Prognostic Accuracy of Five Collateral Circulation Scores derived from computed tomography angiography for Predicting 3-Month Outcomes in Acute Ischemic Stroke

Prognostic Accuracy of Five Collateral Circulation Scores derived from computed tomography angiography for Predicting 3-Month Outcomes in Acute Ischemic Stroke | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prognostic Accuracy of Five Collateral Circulation Scores derived from computed tomography angiography for Predicting 3-Month Outcomes in Acute Ischemic Stroke Dong Liu, Xiaojun Qin, Yanan Wang, Jie Li, Junfeng Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8514418/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose This study aims to explore the optimal collateral circulation score for predicting 3-month function outcomes in patients with acute ischemic stroke. Methods We included patients with acute ischemic stroke. Five collateral circulation scores derived from computed tomography angiography(CTA) including the Maas score, Tan score, rLMC score, Miteff score, and ASPECTS score were assessed. The outcome measure was poor functional outcomes at 3 months, defined as a modified Rankin Scale (mRS) score of 3–6. Receiver operating characteristic (ROC) curve analysis was used to evaluate the prognostic performance of each score. Sensitivity analyses were conducted by only including patients with large artery stenosis (stenosis > 30%) or those who did not receive reperfusion therapy. Results A total of 515 patients with acute ischemic stroke within 24 hours of onset were included, with a mean age of 67.1 ± 14.3 years and 59.2% were male. The proportion of patients received reperfusion therapy was 19.2%(99/515 cases). 252 patients had poor functional outcomes after 3-month follow-up. The rLMC score demonstrated the highest area under the curve (AUC) for predicting poor functional outcomes (AUC = 0.677), followed by the Tan score (AUC = 0.669), Maas score (AUC = 0.668), Miteff score (AUC = 0.666), and ASPECTS score (AUC = 0.660). Similar results were found in sensitivity analysis. Conclusion The rLMC score shows the highest prognostic accuracy for predicting 3-month poor outcomes in patients with acute ischemic stroke. The results remained when only included patients with large artery stenosis/occlusion or patients without reperfusion treatment. Acute ischemic stroke Computed tomography angiography Collateral circulation score Prognosis Predictive value Introduction The compensatory collateral circulation determines the ischemic penumbra and infarct volume when the cerebral blood supply artery is severely narrowed or occluded. Good collateral circulation can reduce the volume of cerebral infarction, improve the prognosis of patients, and effectively reduce the recurrence of cerebral infarction [ 1 ] . Therefore, rapid and accurate assessment of collateral circulation is of great significance for clinical decision-making and predicting the prognosis of stroke. Digital Subtraction Angiography (DSA) is the golden standard for assessing collateral circulation in ischemic stroke, but its clinical application is limited due to its invasiveness and high cost [ 2 ] . Previous study has found that collateral circulation assessment based on CT angiography (CTA) original images was consistent with DSA [ 3 ] . As a non-invasive examination method, CTA can dynamically display the anatomical structure of intracranial and extracranial vessels from different angles, so as to more intuitively evaluate the degree of vascular stenosis and collateral blood flow [ 4 ] . Although multiple scoring scores for collateral circulation using CTA have been proposed, a consensus on the optimal system has not been established as studies on different scores have yielded inconsistent results [ 2 , 5 – 8 ] . To identify the optimal collateral circulation scoring system for predicting prognosis in patients with acute ischemic stroke(AIS), this study will evaluate and compare five CTA-based collateral circulation scores on admission including lateral fissure, convex collateral score (Maas score) [ 9 ] , middle cerebral artery regional collateral score (Tan score) [ 10 ] , regional leptomeningeal collateral circulation score (rLMCs) [ 11 , 12 ] , middle cerebral artery occlusion distal collateral score (Miteff score) [ 13 ] , and the improved Alberta stroke program early CT Score(ASPECTS)based on multiphase CTA) [ 11 , 14 – 16 ] . Materials and Methods Study design and participants We conducted a retrospective analysis of patients with AIS who were admitted to the Department of Neurology, West China Hospital, Sichuan University between January 2016 and June 2018. Patients were included if they were admitted within 24 hours of stroke onset and underwent head and neck CTA within 24 hours following admission. The above five collateral circulation scores based on CTA were evaluated on admission, and they were divided into poor and good collateral circulation group. Patients were excluded for the following reasons: poor image quality that hampered reader evaluation, major organ dysfunction, or refusal to participate/loss to follow-up. Data Collection Data on demographic characteristics (age, gender), medical history, the initial National Institutes of Health Stroke Scale (NIHSS) score, smoking and alcohol consumption were collected. We also recorded the Trial of Org 10172 in Acute Stroke Treatment (TOAST) classification and the degree of arterial stenosis [ 17 – 19 ] . For intracranial vascular lesions: arterial stenosis rate (%) = (1 - Diameter of the most severely stenotic segment / Diameter of the normal proximal segment) × 100%; For extracranial vascular lesions: arterial stenosis rate (%) = (1 - Diameter of the most severely stenotic segment / Diameter of the normal distal segment) × 100%. Patients were diagnosed with large artery stenosis/occlusion, defined as stenosis > 30%. Treatments given to patients in hospitalization including antiplatelets, anticoagulation, reperfusion therapy(thrombolysis/thrombectomy) and lipid-lowering therapy were recorded. CTA Protocol and Image Analysis CTA Protocol and Image Analysis All patients underwent head and neck computed tomography angiography (CTA) within 24 hours of admission. Scans were performed using a helical mode with the following parameters: 100 kVp, 86 mAs, and a slice thickness of 0.625 mm at 0.625 mm intervals. A bolus of 100 mL of non-ionic iodinated contrast medium was administered intravenously at a rate of 5 mL/s prior to imaging. Assessment of Collateral Circulation Collateral circulation was assessed using five established CTA-based scoring systems: the Maas, Tan, rLMCs, Miteff, and improved ASPECTS scores [ 9 – 13 ] . Based on these scores, patients were stratified into two groups:(1)Poor collateral circulation, defined as a Maas score of 1–2, Tan score of 0–1, rLMCs score of 0–10, Miteff score of 1–2, or an improved ASPECTS of 0–3;(2)Good collateral circulation, defined as a Maas score of 3–5, Tan score of 2–3, rLMCs score of 11–20, Miteff score of 3, or an improved ASPECTS of 4–5. Outcome measure The primary outcome was a poor functional outcome at 3 months, defined as a modified Rankin Scale (mRS) score of 3 to 6, which was assessed via telephone interview [ 20 ] . Statistical Analyses Statistical analyses were performed using SPSS 24.0 (IBM Corp., USA). Categorical variables were compared using the chi-square test, normally distributed continuous variables with the independent-samples t-test, and non-normally distributed continuous variables with the Mann-Whitney U test. Variables associated with the 3-month outcome in univariate analysis (P < 0.05) were entered into a multivariate logistic regression model to identify independent predictors. The discriminatory power of the collateral scores was evaluated and compared by receiver operating characteristic (ROC) curve analysis, with the area under the curve (AUC) quantifying performance. A two-sided P-value < 0.05 defined statistical significance. Results Baseline characteristics Of the 573 patients with ischemic stroke initially screened, 515 patients were finally included in the analysis. The baseline characteristics of the study participants were summarized in Table 1 . The mean age of the included patients was 67.1 ± 14.3 years and 59.2% (305/515 cases) were male. Large artery stenosis /occlusion was present in 70.1% (361/515 cases)of the patients and 19.2% (99/515 cases) received reperfusion treatment. Subsequently, at the 3-month follow-up, 252 patients (48.9%) had a poor functional outcome. Table 1 Baseline Characteristics and Univariate Analysis of Outcome Metrics in included Patients with Acute Ischemic Stroke Variable All patients n = 515 Poor prognosis at 3 months n = 252 Good prognosis at 3 months n = 263 P value Age, years (mean ± SD) 67.1 ± 14.3 70.3 ± 14.2 64.0 ± 13.9 <0.001 Male(%) 305(59.2) 130(51.6) 175(66.5) 0.001 Previous medication history Pre hospital antiplatelet(%) 47(9.1) 27(10.7) 20(7.6) 0.221 Pre hospital lipid-lowering(%) 28(5.4) 15(6.0) 13(4.9) 0.614 Pre hospital anticoagulation(%) 30(5.8) 18(7.1) 12(4.6) 0.211 Baseline NIHSS score, median (interquartile interval) 7(3–14) 11.5(6–18) 4(2–8) < 0.001 Medical history Hypertension(%) 280(54.4) 142(56.3) 138(52.5) 0.377 Diabetes(%) 96(18.6) 52(20.6) 44(16.7) 0.255 Hyperlipidemia(%) 16(3.1) 5(2.0) 11(4.2) 0.151 Atrial fibrillation(%) 83(16.1) 53(21.0) 30(11.4) 0.003 Myocardial infarction(%) 4(0.8) 3(1.2) 1(0.4) 0.363 Valvular heart disease(%) 33(6.4) 15(6.0) 18(6.8) 0.680 Coronary heart disease(%) 46(8.9) 25(9.9) 21(8.0) 0.441 Previous stroke(%) 89(17.3) 45(17.9) 44(16.7) 0.735 Ischemic(%) 84(16.3) 43(17.1) 41(15.6) 0.651 Hemorrhagic(%) 6(1.2) 2(0.8) 4(1.5) 0.686 Smoking(%) 200(38.8) 88(34.9) 112(42.6) 0.074 History of drinking(%) 141(27.4) 58(23.0) 83(31.6) 0.030 Systolic blood pressure, mmHg (mean ± standard deviation) 148.2 ± 24.7 147.1 ± 26.5 149.3 ± 22.9 0.318 Diastolic pressure, mmHg (mean ± standard deviation) 85.6 ± 16.3 84.6 ± 18.1 86.6 ± 14.3 0.164 In-hospital treatment Antiplatelet(%) 477(92.6) 230(91.3) 247(93.9) 0.251 Thrombolysis(%) 72(14.0) 30(11.9) 42(16.0) 0.184 Anticoagulation(%) 88(17.1) 41(16.3) 47(17.9) 0.629 Lipid-lowering(%) 447(86.8) 218(86.5) 229(87.1) 0.850 Endovascular intervention(%) 33(6.4) 18(7.1) 15(5.7) 0.505 Reperfusion therapy(%) 99(19.2) 44(17.5) 55(20.9) 0.320 DSA(%) 54(10.5) 20(7.9) 34(12.9) 0.065 TOAST classification Atherosclerotic type(%) 148(28.7) 76(30.2) 72(27.4) < 0.001 Arteriolar occlusive type(%) 105(20.4) 30(11.9) 75(28.5) < 0.001 Cardioembolic type(%) 150(29.1) 90(35.7) 60(22.8) < 0.001 Other definite etiological types(%) 12(2.3) 6(2.4) 6(2.3) < 0.001 Undetermined type(%) 100(19.4) 50(19.8) 50(19.0) < 0.001 Maas score, 1–2 points(%) 191(37.1) 131(52.0) 60(22.8) < 0.001 Tan score, 0–1 points(%) 110(21.4) 90(35.7) 20(7.6) < 0.001 rLMC score, 0–10 points(%) 101(19.6) 83(32.9) 18(6.8) < 0.001 Miteff score, 1–2 points(%) 185(35.9) 128(50.8) 57(21.7) < 0.001 ASPECTS scale, 0–3 points(%) 231(44.9) 136(54.0) 95(36.1) < 0.001 Abbreviations: SD, Standard Deviation; NIHSS, National Institutes of Health Stroke Scale; DSA, Digital Subtraction Angiography; TOAST, Trial of Org 10172 in Acute Stroke treatment. Data are presented as n (%), mean ± standard deviation (SD), or median (interquartile range [IQR]). P values were calculated using the χ² test. P-values < 0.05 are represented in bold. Abbreviations: rLMCs, regional leptomeningeal collateral circulation score; ASPECTS, Alberta Stroke Program Early CT Score. Patients with poor functional outcomes were significantly older (70.3 ± 14.2 vs. 64.0 ± 13.9 years) and had a higher median NIHSS score on admission (11.5 [IQR: 6–18] vs. 4 [IQR: 2–8]). They also had a higher prevalence of atrial fibrillation (21.0% vs. 11.4%). Patients in the good outcome group were more likely to be male (66.5% vs. 51.6%) and have a history of alcohol use (31.6% vs. 23.0%).(Table 1 ) Correlation between five CTA collateral circulation scores and prognosis of acute ischemic stroke All five collateral scores were significantly associated with 3-month poor outcomes in the univariate analysis (all P < 0.001; Table 1 ). After adjustment confounders, the Maas (OR 1.681, 95% CI 1.039–2.720; P = 0.034), Tan (OR 3.205, 95% CI 1.753–5.858; P < 0.001), rLMC (OR 2.945, 95% CI 1.573–5.513; P = 0.001), and Miteff (OR 1.748, 95% CI 1.082–2.826; P = 0.023) scores were independently associated with 3-month poor outcomes, except the ASPECTS score (OR 0.991, 95% CI 0.639–1.537; P = 0.966;Table 2 ). Table 2 Multivariate logistic regression analysis between CTA based five collateral circulation scores and 3-month poor prognosis Variable OR value* 95%CI P value Maas score 3–5 points 1.681 1.039–2.720 0.034 Tan score 2–3 points 3.205 1.753–5.858 < 0.001 rLMC score 11–20 points 2.945 1.573–5.513 0.001 Miteff score 3 points 1.748 1.082–2.826 0.023 ASPECTS score 4–5 points 0.991 0.639–1.537 0.966 * Adjustment for age, gender, atrial fibrillation, alcohol consumption, TOAST classification, and baseline NIHSS score. Abbreviations: OR, Odds Ratio; CI, Confidence Interval. Comparative analysis of Five CTA-Based Collateral Scores for Predicting Prognosis in Acute Ischemic Stroke The rLMC score demonstrated the highest AUC for predicting poor functional outcomes (0.677), followed by the Tan score (0.669), Maas score (0.668), Miteff score (0.666), and ASPECTS score (AUC = 0.660) (Table 3 ). Similar results were found in sensitivity analysis after only including patients with large artery stenosis/occlusion or patients without reperfusion treatment. A total of 361 patients with large artery stenosis/occlusion and 416 patients without reperfusion therapy were analyzed respectively. The rLMC score still showed the highest predictive value for 3-month poor functional outcome in both the stenosis/occlusion group (AUC 0.681, 95% CI 0.626–0.735, P < 0.001) and the non-reperfusion group (AUC 0.702, 95% CI 0.652–0.753, P < 0.001). Table 3 Results of ROC Curve Analysis for 3-Month poor Prognosis in Acute Ischemic Stroke Based on Five Collateral Circulation Scores from CTA AUC value 95%CI P value rLMC score 0.677 0.630–0.723 < 0.001 Mass score 0.668 0.621–0.714 < 0.001 Tan score 0.669 0.622–0.716 < 0.001 Miteff score 0.666 0.619–0.713 < 0.001 ASPECTS scale 0.660 0.613–0.706 < 0.001 Abbreviations: AUC, Area under the Curve; CI, Confidence Interval. rLMC score, regional leptomeningeal collateral circulation score; Mass score, Lateral sulcus and cerebral convexity collateral circulation score; Tan score, MCA territory collateral score; Miteff score, Collateral Circulation Scoring in Distal Middle Cerebral Artery Occlusion; ASPECTS scale, Alberta Stroke Program Early CT Score scale. Discussion This study compared five CTA-based collateral scores for predicting 3-month functional outcomes in AIS, and the rLMC score achieved the highest predictive accuracy. The results remained when we only included large artery stenosis/occlusion or patients without reperfusion therapy. Collateral circulation compensation is important in the pathophysiology of cerebral ischemia, as it helps salvage the ischemic penumbra and reduces the final infarct volume [ 21 , 22 ] . Despite its acknowledged role in predicting prognosis and therapeutic decisions, assessing collateral status remains challenging due to methodological complexities. Our study confirms that collateral scores derived from CTA provide valuable prognostic information in AIS. Moreover, the rLMC score demonstrated highest performance for prediction prognosis at 3 months—a finding consistent with other studies. For instance, in a retrospective analysis of 98 AIS patients with large vessel occlusion, Bao et al. [ 23 ] reported