Non-linear relationship between blood urea nitrogen to albumin ratio and 3-month outcomes in acute ischemic stroke: a second analysis based on a prospective cohort study

Non-linear relationship between blood urea nitrogen to albumin ratio and 3-month outcomes in acute ischemic stroke: a second analysis based on a prospective cohort study | 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 Research Article Non-linear relationship between blood urea nitrogen to albumin ratio and 3-month outcomes in acute ischemic stroke: a second analysis based on a prospective cohort study PAN Zhou, Xin Li, Gang-gang Peng, Haofei Hu, Zhe Deng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4570371/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: Patients with acute ischemic stroke (AIS) have limited evidence regarding the relationship between blood urea nitrogen and albumin (BUN/ALB). Aiming to investigate the relationship between the BUN/ALB ratio and poor outcomes in AIS patients at 3-months was the purpose of this study. Methods: AIS participants at a Korean hospital from January 2010 to December 2016 were included in a secondary analysis of a prospective cohort study. Logistic regression and restricted cubic splines were used to examine the relationship between BUN/ALB ratio and poor outcomes after 3 months. Results: There is a skewed distribution of BUN/ALB ratios, ranging from 0.114 to 1.250. Model II of the binary logistic regression showed that the BUN/ALB ratio was not statistically significant in predicting poor outcomes for AIS patients after three months. However, there was a notable nonlinear relationship between them, with the inflection point of the BUN/ALB ratio identified as 0.326. The BUN/ALB ratio on the left side of the inflection point was associated with a 42% reduction in 3-month poor outcomes (OR=0.58, 95% CI: 0.40 to 0.83, P = 0.0033). Conversely, the relationship was not statistically significant on the right side of the inflection point. Conclusion: The BUN/ALB ratio and poor outcomes in AIS patients show a nonlinear correlation with a saturation effect. For AIS patients, a BUN/ALB ratio of approximately 0.326 is associated with the lowest risk of adverse outcomes at 3 months. Specifically, for non-smoking AIS patients, a BUN/ALB ratio of approximately 0.295 is associated with the lowest risk of adverse outcomes at 3 months. Blood urea nitrogen to albumin ratio Acute ischemic stroke 3-month poor outcomes，Nonlinear relationship Smooth curve fitting Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Acute ischemic stroke (AIS), which has high mortality and morbidity rates, is a major global health concern( 1 , 2 ). There were approximately 12.2 million strokes worldwide in 2019, with 101 million cases and 6.55 million deaths attributed to strokes( 3 ). Annually, more than 2 million stroke cases are reported in China( 4 ). Moreover, strokes in low-income countries are also expected to increase due to factors such as aging populations, persistently high-risk factors like hypertension, and inadequate management( 3 , 4 ). In spite of advances in treatment, approximately 40% of AIS patients continue to have poor clinical outcomes( 5 , 6 ). As a result, AIS poses significant economic and humanistic burdens( 3 , 7 ). Several factors have been identified as being associated with poor prognosis in AIS, including age, obesity, diabetes, hypertension, heart disease, and stroke etiology( 7 – 10 ). However, it is challenging to intervene on these factors once a stroke has occurred, so some studies now examine the relationship between laboratory biomarkers and poor prognosis risk in AIS( 11 – 13 ). As laboratory biomarkers are accessible for intervention, they are potentially useful in improving outcomes in AIS patients. Studies have shown an independent association between higher BUN levels and mortality among critically ill patients( 14 , 15 ). One prospective cohort study of 9420 patients with acute coronary syndrome found that mortality was closely correlated with BUN levels, while glomerular filtration rate (eGFR) and serum creatinine (Scr) were unrelated( 16 ). Similarly, BUN, rather than eGFR and Scr, was associated with in-hospital mortality in another prospective study of 3355 patients with AIS( 12 ). Moreover, combined biomarkers have shown greater predictive value than single biomarkers( 17 ). The BUN/ALB ratio has shown superior predictive value for prognosis in various diseases, including sepsis, respiratory diseases, and chronic heart failure( 18 – 23 ), but its precise effect on AIS outcomes remains unknown. Hence, we examined the relationship between BUN/ALB ratio and 3-month outcomes in patients with AIS by utilizing data from a prospective cohort study conducted in South Korea. 2. Methods 2.1. Study design Korean single center prospective registry system patients with AIS between January 2010 and December 2016 were identified for this study( 24 ). The target-independent variable was the BUN/ALB ratio of AIS patients, while the dependent variable was their 3-month outcomes (dichotomous variable: favorable or poor). 2.2. Data source This study's raw data are available for free download from the DATADRYAD database, provided by Kang MK, Kim TJ, Kim Y, and colleagues( 24 ). Data can be accessed at https://doi.org/10.1371/journal.pone.0228738 . These data can be used for secondary analysis in accordance with Dryad's terms of service, without infringing the author's rights. We thank the authors for providing the data for research. 2.3. Study population AIS patients admitted within 7 days after the onset of symptoms were included in the initial study( 24 ). In addition to the approval of the original research by the Institutional Review Board at Seoul National University Hospital, patient consent was waived (IRB No. 1009-062-332). Therefore, ethics approval is not required for the current secondary analysis. According to the Declarations section, the initial study adhered to the principles outlined in the Declaration of Helsinki, and all methods followed relevant standards and regulations. Secondary analysis is subject to the same standards. In the original study, 2,084 patients with AIS were recruited. The following 178 participants were excluded: ( 1 ) no swallowing test or laboratory data within 24 hours of discharge (n = 72); ( 2 ) no modified 3-month Rankin Scale (mRS) score after hospitalization (n = 106). Those with abnormal or excessive BUN/ALB ratios (three standard deviations above or below the mean) were excluded from the current study (n = 38). Ultimately, Fig. 1 illustrates the participants selection process for the secondary analysis, which involved 1868 participants. 2.4. Variables 2.4.1. BUN/ALB ratio By dividing serum BUN by ALB, the BUN/ALB ratio was calculated as a continuous variable and categorized into quartiles as follows: Q1: <0.305, Q2: 0.306–0.384, Q3: 0.385–0.488, Q4: ≥0.489. 2.4.2. 3-month outcomes in individuals with AIS A 3-month outcome assessment was conducted using the mRS score( 24 , 25 ). Based on the mRS scores obtained through the telephone or through structured outpatient interviews, participants were categorized into two groups based on their mRS scores: favorable outcomes (mRS scores < 3) and poor outcomes (mRS scores ≥ 3)( 25 ). 2.5. Covariates Based on previous reports and clinical expertise, covariates were selected( 11 – 13 , 26 – 28 ). There were two categories of variables: 1. Categorical: sex, age, hypertension, diabetes mellitus (DM), hyperlipidemia, smoking, previous stroke or transient ischemic attack (TIA), atrial fibrillation (AF), coronary heart disease (CHD), and stroke etiology. 2. Continuous: white blood cell count (WBC), red blood cell count (RBC), hematocrit (HCT), hemoglobin concentration (HGB), platelet count (PLT), fasting blood glucose (FBG), serum creatinine (Scr), aspartate aminotransferase (AST), alanine aminotransferase (ALT), serum low-density lipoprotein cholesterol (LDL-c), serum high-density lipoprotein cholesterol (HDL-c), serum triglycerides (TG), serum cholesterol (TC), fibrinogen (FIB), and National Institutes of Health Stroke Scale (NIHSS) score( 3 ). Data from electronic medical records were collected, and tests were conducted within 24 hours of admission( 24 ). Trial of Org 10172 in Acute Stroke Treatment criteria were used to classify stroke subtypes( 24 ). 2.6. Missing data processing A total of 1 (0.05%) participant had missing data for WBC, 107 (5.61%) for TG, 1 (0.05%) for TC, 75 (3.93%) for LDL-c, 99 (5.19%) for HDL-c, 139 (7.29%) for FBG, and 22 (1.15%) for FIB. Multiple imputations were used to manage missing covariate data( 29 ). Variables were included in the imputation model such as age, sex, WBC, RBC, HCT, HGB, MCV, MCH, PLT, Scr, LDL-c, HDL-c, TG, TC, AST, ALT, FBG, HbA1c, FIB, BMI, NIHSS score, previous stroke or TIA, hyperlipidemia, hypertension, DM, smoking, AF, CHD, and stroke etiology. Under the assumption of missing at random (MAR), missing data analysis was conducted( 30 ). 2.7. Statistical analysis Means (standard deviations) or median (range) for non-normally distributed data were presented for continuous variables. Numbers (%) of participants were presented for categorical variables. A one-way analysis of variance was used to examine differences between BUN/ALB ratio groups with normally distributed continuous variables, the χ2 method for categorical variables, and the Kruskal-Wallis H test for skewed continuous variables. 2.7.1. To analyze the independent linear relationship of the BUN/ALB ratio and 3-month poor outcomes in patients with AIS To further elucidate the impact of the BUN/ALB ratio on 3-month poor prognosis, we scaled down the ratio by a factor of 10 in the multivariate analysis. This adjustment allowed for a more granular examination of its association with outcomes. As a result of screening for collinearity (Table S1 , where HGBs, HCTs, and TCs were omitted), three models were constructed to assess the relationship between the BUN/ALB ratio and 3-month poor outcomes in AIS patients( 31 ). The models included: ( 1 ) a non-adjusted model (no covariates were adjusted); ( 2 ) a minimally-adjusted model (Model I: adjusted for sex, age, hyperlipidemia, hypertension, DM, AF, and CHD); and ( 3 ) a fully-adjusted model (Model II: adjusted for age, sex, WBC, RBC, PLT, Scr, AST, ALT, TG, LDL-c, HDL-c, FBG, FIB, hyperlipidemia, hypertension, DM, smoking, AF, CHD, previous stroke or TIA, NIHSS score, and stroke etiology). Changes in the odds ratio (OR) were noted when adjusting for covariates, with adjustments made if the OR changed by 10% or more( 31 ). Effect sizes were reported with 95% confidence intervals (95% CI) 2.7.2. To analyze the nonlinear relationship of the BUN/ALB ratio and 3-month poor outcomes in patients with AIS To address concerns about the ability of binary logistic regression models to handle nonlinear relationships, generalized additive models (GAM) and smooth curve fitting using the penalized spline method were employed to investigate the potential nonlinear relationship between the BUN/ALB ratio and 3-month poor outcomes. Whenever nonlinearity was detected, a recursive algorithm was used to calculate the inflection point, followed by establishing a binary logistic regression model on both sides of the inflection point. By using the log-likelihood ratio test, the appropriateness of the BUN/ALB ratio model was examined( 32 ). 2.7.3. Sensitivity analysis We conducted a sensitivity analysis to assess the robustness of our findings. Since smoking has been shown to significantly elevate the risk of poor outcomes in patients and is a modifiable risk factor( 2 , 3 ), we excluded smoking from our analysis. Statistical analyses were performed using the packages R (R Foundation)2 and EmpowerStats3 (X&Y Solutions, Inc., Boston, MA) in accordance with the STROBE statement( 33 ). Statistical significance was defined as a P-value less than 0.05 (two-sided). 3. Results 3.1. Characteristics of Individuals This study included 1868 individuals, whose characteristics are presented in Table 1. There was a skewed distribution of BUN/ALB ratios, ranging from 0.114 to 1.250, with a median (Q1-Q3) of 0.385 (0.305-0.488) (Figure 2). Of these, 1143 (61.19%) were male. The distribution of age groups was as follows: < 60 years: 431 (23.07%), 60 to < 70 years: 496 (26.55%), 70 to < 80 years: 651 (34.85%), and ≥ 80 years: 290 (15.52%). The NIHSS scores ranged from 0 to 33, with a median (interquartile range) of 3 (1, 7). Stroke etiology classification revealed that 354 (18.95%) individuals had SVO, 597 (31.96%) had LAA, 483 (25.86%) had CE, 168 (8.99%) had other determined etiology, and 266 (14.24%) had undetermined etiology. Compared to group Q1, individuals in group Q4 showed significantly higher levels of male gender, older age, BUN, Scr, NIHSS score, previous stroke, hypertension, DM, AF, and CHD. Conversely, RBC, HGB, HCT, PLT, ALB, LDL-c, HDL-c, and TC were lower in group Q4. Furthermore, AIS in the Q4 group was more likely to be caused by LAA or CE. Table 1. Baseline Characteristics of participants (N =1868) BUN/ALB Q1( ＜ 0.305) Q2(0.306-0.384) Q3(0.385-0.488) Q4(≥0.489) P -value Participants 467 461 471 469 Sex, n (%) <0.001 Male 250 (53.53%) 292 (63.34%) 293 (62.21%) 308 (65.67%) Female 217 (46.47%) 169 (36.66%) 178 (37.79%) 161 (34.33%) Age(years) <0.001 ＜60 186 (39.83%) 111 (24.08%) 77 (16.35%) 57 (12.15%) 60-70 124 (26.55%) 141 (30.59%) 126 (26.75%) 105 (22.39%) 70-80 120 (25.70%) 140 (30.37%) 194 (41.19%) 197 (42.00%) ≥80 37 (7.92%) 69 (14.97%) 74 (15.71%) 110 (23.45%) WBC(×109/L) 7.90 ± 2.63 8.02 ± 2.65 8.08 ± 2.89 8.49 ± 3.27 0.141 RBC(×1012/L) 4.49 ± 0.59 4.47 ± 0.56 4.36 ± 0.55 4.07 ± 0.69 <0.001 HGB(g/dl) 13.96 ± 1.84 13.92 ± 1.73 13.58 ± 1.80 12.72 ± 2.15 <0.001 HCT (%) 41.33 ± 5.07 41.32 ± 4.80 40.43 ± 4.98 37.97 ± 6.07 <0.001 PLT(×109/L) 235.00 ± 64.85 228.11 ± 64.78 220.70 ± 68.24 211.78 ± 78.84 <0.001 BUN (mg/dl) 10.58 ± 1.92 13.93 ± 1.60 17.39 ± 1.76 25.18 ± 6.57 <0.001 Scr(mg/dl) 0.79 ± 0.23 0.85 ± 0.19 0.94 ± 0.24 1.40 ± 1.21 <0.001 AST(U/L) 23.00 (19.00-29.00) 23.00 (19.00-30.00) 23.00 (19.00-29.00) 23.00 (18.00-30.00) 0.988 ALT(U/L) 19.00 (14.00-27.00) 18.00 (14.00-26.00) 18.00 (13.00-26.00) 18.00 (12.00-26.00) 0.179 ALB(g/L) 42.08 ± 3.64 40.83 ± 3.87 40.27 ± 3.32 38.16 ± 4.48 <0.001 LDL-c(mg/dl) 114.09 ± 38.63 112.47 ± 38.40 106.60 ± 36.32 98.51 ± 35.91 <0.001 HDL-c(mg/dl) 47.40 ± 14.02 46.58 ± 14.40 45.54 ± 13.48 44.36 ± 14.25 0.008 TG (mg/dl) 111.49 ± 57.47 116.37 ± 60.49 111.15 ± 56.70 105.80 ± 54.60 0.026 TC (mg/dl) 188.09 ± 42.74 185.38 ± 43.33 178.13 ± 40.91 168.96 ± 44.58 <0.001 FIB (mg/l) 315.84 ± 68.41 328.97 ± 73.96 332.27 ± 82.28 354.09 ± 100.29 <0.001 FBG (mmol/l) 5.68 ± 1.84 5.84 ± 2.17 5.98 ± 2.15 6.17 ± 2.35 0.021 NIHSS score 3.00 (1.00-6.00) 3.00 (1.00-6.00) 3.00 (1.00-7.00) 4.00 (2.00-10.00) <0.001 Previous stroke/TIA 75 (16.06%) 82 (17.79%) 117 (24.84%) 118 (25.16%) <0.001 Hyperlipidemia 180 (38.54%) 187 (40.56%) 164 (34.82%) 159 (33.90%) 0.010 Hypertension 261 (55.89%) 263 (57.05%) 309 (65.61%) 349 (74.41%) <0.001 DM 109 (23.34%) 124 (26.90%) 157 (33.33%) 199 (42.43%) <0.001 Smoking 183 (39.19%) 192 (41.65%) 191 (40.55%) 171 (36.46%) 0.401 AF 69 (14.78%) 89 (19.31%) 107 (22.72%) 133 (28.36%) <0.001 CHD 31 (6.64%) 49 (10.63%) 56 (11.89%) 76 (16.20%) <0.001 Stroke etiology <0.001 SVO 94 (20.13%) 104 (22.56%) 90 (19.11%) 66 (14.07%) LAA 165 (35.33%) 143 (31.02%) 151 (32.06%) 138 (29.42%) CE 80 (17.13%) 112 (24.30%) 132 (28.03%) 159 (33.90%) Other determined 53 (11.35%) 41 (8.89%) 34 (7.22%) 40 (8.53%) Undetermined 75 (16.06%) 61 (13.23%) 64 (13.59%) 66 (14.07%) mRS ≥ 3 119 (25.48%) 105 (22.78%) 133 (28.24%) 173 (36.89%) <0.001 SD, standard deviation; n, number. WBC, white blood cell; RBC, red blood cell; HGB, hemoglobin concentration; HCT, hematocrit; PLT, platelet; BUN, blood urea nitrogen; Scr, serum creatinine; AST, aspartate aminotransferase; ALT, alanine aminotransferase; LDL-c, low-density lipoproteins cholesterol; HDL-c, high-density lipoprotein cholesterol; TG, triglyceride; TC, total cholesterol; FIB, fibrinogen; FBG, fasting blood glucose; NIHSS, national institute of health stroke scale; TIA, transient ischemia attack; DM, diabetes mellitus; AF, atrial fibrillation; CHD, coronary heart disease; LAA, large artery atherosclerosis; SVO, small vessel occlusion; CE, cardio embolism; mRS, modified 3-month Rankin Scale 3.2. The incidence rate of poor outcomes 3‑month after AIS As shown in Table 2, 530 of the 1868 participants had poor outcomes, which had an overall incidence of 28.37% (95% CI: 26.33% - 30.42%). Specifically, the incidence of poor outcomes in each quartile of the BUN/ALB ratio was as follows: Q1: 25.48% (95% CI: 21.52% - 29.45%), Q2: 22.78% (95% CI: 18.93% - 26.61%), Q3: 28.24% (95% CI: 24.16% - 32.32%), and Q4: 36.89% (95% CI: 32.50% - 41.27%). Figure 3 shows the pattern of poor outcomes by quarter: Q4 > Q3 > Q1 > Q2. Table 2. Incidence rate of unfavorable outcome 3-month after stroke BUN/ALB ratio Participants(n) unfavorable outcome events (mRS score≥3) Incidence of unfavorable outcome (95% CI) (%) Total 1868 530 28.37(26.33,30,42) Q1 467 119 25.48(21.52,29,45) Q2 461 105 22.78(18.93,26.61) Q3 471 133 28.24(24.16,32.32) Q4 469 173 36.89(32.50,41.27) 3.3. The results of univariate analyses using a binary logistic regression model In univariate analyses, PLT, Scr, HDL-c, and CHD did not significantly predict 3-month poor outcomes (all P>0.05). However, they showed positive associations with WBC (OR = 1.08, 95% CI: 1.05-1.12), BUN (OR = 1.02, 95% CI: 1.01-1.04), AST (OR = 1.01, 95% CI: 1.00-1.02), FBG (OR = 1.15, 95% CI: 1.10-1.21), and BUN/ALB ratio (OR = 4.39, 95% CI: 2.58-7.49) (all P 80 years old (OR = 4.03, 95% CI: 2.89-5.62), previous stroke or TIA (OR = 1.79, 95% CI: 1.41-2.26), hypertension (OR = 1.35, 95% CI: 1.09-1.66), DM (OR = 1.43, 95% CI: 1.16-1.76), AF (OR = 2.06, 95% CI: 1.63-2.59), NIHSS score ≥ 14 (OR = 17.31, 95% CI: 12.27-24.41), and stroke etiology of LAA (OR = 1.67, 95% CI: 1.21-2.31), CE (OR = 2.46, 95% CI: 1.77-3.42), or other determined (OR = 3.48, 95% CI: 2.32-5.24) were associated with higher likelihood of experiencing 3-month poor outcomes (all P < 0.05). Conversely, RBC (OR = 0.57, 95% CI: 0.49-0.68), ALB (OR = 0.26, 95% CI: 0.20-0.34), LDL-c (OR = 1.00, 95% CI: 0.99-1.00), TG (OR = 1.00, 95% CI: 0.99-1.00), hyperlipidemia (OR = 0.79, 95% CI: 0.64-0.97), and smoking (OR = 0.61, 95% CI: 0.49-0.75) were negatively associated with the risk of 3-month poor outcomes (all P < 0.05). Table 3. Univariate regression analysis of factors influencing poor outcomes in AIS Variable Statistic OR95%CI P WBC(×109/L) 8.12 ± 2.88 1.08 (1.05, 1.12) <0.0001 RBC(×1012/L) 4.34 ± 0.62 0.57 (0.49, 0.68) <0.0001 PLT(×10 9 /L) 223.87 ± 69.92 1.00 (1.00, 1.00) 0.3502 BUN (mg/dl) 16.79 ± 6.53 1.02 (1.01, 1.04) 0.0040 Scr(mg/dl) 1.00 ± 0.68 1.07 (0.93, 1.23) 0.3625 AST(U/L) 26.14 ± 14.26 1.01 (1.00, 1.02) 0.0115 ALT(U/L) 22.43 ± 15.79 0.99 (0.98, 1.00) 0.0239 ALB(g/dl) 40.33 ± 4.10 0.26 (0.20, 0.34) <0.0001 LDL-c(mg/dl) 107.89 ± 37.80 1.00 (0.99, 1.00) 0.0226 HDL-c(mg/dl) 45.96 ± 14.08 1.00 (0.99, 1.00) 0.1821 TG (mg/dl) 111.18 ± 57.41 1.00 (0.99, 1.00) <0.0001 FIB (mg/l) 332.83 ± 83.25 1.00 (1.00, 1.00) <0.0001 FBG (mmol/l) 5.92 ± 2.14 1.15 (1.10, 1.21) <0.0001 BUN/ALB ratio 0.42 ± 0.18 4.39 (2.58, 7.49) <0.0001 SEX Male 1143 (61.19%) 1.0 Female 725 (38.81%) 1.68 (1.37, 2.06) <0.0001 Age (years), n (%) <60 431 (23.07%) 1.0 60-70 496 (26.55%) 1.20 (0.87, 1.65) 0.2683 70-80 651 (34.85%) 1.86 (1.39, 2.49) <0.0001 ≥80 290 (15.52%) 4.03 (2.89, 5.62) <0.0001 Previous stroke/TIA, n (%) No 1476 (79.01%) 1.0 Yes 392 (20.99%) 1.79 (1.41, 2.26) <0.0001 Hyperlipidemia 0.0274 0 1178 (63.06%) 1.0 1 690 (36.94%) 0.79 (0.64, 0.97) Hypertension No 686 (36.72%) 1.0 Yes 1182 (63.28%) 1.35 (1.09, 1.66) 0.0064 DM No 1279 (68.47%) 1.0 Yes 589 (31.53%) 1.43 (1.16, 1.76) 0.0010 Smoking, n (%) No 1131 (60.55%) 1.0 Yes 737 (39.45%) 0.61 (0.49, 0.75) <0.0001 AF No 1470 (78.69%) 1.0 Yes 398 (21.31%) 2.06 (1.63, 2.59) <0.0001 CHD, n (%) No 1656 (88.65%) 1.0 Yes 212 (11.35%) 0.97 (0.71, 1.33) 0.8524 NIHSS score, n (%) =6, <14 378 (20.24%) 6.58 (5.09, 8.50) =14 213 (11.40%) 17.31 (12.27, 24.41) <0.0001 Stroke etiology, n (%) SVO 354 (18.95%) 1.0 LAA 597 (31.96%) 1.676(1.20, 2.30) 0.0023 CE 483 (25.86%) 2.46 (1.77, 3.42) <0.0001 Other determined 168 (8.99%) 3.48 (2.32, 5.24) <0.0001 Undetermined 266(14.19%) 1.41(0.95, 2.08) 0.0878 WBC, white blood cell; RBC, red blood cell; PLT, platelet; BUN, blood urea nitrogen; Scr, serum creatinine; AST, aspartate aminotransferase; ALT, alanine aminotransferase; LDL-c, low-density lipoproteins cholesterol; HDL-c, high-density lipoprotein cholesterol; TG, triglyceride; TC, total cholesterol; FIB, fibrinogen; FBG, fasting blood glucose; NIHSS, national institute of health stroke scale; TIA, transient ischemia attack; DM, diabetes mellitus; AF, atrial fibrillation; CHD, coronary heart disease; LAA, large artery atherosclerosis; SVO, small vessel occlusion; CE, cardio embolism; mRS, modified 3-month Rankin Scale. 3.4. Multivariate analyses using binary logistic regression Three logistic regression models were constructed (Table 4) to explore the relationship between BUN/ALB ratio and poor 3-month outcomes. In the univariate analysis, the odds ratio (OR) for the BUN/ALB ratio appears to be disproportionately large. To address this issue and achieve more interpretable results in the multivariate analysis, we scaled down the BUN/ALB ratio by a factor of 10. In the unadjusted model, a 0.1-unit increase in the BUN/ALB ratio was associated with a 16% increase in the risk of 3-month poor outcomes (OR = 1.16, 95% CI: 1.10-1.22, P < 0.0001). After adjusting for sex, age, hyperlipidemia, hypertension, DM, smoking, AF, and CHD (model I), a 0.1-unit increase in the BUN/ALB ratio corresponded to a 10% increase in the risk of 3-month poor outcomes (OR = 1.10, 95% CI: 1.04-1.17, P = 0.0009). However, in the fully adjusted model (model II), which included additional covariates such as WBC, RBC, PLT, AST, ALT, TG, LDL-c, HDL-c, Scr, FIB, FBG, previous stroke or TIA, NIHSS score, and stroke etiology, there was no significant correlation between BUN/ALB ratio and 3-month poor outcomes (OR = 1.01, 95% CI: 0.93-1.10, P = 0.7901). Furthermore, when the BUN/ALB ratio was categorized into quartiles, there was no statistically significant trend observed in the 3-month poor outcomes rate across the Q1 to Q4 groups in both model I (P for trend = 0.0748) and model II (P for trend = 0.3719). These findings suggest that the BUN/ALB ratio was statistically nonsignificant after adjusting for various confounding factors in patients with AIS. Table 4. Relationship between BUN/ALB ratio and poor outcome 3-month after stroke Exposure Crude model (OR, 95%CI, P ) Model I (OR, 95%CI, P ) Model II (OR, 95%CI, P ) BUN/ALB ratio×10 1.16 (1.10, 1.22) <0.0001 1.10 (1.04, 1.17) 0.0009 1.01 (0.93, 1.10) 0.7901 BUN/ALB ratio (quartile) Q1 Ref. Ref. Ref. Q2 0.86 (0.64, 1.17) 0.3358 0.78 (0.57, 1.07) 0.1254 0.64 (0.44, 0.93) 0.0199 Q3 1.15 (0.86, 1.54) 0.3412 0.93 (0.68, 1.27) 0.6646 0.80 (0.56, 1.15) 0.2369 Q4 1.71 (1.29, 2.26) 0.0002 1.24 (0.91, 1.69) 0.1744 0.76 (0.52, 1.13) 0.1776 P for trend <0.0001 0.0748 0.3719 Abbreviations: CI, confidence interval. Model Ⅰ adjusted for sex, age, hyperlipidemia, hypertension, DM, Smoking, AF, and CHD. Model Ⅱ adjusted for adjusted age, sex, WBC, RBC, PLT, AST, ALT, TG, LDL-c, HDL-c, Scr, FIB, FBG, hyperlipidemia, hypertension, DM, smoking, AF, CHD, previous stroke or TIA, NIHSS score, and stroke etiology. 3.5. The nonlinearity addressed by the GAM GAM analysis revealed a nonlinear relationship between the BUN/ALB ratio and the risk of poor outcomes in AIS patients (Figure 4a). After adjusting for various covariates, including age, sex, WBC, RBC, PLT, AST, ALT, TG, LDL-c, HDL-c, Scr, FIB, FBG, hyperlipidemia, hypertension, DM, smoking, AF, CHD, previous stroke or TIA, NIHSS score, and stroke etiology, the log-likelihood ratio test indicated a significant nonlinear relationship (P = 0.0418). By employing a two-stage linear regression model, we determined the inflection point of the BUN/ALB ratio to be 0.326. For patients with a BUN/ALB ratio ≤ 0.326, a 0.1-unit decrease in the ratio was associated with a 42% increase in the risk of 3-month poor outcomes (OR=0.58, 95%CI: 0.40-0.83, P = 0.0033). However, above the threshold, while elevated BUN/ALB ratios tended to be linked with a higher risk of poor outcomes, the association was not statistically significant (OR: 1.08, 95% CI: 0.99-1.19, P = 0.0944) (Table 5). Table 5 . The result of the two-piecewise linear regression model. Unfavorable outcome: Model I (OR, 95%CI, P ) Model II (OR, 95%CI, P) Fitting model by standard linear regression 1.01 (0.93, 1.10) 0.7901 1.04 (0.94, 1.16) 0.4520 Fitting model by two-piecewise regression Inflection point of BUN/ALB ratio 0.326 0.295 ≤Inflection point 0.58 (0.40, 0.83) 0.0033 0.35 (0.19, 0.64) 0.0007 > Inflection point 1.08 (0.99, 1.19) 0.0944 1.13 (1.01, 1.27) 0.0342 P for log-likelihood ratio test 0.002 <0.001 3.6. Sensitivity Analysis In the sensitivity analysis, where participants with smoking were excluded (n=1131), the nonlinear relationship between the BUN/ALB ratio and 3-month poor outcomes persisted. After adjusting for age, sex, RBC, PLT, Scr, AST, ALT, TG, LDL-c, HDL-c, HBA1c, hypertension, DM, AF, CHD, previous stroke or TIA, NIHSS score, and stroke etiology, the GAM analysis revealed a significant nonlinear relationship (Figure 4b). Using a recursive algorithm, the inflection point was determined to be 0.295. Subsequently, a two-stage binary logistic regression model was employed to calculate the odds ratios (OR) and confidence intervals (CI) around the inflection point. The further the BUN/ALB ratio deviated from the inflection point, the greater the risk for adverse outcomes on both sides (all P<0.05) (Table 5). 4. Discussion In our study, we examined how the BUN/ALB ratio correlated with 3-month outcomes in AIS patients. Our findings revealed a nonlinear relationship between the BUN/ALB ratio at admission and 3-month outcomes. Specifically, for all AIS patients, the inflection point of the BUN/ALB ratio is 0.326, while for non-smoking AIS patients, the inflection point of the BUN/ALB ratio is 0.295, there is a significant difference in the probability of adverse outcomes at 3 months. These findings highlight the importance of considering the BUN/ALB ratio as a potential prognostic marker in AIS patients, with implications for risk stratification and clinical management. According to previous studies(12, 15, 16, 34, 35), BUN demonstrates greater predictive power than Scr and eGFR for critically ill patients. However, recent findings indicate that the BUN/ALB ratio may offer even stronger predictive capabilities for certain patient populations, including those with respiratory diseases, sepsis, and chronic heart failure (18-23). In our study, patients with an elevated BUN/ALB ratio (Q4≥0.489) demonstrated a significantly increased risk of poor outcomes at 3 months (P<0.001). Initially, BUN/ALB ratios were identified as risk factors for poor outcomes at 3 months based on univariate analysis. Nevertheless, multivariate regression analysis (model II) did not reveal that the BUN/ALB ratio was an independent risk factor. Upon categorizing the BUN/ALB ratio and analyzing the data (refer to Figure 2 and Table 2), it became evident that the incidence of poor outcomes followed the order: Q 4 > Q 3 > Q 1 > Q 2. In addition, Table 4 shows that poor outcomes did not exhibit a linear trend from Q1 to Q4 (P for trend was 0.0748 in model I and 0.3719 in model II). These findings suggest that a linear relationship may not adequately capture the correlation between the BUN/ALB ratio and poor outcomes at 3 months. In this study, researchers examined the relationship between BUN/ALB ratio and poor outcomes by using logistic regression with cubic spline functions and smooth curve fitting (cubic spline smoothing). A nonlinear relationship was found between the BUN/ALB ratio and poor outcomes after 3 months. Specifically, an inflection point of 0.326 was identified, leading the authors to conduct further analysis using two piecewise logistic regression models to calculate the odds ratio (OR) and confidence intervals (CI) around this inflection