Abstract

Background: Albumin (ALB) and blood urea nitrogen (BUN) are both associated with the prognosis of acute ischemic stroke (AIS). A recent prognostic marker, the BUN/ALB ratio (BAR), has been suggested as a simple and sensitive method to predict certain acute diseases.
Aims: To determine the predictive value of BAR in relation to the risk of in-hospital mortality among AIS patients.
Study Design: Retrospective cohort study.
Methods: Cox regression analysis was employed to assess the relationship between in-hospital mortality and BAR, with hazard ratios (HRs) and 95% confidence intervals. Subgroup analysis of acute pulmonary embolism, acute myocardial infarction (AMI), thrombolysis, thrombectomy, and septic shock was performed to further examine this relationship. The predictive value of BAR and BAR multivariate models for in-hospital mortality was evaluated and compared to BUN, ALB, the Acute Physiology and Chronic Health Evaluation IV (APACHE IV) score, and the Sequential Organ Failure Assessment Score (SOFA).
Results: Among the 1,635 eligible patients, 226 (13.81%) died during hospitalization. An elevated serum BAR level was associated with an increased in-hospital mortality risk (HR: 1.3) after covariates were adjusted. Additionally, this positive association was observed in patients without AP, AMI, thrombolysis, history of thrombectomy, or septic shock (all; p < 0.05). The efficacy of the BAR multivariate model in predicting in-hospital mortality among AIS patients was superior to that of both APACHE IV and SOFA, with an area under the curve of 0.87.
Conclusion: Serum BAR exhibits the potential to identify AIS patients with high mortality risk, which may contribute to enhanced disease surveillance and risk stratification.

INTRODUCTION

Stroke has been a significant global cause of mortality and lifelong disability, causing immense economic burden over the past few decades.^1,2 Acute ischemic stroke (AIS) is the most prevalent form of stroke, affecting approximately 700,000 individuals annually.^3,4 AIS results from hypoxia and nutrient deficiency caused by the cerebral artery occlusion that induces a local inflammatory immune response.⁵ Therefore, certain biological indicators of inflammation and nutritional status play a vital role in identifying high-risk populations and monitoring AIS progress to appropriately modify therapeutic strategies and vastly enhance patient outcomes.

Albumin (ALB) has been a well-established potent indicator of malnutrition and inflammation (Arques⁷, 2018). In animal models and clinical trials, serum ALB has been demonstrated to function as a neuroprotector.⁶ Research reveals that hypoalbuminemia is linked to poor prognosis in AIS patients.^6,7 Serum blood urea nitrogen (BUN) is a critical parameter that can indicate patients’ kidney function, hemodynamic status, and protein metabolism level. Elevated BUN at admission has been observed to be independently linked to increased in-hospital mortality risk in AIS.⁸ A prognostic marker, the BUN to ALB ratio (BAR), has been recently proposed as a simple and sensitive indicator of prognosis.⁹ Zeng et al.¹⁰ demonstrated that an elevated BAR was a reliable and independent predictor for in-hospital mortality in patients with acute exacerbation of chronic obstructive pulmonary disease, with an area under the curve (AUC) of 0.87. Zou et al.¹¹ discovered that BAR was an easily accessible and independent predictor of 30-day mortality as well as severity in patients with E. coli bacteremia. However, the role of serum BAR in AIS patients has not been fully investigated.

Herein, we aimed to investigate the relationship between serum BAR and in-hospital mortality in AIS patients, evaluate the predictive value of BAR, provide insights on high-risk population identification, and monitor and modify treatment strategies in AIS in a timely manner.

