Impact of Prognostic Nutrition Index and Neutrophil/Lymphocyte Ratio on Outcomes in Acute Ischemic Stroke

Impact of Prognostic Nutrition Index and Neutrophil/Lymphocyte Ratio on Outcomes 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 Impact of Prognostic Nutrition Index and Neutrophil/Lymphocyte Ratio on Outcomes in Acute Ischemic Stroke Zeynep Tanrıverdi, Eren Mingsar, Dilan Düztaş, Hatice Sevil, Mensure Çakırgöz, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3846215/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 This study aims to investigate the impact of the prognostic nutrition index and neutrophil-lymphocyte ratio on survival and neurological outcomes in acute ischemic stroke patients at risk of malnutrition who are monitored in Intensive Care Units. Materials and Methods This retrospective study analysed 215 patients with their first ischemic stroke admitted to the Neurology Intensive Care Unit. The prognostic nutritional index was derived from serum albumin and complete blood count within the first 24 hours using this formula: PNI = (serum albumin level [g/dL] × 10) + (total lymphocyte count [mm³] × 0.005) The prognostic nutritional index was categorised into two groups according to a cut-off value of 42.5 determined by ROC analysis. Results During the 60-day follow-up, multivariable logistic regression analysis of neurological prognosis identified the presence of coronary artery disease (Hazard Ratio [HR]: 3.9, p: 0.021), initial NIHSS score (HR: 1.16, p <0.001), and PNI (HR: 0.022, p <0.001) as independent predictors of neurological outcomes. Cox regression analysis for survival in all patients determined age (HR: 1.93, p = 0.009), initial NIHSS score (HR: 1.04, p = 0.008), BUN level (HR: 1.69, p = 0.012), and prognostic nutritional index (HR: 0.27, p = 0.007) as independent determinants of mortality. Conclusion Our findings suggest that simple, cost-effective, and readily applicable biomarkers such as the prognostic nutritional index and the neutrophil-lymphocyte ratio should be considered practical tools in patient management and predicting neurological outcomes. acute ischemic stroke (AIS) malnutrition prognostic nutrition index (PNI) Neutrophil/Lymphocyte ratio neurological progression survival analysis Figures Figure 1 DETAILS PAGE This manuscript complies with all stated instructions for authors. This article has not been published in another journal and is not under review. I confirm that authorship requirements have been met and all authors approved the final manuscript The protocol of the study was reviewed and approved by the Clinical Research Ethics Committee of İzmir Katip Çelebi University (decision number: 0491, date: 26/10/2023). There are no conflicts of interest for all authors. We confirm the use of the reporting checklist There are no costs for this research other than stationery. The researcher covers these expenses. 1. Introduction Acute Ischemic Stroke (AIS) is a leading cause of death and disability globally, posing a significant health challenge for patients requiring neurologic intensive care. According to the World Health Organization reports, AIS affects millions worldwide annually and is associated with high mortality rates (Thayabaranathan et al., 2022 ). The risk of ischemic stroke increases with age, with a twofold increase in risk observed for each decade after the age of 55 (M. Roy-O’Reilly & McCullough, 2018 ). AIS; while men have a higher incidence, women may experience more severe outcomes and higher mortality following a stroke (Petrea et al., 2009 ). Comorbid conditions, including diabetes, atrial fibrillation, dyslipidemia, and obesity, significantly increase the risk of stroke, with untreated high blood pressure being a primary risk factor (Cipolla et al., 2018 ). Nutritional status significantly affects the prognosis and mortality after ischemic stroke. Malnutrition, which is commonly encountered in stroke patients, can negatively impact recovery and increase the risk of mortality (Aquilani et al., 2008 ). Recent studies have highlighted the potential significance of easily detectable systemic inflammatory indices from peripheral blood, such as the systemic immune-inflammation index (SII), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and total bilirubin, in evaluating the prognosis of AIS patients. A retrospective observational study conducted by Hu and colleagues ( 2023 ) using the MIMIC-IV database demonstrated the successful utilisation of these biomarkers in predicting in-hospital mortality following AIS. Onodera and colleagues ( 1984 ) initially used the Prognostic Nutrition Index (PNI) to evaluate surgical outcomes of cancer patients, and it was later associated with mortality and morbidity in AIS patients by Nergis S and colleagues (2023). These studies highlight the critical impact of immunological and nutritional status on hospital length of stay, mortality, and neurological outcomes. In this context, our study titled 'The Effect of Prognostic Nutrition Index and Neutrophil/Lymphocyte Ratio on Acute Ischemic Stroke Outcomes' aims to comprehensively investigate these biomarkers' effects on AIS patients' neurological prognosis. 2. Materials and Methods This study includes 215 patients who were admitted to the Neurology Intensive Care Unit of İzmir Katip Çelebi University Faculty of Medicine Atatürk Education and Research Hospital between September 1, 2020, and November 30, 2021, and diagnosed with an ischemic stroke for the first time. Data from patients in the intensive care unit were retrospectively obtained from the hospital database. Mortality information for deceased patients was acquired through the National Identity Sharing System. PNI was calculated using serum albumin levels (g/dL) and complete blood count for lymphocytes, leukocytes, and neutrophils (10⁹/L) within the first 24 hours of admission. The PNI calculation formula is below: PNI = (serum albumin level [g/dL] × 10) + (total lymphocyte count [mm³] × 0.005). Based on the Modified Rankin Scale (mRS) scores obtained during the two-month follow-up after the stroke, patients with scores between 0–3 were categorised as having a good neurological prognosis, while those scoring between 4–6 were considered to have a poor neurological prognosis. Our study's primary aim is to analyse the effect of PNI and NLR on the 60-day neurological prognosis in intensive care patients who have suffered an AIS. The secondary objective is to examine the impact of PNI and NLR on survival time and their relationships with other variables. 2.1 Statistical Analysis Statistical analyses were conducted using SPSS version 22.0 software. A significance level of p < 0.05 was accepted. ROC (Receiver Operating Characteristic) analysis was performed to achieve optimal sensitivity and specificity for PNI and NLR. The area under the curve (AUC) was calculated for the ROC, and the hypothesis of a value of 0.5 was tested with a 95% confidence interval for AUC. PNI was divided into two groups based on a cut-off value 42.5 obtained from ROC analysis. A cut-off value 4.2 for NLR was similarly used to create two categories. The normal distribution of variables was assessed using visual methods such as histograms and probability plots and analytical methods like Kolmogorov-Smirnov and Shapiro-Wilk tests. Continuous data were summarised using median and interquartile ranges. The Chi-square test or Fisher's exact test was preferred for the analysis of categorical variables. Survival times were calculated using the Kaplan-Meier method. Univariate analyses were applied to identify factors of prognostic importance. Prognostic factors with a p-value < 0.05 were subsequently examined with multivariate logistic and Cox regression analyses. 2.2 Ethical Approval The protocol of the study was reviewed and approved by the İzmir Katip Çelebi University Clinical Research Ethics Committee (Decision number: 0491, date:26/10/2023) 3. Results The average age of the 215 patients included in the study was 68.3 years (the youngest patient was 21, and the oldest was 104). The gender distribution was 58% male (n = 125) and 42% female (n = 90). 21.9% (n = 47) of the patients were smokers, 62.8% (n = 135) had hypertension, 38.6% (n = 83) had diabetes, 23.5% (n = 51) had coronary artery disease, and 25.5% (n = 54) were diagnosed with atrial fibrillation. The median NIHSS score at hospital admission was 10.0, and the median mRS score at the end of the follow-up period was 3. 51.2% (n = 110) of the patients died in the hospital. According to the TOAST classification, large artery atherosclerosis was 54.4% (n = 117), small artery occlusion was 2.3% (n = 5), cardioembolic causes were 20.5% (n = 44), and other undetermined etiologies were 22.8% (n = 49). Looking at the laboratory parameters, the average hemoglobin level was 12.5 g/dL, median blood urea nitrogen (BUN) level was 19.0 mg/dL, platelet count was median 228x10⁹/L, lymphocyte count was median 1.48x10⁹/L, leukocyte count was median 9.2x10⁹/L, average albumin level was 3.3 g/dL, median PNI was 42.0, and median NLR was 4.3. These findings are detailed in Table 1. Table 1 Acute stroke patient demographic data at the time of diagnosis N=215 % Median Mean Sd Min. Max. Age 215 71.0 68.3 15.0 21 104 Gender Male 125 58.0 Famale 90 42.0 Smoking 47 21.9 Hypertension 135 62.8 Diabetes mellitus 83 38.6 Coronary artery diasease 51 23.5 Atrial fibrillation 54 25.5 NIHSS on admission 10.0 9.4 0.41 0 23 mRS score at 2 months 3 3 2 0 6 In-hospital death 110 51.2 TOAST classifications Large-artery atherosclerosis 117 54.4 Small-artery oclusion 5 2.3 Cardioembolic 44 20.5 Other and unknown causes 49 22.8 Hemoglobin gr/dl 12.0 12.5 1.07 6.7 17.0 Platelet count (10⁹/l) 228 240 5.00 17 559 Lymphocyte count (10⁹/l) 1.48 1.63 0.05 0.3 8.0 Leukocyte (10⁹/l) 9.2 10 3.9 1.1 28 Albümin (g/dl) 3.4 3.3 0.04 1.4 4.8 BUN (mg/dl) 19.0 25.9 1.40 10.0 125 PNI 42.0 41.8 9.1 18.9 74 NLR 4.3 6.8 6.1 0.67 30 BUN: Blood Urea Nitrogen, Min:minimum, Max:maximum, mRS: Modified Rankin Scale, NIHSS: National Institutes of Health Stroke Scale, NLR: Neutrophil to Lymphocyte Ratio , PNI: Prognostic Nutritional Index , TOAST: Trial of ORG 10172 in Acute Stroke Treatment This study evaluated the relationship between PNI and clinical and laboratory findings. Our analysis of 215 patients found that patients with a low PNI had a higher median age (p<0.001). At the same time, this group experienced worse functional outcomes in the 2 months following the stroke, with significantly higher mRS scores (p<0.001). The in-hospital mortality rates for patients with low PNI values (78.4%) were significantly higher compared to the high PNI group (22.1%) (p<0.001). This group also showed a higher prevalence of diabetes mellitus and coronary artery disease, 45% and 30.6%, respectively, compared to 31.7% and 16.3% in the high PNI group (p<0.045 and p<0.014). Looking at the laboratory parameters, the low PNI group had lower average hemoglobin (11 g/dl, SD 4.1), lower lymphocyte count (1.1x10^9/l, IQR 0.6), and higher BUN (22 mg/dl, IQR 15) values. Additionally, this group had lower serum albumin levels (average 2.8 g/dl, SD 0.07) (both p<0.001). These findings indicate a strong association between low PNI and poor stroke outcomes and also with inflammation, nutritional status, and metabolic stress. In conclusion, our study reveals that a low PNI is associated with higher age, worse clinical outcomes, increased in-hospital mortality rates, and adverse changes in some laboratory parameters in stroke patients. In light of these