the inflection point, the effect size (OR = 1.405, 95% CI: 1.018, 1.902, p = 0.039) was determined.

Conclusion

The findings of this study underscore the intricate nature of the relationship between the BUN/Cr ratio and 3-month outcomes in patients with AIS, establishing a robust groundwork for future investigations.

Diagnosis Value of the Blood Urea Nitrogen-to-Creatinine Ratio in Determining the Need for Intervention of Acute Upper Gastrointestinal Bleeding

INTRODUCTION

The blood urea nitrogen (BUN)-to-creatinine (Cr) ratio (BUN/Cr ratio) may be used to evaluate the need for intervention of acute upper gastrointestinal bleeding (AUGIB). This study aimed to explore the predictive value of the BUN/Cr ratio in the need for intervention of AUGIB.

METHODS

This retrospective observational study included patients with AUGIB in the hospital's emergency department between August 2019 and May 2023. The patients were grouped according to whether they underwent an intervention for AUGIB. Patients treated between August 2019 and May 2022 were selected as the training set and the others as the validation set.

RESULTS

A total of 466 patients (328 males, 138 females) with AUGIB were enrolled in the intervention group (n = 167) and the no-intervention group (n = 299). In the training set, multivariable logistic regression showed that the BUN/Cr ratio (odds ratio [OR]: 1.013, 95% confidence interval [CI]: 1.003-1.023, p = 0.009), hemoglobin (OR: 0.989, 95% CI: 0.981-0.997, p = 0.010), and a previous history of esophageal variceal bleeding (OR: 6.898, 95% CI: 3.989-11.929, p < 0.001) were independently associated with intervention for AUGIB. The area under receiver operating characteristic curve of BUN/Cr ratio and the prediction model based on logistic regression to predict the need for intervention of AUGIB were 0.604 (95% CI: 0.544-0.664) and 0.759 (95% CI: 0.706-0.812) in the training set and 0.634 (95% CI: 0.529, 0.740) and 0.708 (95% CI: 0.609, 0.806) in the validation set, respectively.

CONCLUSION

The BUN/Cr ratio was associated with the need for AUGIB intervention. Combining it with other parameters might improve its diagnostic value to predict the need for intervention of AUGIB.

Association between blood urea nitrogen levels and the risk of diabetes mellitus in Chinese adults: secondary analysis based on a multicenter, retrospective cohort study

Background

As one of the recognized indicators of kidney function, blood urea nitrogen (BUN) is a key marker of metabolic diseases and other diseases. Currently, data on the relationship of BUN levels with the risk of diabetes mellitus (DM) in Chinese adults are sparse. This study aimed to investigate the correlation between BUN levels and DM risk in Chinese adults.

Data and methods

This study is a secondary analysis of a multicenter, retrospective cohort study with data from the Chinese health screening program in the DATADRYAD database. From 2010 to 2016, health screening was conducted on 211833 Chinese adults over the age of 20 in 32 locations and 11 cities in China, and there was no DM at baseline. Cox proportional hazards regression analysis assessed an independent correlation between baseline BUN levels and the risk of developing DM. The Generalized Sum Model (GAM) and smoothed curve fitting methods were used to explore the nonlinear relationship. In addition, subgroup analyses were performed to assess the consistency of correlations between different subgroups and further validate the reliability of the results.

Results

After adjusting for potential confounding factors (age, sex, etc.), BUN levels were positively correlated with the occurrence of DM (HR=1.11, 95% CI (1.00~1.23)). BUN level had a nonlinear relationship with DM risk, and its inflection point was 4.2mmol/L. When BUN was greater than 4.2mmol/L, BUN was positively correlated with DM, and the risk of DM increased by 7% for every 1 mmol/L increase in BUN (P<0.05). Subgroup analysis showed that a more significant correlation between BUN levels and DM was observed in terms of sex, BMI, systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol (TC), triglycerides (TG), low-density lipoprotein (LDL), alaninetransaminase (ALT), aspartate transaminase (AST), creatinine (Cr) and smoking status (interaction P<0.05).

Conclusion

High levels of BUN are associated with an increased risk of DM in Chinese adults, suggesting that active control of BUN levels may play an important role in reducing the risk of DM in Chinese adults.