point. A 0.1-unit decrease in the BUN/ALB ratio on the left side of the inflection point was associated with a 42% increase in poor outcomes after 3 months (OR=0.58, 95%CI: 0.40-0.83, P=0.0033). However, this relationship was not statistically significant on the right side of the inflection point (>0.326). Given the difficulty in intervening in factors such as previous hypertension, diabetes, and heart disease when a stroke occurs, the authors conducted a subgroup analysis excluding smoking patients. After this exclusion, the inflection point shifted slightly to 0.295. Notably, the relationship demonstrated statistical significance on both sides of the inflection point (all P<0.05) in the subgroup analysis. This similarity in the curve pattern between the subgroup and the entire participant cohort, along with the proximity of the inflection point to that of all participants, underscores the robustness of the observed nonlinear relationship between the BUN/ALB ratio and 3-month poor outcomes. Although data regarding the BUN/ALB ratio are scarce, several potential explanations can be posited as to why there is a nonlinear relationship between the ratio and poor outcomes at 3-months in AIS patients. BUN serves as a crucial marker of renal function, with numerous studies indicating its robust predictive value in critically ill patients, sometimes surpassing Scr and eGFR(12, 16). This is attributed to its susceptibility to various factors such as age, protein intake, bleeding, catabolism, and hemodynamics. On one hand, low BUN levels may signify inadequate protein intake or malnutrition(36), potentially impeding neurological repair in AIS patients. Given the acute metabolic stress state associated with AIS, characterized by heightened energy demands(37) , reduced BUN levels might deprive patients of the necessary substrate for early neurological recovery. Conversely, elevated BUN levels may reflect deteriorating hemodynamics(38) , which are known to be pivotal in poor stroke prognosis and mortality(39, 40). Albumin, beyond its role in reflecting patients' nutritional status, exerts multiple physiological effects, including regulation of colloidal osmolality, binding and transportation of various substances in blood, antioxidant properties, nitric oxide regulation, and buffering capacity. While low serum albumin levels are commonly associated with worse prognosis in critically ill patients, the efficacy of albumin administration remains contentious(41). Hence, maintaining a proper albumin concentration is crucial. These factors likely contribute to the inverse correlation observed between the BUN/ALB ratio and the likelihood of 3-month poor outcomes on the left side of the inflection point in our study. Conversely, the lack of statistical significance on the right side of the inflection point may be attributed to worsening patient condition and other confounding factors assuming greater importance. This study presents several noteworthy findings. Firstly, it offers a fresh perspective on clinical prognosis evaluation among AIS patients by investigating the nonlinear association between the BUN/ALB ratio and 3-month poor outcomes. Secondly, the utilization of multiple imputation techniques to address missing data enhances statistical robustness and diminishes potential bias, augmenting the credibility of the findings. Thirdly, the robustness of the results was confirmed through meticulous subgroup analyses, further bolstering their reliability and generalizability. Moreover, the practicality of employing the BUN/ALB ratio is underscored by its status as an objective, cost-effective laboratory parameter readily accessible in clinical settings, thereby enhancing its utility in prognostic assessment. This study is subject to several limitations that warrant acknowledgment. Firstly, the inclusion of AIS patients solely from a single center in South Korea may restrict the generalizability of the findings, necessitating validation across diverse geographic and ethnic populations. Secondly, akin to inherent limitations in all observational studies, unmeasured or uncontrolled confounding variables may exist, despite efforts to mitigate potential confounders through identification and control. Thirdly, incomplete variable information, such as the utilization of age stratified data in 10 intervals rather than precise patient ages in the original research database, may introduce inaccuracies and hinder comprehensive analysis. Future research endeavors could explore improved methodologies to procure more precise and comprehensive variable data, thus enhancing the robustness and validity of study outcomes. 5. Conclusion In this study, Korean AIS patients were found to have a distinct nonlinear relationship and saturation effect between their BUN/ALB ratio and 3-month poor outcomes. Specifically, for all AIS patients, when the BUN/ALB ratio is 0.326, the risk of adverse outcomes at 3 months is the lowest. While for non-smoking AIS patients, when the BUN/ALB ratio is 0.295, the risk of adverse outcomes at 3 months is the lowest. These findings have important implications for clinicians and can serve as a reference for identifying high-risk groups among Korean AIS patients who are likely to experience 3-month poor outcomes. Declarations Funding source This study was supported by Shenzhen Science and Technology Program (JCYJ20180228163014668 ; KCXFZ20230731094100002), and Shenzhen Second People’s Hospital Clinical Research Fund of Guangdong Province High-level Hospital Construction Project (No.20223357005). Conflict of Interest The authors declare that they have no conflict of interest. Human Ethics and Consent to Participate declarations not applicable. Availability of Data and Materials Data sharing not applicable to this article as no datasets were generated or analyzed during the current study. Author Contribution Pan Zhou: study concept, design, data analysis and manuscript draft; Xin Li and Gang-gang Peng: critical revision of the manuscript for important intellectual content; Hao-fei Hu: Manuscript writing and review; Zhe Deng: administrative, technical, and material support and study supervision. References Saini V, Guada L, Yavagal DR.2021. Global Epidemiology of Stroke and Access to Acute Ischemic Stroke Interventions. Neurology 97:S6-s16. Campbell BCV, Khatri P.2020. Stroke. Lancet 396:129-142. Anonymous.2021. Global, regional, and national burden of stroke and its risk factors, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol 20:795-820. Wu S, Wu B, Liu M, Chen Z, Wang W, Anderson CS, Sandercock P, Wang Y, Huang Y, Cui L, Pu C, Jia J, Zhang T, Liu X, Zhang S, Xie P, Fan D, Ji X, Wong KL, Wang L.2019. Stroke in China: advances and challenges in epidemiology, prevention, and management. Lancet Neurol 18:394-405. 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Guidelines for the Early Management of Patients With Acute Ischemic Stroke: 2019 Update to the 2018 Guidelines for the Early Management of Acute Ischemic Stroke: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association. Stroke 50:e344-e418. Jacob L, Tanislav C, Kostev K.2020. Long-term risk of stroke and its predictors in transient ischaemic attack patients in Germany. Eur J Neurol 27:723-728. Moalla KS, Damak M, Chakroun O, Farhat N, Sakka S, Hdiji O, Kacem HH, Rekik N, Mhiri C.2020. [Prognostic factors for mortality due to acute arterial stroke in a North African population]. Pan Afr Med J 35:50. Han Y, Huang Z, Zhou J, Wang Z, Li Q, Hu H, Liu D.2022. Association between triglyceride-to-high density lipoprotein cholesterol ratio and three-month outcome in patients with acute ischemic stroke: a second analysis based on a prospective cohort study. BMC Neurol 22:263. You S, Zheng D, Zhong C, Wang X, Tang W, Sheng L, Zheng C, Cao Y, Liu CF.2018. Prognostic Significance of Blood Urea Nitrogen in Acute Ischemic Stroke. Circ J 82:572-578. Aachi RV, Birnbaum LA, Topel CH, Seifi A, Hafeez S, Behrouz R.2017. Laboratory characteristics of ischemic stroke patients with atrial fibrillation on or off therapeutic warfarin. Clin Cardiol 40:1347-1351. Arihan O, Wernly B, Lichtenauer M, Franz M, Kabisch B, Muessig J, Masyuk M, Lauten A, Schulze PC, Hoppe UC, Kelm M, Jung C.2018. Blood Urea Nitrogen (BUN) is independently associated with mortality in critically ill patients admitted to ICU. PLoS One 13:e0191697. Liu EQ, Zeng CL.2021. Blood Urea Nitrogen and In-Hospital Mortality in Critically Ill Patients with Cardiogenic Shock: Analysis of the MIMIC-III Database. Biomed Res Int 2021:5948636. Kirtane AJ, Leder DM, Waikar SS, Chertow GM, Ray KK, Pinto DS, Karmpaliotis D, Burger AJ, Murphy SA, Cannon CP, Braunwald E, Gibson CM.2005. Serum blood urea nitrogen as an independent marker of subsequent mortality among patients with acute coronary syndromes and normal to mildly reduced glomerular filtration rates. J Am Coll Cardiol 45:1781-6. Zemans RL, Jacobson S, Keene J, Kechris K, Miller BE, Tal-Singer R, Bowler RP.2017. Multiple biomarkers predict disease severity, progression and mortality in COPD. Respir Res 18:117. Zeng Z, Ke X, Gong S, Huang X, Liu Q, Huang X, Cheng J, Li Y, Wei L.2022. Blood urea nitrogen to serum albumin ratio: a good predictor of in-hospital and 90-day all-cause mortality in patients with acute exacerbations of chronic obstructive pulmonary disease. BMC Pulm Med 22:476. Ugajin M, Yamaki K, Iwamura N, Yagi T, Asano T.2012. Blood urea nitrogen to serum albumin ratio independently predicts mortality and severity of community-acquired pneumonia. Int J Gen Med 5:583-9. Ryu S, Oh SK, Cho SU, You Y, Park JS, Min JH, Jeong W, Cho YC, Ahn HJ, Kang C.2021. Utility of the blood urea nitrogen to serum albumin ratio as a prognostic factor of mortality in aspiration pneumonia patients. Am J Emerg Med 43:175-179. Fang J, Xu B.2021. Blood Urea Nitrogen to Serum Albumin Ratio Independently Predicts Mortality in Critically Ill Patients With Acute Pulmonary Embolism. Clin Appl Thromb Hemost 27:10760296211010241. Min J, Lu J, Zhong L, Yuan M, Xu Y.2022. The correlation study between blood urea nitrogen to serum albumin ratio and prognosis of patients with sepsis during hospitalization. BMC Anesthesiol 22:404. Lin Z, Zhao Y, Xiao L, Qi C, Chen Q, Li Y.2022. Blood urea nitrogen to serum albumin ratio as a new prognostic indicator in critical patients with chronic heart failure. ESC Heart Fail 9:1360-1369. Kang MK, Kim TJ, Kim Y, Nam KW, Jeong HY, Kim SK, Lee JS, Ko SB, Yoon BW.2020. Geriatric nutritional risk index predicts poor outcomes in patients with acute ischemic stroke - Automated undernutrition screen tool. PLoS One 15:e0228738. Haggag H, Hodgson C.2022. Clinimetrics: Modified Rankin Scale (mRS). J Physiother 68:281. Sun W, Huang Y, Xian Y, Zhu S, Jia Z, Liu R, Li F, Wei JW, Wang JG, Liu M, Anderson CS.2017. Association of body mass index with mortality and functional outcome after acute ischemic stroke. Sci Rep 7:2507. Li SS, Yin MM, Zhou ZH, Chen HS.2017. Dehydration is a strong predictor of long-term prognosis of thrombolysed patients with acute ischemic stroke. Brain Behav 7:e00849. Renner CJ, Kasner SE, Bath PM, Bahouth MN.2022. Stroke Outcome Related to Initial Volume Status and Diuretic Use. J Am Heart Assoc 11:e026903. Groenwold RH, White IR, Donders AR, Carpenter JR, Altman DG, Moons KG.2012. Missing covariate data in clinical research: when and when not to use the missing-indicator method for analysis. Cmaj 184:1265-9. White IR, Royston P, Wood AM.2011. Multiple imputation using chained equations: Issues and guidance for practice. Stat Med 30:377-99. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP.2014. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: guidelines for reporting observational studies. Int J Surg 12:1495-9. Rothenbacher D, Rehm M, Iacoviello L, Costanzo S, Tunstall-Pedoe H, Belch JJF, Söderberg S, Hultdin J, Salomaa V, Jousilahti P, Linneberg A, Sans S, Padró T, Thorand B, Meisinger C, Kee F, McKnight AJ, Palosaari T, Kuulasmaa K, Waldeyer C, Zeller T, Blankenberg S, Koenig W.2020. Contribution of cystatin C- and creatinine-based definitions of chronic kidney disease to cardiovascular risk assessment in 20 population-based and 3 disease cohorts: the BiomarCaRE project. BMC Med 18:300. Diener HC, Hankey GJ.2020. Primary and Secondary Prevention of Ischemic Stroke and Cerebral Hemorrhage: JACC Focus Seminar. J Am Coll Cardiol 75:1804-1818. Wernly B, Lichtenauer M, Vellinga NAR, Boerma EC, Ince C, Kelm M, Jung C.2018. Blood urea nitrogen (BUN) independently predicts mortality in critically ill patients admitted to ICU: A multicenter study. Clin Hemorheol Microcirc 69:123-131. Hong C, Zhu H, Zhou X, Zhai X, Li S, Ma W, Liu K, Shirai K, Sheerah HA, Cao J.2023. Association of Blood Urea Nitrogen with Cardiovascular Diseases and All-Cause Mortality in USA Adults: Results from NHANES 1999-2006. Nutrients 15. Pereira-da-Silva L, Virella D, Fusch C.2019. Nutritional Assessment in Preterm Infants: A Practical Approach in the NICU. Nutrients 11. Chen Z, Venkat P, Seyfried D, Chopp M, Yan T, Chen J.2017. Brain-Heart Interaction: Cardiac Complications After Stroke. Circ Res 121:451-468. Aronson D, Mittleman MA, Burger AJ.2004. Elevated blood urea nitrogen level as a predictor of mortality in patients admitted for decompensated heart failure. Am J Med 116:466-73. Baizabal-Carvallo JF, Alonso-Juarez M, Samson Y.2014. Clinical deterioration following middle cerebral artery hemodynamic changes after intravenous thrombolysis for acute ischemic stroke. J Stroke Cerebrovasc Dis 23:254-8. Bang OY, Kim GM, Chung CS, Kim SJ, Kim KH, Jeon P, Saver JL, Liebeskind DS, Lee KH.2010. Differential pathophysiological mechanisms of stroke evolution between new lesions and lesion growth: perfusion-weighted imaging study. Cerebrovasc Dis 29:328-35. Vincent JL, Russell JA, Jacob M, Martin G, Guidet B, Wernerman J, Ferrer R, McCluskey SA, Gattinoni L.2014. Albumin administration in the acutely ill: what is new and where next? Crit Care 18:231. Additional Declarations No competing interests reported. Supplementary Files supplementarymaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4570371","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":317115031,"identity":"04710ad4-5f48-4720-8fcc-491055df9a33","order_by":0,"name":"PAN