A Neutrophil-Incorporated Nomogram for Predicting 3-Month Disability after Intravenous Thrombolysis in Acute Ischemic Stroke

A Neutrophil-Incorporated Nomogram for Predicting 3-Month Disability after Intravenous Thrombolysis 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 A Neutrophil-Incorporated Nomogram for Predicting 3-Month Disability after Intravenous Thrombolysis in Acute Ischemic Stroke yang zhou, fang fang, Zhenyu Wei, ping zhong, Danhong Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8651893/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background: The present study was designed to create and validate a novel, straightforward and dependable Nomogram for individualized prediction of short-term functional outcomes in AIS patients treated with intravenous thrombolysis. Methods: We enrolled patients who suffered from acute ischemic stroke (AIS) treated with intravenous thrombolysis based on the inclusion and exclusion criteria. The patients from Shaoxing People's Hospital constituted the training set, while those from Shanghai Fifth People's Hospital served as the validation set. The primary outcome measure was a 3-month unfavorable outcome (modified Rankin Scale 3–6). On the basis of LASSO logistic model, the predictive Nomogram was generated. The performance of the Nomogram was evaluated by ROC curves, Hosmer‑Lemeshow test and Calibration plot. Decision curve analysis was utilized to assess the effectiveness of the Nomogram. Results: The training cohort and validation cohort recruited 238 patients (median age 70 years; 61.3% male) and 155 patients (median age 62 years; 74.2% male) respectively. The results indicated that the AUC value of the Nomogram was 0.859 (95% CI: 0.811–0.906) in the training cohort and 0.848 (95% CI: 0.788–0.908) in the validation cohort. Conclusion: The nomogram’s reliance on routinely collected variables (e.g., NIHSS, glucose) facilitates rapid bedside use, potentially guiding post-thrombolysis monitoring for high-risk patients. Ischemic stroke Thrombolysis Prognosis Nomogram Neutrophils Functional outcome Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Ischemic stroke (IS) is the primary subtype of stroke and is currently the leading cause of death worldwide [1, 2] . Recombinant tissue plasminogen activator alteplase (rt-PA) is considered the most effective drug for treating acute ischemic stroke(AIS) and has been shown to improve functional outcomes post-onset [3] . While many patients experience relief within 24-72 hours, a significant number still have lingering neurological deficits after receiving thrombolytic treatment [2, 4] . Therefore, better identification of AIS patients who experience unfavorable outcomes following rt-PA treatment could be useful to develop preventive strategies and reduce the risk of morbidity and mortality after stroke. Nomograms, a scoring system based on various variables, have become increasingly prevalent in clinical decision-making for conditions including ischemic stroke, myocardial infarction, and cancer [5] . They are also utilized in predicting the functional outcomes of AIS patients post-thrombolytic therapy. Cappellari and his colleagues devised a nomogram within a substantial SITS-ISTR cohort, enabling the accurate calculation of the likelihood of an unfavorable three-month outcome in stroke patients who have undergone intravenous thrombolysis (IVT) as a standalone treatment [6] . In addition, a systematic review by Khatri et al. highlights the importance of predictive models in stroke management, emphasizing how they can guide treatment decisions and improve patient outcomes. Strbian et al. derived an outcome score, DRAGON,from a large dataset of nonbasilar artery alteplasetreated stroke patients. They propose that a high DRAGON score can identify those patients who should promptly begin endovascular, hypothermia,or other therapy due to poor outcome after alteplase. [7] Furthermore, shan et al. developed the N2H3 nomogram, which can offer personalized early predictions of unfavorable 3-month outcomes in AIS patients undergoing intravenous rt-PA thrombolysis. This nomogram aids in the prompt identification of patients who may benefit from additional therapeutic interventions [8] . Compared to the above several nomogram, our nomogram integrates inflammatory markers like neutrophils, offering a more comprehensive prediction. Overall, the integration of nomograms into clinical practice represents a significant advancement in the management of complex medical conditions. These tools not only enhance the precision of outcome predictions but also promote a proactive approach to treatment, ensuring that patients receive the most appropriate and timely care based on their unique clinical profiles. As research in this area continues to evolve, the potential for nomograms to further refine clinical decision-making and improve patient outcomes remains promising. Therefore, this research aimed to create and validate a novel, straightforward and dependable Nomogram model for predicting short-term functional outcomes in AIS patients treated with intravenous thrombolysis. Materials and methods Subjects of the study This retrospective study examined AIS patients who underwent intravenous thrombolysis within 4.5 hours of stroke symptom onset at Shaoxing People's Hospital between January 2019 and December 2021 and Shanghai Fifth People's Hospital between January 2018 and December 2020. Inclusion criteria comprised patients aged ≥ 18 years who had a confirmed acute ischemic stroke through brain imaging(computed tomography or magnetic resonance imaging) and received intravenous thrombolysis within 4.5 hours of stroke symptom onset. The confirmation of acute ischemic stroke through brain imaging was conducted by a certified neurologist who had undergone standardized training. Exclusion criteria: 1. patients without complete routine hematological investigations or parameters on the day of emergency or admission; 2. patients with a prior history of stroke; 3. patients with infections in the past 2 weeks; 4. patients with blood disorders; 5. patients with immunosuppressive medication or corticosteroid hormones use; 6. patients with cancers or immune system disorders; 7. patients with severe cardiac, hepatic, or renal diseases; 8. patients who received endovascular treatment; 9. participants who have tested positive for COVID-19 or have exhibited symptoms consistent with the virus within the past 14 days. A flowchart depicting eligible participants is illustrated in Supplementary Fig. 1. The patients from Shaoxing People's Hospital constituted the training set, while those from Shanghai Fifth People's Hospital served as the validation set. The study was approved by the ethics committees of Shaoxing People's Hospital(2021-K-Y-330-01) and Shanghai Fifth People's Hospital(2018 Ethics Approval NO.001). Data collection Upon admission, we documented the patient’s demographics, neurological physical examination, and laboratory results. Demographic information encompassed age, gender, smoking and alcohol consumption habits. The physical examination measured systolic blood pressure (SBP) and diastolic blood pressure (DBP) before thrombolysis. Laboratory data prior to thrombolysis encompassed white blood cell count (WBC), neutrophil count, lymphocyte count, hemoglobin, erythrocyte count, platelet count, total bilirubin (TBil), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), uric acid (UA), creatinine, blood glucose, Glycosylated hemoglobin (GHb), Alanine aminotransferase (AST), Aspartate aminotransferase (AST). Additionally, we gathered information on comorbid conditions such as coronary heart disease (CAD), hypertension, diabetes mellitus(DM), and atrial fibrillation(AF). Ischemic stroke subtypes were classified following the criteria of the Org 10,172 Acute Stroke Treatment Trial [ 9 ] . The National Institutes of Health Stroke Scale (NIHSS) score and modified Rankin Scale (mRS) score were assessed by experienced clinicians on the included patients, and the NIHSS score and mRS score before thrombolysis were defined as the baseline NIHSS score and baseline mRS score, respectively. All certified neurologists underwent standardized training for evaluating NIHSS and mRS scores and were unaware of our study. Two certified neurologists from the medical team evaluated each participant, with a third neurologist resolving any disagreements in assessments. Outcome assessment All enrolled patients were followed up via telephone by the same investigator. The mRS score was used to evaluate the neurological outcomes of stroke patients three months after thrombolysis. A favorable outcome was defined as an mRS score of ≤ 2, while an unfavorable outcome was defined as an mRS score of > 3 [ 10 ] . Statistical analysis Statistical analysis was conducted using R version 3.6.2 software. Categorical variables were presented as n (%) and continuous variables as median (interquartile range, IQR). The independent samples t-test or Mann-Whitney U test was used for continuous variables, while the chi-square test or Fisher's exact test was used for categorical variables. In the training cohort, univariate regression analysis was performed to identify risk factors associated with adverse outcomes at 3 months. LASSO regression in the 'Glmnet' software package was employed to select the optimal feature subset. The least absolute shrinkage and selection operator(LASSO) regression method was used to select the most valuable predictors from the training cohort. To solve the problem of overfitting, based on the concept of variance trade-off, the penalty term λ is added to the selection of predictors. By carrying out penalty regression on the coefficients of all variables,the coefficients of relatively unimportant independent variables are compressed to zero to eliminate these variables, determine the independent variables that can provide important information for the model, and freeze the model in the state of just fitting. According to the established language program, the independent variable and dependent variable matrices were generated in R studio for fitting, a contraction curve was drawn, and cross-validation was carried out. The optimal penalty coefficient λ was obtained using a 10-fold cross-validation method. λ with the least cross-validation error was selected to make the model in the exact fitting state. The shrinkage coefficient plot and the minimum λ curve were drawn, and the variables whose coefficients were not compressed to 0 were selected as the predictors after LASSO screening. The crossing validation graph and shrinkage coefficient diagram of LASSO regression are shown in Fig. 1 . The vertical line on the left represents the optimal λ, and the vertical line on the right represents the double standard error of λ. The variable intersecting the first vertical line was the preserved variable. Subsequently, the relevant risk factors were subjected to LASSO regression analysis, resulting in 7 characteristic variables for multifactor regression analysis. A Nomogram was constructed and validated using data from both the training and validation cohorts. The discriminative ability of the Nomogram was evaluated by calculating the receiver operating characteristic curves (ROC). Calibration of the Nomogram was assessed using the Hosmer-Lemeshow test and calibration curves, which depict the fit between actual and predicted results. Decision curve analysis was utilized to evaluate the effectiveness of the Nomogram. Statistical significance was set at a two-tailed P value of less than 0.05. Results Baseline characteristics of included patients Patients were screened based on specific criteria, resulting in the inclusion of 238 AIS patients who underwent intravenous thrombolysis. These patients were then categorized into either a favorable outcome group or an unfavorable outcome group (refer to Table 1 ). Univariate analysis revealed that factors such as age, baseline mRS score, baseline NIHSS score, TOAST classification, CAD, AF, SBP, WBC, neutrophil count, lymphocyte count, TBil, and blood glucose may be associated with the unfavorable outcomes (P < 0.05). Compared to the favorable outcome group, the unfavorable outcome group had higher values for age, baseline mRS score, baseline NIHSS score, large artery atherosclerosis(LAA), cardio-embolism(CE), SBP, WBC, neutrophil count, TBil, blood glucose, CAD, and AF, while lymphocyte count was lower (Table 1 ). Table 1 Comparison among patients included in different mRS groups Variable mRS(0–2) mRS(3–6) P (n = 143) (n = 95) Age, years a 68[59,75] 74[63,79] 0.003 Male, n(%) b 87(60.8) 59(62.1) 0.844 drinking, n(%) b 44(30.8) 21(22.1) 0.142 smoking, n(%) b 55(38.5) 25(26.3) 0.052 baseline mRS a 2[1,3] 4[4,5] < 0.001 baseline NIHSS score a 3[1,5] 11[5,17] < 0.001 TOAST < 0.001 LAA, n(%) b 40(27.9) 41(43.2) CE, n(%) b 19(13.3) 32(33.7) SAO, n(%) b 57(39.9) 15(15.8) other, n(%) b 27(18.9) 7(7.4) Hypertension, n(%) b 93(65) 56(58.9) 0.342 DM, n(%) b 22(15.4) 20(21.1) 0.261 CAD, n(%) b 15(10.5) 34(35.8) < 0.001 AF, n(%) b 12(8.4) 33(34.7) < 0.001 SBP, mmHg 145[131,160] 151[137,167] 0.015 DBP, mmHg 83[75,90] 84[75,90] 0.481 WBC, 10 9a 6.73[5.35,8.07] 8.32[6.65,10.56] < 0.001 neutrophile, 