scholarly journals Disparities in the Outcomes Following Ischemic Stroke Between the Floating Population and Indigenous Population of Shanghai

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiaochuan Liu ◽  
Qian Sun ◽  
Sichen Yao ◽  
Junhui Zhang ◽  
Huanyin Li
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Logistic Regression Analysis ◽  
Endovascular Therapy ◽  
Indigenous Population ◽  
Arrival Rate ◽  
Floating Population ◽  
Indigenous Populations

Background and Purposes: Through this study, we hope to gain more insights into the differences in outcome following an ischemic stroke between the floating population and the indigenous population of Shanghai.Method: In this retrospective cohort study, we analyzed patients with first-ever acute ischemic stroke who were admitted to a comprehensive stroke center in the Minhang district, Shanghai, from January 1, 2019, to December 31, 2020. All patient's demographic data and medical histories were prospectively collected and they were followed up for at least 3 months. The Indigenous population of Shanghai was defined as patients with an identification number starting with 310. All others were treated as floating population. The primary outcome was defined as an unfavorable prognosis at 3 months, with a modified Rankin Scale (mRS) score above 1. Secondary outcomes included the use of emergency medical service (EMS), 3 h arrival rate, and endovascular therapy in eligible patients. Logistic regression analysis was applied to investigate the differences.Results: Finally, 698 patients with first-ever acute ischemic stroke were included (with mean age of 65.32 years, 74.6% men). Of these, 302 patients belonged to the floating population group. Indigenous populations with ischemic stroke were older than the floating population (68.26 years vs. 61.47 years, P < 0.001). The floating population was more likely to achieve favorable outcomes at 3 months compared with the indigenous population in multivariable logistic regression analysis [Odds ratio (OR): 0.49, 95% CI: 0.32–0.75, P = 0.001]. The use of EMS, 3 h arrival rate, and the application of endovascular therapy were comparable between the floating population and indigenous population (OR: 0.89, 95% CI: 0.62–1.27, P = 0.519; OR: 0.78, 95% CI: 0.56–1.09, P = 0.14; and OR: 0.82, 95% CI: 0.54–1.26, P = 0.365, respectively).Conclusion: Compared with the indigenous population, the floating population with the first-ever ischemic stroke was more likely to have a favorable outcome at 3 months.

Abstract TP21: Is Presence of Diabetes Mellitus a Key Prognostic Factor among Patients Receiving Endovascular Treatment for Acute Ischemic Stroke?

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Seong-Joon Lee ◽  
Yang-Ha Hwang ◽  
Ji Man Hong ◽  
Jin Wook Choi ◽  
Dong-Hun Kang ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Logistic Regression Analysis ◽  
Multiple Logistic Regression Analysis ◽  
Univariate Analysis ◽  
Intravenous Thrombolysis ◽  
The Impact

Introduction: Given the recent positive endovascular therapy trials for acute ischemic stroke (AIS), this therapeutic strategy is now being increasingly incorporated into routine clinical practice. Identifying prognostic factors among AIS patients receiving endovascular revascularization treatments (ERT) in the real world could be important for clinicians and patients. While the impact of diabetes mellitus (DM) on IV thrombolytic outcomes after AIS has been extensively investigated, there is a paucity of data assessing effects of DM on ERT outcomes after AIS. We evaluated the impact of comorbid DM on ERT for AIS. Methods: From Jan 2011 to Feb 2016, patients with AIS who underwent ERT for cervicocephalic occlusions were consecutively enrolled into the Acute Stroke due to Intracranial Atherosclerotic occlusion and Neurointervention - Korean Retrospective (ASIAN KR) registry from 3 hospitals. Patients were excluded if onset to puncture time over 8 hours, in-hospital stroke, or unavailable 3-month mRS. DM was diagnosed if a patient had the history, or hemoglobin A1c on admission was over 6.5. Univariate analysis was performed to compare the characteristics between DM and non-DM population. Multiple logistic regression analysis was used to validate the effect of comorbid DM on 3 month outcomes. Results: Of 721 patients, 667 (93%) were finally included, with 233 DM patients and 434 non-DM patients. In the univariate analysis, comorbidity with hypertension (71.2% vs. 58.3%, p=0.001) and dyslipidemia (36.7% vs. 26.7%, p=0.012) were more frequent in the DM population. Periprocedural factors such as target vessels, intravenous thrombolysis, and final reperfusion grades did not differ. Good outcomes with mRS 0-2 were less frequent in the DM population (43.3% vs. 53.7%, p=0.011). In the logistic regression analysis adjusting age, male sex, initial NIHSS, premorbid mRS, hypertension history, atrial fibrillation, intravenous thrombolysis, onset to puncture time and successful reperfusion, DM was an independent predictor of poor outcomes (mRS 3-6; 1.933, 1.274-2.933, p=0.002). Conclusion: In patients receiving ERT for AIS due to cervicocephalic artery occlusions, the presence of DM as a comorbidity confers greater odds of a poor functional outcome.