Blood Urea Nitrogen-to-Albumin Ratio May Predict Mortality in Patients with Traumatic Brain Injury from the MIMIC Database: A Retrospective Study

Traumatic brain injury (TBI), a major global health burden, disrupts the neurological system due to accidents and other incidents. While the Glasgow coma scale (GCS) gauges neurological function, it falls short as the sole predictor of overall mortality in TBI patients. This highlights the need for comprehensive outcome prediction, considering not just neurological but also systemic factors. Existing approaches relying on newly developed biomolecules face challenges in clinical implementation. Therefore, we investigated the potential of readily available clinical indicators, like the blood urea nitrogen-to-albumin ratio (BAR), for improved mortality prediction in TBI. In this study, we investigated the significance of the BAR in predicting all-cause mortality in TBI patients. In terms of research methodologies, we gave preference to machine learning methods due to their exceptional performance in clinical support in recent years. Initially, we obtained data on TBI patients from the Medical Information Mart for Intensive Care database. A total of 2602 patients were included, of whom 2260 survived and 342 died in hospital. Subsequently, we performed data cleaning and utilized machine learning techniques to develop prediction models. We employed a ten-fold cross-validation method to obtain models with enhanced accuracy and area under the curve (AUC) (Light Gradient Boost Classifier accuracy, 0.905 ± 0.016, and AUC, 0.888; Extreme Gradient Boost Classifier accuracy, 0.903 ± 0.016, and AUC, 0.895; Gradient Boost Classifier accuracy, 0.898 ± 0.021, and AUC, 0.872). Simultaneously, we derived the importance ranking of the variable BAR among the included variables (in Light Gradient Boost Classifier, the BAR ranked fourth; in Extreme Gradient Boost Classifier, the BAR ranked sixth; in Gradient Boost Classifier, the BAR ranked fifth). To further evaluate the clinical utility of BAR, we divided patients into three groups based on their BAR values: Group 1 (BAR < 4.9 mg/g), Group 2 (BAR ≥ 4.9 and ≤10.5 mg/g), and Group 3 (BAR ≥ 10.5 mg/g). This stratification revealed significant differences in mortality across all time points: in-hospital mortality (7.61% vs. 15.16% vs. 31.63%), as well as one-month (8.51% vs. 17.46% vs. 36.39%), three-month (9.55% vs. 20.14% vs. 41.84%), and one-year mortality (11.57% vs. 23.76% vs. 46.60%). Building on this observation, we employed the Cox proportional hazards regression model to assess the impact of BAR segmentation on survival. Compared to Group 1, Groups 2 and 3 had significantly higher hazard ratios (95% confidence interval (CI)) for one-month mortality: 1.77 (1.37-2.30) and 3.17 (2.17-4.62), respectively. To further underscore the clinical potential of BAR as a standalone measure, we compared its performance to established clinical scores, like sequential organ failure assessment (SOFA), GCS, and acute physiology score III(APS-III), using receiver operator characteristic curve (ROC) analysis. Notably, the AUC values (95%CI) of the BAR were 0.67 (0.64-0.70), 0.68 (0.65-0.70), and 0.68 (0.65-0.70) for one-month mortality, three-month mortality, and one-year mortality. The AUC value of the SOFA did not significantly differ from that of the BAR. In conclusion, the BAR is a highly influential factor in predicting mortality in TBI patients and should be given careful consideration in future TBI prediction research. The blood urea nitrogen-to-albumin ratio may predict mortality in TBI patients.

Blood urea nitrogen concentration is associated with severe abdominal aortic calcification in adults: a cross-sectional investigation

The purpose of this research is to examine the correlation between blood urea nitrogen (BUN) and severe abdominal aortic calcification (AAC) among American adults aged 40 years and older. A total of 2757 participants in the NHANES from 2013 to 2014 were included in the final analysis. BUN was measured by means of the enzymatic conductivity rate method. AAC scores were quantified by the Kauppila scoring system, and severe AAC was defined as an AAC score ≥ 6. Multivariable logistic regression and restricted cubic splines were used in the analyses. In the multivariable logistic regression model, the highest BUN level (log 2-transformed) was associated with an increased risk of severe AAC [odds ratio (OR) = 1.77, 95% CI 1.17, 2.71]. The restricted cubic spline plot displayed a reverse L-shaped association between BUN (log2-transformed) and severe AAC (p for nonlinearity < 0.001). In addition,the interactions of BUN were not discover. In general, there is a positive correlation between BUN and the risk of severe AAC.