10 9 /L a 4.59[3.13,5.77] 6.36[4.88,8.35] < 0.001 lymphocyte, 10 9 /L a 1.49[1.08,1.79] 1.22[0.85,1.68] 0.003 Hemoglobin, g/L a 133[123,144] 131[123,142] 0.465 erythrocyte, 10 12 /L a 4.37[3.93,4.64] 4.19[3.88,4.48] 0.085 platelet, 10 9 /L a 201[171,245] 195[161,223] 0.133 TBil, µmol/L a 12.5[9.5,16] 15.2[12.2,19.2] < 0.001 TC, mmol/L a 4.48[3.81,5.23] 4.31[3.61,4.85] 0.067 TG, mmol/L a 1.29[0.91,1.76] 1.11[0.78,1.7] 0.295 HDL, mmol/L a 1.15[0.95,1.28] 1.11[0.96,1.29] 0.73 LDL, mmol/L a 2.66[2.09,3.29] 2.45[2.05,3.1] 0.261 UA, µmol/L a 306.6[257.5,396.7] 291.6[232.2,359.3] 0.051 creatinine, µmol/L a 69.8[59.2,81] 67.2[55.8,77.6] 0.224 glucose, mmol/L a 5.2[4.56,6.05] 5.86[5.17,7.84] < 0.001 GHb, % a 5.9[5.5,6.5] 6[5.6,6.6] 0.091 ALT, U/L a 16.5[12.6,22.6] 15.5[12.6,20.7] 0.277 AST, U/L a 21.1[17.4,25.7] 22.7[18.3,27.4] 0.061 The values are presented as median value (1st quantile, 3rd quantile) or patient number (%). Baseline characteristics of the training and validation cohorts The demographic and clinical characteristics of the training cohort and validation cohort are shown in Table 2 . Table 2 Baseline characteristics of the training and validation cohorts Variable Training cohort Validation cohort P (n = 238) (n = 155) Age, year 70[61,76] 62[53,71] Male, n(%) 146(61.3) 115(74.2) 0.008 Drinking, n(%) 65(27.3) 66(42.6) 0.002 Smoking, n(%) 80(33.6) 85(54.8) baseline mRS 3[2,4] 3[1,4] baseline NIHSS score 4[2,11] 5[3,10] 0.417 TOAST LAA, n(%) 81(34) 62(40) CE, n(%) 51(21.4) 56(36.1) SAO, n(%) 72(30.3) 32(20.6) other, n(%) 34(14.3) 5(3.2) Hypertension, n(%) 149(62.6) 111(71.6) DM, n(%) 42(17.6) 43(27.7) CAD, n(%) 49(20.6) 14(9) AF, n(%) 45(18.9) 32(20.6) SBP, mmHg 147[133,162] 151[135,165] DBP, mmHg 83[75,90] 86[76,95] WBC, 10 9 /L 7.19[5.66,9.09] 7.87[6.16,9.46] neutrophile, 10 9 /L 5.14[3.77,6.82] 5.76[3.98,7.43] lymphocyte, 10 9 /L 1.38[0.99,1.74] 1.52[1.15,2.05] hemoglobin, g/L 133[123,142] 143[134,153] erythrocyte, 10 12 /L 4.24[3.91,4.57] 4.66[4.34,5] platelet, 10 9 /L 199[169,234] 189[160,214] TBil, µmol/L 13.7[10.4,18.3] 15.1[11.6,19] TC, mmol/L 4.37[3.73,5.07] 4.94[4.1,5.62] TG, mmol/L 1.22[0.86,1.72] 1.23[0.82,1.92] HDL, mmol/L 1.13[0.96,1.29] 1.18[0.99,1.32] LDL, mmol/L 2.56[2.08,3.22] 2.79[2.28,3.41] UA, µmol/L 297[253,383] 340[287,402] creatinine, µmol/L 68.6[57.4,79.1] 70[60,80] glucose, mmol/L 5.45[4.77,6.86] 5.9[5.1,7.3] GHb, % 5.9[5.6,6.6] 5.7[5.3,6.5] ALT, U/L 16.2[12.6,21.1] 21[16,29] AST, U/L 21.7[18.1,26.8] 20[16,25] mRS(3–6), n(%) 95(39.9) 32(20.6) The values are presented as median value (1st quantile, 3rd quantile) or patient number (%). Abbreviations: mRS, modified ranking score; NIHSS, National Institute of Health Stroke Scale; LAA, large artery atherosclerosis; CE, cardio-embolism; SAO, small artery occlusion; DM, diabetes mellitus; CAD, coronary artery disease; AF, atrial fibrillation; SBP, systolic blood pressure; DBP, diastolic blood pressure; WBC, white blood cell; TBil, Total bilirubin; TC, total cholesterol; TG, total triglyceride; HDL, high density lipoprotein; LDL, low density lipoprotein ; UA, uric acid; GHb, Glycosylated hemoglobin; ALT, Alanine aminotransferase; AST, Aspartate aminotransferase. Training cohort. The median age was 70 years and 146 subjects (61.3%) were male. The most common vascular risk factor of subjects in this cohort was hypertension (62.6%), followed by CAD (20.6%). The median NIHSS score of subjects was 4 (IQR 2–11) at baseline. There were 95 (39.9%)subjects who experienced unfavorable outcomes(Table 2 ). Validation cohort. The median age was 62 years and 115 subjects (74.2%) were male. The most common vascular risk factor of subjects in this cohort was hypertension (71.6%), followed by DM (27.7%). The median NIHSS score of subjects was 5 (IQR 3–10) at baseline. There were 32 (20.6%)subjects who experienced unfavorable outcomes (Table 2 ). Univariate analysis of risk factors associated with adverse outcomes at 3 months in a training cohort Table 3 displays the results of the univariate logistic regression analysis on risk factors associated with adverse outcomes at the 3months in the training cohort. The study findings indicated that age (OR 1.04, 95% CI 1.01–1.06, p = 0.004), baseline NIHSS score (OR 1.22, 95% CI 1.15–1.28, p < 0.001), TOAST (OR 0.56, 95% CI 0.43–0.73, p < 0.001), CAD (OR 4.76, 95% CI 2.41–9.39, p < 0.001), AF (OR 5.81, 95% CI 2.81–12.02, p < 0.001), SBP (OR 1.02, 95% CI 1.01–1.03, p = 0.006), WBC (OR 1.2, 95% CI 1.09–1.33, p < 0.001), neutrophils (OR 1.27, 95% CI 1.14–1.41, p < 0.001), lymphocytes (OR 0.52, 95% CI 0.33–0.82, p = 0.005), TBil (OR 1.06, 95% CI 1.02–1.11, p = 0.002), UA (OR 1, 95% CI 0.994–0.997, p = 0.049), and blood glucose (OR 1.23, 95% CI 1.09–1.39, p = 0.001) were significantly correlated with adverse outcomes in AIS patients treated with intravenous thrombolysis at 3 months ( Table 3 ). Table 3 univariate logistic regression analysis for unfavorable outcome(mRS score of > 3) Variable B SE OR 95%CI Z P Age 0.035 0.012 1.04 1.01–1.06 2.9 0.004 Male 0.053 0.272 1.05 0.62–1.8 0.196 0.844 drinking -0.449 0.307 0.64 0.35–1.17 -1.464 0.143 smoking -0.56 0.29 0.57 0.32–1.01 -1.933 0.053 baseline NIHSS score 0.196 0.027 1.22 1.15–1.28 7.148 < 0.001 TOAST -0.576 0.135 0.56 0.43–0.73 -4.27 < 0.001 hypertension -0.259 0.272 0.77 0.45–1.32 -0.95 0.342 DM 0.383 0.342 1.47 0.75–2.87 1.119 0.263 CAD 1.559 0.347 4.76 2.41–9.39 4.496 < 0.001 AF 1.76 0.371 5.81 2.81–12.02 4.747 < 0.001 SBP 0.017 0.006 1.02 1.01–1.03 2.760 0.006 DBP 0.007 0.010 1.01 0.99–1.03 0.741 0.459 WBC 0.186 0.049 1.2 1.09–1.33 3.807 < 0.001 neutrophile 0.237 0.054 1.27 1.14–1.41 4.423 < 0.001 lymphocyte -0.65 0.233 0.52 0.33–0.82 -2.79 0.005 hemoglobin -0.005 0.009 1 0.98–1.01 -0.529 0.597 erythrocyte -0.321 0.268 0.73 0.43–1.23 -1.199 0.231 platelet -0.004 0.002 1 0.99-1 -1.734 0.083 TBil 0.063 0.02 1.06 1.02–1.11 3.101 0.002 TC -0.227 0.137 0.8 0.61–1.04 -1.663 0.096 TG -0.022 0.127 0.98 0.76–1.25 -0.174 0.862 HDL -0.069 0.505 0.93 0.35–2.51 -0.137 0.891 LDL -0.174 0.161 0.84 0.61–1.15 -1.075 0.283 UA -0.003 0.001 1.000 0.994–0.997 -1.965 0.049 creatinine -0.002 0.007 1 0.98–1.01 -0.356 0.722 glucose 0.206 0.062 1.23 1.09–1.39 3.307 0.001 GHb 0.212 0.114 1.24 0.99–1.55 1.87 0.061 ALT 0 0.007 1 0.99–1.01 -0.022 0.983 AST 0.031 0.017 1.03 1-1.07 1.858 0.063 Abbreviations: mRS, modified ranking score; NIHSS, National Institute of Health Stroke Scale; LAA, large artery atherosclerosis; CE, cardio-embolism; SAO, small artery occlusion; DM, diabetes mellitus; CAD, coronary artery disease; AF, atrial fibrillation; SBP, systolic blood pressure; DBP, diastolic blood pressure; WBC, white blood cell; TBil, Total bilirubin; TC, total cholesterol; TG, total triglyceride; HDL, high density lipoprotein; LDL, low density lipoprotein; UA, uric acid; GHb, Glycosylated hemoglobin; ALT, Alanine aminotransferase; AST, Aspartate aminotransferase; OR, odd ratio; CI, Confidence interval. LASSO regression analysis Multicollinearity among variables was addressed by employing LASSO regression to identify key variables (Fig. 1 A). The LASSO regression utilized a 10-fold cross-validation approach to select the Lambda parameter associated with the lowest mean square error, defining the optimal model (Fig. 1 B). The analysis revealed a Lambda value of 0.03708948 with seven characteristic variables included in the model: age, baseline NIHSS score, CAD, DM, SBP, neutrophils,and blood glucose. Multivariable logistic regression analysis was conducted on the seven characteristic variables identified through LASSO regression. The results of this analysis, presented in Table 4 , highlight the risk factors associated with unfavorable outcomes at 3 months in the training cohort. Upon adjusting for all potential confounders, it was found that the baseline NIHSS score(OR 1.21, 95%CI 1.13–1.29, p < 0.001), SBP (OR 1.02, 95%CI 1-1.04, p = 0.021), and blood glucose(OR 1.16, 95%CI 1-1.34, p = 0.044) emerged as independent predictors of unfavorable outcomes at 3 months. Table 4 multivariate logistic regression analysis for unfavorable outcome(mRS score of > 3) characteristics B SE OR 95%CI Z P age 0.017 0.016 1.02 0.99–1.05 1.048 0.295 Baseline NIHSS score 0.189 0.033 1.21 1.13–1.29 5.775 < 0.001 CAD 0.471 0.466 1.60 0.64–3.99 1.011 0.312 DM 0.657 0.438 1.93 0.82–4.55 1.501 0.133 SBP 0.019 0.008 1.02 1-1.04 2.300 0.021 neutrophile 0.062 0.067 1.06 0.93–1.21 0.923 0.356 glucose 0.149 0.074 1.16 1-1.34 2.015 0.044 Abbreviations: NIHSS, National Institute of Health Stroke Scale; CAD, coronary artery disease; DM, diabetes mellitus; SBP, systolic blood pressure; OR, odd ratio; CI, Confidence interval. Construction of the Nomogram A Nomogram was developed using 7 characteristic variables (age, baseline NIHSS score, CAD, DM, SBP, neutrophils, and blood glucose) identified through LASSO regression. The Nomogram predicts unfavorable outcomes at 3 months by summing the points assigned to each independent predictor, with scores ranging from 0 to 100. Higher total scores on the Nomogram indicate an increased risk of unfavorable outcomes at 3 months, while lower scores suggest a decreased likelihood of such outcomes (see Fig. 2 ). An illustrative example of using the Nomogram is provided in Fig. 3 . For instance, a 60-year-old patient with CAD and DM, a baseline NIHSS score of 6, SBP of 120mmHg, post-thrombolysis neutrophils of 8*10^9, and blood glucose of 12mmol/L would have a total score of 227, corresponding to an estimated 63.1% probability of an unfavorable outcome. Validation of the Nomogram The calibration between predictions and observations in both the training and validation cohorts was satisfactory, as evidenced by the Hosmer-Lemeshow tests (training cohort: χ = 6.62, P = 0.676; validation cohort: χ2 = 16.57, P = 0.056). The area under the ROC curve was 0.859 (95% CI 0.811–0.906) for the training cohort and 0.848 (95% CI 0.788–0.908) for the validation cohort(Fig. 4 ). Calibration plots comparing Nomogram-predicted probabilities of adverse outcomes at 3 months with actual probabilities of adverse outcomes at 3 months demonstrated significant predictive accuracy in both the training cohort (Fig. 5 A) and validation cohort (Fig. 5 B). Clinical application of the Nomogram Decision curve analysis was conducted on the Nomogram to assess its ability to predict adverse outcomes at 3 months. The analysis revealed that the model offers a greater net benefit when risk thresholds range from 0.05 to 0.82 for the training cohort and from 0.07 to 0.83 for the validation cohort (Fig. 6 A, 7B). Specifically, at a risk threshold of 40% for AIS patients, the net benefit is 0.23 for the training cohort and 0.18 for the validation cohort. Discussion Ischemic stroke is a significant contributor to mortality and disability, with post-stroke disability posing a public health concern. Therefore, timely and accurate detection of adverse outcomes following ischemic stroke is crucial for precise clinical and therapeutic interventions. The innovative aspect of this study lies in its comprehensive analysis of clinical and laboratory parameters that are predictive of outcomes in patients with acute ischemic stroke (AIS) undergoing intravenous thrombolysis. Previous literature has established various risk factors associated with stroke prognosis; however, this study fills a significant knowledge gap by employing a robust retrospective cohort design that examines a large and diverse patient population across multiple centers. Notably, our findings reveal that specific factors, such as age, baseline NIHSS score, CAD, DM, SBP, neutrophil count, and blood glucose are independent predictors of unfavorable outcomes. This contrasts with earlier studies, which often focused on singular risk factors or smaller cohorts, and provides a more nuanced understanding of how these variables interact to influence patient prognosis in the context of thrombolysis treatment [ 11 – 13 ] . Firstly, consistent with previous research [ 14 – 16 ] , our study demonstrated that the baseline NIHSS score plays a significant role in determining clinical outcomes in AIS patients and is frequently utilized in prognostic models. Elevated NIHSS scores are linked to greater infarct size and cerebral edema [ 15 , 17 ] , ultimately resulting in unfavorable outcomes. Secondly, age is a significant predictor of poor neurological status. Regardless of stroke severity, the increasing age of patients significantly influences the incidence, mortality, and long-term prognosis of stroke [ 18 ] . This may be attributed to the higher likelihood of elderly patients having cerebral small vessel disease and its complications, which increases their risk of adverse outcomes. In addition, DM, hypertension and CAD are common risk factors for AIS. Hyperglycemia in patients with ischemic stroke can independently predict adverse outcomes [ 19 – 22 ] . Hyperglycemia is associated with increased recruitment to the ischemic penumbra, which may be detected early after stroke onset but may also be observed within 24 hours due to increased brain lactate production [ 21 , 22 ] . Over time, high blood sugar caused by diabetes can damage the blood vessels and nerves that control the heart, increasing the chance of heart disease. Diabetes itself can