scholarly journals Delayed Stroke Treatment during COVID-19 Pandemic in China

2020 ◽  
Author(s):  
Shiyuan Gu ◽  
Zhengze Dai ◽  
Huachao Shen ◽  
Yongjie Bai ◽  
Xiaohao Zhang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Ischemic Stroke ◽  
Acute Stroke ◽  
Acute Ischemic Stroke ◽  
Logistic Regression Analysis ◽  
Multivariate Logistic Regression Analysis ◽  
Intravenous Thrombolysis ◽  
Multivariate Logistic Regression ◽  
Delayed Treatment

ABSTRACTBackgroundSocial distance, quarantine, pathogen testing and other preventive strategies implemented during COVID-19 pandemic may negatively influence the management of acute stroke.ObjectiveThe current study aimed to evaluate the impacts of COVID-19 pandemic on treatment delay of acute stroke in China.MethodsThis study included patients with acute stroke admitted in two hospitals in Jiangsu, China. Patients admitted before and after the COVID-19 epidemic outbreak (January 31, 2020, as officially announced by Chinese government) were compared for pre- (measured as onset-to-door time) and post-hospital delay (measured as door-to-needle time). The influence factors for delayed treatment (indicated as onset-to-needle time >4.5 hours) were analyzed with multivariate logistic regression analysis.ResultsOnset-to-door time increased from 202 min (IQR 65-492) before to 317 min (IQR 75-790) after the COVID-19 pandemic (P=0.001). Door-to-needle time increased from 50min (IQR 40-75) before to 65 min (IQR 48-84) after the COVID-19 pandemic (P=0.048). The proportion of patients with intravenous thrombolysis in those with acute ischemic stroke was decreased significantly after the pandemic (15.4% vs 20.1%; P=0.030). Multivariate logistic regression analysis indicated that patients after COVID-19 pandemic, lower educational level, rural residency, mild symptoms and transported by other means than ambulance were associated with delayed treatment.ConclusionsCOVID-19 pandemic has remarkable impacts on the management of acute ischemic stroke. Both pre- and post-hospital delays were prolonged significantly, and proportion of patient arrived within the 4.5-hour time window for intravenous thrombolysis treatment was decreased. Given that anti-COVID-19 measures are becoming medical routines, efforts are warranted to shorten the delay so that the outcomes of stroke could be improved.

scholarly journals Serum Albumin Is Negatively Associated with Hemorrhagic Transformation in Acute Ischemic Stroke Patients

2019 ◽  
Vol 47 (1-2) ◽  
pp. 88-94 ◽  
Author(s):  
Changyi Wang ◽  
Linghui Deng ◽  
Shi Qiu ◽  
Haiyang Bian ◽  
Lu Wang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Ischemic Stroke ◽  
Serum Albumin ◽  
Acute Ischemic Stroke ◽  
Logistic Regression Analysis ◽  
Univariate Analysis ◽  
Multivariate Logistic Regression Analysis ◽  
Hemorrhagic Transformation ◽  
Dependent Manner