Searching for Metabolic Markers of Stroke in Human Plasma via NMR Analysis

More than 12 million people around the world suffer a stroke every year, one every 3 s. Stroke has a variety of causes and is often the result of a complex interaction of risk factors related to age, genetics, gender, lifestyle, and some cardiovascular and metabolic diseases. Despite this evidence, it is not possible to prevent the onset of stroke. The use of innovative methods for metabolite analysis has been explored in the last years to detect new stroke biomarkers. We use NMR spectroscopy to identify small molecule variations between different stages of stroke risk. The Framingham Stroke Risk Score was used in people over 63 years of age living in long-term care facilities (LTCF) to calculate the probability of suffering a stroke. Using this parameter, three study groups were formed: low stroke risk (LSR, control), moderate stroke risk (MSR) and high stroke risk (HSR). Univariate statistical analysis showed seven metabolites with increasing plasma levels across different stroke risk groups, from LSR to HSR: isoleucine, asparagine, formate, creatinine, dimethylsulfone and two unidentified molecules, which we termed "unknown-1" and "unknown-3". These metabolic markers can be used for early detection and to detect increasing stages of stroke risk more efficiently.

Plasma urea cycle metabolite levels and the risk of moyamoya disease

Background and purpose

Urea cycle metabolites are expected to be the biomarkers for cerebrovascular diseases. However, the effects of circulating urea cycle metabolites on the risk of MMD and its subcategories remain unclear. The aim of this study was to prospectively investigate the association between plasma urea cycle metabolites and the risk of MMD and its subcategories.

Methods

We measured plasma urea cycle metabolite levels for 360 adult MMD patients and 89 matched healthy controls. Clinical and laboratory characteristics were obtained from the medical record. The study was conducted from July 2020 to December 2021.

Results

After multivariate adjustment, the risk of MMD increased with each increment in ornithine level (per natural log [ornithine] increment: OR, 3.893; 95% CI, 1.366-11.090). The risk of MMD decreased with each increment in arginine level (per natural log [arginine] increment: OR, 0.109; 95% CI, 0.028-0.427), urea level (per natural log [urea] increment: OR, 0.261; 95% CI, 0.072-0.940), and global arginine bioavailability ratio (GABR) level (per natural log [GABR] increment: OR, 0.189; 95% CI, 0.074-0.484). The addition of plasma arginine (integrated discrimination improvement: 1.76%, p = 0.021) or GABR (integrated discrimination improvement: 1.76%, p = 0.004) to conventional risk factors significantly improved the risk reclassification for MMD.

Conclusion

Plasma ornithine levels are positively associated with the risk of MMD. By contrast, the levels of arginine, urea, and GABR are inversely related to the risk of MMD. Plasma urea cycle metabolites might be potential biomarkers for the risk of MMD.

Association between red blood cell distribution width and ischemic stroke recurrence in patients with acute ischemic stroke: a 10-years retrospective cohort analysis

Numerous studies have reported that a higher red blood cell distribution width (RDW) level was associated with adverse outcomes in patients with the first stroke. However, no studies have examined the association between RDW and recurrent ischemic stroke. We performed a population-based cohort data analysis from 2007 to 2017. Baseline RDW was measured in 6402 first ischemic stroke participants, who were followed for about five years on average. During 62 months of median follow-up, 205 participants (3.20%) reported a recurrence (self-reported). RDW showed a nonlinear relationship with the risk of ischemic stroke recurrence. When RDW was assessed as quartiles (quartile 1, RDW<12.4; quartile 2, 12.4 to 12.8; quartile 3,12.8 to 13.3, quartile4, RDW>13.3), compared with the reference group (quartile 1), the hazard ratios (HRs) of ischemic stroke recurrence were 1.372 (95% confidence interval [CI]=0.671-2.805, P=0.386) in quartile 2, 1.835 (95% CI=1.222-2.755, P=0.003) in quartile 3, and 1.732 (95% CI=1.114-2.561, P<0.001) in quartile 4. The trend test was significant (P<0.001). When quartiles 3 and 4 were combined, the adjusted HR of ischemic stroke recurrence was 1.439 (95% CI=1.330-1.556, P<0.001) compared with the combined quartiles 1 and 2 subgroups. This study demonstrated that elevated RDW levels were positively associated with an increased risk of recurrent ischemic stroke. RDW can provide a new perspective for initial risk assessment and identify high-risk patients early. Further research is required to confirm our results.