cause multi-organ disease, which is closely related to death and recurrence after stroke [ 23 ] . Elevated systolic blood pressure is linked to blood-brain barrier damage and increased aquaporin-4 expression through oxidative stress, heightening the risk of neurological deterioration [ 24 ] . Meanwhile, hypertension not only impairs collateral circulation but also diminishes brain tissue's ability to maintain sufficient oxygen levels during cerebral artery occlusion. This condition fosters the accumulation of reactive oxygen species and inflammatory factors, exacerbating blood-brain barrier damage [ 25 ] . The presence and severity of coronary artery disease significantly impact the likelihood of future cardiovascular incidents in ischemic stroke patients. Atherosclerosis, a prevalent pathophysiological change in CAD, is characterized by a lipid-induced inflammatory environment in the arterial intima. The clinical outcomes are contingent on the balance between pro-inflammatory and anti-inflammatory processes [ 26 ] . Simultaneously, the inflammatory response plays a crucial role in the aftermath of cerebral ischemia-reperfusion injury [ 27 ] . Neutrophils serve as key inflammatory mediators, generating pro-inflammatory cytokines, stimulating matrix metalloproteinase-9 (MMP-9) expression, disrupting the blood-brain barrier, and contributing to brain damage [ 28 , 29 ] .It has been reported that elevated white blood cell levels are associated with poor prognosis in patients with acute cerebral infarction [ 30 ] . Additionally, some studies have shown that high levels of neutrophil counts are related to the severity of stroke at the time of admission [ 31 ] . The preferential focus on neutrophils rather than atrial fibrillation (AF) stems from their critical involvement in the acute inflammatory phase post-stroke, during which they mediate both tissue damage and repair processes. In our study, elevated neutrophil levels were significantly correlated with adverse clinical outcomes, indicating that their regulatory role in inflammation may exert a more substantial influence on prognosis than the presence of AF. Therefore, these mechanisms elucidate the adverse consequences associated with ischemic stroke. The implications of these results for clinical practice are profound. The development and validation of a predictive Nomogram based on the identified risk factors can serve as a practical tool for clinicians, enabling them to better assess patient prognosis and tailor treatment strategies accordingly. The Nomogram suggests that adopting the strategy of ending treatment provides a net benefit ensuring that all AIS patients will follow this strategy, thus guaranteeing that no patient with AIS will deviate from it. This is especially pertinent given the urgency surrounding thrombolysis administration in AIS cases, where timely intervention is critical for improving outcomes. By integrating this Nomogram into routine clinical practice, healthcare providers can enhance decision-making processes, allocate resources more effectively, and ultimately improve patient management, particularly for those identified as high-risk [ 32 , 33 ] . Furthermore, our findings underscore the necessity for ongoing research into individualized treatment protocols that consider multifactorial influences on patient outcomes, thereby contributing to enhanced stroke care and recovery strategies [ 34 ] . Our study has several limitations despite these interesting findings. Firstly, being a retrospective study, it is vulnerable to selection bias and recall bias. Secondly, although data from thrombolysis patients at two centers were included, the sample size remained small. We eagerly await replication findings from other institutions. Thirdly, important neurobiological predictors like infarct size were not accessible in the cohort, potentially impacting the predictive accuracy of our model in anticipating adverse outcomes at 3 months. Fourth, the validation cohort participants were significantly younger than those in the training cohort. The age differences between queues may affect the application of the nomogram in other populations. Nonetheless, our Nomogram demonstrated strong predictive accuracy. Future multicenter prospective studies could validate findings and incorporate neuroimaging. In summary, this study not only identifies critical prognostic factors for AIS patients undergoing thrombolysis but also presents a validated Nomogram as a practical tool for clinical application. The findings emphasize the importance of timely intervention and individualized patient management, while also acknowledging the need for continued research to validate and refine these predictive models in diverse clinical settings. In future, the nomogram can be integrated into electronic health record (EHR) systems to provide clinicians with real-time decision support. Furthermore, implementing comprehensive training programs for emergency department staff on nomogram utilization, coupled with the development of an intuitive interface, is critical to ensuring efficient access during time-sensitive clinical scenarios. Conclusions In conclusion, this study has successfully identified critical prognostic factors associated with unfavorable outcomes in acute ischemic stroke patients receiving intravenous thrombolysis, culminating in the development of a clinically applicable Nomogram. The Nomogram incorporates variables such as age, baseline NIHSS score, CAD, DM, SBP, neutrophils, and blood glucose. The findings underscore the significance of timely intervention and personalized management strategies to improve patient outcomes. As we move forward, it is essential to validate these results in larger, more diverse cohorts and explore the potential integration of biological and laboratory metrics into predictive models. In the future, we will further conduct prospective trials to assess the nomogram’s impact on clinical outcomes like mortality or rehab planning. Such endeavors will not only enhance our understanding of acute ischemic stroke but also contribute to the refinement of therapeutic approaches in clinical practice. Abbreviations mRS modified ranking score NIHSS National Institute of Health Stroke Scale LAA large artery atherosclerosis CE cardio-embolism SAO small artery occlusion DM diabetes mellitus CAD coronary artery disease AF atrial fibrillation SBP systolic blood pressure DBP diastolic blood pressure WBC white blood cell TBil Total bilirubin TC total cholesterol TG total triglyceride HDL high density lipoprotein LDL low density lipoprotein UA uric acid GHb Glycosylated hemoglobin ALT Alanine aminotransferase AST Aspartate aminotransferase. Declarations Acknowledgments The authors appreciate the valuable suggestions from other members of their teams. Authors’ contributions PZ and YZ conceived and designed the study. YZ, FF and WZand contributed to the data analysis and drafted the manuscript. DW and PZ critically revised the manuscript. All authors read and approved the final manuscript. Funding This study was supported by the Shanghai Committee of Science and Technology (Grant No. 23JC1401803 and 201409004900) , Enbipu Co., Ltd. of Shiyao Group (Grant No. YXSY-2022-24) , the Zhejiang Medical Science and Technology Project (Grant No.2023RC287 and 2022KY1286), Shaoxing Basic Public Welfare Program(Grant No.2022A14018 and 2022A14017) and Zhejiang Province Traditional Chinese Medicine Technology Program (2023ZL730). Ethics approval and consent to participate This retrospective observational study was approved by the Ethics Committee of the Shaoxing People’s Hospital (2021-K-Y-330–01) and Shanghai Fifth People's Hospital（2018 Ethics Approval NO.001). All methods were carried out in accordance with relevant guidelines and regulations. Written informed consent was obtained from all participants or their relatives.The study has been carried out in accordance with the STROBE Statement. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Availability of data and materials The datasets used and/or analyzed to support this manuscript are available from the corresponding author on reasonable request. References Liu L, Villavicencio F, Yeung D, et al. National, regional, and global causes of mortality in 5-19-year-olds from 2000 to 2019: a systematic analysis[J]. Lancet Glob Health, 2022,10(3):e337-e347. Yu A Y, Hill M D, Coutts S B. Should minor stroke patients be thrombolyzed? A focused review and future directions[J]. Int J Stroke, 2015,10(3):292-297. Wardlaw J M, Murray V, Berge E, et al. Recombinant tissue plasminogen activator for acute ischaemic stroke: an updated systematic review and meta-analysis[J]. Lancet, 2012,379(9834):2364-2372. Romano J G, Smith E E, Liang L, et al. Outcomes in mild acute ischemic stroke treated with intravenous thrombolysis: a retrospective analysis of the Get With the Guidelines-Stroke registry[J]. JAMA Neurol, 2015,72(4):423-431. Zhang S, Ji B, Zhong X, et al. A Dynamic Nomogram Predicting Portal Vein Thrombosis in Cirrhotic Patients During Primary Prophylaxis for Variceal Hemorrhage[J]. Front Med (Lausanne), 2022,9:887995. Cappellari M, Turcato G, Forlivesi S, et al. The START nomogram for individualized prediction of the probability of unfavorable outcome after intravenous thrombolysis for stroke[J]. Int J Stroke, 2018,13(7):700-706. Strbian D, Meretoja A, Ahlhelm F J, et al. Predicting outcome of IV thrombolysis-treated ischemic stroke patients: the DRAGON score[J]. Neurology, 2012,78(6):427-432. Lv S, Song Y, Zhang F L, et al. Early prediction of the 3-month outcome for individual acute ischemic stroke patients who received intravenous thrombolysis using the N2H3 nomogram model[J]. Ther Adv Neurol Disord, 2020,13:1279174622. Adams H J, Bendixen B H, Kappelle L J, et al. Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment[J]. Stroke, 1993,24(1):35-41. Zhou Y, Luo Y, Liang H, et al. Applicability of the low-grade inflammation score in predicting 90-day functional outcomes after acute ischemic stroke[J]. BMC Neurol, 2023,23(1):320. Cho H, Kim T, Kim Y D, et al. A clinical study of 288 patients with anterior cerebral artery infarction[J]. J Neurol, 2022,269(6):2999-3005. Wang Y, Li W, Yang J, et al. Association Between Cystatin C and the Risk of Ischemic Stroke: a Systematic Review and Meta-analysis[J]. J Mol Neurosci, 2019,69(3):444-449. Pawar S S, Hong S, Poetker D M. Delayed presentation of silent sinus syndrome after orbital trauma[J]. Am J Otolaryngol, 2010,31(1):61-63. Cappellari M, Turcato G, Forlivesi S, et al. Introduction of direct oral anticoagulant within 7 days of stroke onset: a nomogram to predict the probability of 3-month modified Rankin Scale score > 2[J]. J Thromb Thrombolysis, 2018,46(3):292-298. Cappellari M, Turcato G, Forlivesi S, et al. The START nomogram for individualized prediction of the probability of unfavorable outcome after intravenous thrombolysis for stroke[J]. Int J Stroke, 2018,13(7):700-706. Turcato G, Cervellin G, Cappellari M, et al. Early function decline after ischemic stroke can be predicted by a nomogram based on age, use of thrombolysis, RDW and NIHSS score at admission[J]. J Thromb Thrombolysis, 2017,43(3):394-400. Wu S, Yuan R, Wang Y, et al. Early Prediction of Malignant Brain Edema After Ischemic Stroke[J]. Stroke, 2018,49(12):2918-2927. Knoflach M, Matosevic B, Rücker M, et al. Functional recovery after ischemic stroke--a matter of age: data from the Austrian Stroke Unit Registry[J]. Neurology, 2012,78(4):279-285. Vora N A, Shook S J, Schumacher H C, et al. A 5-item scale to predict stroke outcome after cortical middle cerebral artery territory infarction: validation from results of the Diffusion and Perfusion Imaging Evaluation for Understanding Stroke Evolution (DEFUSE) Study[J]. Stroke, 2011,42(3):645-649. Yoo D S, Chang J, Kim J T, et al. Various blood glucose parameters that indicate hyperglycemia after intravenous thrombolysis in acute ischemic stroke could predict worse outcome[J]. PLoS One, 2014,9(4):e94364. Parsons M W, Barber P A, Desmond P M, et al. Acute hyperglycemia adversely affects stroke outcome: a magnetic resonance imaging and spectroscopy study[J]. Ann Neurol, 2002,52(1):20-28. Toni D, De Michele M, Fiorelli M, et al. Influence of hyperglycaemia on infarct size and clinical outcome of acute ischemic stroke patients with intracranial arterial occlusion[J]. J Neurol Sci, 1994,123(1-2):129-133. Echouffo-Tcheugui J B, Xu H, Matsouaka R A, et al. Diabetes and long-term outcomes of ischaemic stroke: findings from Get With The Guidelines-Stroke[J]. Eur Heart J, 2018,39(25):2376-2386. He Y, Yang Q, Liu H, et al. Effect of blood pressure on early neurological deterioration of acute ischemic stroke patients with intravenous rt-PA thrombolysis may be mediated through oxidative stress induced blood-brain barrier disruption and AQP4 upregulation[J]. J Stroke Cerebrovasc Dis, 2020,29(8):104997. Wu D, Liu Y. FM Combined With NIHSS Score Contributes to Early AIS Diagnosis and Differential Diagnosis of Cardiogenic and Non-Cardiogenic AIS[J]. Clin Appl Thromb Hemost, 2021,27:1319785663. Bäck M, Yurdagul A J, Tabas I, et al. Inflammation and its resolution in atherosclerosis: mediators and therapeutic opportunities[J]. Nat Rev Cardiol, 2019,16(7):389-406. Kim J Y, Park J, Chang J Y, et al. Inflammation after Ischemic Stroke: The Role of Leukocytes and Glial Cells[J]. Exp Neurobiol, 2016,25(5):241-251. Jin P, Li X, Chen J, et al. Platelet-to-neutrophil ratio is a prognostic marker for 90-days outcome in acute ischemic stroke[J]. J Clin Neurosci, 2019,63:110-115. Huang L, Chen Y, Liu R, et al. P-Glycoprotein Aggravates Blood Brain Barrier Dysfunction in Experimental Ischemic Stroke by Inhibiting Endothelial Autophagy[J]. Aging Dis, 2022,13(5):1546-1561. Furlan J C, Vergouwen M D I, Fang J, et al. White blood cell count is an independent predictor of outcomes after acute ischaemic stroke[J]. Eur J Neurol, 2014,21(2):215-222. Kim J, Song T, Park J H, et al. Different prognostic value of white blood cell subtypes in patients with acute cerebral infarction[J]. Atherosclerosis, 2012,222(2):464-467. Lu B, Shi J, Cheng T, et al. Chemokine ligand 14 correlates with immune cell infiltration in the gastric cancer microenvironment in predicting unfavorable prognosis[J]. Front Pharmacol, 2024,15:1397656. Rost N S, Fitzpatrick K, Biffi A, et al. White matter hyperintensity burden and susceptibility to cerebral ischemia[J]. Stroke, 2010,41(12):2807-2811. Shin H Y, Jeong I H, Kang C K, et al. Relation between left atrial enlargement and stroke subtypes in acute ischemic stroke patients[J]. J Cerebrovasc Endovasc Neurosurg, 2013,15(3):131-136. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure1.docx Supplementary Fig1. Flowchart for the study. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 08 May, 2026 Reviewers agreed at journal 10 Apr, 2026 Reviewers invited by journal 04 Feb, 2026 Editor invited by journal 27 Jan, 2026 Editor assigned by journal 22 Jan, 2026 Submission checks completed at journal 22 Jan, 2026 First submitted to journal 20 Jan, 2026 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-8651893","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587092955,"identity":"d2b459fd-61f7-495c-ba77-83c41734c44b","order_by":0,"name":"yang zhou","email":"","orcid":"","institution":"The First Affiliated Hospital, Shao xing University","correspondingAuthor":false,"prefix":"","firstName":"yang","middleName":"","lastName":"zhou","suffix":""},{"id":587092956,"identity":"d92cd0dc-b98d-4746-b120-a542c0fa0961","order_by":1,"name":"fang fang","email":"","orcid":"","institution":"The First Affiliated Hospital, Shao xing University","correspondingAuthor":false,"prefix":"","firstName":"fang","middleName":"","lastName":"fang","suffix":""},{"id":587092957,"identity":"ee83e91d-4f5d-4e7f-83a0-a796797d003f","order_by":2,"name":"Zhenyu Wei","email":"","orcid":"","institution":"Shanghai Yangpu District Shidong Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhenyu","middleName":"","lastName":"Wei","suffix":""},{"id":587092958,"identity":"e762ae26-fa29-4ba4-9775-5f6455ebb229","order_by":3,"name":"ping zhong","email":"","orcid":"","institution":"Shanghai Yangpu District Shidong Hospital","correspondingAuthor":false,"prefix":"","firstName":"ping","middleName":"","lastName":"zhong","suffix":""},{"id":587092959,"identity":"43b61a7b-ca03-48fe-b5ed-a245afdeb5b2","order_by":4,"name":"Danhong Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYBACewYGNgiLvfEBAw+YlYBfi2EDTAvPYQPitBgcgGmRSCZWy/Gzxx583FGbuOHmYzaJNzV3GPjZcwwYfu7Ao+VMXrrhzDPHEzfcTmaTnHPsGYNkzxsDxt4z+ByWYybN23Ysd8Pt/GPSPGyHGQxu5BgwM7bh0XL+DVTLzcNs0jz/DjPYE9RyA2xLTe6GG8xsQAbQFgkCWgxnvDGTnNl2oH7mmWRmy7l9h3kkzjwrONiLR4s9f46ZxMe2OmO+44cZb7z5dliOvz1544OfeLRAwWE4Cxw1BwhqYGCoI0LNKBgFo2AUjFgAAJhZVc0pNcWfAAAAAElFTkSuQmCC","orcid":"","institution":"Shanghai Fifth People's Hospital, Fudan University","correspondingAuthor":true,"prefix":"","firstName":"Danhong","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2026-01-20 18:03:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8651893/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8651893/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102239200,"identity":"63869922-dd3f-4f45-87b3-e795a99f6c37","added_by":"auto","created_at":"2026-02-09 16:43:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":290089,"visible":true,"origin":"","legend":"\u003cp\u003eSelection of the optimal prognostic factors by LASSO regression analysis. a. LASSO coefficient profiles of potential predictors. b. Screening of the optimal penalization coefficient in the LASSO regression. The left of the 2 vertical lines in the figure represent the optimal λ, and the variables represented by the curves intersecting to this line are preserved. The vertical line on the right is 1 SE of λ.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-8651893/v1/e8d60ad5dc48afa29af7419f.png"},{"id":102297488,"identity":"d4f5dfce-9b6b-451c-8e24-df71e565f1ba","added_by":"auto","created_at":"2026-02-10 10:27:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":438079,"visible":true,"origin":"","legend":"\u003cp\u003eRisk prediction model for unfavorable outcome.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-8651893/v1/928fedf4e685671e14bd1a76.png"},{"id":102297221,"identity":"4095f52e-352a-403c-9016-2fcc797d7c65","added_by":"auto","created_at":"2026-02-10 10:26:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":272074,"visible":true,"origin":"","legend":"\u003cp\u003eExample of using the nomogram.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-8651893/v1/f50d80c8a51c1d62c17bdee1.png"},{"id":102297312,"identity":"784d2a3b-3fcb-4044-9699-cc517c991c42","added_by":"auto","created_at":"2026-02-10 10:26:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":237093,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of the nomogram in the training (A) and test cohort(B).\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-8651893/v1/762e2b6e56940513233bb450.png"},{"id":102239196,"identity":"4c62ee55-9e7e-4a3e-804b-a2965e622f6e","added_by":"auto","created_at":"2026-02-09 16:43:26","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":434286,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration plot of the nomogram in the training (A) and test cohorts (B).\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-8651893/v1/4d389fbb41f0e5cbe1b9687f.png"},{"id":102239202,"identity":"0e264333-32f8-42a1-8636-f9b045dca8c4","added_by":"auto","created_at":"2026-02-09 16:43:26","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":288888,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis of the nomogram of training cohort(A) and test cohort (B).\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-8651893/v1/5c94588253ca492d1c17b214.png"},{"id":102397733,"identity":"501bd97e-9110-4c52-be04-902a2971df4f","added_by":"auto","created_at":"2026-02-11 10:19:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2918008,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8651893/v1/1b721611-e6dd-48af-89d4-b3f1154010c0.pdf"},{"id":102239199,"identity":"7afbab9e-9c32-4bc9-866e-9aa087f628be","added_by":"auto","created_at":"2026-02-09 16:43:26","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":214627,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Fig1. Flowchart for the study.\u003c/p\u003e","description":"","filename":"SupplementaryFigure1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8651893/v1/4762e697f13695537a429725.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Neutrophil-Incorporated Nomogram for Predicting 3-Month Disability after Intravenous Thrombolysis in Acute Ischemic Stroke","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIschemic stroke (IS) is the primary subtype of stroke and is currently the leading cause of death worldwide\u003csup\u003e[1, 2]\u003c/sup\u003e. Recombinant\u0026nbsp;tissue plasminogen activator alteplase (rt-PA) is considered the most effective drug for treating acute ischemic stroke(AIS) and has\u0026nbsp;been\u0026nbsp;shown to improve functional outcomes\u0026nbsp;post-onset\u003csup\u003e[3]\u003c/sup\u003e. While many patients experience relief within 24-72 hours, a significant number still have lingering neurological deficits after receiving thrombolytic treatment\u003csup\u003e[2, 4]\u003c/sup\u003e. Therefore, better identification of AIS patients who experience unfavorable outcomes following rt-PA treatment could be useful to develop preventive strategies and reduce the risk of morbidity and mortality after stroke.\u003c/p\u003e\n\u003cp\u003eNomograms, a scoring system based on various variables, have become increasingly prevalent in clinical decision-making for conditions including ischemic stroke, myocardial infarction, and cancer\u003csup\u003e[5]\u003c/sup\u003e. They are also utilized in predicting the functional outcomes of AIS patients post-thrombolytic therapy. Cappellari and his colleagues devised a nomogram within a substantial SITS-ISTR cohort, enabling the accurate calculation of the likelihood of an unfavorable three-month outcome in stroke patients who have undergone intravenous thrombolysis (IVT) as a standalone treatment\u003csup\u003e[6]\u003c/sup\u003e. In addition, a systematic review by Khatri et al. highlights the importance of predictive models in stroke management, emphasizing how they can guide treatment decisions and improve patient outcomes. Strbian et al. derived an outcome score, DRAGON,from a large dataset of nonbasilar artery alteplasetreated stroke patients. They propose that a high DRAGON score can identify those patients who should promptly begin endovascular, hypothermia,or other therapy due to poor outcome after alteplase.\u003csup\u003e[7]\u003c/sup\u003e Furthermore, shan et al. developed the N2H3 nomogram, which can offer personalized early predictions of unfavorable 3-month outcomes in AIS patients undergoing intravenous rt-PA thrombolysis. This nomogram aids in the prompt identification of patients who may benefit from additional therapeutic interventions\u003csup\u003e[8]\u003c/sup\u003e. Compared to the above several nomogram, our nomogram integrates inflammatory markers like neutrophils, offering a more comprehensive prediction. Overall, the integration of nomograms into clinical practice represents a significant advancement in the management of complex medical conditions. These tools not only enhance the precision of outcome predictions but also promote a proactive approach to treatment, ensuring that patients receive the most appropriate and timely care based on their unique clinical profiles. As research in this area continues to evolve, the potential for nomograms to further refine clinical decision-making and improve patient outcomes remains promising.\u003c/p\u003e\n\u003cp\u003eTherefore, this research aimed to create and validate a novel, straightforward and dependable Nomogram model for predicting short-term functional outcomes in AIS patients treated with intravenous thrombolysis.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eSubjects of the study\u003c/h2\u003e \u003cp\u003eThis retrospective study examined AIS patients who underwent intravenous thrombolysis within 4.5 hours of stroke symptom onset at Shaoxing People's Hospital between January 2019 and December 2021 and Shanghai Fifth People's Hospital between January 2018 and December 2020. Inclusion criteria comprised patients aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years who had a confirmed acute ischemic stroke through brain imaging(computed tomography or magnetic resonance imaging) and received intravenous thrombolysis within 4.5 hours of stroke symptom onset. The confirmation of acute ischemic stroke through brain imaging was conducted by a certified neurologist who had undergone standardized training. Exclusion criteria: 1. patients without complete routine hematological investigations or parameters on the day of emergency or admission; 2. patients with a prior history of stroke; 3. patients with infections in the past 2 weeks; 4. patients with blood disorders; 5. patients with immunosuppressive medication or corticosteroid hormones use; 6. patients with cancers or immune system disorders; 7. patients with severe cardiac, hepatic, or renal diseases; 8. patients who received endovascular treatment; 9. participants who have tested positive for COVID-19 or have exhibited symptoms consistent with the virus within the past 14 days. A flowchart depicting eligible participants is illustrated in Supplementary Fig.\u0026nbsp;1. The patients from Shaoxing People's Hospital constituted the training set, while those from Shanghai Fifth People's Hospital served as the validation set. The study was approved by the ethics committees of Shaoxing People's Hospital(2021-K-Y-330-01) and Shanghai Fifth People's Hospital(2018 Ethics Approval NO.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eUpon admission, we documented the patient\u0026rsquo;s demographics, neurological physical examination, and laboratory results. Demographic information encompassed age, gender, smoking and alcohol consumption habits. The physical examination measured systolic blood pressure (SBP) and diastolic blood pressure (DBP) before thrombolysis. Laboratory data prior to thrombolysis encompassed white blood cell count (WBC), neutrophil count, lymphocyte count, hemoglobin, erythrocyte count, platelet count, total bilirubin (TBil), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), uric acid (UA), creatinine, blood glucose, Glycosylated hemoglobin (GHb), Alanine aminotransferase (AST), Aspartate aminotransferase (AST). Additionally, we gathered information on comorbid conditions such as coronary heart disease (CAD), hypertension, diabetes mellitus(DM), and atrial fibrillation(AF).