Zhou","email":"","orcid":"","institution":"the First Affiliated Hospital of Shenzhen University","correspondingAuthor":false,"prefix":"","firstName":"PAN","middleName":"","lastName":"Zhou","suffix":""},{"id":317115032,"identity":"cf255cbe-9d16-45d0-8f8f-5a57ef59c0ed","order_by":1,"name":"Xin Li","email":"","orcid":"","institution":"the First Affiliated Hospital of Shenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Li","suffix":""},{"id":317115033,"identity":"b43d0621-3417-47da-bca9-46aed4b24f54","order_by":2,"name":"Gang-gang Peng","email":"","orcid":"","institution":"the First Affiliated Hospital of Shenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Gang-gang","middleName":"","lastName":"Peng","suffix":""},{"id":317115035,"identity":"a4cea747-01fd-4967-81e6-9272804629e3","order_by":3,"name":"Haofei Hu","email":"","orcid":"","institution":"the First Affiliated Hospital of Shenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Haofei","middleName":"","lastName":"Hu","suffix":""},{"id":317115037,"identity":"de7139c8-d1b0-4e13-9283-0db5d794d547","order_by":4,"name":"Zhe Deng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYDCCAyDCwMaOn5n58AMStBSkJUu2s6UZkKDlwyHGDed5FCSI0sF3vPfwyx8GB5iND/MwGDDU2EQT1CJ55lyahYTBHT6zw7wHHjAcS8ttIKTF4EaOmYGBwTNms8N8CQaMDYeJ0HL/jZlBgsFhxs3NPAYSxGm5wWP84ABQywZmYrVInskxY2wwSEuWOAwM5ARi/MJ3/Izxxx9/gFHZf/jwgw81NoS1AAEbIjoSiFAOAswfiFQ4CkbBKBgFIxUAAGaJQqpkh0PpAAAAAElFTkSuQmCC","orcid":"","institution":"the First Affiliated Hospital of Shenzhen University","correspondingAuthor":true,"prefix":"","firstName":"Zhe","middleName":"","lastName":"Deng","suffix":""}],"badges":[],"createdAt":"2024-06-12 13:00:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4570371/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4570371/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59871898,"identity":"99a7f73c-3941-411a-9a32-5fcacc69349d","added_by":"auto","created_at":"2024-07-08 17:09:45","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":108087,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of study participants\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4570371/v1/38e75264537d6b897e9be6bd.jpg"},{"id":59871900,"identity":"7ffdfafa-909c-497b-9f3f-1b65f8d81a8b","added_by":"auto","created_at":"2024-07-08 17:09:46","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":45206,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of BUN/ALB ratio. Between 0.114 and 1.250, the BUN/ALB ratio has a skewed distribution.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4570371/v1/0e0469bd218e96714bbc2a12.jpg"},{"id":59871897,"identity":"dca3de0f-8edc-4edb-a159-f82452aa7707","added_by":"auto","created_at":"2024-07-08 17:09:45","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":74825,"visible":true,"origin":"","legend":"\u003cp\u003e3-month poor outcomes according to BUN/ALB ratio quartiles.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4570371/v1/3cb69a4c1fb45c3646a044f8.jpg"},{"id":59871899,"identity":"2d1ce43f-5e04-4d9d-8495-08549a69209c","added_by":"auto","created_at":"2024-07-08 17:09:46","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":58290,"visible":true,"origin":"","legend":"\u003cp\u003eThe nonlinear relationship between BUN/ALB ratio and 3-month poor outcomes. A nonlinear relationship was detected after adjusting for age, sex, WBC, RBC, PLT, AST, ALT, TG, LDL-c, HDL-c, Scr, FIB, FBG, hyperlipidemia, hypertension, DM, smoking, AF, CHD, previous stroke or TIA, NIHSS score, and stroke etiology.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4570371/v1/87dbfb20817a97610c126fa0.jpg"},{"id":69781888,"identity":"b292da5b-d72a-4e03-940b-977c0c855c77","added_by":"auto","created_at":"2024-11-25 08:09:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1060957,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4570371/v1/ba1aecff-9c65-4e1a-973f-066c48419962.pdf"},{"id":59871896,"identity":"05526973-c115-4001-bf26-95479f093cc5","added_by":"auto","created_at":"2024-07-08 17:09:45","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":26649,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4570371/v1/3ecc8336b0f5d929affc735e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Non-linear relationship between blood urea nitrogen to albumin ratio and 3-month outcomes in acute ischemic stroke: a second analysis based on a prospective cohort study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAcute ischemic stroke (AIS), which has high mortality and morbidity rates, is a major global health concern(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). There were approximately 12.2\u0026nbsp;million strokes worldwide in 2019, with 101\u0026nbsp;million cases and 6.55\u0026nbsp;million deaths attributed to strokes(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Annually, more than 2\u0026nbsp;million stroke cases are reported in China(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Moreover, strokes in low-income countries are also expected to increase due to factors such as aging populations, persistently high-risk factors like hypertension, and inadequate management(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In spite of advances in treatment, approximately 40% of AIS patients continue to have poor clinical outcomes(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). As a result, AIS poses significant economic and humanistic burdens(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral factors have been identified as being associated with poor prognosis in AIS, including age, obesity, diabetes, hypertension, heart disease, and stroke etiology(\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). However, it is challenging to intervene on these factors once a stroke has occurred, so some studies now examine the relationship between laboratory biomarkers and poor prognosis risk in AIS(\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). As laboratory biomarkers are accessible for intervention, they are potentially useful in improving outcomes in AIS patients.\u003c/p\u003e \u003cp\u003eStudies have shown an independent association between higher BUN levels and mortality among critically ill patients(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). One prospective cohort study of 9420 patients with acute coronary syndrome found that mortality was closely correlated with BUN levels, while glomerular filtration rate (eGFR) and serum creatinine (Scr) were unrelated(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Similarly, BUN, rather than eGFR and Scr, was associated with in-hospital mortality in another prospective study of 3355 patients with AIS(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Moreover, combined biomarkers have shown greater predictive value than single biomarkers(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The BUN/ALB ratio has shown superior predictive value for prognosis in various diseases, including sepsis, respiratory diseases, and chronic heart failure(\u003cspan additionalcitationids=\"CR19 CR20 CR21 CR22\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), but its precise effect on AIS outcomes remains unknown.\u003c/p\u003e \u003cp\u003eHence, we examined the relationship between BUN/ALB ratio and 3-month outcomes in patients with AIS by utilizing data from a prospective cohort study conducted in South Korea.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study design\u003c/h2\u003e \u003cp\u003eKorean single center prospective registry system patients with AIS between January 2010 and December 2016 were identified for this study(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The target-independent variable was the BUN/ALB ratio of AIS patients, while the dependent variable was their 3-month outcomes (dichotomous variable: favorable or poor).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data source\u003c/h2\u003e \u003cp\u003eThis study's raw data are available for free download from the DATADRYAD database, provided by Kang MK, Kim TJ, Kim Y, and colleagues(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Data can be accessed at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0228738\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0228738\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. These data can be used for secondary analysis in accordance with Dryad's terms of service, without infringing the author's rights. We thank the authors for providing the data for research.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Study population\u003c/h2\u003e \u003cp\u003eAIS patients admitted within 7 days after the onset of symptoms were included in the initial study(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). In addition to the approval of the original research by the Institutional Review Board at Seoul National University Hospital, patient consent was waived (IRB No. 1009-062-332). Therefore, ethics approval is not required for the current secondary analysis. According to the Declarations section, the initial study adhered to the principles outlined in the Declaration of Helsinki, and all methods followed relevant standards and regulations. Secondary analysis is subject to the same standards.\u003c/p\u003e \u003cp\u003eIn the original study, 2,084 patients with AIS were recruited. The following 178 participants were excluded: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) no swallowing test or laboratory data within 24 hours of discharge (n\u0026thinsp;=\u0026thinsp;72); (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) no modified 3-month Rankin Scale (mRS) score after hospitalization (n\u0026thinsp;=\u0026thinsp;106). Those with abnormal or excessive BUN/ALB ratios (three standard deviations above or below the mean) were excluded from the current study (n\u0026thinsp;=\u0026thinsp;38). Ultimately, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the participants selection process for the secondary analysis, which involved 1868 participants.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Variables\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1. BUN/ALB ratio\u003c/h2\u003e \u003cp\u003eBy dividing serum BUN by ALB, the BUN/ALB ratio was calculated as a continuous variable and categorized into quartiles as follows: Q1: \u0026lt;0.305, Q2: 0.306\u0026ndash;0.384, Q3: 0.385\u0026ndash;0.488, Q4: \u0026ge;0.489.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2. 3-month outcomes in individuals with AIS\u003c/h2\u003e \u003cp\u003eA 3-month outcome assessment was conducted using the mRS score(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Based on the mRS scores obtained through the telephone or through structured outpatient interviews, participants were categorized into two groups based on their mRS scores: favorable outcomes (mRS scores\u0026thinsp;\u0026lt;\u0026thinsp;3) and poor outcomes (mRS scores\u0026thinsp;\u0026ge;\u0026thinsp;3)(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Covariates\u003c/h2\u003e \u003cp\u003eBased on previous reports and clinical expertise, covariates were selected(\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). There were two categories of variables: 1. Categorical: sex, age, hypertension, diabetes mellitus (DM), hyperlipidemia, smoking, previous stroke or transient ischemic attack (TIA), atrial fibrillation (AF), coronary heart disease (CHD), and stroke etiology. 2. Continuous: white blood cell count (WBC), red blood cell count (RBC), hematocrit (HCT), hemoglobin concentration (HGB), platelet count (PLT), fasting blood glucose (FBG), serum creatinine (Scr), aspartate aminotransferase (AST), alanine aminotransferase (ALT), serum low-density lipoprotein cholesterol (LDL-c), serum high-density lipoprotein cholesterol (HDL-c), serum triglycerides (TG), serum cholesterol (TC), fibrinogen (FIB), and National Institutes of Health Stroke Scale (NIHSS) score(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Data from electronic medical records were collected, and tests were conducted within 24 hours of admission(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Trial of Org 10172 in Acute Stroke Treatment criteria were used to classify stroke subtypes(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Missing data processing\u003c/h2\u003e \u003cp\u003eA total of 1 (0.05%) participant had missing data for WBC, 107 (5.61%) for TG, 1 (0.05%) for TC, 75 (3.93%) for LDL-c, 99 (5.19%) for HDL-c, 139 (7.29%) for FBG, and 22 (1.15%) for FIB. Multiple imputations were used to manage missing covariate data(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Variables were included in the imputation model such as age, sex, WBC, RBC, HCT, HGB, MCV, MCH, PLT, Scr, LDL-c, HDL-c, TG, TC, AST, ALT, FBG, HbA1c, FIB, BMI, NIHSS score, previous stroke or TIA, hyperlipidemia, hypertension, DM, smoking, AF, CHD, and stroke etiology. Under the assumption of missing at random (MAR), missing data analysis was conducted(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Statistical analysis\u003c/h2\u003e \u003cp\u003eMeans (standard deviations) or median (range) for non-normally distributed data were presented for continuous variables. Numbers (%) of participants were presented for categorical variables. A one-way analysis of variance was used to examine differences between BUN/ALB ratio groups with normally distributed continuous variables, the χ2 method for categorical variables, and the Kruskal-Wallis H test for skewed continuous variables.