that the rLMC score showed higher inter-rater reliability, validity compared to DSA-based American Society of Interventional and Therapeutic Neuroradiology༏Society of Interventional Radiology(ASITN/SIR) grading. In addition, its predictive value for prognosis after thrombectomy was assessed and was found to outperform the Miteff, Maas, and Tan scores. Similarly, Li et al. [ 24 ] reported that the rLMC score correlated significantly with CT perfusion (CTP) parameters in a cohort of 79 patients with AIS, further validating its physiological relevance. Nevertheless, several studies have reported inconsistent findings. In a cohort of 130 patients with AIS, Havenon et al. [ 25 ] demonstrated that a favorable collateral status on single-phase CTA was not an independent predictor of functional outcome, a finding they attributed to non-standardized CTA acquisition parameters and an insufficiently sensitive collateral assessment method. According to the retrospective analysis by Conrad et al. [ 26 ] of 686 patients with suspected AIS, CTA-based collateral status showed no significant prognostic value. The discrepancy between these findings and our own may be explained by the inclusion of patients with a suspected stroke rather than confirmed stroke, which introduces diagnostic heterogeneity and potentially compromises the accuracy of collateral grading. Despite these contrasting reports, the rLMC score remains a valuable tool in settings where DSA is not readily available, offering a reliable and non-invasive alternative for collateral assessment [ 23 , 24 ] . This study has several limitations. First, its retrospective design and single-center recruitment may introduce selection bias and limit the generalizability of the findings. Second, the collateral circulation assessment was static rather than dynamic, which may have affected the accuracy of the results. Lastly, direct comparison with DSA, the golden standard for collateral circulation evaluation, was not available as only a small subset of patients (n = 54) underwent this procedure.Future multi-center, large-sample, prospective studies are needed to validate these findings and further explore the role of the rLMC score in clinical practice. Conclusion In the study, the rLMC score showed the best predictive value for 3-month functional outcomes in patients with AIS. Similar results were also found only included patients with large artery stenosis/embolism or those without reperfusion therapy. Declarations Consent to participate Informed consent was obtained from all individual participants included in the study. Conflict of interest statement The authors declare that there are no conflicts of interest associated with this study. No financial or personal relationships have influenced the design, execution, or reporting of the research. Funding This study was supported by the Noncommunicable Chronic Diseases-National Science and Technology Major Project ( 2024ZD0527700) and National Natural Science Foundation of China (82371323). Author Contribution Junfeng Liu and Jie Li designed the study. Dong Liu and Yanan Wang collected data. Dong Liu and Junfeng Liu writed the manuscript. Junfeng Liu, Jie Li and Xiaojun Qin critically revised the manuscript. All authors read and approved the final manuscript. Acknowledgements None. Data Availability Anonymized datasets are available upon reasonable request to the corresponding author. References Bang OY, Goyal M, Liebeskind DS. Collateral Circulation in Ischemic Stroke: Assessment Tools and Therapeutic Strategies. Stroke. 2015;46(11):3302–9. Liu L, Ding J, Leng X, et al. Guidelines for evaluation and management of cerebral collateral circulation in ischaemic stroke 2017. Stroke Vasc Neurol. 2018;3(3):117–30. He H, Qian ZM, Sheng Y, Liu Y. Assessment of Cerebral Collateral Flow With Single-Phase Computed Tomography Angiography-Based Multimodal Scales in Patients With Acute Ischemic Stroke. J Comput Assist Tomogr. 2020;44(5):708–13. Lima F, Furie et al. O-004 The pattern of leptomeningeal collaterals on CT angiography is a strong predictor of good functional outcome in stroke patients with intracranial large vessel occlusion. J NeuroInterventional Surg. 2010. Chatterjee D, Nagarajan K, Narayan SK, Narasimhan RL. Regional leptomeningeal collateral score by computed tomographic angiography correlates with 3-month clinical outcome in acute ischemic stroke. Brain Circ. 2020;6(2):107–15. Baydemir R, Aykaç Ö, Acar BA, et al. Role of modified TAN score in predicting prognosis in patients with acute ischemic stroke undergoing endovascular therapy. Clin Neurol Neurosurg. 2021;210:106978. Shafie M, Yu W. Recanalization Therapy for Acute Ischemic Stroke with Large Vessel Occlusion: Where We Are and What Comes Next. Transl Stroke Res. 2021;12(3):369–81. Seker F, Pereira-Zimmermann B, Pfaff J, et al. Collateral Scores in Acute Ischemic Stroke: A retrospective study assessing the suitability of collateral scores as standalone predictors of clinical outcome. Clin Neuroradiol. 2020;30(4):789–93. Maas MB, Lev MH, Ay H, et al. Collateral vessels on CT angiography predict outcome in acute ischemic stroke. Stroke. 2009;40(9):3001–5. Tan IY, Demchuk AM, Hopyan J, et al. CT angiography clot burden score and collateral score: correlation with clinical and radiologic outcomes in acute middle cerebral artery infarct. AJNR Am J Neuroradiol. 2009;30(3):525–31. Chinese Stroke Association, Branch of Cerebral Blood Flow and Metabolism. Chinese Guidelines for Assessment and Intervention of Cerebral Collateral Circulation in Ischemic Stroke. (2017). Chinese Journal of Internal Medicine. 2017; 56(6):460–471. (in Chinese). Menon BK, Smith EE, Modi J et al. Regional leptomeningeal score on CT angiography predicts clinical and imaging outcomes in patients with acute anterior circulation occlusions. AJNR Am J Neuroradiol. 2011. 32(9). Miteff F, Levi CR, Bateman GA, Spratt N, McElduff P, Parsons MW. The independent predictive utility of computed tomography angiographic collateral status in acute ischaemic stroke. Brain. 2009;132(Pt 8):2231–8. Menon BK, d'Esterre CD, Qazi EM, et al. Multiphase CT Angiography: A New Tool for the Imaging Triage of Patients with Acute Ischemic Stroke. Radiology. 2015;275(2):510–20. Yu Xiang Z, Xiaoying L. Bin. Research progress of collateral establishment in acute ischemic stroke based on different CTA scales. Int J Med Radiol 2020. 43(5): 550–4. (in Chinese). Xie Jian. The relationship between collateral circulation of CTA and the outcome of reperfusion therapy in patients with acute ischemic stroke. Nanjing: Southeast University; 2019. (in Chinese). Hao Junwei L, Benyan, et al. Neurology. 9th ed. Beijing: People's Medical Publishing House; 2024. pp. 166–217. (in Chinese). Lan Y, Shang J, Ma Y, et al. A new predictor of coronary artery disease in acute ischemic stroke or transient ischemic attack patients: pericarotid fat density. Eur Radiol. 2024;34(3):1667–76. Adams HP, Bendixen BH, Kappelle LJ, 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. Stroke. 1993;24(1):35–41. Janssen PM, Visser NA, Dorhout Mees SM, Klijn CJ, Algra A, Rinkel GJ. Comparison of telephone and face-to-face assessment of the modified Rankin Scale. Cerebrovasc Dis. 2010;29(2):137–9. Muehlen I, Kloska SP, Gölitz P, et al. Noninvasive Collateral Flow Velocity Imaging in Acute Ischemic Stroke: Intraindividual Comparison of 4D-CT Angiography with Digital Subtraction Angiography. Rofo. 2019;191(9):827–35. Liebeskind DS, Cotsonis GA, Saver JL, et al. Collaterals dramatically alter stroke risk in intracranial atherosclerosis. Ann Neurol. 2011;69(6):963–74. Bao HT, Li ZQ. Study on the Application Value of Collateral Circulation Scoring Based on CTA in Acute Ischemic Stroke with Large Vessel Occlusion. Zhejiang Med J. 2024;46(10):1047–55. (in Chinese). Li QY. Comparative Study on Different Evaluation Methods of Cerebral Collateral Circulation Based on CTA. Imaging Med Nuclear Med. 2022. (in Chinese). de Havenon A, Mlynash M, Kim-Tenser MA, et al. Results From DEFUSE 3: Good Collaterals Are Associated With Reduced Ischemic Core Growth but Not Neurologic Outcome. Stroke. 