\u003c/p\u003e \u003cp\u003eIschemic stroke subtypes were classified following the criteria of the Org 10,172 Acute Stroke Treatment Trial\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. The National Institutes of Health Stroke Scale (NIHSS) score and modified Rankin Scale (mRS) score were assessed by experienced clinicians on the included patients, and the NIHSS score and mRS score before thrombolysis were defined as the baseline NIHSS score and baseline mRS score, respectively. All certified neurologists underwent standardized training for evaluating NIHSS and mRS scores and were unaware of our study. Two certified neurologists from the medical team evaluated each participant, with a third neurologist resolving any disagreements in assessments.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOutcome assessment\u003c/h3\u003e\n\u003cp\u003eAll enrolled patients were followed up via telephone by the same investigator. The mRS score was used to evaluate the neurological outcomes of stroke patients three months after thrombolysis. A favorable outcome was defined as an mRS score of \u0026le;\u0026thinsp;2, while an unfavorable outcome was defined as an mRS score of \u0026gt;\u0026thinsp;3\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was conducted using R version 3.6.2 software. Categorical variables were presented as n (%) and continuous variables as median (interquartile range, IQR). The independent samples t-test or Mann-Whitney U test was used for continuous variables, while the chi-square test or Fisher's exact test was used for categorical variables. In the training cohort, univariate regression analysis was performed to identify risk factors associated with adverse outcomes at 3 months. LASSO regression in the 'Glmnet' software package was employed to select the optimal feature subset. The least absolute shrinkage and selection operator(LASSO) regression method was used to select the most valuable predictors from the training cohort. To solve the problem of overfitting, based on the concept of variance trade-off, the penalty term λ is added to the selection of predictors. By carrying out penalty regression on the coefficients of all variables,the coefficients of relatively unimportant independent variables are compressed to zero to eliminate these variables, determine the independent variables that can provide important information for the model, and freeze the model in the state of just fitting. According to the established language program, the independent variable and dependent variable matrices were generated in R studio for fitting, a contraction curve was drawn, and cross-validation was carried out. The optimal penalty coefficient λ was obtained using a 10-fold cross-validation method. λ with the least cross-validation error was selected to make the model in the exact fitting state. The shrinkage coefficient plot and the minimum λ curve were drawn, and the variables whose coefficients were not compressed to 0 were selected as the predictors after LASSO screening. The crossing validation graph and shrinkage coefficient diagram of LASSO regression are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The vertical line on the left represents the optimal λ, and the vertical line on the right represents the double standard error of λ. The variable intersecting the first vertical line was the preserved variable. Subsequently, the relevant risk factors were subjected to LASSO regression analysis, resulting in 7 characteristic variables for multifactor regression analysis. A Nomogram was constructed and validated using data from both the training and validation cohorts. The discriminative ability of the Nomogram was evaluated by calculating the receiver operating characteristic curves (ROC). Calibration of the Nomogram was assessed using the Hosmer-Lemeshow test and calibration curves, which depict the fit between actual and predicted results. Decision curve analysis was utilized to evaluate the effectiveness of the Nomogram. Statistical significance was set at a two-tailed P value of less than 0.05.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics of included patients\u003c/h2\u003e \u003cp\u003ePatients were screened based on specific criteria, resulting in the inclusion of 238 AIS patients who underwent intravenous thrombolysis. These patients were then categorized into either a favorable outcome group or an unfavorable outcome group (refer to Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Univariate analysis revealed that factors such as age, baseline mRS score, baseline NIHSS score, TOAST classification, CAD, AF, SBP, WBC, neutrophil count, lymphocyte count, TBil, and blood glucose may be associated with the unfavorable outcomes (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Compared to the favorable outcome group, the unfavorable outcome group had higher values for age, baseline mRS score, baseline NIHSS score, large artery atherosclerosis(LAA), cardio-embolism(CE), SBP, WBC, neutrophil count, TBil, blood glucose, CAD, and AF, while lymphocyte count was lower (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison among patients included in different mRS groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003emRS(0\u0026ndash;2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003emRS(3\u0026ndash;6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;143)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;95)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68[59,75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74[63,79]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n(%)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87(60.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59(62.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edrinking, n(%)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44(30.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21(22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esmoking, n(%)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55(38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25(26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebaseline mRS\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2[1,3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4[4,5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebaseline NIHSS score\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3[1,5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11[5,17]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTOAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAA, n(%)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40(27.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41(43.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCE, n(%)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19(13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32(33.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAO, n(%)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57(39.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eother, n(%)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27(18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n(%)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93(65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56(58.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM, n(%)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22(15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAD, n(%)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34(35.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAF, n(%)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33(34.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e145[131,160]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e151[137,167]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83[75,90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84[75,90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC, 10\u003csup\u003e9a\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.73[5.35,8.07]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.32[6.65,10.56]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eneutrophile, 10\u003csup\u003e9\u003c/sup\u003e/L\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.59[3.13,5.77]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.36[4.88,8.35]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elymphocyte, 10\u003csup\u003e9\u003c/sup\u003e/L\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.49[1.08,1.79]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22[0.85,1.68]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, g/L\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133[123,144]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131[123,142]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.465\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eerythrocyte, 10\u003csup\u003e12\u003c/sup\u003e/L\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.37[3.93,4.64]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.19[3.88,4.48]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eplatelet, 10\u003csup\u003e9\u003c/sup\u003e/L\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e201[171,245]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e195[161,223]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBil, \u0026micro;mol/L\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.5[9.5,16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.2[12.2,19.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC, mmol/L\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.48[3.81,5.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.31[3.61,4.85]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG, mmol/L\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.29[0.91,1.76]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11[0.78,1.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL, mmol/L\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15[0.95,1.28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11[0.96,1.29]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL, mmol/L\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.66[2.09,3.29]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.45[2.05,3.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA, \u0026micro;mol/L\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e306.6[257.5,396.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e291.6[232.2,359.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecreatinine, \u0026micro;mol/L\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.8[59.2,81]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.2[55.8,77.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eglucose, mmol/L\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.2[4.56,6.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.86[5.17,7.84]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGHb, %\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.9[5.5,6.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6[5.6,6.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT, U/L\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.5[12.6,22.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.5[12.6,20.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST, U/L\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.1[17.4,25.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.7[18.3,27.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eThe values are presented as median value (1st quantile, 3rd quantile) or patient number (%).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics of the training and validation cohorts\u003c/h2\u003e \u003cp\u003eThe demographic and clinical characteristics of the training cohort and validation cohort are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the training and validation cohorts\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraining cohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation cohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;238)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;155)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70[61,76]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62[53,71]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146(61.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115(74.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65(27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66(42.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80(33.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85(54.