MATERIALS AND METHODS

Data on participants

This retrospective cohort study included data of adult AIS patients that were extracted from the e-intensive care unit (eICU) collaborative research database, which contains information pertaining to 200,859 ICU admissions of 139,367 patients during 2014-2015 at 208 hospitals in the United States.¹² The eICU-CRD version 2.0 was accessed using Google BigQuery, and structured query language scripts were employed to acquire relevant data. The complete code is available on Github (https://github.com/CONDUITlab/eICU-CRD/tree/master).¹³ EICU-CRD includes comprehensive de-identified records, including details pertaining to demographics, diagnoses, and treatment.¹⁴

This study initially included 3,379 AIS patients. The diagnosis of AIS was defined using ICD-9¹⁵ and ICD-10-CM codes.¹⁶ We also extracted data from the fields “ais,” “acute cerebral infarct,” and “AIS.” The study excluded patients hospitalized in the ICU < 24 hours and those without information on their BUN/ALB levels or survival status. A total of 1,635 patients were finally eligible. The eICU database has been granted ethical approval by the relevant ethics committee, and the patients who are involved have provided informed consent. Since the data were publicly accessible, the requirement for ethical approval was waived by the ethics committee of our hospital.

Measurement of BAR level

The BUN (mg/dl) to ALB (g/dl) ratio was used to calculate the serum BAR. Based on the cut-off value calculated by the X-tile software,⁹ the serum BAR was classified into two levels: BAR level ≥ 5.28 and BAR level < 5.28.

In the analyses, included both the continuous variable and categorical variable of ALB and BUN. The ALB concentrations were divided into ALB ≥ 3.5 g/dl and ALB < 3.5 g/dl based on the median value, while the BUN concentrations were classified as BUN ≥ 19 mg/dl and BUN < 19 mg/dl using the same method.^2,17

Potential confounding factors

Variables selected as potential covariates in this study included age, sex, ethnicity, ventilation use, vasopressor use, thrombolysis, thrombectomy, antiplatelet agents, anticoagulation agents, acute kidney injury (AKI), acute pulmonary embolism (APE), renal replacement therapy, hypertension, chronic obstructive pulmonary disease (COPD), diabetes mellitus (DM), liver diseases, acute myocardial infarction (AMI), septic shock, cerebral hemorrhage, surgical treatment, blood transfusion, height, weight, heart rate, body mass index, systolic blood pressure (SBP), diastolic blood pressure (DBP), respiration rate (RR), temperature, Acute Physiology and Chronic Health Evaluation IV (APACHE IV) score, white blood cell (WBC), lymphocyte count, hemoglobin (HB) levels, platelet count, red blood cell distribution width (RDW), bilirubin concentration, international normalized ratio (INR), creatinine levels, prothrombin time (PT), levels of glucose, bicarbonate, potassium (K), sodium (Na), and chloride, Sequential Organ Failure Assessment (SOFA) score, and multiple organ dysfunction syndrome (MODS). The information was initially recorded within 24 hours of hospital admission.

Outcome and follow-up duration

The primary outcome was in-hospital mortality, which was defined as patients’ survival status at the time of ICU discharge, as documented in the hospital department records. The Social Security Bureau documented the time of death through the out-of-hospital social security account, which served as the endpoint of the
follow-up.

Statistical analysis

The normality assumption was evaluated using the Shapiro-Wilk test. The normally distributed data were described by mean ± standard deviation. The Levene’s test was used to assess the variance homogeneity. The t-test was employed to compare normally distributed data that exhibited variance homogeneity between two groups, while the Welch’s t-test was employed to compare normally distributed data that did not exhibit variance homogeneity. Non-normally distributed data were described using medians and quartiles [M (Q₁, Q₃)], and the Mann-Whitney U rank test was employed for comparison. Categorical data were presented as frequency and constituent ratio [n (%)]. The chi-square test (χ²) was employed to compare the two groups.

The confounding factors for in-hospital mortality were determined using univariate Cox regression and incremental analyses. Variables with a p value of < 0.05 were considered statistically significant and incorporated in the multivariate model adjustment. The association between serum BAR and in-hospital mortality in AIS patients was investigated using univariate and multivariate Cox regression analyses. When considering AIS treatment modalities, thrombolysis should be included in the covariates, with hazard ratios (HRs) and 95% confidence intervals (CIs). A two-sided p < 0.05 was deemed statistically significant. Multivariate models were adjusted for specific covariates (age, ethnicity, ventilation use, vasopressor use, thrombolysis, antiplatelet agents use, AKI, APE, septic shock, Glasgow Coma Scale (GCS), WBC, platelets, bilirubin, INR, PT, K, and MODS). The relationship between BAR and in-hospital mortality was further investigated through subgroup analysis of APE, AMI, thrombolysis, thrombectomy, and septic shock.