2019;50(3):632–8. Conrad J, Ertl M, Oltmanns MH, Zu Eulenburg P. Prediction contribution of the cranial collateral circulation to the clinical and radiological outcome of ischemic stroke. J Neurol. 2020;267(7):2013–21. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8514418","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":582049419,"identity":"1b363137-f463-4adf-a480-d058cf563144","order_by":0,"name":"Dong Liu","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Dong","middleName":"","lastName":"Liu","suffix":""},{"id":582049421,"identity":"d5922e7e-1062-41df-8618-a231d5690471","order_by":1,"name":"Xiaojun Qin","email":"","orcid":"","institution":"Deyang Second People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaojun","middleName":"","lastName":"Qin","suffix":""},{"id":582049422,"identity":"509aa80e-25ad-491e-a4b2-7424e59f6747","order_by":2,"name":"Yanan Wang","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Yanan","middleName":"","lastName":"Wang","suffix":""},{"id":582049424,"identity":"f0892970-3bf4-404c-88b1-64c112af9a1a","order_by":3,"name":"Jie Li","email":"","orcid":"","institution":"Deyang People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Li","suffix":""},{"id":582049428,"identity":"e1d131d5-59fe-44fa-becb-eb06cb2de3f6","order_by":4,"name":"Junfeng Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYBACxgYGhg9AWoaNgfng4x8VQCY7RBSfFsYZQJqHjYEt2ZjhDJDFTEALSBdYCxCZSTO2EaGFeUbuwYaPO2p5+CQSDKQL59kk7mdmPvhwBoOdnC4OfYwz8hIbZ545zsMmkZBgPHNbWmIPM1uy4QaGZGOzA7i05Jg/5m07BtJyIIF322GgFh4zyQcMBxK34dZi2PwXrCWx4QDvHGK1MLbVALUkMzbzNkC1bMCnpeeNYWNv2wEeNp5nzIwzjqUZ9xwG+mWGAW6/GLbnGDb8bKuTk2/P//7jQ42NbHt788GHPRV2cji1NICpw+jiBtiVg4A8hKrDrWIUjIJRMApGAQCfrlzu9u1MXgAAAABJRU5ErkJggg==","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Junfeng","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2026-01-04 16:23:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8514418/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8514418/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109459325,"identity":"6435df5b-78c0-4675-b455-62aaf5f766f9","added_by":"auto","created_at":"2026-05-18 10:41:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":264695,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8514418/v1/29ab6643-c67e-46ef-9308-859f7ca8d622.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic Accuracy of Five Collateral Circulation Scores derived from computed tomography angiography for Predicting 3-Month Outcomes in Acute Ischemic Stroke","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe compensatory collateral circulation determines the ischemic penumbra and infarct volume when the cerebral blood supply artery is severely narrowed or occluded. Good collateral circulation can reduce the volume of cerebral infarction, improve the prognosis of patients, and effectively reduce the recurrence of cerebral infarction\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Therefore, rapid and accurate assessment of collateral circulation is of great significance for clinical decision-making and predicting the prognosis of stroke. Digital Subtraction Angiography (DSA) is the golden standard for assessing collateral circulation in ischemic stroke, but its clinical application is limited due to its invasiveness and high cost\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Previous study has found that collateral circulation assessment based on CT angiography (CTA) original images was consistent with DSA\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. As a non-invasive examination method, CTA can dynamically display the anatomical structure of intracranial and extracranial vessels from different angles, so as to more intuitively evaluate the degree of vascular stenosis and collateral blood flow\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough multiple scoring scores for collateral circulation using CTA have been proposed, a consensus on the optimal system has not been established as studies on different scores have yielded inconsistent results\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. To identify the optimal collateral circulation scoring system for predicting prognosis in patients with acute ischemic stroke(AIS), this study will evaluate and compare five CTA-based collateral circulation scores on admission including lateral fissure, convex collateral score (Maas score)\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e, middle cerebral artery regional collateral score (Tan score)\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e, regional leptomeningeal collateral circulation score (rLMCs)\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, middle cerebral artery occlusion distal collateral score (Miteff score)\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, and the improved Alberta stroke program early CT Score(ASPECTS)based on multiphase CTA)\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\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.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective analysis of patients with AIS who were admitted to the Department of Neurology, West China Hospital, Sichuan University between January 2016 and June 2018. Patients were included if they were admitted within 24 hours of stroke onset and underwent head and neck CTA within 24 hours following admission. The above five collateral circulation scores based on CTA were evaluated on admission, and they were divided into poor and good collateral circulation group. Patients were excluded for the following reasons: poor image quality that hampered reader evaluation, major organ dysfunction, or refusal to participate/loss to follow-up.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eData on demographic characteristics (age, gender), medical history, the initial National Institutes of Health Stroke Scale (NIHSS) score, smoking and alcohol consumption were collected. We also recorded the Trial of Org 10172 in Acute Stroke Treatment (TOAST) classification and the degree of arterial stenosis\u003csup\u003e[\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. For intracranial vascular lesions: arterial stenosis rate (%) = (1 - Diameter of the most severely stenotic segment / Diameter of the normal proximal segment) \u0026times; 100%; For extracranial vascular lesions: arterial stenosis rate (%) = (1 - Diameter of the most severely stenotic segment / Diameter of the normal distal segment) \u0026times; 100%. Patients were diagnosed with large artery stenosis/occlusion, defined as stenosis\u0026thinsp;\u0026gt;\u0026thinsp;30%. Treatments given to patients in hospitalization including antiplatelets, anticoagulation, reperfusion therapy(thrombolysis/thrombectomy) and lipid-lowering therapy were recorded.\u003c/p\u003e\n\u003ch3\u003eCTA Protocol and Image Analysis\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eCTA Protocol and Image Analysis\u003c/div\u003e \u003cp\u003eAll patients underwent head and neck computed tomography angiography (CTA) within 24 hours of admission. Scans were performed using a helical mode with the following parameters: 100 kVp, 86 mAs, and a slice thickness of 0.625 mm at 0.625 mm intervals. A bolus of 100 mL of non-ionic iodinated contrast medium was administered intravenously at a rate of 5 mL/s prior to imaging.