Association of Admission Albumin and White Blood Cell Count with Stroke Severity and Hospital Length of Stay in Acute Ischemic Stroke

Association of Admission Albumin and White Blood Cell Count with Stroke Severity and Hospital Length of Stay in Acute Ischemic Stroke | 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 Association of Admission Albumin and White Blood Cell Count with Stroke Severity and Hospital Length of Stay in Acute Ischemic Stroke Weiwen Chen, Shuyan Zhang, Jun Lin, Yinan Zhuo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9186249/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 Background Inflammation and nutritional status play crucial roles in the pathophysiology and prognosis of acute ischemic stroke (AIS). However, the independent associations of admission white blood cell (WBC) count and serum albumin with both stroke severity and hospital length of stay (LOS) remain insufficiently characterized. This study aimed to investigate these relationships in a cohort of Chinese patients with AIS. Methods We retrospectively analyzed 434 AIS patients admitted within 24 hours of symptom onset. Patients were stratified into tertiles based on admission albumin and WBC levels. Multivariable linear regression models were used to assess associations with admission NIHSS score and log-transformed LOS, with progressive adjustment for confounders. Generalized additive models (GAM) were applied to explore nonlinear relationships, and subgroup analyses were performed to examine consistency across populations. Results Higher WBC count was independently associated with greater stroke severity. In the fully adjusted model, patients in the highest WBC tertile had significantly higher admission NIHSS scores (β = 2.03, 95% CI 0.90–3.16, P < 0.001). Atrial fibrillation was the strongest predictor of stroke severity (β = 5.45, 95% CI 3.92–6.98, P < 0.001). For albumin, no significant association was found with admission NIHSS (P for trend = 0.83). However, lower albumin levels were associated with prolonged hospital stay. In the fully adjusted model, patients in the highest albumin tertile had shorter LOS (β = − 0.143, 95% CI − 0.298 to 0.012, P = 0.071), with a borderline significant trend (P = 0.071). GAM analysis confirmed a significant near-linear inverse relationship between albumin and log-LOS (P = 0.0012). Subgroup analyses showed consistent effects across sex, age, diabetes, and atrial fibrillation groups, with notable effect modification by sex and age. Conclusions Elevated admission WBC count is independently associated with increased stroke severity, while lower serum albumin levels are independently associated with prolonged hospitalization in AIS patients. These readily available biomarkers may serve as simple and cost-effective tools for early risk stratification and may guide nutritional and anti-inflammatory interventions in acute stroke care. Trial registration Not applicable. This study was a retrospective cohort study and did not involve a clinical trial. Acute ischemic stroke Albumin White blood cell count Stroke severity Length of stay Biomarker Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Stroke is the second leading cause of death and a major cause of long-term disability worldwide, with ischemic stroke accounting for approximately 80% of all cases [ 1 , 2 ] . Despite advances in acute reperfusion therapies, the clinical course of acute ischemic stroke (AIS) remains highly variable, and reliable predictors of stroke severity and early outcomes are needed to optimize patient management [ 3 ] . Inflammation plays a critical role in the pathophysiology of AIS. Leukocytosis, reflected by elevated white blood cell (WBC) count, is a common systemic response to cerebral ischemia and has been associated with larger infarct volume and poorer functional outcomes [ 4 – 6 ] . Recent advances have highlighted the prognostic value of various composite inflammatory biomarkers, such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII), which integrate information from multiple cell lineages and may offer superior predictive performance [ 7 – 9 ] . A decade of research has established that these indices reflect the complex interplay between innate and adaptive immunity and are robust predictors of stroke outcomes [ 10 ] . On the other hand, nutritional status, often assessed by serum albumin level, may influence stroke recovery. Hypoalbuminemia has been linked to increased mortality and disability after stroke, possibly due to its antioxidant properties and role in maintaining colloid osmotic pressure [ 11 – 13 ] . A recent large-scale meta-analysis including 23,597 patients confirmed that low or low-normal albumin on admission is associated with increased long-term mortality in AIS patients [ 14 ] . Furthermore, albumin-based nutritional indices, such as the Prognostic Nutritional Index (PNI) and Controlling Nutritional Status (CONUT) score, have emerged as promising predictors of functional outcomes after reperfusion therapy [ 15 – 17 ] . Previous studies have examined these markers individually, but few have simultaneously evaluated their associations with both stroke severity and hospital length of stay in a well-adjusted model. We hypothesized that low albumin may delay recovery through impaired immune function and increased susceptibility to infections, while elevated WBC may reflect a more severe inflammatory response that exacerbates neurological injury. Understanding these relationships could aid in early risk stratification and identify modifiable factors to improve stroke care. Therefore, we aimed to investigate the independent associations of admission WBC and albumin with stroke severity (admission NIHSS [ 18 , 19 ] ) and hospital length of stay in a cohort of patients with AIS. Methods Study Design and Population This retrospective cohort study was conducted at the Second Affiliated Hospital of Guangdong Medical University, a tertiary care hospital in Zhanjiang, China. Consecutive patients with acute ischemic stroke admitted between January 2023 and January 2024 were screened for eligibility. Inclusion criteria were (1) age ≥ 18 years; (2) diagnosis of acute ischemic stroke according to the Chinese Stroke Association guidelines for clinical management of ischaemic cerebrovascular diseases [ 20 ] , confirmed by computed tomography or magnetic resonance imaging; (3) admission within 24 hours of symptom onset; (4) availability of admission laboratory data (WBC, albumin). Patients with transient ischemic attack, intracerebral hemorrhage, or incomplete medical records were excluded. The study was approved by the Medical Ethics Committee of the Second Affiliated Hospital of Guangdong Medical University (approval number KT2023-063), and the requirement for informed consent was waived due to the retrospective nature of the study. Data Collection Data were extracted from electronic medical records using a standardized data collection form. The following variables were collected: demographics (age, sex); vascular risk factors (hypertension, diabetes mellitus, coronary artery disease, atrial fibrillation, smoking, alcohol consumption); stroke characteristics (admission NIHSS score); laboratory markers (WBC count, serum albumin, creatinine); and outcomes (hospital length of stay, in-hospital mortality). All laboratory measurements were performed within 24 hours of admission using standard