The predictive efficacy of BAR, BUN, and ALB for in-hospital mortality in AIS patients was compared using the DeLong’s test. The time-dependent receiver operator characteristic (ROC) curves were constructed to represent the predictive value of BAR, BUN, and ALB. This function is applicable in both the conventional survival setting and the competing risks setting, which involves estimating the cumulative/dynamic time-dependent ROC curve using the inverse probability of censoring weighting. We also constructed a BAR multivariate model involving both BAR and variables that were substantially associated with in-hospital mortality, including vasopressor use, COPD, temperature, GCS, total bilirubin, platelets, INR, and thrombolysis. The predictive value of the BAR multivariate model on in-hospital mortality in AIS was compared to that of SOFA and APACHE IV scores, by determining the C-indexes of these three prediction systems.

Statistical analysis

Statistical analysis was conducted using the SAS 9.4 software (SAS Institute, Cary, NC, USA) and R software version 4.2.2 (2022-10-31 ucrt) (Institute for Statistics and Mathematics, Vienna, Austria). Variables that were missing values were depicted in the Supplementary Table 1 and were supplemented using multiple imputation. This process generated a complete data set from a data set that was missing values, because of repeated simulation using the “mice” package of R. The Supplementary Table 2 illustrates the sensitivity analysis of the characteristics of the participants before and after multiple imputation of missing data.

RESULTS

The characteristics of AIS patients

The flow chart of the study procedure is depicted in Figure 1. Initially, 3,379 adult AIS patients were included. Those who were hospitalized in the ICU for less than 24 hours (n = 639), those lacking data related to their BUN or ALB levels (n = 1,078) or survival data
(n = 26), were excluded. Finally, 1,635 study participants were eligible.

The characteristics of AIS patients in different BAR level groups are shown in Table 1. Of the eligible patients, 226 (13.82%) died during hospitalization. The total population had a mean age of 69 years, with 802 (49.05%) females and 833 (50.95%) males. The median serum ALB (3.8 vs. 3.3 g/dl) and BUN (14 vs. 26 mg/dl) concentrations were substantially different between the low BAR level group and the high BAR level group. The median APACHE IV scores in these two groups were 43 vs. 61, and the median SOFA scores were respectively 4 and 5. Additionally, significant differences were detected in the following parameters: ventilation use, thrombolysis, vasopressor use, AKI, renal replacement therapy, APE, DM, AMI, septic shock, height, SBP, DBP, RR, temperature, GCS, WBC, lymphocytes, platelet levels, HB levels, RDW, total bilirubin, creatinine, INR, PT, MODS, and levels of glucose, bicarbonate, Na, K, and chloride (all; p < 0.05).

Screening for the covariates associated with in-hospital mortality

Table 2 illustrates the selected covariates. The univariate Cox regression analysis revealed that in-hospital mortality was significantly correlated with age, ethnicity, ventilation use, vasopressor use, thrombolysis, thrombectomy, antiplatelet agents use, AKI, APE, septic shock, APACHE IV score, GCS, WBC, platelet levels, total bilirubin concentration, INR, PT, K, and MODS. Then, following stepwise regression, the final selected covariates were incorporated in adjustment of multivariate models.

Association between BAR and in-hospital mortality

Table 3 depicts the correlation between BAR and in-hospital mortality. After adjusting for covariates, both serum BUN concentration ≥ 19 mg/dl [HR: 1.65, 95% CI: (1.22-2.22)] and BAR ≥ 5.28 [HR: 1.39, 95% CI: (1.01-1.91)] were observed to be associated with an elevated in-hospital mortality risk.

Further subgroup analysis of common comorbidities was conducted. Table 4 demonstrates the investigation of the association of BAR with in-hospital mortality in AMI, APE, thrombolysis, thrombectomy and septic shock subgroups.

