Association between the blood urea nitrogen–to–serum albumin ratio and all-cause mortality in critically ill patients with hypertension: a retrospective cohort study of MIMIC-IV database

Association between the blood urea nitrogen–to–serum albumin ratio and all-cause mortality in critically ill patients with hypertension: a retrospective cohort study of MIMIC-IV database | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Association between the blood urea nitrogen–to–serum albumin ratio and all-cause mortality in critically ill patients with hypertension: a retrospective cohort study of MIMIC-IV database keyang li, Debao li, Dandan Gong, Dongmei Ren, Meng li, Yuanyuan Wu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8827280/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background This study aims to investigate the association between the blood urea nitrogen–to–albumin ratio (BAR) and short- and long-term all-cause mortality in critically ill patients with hypertension admitted to the intensive care unit. Methods A retrospective cohort study was conducted using data from the Medical Information Mart for Intensive Care IV database, which includes ICU patients from 2008 to 2022. Patients with hypertension were identified using ICD codes and categorized into quartiles according to BAR values. Kaplan–Meier survival analyses were performed to compare mortality across BAR quartiles. Multivariate Cox regression models were employed to assess the association between BAR and 30-day, 90-day, and 365-day mortality. Receiver operating characteristic curve was plotted to evaluate the predictive value of BAR, BUN, and SOFA for mortality outcomes. Subgroup analyses were conducted based on comorbidities and clinical characteristics. The external validation was performed in eICU 2.0 database. Results A total of 8,538 patients with hypertension were included. The 30-day, 90-day, and 365-day mortality rates were 20.3%, 26.6%, and 35.5%, respectively. Kaplan–Meier analyses demonstrated progressively higher all-cause mortality across increasing BAR quartiles (log-rank p < 0.001). In fully adjusted Cox models, patients in the highest BAR quartile (Q4) exhibited significantly higher risks of all-cause mortality compared with those in the lowest quartile (Q1), including 30-day mortality (HR 1.92, 95% CI: 1.58–2.32; p < 0.001), 90-day mortality (HR 1.86, 95% CI: 1.58–2.20; p < 0.001), and 365-day mortality (HR 1.70, 95% CI: 1.48–1.96; p < 0.001). The area under the curve (AUC) for BAR in predicting 30-day, 90-day, and 365-day mortality was 0.653, 0.655, and 0.652, respectively. Subgroup analyses demonstrated generally consistent associations across most strata. In the external eICU validation cohort including 6,644 hypertensive patients, higher BAR quartiles were independently associated with increased in-hospital all-cause mortality, while a similar but non-significant risk-increasing trend was observed for in-ICU mortality. Conclusion Higher BAR levels were associated with increased short- and long-term all-cause mortality in critically ill patients with hypertension. BAR may provide incremental information for risk assessment in this population; however, further prospective studies are warranted to confirm its clinical utility. Health sciences/Cardiology Health sciences/Diseases Health sciences/Medical research Health sciences/Risk factors blood urea nitrogen to serum albumin ratio BAR hypertensive all-cause mortality MIMIC-IV database Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Hypertension is a major risk factor for cardiovascular disease and all-cause mortality worldwide and represents a substantial global health burden due to its high prevalence and significant impact on mortality [ 1 – 3 ] . Rapid population aging and lifestyle changes have contributed to an increasing prevalence of hypertension and its associated risk factors [ 4 ] . Hypertension-related vascular injury involves complex pathophysiological mechanisms, including endothelial dysfunction, vascular remodeling, and chronic inflammation, which may further exacerbate organ damage in conditions such as heart failure, kidney disease, and stroke [ 5 , 6 ] . In the intensive care unit (ICU) setting, patients with hypertension often present with complex and heterogeneous clinical conditions, and these disorders frequently coexist as common comorbidities [ 7 ] . Therefore, early identification of patients with hypertension at high risk of mortality in the ICU is of critical importance for optimizing risk stratification and clinical management. Blood urea nitrogen (BUN) is the final product of protein catabolism, and its circulating level is primarily influenced by renal function, systemic catabolic status, and effective circulating blood volume. Elevated BUN levels have been reported to be associated with increased short- and long-term mortality in patients with primary pulmonary arterial hypertension, as well as critically ill patients with intracerebral hemorrhage admitted to the intensive care unit [ 8 , 9 ] . Serum albumin, the most abundant plasma protein synthesized mainly by the liver, serves as a key indicator of nutritional status and systemic inflammatory response. Previous studies have demonstrated that serum albumin levels are reliable prognostic markers in patients with coronavirus disease 2019 (COVID-19) and heart failure [ 10 , 11 ] . In recent years, the blood urea nitrogen-to-albumin ratio (BAR) has been proposed as a composite biomarker that integrates information on renal function, nutritional status, and inflammatory burden. Emerging evidence suggests that elevated BAR levels are associated with adverse outcomes in a variety of clinical settings, including acute pancreatitis [ 12 ] , immunological and surgical diseases [ 13 ] , cerebrovascular disorders [ 14 ] , and sepsis [ 15 ] . However, the association between BAR and short- and long-term all-cause mortality among critically ill hypertensive patients admitted to the ICU has not been adequately elucidated. Therefore, the present study aimed to investigate the association between BAR and short- and long-term mortality in critically ill patients with hypertension admitted to the ICU, with external validation performed using the eICU 2.0 cohort. Subject and methods Data source The data used in this study were obtained from the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 3.1) database, a publicly accessible critical care database developed and maintained by the Computational Physiology Laboratory at the Massachusetts Institute of Technology in collaboration with Beth Israel Deaconess Medical Center (BIDMC), Boston, Massachusetts. The MIMIC-IV database contains detailed, de-identified clinical information from over 90,000 ICU patients admitted between 2008 and 2022 [ 16 , 17 ] , including demographics, laboratory measurements, vital signs, diagnoses, treatments, and survival outcomes. Access to the MIMIC-IV database is granted through PhysioNet following completion of the required data use agreement and Collaborative Institutional Training Initiative (CITI) certification. All data are fully de-identified in accordance with the Health Insurance Portability and Accountability Act (HIPAA) Safe Harbor provisions. As a result, the BIDMC Institutional Review Board approved a waiver of informed consent and permitted the use of the data for research purposes. Data extraction was performed by an authorized investigator (Keyang Li), who completed the CITI Program training for research involving only de-identified data (Certification ID: 40092459). The study was conducted in accordance with the Declaration of Helsinki and complied with all relevant ethical guidelines and regulations. Study participants and data selection Data extraction was performed using pgAdmin PostgreSQL (version 17.4.1) and Navicat Premium (version 15.0.29). This study included adult patients with hypertension who were admitted to the intensive care unit (ICU) for the first time. Hypertension was identified according to the International Classification of Diseases (ICD) codes (ICD-9 codes 401, 402, 403, 404, and 405; ICD-10 codes I10, I11, I12, I13, and I15). Only patients aged 18 years or older at the time of their first ICU admission were eligible. Patients were excluded if blood urea nitrogen (BUN) or serum albumin (ALB) values were missing or implausible (n = 30,692), if survival time was less than 24 h after ICU admission (n = 354), or if ICU length of stay was shorter than 24 h (n = 1,552). After applying these exclusion criteria, a total of 8,538 patients were included in the final analysis, as illustrated in Fig. 1. Baseline data were extracted from the first 24 h after ICU admission and included sex, age, ethnicity, Sequential Organ Failure Assessment (SOFA) score, Charlson Comorbidity Index (CCI), Oxford Acute Severity of Illness Score (OASIS), laboratory test results, comorbidities, therapeutic interventions, and other relevant variables. Laboratory parameters comprised white blood cell count, red blood cell count, platelet count, hemoglobin, serum creatinine, blood urea nitrogen, serum albumin, blood glucose, serum sodium, and serum potassium; all laboratory values represented the initial measurements obtained within 24 h of ICU admission. Comorbidities included chronic kidney disease, sepsis, acute myocardial infarction, diabetes mellitus, heart failure, respiratory failure, and malignancy. Therapeutic interventions included renal replacement therapy, percutaneous coronary intervention or coronary artery bypass grafting, continuous renal replacement therapy, use of vasoactive agents, and antihypertensive medications, including angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, aspirin, beta-blockers, calcium channel blockers, and statins. The primary outcomes were all-cause mortality at 30, 90, and 365 days after ICU admission. All diseases were defined according to ICD-9 and ICD-10 codes. The blood urea nitrogen-to-albumin ratio (BAR, mg/g) was calculated as the initial BUN level (mg/dL) divided by the serum albumin level (g/dL). External validation in eICU 2.0 database The population used for external validation was derived from the eICU Collaborative Research Database (version 2.0), a publicly accessible clinical database containing comprehensive information on 200,859 intensive care unit (ICU) admissions from 139,367 patients across 208 hospitals in the United States between 2014 and 2015. Patients diagnosed with hypertension were identified and included in the study cohort based on their recorded diagnoses (Figure S2). Demographic characteristics, laboratory test results, and clinical outcomes were subsequently extracted using Structured Query Language (SQL). Statistical analysis Continuous variables with a normal distribution were analyzed using Student’s t-test or one-way analysis of variance (ANOVA) and are presented as the mean ± standard deviation (SD). Non-normally distributed continuous variables were compared using the Kruskal–Wallis test and are expressed as median with interquartile range (IQR). Categorical variables were analyzed using the chi-square test or Fisher’s exact test, as appropriate, and are reported as counts and percentages. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between the blood urea nitrogen-to-albumin ratio (BAR) and mortality. Three models were constructed: Model 1 was unadjusted; Model 2 was adjusted for age, gender, and ethnicity; and Model 3 was fully adjusted for gender, age, ethnicity, SOFA score, OASIS score, Charlson Comorbidity Index, percutaneous coronary intervention, coronary artery bypass grafting, continuous renal replacement therapy, use of vasoactive agents, sepsis, respiratory failure, heart failure, diabetes mellitus, chronic kidney disease, acute myocardial infarction, malignancy, medication use (angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, aspirin, beta-blockers, calcium channel blockers, and statins), and laboratory variables including serum creatinine, glucose, sodium, red blood cell count, platelet count, hemoglobin, potassium, and white blood cell count. Covariates were selected using a stepwise regression approach with an entry criterion of p 5 were excluded to reduce multicollinearity. Kaplan–Meier survival curves were constructed to assess mortality across BAR categories, and differences between groups were evaluated using the log-rank test. Subgroup analyses were conducted to examine the robustness of the association between BAR and mortality across predefined subgroups stratified by gender, age, ethnicity, SOFA score, OASIS score, sepsis, congestive heart failure (CHF), chronic kidney disease (CKD), diabetes mellitus, use of renal replacement therapy (RRT), respiratory failure (RF), acute myocardial infarction (AMI), and use of vasopressors. In addition, receiver operating characteristic (ROC) analysis was used to assess the predictive ability of BAR, BUN, and SOFA for 30-day,90-day, and 365-day mortality. Variables with outliers were processed using the winsorization method at the 1st and 99th percentile cutoffs in R. All statistical analyses were performed using Empower-Stats ( http://www.empowerstats.com ; X&Y Solutions, Inc., Boston, MA) and R software. A two-sided P value < 0.05 was considered statistically significant. Results Baseline characteristics The baseline demographic and clinical characteristics of the study population are summarized in Table 1 . A total of 8,538 hypertensive patients admitted to the ICU were included and stratified into four quartiles according to BAR levels: Q1 (< 4.59, n = 2,120), Q2 (4.59–7.50, n = 2,133), Q3 (7.50 -13.33, n = 2,148), and Q4 (≥ 13.33, n = 2,137). Overall, 56.9% of the patients were male, and the mean age was 69.7 ± 14.0 years. Baseline characteristics differed significantly across BAR quartiles (all p < 0.05). The proportion of male patients increased progressively from Q1 to Q4, and patients in higher quartiles tended to be older. Ethnic distribution varied significantly among the groups, with a lower proportion of White patients and a higher proportion of other or unknown ethnicities observed in the higher BAR quartiles. The prevalence of major comorbidities—including sepsis, respiratory failure, heart failure, diabetes mellitus, and chronic kidney disease—increased consistently across quartiles (all p < 0.001). Significant differences were also observed for acute myocardial infarction and malignant tumors. Laboratory parameters showed marked intergroup differences (all p < 0.001). With increasing BAR quartiles, white blood cell count, blood urea nitrogen, creatinine, glucose, and potassium levels tended to rise, whereas red blood cell count, hemoglobin, platelet count, and serum albumin levels demonstrated decreasing trends. Serum sodium levels differed modestly but significantly across groups (p = 0.002). Clinical severity scores, including the Sequential Organ Failure Assessment (SOFA) score, Oxford Acute Severity of Illness Score (OASIS), and Charlson comorbidity index, increased progressively across BAR quartiles (all p < 0.001). Several treatments and supportive interventions varied among groups, most notably the use of continuous renal replacement therapy and vasopressors (all p < 0.001). Hospital length of stay and ICU length of stay differed significantly across quartiles, with longer durations observed in patients with higher BAR levels (all p < 0.001). Mortality at 30, 90, and 365 days increased stepwise across the four quartiles(Figure S1 ), and all between-group differences were statistically significant (all p < 0.001). Table 1 Baseline characteristics and clinical outcomes of patients Variable Overall N = 8538 Q1(< 4.59) N = 2120 Q2(4.59–7.50) N = 2133 Q3(7.50 -13.33) N = 2148 Q4(≥ 13.33) N = 2137 p-value Gender, (Male) 4,860 (56.9%) 1,113 (52.5%) 1,207 (56.6%) 1,235 (57.5%) 1,305 (61.1%) < 0.001 Age (years) 69.7 ± 14.0 65.4 ± 14.4 70.5 ± 13.6 71.8 ± 13.5 71.0 ± 13.6 < 0.001 Ethnicity < 0.001 White 2,970 (34.8%) 825 (38.9%) 716 (33.6%) 719 (33.5%) 710 (33.2%) Others and unknown 5,568 (65.2%) 1,295 (61.1%) 1,417 (66.4%) 1,429 (66.5%) 1,427 (66.8%) Comorbidities Sepsis 5,122 (60.0%) 899 (42.4%) 1,181 (55.4%) 1,435 (66.8%) 1,607 (75.2%) < 0.001 Respiratory failure 3,330 (39.0%) 592 (27.9%) 771 (36.1%) 938 (43.7%) 1,029 (48.2%) < 0.001 Heart failure 2,862 (33.5%) 368 (17.4%) 675 (31.6%) 826 (38.5%) 993 (46.5%) < 0.001 Diabetes mellitus 3,282 (38.4%) 599 (28.3%) 742 (34.8%) 888 (41.3%) 1,053 (49.3%) < 0.001 Chronic kidney disease 2,431 (28.5%) 127 (6.0%) 372 (17.4%) 765 (35.6%) 1,167 (54.6%) < 0.001 Acute myocardial infarction 430 (5.0%) 70 (3.3%) 126 (5.9%) 123 (5.7%) 111 (5.2%) < 0.001 Malignant tumors 1,193 (14.0%) 260 (12.3%) 323 (15.1%) 299 (13.9%) 311 (14.6%) 0.042 Laboratory parameters Wbc, 10 9 /L 10.9 (7.8–15.4) 9.9 (7.2–13.3) 11.0 (7.9–15.0) 11.2 (7.8–16.4) 12.0 (8.1–17.4) < 0.001 Rbc, 10 9 /L 3.6 ± 0.8 3.9 ± 0.7 3.8 ± 0.8 3.5 ± 0.8 3.3 ± 0.8 < 0.001 Platelet, 10 9 /L 194.0 (138.0-261.0) 208.0 (156.0-263.0) 196.0 (144.0-262.0) 186.5 (129.0-253.0) 180.0 (123.0-263.0) < 0.001 Hemoglobin (g/mL) 10.8 ± 2.3 11.8 ± 2.2 11.2 ± 2.2 10.4 ± 2.2 9.7 ± 2.1 < 0.001 Bun (mg/dL) 23.0 (15.0–39.0) 12.0 (10.0–14.0) 19.0 (17.0–22.0) 30.0 (25.0–35.0) 57.0 (46.0–75.0) < 0.001 Albumin (g/dL) 3.2 (2.8–3.6) 3.6 (3.2-4.0) 3.3 (2.9–3.7) 3.1 (2.7–3.5) 2.9 (2.4–3.3) < 0.001 Creatinine (mg/dL) 1.1 (0.8–1.8) 0.8 (0.6–0.9) 1.0 (0.8–1.2) 1.4 (1.0-1.8) 2.5 (1.7-4.0) < 0.001 Glucose (mg/dL) 133.0 (107.0-178.0) 125.0 (104.0-160.0) 135.0 (108.0-177.0) 138.0 (110.0-185.0) 137.0 (106.0-189.0) < 0.001 Sodium (mmol/L) 138.0 ± 5.8 137.8 ± 5.6 138.3 ± 5.0 138.1 ± 5.6 137.7 ± 6.8 0.002 Potassium (mmol/L) 4.3 ± 0.8 4.0 ± 0.6 4.2 ± 0.7 4.3 ± 0.8 4.6 ± 0.9 < 0.001 Clinical severity scores SOFA score 5.0 (2.0–7.0) 3.0 (1.0–5.0) 4.0 (2.0–6.0) 5.0 (3.0–8.0) 7.0 (5.0–9.0) < 0.001 OASIS score 33.4 ± 8.6 30.7 ± 7.9 32.7 ± 8.2 34.4 ± 8.5 35.7 ± 9.1 < 0.001 Charlson comorbidity index 6.0 (4.0–8.0) 4.0 (3.0–6.0) 6.0 (4.0–7.0) 6.0 (5.0–8.0) 7.0 (5.0–9.0) < 0.001 Treatments Percutaneous coronary intervention 218 (2.6%) 49 (2.3%) 68 (3.2%) 63 (2.9%) 38 (1.8%) 0.010 Coronary artery bypass grafting 424 (5.0%) 169 (8.0%) 145 (6.8%) 80 (3.7%) 30 (1.4%) < 0.001 Renal replacement therapy 545 (6.4%) 28 (1.3%) 71 (3.3%) 134 (6.2%) 312 (14.6%) < 0.001 Vasopressor use 2,765 (32.4%) 440 (20.8%) 654 (30.7%) 763 (35.5%) 908 (42.5%) < 0.001 ACEI 2,259 (26.5%) 704 (33.2%) 660 (30.9%) 540 (25.1%) 355 (16.6%) < 0.001 ARB 811 (9.5%) 230 (10.8%) 250 (11.7%) 194 (9.0%) 137 (6.4%) < 0.001 Aspirin 4,215 (49.4%) 994 (46.9%) 1,117 (52.4%) 1,086 (50.6%) 1,018 (47.6%) < 0.001 Beta blocker 5,506 (64.5%) 1,338 (63.1%) 1,481 (69.4%) 1,402 (65.3%) 1,285 (60.1%) < 0.001 CCB 2,196 (25.7%) 682 (32.2%) 549 (25.7%) 497 (23.1%) 468 (21.9%) < 0.001 Statin 4,319 (50.6%) 1,033 (48.7%) 1,138 (53.4%) 1,106 (51.5%) 1,042 (48.8%) < 0.001 Hospital LOS (days) 9.0 (5.5–16.1) 7.7 (4.8–13.1) 8.4 (5.1–15.1) 9.4 (5.7–16.8) 11.1 (6.1–19.5) < 0.001 ICU LOS (days) 3.0 (1.9–5.9) 2.8 (1.8–5.2) 3.0 (1.8–5.4) 3.0 (1.9–5.8) 3.5 (2.0–7.0) < 0.001 30-day mortality 1,737 (20.3%) 215 (10.1%) 326 (15.3%) 500 (23.3%) 696 (32.6%) < 0.001 90-day mortality 2,273 (26.6%) 301 (14.2%) 443 (20.8%) 646 (30.1%) 883 (41.3%) < 0.001 365-day mortality 3,027 (35.5%) 427 (20.1%) 644 (30.2%) 855 (39.8%) 1,101 (51.5%) < 0.001 Notes : Data are expressed as number (%) or mean ± SD or median (IQR). Among the 8538 patients, the amount of missing values for the covariates were 12 (0.14%) for SOFA score, 3 (0.04%) for creatinine, 5 (0.06%) for glucose, 1 (0.01%) for sodium, 11 (0.13%) for potassium, 74 (0.87%) for red blood cell count, 86 (1.01%) for platelet count, 78 (0.91%) for hemoglobin, and 78 (0.91%) for white blood cell count. Association between BAR and mortality in hypertensive patients In the multivariate Cox regression analysis (Table 2 ), the association between BAR and mortality among critically ill patients with hypertension was evaluated using fully adjusted models (Model III). When BAR was analyzed as a continuous variable, higher BAR levels were independently associated with increased risks of 30-, 90-, and 365-day mortality. Specifically, each unit increase in BAR was associated with a 3% higher risk of death at 30 days (HR: 1.03, 95% CI: 1.02–1.03; p < 0.001) and 90 days (HR: 1.03, 95% CI: 1.02–1.03; p < 0.001), and a 2% higher risk at 365 days (HR: 1.02, 95% CI: 1.02–1.03; p < 0.001).When BAR was categorized into quartiles, with the lowest quartile (Q1) serving as the reference, a stepwise increase in mortality risk was observed across higher BAR quartiles. For 30-day mortality, the adjusted hazard ratios were 1.22 (95% CI: 1.02–1.45; p = 0.006) for Q2, 1.53 (95% CI: 1.28–1.82; p < 0.001) for Q3, and 1.92 (95% CI: 1.58–2.32; p < 0.001) for Q4. Similar graded associations were observed for 90-day mortality, with adjusted HRs of 1.17 (95% CI: 1.01–1.36; p = 0.041), 1.45 (95% CI: 1.25–1.68; p < 0.001), and 1.86 (95% CI: 1.58–2.20; p < 0.001) for Q2, Q3, and Q4, respectively. For 365-day mortality, patients in higher BAR quartiles continued to demonstrate elevated risks after full adjustment, with HRs of 1.19 (95% CI: 1.05–1.35; p = 0.005) for Q2, 1.40 (95% CI: 1.23–1.59; p < 0.001) for Q3, and 1.70 (95% CI: 1.48–1.96; p < 0.001) for Q4.A significant dose–response relationship was observed across increasing BAR quartiles for 30-, 90-, and 365-day mortality (all P for trend < 0.001). The Kaplan–Meier survival curves further illustrated the differences in mortality among hypertensive patients across BAR quartiles (Fig. 2 ). Patients in higher BAR quartiles consistently exhibited lower survival probabilities compared with those in lower quartiles at all evaluated time points. For 30-day mortality, survival probability declined progressively from Q1 to Q4, with patients in the highest BAR quartile (Q4) demonstrating the poorest short-term survival. Similar separation of survival curves was observed for 90-day mortality, where higher BAR levels were associated with a markedly reduced probability of survival over time. This gradient became more pronounced in the analysis of 365-day mortality, with sustained divergence among quartiles throughout the follow-up period. Overall, the KM curves demonstrated a clear stepwise pattern, with survival decreasing monotonically across increasing BAR quartiles for short-, intermediate-, and long-term outcomes. The differences among groups were statistically significant, as indicated by the log-rank test (p < 0.0001) for all three time horizons. Table 2 The association between BAR and all-cause mortality in MIMIC IV database by Cox regression analysis. Exposure Model I Model II Model III HR (95% CI) P-value HR (95% CI) P-value HR (95% CI) P-value 30-day mortality BAR (continuous) 1.04 (1.04–1.05) < 0.001 1.04 (1.04–1.05) < 0.001 1.03 (1.02–1.03) < 0.001 Q1 Reference Reference Reference Q2 1.56 (1.32–1.85) < 0.001 1.44 (1.21–1.71) < 0.001 1.22 (1.02–1.45) 0.006 Q3 2.49 (2.12–2.92) < 0.001 2.21 (1.88–2.60) < 0.001 1.53 (1.28–1.82) < 0.001 Q4 3.69 (3.17–4.29) < 0.001 3.38 (2.90–3.94) < 0.001 1.92 (1.58–2.32) < 0.001 P for trend p < 0.001 p < 0.001 p < 0.001 90-day mortality BAR (continuous) 1.04 (1.04–1.05) < 0.001 1.04 (1.04–1.05) < 0.001 1.03 (1.02–1.03) < 0.001 Q1 Reference Reference Reference Q2 1.52 (1.31–1.76) < 0.001 1.37 (1.18–1.58) < 0.001 1.17 (1.01–1.36) 0.041 Q3 2.36 (2.06–2.71) < 0.001 2.05 (1.79–2.35) < 0.001 1.45 (1.25–1.68) < 0.001 Q4 3.50 (3.07–3.98) < 0.001 3.15 (2.76–3.59) < 0.001 1.86 (1.58–2.20) < 0.001 P for trend p < 0.001 p < 0.001 p < 0.001 365-day mortality BAR (continuous) 1.04 (1.04–1.04) < 0.001 1.04 (1.04–1.04) < 0.001 1.02 (1.02–1.03) < 0.001 Q1 Reference Reference Reference Q2 1.59 (1.41–1.79) < 0.001 1.42 (1.25–1.60) < 0.001 1.19 (1.05–1.35) 0.005 Q3 2.30 (2.05–2.58) < 0.001 1.99 (1.77–2.23) < 0.001 1.40 (1.23–1.59) < 0.001 Q4 3.27 (2.92–3.66) < 0.001 2.95 (2.63–3.30) < 0.001 1.70 (1.48–1.96) < 0.001 P for trend p < 0.001 p < 0.001 p < 0.001 Subgroup analysis Subgroup analyses were conducted to evaluate the association between BAR and mortality across different demographic and clinical characteristics in patients with hypertension (Fig. 3 ). Stratification was performed according to age, gender, disease severity scores (SOFA and OASIS), and major comorbidities, including sepsis, congestive heart failure, chronic kidney disease, diabetes, respiratory failure, acute myocardial infarction, renal replacement therapy, and vasopressor use. Overall, higher BAR levels were consistently associated with increased risks of 30-day, 90-day, and 365-day mortality across most examined subgroups. The associations remained