findings, using PNI as a potential prognostic marker in clinical practice warrants further investigation. The details of the results are explained in Table 2. Table 2 Relationship between PNI and Laboratory-Clinicopathological Findings Total Low PNI High PNI P n:215 n:111 n:104 Age year median (IQR) 71 (20) 75 (14) 63.5 (23) <0.001 Male n (%) 125 (58.1) 60 (54.1) 65 (62.5) 0.21 Smoking n (%) 47 (21.9) 25 (19.8) 22 (19.8) 0.45 Hypertension n (%) 135 (62.8) 74 (66.7) 61 (58.7) 0.22 Diabetes mellitus n (%) 83 (38.0) 50(45.0) 33 (31.7) 0.045 Coronary artery diasease n (%) 51(23.0) 34 (30.6) 17 (16.3 0.014 Atrial fibrillation n (%) 54 (25.5) 30 (27.0) 24 (23.8) 0.58 NIHSS on admission, median (IQR) 10 (11) 11 (10) 6.0 (10) 0.001 mRS score at 2 months 3.0 (4) 5.0 (2) 2.0 (2) 0.001 In-hospital death 110 (51.1) 87 (78.4) 23 (22.1) < 0.001 TOAST classifications, n (%) Large-artery atherosclerosis 117(54.4) 58(52.3) 59 (56.7) 0.90 Small-artery oclusion 5 (2.3) 3 (2.7) 2 (1.9) 0.65 C ardioembolic 44 (20.5) 23(20.7) 21 (20.2) 0.74 Other and unknown causes 49 (22.8) 27 (24.3) 22 (21.2) 0.83 mRSscore at 2 months >3 n(%) 63 (29.3) 60 (54) 3 (2.8) < 0.001 Hemoglobin mean (SD) 12 (4) 11 (4.1) 13 (2) < 0.001 Platelet count (10⁹/l), median (IQR) 228 (98) 223 (80) 224 (118) 0.15 Lymphocyte count (10⁹/l), median (IQR) 1.48 (1.1) 1.1 (0.6) 2.0 (0.9) < 0.001 Leukocyte (10⁹/l), median (IQR) 9.2 (4.7) 9.0 (3.1) 10.0 (6.7) 0.50 BUN (mg/dl) median (IQR) 19 (15) 22 (15) 10.0 (8) < 0.001 NLR (<4.2) (n%) 106 (49.3) 30 (27.0) 76 (73.1) < 0.001 Albümin (g/dl) mean (SD) 3.36 (0.04) 2.8 (0.07) 3.8 (0.05) 42.5, Low PNI <42.5 In our study's investigation of the relationship between survival and neurological progression, the variables found significant in univariate analysis for neurological progression were age (OR=4.27, p<0.001), male gender (OR=1.94, p=0.035), coronary artery disease (OR=4.5, p<0.001), admission NIHSS value (OR=1.2, p<0.001), hemoglobin levels (OR=0.77, p<0.001), lymphocyte count (OR=0.20, p<0.001), leukocyte count (OR=1.085, p=0.041), albumin level (OR=0.01, p<0.001), BUN (OR=1.09, p=0.009), PNI (OR=0.013, p<0.001), and NLR (OR=0.17, p<0.001). Independent predictors for neurological progression in multivariate analysis were coronary artery disease (HR=3.92, p=0.021), admission NIHSS value (HR=1.16, p=0.001), and PNI (HR =0.022, p<0.001). For overall survival, the variables found significant in univariate analysis were age (OR=2.97, p<0.001), coronary artery disease (OR=1.69, p=0.02), presence of atrial fibrillation (OR=1.2, p=0.03), admission NIHSS value (OR=0.007, p<0.001), lymphocyte count (OR=0.25, p<0.001), leukocyte count (OR=1.065, p=0.002), albumin level (OR=0.25, p<0.001), BUN (OR=1.02, p<0.001), and NLR (OR=4.05, p<0.001). In multivariate analysis, independent predictors for overall survival were age (HR=1.93, p=0.009), admission NIHSS value (HR=1.04, p=0.008), lymphocyte count (HR=1.7, p=0.041), BUN (HR=1.69, p=0.012), and PNI (HR=0.27, p=0.007). These findings were obtained using multivariate models that consider confounding factors to determine the independent effect of each variable and are specified in Table 3. The Kaplan-Meier graph related to overall survival shows a survival curve consistent with our findings, as depicted in Figure 1. Table 3 Univariate and multivariate logistic regression analysis and survival analysis Neurological Progression Overall Survival Univariable Multivariable Univariable Multivariable Variables OR (95% CI) P HR (95% CI) P OR (95% CI) P HR (95% CI) P Age 4.27 <0.001 2.97 <0.001 1.93 0.009 Gender man 1.94 0.035 1.2 0.33 Smoking 0.96 0.93 1.19 0.43 Hypertension 1.2 0.58 0.99 0.98 Diabetes mellitus 1.03 <0.001 1.38 0.91 Coronary artery diasease 4.5 <0.001 3.92 0.021 1.69 0.02 Atrial fibrillation 2.1 0.065 1.2 0.03 TOAST classifications Large-artery atherosclerosis referance referance Small-artery oclusion 2.4 0.21 1.19 0.45 Cardioembolic 0.73 0.41 0.99 0.97 Other and unknown causes 0.73 0.79 1.6 0.42 NIHSS on admission 1.2 <0.001 1.16 0.001 1.02 <0.001 1.04 0.008 Hemoglobin gr/dl 0.77 <0.001 0.93 0.12 Platelet count (10⁹/l) 0.99 0.064 0.99 0.009 Lymphocyte count (10⁹/l) 0.20 <0.001 0.25 <0.001 1.7 0.041 Leukocyte (10⁹/l) 1.085 0.041 1.065 0.002 Albümin (g/dl) 0.01 <0.001 0.25 <0.001 BUN (mg/dl) 1.09 0.009 1.02 <0.001 1.69 0.012 PNI 0.013 <0.001 0.022 <0.001 0.16 <0.001 0.27 0.007 NLR 0.17 <0.001 4.05 <0.001 BUN: Blood Urea Nitrogen, mRS: Modified Rankin Scale, NIHSS: National Institutes of Health Stroke Scale, NLR: Neutrophil to Lymphocyte Ratio , PNI: Prognostic Nutritional Index , TOAST: Trial of ORG 10172 in Acute Stroke Treatment 4. Discussion The relationship between age and outcomes in ischemic stroke is a pivotal factor for management and prognosis. Our study, including a broad age range of stroke patients, highlights the significant influence of age on morbidity and mortality. Our findings align with the literature that older patients have worse neurological prognosis. Roy-O’Reilly et al. ( 2020 ) have shown that age enhances the pro-inflammatory functions of neutrophils after ischemic stroke, leading to poorer outcomes in older people. This suggests that the observed high morbidity and mortality in older stroke patients in our study could stem from an age-related increase in inflammatory response. Furthermore, our study distinctly observed the interaction between atrial fibrillation (AF) and coronary artery disease (CAD) and their effects on mortality and neurological progression in ischemic stroke patients. AF, a common cardiac arrhythmia, was associated with an increased risk of ischemic stroke, corroborating findings by Hudzik et al. (2019). This study also underscores the crucial role of antithrombotic therapy in preventing thromboembolic events in AF patients. Our observations indicate that CAD in our patients is linked with higher mortality rates and more severe stroke outcomes, consistent with Zhao et al. ( 2019 ), who reported that CAD could exacerbate stroke outcomes. The co-occurrence of CAD and AF complicates stroke patient management, increasing the risk of both stroke and cardiac complications (Lip, 2007 ). The concurrent use of anticoagulant and antiplatelet therapies is vital in preventing stroke recurrence and reducing cardiac event risks (Berge & Sandercock, 2002 ). In our study, we conclude that treatment strategies for patients diagnosed with AF and CAD should be individualised, considering each patient's individual risk profile and clinical status. The choice and dosage of anticoagulant therapy are crucial in these patients. The NIHSS score is a critical tool for evaluating ischemic stroke severity and predicting patient outcomes, serving as a cornerstone of stroke assessment globally. Consistent with the extensive literature, our study reveals that high NIHSS scores at admission strongly correlate with increased morbidity and mortality. Zöllner et al. ( 2020 ) found that the risk of acute symptomatic seizures in ischemic stroke patients significantly increases with higher NIHSS scores at admission. This supports our observation about the predictive value of the NIHSS score for stroke severity and subsequent outcomes. Khalifa et al. ( 2020 ) noted that high NIHSS scores of 15 and above predict the need for intensive care and the risk of mortality in ischemic stroke patients. This further supports our findings on the guiding role of the NIHSS score in treatment decisions. Finally, Elatroush et al. ( 2023 ) emphasised that a high NIHSS score at hospital admission is an independent early predictor of functional outcomes in acute ischemic stroke patients. This reinforces the importance of NIHSS in early prognosis and management strategies. BUN levels have been identified as a significant prognostic factor for ischemic stroke. We observed a significant association between high BUN levels and increased mortality, in line with recent global research. You et al. ( 2018 ) demonstrated that increased BUN at admission is a significant prognostic marker linked with in-hospital mortality in AIS patients. Bae et al. ( 2021 ) examined the blood urea nitrogen to serum albumin ratio (B/A ratio) in patients with acute ischemic stroke (AIS). The study identified a high B/A ratio as a significant indicator for mortality and intensive care unit admission and suggested that this ratio, combining BUN levels and serum albumin, could be a valuable tool for predicting stroke severity and patient outcomes. NLR, a nutrition parameter and a focus of our study, has been established as a significant prognostic marker for ischemic stroke associated with both mortality and morbidity. Our study observed a positive correlation between high NLR values and increased mortality and morbidity, mirroring findings in the current literature. İyigündoğdu et al. (2021) determined that NLR measured at patient admission positively related to stroke severity, short-term functional outcomes, and mortality in acute ischemic stroke patients. This corroborates our findings that a high NLR is linked with poorer clinical outcomes in stroke patients. In the same way, Wu ( 2023 ) revealed that NLR in patients undergoing intravenous thrombolysis for acute ischemic stroke is an effective indicator for predicting three-month outcomes and mortality. The research findings suggest that a high NLR can negatively affect long-term results. In a study on AIS patients by Wirawan and Widyadharma (2021), it was demonstrated that high NLR following the acute phase of the event could adversely affect long-term outcomes, such as the recurrence of ischemic stroke, morbidity, and mortality. Additionally, Majid et al. ( 2021 ) have reported that in patients with acute ischemic stroke receiving thrombolysis treatment, high NLR values are associated with poor functional outcomes after three months. These findings align with our observations on the predictive value of NLR in the management and rehabilitation phases of acute stroke. Another scale we evaluated in our study, the PNI, was initially utilised as an indicator of nutrition and inflammation. It has subsequently garnered attention for its predictive value in ischemic stroke outcomes. We observed that low PNI values were significantly associated with high mortality and morbidity rates, a finding that aligns with the current literature. Xiang et al. ( 2020 ) demonstrated that PNI is an independent predictor of three-month outcomes in patients with ischemic stroke who underwent thrombolysis, with lower PNI values indicating worse outcomes. This is consistent with our study's findings that emphasise the importance of PNI in predicting stroke recovery. Liu et al. ( 2022 ) examined the PNI in patients with critical stroke conditions and determined it to be an independent prognosticator of 30-day, 90-day, and 1-year mortality rates. Additionally, they described a U-shaped association between PNI and all-cause mortality, proposing a multifaceted interplay among nutrition, inflammation, and stroke sequelae. Nergiz and Ozturk ( 2023 ) concentrated on the association between Prognostic Nutritional Index (PNI) and infection in patients with acute ischemic stroke (AIS). They found that lower PNI scores are associated with higher infection rates and increased mortality in these patients. This finding corroborates our observations on the extensive influence of PNI on stroke prognosis. In a recent study by Liu et al. ( 2023 ), the PNI has been highlighted as a potential predictive marker for perioperative ischemic stroke, delineating its significance across various