Nomogram prediction model for the risk of intracranial hemorrhagic transformation after intravenous thrombolysis in patients with acute ischemic stroke

Nomogram prediction model for the risk of intracranial hemorrhagic transformation after intravenous thrombolysis in patients with 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 Article Nomogram prediction model for the risk of intracranial hemorrhagic transformation after intravenous thrombolysis in patients with acute ischemic stroke Yong Ma, Dong-Yan Xu, Qian Liu, He-Cheng Chen, Er-Qing Chai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3804290/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Mar, 2024 Read the published version in Frontiers in Neurology → Version 1 posted You are reading this latest preprint version Abstract Background: Hemorrhagic transformation (HT) after intravenous thrombolysis (IVT) leads to poor clinical prognosis, and a reliable predictive system is needed to identify the risk of hemorrhagic transformation after IVT. Methods: Retrospective collection of patients with acute cerebral infarction treated with intravenous thrombolysis in our hospital from 2018 to 2022. 197 patients were included in the research study. Multivariate logistic regression analysis was used to screen the factors in the predictive nomogram. The performance of nomogram was assessed on the basis of area under the curve (AUC-ROC) of subjects' work characteristics, calibration plots and decision curve analysis (DCA). Results: A total of 197 patients were recruited, of whom 24 (12.1%) developed HT. In multivariate logistic regression model National Institute of Health Stroke Scale (NIHSS) (OR, 1.362; 95% CI, 1.161 −1.652; P = 0.001), N-terminal pro-brain natriuretic peptide (NT-pro BNP) (OR, 1.012; 95% CI, 1.004 −1.020; P = 0.003), neutrophil to lymphocyte ratio (NLR) (OR, 3.430; 95% CI, 2.082 −6.262; P < 0.001), systolic blood pressure (SBP) (OR, 1.039; 95% CI, 1.009 −1.075; P = 0.016) were the independent predictors of HT which were used to generate nomogram. The nomogram showed good discrimination due to AUC-ROC values. Calibration plot showed good calibration. DCA showed that nomogram is clinically useful. Conclusion: Nomograms consisting of NIHSS, NT-pro BNP, NLR, SBP scores predict the risk of HT in AIS patients treated with IVT. Biological sciences/Neuroscience/Diseases of the nervous system Biological sciences/Neuroscience/Neuro vascular interactions acute ischemic stroke intravenous thrombolysis Hemorrhagic transformation nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Worldwide, neurological disorders are the leading cause of disability and the second leading cause of death, with stroke being the largest cause ( 1 ). With the rapid development of interventional techniques and materials in recent years, endovascular intervention has become a primary treatment for acute ischemic stroke, but even so, intravenous thrombolysis (IVT) is now the preferred and effective treatment for patients within the 4.5-hour time window ( 2 ). Within 24 hours of IVT, a subset of patients may experience a worsening of neurological deficits, which has been described as early neurological deterioration ( 3 ), which has been reported to be associated with poor outcomes ( 4 ). One of the higher risks of intravenous thrombolysis is hemorrhagic conversion ( 5 ). As the risk of hemorrhagic conversion increases, the clinical lethality and disability rates also increase ( 6 ). Therefore, it is necessary to develop a predictive model to determine the risk of hemorrhagic conversion after intravenous thrombolysis in patients with AIS. Currently, nomograms are used as a predictive tool to personalize, visualize and accurately determine such risk. Based on this, the present study aimed to create a nomogram to predict the probability of HT after IVT in Chinese stroke patients. METHODS Study design and data sources In this study, we consecutively recruited patients diagnosed with AIS from October 2018 to October 2022 in Gansu Provincial People's Hospital. Included patients met the following criteria:1) age ≥ 18 years; 2) diagnosis of acute ischemic stroke; 3) time to treatment initiation < 4.5 hours; and 4) patients receiving intravenous thrombolysis with rt-PA. Patients who met the following criteria were excluded: 1) intra-arterial thrombolysis or endovascular thrombolysis after intravenous thrombolysis; 2) those diagnosed with intracranial hemorrhage, including subarachnoid hemorrhage, parenchymal hemorrhage, intraventricular hemorrhage, epidural hemorrhage and so on; 3) incomplete clinical data. The study was approved by the Ethics Committee of Gansu Provincial People's Hospital (Approval No. 2023 − 350), Written informed consent was waived due to the retrospective nature of this study.. All procedures performed in the study complied with the 1964 Declaration of Helsinki and its subsequent amendments or similar ethical standards. Baseline data collection Demographic characteristics, medical history, and clinical and laboratory data were obtained at admission. Stroke severity was assessed by National Institutes of Health Stroke Scale (NIHSS) score. Laboratory data included baseline blood glucose, systolic blood pressure (SBP), diastolic blood pressure (DBP), neutrophil-to-lymphocyte ratio (NLR), high-density lipoproteins (HDL), low-density lipoproteins (LDL), triglycerides (TG), N-terminal pro-brain natriuretic peptide (NT-pro BNP), and total cholesterol (TC), et al. The NLR values were calculated as neutrophil count/lymphocyte count. Definition of hemorrhagic transformation Hemorrhagic transformation (HT) was defined as any type of intracranial hemorrhage detected by follow-up CT or MRI within 22–36 hours after intravenous thrombolysis, according to the criteria of the European Cooperative Acute Stroke Study II ( 7 ). All images were judged by two experienced neurologists without knowledge of the clinical data and final diagnosis. statistical analysis Statistical analyses Descriptive analyses were as follows: Continuous variables were expressed as having mean ± standard deviation or median (interquartile range); Categorical variables are described as numbers with percentages. Differences between groups with and without HT were investigated using Mann-Whitney U tests or t tests for appropriate continuous variables. Where appropriate, differences between the two groups of categorical variables were analyzed by Fisher's exact test or χ2 test. To construct nomograms, we used multivariate logistic regression analyses to identify independent factors for HT, and all variables with P values < 0.05 in univariate analyses were included. Variables with P values < 0.05 in multivariate logistic regression were entered to generate predictive models. Regression coefficients and 95% confidence intervals (CI) for each variable in the model were calculated for the odds ratio (OR). The regression coefficients for each variable in the model were used to calculate the corresponding scores in the scale and ultimately to obtain the scoring system. The discriminative power of the Nomogram was assessed by calculating the area under the operating characteristic curve (AUC-ROC) of the subjects. The calibration of the prediction model describing the agreement between observed and predicted probabilities based on nomograms was tested using 1000 resampled calibration plots. All statistical analyses were performed using statistical methods Software SPSS version 26.0 (IBM, New York, NY) and R version 4.3 (R Foundation, Vienna, Austria). RESULTS Patients A total of 243 patients with ischemic stroke were treated with IVT. Patients who underwent intra-arterial thrombolysis (n = 7) or endovascular thrombectomy (n = 29) and those who lacked complete data (n = 10) were excluded. As shown in Table 1 , 24 (11%) of the baseline profile characteristics were post-thrombolytic HT. Table 2 Univariate logistic analysis showing NIHSS score, NLR, SBP, and NT-pro BNP (P < 0.05). After multivariate logistic analysis, NIHSS score, NLR, SBP, and NT-pro BNP were shown to be independent predictors of HT after intravenous thrombolysis