generally robust across categories of gender, ethnicity, and disease severity, as well as in patients with or without several common comorbid conditions. For 30-day mortality, significant associations were observed in all subgroups except patients aged < 60 years and those with AMI. For 90-day mortality, BAR was significantly associated with mortality in most subgroups, except for patients with AMI. Interaction analyses indicated significant effect modification by sepsis, RRT, and respiratory failure. For 365-day mortality, elevated BAR remained significantly associated with increased risk in the majority of subgroups. However, the association was not significant among patients aged < 60 years and those with AMI. Significant interactions were noted for SOFA score, sepsis, and RRT, indicating that the association between BAR and long-term mortality may vary by disease severity and renal support status. Prediction of all-cause mortality in patients with hypertension by BAR We plotted ROC curves for BAR, BUN, and SOFA to assess their predictive value for 30-day,90-day and 365-day mortality in patients with hypertension. As shown in Fig. 4 , the AUC of 30-day mortality for BAR was 0.653, which was superior to BUN (AUC = 0.633) and SOFA (AUC = 0.662), the AUC of 90-day mortality for BAR was 0.655, which was superior to BUN (AUC = 0.632) and SOFA (AUC = 0.647). The AUC of 365-day mortality for BAR was also better than BUN and SOFA (BAR: AUC = 0.652; BUN: AUC = 0.631; SOFA = 0.630). External validation The external validation was performed in 6644 patients with hypertension from eICU 2.0 database (Figure S2). Figure S3 revealed that patients with hypertension in higher BAR group had higher in-ICU (2.9% vs. 4.1% vs. 5.9% vs. 7.1%, p < 0.001) and in-hospital (5.0% vs. 7.0% vs. 10.8% vs. 13.0%, p < 0.001) all-cause mortality. After fully adjustment by gender, age, ethnicity, SOFA score, OASIS, Charlson comorbidity index, PCI, CABG, renal replacement therapy, vasopressor use, sepsis, respiratory failure, heart failure, diabetes mellitus, chronic kidney disease, acute myocardial infarction, tumors, use of ACE inhibitors, ARBs, aspirin, beta-blockers, calcium channel blockers, statins, serum creatinine, glucose, sodium, red blood cell count, platelet count, hemoglobin, potassium, and white blood cell count compared with patients in lowest BAR quartile, Patients in the highest BAR quartile tended to have a higher risk of in-ICU all-cause mortality compared with those in the lowest quartile; however, this association did not reach statistical significance (highest vs. lowest BAR: HR = 1.27, 95% CI 0.82–1.94, p = 0.281; p for trend = 0.222, Table 3 ). In contrast, the highest BAR quartile was significantly associated with an increased risk of in-hospital all-cause mortality (highest vs. lowest BAR: HR = 1.42, 95% CI 1.03–1.95, p = 0.031; p for trend = 0.012, Table 3 ).When BAR was analyzed as a continuous variable, the association with in-ICU all-cause mortality was attenuated and no longer statistically significant after full adjustment (HR = 1.01, 95% CI 0.99–1.02, p = 0.348, Table 3 ). Conversely, each unit increase in BAR remained independently associated with a higher risk of in-hospital all-cause mortality across all models, including the fully adjusted model (HR = 1.02, 95% CI 1.01–1.02, p < 0.001, Table 3 ). Table 3 The association between BAR and all-cause mortality in eICU 2.0 database by Cox regression analysis. Exposure Model I Model II Model III HR (95% CI) P-value HR (95% CI) P-value HR (95% CI) P-value In-ICU all-cause mortality BAR (continuous) 1.01 (1.00-1.02) 0.003 1.01 (1.00-1.02) 0.006 1.01 (0.99–1.02) 0.348 Q1 Reference Reference Reference Q2 1.20 (0.84–1.72) 0.317 1.12 (0.78–1.61) 0.523 0.97 (0.66–1.41) 0.86 Q3 1.68 (1.19–2.36) 0.003 1.52 (1.07–2.15) 0.019 1.07 (0.72–1.58) 0.751 Q4 1.86 (1.35–2.57) < 0.001 1.72 (1.24–2.40) 0.001 1.27 (0.82–1.94) 0.281 P for trend p < 0.001 p < 0.001 P = 0.222 In-hospital all-cause mortality BAR (continuous) 1.02 (1.01–1.02) < 0.001 1.01 (1.01–1.02) < 0.001 1.02 (1.01–1.02) < 0.001 Q1 Reference Reference Reference Q2 1.23 (0.94–1.62) 0.136 1.08 (0.82–1.42) 0.603 0.95 (0.71–1.26) 0.704 Q3 1.74 (1.34–2.24) < 0.001 1.45 (1.11–1.88) 0.006 1.11 (0.83–1.49) 0.486 Q4 1.88 (1.47–2.40) < 0.001 1.66 (1.29–2.12) < 0.001 1.42 (1.03–1.95) 0.031 P for trend p < 0.001 p < 0.001 p < 0.012 Discussion Hypertension remains a major global public health challenge and is strongly associated with adverse outcomes in critically ill patients, underscoring the importance of early risk stratification in this population [ 18 , 19 ] . In the present study, higher BAR levels were associated with increased short- and long-term all-cause mortality among critically ill patients with hypertension in the MIMIC database after multivariable adjustment. Cox proportional hazards analyses demonstrated that BAR was significantly associated with 30-, 90-, and 365-day mortality, whereas receiver operating characteristic analyses suggested that BAR exhibited a stronger predictive performance for long-term mortality than for short-term mortality. This time-dependent prognostic pattern was further corroborated in the external validation cohort derived from the eICU Database, in which higher BAR was significantly associated with in-hospital all-cause mortality, while the association with in-ICU mortality showed a similar risk-increasing trend but did not reach statistical significance. Kaplan–Meier survival analyses consistently demonstrated that patients with higher BAR values had significantly lower survival probabilities at 30, 90, and 365 days compared with those with lower BAR levels. Moreover, subgroup analyses indicated that the association between BAR and mortality was generally consistent across most demographic and clinical subgroups. Collectively, these results indicate a robust association between BAR and mortality risk in critically ill patients with hypertension. Blood urea nitrogen (BUN) is a key end product of protein metabolism and serves as an important indicator of renal function and systemic catabolic status. Previous studies in pediatric hypertension have reported that elevated BUN levels are independently associated with secondary hypertension and target organ damage, including left ventricular hypertrophy and hypertensive encephalopathy [ 20 ] . Importantly, BUN concentrations are influenced by multiple factors beyond renal clearance alone, such as dietary protein intake, metabolic state, and renal hemodynamics. Therefore, BUN should be interpreted in conjunction with other clinical and laboratory parameters to more accurately reflect its significance in patients with hypertension [ 21 ] . Serum albumin is widely recognized as a marker of nutritional status and systemic inflammation [ 22 ] . Accumulating evidence indicates that lower albumin levels are associated with increased arterial stiffness and greater severity of atherosclerosis in hypertensive populations, suggesting a potential role in vascular remodeling and endothelial dysfunction [ 23 , 24 ] . The nutritional and inflammatory states reflected by albumin levels have also been shown to influence outcomes in hypertension. For instance, the C-reactive protein–to–albumin ratio (CAR) has emerged as a strong prognostic marker for COVID-19–related outcomes in patients with hypertension, linking hypoalbuminemia to systemic inflammation and mortality risk [ 25 ] . Moreover, stable or moderately elevated albumin concentrations have been associated with a reduced incidence of hypertension, indicating a possible protective role [ 26 ] . The blood urea nitrogen–to–albumin ratio (BAR) is a composite biomarker derived from the simple calculation of serum BUN divided by serum albumin, integrating information on renal function and the nutrition–inflammation axis into a single index [ 27 ] . Elevated BAR levels have demonstrated prognostic value in hypertension-related complications, including heart failure [ 28 ] , emergency surgery for acute type A aortic dissection [ 29 ] , and acute ischemic stroke. In cohorts of patients with acute ischemic stroke, higher BAR has been independently associated with increased short- and long-term mortality, underscoring its prognostic relevance [ 30 ] . This association may reflect the combined effects of renal dysfunction, systemic inflammation, and malnutrition, all of which contribute to adverse cardiovascular outcomes. Similarly, in patients with severe tricuspid regurgitation [ 31 ] , the combination of elevated BUN and reduced serum albumin has been shown to be associated with adverse clinical events, suggesting that this ratio may reflect cardiovascular risk beyond hypertension alone. In our study, BAR demonstrated superior predictive performance to SOFA for longer-term mortality (90-day and 365-day), whereas SOFA exhibited better discriminative ability for short-term (30-day) mortality in critically ill hypertensive patients. This temporal difference in prognostic utility is consistent with observations from other cohorts [ 32 , 33 ] . Additionally, subgroup analysis revealed an interaction between sepsis and BAR in predicting outcomes among hypertensive patients. Specifically, BAR appeared to have a stronger predictive value for mortality in non-septic patients, which may be related to the higher protein catabolism rate and inflammation levels in septic patients [ 34 ] , factors that could partially confound the relationship between BAR and mortality outcomes in critically ill patients with hypertension. Several limitations should be acknowledged. First, this was a retrospective observational study, and causal inference cannot be established; residual confounding from unmeasured variables may persist despite multivariable adjustment for measured factors. Second, BAR was assessed only at ICU admission, and dynamic changes during hospitalization were not evaluated. Third, certain subgroup analyses may have been underpowered and should be interpreted cautiously. In addition, although external validation was performed using the eICU Collaborative Research Database, outcome assessment in that cohort was limited to in-ICU and in-hospital mortality, without post-discharge follow-up. Differences in follow-up duration between the derivation cohort from the Medical Information Mart for Intensive Care IV and the validation cohort may limit direct comparability of long-term prognostic performance. Finally, as the study population consisted of critically ill hypertensive patients admitted to the ICU, the generalizability of these findings to non-ICU settings or broader hypertensive populations remains uncertain. Conclusion In this retrospective cohort of critically ill patients with hypertension, higher BAR levels were associated with increased short- and long-term all-cause mortality after multivariable adjustment. BAR may provide additional information for risk assessment; however, prospective studies are needed to confirm its clinical relevance. Abbreviations BAR Blood urea nitrogen-to-albumin ratio BUN Blood urea nitrogen SOFA Sequential Organ Failure Assessment ICU Intensive care unit RBC Red blood cell count WBC White blood cell count CVD Cardiovascular disease CKD Chronic kidney disease LOS Length of stay ACEI Angiotensin-converting enzyme inhibitor ARB Angiotensin receptor blocker CCB Calcium channel blocker OASIS Oxford Acute Severity of Illness Score RF Respiratory failure RRT Renal replacement therapy AMI Acute myocardial infarction. Declarations Competing interests The authors declare no competing interests. Funding None. Author Contribution The project was designed by Hongliang Dong, Yangang Wang. Material preparation, data collection, and analysis were performed by Keyang Li, Dandan Gong; The first draft of the manuscript was written by Debao Li, Dongmei Ren, and critically revised by Yuanyuan Wu, Meng Li. Hongliang Dong, Yangang Wang revised the manuscript. All authors reviewed and approved the final manuscript. Acknowledgement We would like to thank the Massachusetts Institute of Technology and the Beth Israel Deaconess Medical Center for the MIMIC-IV database. Data Availability The data supporting the findings of the present paper could be found at: [https://mimic.mit.edu](https:/mimic.mit.edu) . References Mills, K. T., Stefanescu, A. & He, J. The global epidemiology of hypertension. Nat. Rev. Nephrol. 