Background and Objective: Hemorrhagic transformation (HT) is a major complication of acute ischemic stroke (AIS). Serum albumin is known for its neuroprotective effects and is a marker of improved AIS patient outcomes. However, it is not known whether there is a relationship between serum albumin and HT. Methods: AIS patients admitted to the Department of Neurology of West China Hospital from 2012 to 2016 were prospectively and consecutively enrolled. Baseline characteristics were collected. HT during hospitalization was diagnosed by brain imaging. Multivariate logistic regression analysis was performed to determine the relationship between serum albumin and HT. Confounding factors were identified by univariate analysis. Stratified logistic regression analysis was performed to identify effect modifiers. Results: A total of 1996 AIS patients were recruited, of whom 135 (6.8%) developed HT. Serum albumin negatively correlated with HT. Patients in the upper serum albumin tertile (42.6–54.1 g/L) had a 46% lower risk of HT than patients in the lower tertile (19.3–39.1 g/L) after adjustment for potential confounders (OR 0.54, 95% CI 0.29–0.99, p = 0.04). Risk of HT decreased stepwise with higher serum albumin tertile (p for trend = 0.04). There was a significant interaction between serum albumin and age (p = 0.02), with no significant correlation between serum albumin and HT in patients over 60 years of age. Conclusions: Higher serum albumin is associated with lower HT risk in a dose-dependent manner in AIS patients younger than 60 years.

scholarly journals Predictive Value of the Alberta Stroke Program Early CT Score (ASPECTS) in the Outcome of the Acute Ischemic Stroke and Its Correlation with Stroke Subtypes, NIHSS, and Cognitive Impairment

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ahmed Esmael ◽  
Mohammed Elsherief ◽  
Khaled Eltoukhy
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Ischemic Stroke ◽  
Middle Cerebral Artery ◽  
Acute Ischemic Stroke ◽  
Logistic Regression Analysis ◽  
Middle Cerebral Artery Territory ◽  
Spearman Correlation ◽  
Cutoff Value

Objectives. This study is aimed at correlating ASPECTS with mortality and morbidity in patients with acute middle cerebral artery territory infarction and at determining the cutoff value of ASPECTS that may predict the outcome. Methods. 150 patients diagnosed with acute middle cerebral artery territory infarction were involved in this study. Risk factors, initial NIHSS, and GCS were determined. An initial or follow-up noncontrast CT brain was done and assessed by ASPECTS. Outcomes were determined by mRS during the follow-up of cases after 3 months. Correlations of ASPECTS and outcome variables were done by Spearman correlation. Logistic regression analysis and ROC curve were done to detect the cutoff value of ASPECTS that predicts unfavorable outcomes. Results. The most common subtypes of ischemic strokes were lacunar stroke in 66 patients (44%), cardioembolic stroke in 39 patients (26%), and LAA stroke in 30 cases (20%). The cardioembolic stroke had a statistically significant lower ASPECT score than other types of ischemic strokes ( P < 0.05 ). Spearman correlation showed that lower ASPECTS values (worse outcome) were more in older patients and associated with lower initial GCS. ASPECTS values were inversely correlated with initial NIHSS, inpatient stay, inpatient complications, mortality, and mRS. The ASPECTS cutoff value determined for the prediction of unfavorable outcomes was equal to ≤7. The binary logistic regression analysis detected that patients with ASPECTS ≤ 7 were significantly associated with about fourfold increased risk of poor outcomes (OR 3.95, 95% CI 2.09–11.38, and P < 0.01 ). Conclusions. ASPECTS is a valuable and appropriate technique for the evaluation of the prognosis in acute ischemic stroke. Patients with high ASPECTS values are more likely to attain favorable outcomes, and the cutoff value of ASPECTS is a strong predictor for unfavorable outcomes. This trial is registered with ClinicalTrials.gov NCT04235920.

scholarly journals Development and validation of a model to estimate the risk of acute ischemic stroke in geriatric patients with primary hypertension

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xifeng Zheng ◽  
Fang Fang ◽  
Weidong Nong ◽  
Dehui Feng ◽  
Yu Yang
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Ischemic Stroke ◽  
Prediction Model ◽  
Acute Ischemic Stroke ◽  
Logistic Regression Analysis ◽  
Geriatric Patients ◽  
Primary Hypertension ◽  
Multivariable Logistic Regression Analysis ◽  
Training Set