automated analyzers. Statistical Analysis Continuous variables were summarized as mean ± standard deviation (SD) or median with interquartile range (IQR) according to distribution. Categorical variables were presented as frequencies (percentages). Patients were stratified into tertiles based on admission albumin and WBC levels. Spearman's rank correlation was used to assess bivariate correlations. Multivariable linear regression was used to examine associations of albumin and WBC tertiles with admission NIHSS and log-transformed LOS (due to right-skewed distribution). Three models were constructed: Model 1 (unadjusted); Model 2 (adjusted for age and sex); and Model 3 (further adjusted for hypertension, diabetes, atrial fibrillation, and additionally for NIHSS when LOS was the outcome). Trend tests were performed by entering the tertile groups as ordinal variables. Generalized additive models (GAM) with smooth terms were applied to explore potential nonlinear relationships between albumin and log-LOS. Subgroup analyses stratified by sex, age group (≤ 65 vs. >65 years), diabetes, and atrial fibrillation were performed to examine consistency of the albumin-LOS association; interaction P values were calculated to assess effect modification, and results were presented in a forest plot. All statistical tests were two-tailed, and P < 0.05 was considered statistically significant. Analyses were performed using R version 4.x (R Foundation for Statistical Computing, Vienna, Austria). Results Study Population A total of 434 patients with acute ischemic stroke were included. Baseline characteristics stratified by albumin tertiles are presented in Table 1 . The mean age was 72.9 ± 11.6 years, and 64.1% were male. Patients in the lowest albumin tertile (T1) were older, had higher prevalence of hypertension, higher creatinine levels, and a trend toward longer hospital stay (P = 0.068) compared to those in higher tertiles. Table 1 Baseline characteristics stratified by albumin tertiles Characteristic T1 (low) (n = 145) T2 (middle) (n = 145) T3 (high) (n = 144) P value Age, years, mean (SD) 75.83 (11.56) 73.42 (11.58) 70.09 (11.03) < 0.001 Male, n (%) 99 (68.3) 84 (57.9) 95 (66.0) 0.156 Hypertension, n (%) 91 (62.8) 113 (77.9) 122 (84.7) < 0.001 Diabetes, n (%) 45 (31.0) 55 (37.9) 56 (38.9) 0.315 Coronary artery disease, n (%) 26 (17.9) 39 (26.9) 25 (17.4) 0.080 Atrial fibrillation, n (%) 16 (11.0) 20 (13.8) 8 (5.6) 0.062 Smoking, n (%) 1 (0.7) 0 (0.0) 0 (0.0) 0.368 Alcohol consumption, n (%) 0 (0.0) 0 (0.0) 0 (0.0) - Prior stroke, n (%) 49 (33.8) 48 (33.1) 40 (27.8) 0.485 Admission NIHSS, mean (SD) 4.74 (5.23) 4.64 (4.99) 4.52 (5.25) 0.938 WBC, ×10⁹/L, mean (SD) 8.23 (2.75) 8.69 (2.19) 8.53 (1.62) 0.213 Creatinine, µmol/L, mean (SD) 103.43 (66.96) 81.12 (22.65) 85.95 (43.07) < 0.001 Length of stay, days, mean (SD) 8.10 (4.55) 7.40 (4.11) 6.99 (3.44) 0.068 In-hospital death, n (%) 1 (0.7) 1 (0.7) 0 (0.0) 0.607 WBC Count and Stroke Severity Higher WBC count was significantly associated with greater stroke severity (Table 2 ). In the fully adjusted model (Model 3), patients in the highest WBC tertile (T3) had significantly higher admission NIHSS scores compared to those in the lowest tertile (β = 2.03, 95% CI 0.90–3.16, P < 0.001). Atrial fibrillation showed the strongest association with stroke severity (β = 5.45, 95% CI 3.92–6.98, P < 0.001). Table 2 Multivariable linear regression for admission NIHSS (WBC tertiles) Variable Model 1 β (95% CI) P Model 2 β (95% CI) P Model 3 β (95% CI) P WBC tertile T1 (low) Reference Reference Reference T2 (middle) 0.81 (–0.37, 1.98) 0.177 0.85 (–0.33, 2.03) 0.157 0.62 (–0.51, 1.74) 0.281 T3 (high) 2.16 (0.98, 3.33) < 0.001 2.18 (1.00, 3.37) < 0.001 2.03 (0.90, 3.16) < 0.001 Age, per year 0.02 (–0.02, 0.06) 0.339 0.003 (–0.04, 0.04) 0.883 Female sex –0.05 (–1.07, 0.96) 0.919 –0.21 (–1.18, 0.76) 0.672 Hypertension 0.77 (–0.30, 1.84) 0.158 Diabetes 0.44 (–0.52, 1.41) 0.368 Atrial fibrillation 5.45 (3.92, 6.98) < 0.001 Albumin and Stroke Severity No significant association was found between albumin tertiles and admission NIHSS in any of the models (Table 3 ). The trend test was not significant (P = 0.83). Table 3 Multivariable linear regression for admission NIHSS (albumin tertiles) Variable Model 1 β (95% CI) P Model 2 β (95% CI) P Model 3 β (95% CI) P Albumin tertile T1 (low) Reference Reference Reference T2 (middle) –0.10 (–1.29, 1.09) 0.873 –0.08 (–1.28, 1.13) 0.900 –0.41 (–1.57, 0.74) 0.484 T3 (high) –0.22 (–1.41, 0.98) 0.721 –0.13 (–1.35, 1.10) 0.840 –0.13 (–1.32, 1.06) 0.830 Age, per year 0.02 (–0.03, 0.06) 0.443 –0.0001 (–0.04, 0.04) 0.996 Female sex 0.20 (–0.82, 1.22) 0.701 0.07 (–0.91, 1.04) 0.890 Hypertension 0.85 (–0.26, 1.95) 0.133 Diabetes 0.38 (–0.60, 1.36) 0.449 Atrial fibrillation 5.57 (4.01, 7.12) < 0.001 Albumin and Hospital Length of Stay Lower albumin levels were associated with prolonged hospital stay (Table 4 ). In the fully adjusted model, patients in the highest albumin tertile (T3) had shorter LOS compared to those in the lowest tertile (β = − 0.143, 95% CI − 0.298 to 0.012, P = 0.071), with a borderline significant trend (P = 0.071). When analyzed as a continuous variable, each 1 g/L decrease in albumin was associated with an approximately 3% increase in LOS (β = − 0.030, P < 0.001, data not shown). Table 4 Multivariable linear regression for log-transformed length of stay (albumin tertiles) Variable Model 1 β (95% CI) P Model 2 β (95% CI) P Model 3 β (95% CI) P Albumin tertile T1 (low) Reference Reference Reference T2 (middle) –0.097 (–0.243, 0.050) 0.195 –0.095 (–0.243, 0.053) 0.208 –0.102 (–0.253, 0.049) 0.184 T3 (high) –0.141 (–0.288, 0.006) 0.060 –0.134 (–0.284, 0.017) 0.082 –0.143 (–0.298, 0.012) 0.071 Age, per year 0.001 (–0.004, 0.007) 0.635 0.001 (–0.004, 0.007) 0.672 Female sex 0.012 (–0.114, 0.138) 0.850 0.010 (–0.117, 0.137) 0.872 Hypertension 0.040 (–0.104, 0.185) 0.584 Diabetes 0.003 (–0.125, 0.131) 0.963 Atrial fibrillation 0.017 (–0.196, 0.231) 0.873 NIHSS admission –0.002 (–0.014, 0.010) 0.750 The generalized additive model (GAM) confirmed a significant near-linear inverse relationship between albumin and log-LOS (P for smooth term = 0.0012; Fig. 1 ). Subgroup analyses showed consistent effects across most strata, with notable differences by sex and age (Fig. 2 ). The inverse association between albumin and LOS was more pronounced in male patients (β = − 0.032, 95% CI − 0.054 to − 0.010) compared with female patients (β = − 0.026, 95% CI − 0.050 to − 0.002), and in younger patients (≤ 65 years: β = − 0.045, 95% CI − 0.075 to − 0.015) compared with older patients (> 65 years: β = − 0.025, 95% CI − 0.045 to − 0.005). Interaction tests were not statistically significant (all P for interaction > 0.05), suggesting that the overall association was robust across subgroups. The scatter plot of albumin versus admission NIHSS (Fig. 3 ) visually confirmed the absence of a linear relationship, consistent with the regression analysis. Similarly, the boxplot of length of stay across WBC quartiles (Fig. 4 ) showed no clear trend, supporting the finding that WBC was not significantly associated with LOS after multivariable adjustment. Discussion In this retrospective cohort study of 434 patients with acute ischemic stroke, we found that higher admission WBC count was independently associated with greater stroke severity, while lower serum albumin levels were associated with