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebaseline mRS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3[2,4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3[1,4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebaseline NIHSS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4[2,11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5[3,10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTOAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAA, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81(34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62(40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCE, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51(21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56(36.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAO, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72(30.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32(20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eother, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34(14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e149(62.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111(71.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42(17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43(27.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAD, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49(20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAF, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45(18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32(20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e147[133,162]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e151[135,165]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83[75,90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86[76,95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.19[5.66,9.09]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.87[6.16,9.46]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eneutrophile, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.14[3.77,6.82]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.76[3.98,7.43]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elymphocyte, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.38[0.99,1.74]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.52[1.15,2.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehemoglobin, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133[123,142]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143[134,153]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eerythrocyte, 10\u003csup\u003e12\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.24[3.91,4.57]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.66[4.34,5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eplatelet, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e199[169,234]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e189[160,214]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBil, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.7[10.4,18.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.1[11.6,19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.37[3.73,5.07]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.94[4.1,5.62]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.22[0.86,1.72]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23[0.82,1.92]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.13[0.96,1.29]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18[0.99,1.32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.56[2.08,3.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.79[2.28,3.41]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e297[253,383]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e340[287,402]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecreatinine, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.6[57.4,79.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70[60,80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eglucose, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.45[4.77,6.86]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.9[5.1,7.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGHb, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.9[5.6,6.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.7[5.3,6.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.2[12.6,21.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21[16,29]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.7[18.1,26.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20[16,25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emRS(3\u0026ndash;6), n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95(39.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32(20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eThe values are presented as median value (1st quantile, 3rd quantile) or patient number (%).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: mRS, modified ranking score; NIHSS, National Institute of Health Stroke Scale; LAA, large artery atherosclerosis; CE, cardio-embolism; SAO, small artery occlusion; DM, diabetes mellitus; CAD, coronary artery disease; AF, atrial fibrillation; SBP, systolic blood pressure; DBP, diastolic blood pressure; WBC, white blood cell; TBil, Total bilirubin; TC, total cholesterol; TG, total triglyceride; HDL, high density lipoprotein; LDL, low density lipoprotein ; UA, uric acid; GHb, Glycosylated hemoglobin; ALT, Alanine aminotransferase; AST, Aspartate aminotransferase.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTraining cohort.\u003c/b\u003e The median age was 70 years and 146 subjects (61.3%) were male. The most common vascular risk factor of subjects in this cohort was hypertension (62.6%), followed by CAD (20.6%). The median NIHSS score of subjects was 4 (IQR 2\u0026ndash;11) at baseline. There were 95 (39.9%)subjects who experienced unfavorable outcomes(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eValidation cohort.\u003c/b\u003e The median age was 62 years and 115 subjects (74.2%) were male. The most common vascular risk factor of subjects in this cohort was hypertension (71.6%), followed by DM (27.7%). The median NIHSS score of subjects was 5 (IQR 3\u0026ndash;10) at baseline. There were 32 (20.6%)subjects who experienced unfavorable outcomes (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eUnivariate analysis of risk factors associated with adverse outcomes at 3 months in a training cohort\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the results of the univariate logistic regression analysis on risk factors associated with adverse outcomes at the 3months in the training cohort. The study findings indicated that age (OR 1.04, 95% CI 1.01\u0026ndash;1.06, p\u0026thinsp;=\u0026thinsp;0.004), baseline NIHSS score (OR 1.22, 95% CI 1.15\u0026ndash;1.28, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), TOAST (OR 0.56, 95% CI 0.43\u0026ndash;0.73, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), CAD (OR 4.76, 95% CI 2.41\u0026ndash;9.39, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), AF (OR 5.81, 95% CI 2.81\u0026ndash;12.02, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), SBP (OR 1.02, 95% CI 1.01\u0026ndash;1.03, p\u0026thinsp;=\u0026thinsp;0.006), WBC (OR 1.2, 95% CI 1.09\u0026ndash;1.33, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), neutrophils (OR 1.27, 95% CI 1.14\u0026ndash;1.41, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), lymphocytes (OR 0.52, 95% CI 0.33\u0026ndash;0.82, p\u0026thinsp;=\u0026thinsp;0.005), TBil (OR 1.06, 95% CI 1.02\u0026ndash;1.11, p\u0026thinsp;=\u0026thinsp;0.002), UA (OR 1, 95% CI 0.994\u0026ndash;0.997, p\u0026thinsp;=\u0026thinsp;0.049), and blood glucose (OR 1.23, 95% CI 1.09\u0026ndash;1.39, p\u0026thinsp;=\u0026thinsp;0.001) were significantly correlated with adverse outcomes in AIS patients treated with intravenous thrombolysis at 3 months ( Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eunivariate logistic regression analysis for unfavorable outcome(mRS score of \u0026gt;\u0026thinsp;3)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.01\u0026ndash;1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.62\u0026ndash;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edrinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.35\u0026ndash;1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.32\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebaseline NIHSS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.15\u0026ndash;1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTOAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.43\u0026ndash;0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-4.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.45\u0026ndash;1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.75\u0026ndash;2.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.41\u0026ndash;9.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.81\u0026ndash;12.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.01\u0026ndash;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99\u0026ndash;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.09\u0026ndash;1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eneutrophile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.14\u0026ndash;1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elymphocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.33\u0026ndash;0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehemoglobin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.98\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.597\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eerythrocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.43\u0026ndash;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eplatelet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.02\u0026ndash;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.61\u0026ndash;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.76\u0026ndash;1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.862\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.35\u0026ndash;2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.61\u0026ndash;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.283\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.994\u0026ndash;0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecreatinine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.98\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.722\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eglucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.09\u0026ndash;1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGHb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99\u0026ndash;1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1-1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviations: mRS, modified ranking score; NIHSS, National Institute of Health Stroke Scale; LAA, large artery atherosclerosis; CE, cardio-embolism; SAO, small artery occlusion; DM, diabetes mellitus; CAD, coronary artery disease; AF, atrial fibrillation; SBP, systolic blood pressure; DBP, diastolic blood pressure; WBC, white blood cell; TBil, Total bilirubin; TC, total cholesterol; TG, total triglyceride; HDL, high density lipoprotein; LDL, low density lipoprotein; UA, uric acid; GHb, Glycosylated hemoglobin; ALT, Alanine aminotransferase; AST, Aspartate aminotransferase; OR, odd ratio; CI, Confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eLASSO regression analysis\u003c/p\u003e \u003cp\u003eMulticollinearity among variables was addressed by employing LASSO regression to identify key variables (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The LASSO regression utilized a 10-fold cross-validation approach to select the Lambda parameter associated with the lowest mean square error, defining the optimal model (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The analysis revealed a Lambda value of 0.03708948 with seven characteristic variables included in the model: age, baseline NIHSS score, CAD, DM, SBP, neutrophils,and blood glucose.\u003c/p\u003e \u003cp\u003eMultivariable logistic regression analysis was conducted on the seven characteristic variables identified through LASSO regression. The results of this analysis, presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, highlight the risk factors associated with unfavorable outcomes at 3 months in the training cohort. Upon adjusting for all potential confounders, it was found that the baseline NIHSS score(OR 1.21, 95%CI 1.13\u0026ndash;1.29, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), SBP (OR 1.02, 95%CI 1-1.04, p\u0026thinsp;=\u0026thinsp;0.021), and blood glucose(OR 1.16, 95%CI 1-1.34, p\u0026thinsp;=\u0026thinsp;0.044) emerged as independent predictors of unfavorable outcomes at 3 months.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003emultivariate logistic regression analysis for unfavorable outcome(mRS score of \u0026gt;\u0026thinsp;3)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003echaracteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99\u0026ndash;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline NIHSS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.13\u0026ndash;1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.64\u0026ndash;3.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.82\u0026ndash;4.