in patients with ischemic stroke. A nomogram of the HT predictive model was created based on these risk factors. The score for each independent predictor is the score corresponding to the upper scale, and the total score for each subject is the sum of the scores for each independent predictor. The total number of points corresponding to the HT risk axis is the risk of HT. The higher the total score, the higher the risk of HT. Internal validation of the Nomogram was performed by repeated sampling 1000 times using the Bootstrap method. The model was created by combining the independent predictor values as described above and shown as a nomogram in Figure 1 . The score for each predictor in the Nomo plot is determined by drawing a vertical line between the predictor fold and the preliminary score line. The total score is calculated by totaling the scores for each predictor, and the corresponding HT prediction probabilities are obtained by drawing a vertical line between the total score and the probability line. The area under the subject's work characteristics (ROC) curve for the prediction model was (Figure 2). In addition, the calibration curves of the nomograms for the likelihood of HT in patients showed good agreement (Figure 3) , predicting the model HT probability. The calibration curves are shown in Figure 3. The calibration curves used to estimate HT showed no significant deviation from perfect match and good agreement between predicted and actual results. The analysis of the decision curve (DCA) (Figure 4) showed that clinical decision making based on the predictive model was beneficial and implied the practical clinical application and operability of the predictive model. TABLE 1 | Baseline characteristics of AIS patients with IVT. Variable With HT(n=24) Without HT(n=173) Overall(n=197) P -value Demographics Age，years 69.83±11.82 67.86±12.28 68.10±12.21 0.459 Male, n (%) 17 (70.8) 117 (67.6) 134 (68.0) 0.753 Medical history, n (%) Hypertension 16 (66.7) 112 (64.7) 128 (65.0) 0.853 Diabetes mellitus 6 (25.0) 36 (20.8) 42 (21.3) 0.639 Coronary heart disease 5 (20.8) 30 (17.3) 35 (17.8) 0.675 smoking 7 (29.2) 43 (24.9) 50 (25.4) 0.649 drinking 6 (25.0) 30 (17.3) 36 (18.3) 0.363 Previous stroke 5 (20.8) 29 (16.8) 34 (17.3) 0.621 Clinical data SBP, mmHg 164.88±22.94 151.35±19.51 152.99±20.38 0.002 DBP, mmHg 89.50 ±22.49 87.72 ±17.44 87.93±18.08 0.652 NIHSS, score 10.50 [7.00, 15.25] 6.00 [5.00, 8.00] 7.00 [5.00, 9.00] <0.001 Time from onset to treatment, min 144.50 [112.50, 215.25] 171.00 [136.00, 211.00] 170.00 [125.00, 212.00] 0.651 Laboratory data Glucose, mmol/L 7.70 ±2.24 7.17 ±2.27 7.23±2.26 0.286 NEUT, % 69.83±11.55 67.90±13.99 68.13±13.70 0.519 NLR 4.86 [3.40, 6.03] 3.07 [2.39, 3.64] 3.21 [2.45, 3.92] <0.001 Platelet, 10∧9/L 172.50 [142.75, 211.25] 192.00 [160.00, 230.00] 191.00 [159.00, 229.00] 0.168 PT, s 13.78 [12.63, 15.12] 12.90 [10.89, 15.19] 13.12 [11.14, 15.19] 0.118 APTT, s 27.91 [24.41, 31.13] 29.04 [25.58, 31.81] 28.84 [25.50, 31.81] 0.384 INR 0.98 [0.90, 1.03] 0.98 [0.92, 1.04] 0.98 [0.92, 1.04] 0.574 TG, mmol/L 1.91±0.70 1.79 ±0.72 1.81 ±0.71 0.443 TC, mmol/L 3.38 [2.01, 4.94] 3.60 [2.52, 4.50] 3.60 [2.34, 4.51] 0.756 HDL, mmol/L 1.40 [0.97, 2.19] 1.77 [1.24, 2.24] 1.72 [1.20, 2.24] 0.383 LDL, mmol/L 3.09 [2.57, 3.79] 3.10 [2.42, 3.90] 3.10 [2.43, 3.89] 0.598 NT-pro BNP 302.67±127.53 205.39 ±100.24 217.25 ±108.37 <0.001 AIS, acute ischemic stroke; IVT, intravenous thrombolysis; HT, Hemorrhagic transformation; SBP, systolic blood pressure; DBP, diastolic blood pressure; NIHSS, National Institutes of Health Stroke Scale; NEUT, Neutrophilic granulocyte percentage; NLR, neutrophil-to-lymphocyte ratio; PT, prothrombin time; APTT, activated partial thromboplastin time; INR, International normalized ratio; TG, triglyceride; TC, total cholesterol; HDL, High-density lipoprotein; LDL, low-density lipoprotein; NT-pro BNP, the N-terminal of the prohormone brain natriuretic peptide. TABLE 2 | Univariable and multivariable analyses of HT in AIS patients with Intravenous thrombolysis. Variable Crude OR (95%CI) Uni - P value Adj OR (95%CI) multi- P value Age 1.014 [0.979, 1.052] 0.457 Male 1.162 [0.472, 3.152] 0.753 Hypertension 1.089 [0.452, 2.820] 0.853 Diabetes mellitus 1.269 [0.434, 3.276] 0.639 Coronary heart disease 1.254 [0.392, 3.409] 0.675 smoking 1.245 [0.456, 3.099] 0.65 drinking 1.589 [0.540, 4.153] 0.366 Previous stroke 1.307 [0.408, 3.560] 0.622 SBP 1.035 [1.012, 1.060] 0.003 1.039 [1.009, 1.075] 0.016 DBP 1.006 [0.982, 1.030] 0.65 NIHSS 1.335 [1.194, 1.519] <0.001 1.362 [1.161, 1.652] 0.001 Time from onset to treatment 0.998 [0.991, 1.006] 0.674 GLU 1.099 [0.915, 1.301] 0.287 NEUT 1.011 [0.979, 1.044] 0.517 NLR 2.712 [1.889, 4.107] <0.001 3.430 [2.082, 6.262] <0.001 PLT 0.994 [0.986, 1.002] 0.146 PT 1.125 [0.980, 1.303] 0.104 APTT 0.961 [0.876, 1.050] 0.381 INR 0.204 [0.001, 29.445] 0.531 TG 1.266 [0.694, 2.318] 0.441 TC 0.949 [0.701, 1.279] 0.733 HDL 0.760 [0.405, 1.390] 0.379 LDL 1.143 [0.749, 1.763] 0.537 NT-pro BNP 1.008 [1.004, 1.013] <0.001 1.012 [1.004, 1.020] 0.003 AIS, acute ischemic stroke; IVT, intravenous thrombolysis; HT, Hemorrhagic transformation; CI, confidence interval; OR, odds ratio; SBP, systolic blood pressure; DBP, diastolic blood pressure; NIHSS, National Institutes of Health Stroke Scale; NEUT, Neutrophilic granulocyte percentage; NLR, neutrophil-to-lymphocyte ratio; PT, prothrombin time; APTT, activated partial thromboplastin time; INR, International normalized ratio; TG, triglyceride; TC, total cholesterol; HDL, High-density lipoprotein; LDL, low-density lipoprotein; NT-pro BNP, the N-terminal of the prohormone brain natriuretic peptide. DISCUSSION In this study, we found that NIHSS score, NLR, SBP, and NT-pro BNP were independent predictors of HT. Based on these four independent factors, we constructed a predictive nomogram. This model can help us to predict the probability of HT in acute ischemic stroke patients treated with IVT. The developed nomogram showed good discrimination and calibration. In addition, DCA results showed that the developed nomogram had a significant net benefit in predicting the risk of cerebral hemorrhage. Firstly, similar to previous studies, the higher the pre-treatment NIHSS score, the greater the risk of HT ( 8 , 9 ). One article showed a 1.6-fold increased risk of HT in patients with acute cerebral infarction with an NIHSS score of 7–12 and a 2.22-fold increased risk of HT in patients with a baseline NIHSS score of ≥ 13 ( 10 ). In this study, it was shown that patients with high NIHSS scores also had a higher risk of HT. This is because the higher the NIHSS score, the larger the area of cerebral infarction and oedema in the patient, the higher the risk of HT ( 11 , 12 ). The above previous studies have shown that high NIHSS score is closely associated with hemorrhagic transformation, which is consistent with the findings of the current study. NLR is easily accessible in the clinic and reproduces new biomarkers of inflammation ( 13 ). It plays a crucial role in HT due to the inflammatory response of migrating inflammatory cells leading to disruption of blood-brain barrier integrity ( 14 ). Neutrophils can increase the permeability of the blood-brain barrier by releasing, among other things, associated cytokines, whereas activation of lymphocytes can reduce blood-brain barrier disruption. Due to the balance between neutrophils and lymphocytes, NLR is considered a biomarker of systemic inflammation. High NLR (≥ 4.255) on admission has been reported to increase the risk of HT in patients with AIS after IVT ( 15 ). A clinical study by Guo et al. reported that the dynamics of HT were associated with IVT in patients with acute ischemic stroke ( 16 ). The underlying mechanism by which NLR increases the risk of cerebral hemorrhage in patients with AIS treated with IVT has not been elucidated. A plausible explanation may be that NLR influences outcome as it is associated with inflammatory destruction of neutrophils and reduced lymphocyte protection ( 17 ). Hypertension was found to be a risk factor for hemorrhagic transformation after intravenous