The results revealed that the positive association between BAR and in-hospital mortality risk was also significant in patients without APE (HR: 1.40, 95% CI: 1.02-1.93), AMI (HR: 1.39, 95% CI: 1.01-1.92), thrombolysis (HR: 1.47, 95% CI: 1.04-2.09), or septic shock (HR: 1.42, 95% CI: 1.03-1.97), and a history of undergoing thrombectomy (HR: 1.39, 95% CI: 1.01-1.91).

Predictive value of BAR on in-hospital mortality in AIS patients

Figure 2 illustrates the ROC curves of the predictive value of BAR, BUN, and ALB on in-hospital mortality. Both in univariate analysis and multivariate analysis, it was evident that the AUC of time related-ROC for BAR was greater than that of BUN or ALB.

Additionally, we calculated the C-index of the BAR multivariate model, the APACHE IV score, and the SOFA score to assess their efficacy in predicting in-hospital mortality (Table 5). The findings revealed that compared to the BAR multivariate model [C-index: 0.789, 95% CI: (0.760-0.818)], both APACHE IV [C-index: 0.712, 95% CI: (0.675-0.749)] and SOFA scores [C-index: 0.683, 95% CI: (0.646-0.720)] exhibited lower C-indexes, indicating a superior prediction value of BAR multivariate model for in-hospital mortality than the other two scoring systems.

DISCUSSION

This study examined the link between BAR and in-hospital mortality risk in AIS patients. The results indicated that a higher serum BAR was associated with an elevated in-hospital mortality risk. This positive correlation was also significant in patients who had not experienced APE, AMI, thrombolysis, thrombectomy, or septic shock. Furthermore, the efficacy of BAR and its multivariate model in predicting in-hospital mortality is superior to both ALB and BUN, as well as the APACHE IV and SOFA scores.

We did not find any other study that explored the role of BAR in AIS prognosis. According to recent studies, BAR has the potential to serve as a mortality predictor in both pneumonia and ICU patients.^18-20 Zhao et al.⁹ reported that a higher BAR level at the time of ICU admission was associated with a higher four-year all-cause mortality risk in AMI patients, and thus, it may serve as an independent predictor. Dundar et al.²¹ revealed that an increased BAR could predict in-hospital mortality among older emergency department patients. A cohort study conducted by Ye et al.²² suggested that BAR was linked to poor prognosis in patients undergoing cardiac surgery and may offer prognostic information regarding in-hospital mortality. Similarly, the current study revealed that serum BAR exhibited a positive association with in-hospital mortality risk in AIS patients even after adjusting for a variety of potential confounding factors.

The BUN to ALB ratio is determined by the ratio of BUN to ALB. The serum BUN level has been identified as a predictor of mortality in various acute diseases, including AMI, pneumonia, acute pancreatitis, AIS, and APE.^8,20,23-26 Also, studies have reported that an elevated serum BUN concentration at the time of ICU admission is directly linked to mortality.^27,28 Pan et al.²⁹ indicated that the maximal serum BUN level, which was examined during an ICU stay, could independently predict mortality in critically ill older patients. Furthermore, the role of ALB in disease prognosis has garnered significant attention in recent decades. After a ten-year follow-up, Plakht et al.³⁰ demonstrated that a decreased serum ALB level is substantially associated with long-term all-cause mortality in AMI survivors. Xia et al.³¹ also discovered that in initial-onset AMI patients, a low baseline ALB level was an independent predictor of long-term cardiovascular, all-cause, and cardiac mortality. In the present study, we observed that BAR exhibited a significantly superior efficacy for predicting in-hospital mortality in AIS patients. These findings fill the gap in research on this population, indicating that the combination of these two indicators may provide a more complete picture of AIS patients’ risk of death than either indicator alone. Potentially complicated and diverse mechanisms may underlie BAR’s potential to predict in-hospital mortality in AIS patients. Increasing evidence indicates that inflammation is a significant factor in the AIS process.^5,32 The anti-inflammatory effect of ALB was observed at physiological concentrations by selectively inhibiting the expression of vascular cell adhesion molecule-1 and monocyte adhesion in human aortic endothelial cells that were induced by tumor necrosis factor-alpha.³³ Inflammatory biomarkers, including lipoprotein-associated phospholipase A2 and C-reactive protein, are associated with hypoalbuminemia, as well as significant factors in risk stratification after AIS.³⁴ In addition, since ALB is an extracellular antioxidant, the increase in reactive oxygen species and free radicals worsens the long-term prognosis for AIS patients with hypoalbuminemia.³⁵