16 (4), 223–237. 10.1038/s41581-019-0244-2 (2020). Epub 2020 Feb 5. PMID: 32024986; PMCID: PMC7998524. Vaduganathan, M., Mensah, G. A., Turco, J. V., Fuster, V. & Roth, G. A. The Global Burden of Cardiovascular Diseases and Risk: A Compass for Future Health. J. Am. Coll. Cardiol. 80 (25), 2361–2371 (2022). Epub 2022 Nov 9. PMID: 36368511. Jobe, M. et al. Hypertension in Sub-Saharan Africa: Burden, Barriers and Priorities for Improving Treatment Outcomes. Circ. Res. 137 (1), 106–118 (2025). Epub 2025 Jun 19. PMID: 40536937; PMCID: PMC12175831. Wang, Z. et al. 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Preoperative blood urea nitrogen-to-serum albumin ratio for prediction of in-hospital mortality in patients who underwent emergency surgery for acute type A aortic dissection. Hypertens. Res. 47 (7), 1934–1942. 10.1038/s41440-024-01673-z (2024). Epub 2024 May 20. PMID: 38769137; PMCID: PMC11224014. Huang, Y., Li, Z., Wang, J., Wang, D. & Yin, X. Association of the blood urea nitrogen to serum albumin ratio and all-cause mortality in critical ill acute ischemic stroke patients: a retrospective cohort study of MIMIC-IV database 3.0. Front. Nutr. 11 , 1509284 (2025). PMID: 39839282; PMCID: PMC11747420. Nishiura, N. et al. Long-Term Clinical Outcomes in Patients With Severe Tricuspid Regurgitation. J. Am. Heart Assoc. 12 (1), e025751 (2023). Epub 2022 Dec 24. PMID: 36565178; PMCID: PMC9973603. Wang, Y. et al. Prognostic impact of blood urea nitrogen to albumin ratio on patients with sepsis: a retrospective cohort study. Sci. Rep. 13 (1), 10013. 10.1038/s41598-023-37127-8 (2023). 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legend.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8827280/v1/f03e97d0078bef2939d27ba9.jpeg"},{"id":105031715,"identity":"5b79f7be-45a8-4216-ad5b-0cd8e89226eb","added_by":"auto","created_at":"2026-03-20 06:51:30","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":135563,"visible":true,"origin":"","legend":"\u003cp\u003eThe Kaplan–Meier survival curves for the differences in mortality among hypertensive patients across BAR quartiles\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8827280/v1/800eb06e922109db3b913624.jpeg"},{"id":105031716,"identity":"0738a8da-5f96-46b6-9a07-8dfd1f8cb7a5","added_by":"auto","created_at":"2026-03-20 06:51:30","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":272282,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analyses of \u003cstrong\u003e(A)\u003c/strong\u003e 30-day, \u003cstrong\u003e(B)\u003c/strong\u003e 90-day, and \u003cstrong\u003e(C)\u003c/strong\u003e 365-day all-cause mortality\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8827280/v1/461ff7928f4399693644765e.jpeg"},{"id":105035903,"identity":"3322bcb8-6278-4e1e-ac85-1efd11079a02","added_by":"auto","created_at":"2026-03-20 07:26:52","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":106822,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curves of BAR for all-cause mortality of patients with hypertension\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8827280/v1/b3480462180aa029f2e7e000.jpeg"},{"id":105036885,"identity":"d629fc49-5122-4b0b-940f-7e78b9207fb6","added_by":"auto","created_at":"2026-03-20 07:36:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2180320,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8827280/v1/b0159f9b-bca8-439e-9956-b1aa2862245c.pdf"},{"id":105031718,"identity":"12bde4e7-f4b8-4b99-8a2f-06651996b79d","added_by":"auto","created_at":"2026-03-20 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citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Rapid population aging and lifestyle changes have contributed to an increasing prevalence of hypertension and its associated risk factors \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Hypertension-related vascular injury involves complex pathophysiological mechanisms, including endothelial dysfunction, vascular remodeling, and chronic inflammation, which may further exacerbate organ damage in conditions such as heart failure, kidney disease, and stroke \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. In the intensive care unit (ICU) setting, patients with hypertension often present with complex and heterogeneous clinical conditions, and these disorders frequently coexist as common comorbidities \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Therefore, early identification of patients with hypertension at high risk of mortality in the ICU is of critical importance for optimizing risk stratification and clinical management.\u003c/p\u003e \u003cp\u003eBlood urea nitrogen (BUN) is the final product of protein catabolism, and its circulating level is primarily influenced by renal function, systemic catabolic status, and effective circulating blood volume. Elevated BUN levels have been reported to be associated with increased short- and long-term mortality in patients with primary pulmonary arterial hypertension, as well as critically ill patients with intracerebral hemorrhage admitted to the intensive care unit \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Serum albumin, the most abundant plasma protein synthesized mainly by the liver, serves as a key indicator of nutritional status and systemic inflammatory response. Previous studies have demonstrated that serum albumin levels are reliable prognostic markers in patients with coronavirus disease 2019 (COVID-19) and heart failure \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. In recent years, the blood urea nitrogen-to-albumin ratio (BAR) has been proposed as a composite biomarker that integrates information on renal function, nutritional status, and inflammatory burden. Emerging evidence suggests that elevated BAR levels are associated with adverse outcomes in a variety of clinical settings, including acute pancreatitis \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, immunological and surgical diseases \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, cerebrovascular disorders \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, and sepsis \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. However, the association between BAR and short- and long-term all-cause mortality among critically ill hypertensive patients admitted to the ICU has not been adequately elucidated. Therefore, the present study aimed to investigate the association between BAR and short- and long-term mortality in critically ill patients with hypertension admitted to the ICU, with external validation performed using the eICU 2.0 cohort.\u003c/p\u003e"},{"header":"Subject and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003e The data used in this study were obtained from the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 3.1) database, a publicly accessible critical care database developed and maintained by the Computational Physiology Laboratory at the Massachusetts Institute of Technology in collaboration with Beth Israel Deaconess Medical Center (BIDMC), Boston, Massachusetts. The MIMIC-IV database contains detailed, de-identified clinical information from over 90,000 ICU patients admitted between 2008 and 2022 \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, including demographics, laboratory measurements, vital signs, diagnoses, treatments, and survival outcomes. Access to the MIMIC-IV database is granted through PhysioNet following completion of the required data use agreement and Collaborative Institutional Training Initiative (CITI) certification. All data are fully de-identified in accordance with the Health Insurance Portability and Accountability Act (HIPAA) Safe Harbor provisions. As a result, the BIDMC Institutional Review Board approved a waiver of informed consent and permitted the use of the data for research purposes. Data extraction was performed by an authorized investigator (Keyang Li), who completed the CITI Program training for research involving only de-identified data (Certification ID: 40092459). The study was conducted in accordance with the Declaration of Helsinki and complied with all relevant ethical guidelines and regulations.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy participants and data selection\u003c/h3\u003e\n\u003cp\u003eData extraction was performed using pgAdmin PostgreSQL (version 17.4.1) and Navicat Premium (version 15.0.29). This study included adult patients with hypertension who were admitted to the intensive care unit (ICU) for the first time. Hypertension was identified according to the International Classification of Diseases (ICD) codes (ICD-9 codes 401, 402, 403, 404, and 405; ICD-10 codes I10, I11, I12, I13, and I15). Only patients aged 18 years or older at the time of their first ICU admission were eligible. Patients were excluded if blood urea nitrogen (BUN) or serum albumin (ALB) values were missing or implausible (n\u0026thinsp;=\u0026thinsp;30,692), if survival time was less than 24 h after ICU admission (n\u0026thinsp;=\u0026thinsp;354), or if ICU length of stay was shorter than 24 h (n\u0026thinsp;=\u0026thinsp;1,552). After applying these exclusion criteria, a total of 8,538 patients were included in the final analysis, as illustrated in Fig.\u0026nbsp;1.\u003c/p\u003e \u003cp\u003eBaseline data were extracted from the first 24 h after ICU admission and included sex, age, ethnicity, Sequential Organ Failure Assessment (SOFA) score, Charlson Comorbidity Index (CCI), Oxford Acute Severity of Illness Score (OASIS), laboratory test results, comorbidities, therapeutic interventions, and other relevant variables. Laboratory parameters comprised white blood cell count, red blood cell count, platelet count, hemoglobin, serum creatinine, blood urea nitrogen, serum albumin, blood glucose, serum sodium, and serum potassium; all laboratory values represented the initial measurements obtained within 24 h of ICU admission. Comorbidities included chronic kidney disease, sepsis, acute myocardial infarction, diabetes mellitus, heart failure, respiratory failure, and malignancy. Therapeutic interventions included renal replacement therapy, percutaneous coronary intervention or coronary artery bypass grafting, continuous renal replacement therapy, use of vasoactive agents, and antihypertensive medications, including angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, aspirin, beta-blockers, calcium channel blockers, and statins. The primary outcomes were all-cause mortality at 30, 90, and 365 days after ICU admission. All diseases were defined according to ICD-9 and ICD-10 codes. The blood urea nitrogen-to-albumin ratio (BAR, mg/g) was calculated as the initial BUN level (mg/dL) divided by the serum albumin level (g/dL).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eExternal validation in eICU 2.0 database\u003c/h3\u003e\n\u003cp\u003eThe population used for external validation was derived from the eICU Collaborative Research Database (version 2.0), a publicly accessible clinical database containing comprehensive information on 200,859 intensive care unit (ICU) admissions from 139,367 patients across 208 hospitals in the United States between 2014 and 2015. Patients diagnosed with hypertension were identified and included in the study cohort based on their recorded diagnoses (Figure S2). Demographic characteristics, laboratory test results, and clinical outcomes were subsequently extracted using Structured Query Language (SQL).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables with a normal distribution were analyzed using Student\u0026rsquo;s t-test or one-way analysis of variance (ANOVA) and are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Non-normally distributed continuous variables were compared using the Kruskal\u0026ndash;Wallis test and are expressed as median with interquartile range (IQR). Categorical variables were analyzed using the chi-square test or Fisher\u0026rsquo;s exact test, as appropriate, and are reported as counts and percentages. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between the blood urea nitrogen-to-albumin ratio (BAR) and mortality. Three models were constructed: Model 1 was unadjusted; Model 2 was adjusted for age, gender, and ethnicity; and Model 3 was fully adjusted for gender, age, ethnicity, SOFA score, OASIS score, Charlson Comorbidity Index, percutaneous coronary intervention, coronary artery bypass grafting, continuous renal replacement therapy, use of vasoactive agents, sepsis, respiratory failure, heart failure, diabetes mellitus, chronic kidney disease, acute myocardial infarction, malignancy, medication use (angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, aspirin, beta-blockers, calcium channel blockers, and statins), and laboratory variables including serum creatinine, glucose, sodium, red blood cell count, platelet count, hemoglobin, potassium, and white blood cell count. Covariates were selected using a stepwise regression approach with an entry criterion of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Variables with a variance inflation factor\u0026thinsp;\u0026gt;\u0026thinsp;5 were excluded to reduce multicollinearity. Kaplan\u0026ndash;Meier survival curves were constructed to assess mortality across BAR categories, and differences between groups were evaluated using the log-rank test. Subgroup analyses were conducted to examine the robustness of the association between BAR and mortality across predefined subgroups stratified by gender, age, ethnicity, SOFA score, OASIS score, sepsis, congestive heart failure (CHF), chronic kidney disease (CKD), diabetes mellitus, use of renal replacement therapy (RRT), respiratory failure (RF), acute myocardial infarction (AMI), and use of vasopressors. In addition, receiver operating characteristic (ROC) analysis was used to assess the predictive ability of BAR, BUN, and SOFA for 30-day,90-day, and 365-day mortality. Variables with outliers were processed using the winsorization method at the 1st and 99th percentile cutoffs in R. All statistical analyses were performed using Empower-Stats (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.empowerstats.com\u003c/span\u003e\u003cspan address=\"http://www.empowerstats.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; X\u0026amp;Y Solutions, Inc., Boston, MA) and R software. A two-sided P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eThe baseline demographic and clinical characteristics of the study population are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A total of 8,538 hypertensive patients admitted to the ICU were included and stratified into four quartiles according to BAR levels: Q1 (\u0026lt;\u0026thinsp;4.59, n\u0026thinsp;=\u0026thinsp;2,120), Q2 (4.59\u0026ndash;7.50, n\u0026thinsp;=\u0026thinsp;2,133), Q3 (7.50 -13.33, n\u0026thinsp;=\u0026thinsp;2,148), and Q4 (\u0026ge;\u0026thinsp;13.33, n\u0026thinsp;=\u0026thinsp;2,137). Overall, 56.9% of the patients were male, and the mean age was 69.7\u0026thinsp;\u0026plusmn;\u0026thinsp;14.0 years. Baseline characteristics differed significantly across BAR quartiles (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The proportion of male patients increased progressively from Q1 to Q4, and patients in higher quartiles tended to be older. Ethnic distribution varied significantly among the groups, with a lower proportion of White patients and a higher proportion of other or unknown ethnicities observed in the higher BAR quartiles. The prevalence of major comorbidities\u0026mdash;including sepsis, respiratory failure, heart failure, diabetes mellitus, and chronic kidney disease\u0026mdash;increased consistently across quartiles (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Significant differences were also observed for acute myocardial infarction and malignant tumors. Laboratory parameters showed marked intergroup differences (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). With increasing BAR quartiles, white blood cell count, blood urea nitrogen, creatinine, glucose, and potassium levels tended to rise, whereas red blood cell count, hemoglobin, platelet count, and serum albumin levels demonstrated decreasing trends. Serum sodium levels differed modestly but significantly across groups (p\u0026thinsp;=\u0026thinsp;0.002). Clinical severity scores, including the Sequential Organ Failure Assessment (SOFA) score, Oxford Acute Severity of Illness Score (OASIS), and Charlson comorbidity index, increased progressively across BAR quartiles (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Several treatments and supportive interventions varied among groups, most notably the use of continuous renal replacement therapy and vasopressors (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Hospital length of stay and ICU length of stay differed significantly across quartiles, with longer durations observed in patients with higher BAR levels (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Mortality at 30, 90, and 365 days increased stepwise across the four quartiles(Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), and all between-group differences were statistically significant (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics and clinical outcomes of patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;8538\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1(\u0026lt;\u0026thinsp;4.59)\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;2120\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2(4.59\u0026ndash;7.50)\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;2133\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ3(7.50 -13.33)\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;2148\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ4(\u0026ge;\u0026thinsp;13.33)\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;2137\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender, (Male)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,860 (56.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,113 (52.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,207 (56.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,235 (57.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,305 (61.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.7\u0026thinsp;\u0026plusmn;\u0026thinsp;14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.5\u0026thinsp;\u0026plusmn;\u0026thinsp;13.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.8\u0026thinsp;\u0026plusmn;\u0026thinsp;13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71.0\u0026thinsp;\u0026plusmn;\u0026thinsp;13.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eWhite\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,970 (34.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e825 (38.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e716 (33.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e719 (33.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e710 (33.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOthers and unknown\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,568 (65.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,295 (61.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,417 (66.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,429 (66.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,427 (66.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSepsis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,122 (60.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e899 (42.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,181 (55.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,435 (66.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,607 (75.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRespiratory failure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,330 (39.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e592 (27.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e771 (36.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e938 (43.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,029 (48.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeart failure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,862 (33.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e368 (17.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e675 (31.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e826 (38.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e993 (46.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes mellitus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,282 (38.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e599 (28.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e742 (34.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e888 (41.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,053 (49.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChronic kidney disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,431 (28.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127 (6.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e372 (17.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e765 (35.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,167 (54.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAcute myocardial infarction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e430 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126 (5.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e123 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e111 (5.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMalignant tumors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,193 (14.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e260 (12.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e323 (15.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e299 (13.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e311 (14.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory parameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWbc, 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e9\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.9 (7.8\u0026ndash;15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.9 (7.2\u0026ndash;13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.0 (7.9\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.2 (7.8\u0026ndash;16.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.0 (8.1\u0026ndash;17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRbc, 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e9\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlatelet, 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e9\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e194.0 (138.0-261.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e208.0 (156.0-263.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e196.0 (144.0-262.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e186.5 (129.0-253.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e180.0 (123.0-263.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHemoglobin (g/mL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBun (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.0 (15.0\u0026ndash;39.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.0 (10.0\u0026ndash;14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.0 (17.0\u0026ndash;22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.0 (25.0\u0026ndash;35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.0 (46.0\u0026ndash;75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlbumin (g/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.2 (2.8\u0026ndash;3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.6 (3.2-4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.3 (2.9\u0026ndash;3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.1 (2.7\u0026ndash;3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.9 (2.4\u0026ndash;3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCreatinine (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1 (0.8\u0026ndash;1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8 (0.6\u0026ndash;0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0 (0.8\u0026ndash;1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.4 (1.0-1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.5 (1.7-4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlucose (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133.0 (107.0-178.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125.0 (104.0-160.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135.0 (108.0-177.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e138.0 (110.0-185.