Stroke mortality prediction based on ensemble learning and the combination of structured and textual data

For severe cerebrovascular diseases such as stroke, the prediction of short-term mortality of patients has tremendous medical significance. In this study, we combined machine learning models Random Forest classifier (RF), Adaptive Boosting (AdaBoost), Extremely Randomised Trees (ExtraTree) classifier, XGBoost classifier, TabNet, and DistilBERT to construct a multi-level prediction model that used bioassay data and radiology text reports from haemorrhagic and ischaemic stroke patients to predict six-month mortality. The performances of the prediction models were measured using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), precision, recall, and F1-score. The prediction models were built with the use of data from 19,616 haemorrhagic stroke patients and 50,178 ischaemic stroke patients. Novel six-month mortality prediction models for these patients were developed, which enhanced the performance of the prediction models by combining laboratory test data, structured data, and textual radiology report data. The achieved performances were as follows: AUROC = 0.89, AUPRC = 0.70, precision = 0.52, recall = 0.78, and F1 score = 0.63 for haemorrhagic patients, and AUROC = 0.88, AUPRC = 0.54, precision = 0.34, recall = 0.80, and F1 score = 0.48 for ischaemic patients. Such models could be used for mortality risk assessment and early identification of high-risk stroke patients. This could contribute to more efficient utilisation of healthcare resources for stroke survivors.

Establishment and validation of a predictive nomogram for gestational diabetes mellitus during early pregnancy term: A retrospective study

Objective

This study aims to develop and evaluate a predictive nomogram for early assessment risk factors of gestational diabetes mellitus (GDM) during early pregnancy term, so as to help early clinical management and intervention.

Methods

A total of 824 pregnant women at Zhongnan Hospital of Wuhan University and Maternal and Child Health Hospital of Hubei Province from 1 February 2020 to 30 April 2020 were enrolled in a retrospective observational study and comprised the training dataset. Routine clinical and laboratory information was collected; we applied least absolute shrinkage and selection operator (LASSO) logistic regression and multivariate ROC risk analysis to determine significant predictors and establish the nomogram, and the early pregnancy files (gestational weeks 12-16, n = 392) at the same hospital were collected as a validation dataset. We evaluated the nomogram via the receiver operating characteristic (ROC) curve, C-index, calibration curve, and decision curve analysis (DCA).

Results

We conducted LASSO analysis and multivariate regression to establish a GDM nomogram during the early pregnancy term; the five selected risk predictors are as follows: age, blood urea nitrogen (BUN), fibrinogen-to-albumin ratio (FAR), blood urea nitrogen-to-creatinine ratio (BUN/Cr), and blood urea nitrogen-to-albumin ratio (BUN/ALB). The calibration curve and DCA present optimal predictive power. DCA demonstrates that the nomogram could be applied clinically.

Conclusion

An effective nomogram that predicts GDM should be established in order to help clinical management and intervention at the early gestational stage.

Association of Blood Urea Nitrogen with Cardiovascular Diseases and All-Cause Mortality in USA Adults: Results from NHANES 1999-2006

In the general population, there is little evidence of a link between blood urea nitrogen (BUN) and long-term mortality. The goal of this study was to explore whether higher BUN concentration is a predictor of cardiovascular disease (CVD) and all-cause mortality. From 1999 to 2006, the National Health and Nutrition Examination Survey (NHANES) included 17,719 adult individuals. Death outcomes were ascertained by linkage to the database records through 31 December 2015. The Cox proportional hazard regression model was used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for CVD and all-cause mortality in individuals. We also performed stratified analyses based on age, gender, drinking, smoking, history of hypertension and diabetes. During a mean follow-up 11.65 years, a total of 3628 deaths were documented, of which 859 were due to CVD. Participants with higher BUN had a higher risk of CVD and all-cause death compared to those with lower BUN. After multifactor adjustment for demographics, major lifestyle factors, and hypertension and diabetes history, higher BUN levels compared with lower levels were significantly associated with higher risk of CVD (HR: 1.48 [1.08, 2.02], P-trend < 0.001) and all-cause mortality (HR: 1.48 [1.28, 1.72], P-trend < 0.001). In subgroup analyses, we found that the trend in the association of BUN with the risk of death remained strong in female subjects. Greater BUN levels were linked to higher CVD and all-cause mortality in the NHANES of American adults. The importance of BUN in predicting death is supported by our research.