stroke-associated outcomes. These literature reviews substantiate that PNI is a beneficial parameter for predicting the clinical trajectory of patients with acute ischemic stroke. This study is subject to several significant limitations. Firstly, it is a single-center study, which may restrict the applicability of the findings to the general population or to individuals with different geographic and demographic profiles. Secondly, the values measured in this study were collected post-event, which does not account for dynamic changes at the time of the event. Thirdly, due to the retrospective design, we did not evaluate patients' post-event nutritional habits and the potential impact of these habits on health outcomes. Moreover, the retrospective nature of the study raises concerns about the accuracy of data collection looking back in time, which could affect the veracity of the findings. Lastly, the study lacks dynamic measurements, limiting understanding of the event's effects over time and the process itself. These limitations should be considered when interpreting the findings and addressed in future research endeavors to fill in these gaps. 5. Conclusion Our findings show that NLR is significant in univariate analysis and may serve as an initial indicator for stroke outcomes. However, based on the results of multivariate analysis, PNI emerges as a more prominent prognostic factor. This distinction highlights the multifactorial nature of stroke prognosis. PNI offers a more comprehensive perspective on the patient's overall health status by reflecting on nutritional status and the inflammatory response. We recommend a holistic assessment beyond individual biomarkers to consider multiple biological indicators. This approach aligns with the current medical research and practice trend, which increasingly recognises the value of multifactorial analysis in understanding complex health conditions. Enhancing the accuracy and granularity of our prognostic assessments with a wide range of biomarkers can ultimately lead to more informed and effective patient care strategies in ischemic stroke treatment. Declarations Funding: Not applicable (N/A) Conflict of Interest: Not applicable (N/A) Ethical approval: This study was approved by the Katip Çelebi University Ethics Committee (Decision No: 0491). Informed consent: Informed consent was obtained from all individual participants included in the study. Author contribution: - Zeynep Tanrıverdi designed the study and wrote the main manuscript, setting the research foundation. - Eren Mingsar performed data analysis, enriching the discussion with in-depth insights. - Dilan Düztaş, Hatice Sevil, and Enise Nur Özlem Tiryaki reviewed the literature across disciplines, broadening the study's perspective and informing the discussion. - Mensure Çakırgöz mentored the team, guided the project direction, and ensured the scientific rigor in the findings, adding to the study's trustworthiness. Data Availability Statement: Data used in this study are available from Dr. Zeynep Tanrıverdi upon reasonable request. References Aquilani R, Scocchi M, Boschi F, Viglio S, Iadarola P, Pastoris O, Verri M (2008) Effect of calorie-protein supplementation on the cognitive recovery of patients with subacute stroke. Nutr Neurosci 11(5):235–240. https://doi.org/10.1179/147683008X301586 Bae SJ, Lee SH, Yun SJ, Kim K (2021) Usefulness of the blood urea nitrogen-to-serum albumin ratio as a prognostic indicator of severity in acute ischemic stroke. Signa Vitae 17(4):163–170. https://doi.org/10.22514/sv.2021.028 Berge E, Sandercock PA (2002) Anticoagulants versus antiplatelet agents for acute ischaemic stroke. Cochrane Database of Systematic Reviews. 4 https://doi.org/10.1002/14651858.CD003242 Cipolla MJ, Liebeskind DS, Chan SL (2018) The importance of comorbidities in ischemic stroke: Impact of hypertension on the cerebral circulation. J Cereb Blood Flow Metabolism 38(12):2129. https://doi.org/10.1177/0271678X18800589 Elatroush H, Ragab D, Mashhour K, Afify M, Salah A (2023) Early prognostication of ischemic stroke using computed tomography perfusion and S100 beta level. Res Opin Anesth Intensive Care 10(2):122. https://doi.org/10.4103/ROAIC.ROAIC_86_21 Hu X, Liang J, Hao W, Zhou J, Gao Y, Gong X, Liu Y (2023) Prognostic value of inflammatory markers for in-hospital mortality in intensive care patients with acute ischemic stroke: a retrospective observational study based on MIMIC-IV. Front Neurol 14:1174711. https://doi.org/10.3389/FNEUR.2023.1174711/BIBTEX Hudzik B, Registry TrAT (TREAT), Desperak A, Registry TrAT (TREAT), Freedman B, Registry, Tr. A. T. (TREAT), Nielsen, PB, Registry TrAT (TREAT), Larsen TB, Registry, Tr AT (TREAT), Desperak P, Registry TrAT (TREAT), Trzeciak P, Registry TAT (TREAT), Gasior, M.,Registry, Tr. A. T. (TREAT) (2019) P3760Temporal trends in stroke prevalence and its associations with changing patterns of antithrombotic regimens in patients with atrial fibrillation and a wide spectrum of coronary artery disease. European Heart Journal , 40 (Supplement_1). https://doi.org/10.1093/EURHEARTJ/EHZ745.0612 Iyigundogdu I, Derle E, Kibaroglu S, Can U (n.d.). Neutrophil to lymphocyte ratio, stroke severity and short term clinical outcomes in acute ischemic stroke . https://doi.org/10.54029/2021cmz Khalifa A, Al Kharusi A, Al Jadidi A, Malik MA, Khan RM (2020) Saudi Journal of Medicine Predicting the Need for ICU Care in Patients with Acute Ischemic Stroke. Saudi J Med. https://doi.org/10.36348/sjm.2020.v05i12.001 Lip GYH (2007) Coronary artery disease and ischemic stroke in atrial fibrillation. Chest 132(1):8–10. https://doi.org/10.1378/chest.07-0500 Liu M, Sun M, Zhang T, Li P, Liu J, Liu Y, Song Y, Liu S, Yang H, Zhou Z, Chang D, Wang G, Mi W, Ma Y (2023) Prognostic Nutritional Index (PNI) as a potential predictor and intervention target for perioperative ischemic stroke: a retrospective cohort study. BMC Anesthesiol 23(1):1–10. https://doi.org/10.1186/S12871-023-02216-8/FIGURES/3 Liu Y, Yang X, Kadasah S, Peng C (2022) Clinical Value of the Prognostic Nutrition Index in the Assessment of Prognosis in Critically Ill Patients with Stroke: A Retrospective Analysis. Computational and Mathematical Methods in Medicine , 2022 . https://doi.org/10.1155/2022/4889920 Majid S, Lodhi OUH, Niazi AK, Lodhi SUH, Siddiqui M (2021) Usefulness of neutrophil-to-lymphocyte ratio as a predictor of functional outcome in patients with acute ischemic stroke after thrombolysis therapy. Baylor University Medical Center Proceedings , 34 (6), 664–667. https://doi.org/10.1080/08998280.2021.1938471 Nergiz S, Ozturk U (2023) The Effect of Prognostic Nutritional Index on Infection in Acute Ischemic Stroke Patients. Med (Kaunas Lithuania) 59(4). https://doi.org/10.3390/MEDICINA59040679 Onodera T, Goseki N, zasshi GK-N, geka gakkai (1984) & undefined. (n.d.). Prognostic nutritional index in gastrointestinal surgery of malnourished cancer patients. Europepmc.OrgT Onodera, N Goseki, G KosakiNihon Geka Gakkai Zasshi, 1984•europepmc.Org . Retrieved December 15, 2023, from https://europepmc.org/article/med/6438478 Petrea RE, Beiser AS, Seshadri S, Kelly-Hayes M, Kase CS, Wolf PA (2009) Gender differences in stroke incidence and poststroke disability in the Framingham heart study. Stroke 40(4):1032–1037. https://doi.org/10.1161/STROKEAHA.108.542894 Roy-O’Reilly MA, Ahnstedt H, Spychala MS, Munshi Y, Aronowski J, Sansing LH, McCullough LD (2020) Aging exacerbates neutrophil pathogenicity in ischemic stroke. Aging 12(1):436. https://doi.org/10.18632/AGING.102632 Roy-O’Reilly M, McCullough LD (2018) Age and Sex Are Critical Factors in Ischemic Stroke Pathology. Endocrinology 159(8):3120–3131. https://doi.org/10.1210/EN.2018-00465 Thayabaranathan T, Kim J, Cadilhac DA, Thrift AG, Donnan GA, Howard G, Howard VJ, Rothwell PM, Feigin V, Norrving B, Owolabi M, Pandian J, Liu L, Olaiya MT (2022) Global stroke statistics 2022. Int J Stroke 17(9):946–956. https://doi.org/10.1177/17474930221123175/ASSET/IMAGES/LARGE/10.1177_17474930221123175-FIG11.JPEG Wirawan C, Putu I, Widyadharma E (2021) Neutrophil-to-lymphocyte ratio is modality to predict recurrence, disability and mortality of ischemic stroke following acute phase. Romanian JouRnal of NeuRology , XX (3). https://doi.org/10.37897/RJN.2021.3.1 Wu Q, Chen HS (2023) Neutrophil-to-lymphocyte ratio and its changes predict the 3-month outcome and mortality in acute ischemic stroke patients after intravenous thrombolysis. Brain and Behavior 13(9). https://doi.org/10.1002/BRB3.3162 Xiang W, Chen X, Ye W, Li J, Zhang X, Xie D (2020) Prognostic Nutritional Index for Predicting 3-Month Outcomes in Ischemic Stroke Patients Undergoing Thrombolysis. Frontiers in Neurology , 11 . https://doi.org/10.3389/FNEUR.2020.00599 You S, Zheng D, Zhong C, Wang X, Tang W, Sheng L, Zheng C, Cao Y, Liu CF (2018) Prognostic Significance of Blood Urea Nitrogen in Acute Ischemic Stroke. Circulation Journal: Official Journal of the Japanese Circulation Society 82(2):572–578. https://doi.org/10.1253/CIRCJ.CJ-17-0485 Zhao Q, Wang L, Kurlansky PA, Schein J, Baser O, Berger JS (2019) Cardiovascular outcomes among elderly patients with heart failure and coronary artery disease and without atrial fibrillation: A retrospective cohort study. BMC Cardiovasc Disord 19(1):1–10. https://doi.org/10.1186/S12872-018-0991-1/FIGURES/4 Zöllner JP, Misselwitz B, Kaps M, Stein M, Konczalla J, Roth C, Krakow K, Steinmetz H, Rosenow F, Strzelczyk A (2020) National Institutes of Health Stroke Scale (NIHSS) on admission predicts acute symptomatic seizure risk in ischemic stroke: a population-based study involving 135,117 cases. Scientific Reports 2020 10:1 , 10 (1), 1–7. https://doi.org/10.1038/s41598-020-60628-9 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3846215","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":266284080,"identity":"f4adf4b2-a114-4223-99d6-0429c941c2aa","order_by":0,"name":"Zeynep Tanrıverdi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYDCCAzAGewOQMLAgRQsPiGUgQYoWiQQwSVgH3+3jDx/ztt2RM5d8fnXDjwIJBv727gS8WiTPJSQb87Y9M7acnVN2swfoMIkzZzfg1WJwhuGYNG/b4cQNt3PSbvAAtRhI5BLSwtj+G6ilfsPNM2k3/xCnhZmNGaglweAG+7HbRNkieYaNWXLOucOGO3ty2G7LGEjwEPQL3xn2hx/elB2WN2c//uzmmz82cvztvfi1gAATLxvQhQw8BiAOD0HlIMD44w9IC/sDolSPglEwCkbByAMAxrhLSUSyLGgAAAAASUVORK5CYII=","orcid":"","institution":"Izmir Kâtip Çelebi University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Zeynep","middleName":"","lastName":"Tanrıverdi","suffix":""},{"id":266284081,"identity":"ae427797-e746-4003-a22b-7757e3eab4ec","order_by":1,"name":"Eren Mingsar","email":"","orcid":"","institution":"Izmir Kâtip Çelebi University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Eren","middleName":"","lastName":"Mingsar","suffix":""},{"id":266284082,"identity":"865f8260-4136-40c7-837b-fbae5484d9a9","order_by":2,"name":"Dilan Düztaş","email":"","orcid":"","institution":"Izmir Kâtip Çelebi University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dilan","middleName":"","lastName":"Düztaş","suffix":""},{"id":266284083,"identity":"10b95bf2-b73a-443e-92b1-dba36e982a16","order_by":3,"name":"Hatice Sevil","email":"","orcid":"","institution":"Izmir Kâtip Çelebi University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hatice","middleName":"","lastName":"Sevil","suffix":""},{"id":266284084,"identity":"f87ca310-951a-4295-ab1a-de3f1df26133","order_by":4,"name":"Mensure Çakırgöz","email":"","orcid":"","institution":"Izmir Tepecik Eğitim ve Araştırma Hastanesi","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mensure","middleName":"","lastName":"Çakırgöz","suffix":""},{"id":266284085,"identity":"a7e657cd-faa5-47ea-98c6-89a7bfea9e93","order_by":5,"name":"Enise Nur Özlem Tiryaki","email":"","orcid":"","institution":"Izmir Kâtip Çelebi University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Enise","middleName":"Nur Özlem","lastName":"Tiryaki","suffix":""}],"badges":[],"createdAt":"2024-01-08 19:29:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3846215/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3846215/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49540590,"identity":"32a21a3c-270c-4c58-959f-36682c2b7b2e","added_by":"auto","created_at":"2024-01-12 17:19:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":336446,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePNI and NLR Kaplan Meier survival chart results of our article\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3846215/v1/9e870898d67df834240b84f5.png"},{"id":51616214,"identity":"3180b577-d6c2-4794-b5aa-cd8145abed9f","added_by":"auto","created_at":"2024-02-26 03:49:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":458061,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3846215/v1/41adf475-4e4f-441e-a3c6-8a2c2d53f96a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eImpact of Prognostic Nutrition Index and Neutrophil/Lymphocyte Ratio on Outcomes in Acute Ischemic Stroke\u003c/p\u003e","fulltext":[{"header":"DETAILS PAGE","content":"\u003col\u003e\n \u003cli\u003eThis manuscript\u0026nbsp;complies with all stated instructions for authors.