Abstract Objectives This study aimed to construct and validate a prediction model of acute ischemic stroke in geriatric patients with primary hypertension. Methods This retrospective file review collected information on 1367 geriatric patients diagnosed with primary hypertension and with and without acute ischemic stroke between October 2018 and May 2020. The study cohort was randomly divided into a training set and a testing set at a ratio of 70 to 30%. A total of 15 clinical indicators were assessed using the chi-square test and then multivariable logistic regression analysis to develop the prediction model. We employed the area under the curve (AUC) and calibration curves to assess the performance of the model and a nomogram for visualization. Internal verification by bootstrap resampling (1000 times) and external verification with the independent testing set determined the accuracy of the model. Finally, this model was compared with four machine learning algorithms to identify the most effective method for predicting the risk of stroke. Results The prediction model identified six variables (smoking, alcohol abuse, blood pressure management, stroke history, diabetes, and carotid artery stenosis). The AUC was 0.736 in the training set and 0.730 and 0.725 after resampling and in the external verification, respectively. The calibration curve illustrated a close overlap between the predicted and actual diagnosis of stroke in both the training set and testing validation. The multivariable logistic regression analysis and support vector machine with radial basis function kernel were the best models with an AUC of 0.710. Conclusion The prediction model using multiple logistic regression analysis has considerable accuracy and can be visualized in a nomogram, which is convenient for its clinical application.

scholarly journals Early prediction of the 3-month outcome for individual acute ischemic stroke patients who received intravenous thrombolysis using the N2H3 nomogram model

2020 ◽  
Vol 13 ◽  
pp. 175628642095305
Author(s):  
Shan Lv ◽  
Yu Song ◽  
Fu-Liang Zhang ◽  
Xiu-Li Yan ◽  
Jie Chen ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Logistic Regression Analysis ◽  
Density Lipoprotein ◽  
Unfavorable Outcome ◽  
Early Prediction ◽  
Training Cohort ◽  
Nihss Score

Background: The aim of this study was to establish a nomogram model for individualized early prediction of the 3-month prognosis in patients with acute ischemic stroke (AIS) who were treated with intravenous recombinant tissue plasminogen activator (rt-PA) thrombolysis. Methods: A total of 691 patients were included in this study; 564 patients were included in the training cohort, while 127 patients were included in the test cohort. The main outcome measure was a 3-month unfavorable outcome (modified Rankin Scale 3–6). To construct the nomogram model, stepwise logistic regression analysis was applied to select the significant predictors of the outcome. The discriminative performance of the model was assessed by calculating the area under the receiver operating characteristic curve (AUC-ROC). A decision curve analysis was used to evaluate prognostic value of the model. Results: The initial National Institutes of Health Stroke Scale [NIHSS, odds ratio (OR), 1.35; 95% confidence interval (CI), 1.28–1.44; p < 0.001], delta NIHSS (changes in the NIHSS score from baseline to 24 h, OR, 0.75; 95% CI, 0.70–0.79; p < 0.001), hypertension (OR, 2.07; 95% CI, 1.32–3.31; p = 0.002), hyperhomocysteinemia (Hhcy, OR, 2.18; 95% CI, 1.20–4.11; p = 0.013), and the ratio of high-density lipoprotein cholesterol (HDL-C) to low-density lipoprotein cholesterol (LDL-C) (HDL-C/LDL-C, OR, 3.29; 95% CI, 1.00–10.89; p = 0.049) (N2H3) were found to be independent predictors of a 3-month unfavorable outcome from multivariate logistic regression analysis and were incorporated in the N2H3 nomogram model. The AUC-ROC of the training cohort was 0.872 (95% CI, 0.841–0.902), and the AUC-ROC of the test cohort was 0.900 (95% CI, 0.848–0.953). Conclusion: The study presented the N2H3 nomogram model, with initial NIHSS score, delta NIHSS, hypertension, Hhcy, and HDL-C/LDL-C as predictors. It therefore provides an individualized early prediction of the 3-month unfavorable outcome in AIS patients treated with intravenous rt-PA thrombolysis.