\u003c/p\u003e \u003cp\u003e \u003cem\u003e2.7.1. To analyze the independent linear relationship of the BUN/ALB ratio and 3-month poor outcomes in patients with AIS\u003c/em\u003e \u003c/p\u003e \u003cp\u003eTo further elucidate the impact of the BUN/ALB ratio on 3-month poor prognosis, we scaled down the ratio by a factor of 10 in the multivariate analysis. This adjustment allowed for a more granular examination of its association with outcomes. As a result of screening for collinearity (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, where HGBs, HCTs, and TCs were omitted), three models were constructed to assess the relationship between the BUN/ALB ratio and 3-month poor outcomes in AIS patients(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). The models included: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) a non-adjusted model (no covariates were adjusted); (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) a minimally-adjusted model (Model I: adjusted for sex, age, hyperlipidemia, hypertension, DM, AF, and CHD); and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) a fully-adjusted model (Model II: adjusted for age, sex, WBC, RBC, PLT, Scr, AST, ALT, TG, LDL-c, HDL-c, FBG, FIB, hyperlipidemia, hypertension, DM, smoking, AF, CHD, previous stroke or TIA, NIHSS score, and stroke etiology). Changes in the odds ratio (OR) were noted when adjusting for covariates, with adjustments made if the OR changed by 10% or more(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Effect sizes were reported with 95% confidence intervals (95% CI)\u003c/p\u003e \u003cp\u003e \u003cem\u003e2.7.2. To analyze the nonlinear relationship of the BUN/ALB ratio and 3-month poor outcomes in patients with AIS\u003c/em\u003e \u003c/p\u003e \u003cp\u003eTo address concerns about the ability of binary logistic regression models to handle nonlinear relationships, generalized additive models (GAM) and smooth curve fitting using the penalized spline method were employed to investigate the potential nonlinear relationship between the BUN/ALB ratio and 3-month poor outcomes. Whenever nonlinearity was detected, a recursive algorithm was used to calculate the inflection point, followed by establishing a binary logistic regression model on both sides of the inflection point. By using the log-likelihood ratio test, the appropriateness of the BUN/ALB ratio model was examined(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.7.3. Sensitivity analysis\u003c/h2\u003e \u003cp\u003eWe conducted a sensitivity analysis to assess the robustness of our findings. Since smoking has been shown to significantly elevate the risk of poor outcomes in patients and is a modifiable risk factor(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), we excluded smoking from our analysis.\u003c/p\u003e \u003cp\u003eStatistical analyses were performed using the packages R (R Foundation)2 and EmpowerStats3 (X\u0026amp;Y Solutions, Inc., Boston, MA) in accordance with the STROBE statement(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). \u003cem\u003eStatistical significance was defined as a P-value less than 0.05 (two-sided).\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cem\u003e3.1. Characteristics of Individuals\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study included 1868 individuals, whose characteristics are presented in Table 1. There was a skewed distribution of BUN/ALB ratios, ranging from 0.114 to 1.250, with a median (Q1-Q3) of 0.385 (0.305-0.488) (Figure 2). Of these, 1143 (61.19%) were male. The distribution of age groups was as follows: \u0026lt; 60 years: 431 (23.07%), 60 to \u0026lt; 70 years: 496 (26.55%), 70 to \u0026lt; 80 years: 651 (34.85%), and\u0026nbsp;\u0026ge;\u0026nbsp;80 years: 290 (15.52%). The NIHSS scores ranged from 0 to 33, with a median (interquartile range) of 3 (1, 7). Stroke etiology classification revealed that 354 (18.95%) individuals had SVO, 597 (31.96%) had LAA, 483 (25.86%) had CE, 168 (8.99%) had other determined etiology, and 266 (14.24%) had undetermined etiology. Compared to group Q1, individuals in group Q4 showed significantly higher levels of male gender, older age, BUN, Scr, NIHSS score, previous stroke, hypertension, DM, AF, and CHD. Conversely, RBC, HGB, HCT, PLT, ALB, LDL-c, HDL-c, and TC were lower in group Q4. Furthermore, AIS in the Q4 group was more likely to be caused by LAA or CE.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Baseline Characteristics of participants (N =1868)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"101%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBUN/ALB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1(\u003c/strong\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.305)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ2(0.306-0.384)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3(0.385-0.488)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ4(\u0026ge;0.489)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" valign=\"top\"\u003e\n \u003cp\u003eParticipants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003eSex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e250 (53.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e292 (63.34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e293 (62.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e308 (65.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e217 (46.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e169 (36.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e178 (37.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e161 (34.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003eAge(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e＜60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e186 (39.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e111 (24.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e77 (16.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e57 (12.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e60-70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e124 (26.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e141 (30.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e126 (26.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e105 (22.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e70-80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e120 (25.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e140 (30.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e194 (41.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e197 (42.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e\u0026ge;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e37 (7.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e69 (14.97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e74 (15.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e110 (23.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" valign=\"top\"\u003e\n \u003cp\u003eWBC(\u0026times;109/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e7.90 \u0026plusmn; 2.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e8.02 \u0026plusmn; 2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e8.08 \u0026plusmn; 2.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e8.49 \u0026plusmn; 3.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" valign=\"top\"\u003e\n \u003cp\u003eRBC(\u0026times;1012/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e4.49 \u0026plusmn; 0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e4.47 \u0026plusmn; 0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e4.36 \u0026plusmn; 0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e4.07 \u0026plusmn; 0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" valign=\"top\"\u003e\n \u003cp\u003eHGB(g/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e13.96 \u0026plusmn; 1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e13.92 \u0026plusmn; 1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e13.58 \u0026plusmn; 1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e12.72 \u0026plusmn; 2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" valign=\"top\"\u003e\n \u003cp\u003eHCT (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e41.33 \u0026plusmn; 5.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e41.32 \u0026plusmn; 4.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e40.43 \u0026plusmn; 4.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e37.97 \u0026plusmn; 6.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" valign=\"top\"\u003e\n \u003cp\u003ePLT(\u0026times;109/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e235.00 \u0026plusmn; 64.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e228.11 \u0026plusmn; 64.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e220.70 \u0026plusmn; 68.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e211.78 \u0026plusmn; 78.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" valign=\"top\"\u003e\n \u003cp\u003eBUN (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e10.58 \u0026plusmn; 1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e13.93 \u0026plusmn; 1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e17.39 \u0026plusmn; 1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e25.18 \u0026plusmn; 6.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" valign=\"top\"\u003e\n \u003cp\u003eScr(mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.79 \u0026plusmn; 0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.85 \u0026plusmn; 0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e0.94 \u0026plusmn; 0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e1.40 \u0026plusmn; 1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" valign=\"top\"\u003e\n \u003cp\u003eAST(U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e23.00 (19.00-29.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e23.00 (19.00-30.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e23.00 (19.00-29.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e23.00 (18.00-30.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" valign=\"top\"\u003e\n \u003cp\u003eALT(U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e19.00 (14.00-27.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e18.00 (14.00-26.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e18.00 (13.00-26.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e18.00 (12.00-26.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" valign=\"top\"\u003e\n \u003cp\u003eALB(g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e42.08 \u0026plusmn; 3.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e40.83 \u0026plusmn; 3.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e40.27 \u0026plusmn; 3.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e38.16 \u0026plusmn; 4.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" valign=\"top\"\u003e\n \u003cp\u003eLDL-c(mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e114.09 \u0026plusmn; 38.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e112.47 \u0026plusmn; 38.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e106.60 \u0026plusmn; 36.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e98.51 \u0026plusmn; 35.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" valign=\"top\"\u003e\n \u003cp\u003eHDL-c(mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e47.40 \u0026plusmn; 14.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e46.58 \u0026plusmn; 14.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e45.54 \u0026plusmn; 13.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e44.36 \u0026plusmn; 14.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" valign=\"top\"\u003e\n \u003cp\u003eTG (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e111.49 \u0026plusmn; 57.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e116.37 \u0026plusmn; 60.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e111.15 \u0026plusmn; 56.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e105.80 \u0026plusmn; 54.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" valign=\"top\"\u003e\n \u003cp\u003eTC (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e188.09 \u0026plusmn; 42.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e185.38 \u0026plusmn; 43.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e178.13 \u0026plusmn; 40.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e168.96 \u0026plusmn; 44.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" valign=\"top\"\u003e\n \u003cp\u003eFIB (mg/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e315.84 \u0026plusmn; 68.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e328.97 \u0026plusmn; 73.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e332.27 \u0026plusmn; 82.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e354.09 \u0026plusmn; 100.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" valign=\"top\"\u003e\n \u003cp\u003eFBG (mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e5.68 \u0026plusmn; 1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e5.84 \u0026plusmn; 2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e5.98 \u0026plusmn; 2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e6.17 \u0026plusmn; 2.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003eNIHSS score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e3.00 (1.00-6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e3.00 (1.00-6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e3.00 (1.00-7.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e4.00 (2.00-10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003ePrevious stroke/TIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e75 (16.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e82 (17.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e117 (24.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e118 (25.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003eHyperlipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e180 (38.54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e187 (40.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e164 (34.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e159 (33.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e261 (55.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e263 (57.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e309 (65.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e349 (74.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e109 (23.34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e124 (26.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e157 (33.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e199 (42.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e183 (39.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e192 (41.