Relationship between the Blood Urea Nitrogen to Creatinine Ratio and In-Hospital Mortality in Non-Traumatic Subarachnoid Hemorrhage Patients: Based on Propensity Score Matching Method

(1) Background: To explore the correlation between the blood urea nitrogen to creatinine ratio (UCR) and in-hospital mortality in non-traumatic subarachnoid hemorrhage patients. (2) Methods: Specific clinical information was collected from the Medical Information Mart for Intensive Ⅳ (MIMIC-Ⅳ) database. The optimal cut-off value of the UCR was calculated with ROC curve analysis conducted using the maximum Youden index for the prediction of survival status. Univariable and multivariable logistic regression analyses were also carried out to assess the prognostic significance of UCR, and the Kaplan−Meier (K−M) analysis was conducted to draw the survival curves. Then, the 1:1 propensity score matching (PSM) method was applied to improve the reliability of the research results while balancing the unintended influence of underlying confounders. (3) Results: This retrospective cohort study included 961 patients. The optimal cut-off value of the UCR for in-hospital mortality was 27.208. The PSM was performed to identify 92 pairs of score-matched patients, with balanced differences exhibited for nearly all variables. According to the K−M analysis, those patients with a UCR of more than 27.208 showed a significantly higher level of in-hospital mortality compared to the patients with a UCR of less than 27.208 (p < 0.05). After the adjustment for possible confounders, those patients whose UCR was more than 27.208 still had a significantly higher level of in-hospital mortality than the patients whose UCR was less than 27.208, as revealed by the multivariable logistic regression analysis (OR = 3.783, 95% CI: 1.959~7.305, p < 0.001). Similarly, the in-hospital mortality remained substantially higher for those patients in the higher UCR group than for the patients in the lower UCR group after PSM. (4) Conclusion: A higher level of the UCR was evidently associated with an increased risk of in-hospital mortality, which made the ratio useful as a prognostic predictor of clinical outcomes for those patients with non-traumatic subarachnoid hemorrhage.

The Association of Blood Urea Nitrogen to Creatinine Ratio and the Prognosis of Critically Ill Patients with Cerebral Infarction: A Cohort Study

Background

To evaluate the association between blood urea nitrogen (BUN) to creatinine (Cr) (BUN/Cr) ratio and the in-hospital mortality of critically ill patients with cerebral infarction in intensive care unit (ICU).

Methods

In this cohort study, the data of 3059 participants with cerebral infarction were collected from the Medical Information Mart for Intensive Care (MIMIC)-III and the MIMIC-IV database. After propensity score matching (PSM) on age and gender, 2085 people were involved in and divided into the alive group (n = 1390) and the dead group (n = 695) based on the results of follow-up. Multivariate logistic analyses were applied to identify the confounders and the association between BUN/Cr and mortality of cerebral infarction.

Results

The median follow-up time was 10.5 days. Among 2778 participants, 695 were dead at the end of follow-up. Univariate analysis revealed that BUN/Cr [risk ratio (RR) = 1.01, 95% confidence interval (CI): 1.01-1.02] might be associated with the in-hospital mortality of cerebral infarction patients. After adjusting for respiratory failure, malignant cancer, anticoagulation, liver disease, white blood cell (WBC), red cell distribution width (RDW), glucose, bicarbonate, and temperature, BUN/Cr had week correlation with the increased risk of in-hospital mortality of cerebral infarction patients (RR = 1.01, 95% CI: 1.01-1.02).

Conclusion

This study evaluated the association between BUN/Cr and the in-hospital mortality of cerebral infarction patients in ICU and found that BUN/Cr had weak correlation with the increased risk of in-hospital mortality of patients with cerebral infarction in ICU especially in males and those with respiratory failure, malignant cancer, and without liver disease, as well as those receiving anticoagulation.

Elevated Blood Urea Nitrogen to Serum Albumin Ratio Is an Adverse Prognostic Predictor for Patients Undergoing Cardiac Surgery

Background

Elevated blood urea nitrogen (BUN) and reduced albumin have been prominently correlated with unfavorable outcomes in patients with cardiovascular diseases. However, whether combination BUN and albumin levels could predict the adverse outcomes of cardiac surgery patients remains to be confirmed. Here, we investigated the prognostic effect of the preoperative BUN to serum albumin ratio (BAR) in cardiac surgery patients.