\u003c/p\u003e\n\u003ch3\u003eAssessment of Collateral Circulation\u003c/h3\u003e\n\u003cp\u003eCollateral circulation was assessed using five established CTA-based scoring systems: the Maas, Tan, rLMCs, Miteff, and improved ASPECTS scores\u003csup\u003e[\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Based on these scores, patients were stratified into two groups:(1)Poor collateral circulation, defined as a Maas score of 1\u0026ndash;2, Tan score of 0\u0026ndash;1, rLMCs score of 0\u0026ndash;10, Miteff score of 1\u0026ndash;2, or an improved ASPECTS of 0\u0026ndash;3;(2)Good collateral circulation, defined as a Maas score of 3\u0026ndash;5, Tan score of 2\u0026ndash;3, rLMCs score of 11\u0026ndash;20, Miteff score of 3, or an improved ASPECTS of 4\u0026ndash;5.\u003c/p\u003e\n\u003ch3\u003eOutcome measure\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was a poor functional outcome at 3 months, defined as a modified Rankin Scale (mRS) score of 3 to 6, which was assessed via telephone interview\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analyses\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using SPSS 24.0 (IBM Corp., USA). Categorical variables were compared using the chi-square test, normally distributed continuous variables with the independent-samples t-test, and non-normally distributed continuous variables with the Mann-Whitney U test. Variables associated with the 3-month outcome in univariate analysis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were entered into a multivariate logistic regression model to identify independent predictors. The discriminatory power of the collateral scores was evaluated and compared by receiver operating characteristic (ROC) curve analysis, with the area under the curve (AUC) quantifying performance. A two-sided P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 defined statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eOf the 573 patients with ischemic stroke initially screened, 515 patients were finally included in the analysis. The baseline characteristics of the study participants were summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean age of the included patients was 67.1\u0026thinsp;\u0026plusmn;\u0026thinsp;14.3 years and 59.2% (305/515 cases) were male. Large artery stenosis /occlusion was present in 70.1% (361/515 cases)of the patients and 19.2% (99/515 cases) received reperfusion treatment. Subsequently, at the 3-month follow-up, 252 patients (48.9%) had a poor functional outcome.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics and Univariate Analysis of Outcome Metrics in included Patients with Acute Ischemic Stroke\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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=\"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 \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\u003eAll patients n\u0026thinsp;=\u0026thinsp;515\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoor prognosis at 3 months n\u0026thinsp;=\u0026thinsp;252\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGood prognosis at 3 months\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;263\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.1\u0026thinsp;\u0026plusmn;\u0026thinsp;14.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.0\u0026thinsp;\u0026plusmn;\u0026thinsp;13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\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\u003e305(59.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130(51.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e175(66.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious medication history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre hospital antiplatelet(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47(9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27(10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20(7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre hospital lipid-lowering(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28(5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13(4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre hospital anticoagulation(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30(5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12(4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline NIHSS score, median (interquartile interval)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(3\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.5(6\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(2\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \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\u003e280(54.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142(56.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138(52.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96(18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52(20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44(16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16(3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83(16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53(21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30(11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyocardial infarction(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.363\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValvular heart disease(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33(6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18(6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary heart disease(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46(8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25(9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21(8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious stroke(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89(17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45(17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44(16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIschemic(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84(16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43(17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41(15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemorrhagic(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.686\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\u003e200(38.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88(34.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112(42.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of drinking(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e141(27.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58(23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83(31.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure, mmHg (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e148.2\u0026thinsp;\u0026plusmn;\u0026thinsp;24.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e147.1\u0026thinsp;\u0026plusmn;\u0026thinsp;26.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e149.3\u0026thinsp;\u0026plusmn;\u0026thinsp;22.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic pressure, mmHg (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.6\u0026thinsp;\u0026plusmn;\u0026thinsp;16.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.6\u0026thinsp;\u0026plusmn;\u0026thinsp;18.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.6\u0026thinsp;\u0026plusmn;\u0026thinsp;14.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn-hospital treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntiplatelet(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e477(92.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e230(91.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e247(93.