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e137.0 (106.0-189.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSodium (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e138.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e137.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePotassium (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical severity scores\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSOFA score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.0 (2.0\u0026ndash;7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0 (1.0\u0026ndash;5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.0 (2.0\u0026ndash;6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.0 (3.0\u0026ndash;8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.0 (5.0\u0026ndash;9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOASIS score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCharlson comorbidity index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.0 (4.0\u0026ndash;8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0 (3.0\u0026ndash;6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0\u0026ndash;7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.0 (5.0\u0026ndash;8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.0 (5.0\u0026ndash;9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTreatments\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePercutaneous coronary intervention\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e218 (2.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCoronary artery bypass grafting\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e424 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e169 (8.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e145 (6.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80 (3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRenal replacement therapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e545 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (1.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e134 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e312 (14.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVasopressor use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,765 (32.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e440 (20.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e654 (30.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e763 (35.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e908 (42.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eACEI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,259 (26.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e704 (33.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e660 (30.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e540 (25.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e355 (16.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eARB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e811 (9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e230 (10.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e250 (11.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e194 (9.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e137 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAspirin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,215 (49.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e994 (46.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,117 (52.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,086 (50.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,018 (47.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBeta blocker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,506 (64.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,338 (63.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,481 (69.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,402 (65.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,285 (60.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCCB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,196 (25.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e682 (32.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e549 (25.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e497 (23.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e468 (21.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStatin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,319 (50.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,033 (48.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,138 (53.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,106 (51.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,042 (48.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHospital LOS (days)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.0 (5.5\u0026ndash;16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.7 (4.8\u0026ndash;13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.4 (5.1\u0026ndash;15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.4 (5.7\u0026ndash;16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.1 (6.1\u0026ndash;19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eICU LOS (days)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.0 (1.9\u0026ndash;5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.8 (1.8\u0026ndash;5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.0 (1.8\u0026ndash;5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.0 (1.9\u0026ndash;5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.5 (2.0\u0026ndash;7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e30-day mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,737 (20.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e215 (10.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e326 (15.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e500 (23.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e696 (32.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e90-day mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,273 (26.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e301 (14.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e443 (20.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e646 (30.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e883 (41.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e365-day mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,027 (35.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e427 (20.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e644 (30.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e855 (39.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,101 (51.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNotes\u003c/b\u003e: Data are expressed as number (%) or mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median (IQR).\u003c/p\u003e \u003cp\u003eAmong the 8538 patients, the amount of missing values for the covariates were 12 (0.14%) for SOFA score, 3 (0.04%) for creatinine, 5 (0.06%) for glucose, 1 (0.01%) for sodium, 11 (0.13%) for potassium, 74 (0.87%) for red blood cell count, 86 (1.01%) for platelet count, 78 (0.91%) for hemoglobin, and 78 (0.91%) for white blood cell count.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssociation between BAR and mortality in hypertensive patients\u003c/h3\u003e\n\u003cp\u003eIn the multivariate Cox regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the association between BAR and mortality among critically ill patients with hypertension was evaluated using fully adjusted models (Model III). When BAR was analyzed as a continuous variable, higher BAR levels were independently associated with increased risks of 30-, 90-, and 365-day mortality. Specifically, each unit increase in BAR was associated with a 3% higher risk of death at 30 days (HR: 1.03, 95% CI: 1.02\u0026ndash;1.03; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 90 days (HR: 1.03, 95% CI: 1.02\u0026ndash;1.03; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and a 2% higher risk at 365 days (HR: 1.02, 95% CI: 1.02\u0026ndash;1.03; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).When BAR was categorized into quartiles, with the lowest quartile (Q1) serving as the reference, a stepwise increase in mortality risk was observed across higher BAR quartiles. For 30-day mortality, the adjusted hazard ratios were 1.22 (95% CI: 1.02\u0026ndash;1.45; p\u0026thinsp;=\u0026thinsp;0.006) for Q2, 1.53 (95% CI: 1.28\u0026ndash;1.82; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for Q3, and 1.92 (95% CI: 1.58\u0026ndash;2.32; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for Q4. Similar graded associations were observed for 90-day mortality, with adjusted HRs of 1.17 (95% CI: 1.01\u0026ndash;1.36; p\u0026thinsp;=\u0026thinsp;0.041), 1.45 (95% CI: 1.25\u0026ndash;1.68; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and 1.86 (95% CI: 1.58\u0026ndash;2.20; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for Q2, Q3, and Q4, respectively. For 365-day mortality, patients in higher BAR quartiles continued to demonstrate elevated risks after full adjustment, with HRs of 1.19 (95% CI: 1.05\u0026ndash;1.35; p\u0026thinsp;=\u0026thinsp;0.005) for Q2, 1.40 (95% CI: 1.23\u0026ndash;1.59; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for Q3, and 1.70 (95% CI: 1.48\u0026ndash;1.96; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for Q4.A significant dose\u0026ndash;response relationship was observed across increasing BAR quartiles for 30-, 90-, and 365-day mortality (all P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eThe Kaplan\u0026ndash;Meier survival curves further illustrated the differences in mortality among hypertensive patients across BAR quartiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Patients in higher BAR quartiles consistently exhibited lower survival probabilities compared with those in lower quartiles at all evaluated time points. For 30-day mortality, survival probability declined progressively from Q1 to Q4, with patients in the highest BAR quartile (Q4) demonstrating the poorest short-term survival. Similar separation of survival curves was observed for 90-day mortality, where higher BAR levels were associated with a markedly reduced probability of survival over time. This gradient became more pronounced in the analysis of 365-day mortality, with sustained divergence among quartiles throughout the follow-up period. Overall, the KM curves demonstrated a clear stepwise pattern, with survival decreasing monotonically across increasing BAR quartiles for short-, intermediate-, and long-term outcomes. The differences among groups were statistically significant, as indicated by the log-rank test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) for all three time horizons.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe association between BAR and all-cause mortality in MIMIC IV database by Cox regression analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel I\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel II\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eModel III\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e30-day mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBAR (continuous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.04 (1.04\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.04 (1.04\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e1.03 (1.02\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.56 (1.32\u0026ndash;1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.44 (1.21\u0026ndash;1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e1.22 (1.02\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2.49 (2.12\u0026ndash;2.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.21 (1.88\u0026ndash;2.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e1.53 (1.28\u0026ndash;1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e3.69 (3.17\u0026ndash;4.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.38 (2.90\u0026ndash;3.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e1.92 (1.58\u0026ndash;2.