thrombolysis. Similar results were obtained by H Y et al, who suggested that increased SBP mediates brain-blood barrier damage and upregulation of aquaporin Protein-4 via oxidative stress, leading to an increased risk of neurological deterioration ( 18 ). Meanwhile, hypertension impairs collateral circulation, reduces the ability of brain tissue to maintain adequate oxygenation during cerebral artery occlusion, and promotes the accumulation of reactive oxygen species and the release of inflammatory factors, leading to further damage to the blood-brain barrier, which in turn leads to hemorrhagic transformation after thrombolysis ( 19 ). There is still some controversy about whether a history of previous hypertension serves as a risk factor for hemorrhagic transformation ( 20 , 21 ), which is because chronic hypertension leads to increased permeability of the blood-brain barrier, impaired reperfusion of blood flow, and damage to the inner wall of blood vessels leading to blood leakage, secondary to hemorrhagic transformation. And the history of hypertension was not found to affect HT during the study of this paper, which needs to be supported by subsequent studies. Another major finding of this paper is the correlation between elevated levels of NT-pro BNP and hemorrhagic conversion in stroke patients treated with intravenous thrombolysis. NT-pro BNP is released from ventricular myocardium with stretching ( 22 ). Several studies have also shown that the brain secretes NT-pro BNP and that the concentration of NT-pro BNP in the cerebrospinal fluid may be greatly increased after brain injury ( 23 , 24 ). Hemorrhagic transformation is a major complication in stroke patients treated with intravenous thrombolysis. The association between elevated NT-pro BNP levels and cerebral hemorrhage has been demonstrated It has been demonstrated ( 25 ) that elevated NT-pro BNP levels are associated with increased hematoma volume and a poor prognosis ( 26 , 27 ). Our findings suggest that NT-pro BNP levels are independently associated with hemorrhagic hypertension in stroke patients receiving intravenous thrombolytic therapy for transformation. Another potential reason for elevated NT-pro BNP levels in hemorrhagic transformation may be that hemorrhagic transformation exacerbates ischemic stroke-induced neurological damage ( 28 ), and may also exacerbate stroke-induced cardiac dysfunction in the same way ( 29 , 30 ). However, whether thrombolytic therapy affects NT-pro BNP levels remains unclear. Future studies are needed to further elucidate the mechanism of elevated NT-pro BNP levels in stroke patients receiving intravenous thrombolytic therapy. CONCLUSION Our study presents a novel and practical nomogram of NIHSS, NT-pro BNP, NLR, SBP that can well predict the probability of HT after intravenous thrombolysis in ischemic stroke patients. The qualitative and discriminative properties of the graph were verified in an internal validation. The graph can be used to predict the probability of HT after IVT and to help clinicians assess whether to continue IVT in patients at high risk for HT. However, further studies are needed to confirm the validity of the nomogram. Declarations DATA AVAILABILITY STATEMENT The raw data supporting the conclusions of this article will be made available by the authors, upon request to the corresponding author. If anyone would like to receive data from this study, they can contact Yong Ma at [email protected] . ETHICS STATEMENT The study was approved by the Ethics Committee of Gansu Provincial People's Hospital (approval number: 2023-350). Written informed consent was waived due to the retrospective nature of this study. AUTHOR CONTRIBUTIONS YM, D-YX, QL, H-CC and E-QC were involved in the conception and design of the study. YM, D-YX, and QL were used for material preparation and data collection. Analysis and interpretation of the data were done by YM. The first draft of the manuscript was written by YM, D-YX, QL, H-CC and E-QC. All authors read and approved the final manuscript. YM, D-YX, and QL contributed equally to this work. All authors read and approved the final manuscript. FUNDING This study was supported by the Natural Science Foundation of Gansu Province(22JR5RA673), Natural Science Foundation of Gansu Province(23JRRA1308), Natural Science Foundation of Gansu Province (20JR10RA384), Gansu Provincial Key Laboratory of Cerebrovascular Disease(20JR10RA431). CONFLICT INTEREST The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. References GBD 2016 Neurology Collaborators. Global, regional, and national burden of neurological disorders, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 2019 May;18(5):459-480. doi: 10.1016/S1474-4422(18)30499-X. 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J Intensive Care Med. 2018 Dec;33(12):663-670. doi: 10.1177/0885066616683677. Hajdinjak E, Klemen P, Grmec S. Prognostic value of a single prehospital measurement of N-terminal pro-brain natriuretic peptide and troponin T after acute ischaemic stroke. J Int Med Res. 2012;40(2):768-76. doi: 10.1177/147323001204000243. Chen Z, Venkat P, Seyfried D, Chopp M, Yan T, Chen J. Brain-Heart Interaction: Cardiac Complications After Stroke. Circ Res. 2017 Aug 4;121(4):451-468. doi: 10.1161/CIRCRESAHA.117.311170. Battaglini D, Robba C, Lopes da Silva A, Dos Santos Samary C, Leme Silva P, et al. Brain-heart interaction after acute ischemic stroke. Crit Care. 2020 Apr 21;24(1):163. doi: 10.1186/s13054-020-02885-8. Additional Declarations No competing interests reported. Supplementary Files HT.xlsx Cite Share Download PDF Status: Published Journal Publication published 07 Mar, 2024 Read the published version in Frontiers in Neurology → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3804290","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":264555445,"identity":"23dc6184-3814-4bd0-a20c-4e37f3ceffe7","order_by":0,"name":"Yong Ma","email":"","orcid":"","institution":"Ningxia Medical University，Yinchuan","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Ma","suffix":""},{"id":264555446,"identity":"58c968b3-c6f2-44c7-aa24-4c38c90c547c","order_by":1,"name":"Dong-Yan Xu","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Dong-Yan","middleName":"","lastName":"Xu","suffix":""},{"id":264555447,"identity":"43bdb543-d6af-4ae4-b475-514177467364","order_by":2,"name":"Qian Liu","email":"","orcid":"","institution":"Gansu Provincial People's Hospital，Lanzhou","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Liu","suffix":""},{"id":264555448,"identity":"2faabb9b-eea1-4bef-a6c7-f5fa5a339ff9","order_by":3,"name":"He-Cheng Chen","email":"","orcid":"","institution":"Gansu Provincial People's Hospital，Lanzhou","correspondingAuthor":false,"prefix":"","firstName":"He-Cheng","middleName":"","lastName":"Chen","suffix":""},{"id":264555449,"identity":"ed1ff883-7183-4074-a3e3-c81499ec725a","order_by":4,"name":"Er-Qing Chai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYJCCA0CcwMDMfPDhhwobHn7+BqK1sCUbS5xJk5GccYA4mxIYGHjUBHhbDtsYNCTgV8ovfXjj4YJfdnn87TxsDJIN53kMGA4wfviYg1uLZF9aweGZfcnFEod5jz0o3HGbx5y5gVly5jbcWgzO8Bgc5u05kNhwmC/dQPLMbR7LhgNszLx4tNjDtMw/zGMmwdt2jsfgQAJ+LQY8QC08Pw4kboBoOUBYi8QZtoLDvA3JiRsPgwM5mUdyxsFmvH7h72He/Jnnj13ivPOHQVFpZ8/P33zww0c8WkBuY2BsQxFgbMCrHqyF4Q8hNaNgFIyCUTCiAQDOfVdMeOfjbQAAAABJRU5ErkJggg==","orcid":"","institution":"Gansu Provincial People's Hospital，Lanzhou","correspondingAuthor":true,"prefix":"","firstName":"Er-Qing","middleName":"","lastName":"Chai","suffix":""}],"badges":[],"createdAt":"2023-12-25 11:44:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3804290/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3804290/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.3389/fneur.2024.1361035","type":"published","date":"2024-03-07T05:01:33+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":49091662,"identity":"7d1856a5-8d85-4c61-95f4-4edfad05e721","added_by":"auto","created_at":"2024-01-03 01:56:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":62064,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting HT after IVT. A line graph consisting of the SBP, NIHSS, NLR, NT-pro BNP. A vertical line was drawn from the axis corresponding to each predictor until the top line labeled \"points\" was reached totaling the number of points for all predictors, and then a line was drawn down the axis labeled \"total points\" until it intersected the risk of intracranial hemorrhagic transformation after intravenous thrombolysis in patients with acute ischemic stroke.