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e191 (40.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e171 (36.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.401\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003eAF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e69 (14.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e89 (19.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e107 (22.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e133 (28.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003eCHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e31 (6.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e49 (10.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e56 (11.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e76 (16.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003eStroke etiology\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003eSVO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e94 (20.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e104 (22.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e90 (19.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e66 (14.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003eLAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e165 (35.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e143 (31.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e151 (32.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e138 (29.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003eCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e80 (17.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e112 (24.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e132 (28.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e159 (33.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003eOther determined\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e53 (11.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e41 (8.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e34 (7.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e40 (8.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003eUndetermined\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e75 (16.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e61 (13.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e64 (13.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e66 (14.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003emRS\u0026nbsp;\u0026ge;\u0026nbsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e119 (25.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e105 (22.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e133 (28.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e173 (36.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSD, standard deviation; n, number.\u003c/p\u003e\n\u003cp\u003eWBC, white blood cell; RBC, red blood cell; HGB, hemoglobin concentration; HCT, hematocrit; PLT, platelet; BUN, blood urea nitrogen; Scr, serum creatinine; AST, aspartate aminotransferase; ALT, alanine aminotransferase; LDL-c, low-density lipoproteins cholesterol; HDL-c, high-density lipoprotein cholesterol; TG, triglyceride; TC, total cholesterol; FIB, fibrinogen; FBG, fasting blood glucose; NIHSS, national institute of health stroke scale; TIA, transient ischemia attack; DM, diabetes mellitus; AF, atrial fibrillation; CHD, coronary heart disease; LAA, large artery atherosclerosis; SVO, small vessel occlusion; CE, cardio embolism; mRS, modified 3-month Rankin Scale\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.2. The incidence rate of poor outcomes 3‑month after AIS\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Table 2, 530 of the 1868 participants had poor outcomes, which had an overall incidence of 28.37% (95% CI: 26.33% - 30.42%). Specifically, the incidence of poor outcomes in each quartile of the BUN/ALB ratio was as follows: Q1: 25.48% (95% CI: 21.52% - 29.45%), Q2: 22.78% (95% CI: 18.93% - 26.61%), Q3: 28.24% (95% CI: 24.16% - 32.32%), and Q4: 36.89% (95% CI: 32.50% - 41.27%). Figure 3 shows the pattern of poor outcomes by quarter: Q4 \u0026gt; Q3 \u0026gt; Q1 \u0026gt; Q2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Incidence rate of unfavorable outcome 3-month after stroke\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.684684684684683%\" valign=\"top\"\u003e\n \u003cp\u003eBUN/ALB ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.045045045045047%\" valign=\"top\"\u003e\n \u003cp\u003eParticipants(n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.864864864864863%\" valign=\"top\"\u003e\n \u003cp\u003eunfavorable outcome events\u003c/p\u003e\n \u003cp\u003e(mRS score\u0026ge;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.405405405405407%\" valign=\"top\"\u003e\n \u003cp\u003eIncidence of\u003c/p\u003e\n \u003cp\u003eunfavorable outcome\u003c/p\u003e\n \u003cp\u003e(95% CI) (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.684684684684683%\" valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.045045045045047%\" valign=\"top\"\u003e\n \u003cp\u003e1868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.864864864864863%\" valign=\"top\"\u003e\n \u003cp\u003e530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.405405405405407%\" valign=\"top\"\u003e\n \u003cp\u003e28.37(26.33,30,42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.684684684684683%\" valign=\"top\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.045045045045047%\" valign=\"top\"\u003e\n \u003cp\u003e467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.864864864864863%\" valign=\"top\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.405405405405407%\" valign=\"top\"\u003e\n \u003cp\u003e25.48(21.52,29,45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.684684684684683%\" valign=\"top\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.045045045045047%\" valign=\"top\"\u003e\n \u003cp\u003e461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.864864864864863%\" valign=\"top\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.405405405405407%\" valign=\"top\"\u003e\n \u003cp\u003e22.78(18.93,26.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.684684684684683%\" valign=\"top\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.045045045045047%\" valign=\"top\"\u003e\n \u003cp\u003e471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.864864864864863%\" valign=\"top\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.405405405405407%\" valign=\"top\"\u003e\n \u003cp\u003e28.24(24.16,32.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.684684684684683%\" valign=\"top\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.045045045045047%\" valign=\"top\"\u003e\n \u003cp\u003e469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.864864864864863%\" valign=\"top\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.405405405405407%\" valign=\"top\"\u003e\n \u003cp\u003e36.89(32.50,41.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.3. The results of univariate analyses using a binary logistic regression model\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn univariate analyses, PLT, Scr, HDL-c, and CHD did not significantly predict 3-month poor outcomes (all P\u0026gt;0.05). However, they showed positive associations with WBC (OR = 1.08, 95% CI: 1.05-1.12), BUN (OR = 1.02, 95% CI: 1.01-1.04), AST (OR = 1.01, 95% CI: 1.00-1.02), FBG (OR = 1.15, 95% CI: 1.10-1.21), and BUN/ALB ratio (OR = 4.39, 95% CI: 2.58-7.49) (all P \u0026lt; 0.05). Furthermore, being female (OR = 1.68, 95% CI: 1.37-2.06), age \u0026gt; 80 years old (OR = 4.03, 95% CI: 2.89-5.62), previous stroke or TIA (OR = 1.79, 95% CI: 1.41-2.26), hypertension (OR = 1.35, 95% CI: 1.09-1.66), DM (OR = 1.43, 95% CI: 1.16-1.76), AF (OR = 2.06, 95% CI: 1.63-2.59), NIHSS score\u0026nbsp;\u0026ge;\u0026nbsp;14 (OR = 17.31, 95% CI: 12.27-24.41), and stroke etiology of LAA (OR = 1.67, 95% CI: 1.21-2.31), CE (OR = 2.46, 95% CI: 1.77-3.42), or other determined (OR = 3.48, 95% CI: 2.32-5.24) were associated with higher likelihood of experiencing 3-month poor outcomes (all P \u0026lt; 0.05). Conversely, RBC (OR = 0.57, 95% CI: 0.49-0.68), ALB (OR = 0.26, 95% CI: 0.20-0.34), LDL-c (OR = 1.00, 95% CI: 0.99-1.00), TG (OR = 1.00, 95% CI: 0.99-1.00), hyperlipidemia (OR = 0.79, 95% CI: 0.64-0.97), and smoking (OR = 0.61, 95% CI: 0.49-0.75) were negatively associated with the risk of 3-month poor outcomes (all P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eTable 3. Univariate regression analysis of factors influencing poor outcomes in AIS\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\" valign=\"top\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\" valign=\"top\"\u003e\n \u003cp\u003eStatistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\" valign=\"top\"\u003e\n \u003cp\u003eOR95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003eWBC(\u0026times;109/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\" valign=\"top\"\u003e\n \u003cp\u003e8.12 \u0026plusmn; 2.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\" valign=\"top\"\u003e\n \u003cp\u003e1.08 (1.05, 1.12)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003eRBC(\u0026times;1012/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\" valign=\"top\"\u003e\n \u003cp\u003e4.34 \u0026plusmn; 0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\" valign=\"top\"\u003e\n \u003cp\u003e0.57 (0.49, 0.68)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003ePLT(\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\" valign=\"top\"\u003e\n \u003cp\u003e223.87 \u0026plusmn; 69.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (1.00, 1.00)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e0.3502\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003eBUN (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\" valign=\"top\"\u003e\n \u003cp\u003e16.79 \u0026plusmn; 6.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\" valign=\"top\"\u003e\n \u003cp\u003e1.02 (1.01, 1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e0.0040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003eScr(mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 \u0026plusmn; 0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\" valign=\"top\"\u003e\n \u003cp\u003e1.07 (0.93, 1.23)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e0.3625\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003eAST(U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\" valign=\"top\"\u003e\n \u003cp\u003e26.14 \u0026plusmn; 14.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\" valign=\"top\"\u003e\n \u003cp\u003e1.01 (1.00, 1.02)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e0.0115\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003eALT(U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\" valign=\"top\"\u003e\n \u003cp\u003e22.43 \u0026plusmn; 15.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\" valign=\"top\"\u003e\n \u003cp\u003e0.99 (0.98, 1.00)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e0.0239\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\" valign=\"top\"\u003e\n \u003cp\u003eALB(g/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\" valign=\"top\"\u003e\n \u003cp\u003e40.33 \u0026plusmn; 4.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\" valign=\"top\"\u003e\n \u003cp\u003e0.26 (0.20, 0.34)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\" valign=\"top\"\u003e\n \u003cp\u003eLDL-c(mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\" valign=\"top\"\u003e\n \u003cp\u003e107.89 \u0026plusmn; 37.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (0.99, 1.00)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\"\u003e\n \u003cp\u003e0.0226\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\" valign=\"top\"\u003e\n \u003cp\u003eHDL-c(mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\" valign=\"top\"\u003e\n \u003cp\u003e45.96 \u0026plusmn; 14.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (0.99, 1.00)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\"\u003e\n \u003cp\u003e0.1821\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\" valign=\"top\"\u003e\n \u003cp\u003eTG (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\" valign=\"top\"\u003e\n \u003cp\u003e111.18 \u0026plusmn; 57.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (0.99, 1.00)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\" valign=\"top\"\u003e\n \u003cp\u003eFIB (mg/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\" valign=\"top\"\u003e\n \u003cp\u003e332.83 \u0026plusmn; 83.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (1.00, 1.00)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003eFBG (mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\" valign=\"top\"\u003e\n \u003cp\u003e5.92 \u0026plusmn; 2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\" valign=\"top\"\u003e\n \u003cp\u003e1.15 (1.10, 1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003eBUN/ALB ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\" valign=\"top\"\u003e\n \u003cp\u003e0.42 \u0026plusmn; 0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\" valign=\"top\"\u003e\n \u003cp\u003e4.39 (2.58, 7.49)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003eSEX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\"\u003e\n \u003cp\u003e1143 (61.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\"\u003e\n \u003cp\u003e725 (38.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\"\u003e\n \u003cp\u003e1.68 (1.37, 2.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003eAge (years), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003e\u0026lt;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\"\u003e\n \u003cp\u003e431 (23.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003e60-70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\"\u003e\n \u003cp\u003e496 (26.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\"\u003e\n \u003cp\u003e1.20 (0.87, 1.65)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e0.2683\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003e70-80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\"\u003e\n \u003cp\u003e651 (34.