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1-1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eneutrophile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.93\u0026ndash;1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eglucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1-1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviations: NIHSS, National Institute of Health Stroke Scale; CAD, coronary artery disease; DM, diabetes mellitus; SBP, systolic blood pressure; OR, odd ratio; CI, Confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eConstruction of the Nomogram\u003c/h3\u003e\n\u003cp\u003eA Nomogram was developed using 7 characteristic variables (age, baseline NIHSS score, CAD, DM, SBP, neutrophils, and blood glucose) identified through LASSO regression. The Nomogram predicts unfavorable outcomes at 3 months by summing the points assigned to each independent predictor, with scores ranging from 0 to 100. Higher total scores on the Nomogram indicate an increased risk of unfavorable outcomes at 3 months, while lower scores suggest a decreased likelihood of such outcomes (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). An illustrative example of using the Nomogram is provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. For instance, a 60-year-old patient with CAD and DM, a baseline NIHSS score of 6, SBP of 120mmHg, post-thrombolysis neutrophils of 8*10^9, and blood glucose of 12mmol/L would have a total score of 227, corresponding to an estimated 63.1% probability of an unfavorable outcome.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eValidation of the Nomogram\u003c/h3\u003e\n\u003cp\u003eThe calibration between predictions and observations in both the training and validation cohorts was satisfactory, as evidenced by the Hosmer-Lemeshow tests (training cohort: χ\u0026thinsp;=\u0026thinsp;6.62, P\u0026thinsp;=\u0026thinsp;0.676; validation cohort: χ2\u0026thinsp;=\u0026thinsp;16.57, P\u0026thinsp;=\u0026thinsp;0.056). The area under the ROC curve was 0.859 (95% CI 0.811\u0026ndash;0.906) for the training cohort and 0.848 (95% CI 0.788\u0026ndash;0.908) for the validation cohort(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Calibration plots comparing Nomogram-predicted probabilities of adverse outcomes at 3 months with actual probabilities of adverse outcomes at 3 months demonstrated significant predictive accuracy in both the training cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA) and validation cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eClinical application of the Nomogram\u003c/h2\u003e \u003cp\u003eDecision curve analysis was conducted on the Nomogram to assess its ability to predict adverse outcomes at 3 months. The analysis revealed that the model offers a greater net benefit when risk thresholds range from 0.05 to 0.82 for the training cohort and from 0.07 to 0.83 for the validation cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, 7B). Specifically, at a risk threshold of 40% for AIS patients, the net benefit is 0.23 for the training cohort and 0.18 for the validation cohort.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIschemic stroke is a significant contributor to mortality and disability, with post-stroke disability posing a public health concern. Therefore, timely and accurate detection of adverse outcomes following ischemic stroke is crucial for precise clinical and therapeutic interventions.\u003c/p\u003e \u003cp\u003eThe innovative aspect of this study lies in its comprehensive analysis of clinical and laboratory parameters that are predictive of outcomes in patients with acute ischemic stroke (AIS) undergoing intravenous thrombolysis. Previous literature has established various risk factors associated with stroke prognosis; however, this study fills a significant knowledge gap by employing a robust retrospective cohort design that examines a large and diverse patient population across multiple centers. Notably, our findings reveal that specific factors, such as age, baseline NIHSS score, CAD, DM, SBP, neutrophil count, and blood glucose are independent predictors of unfavorable outcomes. This contrasts with earlier studies, which often focused on singular risk factors or smaller cohorts, and provides a more nuanced understanding of how these variables interact to influence patient prognosis in the context of thrombolysis treatment\u003csup\u003e[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFirstly, consistent with previous research\u003csup\u003e[\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e, our study demonstrated that the baseline NIHSS score plays a significant role in determining clinical outcomes in AIS patients and is frequently utilized in prognostic models. Elevated NIHSS scores are linked to greater infarct size and cerebral edema\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, ultimately resulting in unfavorable outcomes. Secondly, age is a significant predictor of poor neurological status. Regardless of stroke severity, the increasing age of patients significantly influences the incidence, mortality, and long-term prognosis of stroke\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. This may be attributed to the higher likelihood of elderly patients having cerebral small vessel disease and its complications, which increases their risk of adverse outcomes. In addition, DM, hypertension and CAD are common risk factors for AIS. Hyperglycemia in patients with ischemic stroke can independently predict adverse outcomes\u003csup\u003e[\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Hyperglycemia is associated with increased recruitment to the ischemic penumbra, which may be detected early after stroke onset but may also be observed within 24 hours due to increased brain lactate production\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Over time, high blood sugar caused by diabetes can damage the blood vessels and nerves that control the heart, increasing the chance of heart disease. Diabetes itself can cause multi-organ disease, which is closely related to death and recurrence after stroke\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Elevated systolic blood pressure is linked to blood-brain barrier damage and increased aquaporin-4 expression through oxidative stress, heightening the risk of neurological deterioration\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Meanwhile, hypertension not only impairs collateral circulation but also diminishes brain tissue's ability to maintain sufficient oxygen levels during cerebral artery occlusion. This condition fosters the accumulation of reactive oxygen species and inflammatory factors, exacerbating blood-brain barrier damage\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. The presence and severity of coronary artery disease significantly impact the likelihood of future cardiovascular incidents in ischemic stroke patients. Atherosclerosis, a prevalent pathophysiological change in CAD, is characterized by a lipid-induced inflammatory environment in the arterial intima. The clinical outcomes are contingent on the balance between pro-inflammatory and anti-inflammatory processes\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Simultaneously, the inflammatory response plays a crucial role in the aftermath of cerebral ischemia-reperfusion injury\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Neutrophils serve as key inflammatory mediators, generating pro-inflammatory cytokines, stimulating matrix metalloproteinase-9 (MMP-9) expression, disrupting the blood-brain barrier, and contributing to brain damage\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e.It has been reported that elevated white blood cell levels are associated with poor prognosis in patients with acute cerebral infarction\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Additionally, some studies have shown that high levels of neutrophil counts are related to the severity of stroke at the time of admission\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. The preferential focus on neutrophils rather than atrial fibrillation (AF) stems from their critical involvement in the acute inflammatory phase post-stroke, during which they mediate both tissue damage and repair processes. In our study, elevated neutrophil levels were significantly correlated with adverse clinical outcomes, indicating that their regulatory role in inflammation may exert a more substantial influence on prognosis than the presence of AF. Therefore, these mechanisms elucidate the adverse consequences associated with ischemic stroke.\u003c/p\u003e \u003cp\u003eThe implications of these results for clinical practice are profound. The development and validation of a predictive Nomogram based on the identified risk factors can serve as a practical tool for clinicians, enabling them to better assess patient prognosis and tailor treatment strategies accordingly. The Nomogram suggests that adopting the strategy of ending treatment provides a net benefit ensuring that all AIS patients will follow this strategy, thus guaranteeing that no patient with AIS will deviate from it. This is especially pertinent given the urgency surrounding thrombolysis administration in AIS cases, where timely intervention is critical for improving outcomes. By integrating this Nomogram into routine clinical practice, healthcare providers can enhance decision-making processes, allocate resources more effectively, and ultimately improve patient management, particularly for those identified as high-risk\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Furthermore, our findings underscore the necessity for ongoing research into individualized treatment protocols that consider multifactorial influences on patient outcomes, thereby contributing to enhanced stroke care and recovery strategies\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur study has several limitations despite these interesting findings. Firstly, being a retrospective study, it is vulnerable to selection bias and recall bias. Secondly, although data from thrombolysis patients at two centers were included, the sample size remained small. We eagerly await replication findings from other institutions. Thirdly, important neurobiological predictors like infarct size were not accessible in the cohort, potentially impacting the predictive accuracy of our model in anticipating adverse outcomes at 3 months. Fourth, the validation cohort participants were significantly younger than those in the training cohort. The age differences between queues may affect the application of the nomogram in other populations. Nonetheless, our Nomogram demonstrated strong predictive accuracy. Future multicenter prospective studies could validate findings and incorporate neuroimaging. In summary, this study not only identifies critical prognostic factors for AIS patients undergoing thrombolysis but also presents a validated Nomogram as a practical tool for clinical application. The findings emphasize the importance of timely intervention and individualized patient management, while also acknowledging the need for continued research to validate and refine these predictive models in diverse clinical settings. In future, the nomogram can be integrated into electronic health record (EHR) systems to provide clinicians with real-time decision support. Furthermore, implementing comprehensive training programs for emergency department staff on nomogram utilization, coupled with the development of an intuitive interface, is critical to ensuring efficient access during time-sensitive clinical scenarios.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, this study has successfully identified critical prognostic factors associated with unfavorable outcomes in acute ischemic stroke patients receiving intravenous thrombolysis, culminating in the development of a clinically applicable Nomogram. The Nomogram incorporates variables such as age, baseline NIHSS score, CAD, DM, SBP, neutrophils, and blood glucose. The findings underscore the significance of timely intervention and personalized management strategies to improve patient outcomes. As we move forward, it is essential to validate these results in larger, more diverse cohorts and explore the potential integration of biological and laboratory metrics into predictive models. In the future, we will further conduct prospective trials to assess the nomogram\u0026rsquo;s impact on clinical outcomes like mortality or rehab planning. Such endeavors will not only enhance our understanding of acute ischemic stroke but also contribute to the refinement of therapeutic approaches in clinical practice.