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3804290/v1/2a91c5919128dd809da54a85.png"},{"id":49091663,"identity":"c0ce373b-5ccc-45e8-a1f5-7a795a7828c2","added_by":"auto","created_at":"2024-01-03 01:56:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":22907,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curve of the nomogram for predicting the risk of HT after IVT.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3804290/v1/bf8dce19ebe9da21667dd114.png"},{"id":49091664,"identity":"0afe4932-018b-4d83-b319-3b8ecff73871","added_by":"auto","created_at":"2024-01-03 01:56:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":45169,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration plot for predicting HT after IVT.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3804290/v1/3ddd561e398c125af76da9c9.png"},{"id":49092439,"identity":"7173d05f-f3df-4984-abe4-6d5cbf372244","added_by":"auto","created_at":"2024-01-03 02:04:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":43309,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis for the nomogram.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3804290/v1/f46281442f02ccbc2c897eeb.png"},{"id":60276498,"identity":"840172cb-7f28-43fd-bdf7-3f205d09c6d0","added_by":"auto","created_at":"2024-07-15 05:24:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":645381,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3804290/v1/0b8fa22a-41ca-49c5-9f6e-e2170bafa952.pdf"},{"id":49091666,"identity":"7f66a54c-1ae8-467e-b816-76d68a5244b0","added_by":"auto","created_at":"2024-01-03 01:56:15","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":69776,"visible":true,"origin":"","legend":"","description":"","filename":"HT.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3804290/v1/5dd6bb9b745b7911c5ea5cd8.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Nomogram prediction model for the risk of intracranial hemorrhagic transformation after intravenous thrombolysis in patients with acute ischemic stroke","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eWorldwide, neurological disorders are the leading cause of disability and the second leading cause of death, with stroke being the largest cause (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). With the rapid development of interventional techniques and materials in recent years, endovascular intervention has become a primary treatment for acute ischemic stroke, but even so, intravenous thrombolysis (IVT) is now the preferred and effective treatment for patients within the 4.5-hour time window (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Within 24 hours of IVT, a subset of patients may experience a worsening of neurological deficits, which has been described as early neurological deterioration (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), which has been reported to be associated with poor outcomes (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). One of the higher risks of intravenous thrombolysis is hemorrhagic conversion (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). As the risk of hemorrhagic conversion increases, the clinical lethality and disability rates also increase (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Therefore, it is necessary to develop a predictive model to determine the risk of hemorrhagic conversion after intravenous thrombolysis in patients with AIS.\u003c/p\u003e \u003cp\u003eCurrently, nomograms are used as a predictive tool to personalize, visualize and accurately determine such risk. Based on this, the present study aimed to create a nomogram to predict the probability of HT after IVT in Chinese stroke patients.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e \u003cb\u003eStudy design and data sources\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn this study, we consecutively recruited patients diagnosed with AIS from October 2018 to October 2022 in Gansu Provincial People's Hospital. Included patients met the following criteria:1) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; 2) diagnosis of acute ischemic stroke; 3) time to treatment initiation\u0026thinsp;\u0026lt;\u0026thinsp;4.5 hours; and 4) patients receiving intravenous thrombolysis with rt-PA. Patients who met the following criteria were excluded: 1) intra-arterial thrombolysis or endovascular thrombolysis after intravenous thrombolysis; 2) those diagnosed with intracranial hemorrhage, including subarachnoid hemorrhage, parenchymal hemorrhage, intraventricular hemorrhage, epidural hemorrhage and so on; 3) incomplete clinical data. The study was approved by the Ethics Committee of Gansu Provincial People's Hospital (Approval No. 2023\u0026thinsp;\u0026minus;\u0026thinsp;350), Written informed consent was waived due to the retrospective nature of this study.. All procedures performed in the study complied with the 1964 Declaration of Helsinki and its subsequent amendments or similar ethical standards.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBaseline data collection\u003c/b\u003e \u003c/p\u003e \u003cp\u003eDemographic characteristics, medical history, and clinical and laboratory data were obtained at admission. Stroke severity was assessed by National Institutes of Health Stroke Scale (NIHSS) score. Laboratory data included baseline blood glucose, systolic blood pressure (SBP), diastolic blood pressure (DBP), neutrophil-to-lymphocyte ratio (NLR), high-density lipoproteins (HDL), low-density lipoproteins (LDL), triglycerides (TG), N-terminal pro-brain natriuretic peptide (NT-pro BNP), and total cholesterol (TC), et al. The NLR values were calculated as neutrophil count/lymphocyte count.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDefinition of hemorrhagic transformation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eHemorrhagic transformation (HT) was defined as any type of intracranial hemorrhage detected by follow-up CT or MRI within 22\u0026ndash;36 hours after intravenous thrombolysis, according to the criteria of the European Cooperative Acute Stroke Study II (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). All images were judged by two experienced neurologists without knowledge of the clinical data and final diagnosis.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003estatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses Descriptive analyses were as follows: Continuous variables were expressed as having mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (interquartile range); Categorical variables are described as numbers with percentages. Differences between groups with and without HT were investigated using Mann-Whitney U tests or t tests for appropriate continuous variables. Where appropriate, differences between the two groups of categorical variables were analyzed by Fisher's exact test or χ2 test.\u003c/p\u003e \u003cp\u003eTo construct nomograms, we used multivariate logistic regression analyses to identify independent factors for HT, and all variables with P values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univariate analyses were included. Variables with P values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in multivariate logistic regression were entered to generate predictive models. Regression coefficients and 95% confidence intervals (CI) for each variable in the model were calculated for the odds ratio (OR). The regression coefficients for each variable in the model were used to calculate the corresponding scores in the scale and ultimately to obtain the scoring system. The discriminative power of the Nomogram was assessed by calculating the area under the operating characteristic curve (AUC-ROC) of the subjects. The calibration of the prediction model describing the agreement between observed and predicted probabilities based on nomograms was tested using 1000 resampled calibration plots. All statistical analyses were performed using statistical methods Software SPSS version 26.0 (IBM, New York, NY) and R version 4.3 (R Foundation, Vienna, Austria).