Methods

Data were obtained from the Medical Information Mart for Intensive Care (MIMIC) III and eICU databases and classified into a training cohort and validation cohort. The BAR (mg/g) was calculated by initial BUN (mg/dl)/serum albumin (g/dl). The primary outcome was in-hospital mortality. Secondary outcomes were 1-year mortality, prolonged length at intensive care unit, and duration of hospital stay. The associations of BAR with outcomes were explored by multivariate regression analysis and subgroup analyses. Then, C statistics were performed to assess the added prognostic impact of BAR beyond a baseline risk model.

Results

Patients with in-hospital death had significantly higher levels of BAR. Multivariate regression analysis identified BAR, as a categorical or continuous variable, as an independent factor for adverse outcomes of cardiac surgery (all p < 0.05). Subgroup analyses demonstrated a significant relationship between elevated BAR and in-hospital mortality in different subclasses. The addition of BAR to a baseline model provided additional prognostic information benefits for assessing primary outcome. Results were concordant in the external validation cohort.

Conclusions

Increased preoperative BAR is a potent predictor of unfavorable outcomes in patients undergoing cardiac surgery.

Predictive Value of Blood Urea Nitrogen to Albumin Ratio in Long-Term Mortality in Intensive Care Unit Patients with Acute Myocardial Infarction: A Propensity Score Matching Analysis

Background

Blood urea nitrogen to albumin ratio (BAR) has been implicated in predicting outcomes of various inflammatory-related diseases. However, the predictive value of BAR in long-term mortality in patients with acute myocardial infarction (AMI) has not yet been evaluated.

Methods

In this retrospective cohort study, the patients were recruited from the Medical Information Mart for Intensive Care III (MIMIC III) database and categorized into two groups by a cutoff value of BAR. Kaplan–Meier (K-M) analysis and Cox proportional hazard model were performed to determine the predictive value of BAR in long-term mortality following AMI. In order to adjust the baseline differences, a 1:1 propensity score matching (PSM) was carried out and the results were further validated.

Results

A total of 1827 eligible patients were enrolled. The optimal cutoff value of BAR for four-year mortality was 7.83 mg/g. Patients in the high BAR group tended to have a longer intensive care unit (ICU) stay and a higher rate of one-, two-, three- and four-year mortality (all p<0.001) compared with those in the low BAR group. K-M curves indicated a significant difference in four-year survival (p<0.001) between low and high BAR groups. The Cox proportional hazards model showed that higher BAR (>7.83) was independently associated with increased four-year mortality in the entire cohort, with a hazard ratio (HR) of 1.478 [95% CI (1.254–1.740), p<0.001]. After PSM, the baseline characteristics of 312 pairs of patients in the high and low BAR groups were well balanced, and similar results were observed in K-M curve (p=0.003).

Conclusion

A higher BAR (>7.83) was associated with four-year mortality in patients with AMI. As an easily available biomarker, BAR can predict the long-term mortality in AMI patients independently.

The blood urea nitrogen/creatinine (BUN/cre) ratio was U-shaped associated with all-cause mortality in general population

Objectives

This study aimed to explore the relationship between the blood urea nitrogen/creatinine (BUN/Cre) ratio and all-cause or cause-specific mortality in the general population.

Methods

Participants were enrolled from the National Health and Nutrition Examination Survey (NHANES) during 1999 to 2014. Baseline variables were acquired from questionnaires and examinations. Death status were ascertained from National Death Index records. Cox proportional hazards models with cubic spines were used to estimate hazard ratios (HRs) and 95% confidence interval (CI) of all-cause mortality, cardiovascular and cancer mortality.

Results

A total of 42038 participants were enrolled in the study with a median 8.13 years of follow-up. Older people and women tend to have a higher BUN/Cre ratio. After multivariable adjustment, BUN/Cre ratio between 11.43 and 14.64 was associated with the lowest all-cause mortality compared with the participants with the lowest quartile (HR 0.83 [0.76, 0.91]; p < 0.001). The highest quartile of BUN/Cre ratio was associated with the lowest risk of cancer mortality (HR 0.64 [0.53, 0.78]; p < 0.001). Restricted cubic splines showed BUN/Cre was nonlinearly associated with all-cause mortality and linearly associated with cancer mortality.

Conclusions

This study confirmed a U-shape relationship between BUN/Cre ratio and all-cause mortality in the general population.