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e90-day mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBAR (continuous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.04 (1.04\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.04 (1.04\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e1.03 (1.02\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.52 (1.31\u0026ndash;1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.37 (1.18\u0026ndash;1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e1.17 (1.01\u0026ndash;1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2.36 (2.06\u0026ndash;2.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.05 (1.79\u0026ndash;2.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e1.45 (1.25\u0026ndash;1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e3.50 (3.07\u0026ndash;3.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.15 (2.76\u0026ndash;3.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e1.86 (1.58\u0026ndash;2.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e365-day mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBAR (continuous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.04 (1.04\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.04 (1.04\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e1.02 (1.02\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c14\" namest=\"c11\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.59 (1.41\u0026ndash;1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.42 (1.25\u0026ndash;1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e1.19 (1.05\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2.30 (2.05\u0026ndash;2.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.99 (1.77\u0026ndash;2.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e1.40 (1.23\u0026ndash;1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e3.27 (2.92\u0026ndash;3.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2.95 (2.63\u0026ndash;3.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e1.70 (1.48\u0026ndash;1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eSubgroup analysis\u003c/h3\u003e\n\u003cp\u003eSubgroup analyses were conducted to evaluate the association between BAR and mortality across different demographic and clinical characteristics in patients with hypertension (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Stratification was performed according to age, gender, disease severity scores (SOFA and OASIS), and major comorbidities, including sepsis, congestive heart failure, chronic kidney disease, diabetes, respiratory failure, acute myocardial infarction, renal replacement therapy, and vasopressor use. Overall, higher BAR levels were consistently associated with increased risks of 30-day, 90-day, and 365-day mortality across most examined subgroups. The associations remained generally robust across categories of gender, ethnicity, and disease severity, as well as in patients with or without several common comorbid conditions. For 30-day mortality, significant associations were observed in all subgroups except patients aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years and those with AMI. For 90-day mortality, BAR was significantly associated with mortality in most subgroups, except for patients with AMI. Interaction analyses indicated significant effect modification by sepsis, RRT, and respiratory failure. For 365-day mortality, elevated BAR remained significantly associated with increased risk in the majority of subgroups. However, the association was not significant among patients aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years and those with AMI. Significant interactions were noted for SOFA score, sepsis, and RRT, indicating that the association between BAR and long-term mortality may vary by disease severity and renal support status.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePrediction of all-cause mortality in patients with hypertension by BAR\u003c/h2\u003e \u003cp\u003eWe plotted ROC curves for BAR, BUN, and SOFA to assess their predictive value for 30-day,90-day and 365-day mortality in patients with hypertension. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the AUC of 30-day mortality for BAR was 0.653, which was superior to BUN (AUC\u0026thinsp;=\u0026thinsp;0.633) and SOFA (AUC\u0026thinsp;=\u0026thinsp;0.662), the AUC of 90-day mortality for BAR was 0.655, which was superior to BUN (AUC\u0026thinsp;=\u0026thinsp;0.632) and SOFA (AUC\u0026thinsp;=\u0026thinsp;0.647). The AUC of 365-day mortality for BAR was also better than BUN and SOFA (BAR: AUC\u0026thinsp;=\u0026thinsp;0.652; BUN: AUC\u0026thinsp;=\u0026thinsp;0.631; SOFA\u0026thinsp;=\u0026thinsp;0.630).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eExternal validation\u003c/h2\u003e \u003cp\u003eThe external validation was performed in 6644 patients with hypertension from eICU 2.0 database (Figure S2). Figure S3 revealed that patients with hypertension in higher BAR group had higher in-ICU (2.9% vs. 4.1% vs. 5.9% vs. 7.1%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and in-hospital (5.0% vs. 7.0% vs. 10.8% vs. 13.0%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) all-cause mortality. After fully adjustment by gender, age, ethnicity, SOFA score, OASIS, Charlson comorbidity index, PCI, CABG, renal replacement therapy, vasopressor use, sepsis, respiratory failure, heart failure, diabetes mellitus, chronic kidney disease, acute myocardial infarction, tumors, use of ACE inhibitors, ARBs, aspirin, beta-blockers, calcium channel blockers, statins, serum creatinine, glucose, sodium, red blood cell count, platelet count, hemoglobin, potassium, and white blood cell count compared with patients in lowest BAR quartile, Patients in the highest BAR quartile tended to have a higher risk of in-ICU all-cause mortality compared with those in the lowest quartile; however, this association did not reach statistical significance (highest vs. lowest BAR: HR\u0026thinsp;=\u0026thinsp;1.27, 95% CI 0.82\u0026ndash;1.94, p\u0026thinsp;=\u0026thinsp;0.281; p for trend\u0026thinsp;=\u0026thinsp;0.222, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In contrast, the highest BAR quartile was significantly associated with an increased risk of in-hospital all-cause mortality (highest vs. lowest BAR: HR\u0026thinsp;=\u0026thinsp;1.42, 95% CI 1.03\u0026ndash;1.95, p\u0026thinsp;=\u0026thinsp;0.031; p for trend\u0026thinsp;=\u0026thinsp;0.012, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).When BAR was analyzed as a continuous variable, the association with in-ICU all-cause mortality was attenuated and no longer statistically significant after full adjustment (HR\u0026thinsp;=\u0026thinsp;1.01, 95% CI 0.99\u0026ndash;1.02, p\u0026thinsp;=\u0026thinsp;0.348, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Conversely, each unit increase in BAR remained independently associated with a higher risk of in-hospital all-cause mortality across all models, including the fully adjusted model (HR\u0026thinsp;=\u0026thinsp;1.02, 95% CI 1.01\u0026ndash;1.02, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe association between BAR and all-cause mortality in eICU 2.0 database by Cox regression analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel I\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel II\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel III\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIn-ICU all-cause mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBAR (continuous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (1.00-1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01 (1.00-1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.01 (0.99\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.20 (0.84\u0026ndash;1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.12 (0.78\u0026ndash;1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97 (0.66\u0026ndash;1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.68 (1.19\u0026ndash;2.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.52 (1.07\u0026ndash;2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.07 (0.72\u0026ndash;1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.751\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.86 (1.35\u0026ndash;2.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.72 (1.24\u0026ndash;2.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.27 (0.82\u0026ndash;1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIn-hospital all-cause mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBAR (continuous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 (1.01\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01 (1.01\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02 (1.01\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.23 (0.94\u0026ndash;1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08 (0.82\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.95 (0.71\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.74 (1.34\u0026ndash;2.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.45 (1.11\u0026ndash;1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.11 (0.83\u0026ndash;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.486\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.88 (1.47\u0026ndash;2.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.66 (1.29\u0026ndash;2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.42 (1.03\u0026ndash;1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eHypertension remains a major global public health challenge and is strongly associated with adverse outcomes in critically ill patients, underscoring the importance of early risk stratification in this population\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. In the present study, higher BAR levels were associated with increased short- and long-term all-cause mortality among critically ill patients with hypertension in the MIMIC database after multivariable adjustment. Cox proportional hazards analyses demonstrated that BAR was significantly associated with 30-, 90-, and 365-day mortality, whereas receiver operating characteristic analyses suggested that BAR exhibited a stronger predictive performance for long-term mortality than for short-term mortality. This time-dependent prognostic pattern was further corroborated in the external validation cohort derived from the eICU Database, in which higher BAR was significantly associated with in-hospital all-cause mortality, while the association with in-ICU mortality showed a similar risk-increasing trend but did not reach statistical significance. Kaplan\u0026ndash;Meier survival analyses consistently demonstrated that patients with higher BAR values had significantly lower survival probabilities at 30, 90, and 365 days compared with those with lower BAR levels. Moreover, subgroup analyses indicated that the association between BAR and mortality was generally consistent across most demographic and clinical subgroups. Collectively, these results indicate a robust association between BAR and mortality risk in critically ill patients with hypertension.