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003ePatients A total of 243 patients with ischemic stroke were treated with IVT. Patients who underwent intra-arterial thrombolysis (n = 7) or endovascular thrombectomy (n = 29) and those who lacked complete data (n = 10) were excluded. As shown in \u003cstrong\u003eTable 1\u003c/strong\u003e, 24 (11%) of the baseline profile characteristics were post-thrombolytic HT. \u003cstrong\u003eTable 2\u003c/strong\u003e Univariate logistic analysis showing NIHSS score, NLR, SBP, and NT-pro BNP (P \u0026lt; 0.05). After multivariate logistic analysis, NIHSS score, NLR, SBP, and NT-pro BNP were shown to be independent predictors of HT after intravenous thrombolysis in patients with ischemic stroke.\u003c/p\u003e\n\u003cp\u003eA nomogram of the HT predictive model was created based on these risk factors. The score for each independent predictor is the score corresponding to the upper scale, and the total score for each subject is the sum of the scores for each independent predictor. The total number of points corresponding to the HT risk axis is the risk of HT. The higher the total score, the higher the risk of HT. Internal validation of the Nomogram was performed by repeated sampling 1000 times using the Bootstrap method.\u003c/p\u003e\n\u003cp\u003eThe model was created by combining the independent predictor values as described above and shown as a nomogram in \u003cstrong\u003eFigure 1\u003c/strong\u003e. The score for each predictor in the Nomo plot is determined by drawing a vertical line between the predictor fold and the preliminary score line. The total score is calculated by totaling the scores for each predictor, and the corresponding HT prediction probabilities are obtained by drawing a vertical line between the total score and the probability line. The area under the subject\u0026apos;s work characteristics (ROC) curve for the prediction model was \u003cstrong\u003e(Figure 2).\u003c/strong\u003e In addition, the calibration curves of the nomograms for the likelihood of HT in patients showed good agreement \u003cstrong\u003e(Figure 3)\u003c/strong\u003e, predicting the model HT probability. The calibration curves are shown in Figure 3. The calibration curves used to estimate HT showed no significant deviation from perfect match and good agreement between predicted and actual results. The analysis of the decision curve (DCA) \u003cstrong\u003e(Figure 4)\u003c/strong\u003e showed that clinical decision making based on the predictive model was beneficial and implied the practical clinical application and operability of the predictive model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 1 |\u0026nbsp;\u003c/strong\u003eBaseline characteristics of AIS patients with IVT.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"575\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.565217391304348%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.956521739130434%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eWith HT(n=24)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eWithout HT(n=173)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall(n=197)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.478260869565217%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.565217391304348%\" valign=\"bottom\"\u003e\n \u003cp\u003eAge，years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.956521739130434%\" valign=\"bottom\"\u003e\n \u003cp\u003e69.83\u0026plusmn;11.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e67.86\u0026plusmn;12.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e68.10\u0026plusmn;12.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.478260869565217%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.565217391304348%\" valign=\"bottom\"\u003e\n \u003cp\u003eMale, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.956521739130434%\" valign=\"bottom\"\u003e\n \u003cp\u003e17 (70.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e117 (67.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e134 (68.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.478260869565217%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedical history, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.565217391304348%\" valign=\"bottom\"\u003e\n \u003cp\u003eHypertension\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.956521739130434%\" valign=\"bottom\"\u003e\n \u003cp\u003e16 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e112 (64.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e128 (65.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.478260869565217%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.853\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.565217391304348%\" valign=\"bottom\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.956521739130434%\" valign=\"bottom\"\u003e\n \u003cp\u003e6 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e36 (20.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e42 (21.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.478260869565217%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.639\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.565217391304348%\" valign=\"bottom\"\u003e\n \u003cp\u003eCoronary heart disease\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.956521739130434%\" valign=\"bottom\"\u003e\n \u003cp\u003e5 (20.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e30 (17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e35 (17.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.478260869565217%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.675\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.565217391304348%\" valign=\"bottom\"\u003e\n \u003cp\u003esmoking\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.956521739130434%\" valign=\"bottom\"\u003e\n \u003cp\u003e7 (29.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e43 (24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e50 (25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.478260869565217%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.565217391304348%\" valign=\"bottom\"\u003e\n \u003cp\u003edrinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.956521739130434%\" valign=\"bottom\"\u003e\n \u003cp\u003e6 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e30 (17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e36 (18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.478260869565217%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.363\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.565217391304348%\" valign=\"bottom\"\u003e\n \u003cp\u003ePrevious stroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.956521739130434%\" valign=\"bottom\"\u003e\n \u003cp\u003e5 (20.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e29 (16.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e34 (17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.478260869565217%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.565217391304348%\" valign=\"bottom\"\u003e\n \u003cp\u003eSBP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.956521739130434%\" valign=\"bottom\"\u003e\n \u003cp\u003e164.88\u0026plusmn;22.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e151.35\u0026plusmn;19.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e152.99\u0026plusmn;20.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.478260869565217%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.565217391304348%\" valign=\"bottom\"\u003e\n \u003cp\u003eDBP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.956521739130434%\" valign=\"bottom\"\u003e\n \u003cp\u003e89.50 \u0026plusmn;22.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e87.72 \u0026plusmn;17.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e87.93\u0026plusmn;18.