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThrombolysis(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72(14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42(16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnticoagulation(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88(17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41(16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47(17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipid-lowering(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e447(86.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e218(86.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e229(87.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndovascular intervention(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33(6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15(5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReperfusion therapy(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99(19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44(17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55(20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDSA(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54(10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34(12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTOAST classification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtherosclerotic type(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e148(28.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76(30.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72(27.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArteriolar occlusive type(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105(20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75(28.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardioembolic type(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150(29.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90(35.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60(22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther definite etiological types(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUndetermined type(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100(19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50(19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50(19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaas score, 1\u0026ndash;2 points(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e191(37.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131(52.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60(22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTan score, 0\u0026ndash;1 points(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110(21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90(35.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20(7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erLMC score, 0\u0026ndash;10 points(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101(19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83(32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18(6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiteff score, 1\u0026ndash;2 points(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e185(35.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128(50.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57(21.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASPECTS scale, 0\u0026ndash;3 points(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e231(44.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136(54.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95(36.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: SD, Standard Deviation; NIHSS, National Institutes of Health Stroke Scale; DSA, Digital Subtraction Angiography; TOAST, Trial of Org 10172 in Acute Stroke treatment. Data are presented as n (%), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), or median (interquartile range [IQR]). P values were calculated using the χ\u0026sup2; test. P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 are represented in bold. Abbreviations: rLMCs, regional leptomeningeal collateral circulation score; ASPECTS, Alberta Stroke Program Early CT Score.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePatients with poor functional outcomes were significantly older (70.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.2 vs. 64.0\u0026thinsp;\u0026plusmn;\u0026thinsp;13.9 years) and had a higher median NIHSS score on admission (11.5 [IQR: 6\u0026ndash;18] vs. 4 [IQR: 2\u0026ndash;8]). They also had a higher prevalence of atrial fibrillation (21.0% vs. 11.4%). Patients in the good outcome group were more likely to be male (66.5% vs. 51.6%) and have a history of alcohol use (31.6% vs. 23.0%).(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between five CTA collateral circulation scores and prognosis of acute ischemic stroke\u003c/h2\u003e \u003cp\u003eAll five collateral scores were significantly associated with 3-month poor outcomes in the univariate analysis (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). After adjustment confounders, the Maas (OR 1.681, 95% CI 1.039\u0026ndash;2.720; P\u0026thinsp;=\u0026thinsp;0.034), Tan (OR 3.205, 95% CI 1.753\u0026ndash;5.858; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), rLMC (OR 2.945, 95% CI 1.573\u0026ndash;5.513; P\u0026thinsp;=\u0026thinsp;0.001), and Miteff (OR 1.748, 95% CI 1.082\u0026ndash;2.826; P\u0026thinsp;=\u0026thinsp;0.023) scores were independently associated with 3-month poor outcomes, except the ASPECTS score (OR 0.991, 95% CI 0.639\u0026ndash;1.537; P\u0026thinsp;=\u0026thinsp;0.966;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\u003eMultivariate logistic regression analysis between CTA based five collateral circulation scores and 3-month poor prognosis\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=\"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 \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\u003eOR value*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaas score 3\u0026ndash;5 points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.039\u0026ndash;2.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTan score 2\u0026ndash;3 points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.753\u0026ndash;5.858\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\u003erLMC score 11\u0026ndash;20 points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.573\u0026ndash;5.513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiteff score 3 points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.082\u0026ndash;2.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASPECTS score 4\u0026ndash;5 points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.639\u0026ndash;1.537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e* Adjustment for age, gender, atrial fibrillation, alcohol consumption, TOAST classification, and baseline NIHSS score. Abbreviations: OR, Odds 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 \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eComparative analysis of Five CTA-Based Collateral Scores for Predicting Prognosis in Acute Ischemic Stroke\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe rLMC score demonstrated the highest AUC for predicting poor functional outcomes (0.677), followed by the Tan score (0.669), Maas score (0.668), Miteff score (0.666), and ASPECTS score (AUC\u0026thinsp;=\u0026thinsp;0.660) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Similar results were found in sensitivity analysis after only including patients with large artery stenosis/occlusion or patients without reperfusion treatment. A total of 361 patients with large artery stenosis/occlusion and 416 patients without reperfusion therapy were analyzed respectively. The rLMC score still showed the highest predictive value for 3-month poor functional outcome in both the stenosis/occlusion group (AUC 0.681, 95% CI 0.626\u0026ndash;0.735, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the