Research suggests that BAR exhibits higher efficacy and sensitivity in predicting mortality in patients with acquired pneumonia than BUN or ALB, although they are both independent predictors of mortality.^18,19,36 This study also revealed that BAR demonstrated a substantially higher AUC value of ROC in predicting in-hospital mortality in AIS patients than ALB or BUN. Dundar et al.²¹ reported that AUC and ORs obtained from BAR were superior to those calculated from BUN and ALB in predicting mortality among older patients. In predicting mortality, Ryu et al.²⁰ indicated that AUC and OR values corresponding to BAR were higher than those corresponding to ALB. BUN and ALB are easily accessible parameters in emergency services; however, performing a combined evaluation by proportioning them may be more advantageous than evaluating them separately.³⁷ Moreover, we compared the predictive value BAR multivariate model with that of APACHE IV and SOFA scores, and found that the BAR model demonstrated a superior prediction performance for in-hospital mortality in AIS compared to the APACHE IV and SOFA scores. Zhao et al.⁹ also observed that the AUC of BAR-based predictive model was greater than those of SOFA score. This indicates a more favorable prognostic efficiency for 4-year mortality after AMI, which could be easily and rapidly calculated in routine clinical practice. Our study findings suggest that it may be more meaningful to evaluate BAR in AIS patients, and use it as a biomarker for prognosis, as this may enable physicians to assess the clinical condition of AIS patients from two distinct perspectives. Also, the C-index of BAR multivariate model, APACHE IV score and SOFA score in this study were respectively 0.789, 0.712, and 0.683. Nevertheless, additional study is required to validate the advantage of serum BAR in predicting mortality compared to BUN or ALB.

There were several limitations in our study. The eICU database is a public multi-center database in the U.S. and is representative to a certain extent. However, the conclusions we have drawn are still limited to the American population and therefore cannot be generalized. There was evident selection bias due to the substantial number of deleted patients who lacked BUN or ALB data. However, based on sensitivity analysis and existing literature, our conclusion that BAR is significantly associated with in-hospital mortality in AIS patients after excluding potential confounding factors is relatively robust. The sample size in APE and thrombectomy subgroups was insufficient to explore the association between BAR and in-hospital mortality. Although we compared the predictive performance of the BAR multivariate model with APACHE IV and SOFA scores, other scoring systems, such as the National Institutes of Health Stroke Scale, which provide significant data regarding factors influencing hospitalization-related mortality in AIS patients, were not included in the eICU database. Additionally, prospective cohort studies with a larger sample size are still required to investigate the causal relationship between BAR and mortality risk in AIS patients.

In conclusion, serum BAR exhibits a positive association with in-hospital mortality risk in AIS patients. Additionally, BAR and its multivariate model both demonstrated reliable predictive value for in-hospital mortality in AIS. Our findings suggested that serum BAR exhibits the potential to identify patients with high mortality risk, which may be beneficial for the disease surveillance and risk stratification of AIS.

Supplementary Table 1: https://balkanmedicaljournal.org/uploads/pdf/SUPLLEMENT--TABLE%201.pdf

Supplementary Table 2: https://balkanmedicaljournal.org/uploads/pdf/SUPLLEMENT--TABLE%202.pdf

Ethics Committee Approval: Since the data were publicly accessible, the requirement for ethical approval was waived by the ethics committee of our hospital.

Informed Consent: The patients who are involved have provided informed consent.

Data Sharing Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.

Authorship Contributions: Concept- W.L.; Design- W.L.; Data Collection or Processing- Q.H., K.Z.; Analysis and/or Interpretation- Q.H., K.Z.; Writing- W.L.; Critical Review- W.L.

Conflict of Interest: The authors declare that they have no conflict of interest.

Funding: The authors declared that this study received no financial support.