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.478260869565217%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.652\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.565217391304348%\" valign=\"bottom\"\u003e\n \u003cp\u003eNIHSS, score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.956521739130434%\" valign=\"bottom\"\u003e\n \u003cp\u003e10.50 [7.00, 15.25]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e6.00 [5.00, 8.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.00 [5.00, 9.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.478260869565217%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.565217391304348%\" valign=\"bottom\"\u003e\n \u003cp\u003eTime from onset to treatment, min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.956521739130434%\" valign=\"bottom\"\u003e\n \u003cp\u003e144.50 [112.50, 215.25]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e171.00 [136.00, 211.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e170.00 [125.00, 212.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.478260869565217%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.651\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eLaboratory data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.565217391304348%\" valign=\"bottom\"\u003e\n \u003cp\u003eGlucose,\u0026nbsp;mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.956521739130434%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.70\u0026nbsp;\u0026plusmn;2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.17\u0026nbsp;\u0026plusmn;2.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.23\u0026plusmn;2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.478260869565217%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.565217391304348%\" valign=\"bottom\"\u003e\n \u003cp\u003eNEUT, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.956521739130434%\" valign=\"bottom\"\u003e\n \u003cp\u003e69.83\u0026plusmn;11.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e67.90\u0026plusmn;13.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e68.13\u0026plusmn;13.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.478260869565217%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.519\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.565217391304348%\" valign=\"bottom\"\u003e\n \u003cp\u003eNLR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.956521739130434%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.86 [3.40, 6.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.07 [2.39, 3.64]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.21 [2.45, 3.92]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.478260869565217%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.565217391304348%\" valign=\"bottom\"\u003e\n \u003cp\u003ePlatelet, 10\u0026and;9/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.956521739130434%\" valign=\"bottom\"\u003e\n \u003cp\u003e172.50 [142.75, 211.25]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e192.00 [160.00, 230.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e191.00 [159.00, 229.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.478260869565217%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.565217391304348%\" valign=\"bottom\"\u003e\n \u003cp\u003ePT, s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.956521739130434%\" valign=\"bottom\"\u003e\n \u003cp\u003e13.78 [12.63, 15.12]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e12.90 [10.89, 15.19]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e13.12 [11.14, 15.19]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.478260869565217%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.565217391304348%\" valign=\"bottom\"\u003e\n \u003cp\u003eAPTT, s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.956521739130434%\" valign=\"bottom\"\u003e\n \u003cp\u003e27.91 [24.41, 31.13]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e29.04 [25.58, 31.81]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e28.84 [25.50, 31.81]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.478260869565217%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.384\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.565217391304348%\" valign=\"bottom\"\u003e\n \u003cp\u003eINR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.956521739130434%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.98 [0.90, 1.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.98 [0.92, 1.04]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.98 [0.92, 1.04]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.478260869565217%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.565217391304348%\" valign=\"bottom\"\u003e\n \u003cp\u003eTG, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.956521739130434%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.91\u0026plusmn;0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.79\u0026nbsp;\u0026plusmn;0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.81\u0026nbsp;\u0026plusmn;0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.478260869565217%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.565217391304348%\" valign=\"bottom\"\u003e\n \u003cp\u003eTC, mmol/L \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.956521739130434%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.38 [2.01, 4.94]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.60 [2.52, 4.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.60 [2.34, 4.51]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.478260869565217%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.565217391304348%\" valign=\"bottom\"\u003e\n \u003cp\u003eHDL, mmol/L\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.956521739130434%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.40 [0.97, 2.19]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.77 [1.24, 2.24]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.72 [1.20, 2.24]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.478260869565217%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.565217391304348%\" valign=\"bottom\"\u003e\n \u003cp\u003eLDL, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.956521739130434%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.09 [2.57, 3.79]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.10 [2.42, 3.90]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.10 [2.43, 3.89]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.478260869565217%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.598\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.565217391304348%\" valign=\"bottom\"\u003e\n \u003cp\u003eNT-pro BNP\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.956521739130434%\" valign=\"bottom\"\u003e\n \u003cp\u003e302.67\u0026plusmn;127.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e205.39\u0026nbsp;\u0026plusmn;100.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e217.25 \u0026plusmn;108.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.478260869565217%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAIS, acute ischemic stroke; IVT, intravenous thrombolysis; HT, Hemorrhagic transformation; SBP, systolic blood pressure; DBP, diastolic blood pressure; NIHSS, National Institutes of Health Stroke Scale; NEUT, Neutrophilic granulocyte percentage; NLR, neutrophil-to-lymphocyte ratio; PT, prothrombin time; APTT, activated partial thromboplastin time; INR, International normalized ratio; TG, triglyceride; TC, total cholesterol; HDL, High-density lipoprotein; LDL, low-density lipoprotein; NT-pro BNP, the N-terminal of the prohormone brain natriuretic peptide.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 2 |\u0026nbsp;\u003c/strong\u003eUnivariable and multivariable analyses of HT in AIS patients with Intravenous thrombolysis.