prolonged hospital stay. These associations persisted after adjusting for potential confounders including age, sex, and major vascular risk factors. Our finding that elevated WBC correlates with higher NIHSS on admission is consistent with previous studies [ 4 – 6 ] . Recent research has extended these observations to composite inflammatory indices. For instance, Chen et al. demonstrated that both NLR and PLR were independent predictors of 3-month functional outcomes, with NLR showing higher predictive value (AUC = 0.776) [ 7 ] . Dynamic changes in NLR during the first week after stroke may provide additional prognostic information [ 21 ] . More complex indices such as SII and the pan-immune inflammation value (PIV) have also been shown to predict mortality in critically ill stroke patients [ 22 – 24 ] . Notably, a nonlinear J-shaped relationship has been observed between PLR and in-hospital mortality, suggesting a threshold effect [ 25 ] . While composite indices may offer superior predictive performance, our findings suggest that even a single marker like WBC retains independent prognostic value. This is particularly relevant in clinical settings where timely access to complete blood count differentials may be limited, making WBC a practical and cost-effective alternative for early risk stratification. The independent inverse relationship between albumin and LOS aligns with recent studies demonstrating the prognostic value of albumin-based nutritional indices. Zhao et al. reported that the CONUT score was independently associated with 3-month poor prognosis in patients undergoing reperfusion therapy (adjusted OR per 1-point increase = 1.59) [ 15 ] . These findings suggest that nutritional status, as reflected by albumin, may influence recovery through multiple mechanisms: (1) albumin maintains colloid osmotic pressure and modulates immune function; (2) hypoalbuminemia is associated with increased susceptibility to infections, which can prolong hospitalization; and (3) albumin has antioxidant properties that may protect against secondary brain injury [ 11 – 13 ] . Our results extend this evidence by demonstrating that even a single marker like albumin can independently predict LOS, a clinically relevant outcome, and that the relationship is near-linear across the entire range of albumin values. The simplicity and low cost of measuring WBC and albumin make them attractive tools for early risk stratification. Elevated WBC on admission may alert clinicians to a more severe inflammatory response, prompting closer monitoring for neurological deterioration and consideration of anti-inflammatory strategies. Low albumin levels could identify patients at risk for prolonged hospitalization, facilitating early nutritional assessment and intervention. In resource-limited settings where complex composite indices may not be readily available, these single markers offer a practical alternative for prognostic assessment. Furthermore, the consistency of the albumin-LOS association across most subgroups suggests that nutritional support may benefit a broad spectrum of AIS patients, with potential for greater impact in younger and male patients who showed stronger effect estimates. The lack of association between albumin and stroke severity is interesting. While some studies have reported an inverse relationship [ 26 ] , others have found no independent association after adjusting for confounders. These findings are consistent with recent systematic reviews that have established low serum albumin as a robust predictor of poor outcomes after ischemic stroke [ 15 ] . This suggests that endogenous albumin levels may reflect different physiological processes than exogenous albumin administration, and their relationship with stroke severity may be complex and nonlinear. Our subgroup analysis revealed that the inverse association between albumin and LOS was more pronounced in male and younger patients. While interaction tests were not statistically significant, these observed differences may reflect underlying biological mechanisms. Male patients may be more susceptible to the catabolic effects of acute illness, and younger patients may have greater potential for recovery when nutritional status is optimized. These findings warrant further investigation in larger cohorts. Our observation that atrial fibrillation is the strongest predictor of stroke severity is consistent with large registry studies [ 27 ] . Notably, newly diagnosed AF after stroke shares similar clinical and echocardiographic features with known AF, suggesting that pre-existing heart disease underlies most post-stroke AF [ 28 ] . Strengths and Limitations Strengths of this study include the relatively large sample size, comprehensive adjustment for major confounders, and the use of multiple analytical approaches (tertile-based, continuous, and nonlinear models). The inclusion of subgroup analyses and forest plots enhances the robustness of our findings. However, several limitations must be acknowledged. First, the single-center retrospective design may introduce selection bias and limit generalizability. Second, we lacked data on other inflammatory markers such as C-reactive protein (CRP) and procalcitonin, which could provide additional insights. Third, the number of deaths was too small to analyze mortality. Fourth, residual confounding cannot be excluded, particularly from unmeasured factors such as pre-stroke functional status, nutritional intake, and in-hospital complications like infections. Fifth, the borderline significance for the albumin-LOS association may reflect limited statistical power; larger prospective studies are needed to confirm this finding. Conclusions In patients with acute ischemic stroke, higher admission WBC count is independently associated with greater stroke severity, and lower serum albumin independently predicts longer hospital stay. These easily available biomarkers may aid in early risk stratification and guide individualized care, particularly in identifying patients who may benefit from closer monitoring, anti-inflammatory strategies, or nutritional support. Abbreviations AIS Acute ischemic stroke CI Confidence interval CONUT Controlling Nutritional Status GAM Generalized additive model IQR Interquartile range LOS Length of stay NIHSS National Institutes of Health Stroke Scale NLR Neutrophil-to-lymphocyte ratio PLR Platelet-to-lymphocyte ratio PNI Prognostic Nutritional Index SII Systemic immune-inflammation index WBC White blood cell. Declarations Funding This work was supported by the Zhanjiang Science and Technology Bureau (2022 Zhanjiang Science and Technology Development Special Fund Competitive Allocation Project, Grant No. 2022A01115), titled “Effectiveness Analysis of the Comprehensive Stroke Treatment Management Platform on Cognitive Function in Patients with Post-Stroke Cognitive Impairment (PSCI)”. Availability of data and materials The anonymized data that support the findings of this study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate This study was approved by the Medical Ethics Committee of the Second Affiliated Hospital of Guangdong Medical University (approval number KT2023-063). Given the retrospective nature of the study, the requirement for informed consent was waived. All data were fully de-identified. The study adhered to the principles of the Declaration of Helsinki. Trial registration Not applicable. This study was a retrospective cohort study and did not involve a clinical trial. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Author contributions Weiwen Chen conceived and designed the study. Jun Lin collected the data. Yinan Zhuo performed the statistical analysis. Shuyan Zhang drafted the manuscript. All authors critically revised the manuscript and approved the final version. Acknowledgements Not applicable. 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Dual Antiplatelet Therapy Versus Aspirin in Patients With Stroke or Transient Ischemic Attack: Meta-Analysis of Randomized Controlled Trials. Stroke. 2021;52(6):e217–23. https://doi.org/10.1161/STROKEAHA.120.033033 . Toyoda K, Yoshimura S, Nakai M, Wada S, Miwa K, Koge J, Yoshida T, Kamiyama K, Mizoue T, Hatano T, Yoshida Y, Sasahara Y, Ishigami A, Iwanaga Y, Miyamoto Y, Minematsu K, Kobayashi S, Koga M. Severity, Outcomes, and their Secular Changes in 33,870 Ischemic Stroke Patients with Atrial Fibrillation in a Hospital-Based Registry: Japan Stroke Data Bank. J Atheroscler Thromb. 2025;32(3):308–20. https://doi.org/10.5551/jat.65117 . & Japan Stroke Data Bank Investigators Rizos T, Horstmann S, Dittgen F, Täger T, Jenetzky E, Heuschmann P, Veltkamp R. Preexisting Heart Disease Underlies Newly Diagnosed Atrial Fibrillation After Acute Ischemic Stroke. Stroke. 2016;47(2):336–41. https://doi.org/10.1161/STROKEAHA.115.011465 . Additional Declarations No competing interests reported. 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Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shuyan","middleName":"","lastName":"Zhang","suffix":""},{"id":618311864,"identity":"21d19f51-a389-4ea3-8c47-190a0ee4c549","order_by":2,"name":"Jun Lin","email":"","orcid":"","institution":"The Second Affiliated Hospital of Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Lin","suffix":""},{"id":618311868,"identity":"74168056-a2c4-4dc6-a280-ebfece65f942","order_by":3,"name":"Yinan Zhuo","email":"data:image/png;base64,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","orcid":"","institution":"The Second Affiliated Hospital of Guangdong Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yinan","middleName":"","lastName":"Zhuo","suffix":""}],"badges":[],"createdAt":"2026-03-21 13:54:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9186249/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9186249/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106581330,"identity":"5c9d60f7-b13d-4b56-bd6e-86a7224f84c1","added_by":"auto","created_at":"2026-04-10 06:45:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":39343,"visible":true,"origin":"","legend":"\u003cp\u003eNonlinear relationship between albumin and log-LOS (GAM)\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9186249/v1/449be64ca39cc9abd04369d7.png"},{"id":106581331,"identity":"d3defe72-d01f-4c2b-a8eb-ecd473994357","added_by":"auto","created_at":"2026-04-10 06:45:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":34536,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis: Association between albumin and log-LOS\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9186249/v1/64e2e9311cc1b7a48c94b76d.png"},{"id":106581333,"identity":"70e3a366-68ee-4409-ba6d-480ff57de7ae","added_by":"auto","created_at":"2026-04-10 06:45:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":67248,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScatter plot of albumin and admission NIHSS\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9186249/v1/bf17c8993b0a6f11f3c73fb6.png"},{"id":106581332,"identity":"9ae212d6-cef6-4280-824a-268b0a01fe2e","added_by":"auto","created_at":"2026-04-10 06:45:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":29825,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBoxplot of length of stay across WBC quartiles\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9186249/v1/0ca13616ffb5a4307e2d8aa5.png"},{"id":107319377,"identity":"2e52abad-0e8f-4274-a83c-2ca55abb848b","added_by":"auto","created_at":"2026-04-20 10:13:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":773382,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9186249/v1/4fc4ab40-8c35-4a83-82c1-57626404bdcb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of Admission Albumin and White Blood Cell Count with Stroke Severity and Hospital Length of Stay in Acute Ischemic Stroke","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStroke is the second leading cause of death and a major cause of long-term disability worldwide, with ischemic stroke accounting for approximately 80% of all cases \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Despite advances in acute reperfusion therapies, the clinical course of acute ischemic stroke (AIS) remains highly variable, and reliable predictors of stroke severity and early outcomes are needed to optimize patient management \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eInflammation plays a critical role in the pathophysiology of AIS. Leukocytosis, reflected by elevated white blood cell (WBC) count, is a common systemic response to cerebral ischemia and has been associated with larger infarct volume and poorer functional outcomes \u003csup\u003e[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Recent advances have highlighted the prognostic value of various composite inflammatory biomarkers, such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII), which integrate information from multiple cell lineages and may offer superior predictive performance \u003csup\u003e[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. A decade of research has established that these indices reflect the complex interplay between innate and adaptive immunity and are robust predictors of stroke outcomes \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOn the other hand, nutritional status, often assessed by serum albumin level, may influence stroke recovery. Hypoalbuminemia has been linked to increased mortality and disability after stroke, possibly due to its antioxidant properties and role in maintaining colloid osmotic pressure \u003csup\u003e[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. A recent large-scale meta-analysis including 23,597 patients confirmed that low or low-normal albumin on admission is associated with increased long-term mortality in AIS patients \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Furthermore, albumin-based nutritional indices, such as the Prognostic Nutritional Index (PNI) and Controlling Nutritional Status (CONUT) score, have emerged as promising predictors of functional outcomes after reperfusion therapy \u003csup\u003e[\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePrevious studies have examined these markers individually, but few have simultaneously evaluated their associations with both stroke severity and hospital length of stay in a well-adjusted model. We hypothesized that low albumin may delay recovery through impaired immune function and increased susceptibility to infections, while elevated WBC may reflect a more severe inflammatory response that exacerbates neurological injury. Understanding these relationships could aid in early risk stratification and identify modifiable factors to improve stroke care. Therefore, we aimed to investigate the independent associations of admission WBC and albumin with stroke severity (admission NIHSS\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e) and hospital length of stay in a cohort of patients with AIS.