REFERENCES

1. Al Alawi AM, Al Busaidi I, Al Shibli E, et al. Health outcomes after acute ischemic stroke:retrospective and survival analysis from Oman. Ann Saudi Med. 2022;42:269-275.
2. Li Z, Bi R, Sun S, et al. The Role of Oxidative Stress in Acute Ischemic Stroke-Related Thrombosis. Oxid Med Cell Longev. 2022;2022:8418820.
3. Feske SK. Ischemic Stroke. Am J Med. 2021;134:1457-1464.
4. Paul S, Candelario-Jalil E. Emerging neuroprotective strategies for the treatment of ischemic stroke: An overview of clinical and preclinical studies. Exp Neurol. 2021;335:113518.
5. Maida CD, Norrito RL, Daidone M, Tuttolomondo A, Pinto A. Neuroinflammatory Mechanisms in Ischemic Stroke: Focus on Cardioembolic Stroke, Background, and Therapeutic Approaches. Int J Mol Sci. 2020;21:6454.
6. Wang C, Deng L, Qiu S, et al. Serum Albumin Is Negatively Associated with Hemorrhagic Transformation in Acute Ischemic Stroke Patients. Cerebrovasc Dis. 2019;47:88-94.
7. Arques S. Human serum albumin in cardiovascular diseases. Eur J Intern Med. 2018;52:8-12.
8. You S, Zheng D, Zhong C, et al. Prognostic Significance of Blood Urea Nitrogen in Acute Ischemic Stroke. Circ J. 2018;82:572-578.
9. Zhao D, Liu Y, Chen S, et al. 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. Int J Gen Med. 2022;15:2247-2259.
10. Zeng Z, Ke X, Gong S, et al. Blood urea nitrogen to serum albumin ratio: a good predictor of in-hospital and 90-day all-cause mortality in patients with acute exacerbations of chronic obstructive pulmonary disease. BMC Pulm Med. 2022;22:476.
11. Zou XL, Feng DY, Wu WB, Yang HL, Zhang TT. Blood urea nitrogen to serum albumin ratio independently predicts 30-day mortality and severity in patients with Escherichia coli bacteraemia. Med Clin. 2021;157:219-225.
12. Pollard TJ, Johnson AEW, Raffa JD, Celi LA, Mark RG, Badawi O. The eICU Collaborative Research Database, a freely available multi-center database for critical care research. Sci Data. 2018;5:180178.
13. O’Halloran HM, Kwong K, Veldhoen RA, Maslove DM. Characterizing the Patients, Hospitals, and Data Quality of the eICU Collaborative Research Database. Crit Care Med. 2020;48:1737-1743.
14. Zhu X, Xue J, Liu Z, et al. Association between serum chloride levels with mortality in critically ill patients with acute kidney injury: An observational multicenter study employing the eICU database. PloS One. 2022;17:e0273283.
15. McCormick N, Bhole V, Lacaille D, Avina-Zubieta JA. Validity of Diagnostic Codes for Acute Stroke in Administrative Databases: A Systematic Review. PloS One. 2015;10:e0135834.
16. Hsieh MT, Hsieh CY, Tsai TT, Wang YC, Sung SF. Performance of ICD-10-CM Diagnosis Codes for Identifying Acute Ischemic Stroke in a National Health Insurance Claims Database. Clin Epidemiol. 2020;12:1007-1013.
17. Hong C, Zhu H, Zhou X, et al. Association of Blood Urea Nitrogen with Cardiovascular Diseases and All-Cause Mortality in USA Adults: Results from NHANES 1999-2006. Nutrients. 2023;15:461.
18. Gundpatil DB, Somani BL, Saha TK, Banerjee M. Serum urea:albumin ratio as a prognostic marker in critical patients with non-chronic kidney disease. Indian J Clin Biochem. 2014;29:97-100.
19. Feng DY, Zhou YQ, Zou XL, et al. Elevated blood urea nitrogen-to-serum albumin ratio as a factor that negatively affects the mortality of patients with hospital-acquired pneumonia. Can J Infect Dis Med Microbiol. 2019;2019:1547405.
20. Ryu S, Oh SK, Cho SU, et al. Utility of the blood urea nitrogen to serum albumin ratio as a prognostic factor of mortality in aspiration pneumonia patients. Am J Emerg Med. 2021;43:175-179.