non-reperfusion group (AUC 0.702, 95% CI 0.652\u0026ndash;0.753, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\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\u003eResults of ROC Curve Analysis for 3-Month poor Prognosis in Acute Ischemic Stroke Based on Five Collateral Circulation Scores from CTA\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erLMC score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.630\u0026ndash;0.723\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\u003eMass score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.621\u0026ndash;0.714\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\u003eTan score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.622\u0026ndash;0.716\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\u003eMiteff score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.619\u0026ndash;0.713\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\u003eASPECTS scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.613\u0026ndash;0.706\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 \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: AUC, Area under the Curve; CI, Confidence Interval. rLMC score, regional leptomeningeal collateral circulation score; Mass score, Lateral sulcus and cerebral convexity collateral circulation score; Tan score, MCA territory collateral score; Miteff score, Collateral Circulation Scoring in Distal Middle Cerebral Artery Occlusion; ASPECTS scale, Alberta Stroke Program Early CT Score scale.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study compared five CTA-based collateral scores for predicting 3-month functional outcomes in AIS, and the rLMC score achieved the highest predictive accuracy. The results remained when we only included large artery stenosis/occlusion or patients without reperfusion therapy.\u003c/p\u003e \u003cp\u003eCollateral circulation compensation is important in the pathophysiology of cerebral ischemia, as it helps salvage the ischemic penumbra and reduces the final infarct volume\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Despite its acknowledged role in predicting prognosis and therapeutic decisions, assessing collateral status remains challenging due to methodological complexities. Our study confirms that collateral scores derived from CTA provide valuable prognostic information in AIS. Moreover, the rLMC score demonstrated highest performance for prediction prognosis at 3 months\u0026mdash;a finding consistent with other studies. For instance, in a retrospective analysis of 98 AIS patients with large vessel occlusion, Bao et al. \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e reported that the rLMC score showed higher inter-rater reliability, validity compared to DSA-based American Society of Interventional and Therapeutic Neuroradiology༏Society of Interventional Radiology(ASITN/SIR) grading. In addition, its predictive value for prognosis after thrombectomy was assessed and was found to outperform the Miteff, Maas, and Tan scores. Similarly, Li et al. \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e reported that the rLMC score correlated significantly with CT perfusion (CTP) parameters in a cohort of 79 patients with AIS, further validating its physiological relevance.\u003c/p\u003e \u003cp\u003eNevertheless, several studies have reported inconsistent findings. In a cohort of 130 patients with AIS, Havenon et al. \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e demonstrated that a favorable collateral status on single-phase CTA was not an independent predictor of functional outcome, a finding they attributed to non-standardized CTA acquisition parameters and an insufficiently sensitive collateral assessment method. According to the retrospective analysis by Conrad et al. \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e of 686 patients with suspected AIS, CTA-based collateral status showed no significant prognostic value. The discrepancy between these findings and our own may be explained by the inclusion of patients with a suspected stroke rather than confirmed stroke, which introduces diagnostic heterogeneity and potentially compromises the accuracy of collateral grading. Despite these contrasting reports, the rLMC score remains a valuable tool in settings where DSA is not readily available, offering a reliable and non-invasive alternative for collateral assessment\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, its retrospective design and single-center recruitment may introduce selection bias and limit the generalizability of the findings. Second, the collateral circulation assessment was static rather than dynamic, which may have affected the accuracy of the results. Lastly, direct comparison with DSA, the golden standard for collateral circulation evaluation, was not available as only a small subset of patients (n\u0026thinsp;=\u0026thinsp;54) underwent this procedure.Future multi-center, large-sample, prospective studies are needed to validate these findings and further explore the role of the rLMC score in clinical practice.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn the study, the rLMC score showed the best predictive value for 3-month functional outcomes in patients with AIS. Similar results were also found only included patients with large artery stenosis/embolism or those without reperfusion therapy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003ch2\u003eConflict of interest statement\u003c/h2\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest associated with this study. No financial or personal relationships have influenced the design, execution, or reporting of the research.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study was supported by the Noncommunicable Chronic Diseases-National Science and Technology Major Project ( 2024ZD0527700) and National Natural Science Foundation of China (82371323).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eJunfeng Liu and Jie Li designed the study. Dong Liu and Yanan Wang collected data. Dong Liu and Junfeng Liu writed the manuscript. Junfeng Liu, Jie Li and Xiaojun Qin critically revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eAnonymized datasets are available upon reasonable request to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBang OY, Goyal M, Liebeskind DS. Collateral Circulation in Ischemic Stroke: Assessment Tools and Therapeutic Strategies. Stroke. 2015;46(11):3302\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu L, Ding J, Leng X, et al. Guidelines for evaluation and management of cerebral collateral circulation in ischaemic stroke 2017. Stroke Vasc Neurol. 2018;3(3):117\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe H, Qian ZM, Sheng Y, Liu Y. Assessment of Cerebral Collateral Flow With Single-Phase Computed Tomography Angiography-Based Multimodal Scales in Patients With Acute Ischemic Stroke. J Comput Assist Tomogr. 2020;44(5):708\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLima F, Furie et al. O-004 The pattern of leptomeningeal collaterals on CT angiography is a strong predictor of good functional outcome in stroke patients with intracranial large vessel occlusion. J NeuroInterventional Surg. 2010.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChatterjee D, Nagarajan K, Narayan SK, Narasimhan RL. Regional leptomeningeal collateral score by computed tomographic angiography correlates with 3-month clinical outcome in acute ischemic stroke. Brain Circ. 2020;6(2):107\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaydemir R, Ayka\u0026ccedil; \u0026Ouml;, Acar BA, et al. Role of modified TAN score in predicting prognosis in patients with acute ischemic stroke undergoing endovascular therapy. Clin Neurol Neurosurg. 2021;210:106978.