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emRS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emodified ranking score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNIHSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Institute of Health Stroke Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLAA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elarge artery atherosclerosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecardio-embolism\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSAO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esmall artery occlusion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ediabetes mellitus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecoronary artery disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eatrial fibrillation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esystolic blood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ediastolic blood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ewhite blood cell\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTBil\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTotal bilirubin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etotal cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etotal triglyceride\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehigh density lipoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elow density lipoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003euric acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGHb\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlycosylated hemoglobin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlanine aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAspartate aminotransferase.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors appreciate the valuable suggestions from other members of their teams.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePZ and YZ conceived and designed the study. YZ, FF and WZand contributed to the data analysis and drafted the manuscript. DW and PZ critically revised the manuscript. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Shanghai Committee of Science and Technology (Grant No. 23JC1401803 and 201409004900) , Enbipu Co., Ltd. of Shiyao Group (Grant No. YXSY-2022-24) , the Zhejiang Medical Science and Technology Project (Grant No.2023RC287 and 2022KY1286), Shaoxing Basic Public Welfare Program(Grant No.2022A14018 and 2022A14017) and Zhejiang Province Traditional Chinese Medicine Technology Program (2023ZL730).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis retrospective observational study was approved by the Ethics Committee of the Shaoxing People’s Hospital (2021-K-Y-330–01) and Shanghai Fifth People's Hospital（2018 Ethics Approval NO.001).\u0026nbsp;\u0026nbsp;All methods were carried out in accordance with relevant guidelines and regulations. Written informed consent was obtained from all participants or their relatives.The study has been carried out in accordance with the STROBE Statement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed to support this manuscript are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLiu L, Villavicencio F, Yeung D, et al. National, regional, and global causes of mortality in 5-19-year-olds from 2000 to 2019: a systematic analysis[J]. Lancet Glob Health, 2022,10(3):e337-e347.\u003c/li\u003e\n\u003cli\u003eYu A Y, Hill M D, Coutts S B. Should minor stroke patients be thrombolyzed? A focused review and future directions[J]. Int J Stroke, 2015,10(3):292-297.\u003c/li\u003e\n\u003cli\u003eWardlaw J M, Murray V, Berge E, et al. Recombinant tissue plasminogen activator for acute ischaemic stroke: an updated systematic review and meta-analysis[J]. Lancet, 2012,379(9834):2364-2372.\u003c/li\u003e\n\u003cli\u003eRomano J G, Smith E E, Liang L, et al. Outcomes in mild acute ischemic stroke treated with intravenous thrombolysis: a retrospective analysis of the Get With the Guidelines-Stroke registry[J]. JAMA Neurol, 2015,72(4):423-431.\u003c/li\u003e\n\u003cli\u003eZhang S, Ji B, Zhong X, et al. A Dynamic Nomogram Predicting Portal Vein Thrombosis in Cirrhotic Patients During Primary Prophylaxis for Variceal Hemorrhage[J]. Front Med (Lausanne), 2022,9:887995.\u003c/li\u003e\n\u003cli\u003eCappellari M, Turcato G, Forlivesi S, et al. The START nomogram for individualized prediction of the probability of unfavorable outcome after intravenous thrombolysis for stroke[J]. Int J Stroke, 2018,13(7):700-706.\u003c/li\u003e\n\u003cli\u003eStrbian D, Meretoja A, Ahlhelm F J, et al. Predicting outcome of IV thrombolysis-treated ischemic stroke patients: the DRAGON score[J]. Neurology, 2012,78(6):427-432.\u003c/li\u003e\n\u003cli\u003eLv S, Song Y, Zhang F L, et al. Early prediction of the 3-month outcome for individual acute ischemic stroke patients who received intravenous thrombolysis using the N2H3 nomogram model[J]. Ther Adv Neurol Disord, 2020,13:1279174622.\u003c/li\u003e\n\u003cli\u003eAdams H J, Bendixen B H, Kappelle L J, et al. Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment[J]. Stroke, 1993,24(1):35-41.\u003c/li\u003e\n\u003cli\u003eZhou Y, Luo Y, Liang H, et al. Applicability of the low-grade inflammation score in predicting 90-day functional outcomes after acute ischemic stroke[J]. BMC Neurol, 2023,23(1):320.\u003c/li\u003e\n\u003cli\u003eCho H, Kim T, Kim Y D, et al. A clinical study of 288 patients with anterior cerebral artery infarction[J]. J Neurol, 2022,269(6):2999-3005.\u003c/li\u003e\n\u003cli\u003eWang Y, Li W, Yang J, et al. Association Between Cystatin C and the Risk of Ischemic Stroke: a Systematic Review and Meta-analysis[J]. J Mol Neurosci, 2019,69(3):444-449.\u003c/li\u003e\n\u003cli\u003ePawar S S, Hong S, Poetker D M. Delayed presentation of silent sinus syndrome after orbital trauma[J]. Am J Otolaryngol, 2010,31(1):61-63.\u003c/li\u003e\n\u003cli\u003eCappellari M, Turcato G, Forlivesi S, et al. Introduction of direct oral anticoagulant within 7 days of stroke onset: a nomogram to predict the probability of 3-month modified Rankin Scale score\u0026thinsp;\u0026gt; 2[J]. J Thromb Thrombolysis, 2018,46(3):292-298.\u003c/li\u003e\n\u003cli\u003eCappellari M, Turcato G, Forlivesi S, et al. The START nomogram for individualized prediction of the probability of unfavorable outcome after intravenous thrombolysis for stroke[J]. Int J Stroke, 2018,13(7):700-706.\u003c/li\u003e\n\u003cli\u003eTurcato G, Cervellin G, Cappellari M, et al. Early function decline after ischemic stroke can be predicted by a nomogram based on age, use of thrombolysis, RDW and NIHSS score at admission[J]. J Thromb Thrombolysis, 2017,43(3):394-400.\u003c/li\u003e\n\u003cli\u003eWu S, Yuan R, Wang Y, et al. Early Prediction of Malignant Brain Edema After Ischemic Stroke[J]. Stroke, 2018,49(12):2918-2927.\u003c/li\u003e\n\u003cli\u003eKnoflach M, Matosevic B, R\u0026uuml;cker M, et al. Functional recovery after ischemic stroke--a matter of age: data from the Austrian Stroke Unit Registry[J]. Neurology, 2012,78(4):279-285.\u003c/li\u003e\n\u003cli\u003eVora N A, Shook S J, Schumacher H C, et al. A 5-item scale to predict stroke outcome after cortical middle cerebral artery territory infarction: validation from results of the Diffusion and Perfusion Imaging Evaluation for Understanding Stroke Evolution (DEFUSE) Study[J]. Stroke, 2011,42(3):645-649.\u003c/li\u003e\n\u003cli\u003eYoo D S, Chang J, Kim J T, et al. Various blood glucose parameters that indicate hyperglycemia after intravenous thrombolysis in acute ischemic stroke could predict worse outcome[J]. PLoS One, 2014,9(4):e94364.\u003c/li\u003e\n\u003cli\u003eParsons M W, Barber P A, Desmond P M, et al. Acute hyperglycemia adversely affects stroke outcome: a magnetic resonance imaging and spectroscopy study[J]. Ann Neurol, 2002,52(1):20-28.\u003c/li\u003e\n\u003cli\u003eToni D, De Michele M, Fiorelli M, et al. Influence of hyperglycaemia on infarct size and clinical outcome of acute ischemic stroke patients with intracranial arterial occlusion[J]. J Neurol Sci, 1994,123(1-2):129-133.\u003c/li\u003e\n\u003cli\u003eEchouffo-Tcheugui J B, Xu H, Matsouaka R A, et al. Diabetes and long-term outcomes of ischaemic stroke: findings from Get With The Guidelines-Stroke[J]. Eur Heart J, 2018,39(25):2376-2386.\u003c/li\u003e\n\u003cli\u003eHe Y, Yang Q, Liu H, et al. Effect of blood pressure on early neurological deterioration of acute ischemic stroke patients with intravenous rt-PA thrombolysis may be mediated through oxidative stress induced blood-brain barrier disruption and AQP4 upregulation[J]. J Stroke Cerebrovasc Dis, 2020,29(8):104997.\u003c/li\u003e\n\u003cli\u003eWu D, Liu Y. FM Combined With NIHSS Score Contributes to Early AIS Diagnosis and Differential Diagnosis of Cardiogenic and Non-Cardiogenic AIS[J]. Clin Appl Thromb Hemost, 2021,27:1319785663.\u003c/li\u003e\n\u003cli\u003eB\u0026auml;ck M, Yurdagul A J, Tabas I, et al. Inflammation and its resolution in atherosclerosis: mediators and therapeutic opportunities[J]. Nat Rev Cardiol, 2019,16(7):389-406.\u003c/li\u003e\n\u003cli\u003eKim J Y, Park J, Chang J Y, et al. Inflammation after Ischemic Stroke: The Role of Leukocytes and Glial Cells[J]. Exp Neurobiol, 2016,25(5):241-251.\u003c/li\u003e\n\u003cli\u003eJin P, Li X, Chen J, et al. Platelet-to-neutrophil ratio is a prognostic marker for 90-days outcome in acute ischemic stroke[J]. J Clin Neurosci, 2019,63:110-115.\u003c/li\u003e\n\u003cli\u003eHuang L, Chen Y, Liu R, et al. P-Glycoprotein Aggravates Blood Brain Barrier Dysfunction in Experimental Ischemic Stroke by Inhibiting Endothelial Autophagy[J]. Aging Dis, 2022,13(5):1546-1561.\u003c/li\u003e\n\u003cli\u003eFurlan J C, Vergouwen M D I, Fang J, et al. White blood cell count is an independent predictor of outcomes after acute ischaemic stroke[J]. Eur J Neurol, 2014,21(2):215-222.\u003c/li\u003e\n\u003cli\u003eKim J, Song T, Park J H, et al. Different prognostic value of white blood cell subtypes in patients with acute cerebral infarction[J]. Atherosclerosis, 2012,222(2):464-467.\u003c/li\u003e\n\u003cli\u003eLu B, Shi J, Cheng T, et al. Chemokine ligand 14 correlates with immune cell infiltration in the gastric cancer microenvironment in predicting unfavorable prognosis[J]. Front Pharmacol, 2024,15:1397656.\u003c/li\u003e\n\u003cli\u003eRost N S, Fitzpatrick K, Biffi A, et al. White matter hyperintensity burden and susceptibility to cerebral ischemia[J]. Stroke, 2010,41(12):2807-2811.\u003c/li\u003e\n\u003cli\u003eShin H Y, Jeong I H, Kang C K, et al. Relation between left atrial enlargement and stroke subtypes in acute ischemic stroke patients[J]. J Cerebrovasc Endovasc Neurosurg, 2013,15(3):131-136.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Ischemic stroke, Thrombolysis, Prognosis, Nomogram, Neutrophils, Functional outcome","lastPublishedDoi":"10.21203/rs.3.rs-8651893/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8651893/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThe present study was designed to create and validate a novel, straightforward and dependable Nomogram for individualized prediction of short-term functional outcomes in AIS patients treated with intravenous thrombolysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe enrolled patients who suffered from acute ischemic stroke (AIS) treated with intravenous thrombolysis based on the inclusion and exclusion criteria. The patients from Shaoxing People's Hospital constituted the training set, while those from Shanghai Fifth People's Hospital served as the validation set. The primary outcome measure was a 3-month unfavorable outcome (modified Rankin Scale 3–6). On the basis of LASSO logistic model, the predictive Nomogram was generated. The performance of the Nomogram was evaluated by ROC curves, Hosmer‑Lemeshow test and Calibration plot. Decision curve analysis was utilized to assess the effectiveness of the Nomogram.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The training cohort and validation cohort recruited 238 patients (median age 70 years; 61.3% male) and 155 patients (median age 62 years; 74.2% male) respectively. The results indicated that the AUC value of the Nomogram was 0.859 (95% CI: 0.811–0.906) in the training cohort and 0.848 (95% CI: 0.788–0.908) in the validation cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe nomogram’s reliance on routinely collected variables (e.g., NIHSS, glucose) facilitates rapid bedside use, potentially guiding post-thrombolysis monitoring for high-risk patients.\u003c/p\u003e","manuscriptTitle":"A Neutrophil-Incorporated Nomogram for Predicting 3-Month Disability after Intravenous Thrombolysis in Acute Ischemic Stroke","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-09 16:43:21","doi":"10.21203/rs.3.rs-8651893/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-08T14:56:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"261192037469296780039405558118260900555","date":"2026-04-10T19:27:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-05T01:55:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-27T21:26:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-22T10:22:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-22T10:14:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Neurology","date":"2026-01-20T16:09:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e2f15144-a0a5-4424-8928-ec883c1838ee","owner":[],"postedDate":"February 9th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-08T14:56:01+00:00","index":239,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T16:43:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-09 16:43:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8651893","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8651893","identity":"rs-8651893","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}