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\"\u003e\n \u003cp\u003e1.86 (1.39, 2.49)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003e\u0026ge;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\"\u003e\n \u003cp\u003e290 (15.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\"\u003e\n \u003cp\u003e4.03 (2.89, 5.62)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003ePrevious stroke/TIA, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\"\u003e\n \u003cp\u003e1476 (79.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\"\u003e\n \u003cp\u003e392 (20.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\"\u003e\n \u003cp\u003e1.79 (1.41, 2.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\" valign=\"top\"\u003e\n \u003cp\u003eHyperlipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e0.0274\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\" valign=\"top\"\u003e\n \u003cp\u003e1178 (63.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\" valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\" valign=\"top\"\u003e\n \u003cp\u003e690 (36.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\" valign=\"top\"\u003e\n \u003cp\u003e0.79 (0.64, 0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\"\u003e\n \u003cp\u003e686 (36.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\"\u003e\n \u003cp\u003e1182 (63.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\"\u003e\n \u003cp\u003e1.35 (1.09, 1.66)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e0.0064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\"\u003e\n \u003cp\u003e1279 (68.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\"\u003e\n \u003cp\u003e589 (31.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\"\u003e\n \u003cp\u003e1.43 (1.16, 1.76)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e0.0010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003eSmoking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\" valign=\"top\"\u003e\n \u003cp\u003e1131 (60.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\" valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\" valign=\"top\"\u003e\n \u003cp\u003e737 (39.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\" valign=\"top\"\u003e\n \u003cp\u003e0.61 (0.49, 0.75)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003eAF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\"\u003e\n \u003cp\u003e1470 (78.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\"\u003e\n \u003cp\u003e398 (21.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\"\u003e\n \u003cp\u003e2.06 (1.63, 2.59)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003eCHD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\"\u003e\n \u003cp\u003e1656 (88.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\"\u003e\n \u003cp\u003e212 (11.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\"\u003e\n \u003cp\u003e0.97 (0.71, 1.33)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e0.8524\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003eNIHSS score, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026lt;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\"\u003e\n \u003cp\u003e1277 (68.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026gt;=6, \u0026lt;14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\"\u003e\n \u003cp\u003e378 (20.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\"\u003e\n \u003cp\u003e6.58 (5.09, 8.50)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026gt;=14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\"\u003e\n \u003cp\u003e213 (11.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\"\u003e\n \u003cp\u003e17.31 (12.27, 24.41)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003eStroke etiology, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003eSVO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\" valign=\"top\"\u003e\n \u003cp\u003e354 (18.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\" valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003eLAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\" valign=\"top\"\u003e\n \u003cp\u003e597 (31.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\" valign=\"top\"\u003e\n \u003cp\u003e1.676(1.20, 2.30)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e0.0023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003eCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\" valign=\"top\"\u003e\n \u003cp\u003e483 (25.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\" valign=\"top\"\u003e\n \u003cp\u003e2.46 (1.77, 3.42)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003eOther determined\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\" valign=\"top\"\u003e\n \u003cp\u003e168 (8.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\" valign=\"top\"\u003e\n \u003cp\u003e3.48 (2.32, 5.24)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.040564373897706%\"\u003e\n \u003cp\u003eUndetermined\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.984126984126984%\" valign=\"top\"\u003e\n \u003cp\u003e266(14.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.27689594356261%\" valign=\"top\"\u003e\n \u003cp\u003e1.41(0.95, 2.08)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e0.0878\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWBC, white blood cell; RBC, red blood cell; PLT, platelet; BUN, blood urea nitrogen; Scr, serum creatinine; AST, aspartate aminotransferase; ALT, alanine aminotransferase; LDL-c, low-density lipoproteins cholesterol; HDL-c, high-density lipoprotein cholesterol; TG, triglyceride; TC, total cholesterol; FIB, fibrinogen; FBG, fasting blood glucose; NIHSS, national institute of health stroke scale; TIA, transient ischemia attack; DM, diabetes mellitus; AF, atrial fibrillation; CHD, coronary heart disease; LAA, large artery atherosclerosis; SVO, small vessel occlusion; CE, cardio embolism; mRS, modified 3-month Rankin Scale.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.4.\u003c/em\u003e \u003cem\u003eMultivariate analyses using binary logistic regression\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThree logistic regression models were constructed (Table 4) to explore the relationship between BUN/ALB ratio and poor 3-month outcomes. In the univariate analysis, the odds ratio (OR) for the BUN/ALB ratio appears to be disproportionately large. To address this issue and achieve more interpretable results in the multivariate analysis, we scaled down the BUN/ALB ratio by a factor of 10. In the unadjusted model, a 0.1-unit increase in the BUN/ALB ratio was associated with a 16% increase in the risk of 3-month poor outcomes (OR = 1.16, 95% CI: 1.10-1.22, P \u0026lt; 0.0001). After adjusting for sex, age, hyperlipidemia, hypertension, DM, smoking, AF, and CHD (model I), a 0.1-unit increase in the BUN/ALB ratio corresponded to a 10% increase in the risk of 3-month poor outcomes (OR = 1.10, 95% CI: 1.04-1.17, P = 0.0009). However, in the fully adjusted model (model II), which included additional covariates such as WBC, RBC, PLT, AST, ALT, TG, LDL-c, HDL-c, Scr, FIB, FBG, previous stroke or TIA, NIHSS score, and stroke etiology, there was no significant correlation between BUN/ALB ratio and 3-month poor outcomes (OR = 1.01, 95% CI: 0.93-1.10, P = 0.7901). Furthermore, when the BUN/ALB ratio was categorized into quartiles, there was no statistically significant trend observed in the 3-month poor outcomes rate across the Q1 to Q4 groups in both model I (P for trend = 0.0748) and model II (P for trend = 0.3719). These findings suggest that the BUN/ALB ratio was statistically nonsignificant after adjusting for various confounding factors in patients with AIS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Relationship between BUN/ALB ratio and poor outcome 3-month after stroke\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"633\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.118483412322274%\" valign=\"top\"\u003e\n \u003cp\u003eExposure\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.96050552922591%\" valign=\"top\"\u003e\n \u003cp\u003eCrude model (OR, 95%CI, \u003cem\u003eP\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.96050552922591%\" valign=\"top\"\u003e\n \u003cp\u003eModel I (OR, 95%CI, \u003cem\u003eP\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.96050552922591%\" valign=\"top\"\u003e\n \u003cp\u003eModel II (OR, 95%CI, \u003cem\u003eP\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.118483412322274%\" valign=\"top\"\u003e\n \u003cp\u003eBUN/ALB ratio\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.96050552922591%\" valign=\"top\"\u003e\n \u003cp\u003e1.16 (1.10, 1.22) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.96050552922591%\" valign=\"top\"\u003e\n \u003cp\u003e1.10 (1.04, 1.17) 0.0009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.96050552922591%\" valign=\"top\"\u003e\n \u003cp\u003e1.01 (0.93, 1.10) 0.7901\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.118483412322274%\" valign=\"top\"\u003e\n \u003cp\u003eBUN/ALB ratio (quartile)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.96050552922591%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.96050552922591%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.96050552922591%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.118483412322274%\" valign=\"top\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.96050552922591%\" valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.96050552922591%\" valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.96050552922591%\" valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.118483412322274%\" valign=\"top\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.96050552922591%\" valign=\"top\"\u003e\n \u003cp\u003e0.86 (0.64, 1.17) 0.3358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.96050552922591%\" valign=\"top\"\u003e\n \u003cp\u003e0.78 (0.57, 1.07) 0.1254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.96050552922591%\" valign=\"top\"\u003e\n \u003cp\u003e0.64 (0.44, 0.93) 0.0199\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.118483412322274%\" valign=\"top\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.96050552922591%\" valign=\"top\"\u003e\n \u003cp\u003e1.15 (0.86, 1.54) 0.3412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.96050552922591%\" valign=\"top\"\u003e\n \u003cp\u003e0.93 (0.68, 1.27) 0.6646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.96050552922591%\" valign=\"top\"\u003e\n \u003cp\u003e0.80 (0.56, 1.15) 0.2369\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.118483412322274%\" valign=\"top\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.96050552922591%\" valign=\"top\"\u003e\n \u003cp\u003e1.71 (1.29, 2.26) 0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.96050552922591%\" valign=\"top\"\u003e\n \u003cp\u003e1.24 (0.91, 1.69) 0.1744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.96050552922591%\" valign=\"top\"\u003e\n \u003cp\u003e0.76 (0.52, 1.13) 0.1776\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.118483412322274%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.96050552922591%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.96050552922591%\" valign=\"top\"\u003e\n \u003cp\u003e0.0748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.96050552922591%\" valign=\"top\"\u003e\n \u003cp\u003e0.3719\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: CI, confidence interval.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModel Ⅰ adjusted for sex, age, hyperlipidemia, hypertension, DM, Smoking, AF, and CHD.\u003c/p\u003e\n\u003cp\u003eModel Ⅱ adjusted for adjusted age, sex, WBC, RBC, PLT, AST, ALT, TG, LDL-c, HDL-c, Scr, FIB, FBG, hyperlipidemia, hypertension, DM, smoking, AF, CHD, previous stroke or TIA, NIHSS score, and stroke etiology.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.5. The nonlinearity addressed by the GAM\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eGAM analysis revealed a nonlinear relationship between the BUN/ALB ratio and the risk of poor outcomes in AIS patients (Figure 4a). After adjusting for various covariates, including age, sex, WBC, RBC, PLT, AST, ALT, TG, LDL-c, HDL-c, Scr, FIB, FBG, hyperlipidemia, hypertension, DM, smoking, AF, CHD, previous stroke or TIA, NIHSS score, and stroke etiology, the log-likelihood ratio test indicated a significant nonlinear relationship (P = 0.0418). By employing a two-stage linear regression model, we determined the inflection point of the BUN/ALB ratio to be 0.326. For patients with a BUN/ALB ratio \u0026le; 0.326, a 0.1-unit decrease in the ratio was associated with a 42% increase in the risk of 3-month poor outcomes (OR=0.58, 95%CI: 0.40-0.83, P = 0.0033). However, above the threshold, while elevated BUN/ALB ratios tended to be linked with a higher risk of poor outcomes, the association was not statistically significant (OR: 1.08, 95% CI: 0.99-1.19, P = 0.0944) (Table 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e. The result of the two-piecewise linear regression model.