Predictors of poor clinical outcome despite complete reperfusion in acute ischemic stroke patients

2020 ◽  
Vol 13 (1) ◽  
pp. 14-18 ◽  
Author(s):  
Noel van Horn ◽  
Helge Kniep ◽  
Hannes Leischner ◽  
Rosalie McDonough ◽  
Milani Deb-Chatterji ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Ischemic Stroke ◽  
Clinical Outcome ◽  
Acute Ischemic Stroke ◽  
Logistic Regression Analysis ◽  
Stroke Severity ◽  
Stroke Patients ◽  
Poor Clinical Outcome ◽  
Poor Outcome

BackgroundIn patients suffering from acute ischemic stroke from large vessel occlusion (LVO), mechanical thrombectomy (MT) often leads to successful reperfusion. Only approximately half of these patients have a favorable clinical outcome. Our aim was to determine the prognostic factors associated with poor clinical outcome following complete reperfusion.MethodsPatients treated with MT for LVO from a prospective single-center stroke registry between July 2015 and April 2019 were screened. Complete reperfusion was defined as Thrombolysis in Cerebral Infarction (TICI) grade 3. A modified Rankin scale at 90 days (mRS90) of 3–6 was defined as ‘poor outcome’. A logistic regression analysis was performed with poor outcome as a dependent variable, and baseline clinical data, comorbidities, stroke severity, collateral status, and treatment information as independent variables.Results123 patients with complete reperfusion (TICI 3) were included in this study. Poor clinical outcome was observed in 67 (54.5%) of these patients. Multivariable logistic regression analysis identified greater age (adjusted OR 1.10, 95% CI 1.04 to 1.17; p=0.001), higher admission National Institutes of Health Stroke Scale (NIHSS) (OR 1.14, 95% CI 1.02 to 1.28; p=0.024), and lower Alberta Stroke Program Early CT Score (ASPECTS) (OR 0.6, 95% CI 0.4 to 0.84; p=0.007) as independent predictors of poor outcome. Poor outcome was independent of collateral score.ConclusionPoor clinical outcome is observed in a large proportion of acute ischemic stroke patients treated with MT, despite complete reperfusion. In this study, futile recanalization was shown to occur independently of collateral status, but was associated with increasing age and stroke severity.

scholarly journals Decreased eGFR Is Associated With Ischemic Stroke in Patients With Dilated Cardiomyopathy

2019 ◽  
Vol 25 ◽  
pp. 107602961986690 ◽  
Author(s):  
Yuqing Deng ◽  
Zhiqing Chen ◽  
Lili Hu ◽  
Zhenyan Xu ◽  
Jinzhu Hu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Ischemic Stroke ◽  
Dilated Cardiomyopathy ◽  
Logistic Regression Analysis ◽  
Multivariate Logistic Regression Analysis ◽  
Analysis Model ◽  
Multivariate Logistic Regression ◽  
Increased Risk

Dilated cardiomyopathy (DCM) is increasingly indicated as a cause of cardioembolic syndrome, in particular, cardioembolic ischemia stroke. However, the potential risk factors for stroke among DCM patients remain under investigated. DCM patients hospitalized from June 2011 to June 2016 were included. The cases were defined as the group of DCM patients with stroke compared with those without stroke. Clinical characteristic data were collected and compared between the two groups including demographic data, complicated diseases, echocardiography index, and laboratory parameters and estimated glomerular filtration rate (eGFR). A multivariate logistic regression analysis model adjusted by sex and age was used to explore the related risk factors for stroke in DCM patients. A total of 779 hospitalized patients with DCM were included. Of these, 55 (7.1%) had experienced a stroke. Significantly lower eGFR levels (68.03 ± 26.22 vs 79.88 ± 24.25 mL/min/1.73 m2, P = .001) and larger left atrial diameters (45.32 ± 7.79 vs 43.25 ± 7.11 mm, P = .04) were found in the group of patients having DCM with stroke compared to those without stroke. When the eGFR was categorized as eGFR >60, 30<eGFR≤ 60 and eGFR ≤ 30, there were more patients with 30<eGFR≤ 60 (30.9% vs 17.7%) and eGFR≤ 30 (9.1% vs 3.3%) in the ischemic stroke group ( P = 0.003). A multivariate logistic regression analysis model adjusted by sex and age showed that 30 <eGFR≤60 (odds ratio [OR]: 2.07, 95% confidence interval [CI]: [1.05-4.07], P = .035) and eGFR≤30 (OR: 4.04, 95% CI: [1.41-11.62], P = .009) were statistically associated with ischemic stroke in patients with DCM. It is concluded that decreased eGFR is significantly associated with an increased risk of ischemic stroke in patients with DCM.