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShafie M, Yu W. Recanalization Therapy for Acute Ischemic Stroke with Large Vessel Occlusion: Where We Are and What Comes Next. Transl Stroke Res. 2021;12(3):369\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeker F, Pereira-Zimmermann B, Pfaff J, et al. Collateral Scores in Acute Ischemic Stroke: A retrospective study assessing the suitability of collateral scores as standalone predictors of clinical outcome. Clin Neuroradiol. 2020;30(4):789\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaas MB, Lev MH, Ay H, et al. Collateral vessels on CT angiography predict outcome in acute ischemic stroke. Stroke. 2009;40(9):3001\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan IY, Demchuk AM, Hopyan J, et al. CT angiography clot burden score and collateral score: correlation with clinical and radiologic outcomes in acute middle cerebral artery infarct. AJNR Am J Neuroradiol. 2009;30(3):525\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChinese Stroke Association, Branch of Cerebral Blood Flow and Metabolism. Chinese Guidelines for Assessment and Intervention of Cerebral Collateral Circulation in Ischemic Stroke. (2017). Chinese Journal of Internal Medicine. 2017; 56(6):460\u0026ndash;471. (in Chinese).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMenon BK, Smith EE, Modi J et al. Regional leptomeningeal score on CT angiography predicts clinical and imaging outcomes in patients with acute anterior circulation occlusions. AJNR Am J Neuroradiol. 2011. 32(9).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiteff F, Levi CR, Bateman GA, Spratt N, McElduff P, Parsons MW. The independent predictive utility of computed tomography angiographic collateral status in acute ischaemic stroke. Brain. 2009;132(Pt 8):2231\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMenon BK, d'Esterre CD, Qazi EM, et al. Multiphase CT Angiography: A New Tool for the Imaging Triage of Patients with Acute Ischemic Stroke. Radiology. 2015;275(2):510\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu Xiang Z, Xiaoying L. Bin. Research progress of collateral establishment in acute ischemic stroke based on different CTA scales. Int J Med Radiol 2020. 43(5): 550\u0026ndash;4. (in Chinese).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie Jian. The relationship between collateral circulation of CTA and the outcome of reperfusion therapy in patients with acute ischemic stroke. Nanjing: Southeast University; 2019. (in Chinese).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHao Junwei L, Benyan, et al. Neurology. 9th ed. Beijing: People's Medical Publishing House; 2024. pp. 166\u0026ndash;217. (in Chinese).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLan Y, Shang J, Ma Y, et al. A new predictor of coronary artery disease in acute ischemic stroke or transient ischemic attack patients: pericarotid fat density. Eur Radiol. 2024;34(3):1667\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdams HP, Bendixen BH, Kappelle LJ, 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. Stroke. 1993;24(1):35\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJanssen PM, Visser NA, Dorhout Mees SM, Klijn CJ, Algra A, Rinkel GJ. Comparison of telephone and face-to-face assessment of the modified Rankin Scale. Cerebrovasc Dis. 2010;29(2):137\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuehlen I, Kloska SP, G\u0026ouml;litz P, et al. Noninvasive Collateral Flow Velocity Imaging in Acute Ischemic Stroke: Intraindividual Comparison of 4D-CT Angiography with Digital Subtraction Angiography. Rofo. 2019;191(9):827\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiebeskind DS, Cotsonis GA, Saver JL, et al. Collaterals dramatically alter stroke risk in intracranial atherosclerosis. Ann Neurol. 2011;69(6):963\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBao HT, Li ZQ. Study on the Application Value of Collateral Circulation Scoring Based on CTA in Acute Ischemic Stroke with Large Vessel Occlusion. Zhejiang Med J. 2024;46(10):1047\u0026ndash;55. (in Chinese).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi QY. Comparative Study on Different Evaluation Methods of Cerebral Collateral Circulation Based on CTA. Imaging Med Nuclear Med. 2022. (in Chinese).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Havenon A, Mlynash M, Kim-Tenser MA, et al. Results From DEFUSE 3: Good Collaterals Are Associated With Reduced Ischemic Core Growth but Not Neurologic Outcome. Stroke. 2019;50(3):632\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConrad J, Ertl M, Oltmanns MH, Zu Eulenburg P. Prediction contribution of the cranial collateral circulation to the clinical and radiological outcome of ischemic stroke. J Neurol. 2020;267(7):2013\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Acute ischemic stroke, Computed tomography angiography, Collateral circulation score, Prognosis, Predictive value","lastPublishedDoi":"10.21203/rs.3.rs-8514418/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8514418/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThis study aims to explore the optimal collateral circulation score for predicting 3-month function outcomes in patients with acute ischemic stroke.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe included patients with acute ischemic stroke. Five collateral circulation scores derived from computed tomography angiography(CTA) including the Maas score, Tan score, rLMC score, Miteff score, and ASPECTS score were assessed. The outcome measure was poor functional outcomes at 3 months, defined as a modified Rankin Scale (mRS) score of 3\u0026ndash;6. Receiver operating characteristic (ROC) curve analysis was used to evaluate the prognostic performance of each score. Sensitivity analyses were conducted by only including patients with large artery stenosis (stenosis\u0026thinsp;\u0026gt;\u0026thinsp;30%) or those who did not receive reperfusion therapy.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 515 patients with acute ischemic stroke within 24 hours of onset were included, with a mean age of 67.1\u0026thinsp;\u0026plusmn;\u0026thinsp;14.3 years and 59.2% were male. The proportion of patients received reperfusion therapy was 19.2%(99/515 cases). 252 patients had poor functional outcomes after 3-month follow-up. The rLMC score demonstrated the highest area under the curve (AUC) for predicting poor functional outcomes (AUC\u0026thinsp;=\u0026thinsp;0.677), followed by the Tan score (AUC\u0026thinsp;=\u0026thinsp;0.669), Maas score (AUC\u0026thinsp;=\u0026thinsp;0.668), Miteff score (AUC\u0026thinsp;=\u0026thinsp;0.666), and ASPECTS score (AUC\u0026thinsp;=\u0026thinsp;0.660). Similar results were found in sensitivity analysis.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe rLMC score shows the highest prognostic accuracy for predicting 3-month poor outcomes in patients with acute ischemic stroke. The results remained when only included patients with large artery stenosis/occlusion or patients without reperfusion treatment.\u003c/p\u003e","manuscriptTitle":"Prognostic Accuracy of Five Collateral Circulation Scores derived from computed tomography angiography for Predicting 3-Month Outcomes in Acute Ischemic Stroke","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-30 21:00:29","doi":"10.21203/rs.3.rs-8514418/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":"4886579d-5e7b-4444-a0f4-898b052f0226","owner":[],"postedDate":"January 30th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-18T10:36:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T20:14:40+00:00","index":41,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T10:41:02+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-30 21:00:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8514418","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8514418","identity":"rs-8514418","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}