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"553\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.96383363471971%\" valign=\"top\"\u003e\n \u003cp\u003eUnfavorable outcome:\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.018083182640144%\" valign=\"top\"\u003e\n \u003cp\u003eModel I (OR, 95%CI, \u003cem\u003eP\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.018083182640144%\" valign=\"top\"\u003e\n \u003cp\u003eModel II (OR, 95%CI, P)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.96383363471971%\" valign=\"top\"\u003e\n \u003cp\u003eFitting model by standard linear regression\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.018083182640144%\" valign=\"top\"\u003e\n \u003cp\u003e1.01 (0.93, 1.10) 0.7901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.018083182640144%\" valign=\"top\"\u003e\n \u003cp\u003e1.04 (0.94, 1.16) 0.4520\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.96383363471971%\" valign=\"top\"\u003e\n \u003cp\u003eFitting model by two-piecewise regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.018083182640144%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.018083182640144%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.96383363471971%\" valign=\"top\"\u003e\n \u003cp\u003eInflection point of BUN/ALB ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.018083182640144%\" valign=\"top\"\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.018083182640144%\" valign=\"top\"\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.96383363471971%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026le;Inflection point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.018083182640144%\"\u003e\n \u003cp\u003e0.58 (0.40, 0.83) 0.0033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.018083182640144%\"\u003e\n \u003cp\u003e0.35 (0.19, 0.64) 0.0007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.96383363471971%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt; Inflection point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.018083182640144%\"\u003e\n \u003cp\u003e1.08 (0.99, 1.19) 0.0944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.018083182640144%\"\u003e\n \u003cp\u003e1.13 (1.01, 1.27) 0.0342\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.96383363471971%\" valign=\"top\"\u003e\n \u003cp\u003eP for log-likelihood ratio test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.018083182640144%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.018083182640144%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e3.6. Sensitivity Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn the sensitivity analysis, where participants with smoking were excluded (n=1131), the nonlinear relationship between the BUN/ALB ratio and 3-month poor outcomes persisted. After adjusting for age, sex, RBC, PLT, Scr, AST, ALT, TG, LDL-c, HDL-c, HBA1c, hypertension, DM, AF, CHD, previous stroke or TIA, NIHSS score, and stroke etiology, the GAM analysis revealed a significant nonlinear relationship (Figure 4b). Using a recursive algorithm, the inflection point was determined to be 0.295. Subsequently, a two-stage binary logistic regression model was employed to calculate the odds ratios (OR) and confidence intervals (CI) around the inflection point. The further the BUN/ALB ratio deviated from the inflection point, the greater the risk for adverse outcomes on both sides (all P\u0026lt;0.05) (Table 5).\u0026nbsp;\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn our study, we examined how the BUN/ALB ratio correlated with 3-month outcomes in AIS patients. Our findings revealed a nonlinear relationship between the BUN/ALB ratio at admission and 3-month outcomes. Specifically, for all AIS patients, the inflection point of the BUN/ALB ratio is 0.326, while for non-smoking AIS patients, the inflection point of the BUN/ALB ratio is 0.295, there is a significant difference in the probability of adverse outcomes at 3 months. These findings highlight the importance of considering the BUN/ALB ratio as a potential prognostic marker in AIS patients, with implications for risk stratification and clinical management.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; According to previous studies(12, 15, 16, 34, 35), BUN demonstrates greater predictive power than Scr and eGFR for critically ill patients. However, recent findings indicate that the BUN/ALB ratio may offer even stronger predictive capabilities for certain patient populations, including those with respiratory diseases, sepsis, and chronic heart failure\u0026nbsp;(18-23). In our study, patients with an elevated BUN/ALB ratio (Q4\u0026ge;0.489) demonstrated a significantly increased risk of poor outcomes at 3 months (P\u0026lt;0.001). Initially, BUN/ALB ratios were identified as risk factors for poor outcomes at 3 months based on univariate analysis. Nevertheless, multivariate regression analysis (model II) did not reveal that the BUN/ALB ratio was an independent risk factor. Upon categorizing the BUN/ALB ratio and analyzing the data (refer to Figure 2 and Table 2), it became evident that the incidence of poor outcomes followed the order: Q 4 \u0026gt; Q 3 \u0026gt; Q 1 \u0026gt; Q 2. In addition, Table 4 shows that poor outcomes did not exhibit a linear trend from Q1 to Q4 (P for trend was 0.0748 in model I and 0.3719 in model II). These findings suggest that a linear relationship may not adequately capture the correlation between the BUN/ALB ratio and poor outcomes at 3 months.\u003c/p\u003e\n\u003cp\u003eIn this study, researchers examined the relationship between BUN/ALB ratio and poor outcomes by using logistic regression with cubic spline functions and smooth curve fitting (cubic spline smoothing). A nonlinear relationship was found between the BUN/ALB ratio and poor outcomes after 3 months. Specifically, an inflection point of 0.326 was identified, leading the authors to conduct further analysis using two piecewise logistic regression models to calculate the odds ratio (OR) and confidence intervals (CI) around this inflection point. A 0.1-unit decrease in the BUN/ALB ratio on the left side of the inflection point was associated with a 42% increase in poor outcomes after 3 months (OR=0.58, 95%CI: 0.40-0.83, P=0.0033). However, this relationship was not statistically significant on the right side of the inflection point (\u0026gt;0.326). Given the difficulty in intervening in factors such as previous hypertension, diabetes, and heart disease when a stroke occurs, the authors conducted a subgroup analysis excluding smoking patients. After this exclusion, the inflection point shifted slightly to 0.295. Notably, the relationship demonstrated statistical significance on both sides of the inflection point (all P\u0026lt;0.05) in the subgroup analysis. This similarity in the curve pattern between the subgroup and the entire participant cohort, along with the proximity of the inflection point to that of all participants, underscores the robustness of the observed nonlinear relationship between the BUN/ALB ratio and 3-month poor outcomes.\u003c/p\u003e\n\u003cp\u003eAlthough data regarding the BUN/ALB ratio are scarce, several potential explanations can be posited as to why there is a nonlinear relationship between the ratio and poor outcomes at 3-months in AIS patients. BUN serves as a crucial marker of renal function, with numerous studies indicating its robust predictive value in critically ill patients, sometimes surpassing Scr and eGFR(12, 16). This is attributed to its susceptibility to various factors such as age, protein intake, bleeding, catabolism, and hemodynamics. On one hand, low BUN levels may signify inadequate protein intake or malnutrition(36), potentially impeding neurological repair in AIS patients. Given the acute metabolic stress state associated with AIS, characterized by heightened energy demands(37)\u0026nbsp;, reduced BUN levels might deprive patients of the necessary substrate for early neurological recovery. Conversely, elevated BUN levels may reflect deteriorating hemodynamics(38) , which are known to be pivotal in poor stroke prognosis and mortality(39, 40). Albumin, beyond its role in reflecting patients\u0026apos; nutritional status, exerts multiple physiological effects, including regulation of colloidal osmolality, binding and transportation of various substances in blood, antioxidant properties, nitric oxide regulation, and buffering capacity. While low serum albumin levels are commonly associated with worse prognosis in critically ill patients, the efficacy of albumin administration remains contentious(41). Hence, maintaining a proper albumin concentration is crucial. These factors likely contribute to the inverse correlation observed between the BUN/ALB ratio and the likelihood of 3-month poor outcomes on the left side of the inflection point in our study. Conversely, the lack of statistical significance on the right side of the inflection point may be attributed to worsening patient condition and other confounding factors assuming greater importance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study presents several noteworthy findings. Firstly, it offers a fresh perspective on clinical prognosis evaluation among AIS patients by investigating the nonlinear association between the BUN/ALB ratio and 3-month poor outcomes. Secondly, the utilization of multiple imputation techniques to address missing data enhances statistical robustness and diminishes potential bias, augmenting the credibility of the findings. Thirdly, the robustness of the results was confirmed through meticulous subgroup analyses, further bolstering their reliability and generalizability. Moreover, the practicality of employing the BUN/ALB ratio is underscored by its status as an objective, cost-effective laboratory parameter readily accessible in clinical settings, thereby enhancing its utility in prognostic assessment.\u003c/p\u003e\n\u003cp\u003eThis study is subject to several limitations that warrant acknowledgment. Firstly, the inclusion of AIS patients solely from a single center in South Korea may restrict the generalizability of the findings, necessitating validation across diverse geographic and ethnic populations. Secondly, akin to inherent limitations in all observational studies, unmeasured or uncontrolled confounding variables may exist, despite efforts to mitigate potential confounders through identification and control. Thirdly, incomplete variable information, such as the utilization of age stratified data in 10 intervals rather than precise patient ages in the original research database, may introduce inaccuracies and hinder comprehensive analysis. Future research endeavors could explore improved methodologies to procure more precise and comprehensive variable data, thus enhancing the robustness and validity of study outcomes.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn this study, Korean AIS patients were found to have a distinct nonlinear relationship and saturation effect between their BUN/ALB ratio and 3-month poor outcomes. Specifically, for all AIS patients, when the BUN/ALB ratio is 0.326, the risk of adverse outcomes at 3 months is the lowest. While for non-smoking AIS patients, when the BUN/ALB ratio is 0.295, the risk of adverse outcomes at 3 months is the lowest. These findings have important implications for clinicians and can serve as a reference for identifying high-risk groups among Korean AIS patients who are likely to experience 3-month poor outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Shenzhen Science and Technology Program (JCYJ20180228163014668 ; \u0026nbsp;KCXFZ20230731094100002), and Shenzhen Second People\u0026rsquo;s Hospital Clinical Research Fund of Guangdong Province High-level Hospital Construction Project (No.20223357005).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData sharing not applicable to this article as no datasets were generated or analyzed during the current study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003ePan Zhou: study concept, design, data analysis and manuscript draft; Xin Li and Gang-gang Peng: critical revision of the manuscript for important intellectual content; Hao-fei Hu: Manuscript writing and review; Zhe Deng: administrative, technical, and material support and study supervision.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSaini V, Guada L, Yavagal DR.2021. 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Crit Care 18:231.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Blood urea nitrogen to albumin ratio, Acute ischemic stroke, 3-month poor outcomes，Nonlinear relationship, Smooth curve fitting","lastPublishedDoi":"10.21203/rs.3.rs-4570371/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4570371/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e Patients with acute ischemic stroke (AIS) have limited evidence regarding the relationship between blood urea nitrogen and albumin (BUN/ALB). Aiming to investigate the relationship between the BUN/ALB ratio and poor outcomes in AIS patients at 3-months was the purpose of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e AIS participants at a Korean hospital from January 2010 to December 2016 were included in a secondary analysis of a prospective cohort study. Logistic regression and restricted cubic splines were used to examine the relationship between BUN/ALB ratio and poor outcomes after 3 months.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e There is a skewed distribution of BUN/ALB ratios, ranging from 0.114 to 1.250. Model II of the binary logistic regression showed that the BUN/ALB ratio was not statistically significant in predicting poor outcomes for AIS patients after three months. However, there was a notable nonlinear relationship between them, with the inflection point of the BUN/ALB ratio identified as 0.326. The BUN/ALB ratio on the left side of the inflection point was associated with a 42% reduction in 3-month poor outcomes (OR=0.58, 95% CI: 0.40 to 0.83, P = 0.0033). Conversely, the relationship was not statistically significant on the right side of the inflection point.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The BUN/ALB ratio and poor outcomes in AIS patients show a nonlinear correlation with a saturation effect. For AIS patients, a BUN/ALB ratio of approximately 0.326 is associated with the lowest risk of adverse outcomes at 3 months. Specifically, for non-smoking AIS patients, a BUN/ALB ratio of approximately 0.295 is associated with the lowest risk of adverse outcomes at 3 months.\u003c/p\u003e","manuscriptTitle":"Non-linear relationship between blood urea nitrogen to albumin ratio and 3-month outcomes in acute ischemic stroke: a second analysis based on a prospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-08 17:09:40","doi":"10.21203/rs.3.rs-4570371/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c0bee797-6d2f-4b1e-a8c8-828a9f313624","owner":[],"postedDate":"July 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-25T08:09:24+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-08 17:09:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4570371","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4570371","identity":"rs-4570371","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}