\u003c/li\u003e\n \u003cli\u003eThis article has not been published in another journal and is not under review.\u003c/li\u003e\n \u003cli\u003eI\u0026nbsp;confirm that authorship requirements have been met and all authors approved the final manuscript\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eThe protocol of the study was reviewed and approved by the Clinical Research Ethics Committee of İzmir Katip \u0026Ccedil;elebi University (decision number: 0491, date: 26/10/2023).\u003c/li\u003e\n \u003cli\u003eThere are no conflicts of interest for all authors.\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"6\" type=\"1\"\u003e\n \u003cli\u003eWe confirm the use of the reporting checklist\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eThere are no costs for this research other than stationery. The researcher covers these expenses.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eAcute Ischemic Stroke (AIS) is a leading cause of death and disability globally, posing a significant health challenge for patients requiring neurologic intensive care. According to the World Health Organization reports, AIS affects millions worldwide annually and is associated with high mortality rates (Thayabaranathan et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The risk of ischemic stroke increases with age, with a twofold increase in risk observed for each decade after the age of 55 (M. Roy-O\u0026rsquo;Reilly \u0026amp; McCullough, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). AIS; while men have a higher incidence, women may experience more severe outcomes and higher mortality following a stroke (Petrea et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Comorbid conditions, including diabetes, atrial fibrillation, dyslipidemia, and obesity, significantly increase the risk of stroke, with untreated high blood pressure being a primary risk factor (Cipolla et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNutritional status significantly affects the prognosis and mortality after ischemic stroke. Malnutrition, which is commonly encountered in stroke patients, can negatively impact recovery and increase the risk of mortality (Aquilani et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Recent studies have highlighted the potential significance of easily detectable systemic inflammatory indices from peripheral blood, such as the systemic immune-inflammation index (SII), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and total bilirubin, in evaluating the prognosis of AIS patients. A retrospective observational study conducted by Hu and colleagues (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) using the MIMIC-IV database demonstrated the successful utilisation of these biomarkers in predicting in-hospital mortality following AIS. Onodera and colleagues (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1984\u003c/span\u003e) initially used the Prognostic Nutrition Index (PNI) to evaluate surgical outcomes of cancer patients, and it was later associated with mortality and morbidity in AIS patients by Nergis S and colleagues (2023). These studies highlight the critical impact of immunological and nutritional status on hospital length of stay, mortality, and neurological outcomes. In this context, our study titled 'The Effect of Prognostic Nutrition Index and Neutrophil/Lymphocyte Ratio on Acute Ischemic Stroke Outcomes' aims to comprehensively investigate these biomarkers' effects on AIS patients' neurological prognosis.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e This study includes 215 patients who were admitted to the Neurology Intensive Care Unit of İzmir Katip Çelebi University Faculty of Medicine Atatürk Education and Research Hospital between September 1, 2020, and November 30, 2021, and diagnosed with an ischemic stroke for the first time. Data from patients in the intensive care unit were retrospectively obtained from the hospital database. Mortality information for deceased patients was acquired through the National Identity Sharing System. PNI was calculated using serum albumin levels (g/dL) and complete blood count for lymphocytes, leukocytes, and neutrophils (10⁹/L) within the first 24 hours of admission.\u003c/p\u003e \u003cp\u003eThe PNI calculation formula is below:\u003c/p\u003e \u003cp\u003ePNI = (serum albumin level [g/dL] × 10) + (total lymphocyte count [mm³] × 0.005).\u003c/p\u003e \u003cp\u003eBased on the Modified Rankin Scale (mRS) scores obtained during the two-month follow-up after the stroke, patients with scores between 0–3 were categorised as having a good neurological prognosis, while those scoring between 4–6 were considered to have a poor neurological prognosis.\u003c/p\u003e \u003cp\u003eOur study's primary aim is to analyse the effect of PNI and NLR on the 60-day neurological prognosis in intensive care patients who have suffered an AIS. The secondary objective is to examine the impact of PNI and NLR on survival time and their relationships with other variables.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Statistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were conducted using SPSS version 22.0 software. A significance level of p \u0026lt; 0.05 was accepted. ROC (Receiver Operating Characteristic) analysis was performed to achieve optimal sensitivity and specificity for PNI and NLR. The area under the curve (AUC) was calculated for the ROC, and the hypothesis of a value of 0.5 was tested with a 95% confidence interval for AUC. PNI was divided into two groups based on a cut-off value 42.5 obtained from ROC analysis. A cut-off value 4.2 for NLR was similarly used to create two categories.\u003c/p\u003e \u003cp\u003eThe normal distribution of variables was assessed using visual methods such as histograms and probability plots and analytical methods like Kolmogorov-Smirnov and Shapiro-Wilk tests. Continuous data were summarised using median and interquartile ranges. The Chi-square test or Fisher's exact test was preferred for the analysis of categorical variables. Survival times were calculated using the Kaplan-Meier method. Univariate analyses were applied to identify factors of prognostic importance. Prognostic factors with a p-value \u0026lt; 0.05 were subsequently examined with multivariate logistic and Cox regression analyses.\u003c/p\u003e \u003c/div\u003e\n\u003ch2\u003e2.2 Ethical Approval\u003c/h2\u003e \u003cp\u003e The protocol of the study was reviewed and approved by the İzmir Katip Çelebi University Clinical Research Ethics Committee (Decision number: 0491, date:26/10/2023)\u003c/p\u003e "},{"header":"3. Results","content":"\u003cp\u003eThe average age of the 215 patients included in the study was 68.3 years (the youngest patient was 21, and the oldest was 104). The gender distribution was 58% male (n = 125) and 42% female (n = 90). 21.9% (n = 47) of the patients were smokers, 62.8% (n = 135) had hypertension, 38.6% (n = 83) had diabetes, 23.5% (n = 51) had coronary artery disease, and 25.5% (n = 54) were diagnosed with atrial fibrillation. The median NIHSS score at hospital admission was 10.0, and the median mRS score at the end of the follow-up period was 3.\u003c/p\u003e\u003cp\u003e51.2% (n = 110) of the patients died in the hospital. According to the TOAST classification, large artery atherosclerosis was 54.4% (n = 117), small artery occlusion was 2.3% (n = 5), cardioembolic causes were 20.5% (n = 44), and other undetermined etiologies were 22.8% (n = 49). Looking at the laboratory parameters, the average hemoglobin level was 12.5 g/dL, median blood urea nitrogen (BUN) level was 19.0 mg/dL, platelet count was median 228x10⁹/L, lymphocyte count was median 1.48x10⁹/L, leukocyte count was median 9.2x10⁹/L, average albumin level was 3.3 g/dL, median PNI was 42.0, and median NLR was 4.3. These findings are detailed in Table\u0026nbsp;1.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 1 Acute stroke patient demographic data at the time of diagnosis\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.611570247933884%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003eN=215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003eSd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003eMin.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003eMax.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.611570247933884%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003e215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e71.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e68.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e15.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.611570247933884%\" valign=\"top\"\u003e\n \u003cp\u003eGender\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.611570247933884%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e58.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.611570247933884%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Famale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e42.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.611570247933884%\" valign=\"top\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e21.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.611570247933884%\" valign=\"top\"\u003e\n \u003cp\u003eHypertension\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e62.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.611570247933884%\" valign=\"top\"\u003e\n \u003cp\u003eDiabetes mellitus\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e38.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.611570247933884%\" valign=\"top\"\u003e\n \u003cp\u003eCoronary artery diasease\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e23.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.611570247933884%\" valign=\"top\"\u003e\n \u003cp\u003eAtrial fibrillation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003e54\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e25.