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Population\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eThis retrospective cohort study was conducted at the Second Affiliated Hospital of Guangdong Medical University, a tertiary care hospital in Zhanjiang, China. Consecutive patients with acute ischemic stroke admitted between January 2023 and January 2024 were screened for eligibility. Inclusion criteria were\u003c/strong\u003e \u003cp\u003e(1) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; (2) diagnosis of acute ischemic stroke according to the Chinese Stroke Association guidelines for clinical management of ischaemic cerebrovascular diseases \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, confirmed by computed tomography or magnetic resonance imaging; (3) admission within 24 hours of symptom onset; (4) availability of admission laboratory data (WBC, albumin). Patients with transient ischemic attack, intracerebral hemorrhage, or incomplete medical records were excluded. The study was approved by the Medical Ethics Committee of the Second Affiliated Hospital of Guangdong Medical University (approval number KT2023-063), and the requirement for informed consent was waived due to the retrospective nature of the study.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eData were extracted from electronic medical records using a standardized data collection form. The following variables were collected: demographics (age, sex); vascular risk factors (hypertension, diabetes mellitus, coronary artery disease, atrial fibrillation, smoking, alcohol consumption); stroke characteristics (admission NIHSS score); laboratory markers (WBC count, serum albumin, creatinine); and outcomes (hospital length of stay, in-hospital mortality). All laboratory measurements were performed within 24 hours of admission using standard automated analyzers.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were summarized as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median with interquartile range (IQR) according to distribution. Categorical variables were presented as frequencies (percentages). Patients were stratified into tertiles based on admission albumin and WBC levels.\u003c/p\u003e \u003cp\u003eSpearman's rank correlation was used to assess bivariate correlations. Multivariable linear regression was used to examine associations of albumin and WBC tertiles with admission NIHSS and log-transformed LOS (due to right-skewed distribution). Three models were constructed: Model 1 (unadjusted); Model 2 (adjusted for age and sex); and Model 3 (further adjusted for hypertension, diabetes, atrial fibrillation, and additionally for NIHSS when LOS was the outcome). Trend tests were performed by entering the tertile groups as ordinal variables.\u003c/p\u003e \u003cp\u003eGeneralized additive models (GAM) with smooth terms were applied to explore potential nonlinear relationships between albumin and log-LOS. Subgroup analyses stratified by sex, age group (\u0026le;\u0026thinsp;65 vs. \u0026gt;65 years), diabetes, and atrial fibrillation were performed to examine consistency of the albumin-LOS association; interaction P values were calculated to assess effect modification, and results were presented in a forest plot.\u003c/p\u003e \u003cp\u003eAll statistical tests were two-tailed, and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Analyses were performed using R version 4.x (R Foundation for Statistical Computing, Vienna, Austria).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eA total of 434 patients with acute ischemic stroke were included. Baseline characteristics stratified by albumin tertiles are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean age was 72.9\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6 years, and 64.1% were male. Patients in the lowest albumin tertile (T1) were older, had higher prevalence of hypertension, higher creatinine levels, and a trend toward longer hospital stay (P\u0026thinsp;=\u0026thinsp;0.068) compared to those in higher tertiles.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics stratified by albumin tertiles\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT1 (low) (n\u0026thinsp;=\u0026thinsp;145)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT2 (middle) (n\u0026thinsp;=\u0026thinsp;145)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT3 (high) (n\u0026thinsp;=\u0026thinsp;144)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75.83 (11.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73.42 (11.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70.09 (11.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e99 (68.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84 (57.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95 (66.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91 (62.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e113 (77.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e122 (84.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45 (31.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55 (37.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56 (38.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary artery disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26 (17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39 (26.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25 (17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16 (11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior stroke, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49 (33.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48 (33.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40 (27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdmission NIHSS, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.74 (5.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.64 (4.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.52 (5.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC, \u0026times;10⁹/L, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.23 (2.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.69 (2.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.53 (1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine, \u0026micro;mol/L, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e103.43 (66.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81.12 (22.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85.95 (43.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of stay, days, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.10 (4.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.40 (4.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.99 (3.