21. Dundar ZD, Kucukceran K, Ayranci MK. Blood urea nitrogen to albumin ratio is a predictor of in-hospital mortality in older emergency department patients. Am J Emerg Med. 2021;46:349-354.
22. Ye L, Shi H, Wang X, Duan Q, Ge P, Shao Y. Elevated Blood Urea Nitrogen to Serum Albumin Ratio Is an Adverse Prognostic Predictor for Patients Undergoing Cardiac Surgery. Front Cardiovasc Med. 2022;9:888736.
23. Sacks D, Baxter B, Campbell BCV, et al. Multisociety Consensus Quality Improvement Revised Consensus Statement for Endovascular Therapy of Acute Ischemic Stroke. Int J Stroke. 2018;13:612-632.
24. Liu J, Sun LL, Wang J, Ji G. Blood urea nitrogen in the prediction of in-hospital mortality of patients with acute aortic dissection. Cardiol J. 2018;25:371-376.
25. Emami A, Javanmardi F, Rajaee M, et al. Predictive Biomarkers for Acute Kidney Injury in Burn Patients. J Burn Care Res. 2019;40:601-605.
26. Yamagata A, Ito A, Nakanishi Y, Ishida T. Prognostic factors in nursing and healthcare-associated pneumonia. J Infect Chemother. 2020;26:563-569.
27. Beier K, Eppanapally S, Bazick HS, et al. Elevation of blood urea nitrogen is predictive of long-term mortality in critically ill patients independent of “normal” creatinine. Crit Care Med. 2011;39:305-313.
28. Arihan O, Wernly B, Lichtenauer M, et al. Blood Urea Nitrogen (BUN) is independently associated with mortality in critically ill patients admitted to ICU. PloS One. 2018;13:e0191697.
29. Pan SW, Kao HK, Yu WK, et al. Synergistic impact of low serum albumin on intensive care unit admission and high blood urea nitrogen during intensive care unit stay on post-intensive care unit mortality in critically ill elderly patients requiring mechanical ventilation. Geriatr Gerontol Int. 2013;13:107-115.
30. Plakht Y, Gilutz H, Shiyovich A. Decreased admission serum albumin level is an independent predictor of long-term mortality in hospital survivors of acute myocardial infarction. Soroka Acute Myocardial Infarction II (SAMI-II) project. Int J Cardiol. 2016;219:20-24.
31. Xia M, Zhang C, Gu J, et al. Impact of serum albumin levels on long-term all-cause, cardiovascular, and cardiac mortality in patients with first-onset acute myocardial infarction. Clin Chim Acta. 2018;477:89-93.
32. Li LH, Chen CT, Chang YC, Chen YJ, Lee IH, How CK. Prognostic role of neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and systemic immune inflammation index in acute ischemic stroke: A STROBE-compliant retrospective study. Medicine. 2021;100:e26354.
33. Zhang WJ, Frei B. Albumin selectively inhibits TNF alpha-induced expression of vascular cell adhesion molecule-1 in human aortic endothelial cells. Cardiovasc Res. 2002;55:820-829.
34. Gerber Y, McConnell JP, Jaffe AS, Weston SA, Killian JM, Roger VL. Lipoprotein-associated phospholipase A2 and prognosis after myocardial infarction in the community. Arterioscler Thromb Vasc Biol. 2006;26:2517-2522.
35. Orellana-Urzúa S, Rojas I, Líbano L, Rodrigo R. Pathophysiology of Ischemic Stroke: Role of Oxidative Stress. Curr Pharm Des. 2020;26:4246-4260.
36. Ugajin M, Yamaki K, Iwamura N, Yagi T, Asano T. Blood urea nitrogen to serum albumin ratio independently predicts mortality and severity of community-acquired pneumonia. Int J Gen Med. 2012;5:583-589.
37. Küçükceran K, Ayrancı MK, Girişgin AS, Koçak S, Dündar ZD. The role of the BUN/albumin ratio in predicting mortality in COVID-19 patients in the emergency department. Am J Emerg Med. 2021;48:33-37.