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.611570247933884%\" valign=\"top\"\u003e\n \u003cp\u003eNIHSS on admission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.611570247933884%\" valign=\"top\"\u003e\n \u003cp\u003emRS score at 2 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.611570247933884%\" valign=\"top\"\u003e\n \u003cp\u003eIn-hospital death\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e51.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.611570247933884%\" valign=\"top\"\u003e\n \u003cp\u003eTOAST classifications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.611570247933884%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Large-artery atherosclerosis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e54.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.611570247933884%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Small-artery oclusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.611570247933884%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Cardioembolic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e20.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.611570247933884%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Other and unknown causes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e22.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.611570247933884%\" valign=\"top\"\u003e\n \u003cp\u003eHemoglobin gr/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e17.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.611570247933884%\" valign=\"top\"\u003e\n \u003cp\u003ePlatelet count (10⁹/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e559\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.611570247933884%\" valign=\"top\"\u003e\n \u003cp\u003eLymphocyte count (10⁹/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.611570247933884%\" valign=\"top\"\u003e\n \u003cp\u003eLeukocyte (10⁹/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.611570247933884%\" valign=\"top\"\u003e\n \u003cp\u003eAlb\u0026uuml;min (g/dl)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.611570247933884%\" valign=\"top\"\u003e\n \u003cp\u003eBUN (mg/dl)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e19.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e25.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.611570247933884%\" valign=\"top\"\u003e\n \u003cp\u003ePNI\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e42.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e41.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e18.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.611570247933884%\" valign=\"top\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"8\" valign=\"top\"\u003e\n \u003cp\u003eBUN: Blood Urea Nitrogen, Min:minimum, Max:maximum, mRS: Modified Rankin Scale, NIHSS: National Institutes of Health Stroke Scale, NLR: Neutrophil to Lymphocyte Ratio , PNI: Prognostic Nutritional Index , TOAST: Trial of ORG 10172 in Acute Stroke Treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThis study evaluated the relationship between PNI and clinical and laboratory findings. Our analysis of \u0026nbsp;215 patients found that patients with a low PNI had a higher median age (p\u0026lt;0.001). At the same time, this group experienced worse functional outcomes in the 2 months following the stroke, with significantly higher mRS scores (p\u0026lt;0.001). The in-hospital mortality rates for patients with low PNI values (78.4%) were significantly higher compared to the high PNI group (22.1%) (p\u0026lt;0.001). This group also showed a higher prevalence of diabetes mellitus and coronary artery disease, 45% and 30.6%, respectively, compared to 31.7% and 16.3% in the high PNI group (p\u0026lt;0.045 and p\u0026lt;0.014). Looking at the laboratory parameters, the low PNI group had lower average hemoglobin (11 g/dl, SD 4.1), lower lymphocyte count (1.1x10^9/l, IQR 0.6), and higher BUN (22 mg/dl, IQR 15) values. Additionally, this group had lower serum albumin levels (average 2.8 g/dl, SD 0.07) (both p\u0026lt;0.001). These findings indicate a strong association between low PNI and poor stroke outcomes and also with inflammation, nutritional status, and metabolic stress.\u003c/p\u003e\n\u003cp\u003eIn conclusion, our study reveals that a low PNI is associated with higher age, worse clinical outcomes, increased in-hospital mortality rates, and adverse changes in some laboratory parameters in stroke patients. In light of these findings, using PNI as a potential prognostic marker in clinical practice warrants further investigation. The details of the results are explained in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e \u003cstrong\u003eRelationship between PNI and Laboratory-Clinicopathological Findings\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.21854304635762%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003eLow PNI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003eHigh PNI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.562913907284768%\" valign=\"top\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.21854304635762%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003en:215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003en:111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003en:104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.562913907284768%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.21854304635762%\" valign=\"top\"\u003e\n \u003cp\u003eAge year median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e71 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e75 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e63.5 (23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.562913907284768%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.21854304635762%\" valign=\"top\"\u003e\n \u003cp\u003eMale n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e125 (58.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e60 (54.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e65 (62.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.562913907284768%\" valign=\"top\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.21854304635762%\" valign=\"top\"\u003e\n \u003cp\u003eSmoking n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e47 (21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e25 (19.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e22 (19.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.562913907284768%\" valign=\"top\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.21854304635762%\" valign=\"top\"\u003e\n \u003cp\u003eHypertension n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e135 (62.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e74 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e61 (58.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.562913907284768%\" valign=\"top\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.21854304635762%\" valign=\"top\"\u003e\n \u003cp\u003eDiabetes mellitus n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e83 (38.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e50(45.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e33 (31.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.562913907284768%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.045\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.21854304635762%\" valign=\"top\"\u003e\n \u003cp\u003eCoronary artery diasease n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e51(23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e34 (30.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e17 (16.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.562913907284768%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.21854304635762%\" valign=\"top\"\u003e\n \u003cp\u003eAtrial fibrillation n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e54 (25.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e30 (27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e24 (23.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.562913907284768%\" valign=\"top\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.21854304635762%\" valign=\"top\"\u003e\n \u003cp\u003eNIHSS on admission, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e10 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e11 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e6.0 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.562913907284768%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.21854304635762%\" valign=\"top\"\u003e\n \u003cp\u003emRS score at 2 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e3.0 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e5.0 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e2.0 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.562913907284768%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.21854304635762%\" valign=\"top\"\u003e\n \u003cp\u003eIn-hospital death\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e110 (51.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e87 (78.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e23 (22.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.562913907284768%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.21854304635762%\" valign=\"top\"\u003e\n \u003cp\u003eTOAST classifications, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.562913907284768%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.21854304635762%\" valign=\"top\"\u003e\n \u003cp\u003eLarge-artery atherosclerosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e117(54.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e58(52.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e59 (56.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.562913907284768%\" valign=\"top\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.21854304635762%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Small-artery oclusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e5 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e3 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e2 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.562913907284768%\" valign=\"top\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.21854304635762%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; C ardioembolic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e44 (20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e23(20.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e21 (20.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.562913907284768%\" valign=\"top\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.21854304635762%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Other and unknown causes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e49 (22.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e27 (24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e22 (21.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.562913907284768%\" valign=\"top\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.21854304635762%\" valign=\"top\"\u003e\n \u003cp\u003emRSscore at 2 months \u0026gt;3 n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e63 (29.