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"521\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude OR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eUni -\u003cem\u003eP\u003c/em\u003e value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdj OR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003emulti-\u003cem\u003eP\u0026nbsp;\u003c/em\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.014 [0.979, 1.052]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.162 [0.472, 3.152]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHypertension\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.089 [0.452, 2.820]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.269 [0.434, 3.276]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCoronary heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.254 [0.392, 3.409]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003esmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.245 [0.456, 3.099]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003edrinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.589 [0.540, 4.153]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePrevious stroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.307 [0.408, 3.560]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.035 [1.012, 1.060]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.039 [1.009, 1.075]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eDBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.006 [0.982, 1.030]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNIHSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.335 [1.194, 1.519]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.362 [1.161, 1.652]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTime from onset to treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.998 [0.991, 1.006]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eGLU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.099 [0.915, 1.301]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNEUT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.011 [0.979, 1.044]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.712 [1.889, 4.107]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.430 [2.082, 6.262]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePLT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.994 [0.986, 1.002]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.125 [0.980, 1.303]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAPTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.961 [0.876, 1.050]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eINR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.204 [0.001, 29.445]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.266 [0.694, 2.318]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.949 [0.701, 1.279]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.760 [0.405, 1.390]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eLDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.143 [0.749, 1.763]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNT-pro BNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.008 [1.004, 1.013]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.012 [1.004, 1.020]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAIS, acute ischemic stroke; IVT, intravenous thrombolysis; HT, Hemorrhagic transformation; CI, confidence interval; OR, odds ratio;\u0026nbsp;SBP, systolic blood pressure; DBP, diastolic blood pressure; NIHSS, National Institutes of Health Stroke Scale; NEUT, Neutrophilic granulocyte percentage; NLR, neutrophil-to-lymphocyte ratio; PT, prothrombin time; APTT, activated partial thromboplastin time; INR, International normalized ratio; TG, triglyceride; TC, total cholesterol; HDL, High-density lipoprotein; LDL, low-density lipoprotein; NT-pro BNP, the N-terminal of the prohormone brain natriuretic peptide.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this study, we found that NIHSS score, NLR, SBP, and NT-pro BNP were independent predictors of HT. Based on these four independent factors, we constructed a predictive nomogram. This model can help us to predict the probability of HT in acute ischemic stroke patients treated with IVT. The developed nomogram showed good discrimination and calibration. In addition, DCA results showed that the developed nomogram had a significant net benefit in predicting the risk of cerebral hemorrhage.\u003c/p\u003e \u003cp\u003eFirstly, similar to previous studies, the higher the pre-treatment NIHSS score, the greater the risk of HT (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). One article showed a 1.6-fold increased risk of HT in patients with acute cerebral infarction with an NIHSS score of 7\u0026ndash;12 and a 2.22-fold increased risk of HT in patients with a baseline NIHSS score of \u0026ge;\u0026thinsp;13 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). In this study, it was shown that patients with high NIHSS scores also had a higher risk of HT. This is because the higher the NIHSS score, the larger the area of cerebral infarction and oedema in the patient, the higher the risk of HT (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The above previous studies have shown that high NIHSS score is closely associated with hemorrhagic transformation, which is consistent with the findings of the current study.\u003c/p\u003e \u003cp\u003eNLR is easily accessible in the clinic and reproduces new biomarkers of inflammation (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). It plays a crucial role in HT due to the inflammatory response of migrating inflammatory cells leading to disruption of blood-brain barrier integrity (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Neutrophils can increase the permeability of the blood-brain barrier by releasing, among other things, associated cytokines, whereas activation of lymphocytes can reduce blood-brain barrier disruption. Due to the balance between neutrophils and lymphocytes, NLR is considered a biomarker of systemic inflammation. High NLR (\u0026ge;\u0026thinsp;4.255) on admission has been reported to increase the risk of HT in patients with AIS after IVT (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). A clinical study by Guo et al. reported that the dynamics of HT were associated with IVT in patients with acute ischemic stroke (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The underlying mechanism by which NLR increases the risk of cerebral hemorrhage in patients with AIS treated with IVT has not been elucidated. A plausible explanation may be that NLR influences outcome as it is associated with inflammatory destruction of neutrophils and reduced lymphocyte protection (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHypertension was found to be a risk factor for hemorrhagic transformation after intravenous thrombolysis. Similar results were obtained by H Y et al, who suggested that increased SBP mediates brain-blood barrier damage and upregulation of aquaporin Protein-4 via oxidative stress, leading to an increased risk of neurological deterioration (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Meanwhile, hypertension impairs collateral circulation, reduces the ability of brain tissue to maintain adequate oxygenation during cerebral artery occlusion, and promotes the accumulation of reactive oxygen species and the release of inflammatory factors, leading to further damage to the blood-brain barrier, which in turn leads to hemorrhagic transformation after thrombolysis (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere is still some controversy about whether a history of previous hypertension serves as a risk factor for hemorrhagic transformation (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), which is because chronic hypertension leads to increased permeability of the blood-brain barrier, impaired reperfusion of blood flow, and damage to the inner wall of blood vessels leading to blood leakage, secondary to hemorrhagic transformation. And the history of hypertension was not found to affect HT during the study of this paper, which needs to be supported by subsequent studies.