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn-hospital death, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eWBC Count and Stroke Severity\u003c/h2\u003e \u003cp\u003eHigher WBC count was significantly associated with greater stroke severity (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the fully adjusted model (Model 3), patients in the highest WBC tertile (T3) had significantly higher admission NIHSS scores compared to those in the lowest tertile (β\u0026thinsp;=\u0026thinsp;2.03, 95% CI 0.90\u0026ndash;3.16, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Atrial fibrillation showed the strongest association with stroke severity (β\u0026thinsp;=\u0026thinsp;5.45, 95% CI 3.92\u0026ndash;6.98, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable linear regression for admission NIHSS (WBC tertiles)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1 β (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 2 β (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 3 β (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC tertile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1 (low)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2 (middle)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.81 (\u0026ndash;0.37, 1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85 (\u0026ndash;0.33, 2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.62 (\u0026ndash;0.51, 1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3 (high)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.16 (0.98, 3.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.18 (1.00, 3.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.03 (0.90, 3.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, per year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02 (\u0026ndash;0.02, 0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003 (\u0026ndash;0.04, 0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.05 (\u0026ndash;1.07, 0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;0.21 (\u0026ndash;1.18, 0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.77 (\u0026ndash;0.30, 1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.44 (\u0026ndash;0.52, 1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.45 (3.92, 6.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAlbumin and Stroke Severity\u003c/h3\u003e\n\u003cp\u003eNo significant association was found between albumin tertiles and admission NIHSS in any of the models (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The trend test was not significant (P\u0026thinsp;=\u0026thinsp;0.83).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable linear regression for admission NIHSS (albumin tertiles)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1 β (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 2 β (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 3 β (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin tertile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1 (low)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2 (middle)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.10 (\u0026ndash;1.29, 1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.08 (\u0026ndash;1.28, 1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;0.41 (\u0026ndash;1.57, 0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3 (high)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.22 (\u0026ndash;1.41, 0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.13 (\u0026ndash;1.35, 1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;0.13 (\u0026ndash;1.32, 1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, per year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02 (\u0026ndash;0.03, 0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;0.0001 (\u0026ndash;0.04, 0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20 (\u0026ndash;0.82, 1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07 (\u0026ndash;0.91, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.85 (\u0026ndash;0.26, 1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.38 (\u0026ndash;0.60, 1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.57 (4.01, 7.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eAlbumin and Hospital Length of Stay\u003c/h3\u003e\n\u003cp\u003eLower albumin levels were associated with prolonged hospital stay (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the fully adjusted model, patients in the highest albumin tertile (T3) had shorter LOS compared to those in the lowest tertile (β = \u0026minus;\u0026thinsp;0.143, 95% CI \u0026minus;\u0026thinsp;0.298 to 0.012, P\u0026thinsp;=\u0026thinsp;0.071), with a borderline significant trend (P\u0026thinsp;=\u0026thinsp;0.071). When analyzed as a continuous variable, each 1 g/L decrease in albumin was associated with an approximately 3% increase in LOS (β = \u0026minus;\u0026thinsp;0.030, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, data not shown).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable linear regression for log-transformed length of stay (albumin tertiles)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1 β (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 2 β (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 3 β (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin tertile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1 (low)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2 (middle)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.097 (\u0026ndash;0.243, 0.050)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.095 (\u0026ndash;0.243, 0.053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;0.102 (\u0026ndash;0.253, 0.049)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3 (high)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.141 (\u0026ndash;0.288, 0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.134 (\u0026ndash;0.284, 0.017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;0.143 (\u0026ndash;0.298, 0.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, per year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001 (\u0026ndash;0.004, 0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001 (\u0026ndash;0.004, 0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.012 (\u0026ndash;0.114, 0.138)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.010 (\u0026ndash;0.117, 0.137)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.040 (\u0026ndash;0.104, 0.185)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003 (\u0026ndash;0.125, 0.131)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.017 (\u0026ndash;0.196, 0.231)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIHSS admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;0.002 (\u0026ndash;0.014, 0.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe generalized additive model (GAM) confirmed a significant near-linear inverse relationship between albumin and log-LOS (P for smooth term\u0026thinsp;=\u0026thinsp;0.0012; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSubgroup analyses showed consistent effects across most strata, with notable differences by sex and age (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The inverse association between albumin and LOS was more pronounced in male patients (β = \u0026minus;\u0026thinsp;0.032, 95% CI \u0026minus;\u0026thinsp;0.054 to \u0026minus;\u0026thinsp;0.010) compared with female patients (β = \u0026minus;\u0026thinsp;0.026, 95% CI \u0026minus;\u0026thinsp;0.050 to \u0026minus;\u0026thinsp;0.002), and in younger patients (\u0026le;\u0026thinsp;65 years: β = \u0026minus;\u0026thinsp;0.045, 95% CI \u0026minus;\u0026thinsp;0.075 to \u0026minus;\u0026thinsp;0.015) compared with older patients (\u0026gt;\u0026thinsp;65 years: β = \u0026minus;\u0026thinsp;0.025, 95% CI \u0026minus;\u0026thinsp;0.045 to \u0026minus;\u0026thinsp;0.005). Interaction tests were not statistically significant (all P for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting that the overall association was robust across subgroups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe scatter plot of albumin versus admission NIHSS (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) visually confirmed the absence of a linear relationship, consistent with the regression analysis. Similarly, the boxplot of length of stay across WBC quartiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) showed no clear trend, supporting the finding that WBC was not significantly associated with LOS after multivariable adjustment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this retrospective cohort study of 434 patients with acute ischemic stroke, we found that higher admission WBC count was independently associated with greater stroke severity, while lower serum albumin levels were associated with prolonged hospital stay. These associations persisted after adjusting for potential confounders including age, sex, and major vascular risk factors.