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e60 (54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e3 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.562913907284768%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.21854304635762%\" valign=\"top\"\u003e\n \u003cp\u003eHemoglobin mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e12 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e11 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e13 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.562913907284768%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.21854304635762%\" valign=\"top\"\u003e\n \u003cp\u003ePlatelet count (10⁹/l), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e228 (98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e223 (80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e224 (118)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.562913907284768%\" valign=\"top\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.21854304635762%\" valign=\"top\"\u003e\n \u003cp\u003eLymphocyte count (10⁹/l), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e1.48 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e1.1 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e2.0 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.562913907284768%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.21854304635762%\" valign=\"top\"\u003e\n \u003cp\u003eLeukocyte (10⁹/l), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e9.2 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e9.0 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e10.0 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.562913907284768%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.50\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.21854304635762%\" valign=\"top\"\u003e\n \u003cp\u003eBUN (mg/dl) median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e19 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e22 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e10.0 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.562913907284768%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.21854304635762%\" valign=\"top\"\u003e\n \u003cp\u003eNLR (\u0026lt;4.2) (n%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e106 (49.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e30 (27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e76 (73.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.562913907284768%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.21854304635762%\" valign=\"top\"\u003e\n \u003cp\u003eAlb\u0026uuml;min (g/dl) mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e3.36 (0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e2.8 (0.07)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.072847682119205%\" valign=\"top\"\u003e\n \u003cp\u003e3.8 (0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.562913907284768%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eIQR: Interquartile Range, mRS: modified Rankin Scale NIHSS: National Institutes of Health Stroke Scale; The TOAST: trial of ORG 10172 in acute stroke treatment), High PNI\u0026gt;42.5, Low PNI \u0026lt;42.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIn our study\u0026apos;s investigation of the relationship between survival and neurological progression, the variables found significant in univariate analysis for neurological progression were age (OR=4.27, p\u0026lt;0.001), male gender (OR=1.94, p=0.035), coronary artery disease (OR=4.5, p\u0026lt;0.001), admission NIHSS value (OR=1.2, p\u0026lt;0.001), hemoglobin levels (OR=0.77, p\u0026lt;0.001), lymphocyte count (OR=0.20, p\u0026lt;0.001), leukocyte count (OR=1.085, p=0.041), albumin level (OR=0.01, p\u0026lt;0.001), BUN (OR=1.09, p=0.009), PNI (OR=0.013, p\u0026lt;0.001), and NLR (OR=0.17, p\u0026lt;0.001). Independent predictors for neurological progression in multivariate analysis were coronary artery disease (HR=3.92, p=0.021), admission NIHSS value (HR=1.16, p=0.001), and PNI (HR =0.022, p\u0026lt;0.001). For overall survival, the variables found significant in univariate analysis were age (OR=2.97, p\u0026lt;0.001), coronary artery disease (OR=1.69, p=0.02), presence of atrial fibrillation (OR=1.2, p=0.03), admission NIHSS value (OR=0.007, p\u0026lt;0.001), lymphocyte count (OR=0.25, p\u0026lt;0.001), leukocyte count (OR=1.065, p=0.002), albumin level (OR=0.25, p\u0026lt;0.001), BUN (OR=1.02, p\u0026lt;0.001), and NLR (OR=4.05, p\u0026lt;0.001). In multivariate analysis, independent predictors for overall survival were age (HR=1.93, p=0.009), admission NIHSS value (HR=1.04, p=0.008), lymphocyte count (HR=1.7, p=0.041), BUN (HR=1.69, p=0.012), and PNI (HR=0.27, p=0.007). These findings were obtained using multivariate models that consider confounding factors to determine the independent effect of each variable and are specified in Table 3. The Kaplan-Meier graph related to overall survival shows a survival curve consistent with our findings, as depicted in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 Univariate and multivariate logistic regression analysis and survival analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.06514657980456%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eNeurological Progression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.530944625407166%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eOverall Survival\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eUnivariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMultivariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eUnivariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.498371335504885%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMultivariable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.214983713355048%\" valign=\"top\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e4.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e2.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.214983713355048%\" valign=\"top\"\u003e\n \u003cp\u003e1.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\"\u003e\n \u003cp\u003eGender man\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.214983713355048%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\"\u003e\n \u003cp\u003eSmoking\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.214983713355048%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\"\u003e\n \u003cp\u003eHypertension\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.214983713355048%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\"\u003e\n \u003cp\u003eDiabetes mellitus\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.214983713355048%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\"\u003e\n \u003cp\u003eCoronary artery diasease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e3.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.214983713355048%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\"\u003e\n \u003cp\u003eAtrial fibrillation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.214983713355048%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\"\u003e\n \u003cp\u003eTOAST classifications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.214983713355048%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\"\u003e\n \u003cp\u003eLarge-artery \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;atherosclerosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ereferance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ereferance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.214983713355048%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\"\u003e\n \u003cp\u003eSmall-artery \u0026nbsp; \u0026nbsp; oclusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.214983713355048%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\"\u003e\n \u003cp\u003eCardioembolic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.214983713355048%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\"\u003e\n \u003cp\u003eOther and unknown causes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.214983713355048%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\"\u003e\n \u003cp\u003eNIHSS on admission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.214983713355048%\" valign=\"top\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\"\u003e\n \u003cp\u003eHemoglobin gr/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.214983713355048%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\"\u003e\n \u003cp\u003ePlatelet count\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(10⁹/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.214983713355048%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\"\u003e\n \u003cp\u003eLymphocyte count (10⁹/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.214983713355048%\" valign=\"top\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\"\u003e\n \u003cp\u003eLeukocyte (10⁹/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e1.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e1.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.214983713355048%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\"\u003e\n \u003cp\u003eAlb\u0026uuml;min (g/dl)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.214983713355048%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\"\u003e\n \u003cp\u003eBUN (mg/dl)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.214983713355048%\" valign=\"top\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\"\u003e\n \u003cp\u003ePNI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.214983713355048%\" valign=\"top\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\"\u003e\n \u003cp\u003eNLR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.749185667752442%\" valign=\"top\"\u003e\n \u003cp\u003e4.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.214983713355048%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.283387622149837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003eBUN: Blood Urea Nitrogen, mRS: Modified Rankin Scale, NIHSS: National Institutes of Health Stroke Scale, NLR: Neutrophil to Lymphocyte Ratio , PNI: Prognostic Nutritional Index , TOAST: Trial of ORG 10172 in Acute Stroke Treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe relationship between age and outcomes in ischemic stroke is a pivotal factor for management and prognosis. Our study, including a broad age range of stroke patients, highlights the significant influence of age on morbidity and mortality. Our findings align with the literature that older patients have worse neurological prognosis. Roy-O’Reilly et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) have shown that age enhances the pro-inflammatory functions of neutrophils after ischemic stroke, leading to poorer outcomes in older people. This suggests that the observed high morbidity and mortality in older stroke patients in our study could stem from an age-related increase in inflammatory response.\u003c/p\u003e\u003cp\u003eFurthermore, our study distinctly observed the interaction between atrial fibrillation (AF) and coronary artery disease (CAD) and their effects on mortality and neurological progression in ischemic stroke patients. AF, a common cardiac arrhythmia, was associated with an increased risk of ischemic stroke, corroborating findings by Hudzik et al. (2019). This study also underscores the crucial role of antithrombotic therapy in preventing thromboembolic events in AF patients. Our observations indicate that CAD in our patients is linked with higher mortality rates and more severe stroke outcomes, consistent with Zhao et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), who reported that CAD could exacerbate stroke outcomes. The co-occurrence of CAD and AF complicates stroke patient management, increasing the risk of both stroke and cardiac complications (Lip, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The concurrent use of anticoagulant and antiplatelet therapies is vital in preventing stroke recurrence and reducing cardiac event risks (Berge \u0026amp; Sandercock, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). In our study, we conclude that treatment strategies for patients diagnosed with AF and CAD should be individualised, considering each patient's individual risk profile and clinical status. The choice and dosage of anticoagulant therapy are crucial in these patients.