\u003c/p\u003e \u003cp\u003eAnother major finding of this paper is the correlation between elevated levels of NT-pro BNP and hemorrhagic conversion in stroke patients treated with intravenous thrombolysis. NT-pro BNP is released from ventricular myocardium with stretching (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Several studies have also shown that the brain secretes NT-pro BNP and that the concentration of NT-pro BNP in the cerebrospinal fluid may be greatly increased after brain injury (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Hemorrhagic transformation is a major complication in stroke patients treated with intravenous thrombolysis. The association between elevated NT-pro BNP levels and cerebral hemorrhage has been demonstrated It has been demonstrated (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) that elevated NT-pro BNP levels are associated with increased hematoma volume and a poor prognosis (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Our findings suggest that NT-pro BNP levels are independently associated with hemorrhagic hypertension in stroke patients receiving intravenous thrombolytic therapy for transformation. Another potential reason for elevated NT-pro BNP levels in hemorrhagic transformation may be that hemorrhagic transformation exacerbates ischemic stroke-induced neurological damage (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), and may also exacerbate stroke-induced cardiac dysfunction in the same way (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). However, whether thrombolytic therapy affects NT-pro BNP levels remains unclear. Future studies are needed to further elucidate the mechanism of elevated NT-pro BNP levels in stroke patients receiving intravenous thrombolytic therapy.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eOur study presents a novel and practical nomogram of NIHSS, NT-pro BNP, NLR, SBP that can well predict the probability of HT after intravenous thrombolysis in ischemic stroke patients. The qualitative and discriminative properties of the graph were verified in an internal validation. The graph can be used to predict the probability of HT after IVT and to help clinicians assess whether to continue IVT in patients at high risk for HT. However, further studies are needed to confirm the validity of the nomogram.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The raw data supporting the conclusions of this article will be made available by the authors, upon request to the corresponding author. If anyone would like to receive data from this study, they can contact Yong Ma at [email protected].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICS STATEMENT\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethics Committee of Gansu Provincial People's Hospital (approval number: 2023-350). Written informed consent was waived due to the retrospective nature of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYM, D-YX, QL, H-CC and E-QC were involved in the conception and design of the study. YM, D-YX, and QL were used for material preparation and data collection. Analysis and interpretation of the data were done by YM. The first draft of the manuscript was written by YM, D-YX, QL, H-CC and E-QC. All authors read and approved the final manuscript. YM, D-YX, and QL contributed equally to this work. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Natural Science Foundation of Gansu Province(22JR5RA673), Natural Science Foundation of Gansu Province(23JRRA1308), Natural Science Foundation of Gansu Province (20JR10RA384),\u0026nbsp;Gansu Provincial Key Laboratory of Cerebrovascular Disease(20JR10RA431).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT INTEREST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGBD 2016 Neurology Collaborators. Global, regional, and national burden of neurological disorders, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. 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J Neurol. 2017 Oct;264(10):2081-2087. doi: 10.1007/s00415-017-8602-0. \u003c/li\u003e\n\u003cli\u003eLi F, Chen QX, Xiang SG, Yuan SZ, Xu XZ. N-Terminal Pro-Brain Natriuretic Peptide Concentrations After Hypertensive Intracerebral Hemorrhage: Relationship With Hematoma Size, Hyponatremia, and Intracranial Pressure. J Intensive Care Med. 2018 Dec;33(12):663-670. doi: 10.1177/0885066616683677. \u003c/li\u003e\n\u003cli\u003eHajdinjak E, Klemen P, Grmec S. Prognostic value of a single prehospital measurement of N-terminal pro-brain natriuretic peptide and troponin T after acute ischaemic stroke. J Int Med Res. 2012;40(2):768-76. doi: 10.1177/147323001204000243. \u003c/li\u003e\n\u003cli\u003eChen Z, Venkat P, Seyfried D, Chopp M, Yan T, Chen J. Brain-Heart Interaction: Cardiac Complications After Stroke. Circ Res. 2017 Aug 4;121(4):451-468. doi: 10.1161/CIRCRESAHA.117.311170. \u003c/li\u003e\n\u003cli\u003eBattaglini D, Robba C, Lopes da Silva A, Dos Santos Samary C, Leme Silva P, et al. Brain-heart interaction after acute ischemic stroke. Crit Care. 2020 Apr 21;24(1):163. doi: 10.1186/s13054-020-02885-8. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"acute ischemic stroke, intravenous thrombolysis, Hemorrhagic transformation, nomogram","lastPublishedDoi":"10.21203/rs.3.rs-3804290/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3804290/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eHemorrhagic transformation (HT) after intravenous thrombolysis (IVT) leads to poor clinical prognosis, and a reliable predictive system is needed to identify the risk of hemorrhagic transformation after IVT.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eRetrospective collection of patients with acute cerebral infarction treated with intravenous thrombolysis in our hospital from 2018 to 2022. 197 patients were included in the research study. Multivariate logistic regression analysis was used to screen the factors in the predictive nomogram. The performance of nomogram was assessed on the basis of area under the curve (AUC-ROC) of subjects' work characteristics, calibration plots and decision curve analysis (DCA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e A total of 197 patients were recruited, of whom 24 (12.1%) developed HT. In multivariate logistic regression model National Institute of Health Stroke Scale (NIHSS) (OR, 1.362; 95% CI, 1.161 −1.652; P = 0.001), N-terminal pro-brain natriuretic peptide (NT-pro BNP) (OR, 1.012; 95% CI, 1.004 −1.020; P = 0.003), neutrophil to lymphocyte ratio (NLR) (OR, 3.430; 95% CI, 2.082 −6.262; P \u0026lt; 0.001), systolic blood pressure (SBP) (OR, 1.039; 95% CI, 1.009 −1.075; P = 0.016) were the independent predictors of HT which were used to generate nomogram. The nomogram showed good discrimination due to AUC-ROC values. Calibration plot showed good calibration. DCA showed that nomogram is clinically useful.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Nomograms consisting of NIHSS, NT-pro BNP, NLR, SBP scores predict the risk of HT in AIS patients treated with IVT.\u003c/p\u003e","manuscriptTitle":"Nomogram prediction model for the risk of intracranial hemorrhagic transformation after intravenous thrombolysis in patients with acute ischemic stroke","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-03 01:56:10","doi":"10.21203/rs.3.rs-3804290/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"51436ea5-7fd3-481b-b964-15a6bd3bd978","owner":[],"postedDate":"January 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":27879790,"name":"Biological sciences/Neuroscience/Diseases of the nervous system"},{"id":27879791,"name":"Biological sciences/Neuroscience/Neuro vascular interactions"}],"tags":[],"updatedAt":"2024-07-15T05:01:33+00:00","versionOfRecord":{"articleIdentity":"rs-3804290","link":"https://doi.org/10.3389/fneur.2024.1361035","journal":{"identity":"frontiers-in-neurology","isVorOnly":true,"title":"Frontiers in Neurology"},"publishedOn":"2024-03-07 05:01:33","publishedOnDateReadable":"March 7th, 2024"},"versionCreatedAt":"2024-01-03 01:56:10","video":"","vorDoi":"10.3389/fneur.2024.1361035","vorDoiUrl":"https://doi.org/10.3389/fneur.2024.1361035","workflowStages":[]},"version":"v1","identity":"rs-3804290","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3804290","identity":"rs-3804290","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}