\u003c/p\u003e \u003cp\u003eOur finding that elevated WBC correlates with higher NIHSS on admission is consistent with previous studies \u003csup\u003e[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Recent research has extended these observations to composite inflammatory indices. For instance, Chen et al. demonstrated that both NLR and PLR were independent predictors of 3-month functional outcomes, with NLR showing higher predictive value (AUC\u0026thinsp;=\u0026thinsp;0.776) \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Dynamic changes in NLR during the first week after stroke may provide additional prognostic information \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. More complex indices such as SII and the pan-immune inflammation value (PIV) have also been shown to predict mortality in critically ill stroke patients \u003csup\u003e[\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Notably, a nonlinear J-shaped relationship has been observed between PLR and in-hospital mortality, suggesting a threshold effect \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. While composite indices may offer superior predictive performance, our findings suggest that even a single marker like WBC retains independent prognostic value. This is particularly relevant in clinical settings where timely access to complete blood count differentials may be limited, making WBC a practical and cost-effective alternative for early risk stratification.\u003c/p\u003e \u003cp\u003eThe independent inverse relationship between albumin and LOS aligns with recent studies demonstrating the prognostic value of albumin-based nutritional indices. Zhao et al. reported that the CONUT score was independently associated with 3-month poor prognosis in patients undergoing reperfusion therapy (adjusted OR per 1-point increase\u0026thinsp;=\u0026thinsp;1.59) \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. These findings suggest that nutritional status, as reflected by albumin, may influence recovery through multiple mechanisms: (1) albumin maintains colloid osmotic pressure and modulates immune function; (2) hypoalbuminemia is associated with increased susceptibility to infections, which can prolong hospitalization; and (3) albumin has antioxidant properties that may protect against secondary brain injury \u003csup\u003e[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Our results extend this evidence by demonstrating that even a single marker like albumin can independently predict LOS, a clinically relevant outcome, and that the relationship is near-linear across the entire range of albumin values.\u003c/p\u003e \u003cp\u003eThe simplicity and low cost of measuring WBC and albumin make them attractive tools for early risk stratification. Elevated WBC on admission may alert clinicians to a more severe inflammatory response, prompting closer monitoring for neurological deterioration and consideration of anti-inflammatory strategies. Low albumin levels could identify patients at risk for prolonged hospitalization, facilitating early nutritional assessment and intervention. In resource-limited settings where complex composite indices may not be readily available, these single markers offer a practical alternative for prognostic assessment. Furthermore, the consistency of the albumin-LOS association across most subgroups suggests that nutritional support may benefit a broad spectrum of AIS patients, with potential for greater impact in younger and male patients who showed stronger effect estimates.\u003c/p\u003e \u003cp\u003eThe lack of association between albumin and stroke severity is interesting. While some studies have reported an inverse relationship \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e, others have found no independent association after adjusting for confounders. These findings are consistent with recent systematic reviews that have established low serum albumin as a robust predictor of poor outcomes after ischemic stroke \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. This suggests that endogenous albumin levels may reflect different physiological processes than exogenous albumin administration, and their relationship with stroke severity may be complex and nonlinear.\u003c/p\u003e \u003cp\u003eOur subgroup analysis revealed that the inverse association between albumin and LOS was more pronounced in male and younger patients. While interaction tests were not statistically significant, these observed differences may reflect underlying biological mechanisms. Male patients may be more susceptible to the catabolic effects of acute illness, and younger patients may have greater potential for recovery when nutritional status is optimized. These findings warrant further investigation in larger cohorts.\u003c/p\u003e \u003cp\u003eOur observation that atrial fibrillation is the strongest predictor of stroke severity is consistent with large registry studies \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Notably, newly diagnosed AF after stroke shares similar clinical and echocardiographic features with known AF, suggesting that pre-existing heart disease underlies most post-stroke AF \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eStrengths of this study include the relatively large sample size, comprehensive adjustment for major confounders, and the use of multiple analytical approaches (tertile-based, continuous, and nonlinear models). The inclusion of subgroup analyses and forest plots enhances the robustness of our findings. However, several limitations must be acknowledged. First, the single-center retrospective design may introduce selection bias and limit generalizability. Second, we lacked data on other inflammatory markers such as C-reactive protein (CRP) and procalcitonin, which could provide additional insights. Third, the number of deaths was too small to analyze mortality. Fourth, residual confounding cannot be excluded, particularly from unmeasured factors such as pre-stroke functional status, nutritional intake, and in-hospital complications like infections. Fifth, the borderline significance for the albumin-LOS association may reflect limited statistical power; larger prospective studies are needed to confirm this finding.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn patients with acute ischemic stroke, higher admission WBC count is independently associated with greater stroke severity, and lower serum albumin independently predicts longer hospital stay. These easily available biomarkers may aid in early risk stratification and guide individualized care, particularly in identifying patients who may benefit from closer monitoring, anti-inflammatory strategies, or nutritional support.