\u003c/p\u003e\u003cp\u003eThe NIHSS score is a critical tool for evaluating ischemic stroke severity and predicting patient outcomes, serving as a cornerstone of stroke assessment globally. Consistent with the extensive literature, our study reveals that high NIHSS scores at admission strongly correlate with increased morbidity and mortality. Zöllner et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found that the risk of acute symptomatic seizures in ischemic stroke patients significantly increases with higher NIHSS scores at admission. This supports our observation about the predictive value of the NIHSS score for stroke severity and subsequent outcomes. Khalifa et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) noted that high NIHSS scores of 15 and above predict the need for intensive care and the risk of mortality in ischemic stroke patients. This further supports our findings on the guiding role of the NIHSS score in treatment decisions. Finally, Elatroush et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) emphasised that a high NIHSS score at hospital admission is an independent early predictor of functional outcomes in acute ischemic stroke patients. This reinforces the importance of NIHSS in early prognosis and management strategies.\u003c/p\u003e\u003cp\u003eBUN levels have been identified as a significant prognostic factor for ischemic stroke. We observed a significant association between high BUN levels and increased mortality, in line with recent global research. You et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) demonstrated that increased BUN at admission is a significant prognostic marker linked with in-hospital mortality in AIS patients. Bae et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) examined the blood urea nitrogen to serum albumin ratio (B/A ratio) in patients with acute ischemic stroke (AIS). The study identified a high B/A ratio as a significant indicator for mortality and intensive care unit admission and suggested that this ratio, combining BUN levels and serum albumin, could be a valuable tool for predicting stroke severity and patient outcomes.\u003c/p\u003e\u003cp\u003eNLR, a nutrition parameter and a focus of our study, has been established as a significant prognostic marker for ischemic stroke associated with both mortality and morbidity. Our study observed a positive correlation between high NLR values and increased mortality and morbidity, mirroring findings in the current literature. İyigündoğdu et al. (2021) determined that NLR measured at patient admission positively related to stroke severity, short-term functional outcomes, and mortality in acute ischemic stroke patients. This corroborates our findings that a high NLR is linked with poorer clinical outcomes in stroke patients.\u003c/p\u003e\u003cp\u003eIn the same way, Wu (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) revealed that NLR in patients undergoing intravenous thrombolysis for acute ischemic stroke is an effective indicator for predicting three-month outcomes and mortality. The research findings suggest that a high NLR can negatively affect long-term results. In a study on AIS patients by Wirawan and Widyadharma (2021), it was demonstrated that high NLR following the acute phase of the event could adversely affect long-term outcomes, such as the recurrence of ischemic stroke, morbidity, and mortality. Additionally, Majid et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) have reported that in patients with acute ischemic stroke receiving thrombolysis treatment, high NLR values are associated with poor functional outcomes after three months. These findings align with our observations on the predictive value of NLR in the management and rehabilitation phases of acute stroke.\u003c/p\u003e\u003cp\u003eAnother scale we evaluated in our study, the PNI, was initially utilised as an indicator of nutrition and inflammation. It has subsequently garnered attention for its predictive value in ischemic stroke outcomes. We observed that low PNI values were significantly associated with high mortality and morbidity rates, a finding that aligns with the current literature. Xiang et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) demonstrated that PNI is an independent predictor of three-month outcomes in patients with ischemic stroke who underwent thrombolysis, with lower PNI values indicating worse outcomes. This is consistent with our study's findings that emphasise the importance of PNI in predicting stroke recovery. Liu et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) examined the PNI in patients with critical stroke conditions and determined it to be an independent prognosticator of 30-day, 90-day, and 1-year mortality rates. Additionally, they described a U-shaped association between PNI and all-cause mortality, proposing a multifaceted interplay among nutrition, inflammation, and stroke sequelae. Nergiz and Ozturk (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) concentrated on the association between Prognostic Nutritional Index (PNI) and infection in patients with acute ischemic stroke (AIS). They found that lower PNI scores are associated with higher infection rates and increased mortality in these patients.\u003c/p\u003e\u003cp\u003eThis finding corroborates our observations on the extensive influence of PNI on stroke prognosis. In a recent study by Liu et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the PNI has been highlighted as a potential predictive marker for perioperative ischemic stroke, delineating its significance across various stroke-associated outcomes. These literature reviews substantiate that PNI is a beneficial parameter for predicting the clinical trajectory of patients with acute ischemic stroke.\u003c/p\u003e\u003cp\u003eThis study is subject to several significant limitations. Firstly, it is a single-center study, which may restrict the applicability of the findings to the general population or to individuals with different geographic and demographic profiles. Secondly, the values measured in this study were collected post-event, which does not account for dynamic changes at the time of the event. Thirdly, due to the retrospective design, we did not evaluate patients' post-event nutritional habits and the potential impact of these habits on health outcomes. Moreover, the retrospective nature of the study raises concerns about the accuracy of data collection looking back in time, which could affect the veracity of the findings. Lastly, the study lacks dynamic measurements, limiting understanding of the event's effects over time and the process itself. These limitations should be considered when interpreting the findings and addressed in future research endeavors to fill in these gaps.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOur findings show that NLR is significant in univariate analysis and may serve as an initial indicator for stroke outcomes. However, based on the results of multivariate analysis, PNI emerges as a more prominent prognostic factor. This distinction highlights the multifactorial nature of stroke prognosis. PNI offers a more comprehensive perspective on the patient's overall health status by reflecting on nutritional status and the inflammatory response. We recommend a holistic assessment beyond individual biomarkers to consider multiple biological indicators. This approach aligns with the current medical research and practice trend, which increasingly recognises the value of multifactorial analysis in understanding complex health conditions. Enhancing the accuracy and granularity of our prognostic assessments with a wide range of biomarkers can ultimately lead to more informed and effective patient care strategies in ischemic stroke treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e Not applicable (N/A)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u003c/strong\u003e Not applicable (N/A)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval:\u003c/strong\u003e This study was approved by the Katip \u0026Ccedil;elebi University Ethics Committee (Decision No: 0491).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent:\u003c/strong\u003e\u0026nbsp; Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e- Zeynep Tanrıverdi designed the study and wrote the main manuscript, setting the research foundation.\u003c/p\u003e\n\u003cp\u003e- Eren Mingsar performed data analysis, enriching the discussion with in-depth insights.\u003c/p\u003e\n\u003cp\u003e- Dilan D\u0026uuml;ztaş, Hatice Sevil, and Enise Nur \u0026Ouml;zlem Tiryaki reviewed the literature across disciplines, broadening the study\u0026apos;s perspective and informing the discussion.\u003c/p\u003e\n\u003cp\u003e- Mensure \u0026Ccedil;akırg\u0026ouml;z mentored the team, guided the project direction, and ensured the scientific rigor in the findings, adding to the study\u0026apos;s trustworthiness.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e Data used in this study are available from Dr. Zeynep Tanrıverdi upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAquilani R, Scocchi M, Boschi F, Viglio S, Iadarola P, Pastoris O, Verri M (2008) Effect of calorie-protein supplementation on the cognitive recovery of patients with subacute stroke. Nutr Neurosci 11(5):235\u0026ndash;240. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1179/147683008X301586\u003c/span\u003e\u003cspan address=\"10.1179/147683008X301586\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBae SJ, Lee SH, Yun SJ, Kim K (2021) Usefulness of the blood urea nitrogen-to-serum albumin ratio as a prognostic indicator of severity in acute ischemic stroke. Signa Vitae 17(4):163\u0026ndash;170. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.22514/sv.2021.028\u003c/span\u003e\u003cspan address=\"10.22514/sv.2021.028\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerge E, Sandercock PA (2002) Anticoagulants versus antiplatelet agents for acute ischaemic stroke. Cochrane Database of Systematic Reviews. \u003cem\u003e4\u003c/em\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/14651858.CD003242\u003c/span\u003e\u003cspan address=\"10.1002/14651858.CD003242\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCipolla MJ, Liebeskind DS, Chan SL (2018) The importance of comorbidities in ischemic stroke: Impact of hypertension on the cerebral circulation. J Cereb Blood Flow Metabolism 38(12):2129. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0271678X18800589\u003c/span\u003e\u003cspan address=\"10.1177/0271678X18800589\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElatroush H, Ragab D, Mashhour K, Afify M, Salah A (2023) Early prognostication of ischemic stroke using computed tomography perfusion and S100 beta level. Res Opin Anesth Intensive Care 10(2):122. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4103/ROAIC.ROAIC_86_21\u003c/span\u003e\u003cspan address=\"10.4103/ROAIC.ROAIC_86_21\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu X, Liang J, Hao W, Zhou J, Gao Y, Gong X, Liu Y (2023) Prognostic value of inflammatory markers for in-hospital mortality in intensive care patients with acute ischemic stroke: a retrospective observational study based on MIMIC-IV. Front Neurol 14:1174711. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/FNEUR.2023.1174711/BIBTEX\u003c/span\u003e\u003cspan address=\"10.3389/FNEUR.2023.1174711/BIBTEX\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHudzik B, Registry TrAT (TREAT), Desperak A, Registry TrAT (TREAT), Freedman B, Registry, Tr. A. T. (TREAT), Nielsen, PB, Registry TrAT (TREAT), Larsen TB, Registry, Tr AT (TREAT), Desperak P, Registry TrAT (TREAT), Trzeciak P, Registry TAT (TREAT), Gasior, M.,Registry, Tr. A. T. (TREAT) (2019) P3760Temporal trends in stroke prevalence and its associations with changing patterns of antithrombotic regimens in patients with atrial fibrillation and a wide spectrum of coronary artery disease. \u003cem\u003eEuropean Heart Journal\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e(Supplement_1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/EURHEARTJ/EHZ745.0612\u003c/span\u003e\u003cspan address=\"10.1093/EURHEARTJ/EHZ745.0612\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIyigundogdu I, Derle E, Kibaroglu S, Can U (n.d.). \u003cem\u003eNeutrophil to lymphocyte ratio, stroke severity and short term clinical outcomes in acute ischemic stroke\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.54029/2021cmz\u003c/span\u003e\u003cspan address=\"10.54029/2021cmz\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhalifa A, Al Kharusi A, Al Jadidi A, Malik MA, Khan RM (2020) Saudi Journal of Medicine Predicting the Need for ICU Care in Patients with Acute Ischemic Stroke. Saudi J Med. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.36348/sjm.2020.v05i12.001\u003c/span\u003e\u003cspan address=\"10.36348/sjm.2020.v05i12.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLip GYH (2007) Coronary artery disease and ischemic stroke in atrial fibrillation. Chest 132(1):8\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1378/chest.07-0500\u003c/span\u003e\u003cspan address=\"10.1378/chest.07-0500\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu M, Sun M, Zhang T, Li P, Liu J, Liu Y, Song Y, Liu S, Yang H, Zhou Z, Chang D, Wang G, Mi W, Ma Y (2023) Prognostic Nutritional Index (PNI) as a potential predictor and intervention target for perioperative ischemic stroke: a retrospective cohort study. BMC Anesthesiol 23(1):1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/S12871-023-02216-8/FIGURES/3\u003c/span\u003e\u003cspan address=\"10.1186/S12871-023-02216-8/FIGURES/3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y, Yang X, Kadasah S, Peng C (2022) Clinical Value of the Prognostic Nutrition Index in the Assessment of Prognosis in Critically Ill Patients with Stroke: A Retrospective Analysis. \u003cem\u003eComputational and Mathematical Methods in Medicine\u003c/em\u003e, \u003cem\u003e2022\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1155/2022/4889920\u003c/span\u003e\u003cspan address=\"10.1155/2022/4889920\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMajid S, Lodhi OUH, Niazi AK, Lodhi SUH, Siddiqui M (2021) Usefulness of neutrophil-to-lymphocyte ratio as a predictor of functional outcome in patients with acute ischemic stroke after thrombolysis therapy. \u003cem\u003eBaylor University Medical Center Proceedings\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(6), 664\u0026ndash;667. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/08998280.2021.1938471\u003c/span\u003e\u003cspan address=\"10.1080/08998280.2021.1938471\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNergiz S, Ozturk U (2023) The Effect of Prognostic Nutritional Index on Infection in Acute Ischemic Stroke Patients. Med (Kaunas Lithuania) 59(4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/MEDICINA59040679\u003c/span\u003e\u003cspan address=\"10.3390/MEDICINA59040679\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOnodera T, Goseki N, zasshi GK-N, geka gakkai (1984) \u0026amp; undefined. (n.d.). Prognostic nutritional index in gastrointestinal surgery of malnourished cancer patients. \u003cem\u003eEuropepmc.OrgT Onodera, N Goseki, G KosakiNihon Geka Gakkai Zasshi, 1984\u0026bull;europepmc.Org\u003c/em\u003e. Retrieved December 15, 2023, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://europepmc.org/article/med/6438478\u003c/span\u003e\u003cspan address=\"https://europepmc.org/article/med/6438478\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetrea RE, Beiser AS, Seshadri S, Kelly-Hayes M, Kase CS, Wolf PA (2009) Gender differences in stroke incidence and poststroke disability in the Framingham heart study. Stroke 40(4):1032\u0026ndash;1037. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/STROKEAHA.108.542894\u003c/span\u003e\u003cspan address=\"10.1161/STROKEAHA.108.542894\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoy-O\u0026rsquo;Reilly MA, Ahnstedt H, Spychala MS, Munshi Y, Aronowski J, Sansing LH, McCullough LD (2020) Aging exacerbates neutrophil pathogenicity in ischemic stroke. Aging 12(1):436. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18632/AGING.102632\u003c/span\u003e\u003cspan address=\"10.18632/AGING.102632\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoy-O\u0026rsquo;Reilly M, McCullough LD (2018) Age and Sex Are Critical Factors in Ischemic Stroke Pathology. Endocrinology 159(8):3120\u0026ndash;3131. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1210/EN.2018-00465\u003c/span\u003e\u003cspan address=\"10.1210/EN.2018-00465\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThayabaranathan T, Kim J, Cadilhac DA, Thrift AG, Donnan GA, Howard G, Howard VJ, Rothwell PM, Feigin V, Norrving B, Owolabi M, Pandian J, Liu L, Olaiya MT (2022) Global stroke statistics 2022. Int J Stroke 17(9):946\u0026ndash;956. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/17474930221123175/ASSET/IMAGES/LARGE/10.1177_17474930221123175-FIG11.JPEG\u003c/span\u003e\u003cspan address=\"10.1177/17474930221123175/ASSET/IMAGES/LARGE/10.1177_17474930221123175-FIG11.JPEG\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWirawan C, Putu I, Widyadharma E (2021) Neutrophil-to-lymphocyte ratio is modality to predict recurrence, disability and mortality of ischemic stroke following acute phase. \u003cem\u003eRomanian JouRnal of NeuRology\u003c/em\u003e, \u003cem\u003eXX\u003c/em\u003e(3). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.37897/RJN.2021.3.1\u003c/span\u003e\u003cspan address=\"10.37897/RJN.2021.3.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu Q, Chen HS (2023) Neutrophil-to-lymphocyte ratio and its changes predict the 3-month outcome and mortality in acute ischemic stroke patients after intravenous thrombolysis. Brain and Behavior 13(9). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/BRB3.3162\u003c/span\u003e\u003cspan address=\"10.1002/BRB3.3162\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiang W, Chen X, Ye W, Li J, Zhang X, Xie D (2020) Prognostic Nutritional Index for Predicting 3-Month Outcomes in Ischemic Stroke Patients Undergoing Thrombolysis. \u003cem\u003eFrontiers in Neurology\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/FNEUR.2020.00599\u003c/span\u003e\u003cspan address=\"10.3389/FNEUR.2020.00599\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYou S, Zheng D, Zhong C, Wang X, Tang W, Sheng L, Zheng C, Cao Y, Liu CF (2018) Prognostic Significance of Blood Urea Nitrogen in Acute Ischemic Stroke. Circulation Journal: Official Journal of the Japanese Circulation Society 82(2):572\u0026ndash;578. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1253/CIRCJ.CJ-17-0485\u003c/span\u003e\u003cspan address=\"10.1253/CIRCJ.CJ-17-0485\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao Q, Wang L, Kurlansky PA, Schein J, Baser O, Berger JS (2019) Cardiovascular outcomes among elderly patients with heart failure and coronary artery disease and without atrial fibrillation: A retrospective cohort study. BMC Cardiovasc Disord 19(1):1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/S12872-018-0991-1/FIGURES/4\u003c/span\u003e\u003cspan address=\"10.1186/S12872-018-0991-1/FIGURES/4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZ\u0026ouml;llner JP, Misselwitz B, Kaps M, Stein M, Konczalla J, Roth C, Krakow K, Steinmetz H, Rosenow F, Strzelczyk A (2020) National Institutes of Health Stroke Scale (NIHSS) on admission predicts acute symptomatic seizure risk in ischemic stroke: a population-based study involving 135,117 cases. \u003cem\u003eScientific Reports 2020 10:1\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(1), 1\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-020-60628-9\u003c/span\u003e\u003cspan address=\"10.1038/s41598-020-60628-9\" 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 (AIS), malnutrition, prognostic nutrition index (PNI), Neutrophil/Lymphocyte ratio, neurological progression, survival analysis","lastPublishedDoi":"10.21203/rs.3.rs-3846215/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3846215/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aims to investigate the impact of the prognostic nutrition index and neutrophil-lymphocyte ratio on survival and neurological outcomes in acute ischemic stroke patients at risk of malnutrition who are monitored in Intensive Care Units.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study analysed 215 patients with their first ischemic stroke admitted to the Neurology Intensive Care Unit. The prognostic nutritional index was derived from serum albumin and complete blood count within the first 24 hours using this formula:\u003c/p\u003e\n\u003cp\u003ePNI = (serum albumin level [g/dL] × 10) + (total lymphocyte count [mm³] × 0.005)\u003c/p\u003e\n\u003cp\u003eThe prognostic nutritional index was categorised into two groups according to a cut-off value of 42.5 determined by ROC analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the 60-day follow-up, multivariable logistic regression analysis of neurological prognosis identified the presence of coronary artery disease (Hazard Ratio [HR]: 3.9, p: 0.021), initial NIHSS score (HR: 1.16, p \u0026lt;0.001), and PNI (HR: 0.022, p \u0026lt;0.001) as independent predictors of neurological outcomes. Cox regression analysis for survival in all patients determined age (HR: 1.93, p = 0.009), initial NIHSS score (HR: 1.04, p = 0.008), BUN level (HR: 1.69, p = 0.012), and prognostic nutritional index (HR: 0.27, p = 0.007) as independent determinants of mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur findings suggest that simple, cost-effective, and readily applicable biomarkers such as the prognostic nutritional index and the neutrophil-lymphocyte ratio should be considered practical tools in patient management and predicting neurological outcomes.\u003c/p\u003e","manuscriptTitle":"Impact of Prognostic Nutrition Index and Neutrophil/Lymphocyte Ratio on Outcomes in Acute Ischemic Stroke","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-12 17:19:52","doi":"10.21203/rs.3.rs-3846215/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":"c09191f0-9770-44b8-a773-5a9358181853","owner":[],"postedDate":"January 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-26T03:49:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-12 17:19:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3846215","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3846215","identity":"rs-3846215","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}