scholarly journals Complications as important predictors of disability in ischemic stroke

2017 ◽  
Vol 36 (3) ◽  
pp. 197
Author(s):  
Rizaldy Taslim Pinzon ◽  
Rosa De Lima Renita Sanyasi
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Ischemic Stroke ◽  
Logistic Regression Analysis ◽  
Predictive Factors ◽  
Recurrent Stroke ◽  
Multiple Logistic Regression Analysis ◽  
Multiple Logistic Regression ◽  
Cross Sectional ◽  
Stroke Registry

Background<br />Stroke is the main cause of disability and death in many countries. The high incidence of disability in stroke survivors requires special attention to determine various predictive factors of disability. This study aimed to identify the various predictive factors of disability in ischemic stroke.<br /><br />Methods<br />This study was a cross sectional study on 4510 ischemic stroke patients. Each patient’s data had been recorded in the electronic stroke registry of Bethesda Hospital. Ischemic stroke diagnosis was confirmed by brain CT scan, which was interpreted by a neurologist and a radiologist. Disability was assessed using the modified Rankin scale. Predictors of disability were assessed. Multiple logistic regression analysis was used to analyse the data. <br /><br />Results<br />The subjects were predominantly males, &gt;60 years of age, and suffered stroke for the first time. The incidence of disability was 31.5% (1420/4510). Multiple logistic regression analysis showed that the presence of complications (OR: 6.43; 95% CI: 4.74-8.73; p&lt;0.001), decreased level of consciousness (OR: 4.82; 95% CI: 3.95-5.90; p &lt;0.001), onset ³3 hours (OR: 1.93; 95% CI: 1.52-2.45; p&lt;0.001), recurrent stroke (OR: 1.63; 95% CI: 1.39-1.90; p&lt;0.001), and age &gt;60 years (OR: 1.55; 95% CI: 1.35-1.79; p&lt;0.001) were independent predictive factors of disability.<br /><br />Conclusion<br />We demonstrated that a substantial proportion of patients with ischemic stroke become disabled. And the presence of complications was the most predictive factor of disability in ischemic stroke.

scholarly journals Total small vessel disease burden and functional outcome in patients with ischemic stroke

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242319
Author(s):  
Wi-Sun Ryu ◽  
Sang-Wuk Jeong ◽  
Dong-Eog Kim
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Ischemic Stroke ◽  
Functional Outcome ◽  
Logistic Regression Analysis ◽  
Small Vessel Disease ◽  
Ordinal Logistic Regression ◽  
Small Vessel ◽  
Vessel Disease ◽  
Ordinal Logistic Regression Analysis

Background Cerebral small vessel disease (SVD) is comprised of lacunes, cerebral microbleeds (CMBs), white matter hyperintensities (WMHs), and enlarged perivascular space (EPVS). We investigated the cumulative effect of SVD on 3-month functional outcome following ischemic stroke using the total SVD score. Methods The total SVD score of 477 acute ischemic stroke patients with adequate brain MRI was analyzed. We used multivariable ordinal logistic regression analysis to investigate the independent impact of total SVD score on ordinal modified Rankin Scale (mRS) score at 3-month after ischemic stroke. Results Mean age was 66±14 years, and 61% were men. The distribution of the total SVD score from 0 to 4 was 27%, 24%, 26%, 16%, and 7%, respectively. The proportion of mRS scores 2 or greater was 16% and 47% in total SVD score 0 and 4, respectively. Multivariable ordinal logistic regression analysis results showed that compared with the total SVD score of 0, total SVD scores of 2, 3, and 4 were independently associated with higher mRS scores with adjusted odds ratios (95% confidence intervals) of 1.68 (1.02–2.76), 2.24 (1.25–4.00), and 2.00 (1.02–4.29). Lacunes, CMBs, WMHs but not EPVS were associated with mRS scores at 3 months. However, the impact of each SVD marker on stroke outcome was smaller than that of the total SVD score. Conclusion We found an independent association between total SVD scores and functional outcome at 3 months following ischemic stroke. The total SVD score may be useful for stratification of patients who are at a high-risk of unfavorable outcomes.

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