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAIS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcute ischemic stroke\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCONUT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eControlling Nutritional Status\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGAM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneralized additive model\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterquartile range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLength of stay\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNIHSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Institutes of Health Stroke Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNLR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNeutrophil-to-lymphocyte ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePLR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePlatelet-to-lymphocyte ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePNI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrognostic Nutritional Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSII\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSystemic immune-inflammation index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWhite blood cell.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Zhanjiang Science and Technology Bureau (2022 Zhanjiang Science and Technology Development Special Fund Competitive Allocation Project, Grant No. 2022A01115), titled \u0026ldquo;Effectiveness Analysis of the Comprehensive Stroke Treatment Management Platform on Cognitive Function in Patients with Post-Stroke Cognitive Impairment (PSCI)\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe anonymized data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Medical Ethics Committee of the Second Affiliated Hospital of Guangdong Medical University (approval number KT2023-063). Given the retrospective nature of the study, the requirement for informed consent was waived. All data were fully de-identified. The study adhered to the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study was a retrospective cohort study and did not involve a clinical trial.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWeiwen Chen \u0026nbsp;conceived and designed the study. Jun Lin collected the data. Yinan Zhuo performed the statistical analysis. Shuyan Zhang drafted the manuscript. All authors critically revised the manuscript and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYang S, Deng M, Ren X, Wang F, Kong Z, Luo J, Cao Y, Han G, Yin H, Xiang X, Liu J, Zhang J, Tan Y. Global burden of disease study highlights the global, regional and national trends of stroke. J Neurol Neurosurg Psychiatry. 2025;97(1):13\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/jnnp-2025-335954\u003c/span\u003e\u003cspan address=\"10.1136/jnnp-2025-335954\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQian N, Lu C, Wei T, Yang W, Wang H, Chen H, Li J, Zhu S, Wang W, Shao N. 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Stroke. 2016;47(2):336\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/STROKEAHA.115.011465\u003c/span\u003e\u003cspan address=\"10.1161/STROKEAHA.115.011465\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\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":"Acute ischemic stroke, Albumin, White blood cell count, Stroke severity, Length of stay, Biomarker","lastPublishedDoi":"10.21203/rs.3.rs-9186249/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9186249/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eInflammation and nutritional status play crucial roles in the pathophysiology and prognosis of acute ischemic stroke (AIS). However, the independent associations of admission white blood cell (WBC) count and serum albumin with both stroke severity and hospital length of stay (LOS) remain insufficiently characterized. This study aimed to investigate these relationships in a cohort of Chinese patients with AIS.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe retrospectively analyzed 434 AIS patients admitted within 24 hours of symptom onset. Patients were stratified into tertiles based on admission albumin and WBC levels. Multivariable linear regression models were used to assess associations with admission NIHSS score and log-transformed LOS, with progressive adjustment for confounders. Generalized additive models (GAM) were applied to explore nonlinear relationships, and subgroup analyses were performed to examine consistency across populations.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eHigher WBC count was independently associated with greater stroke severity. In the fully adjusted model, patients in the highest WBC tertile had significantly higher admission NIHSS scores (β\u0026thinsp;=\u0026thinsp;2.03, 95% CI 0.90\u0026ndash;3.16, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Atrial fibrillation was the strongest predictor of stroke severity (β\u0026thinsp;=\u0026thinsp;5.45, 95% CI 3.92\u0026ndash;6.98, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For albumin, no significant association was found with admission NIHSS (P for trend\u0026thinsp;=\u0026thinsp;0.83). However, lower albumin levels were associated with prolonged hospital stay. In the fully adjusted model, patients in the highest albumin tertile had shorter LOS (β = \u0026minus;\u0026thinsp;0.143, 95% CI \u0026minus;\u0026thinsp;0.298 to 0.012, P\u0026thinsp;=\u0026thinsp;0.071), with a borderline significant trend (P\u0026thinsp;=\u0026thinsp;0.071). GAM analysis confirmed a significant near-linear inverse relationship between albumin and log-LOS (P\u0026thinsp;=\u0026thinsp;0.0012). Subgroup analyses showed consistent effects across sex, age, diabetes, and atrial fibrillation groups, with notable effect modification by sex and age.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eElevated admission WBC count is independently associated with increased stroke severity, while lower serum albumin levels are independently associated with prolonged hospitalization in AIS patients. These readily available biomarkers may serve as simple and cost-effective tools for early risk stratification and may guide nutritional and anti-inflammatory interventions in acute stroke care.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e \u003cp\u003eNot applicable. This study was a retrospective cohort study and did not involve a clinical trial.\u003c/p\u003e","manuscriptTitle":"Association of Admission Albumin and White Blood Cell Count with Stroke Severity and Hospital Length of Stay in Acute Ischemic Stroke","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-10 06:45:36","doi":"10.21203/rs.3.rs-9186249/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":"0985fa83-22bf-4215-ad54-d09af656c300","owner":[],"postedDate":"April 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T10:12:09+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-10 06:45:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9186249","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9186249","identity":"rs-9186249","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}