\u003c/p\u003e \u003cp\u003eBlood urea nitrogen (BUN) is a key end product of protein metabolism and serves as an important indicator of renal function and systemic catabolic status. Previous studies in pediatric hypertension have reported that elevated BUN levels are independently associated with secondary hypertension and target organ damage, including left ventricular hypertrophy and hypertensive encephalopathy\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Importantly, BUN concentrations are influenced by multiple factors beyond renal clearance alone, such as dietary protein intake, metabolic state, and renal hemodynamics. Therefore, BUN should be interpreted in conjunction with other clinical and laboratory parameters to more accurately reflect its significance in patients with hypertension\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSerum albumin is widely recognized as a marker of nutritional status and systemic inflammation\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Accumulating evidence indicates that lower albumin levels are associated with increased arterial stiffness and greater severity of atherosclerosis in hypertensive populations, suggesting a potential role in vascular remodeling and endothelial dysfunction\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. The nutritional and inflammatory states reflected by albumin levels have also been shown to influence outcomes in hypertension. For instance, the C-reactive protein\u0026ndash;to\u0026ndash;albumin ratio (CAR) has emerged as a strong prognostic marker for COVID-19\u0026ndash;related outcomes in patients with hypertension, linking hypoalbuminemia to systemic inflammation and mortality risk\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Moreover, stable or moderately elevated albumin concentrations have been associated with a reduced incidence of hypertension, indicating a possible protective role\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe blood urea nitrogen\u0026ndash;to\u0026ndash;albumin ratio (BAR) is a composite biomarker derived from the simple calculation of serum BUN divided by serum albumin, integrating information on renal function and the nutrition\u0026ndash;inflammation axis into a single index\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Elevated BAR levels have demonstrated prognostic value in hypertension-related complications, including heart failure\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e, emergency surgery for acute type A aortic dissection\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e, and acute ischemic stroke. In cohorts of patients with acute ischemic stroke, higher BAR has been independently associated with increased short- and long-term mortality, underscoring its prognostic relevance\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. This association may reflect the combined effects of renal dysfunction, systemic inflammation, and malnutrition, all of which contribute to adverse cardiovascular outcomes. Similarly, in patients with severe tricuspid regurgitation\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e, the combination of elevated BUN and reduced serum albumin has been shown to be associated with adverse clinical events, suggesting that this ratio may reflect cardiovascular risk beyond hypertension alone. In our study, BAR demonstrated superior predictive performance to SOFA for longer-term mortality (90-day and 365-day), whereas SOFA exhibited better discriminative ability for short-term (30-day) mortality in critically ill hypertensive patients. This temporal difference in prognostic utility is consistent with observations from other cohorts\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Additionally, subgroup analysis revealed an interaction between sepsis and BAR in predicting outcomes among hypertensive patients. Specifically, BAR appeared to have a stronger predictive value for mortality in non-septic patients, which may be related to the higher protein catabolism rate and inflammation levels in septic patients\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e, factors that could partially confound the relationship between BAR and mortality outcomes in critically ill patients with hypertension.\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged. First, this was a retrospective observational study, and causal inference cannot be established; residual confounding from unmeasured variables may persist despite multivariable adjustment for measured factors. Second, BAR was assessed only at ICU admission, and dynamic changes during hospitalization were not evaluated. Third, certain subgroup analyses may have been underpowered and should be interpreted cautiously. In addition, although external validation was performed using the eICU Collaborative Research Database, outcome assessment in that cohort was limited to in-ICU and in-hospital mortality, without post-discharge follow-up. Differences in follow-up duration between the derivation cohort from the Medical Information Mart for Intensive Care IV and the validation cohort may limit direct comparability of long-term prognostic performance. Finally, as the study population consisted of critically ill hypertensive patients admitted to the ICU, the generalizability of these findings to non-ICU settings or broader hypertensive populations remains uncertain.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this retrospective cohort of critically ill patients with hypertension, higher BAR levels were associated with increased short- and long-term all-cause mortality after multivariable adjustment. BAR may provide additional information for risk assessment; however, prospective studies are needed to confirm its clinical relevance.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBAR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBlood urea nitrogen-to-albumin ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBUN\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBlood urea nitrogen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSOFA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSequential Organ Failure Assessment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eICU\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntensive care unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRBC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRed blood cell count\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eWBC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWhite blood cell count\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCVD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCardiovascular disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCKD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChronic kidney disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLOS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLength of stay\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eACEI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAngiotensin-converting enzyme inhibitor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eARB\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAngiotensin receptor blocker\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCCB\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCalcium channel blocker\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eOASIS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOxford Acute Severity of Illness Score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRF\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRespiratory failure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRRT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRenal replacement therapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAMI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcute myocardial infarction.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNone.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe project was designed by Hongliang Dong, Yangang Wang. Material preparation, data collection, and analysis were performed by Keyang Li, Dandan Gong; The first draft of the manuscript was written by Debao Li, Dongmei Ren, and critically revised by Yuanyuan Wu, Meng Li.\u0026nbsp; Hongliang Dong, Yangang Wang revised the manuscript.\u0026nbsp;All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to thank the Massachusetts Institute of Technology and the Beth Israel Deaconess Medical Center for the MIMIC-IV database.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data supporting the findings of the present paper could be found at: [https://mimic.mit.edu](https:/mimic.mit.edu) .\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMills, K. T., Stefanescu, A. \u0026amp; He, J. The global epidemiology of hypertension. \u003cem\u003eNat. Rev. 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PMID: 38553557; PMCID: PMC10980814.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma, K., Mogensen, K. M. \u0026amp; Robinson, M. K. Pathophysiology of Critical Illness and Role of Nutrition. \u003cem\u003eNutr. Clin. Pract.\u003c/em\u003e \u003cb\u003e34\u003c/b\u003e (1), 12\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ncp.10232\u003c/span\u003e\u003cspan address=\"10.1002/ncp.10232\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019). Epub 2018 Dec 23. PMID: 30580456.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"blood urea nitrogen to serum albumin ratio, BAR, hypertensive, all-cause mortality, MIMIC-IV database","lastPublishedDoi":"10.21203/rs.3.rs-8827280/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8827280/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aims to investigate the association between the blood urea nitrogen–to–albumin ratio (BAR) and short- and long-term all-cause mortality in critically ill patients with hypertension admitted to the intensive care unit.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA retrospective cohort study was conducted using data from the Medical Information Mart for Intensive Care IV database, which includes ICU patients from 2008 to 2022. Patients with hypertension were identified using ICD codes and categorized into quartiles according to BAR values. Kaplan–Meier survival analyses were performed to compare mortality across BAR quartiles. Multivariate Cox regression models were employed to assess the association between BAR and 30-day, 90-day, and 365-day mortality. Receiver operating characteristic curve was plotted to evaluate the predictive value of BAR, BUN, and SOFA for mortality outcomes. Subgroup analyses were conducted based on comorbidities and clinical characteristics. The external validation was performed in eICU 2.0 database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 8,538 patients with hypertension were included. The 30-day, 90-day, and 365-day mortality rates were 20.3%, 26.6%, and 35.5%, respectively. Kaplan–Meier analyses demonstrated progressively higher all-cause mortality across increasing BAR quartiles (log-rank p \u0026lt; 0.001). In fully adjusted Cox models, patients in the highest BAR quartile (Q4) exhibited significantly higher risks of all-cause mortality compared with those in the lowest quartile (Q1), including 30-day mortality (HR 1.92, 95% CI: 1.58–2.32; p \u0026lt; 0.001), 90-day mortality (HR 1.86, 95% CI: 1.58–2.20; p \u0026lt; 0.001), and 365-day mortality (HR 1.70, 95% CI: 1.48–1.96; p \u0026lt; 0.001). The area under the curve (AUC) for BAR in predicting 30-day, 90-day, and 365-day mortality was 0.653, 0.655, and 0.652, respectively. Subgroup analyses demonstrated generally consistent associations across most strata. In the external eICU validation cohort including 6,644 hypertensive patients, higher BAR quartiles were independently associated with increased in-hospital all-cause mortality, while a similar but non-significant risk-increasing trend was observed for in-ICU mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHigher BAR levels were associated with increased short- and long-term all-cause mortality in critically ill patients with hypertension. BAR may provide incremental information for risk assessment in this population; however, further prospective studies are warranted to confirm its clinical utility.\u003c/p\u003e","manuscriptTitle":"Association between the blood urea nitrogen–to–serum albumin ratio and all-cause mortality in critically ill patients with hypertension: a retrospective cohort study of MIMIC-IV database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-20 06:51:23","doi":"10.21203/rs.3.rs-8827280/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-03-16T12:05:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-16T12:02:25+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-25T12:03:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-14T02:23:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-14T02:19:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e554ca00-eae9-4b0e-a35a-f17b4768cd1b","owner":[],"postedDate":"March 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":64601767,"name":"Health sciences/Cardiology"},{"id":64601768,"name":"Health sciences/Diseases"},{"id":64601769,"name":"Health sciences/Medical research"},{"id":64601770,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-03-20T06:51:25+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-20 06:51:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8827280","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8827280","identity":"rs-8827280","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}