Objective　To explore the influencing factors of restenosis risk after cerebral vascular intervention,and establish a warning model based on SMOTE algorithm.Methods　A total of 320 patients with cerebrovascular stenosis admitted to the Department of Neurology of Mianyang Central Hospital from May 2017 to May 2021 were selected.The medical records of all patients were retrospectively analyzed.According to whether restenosis occurred after interventional treatment,the patients were divided into stenosis group and non-stenosis group.Single factor and multi-factor Logistic regression analysis were used to screen the risk factors of restenosis after cerebrovascular intervention and establish the prediction model.At the same time,expand the positive group data based on SMOTE algorithm,build the early warning model of improved data set and compare and verify the prediction efficiency of the model.Results　Smoking history,hyperlipidemia,diabetes mellitus,CRP≥5 mg/L and stent length≥16 mm were independent risk factors for restenosis after interventionotherapy in patients with cerebrovascular diseases (P<0.05).Based on the above risk factors,the AUC of early warning model P-1 and P-2 was established to be 0.872 (95%CI 0.821~0.923) and 0.847 (95%CI0.816~0.879),respectively.There was no significant difference in the efficacy of the two prediction models and their AUC was over 0.75,indicating that the prediction model had high efficacy.Conclusion　Based on the history of smoking,hyperlipidemia,diabetes,CRP,bracket and the length of the original data of sampling algorithm to establish early warning model has higher predictive,medical personnel will enable effective intervention,thus reduce the patients with cerebrovascular disease risk for restenosis after interventional treatment,improve the prognosis of patients,improve the quality of survival.

Objective To investigate the predictive value of neutrophil to lymphocyte ratio (NLR) in peripheral blood for the outcomes in patients with acute intracerebral hemorrhage.Methods Consecutive inpatients with intracerebral hemorrhage diagnosed with the head CT were entolled.The modified Rankin Scale (mRS) was used to evaluate the functional outcomes at 90 d,0-2 wvas defined as good outcome,3-6 were defined as poor outcome,and 6 was defined as death.Univariate analysis was used to compare the demographic characteristics,baseline data,imaging,and laboratory findings between the groups.Multivariate logistic regression analysis was used to determine the independent correlation between NLR and the outcomes,and receiver operating characteristics (ROC) analysis was performed to assess the predictive value of NLR for the outcomes.Results A total of 205 patients with acute intracerebral hemorrhage were enrolled in the study,107 (52.2％) had poor outcome and 57 (27.8％) died.There were significant differences in age (P=0.038),Glasgow Coma Scale (GCS) scores (P=0.001),National Institutes of Health Stroke Scale (NIHSS) scores (P =0.001),neutrophil count (P =0.005),lymphocyte count (P =0.002),NLR (P =0.001),fasting blood glucose (P =0.012),hypersensitivity C-reactive protein (P=0.002),hematoma volume (P =0.005),and proportion of bleeding into the ventricles (P =0.002) between the poor outcome group and the good outcome group.There were significant differences in age (P =0.002),previous stroke (P =0.018),GCS scores (P =0.001),NIHSS scores (P =0.001),neutrophil count (P=0.008),lymphocyte count (P=0.001),NLR (P=0.001),fasting blood glucose (P=0.016),hematoma volume (P=0.001),and proportion of bleeding into ventricle (P=0.002) between the death group and the survival group.Multivariate logistic regression analysis showed that NLR was an independent predictive factor for poor outcome (odds ratio [OR] 2.405,95％ confidence interval [CI] 1.613-3.587;P=0.001) and death (OR 2.268,95％ CI 1.532-3.358;P =0.001) after adjusting for confounders.The ROC curve analysis showed that NLR had a higher predictive value for poor outcome at 90 d (area under the ROC curve 0.703,95％ CI 0.632-0.774;P ＜ 0.001).When the cutoff value was 2.3,the sensitivity and specificity were 61.7％ and 72.4％,respectively.NLR also had a predictive value for death within 90 d (area under the ROC curve 0.706,95％ CI 0.629-0.786;P =0.003).When the cutoff value was 2.2,the sensitivity and specificity were 63.2％ and 72.6％,respectively.Conclusion NLR may have certain predict value for outcomes in patients with acute intracerebral hemorrhage.

Objective:To comprehensively evaluate the correlation between migraine and the risk of hemorrhagic stroke using Meta-analysis.Methods:The published observational studies on migraine and the risk of hemorrhagic stroke in PubMed, EMbase, Cochrane library, Chinese Biomedical Database, China Journal Full-text Database, Wanfang Database and VIP Database were retrieved by computers. The retrieval time limit was from the establishment of the databases to December 31, 2019. Two reviewers independently conducted the literature screening and data extraction, and evaluated the quality according to Newcastle Ottawa scale. Stata SE 12.1 software was used for Meta-analysis.Results:Six case-control studies and 7 cohort studies met the inclusion criteria, all of which were in English. The results of Meta-analysis showed that exposure to migraine increased the risk of hemorrhagic stroke (odds ratio [ OR] 1.47, 95% confidence interval [ CI] 1.23-1.76; P<0.001). Sensitivity analysis showed that the results were robust. Subgroup analysis showed that migraine with aura ( OR 1.38, 95% CI 1.05-1.81; P=0.019), migraine without aura ( OR 1.46, 95% CI 1.19-1.80; P<0.001), male ( OR 2.10, 95% CI 1.72-2.56; P<0.001) and female ( OR 1.53, 95% CI 1.22-1.92; P<0.001) migraine could increase the risk of hemorrhagic stroke. Conclusion:Regardless of the gender of patients and presence or absence of migraine aura, migraine can significantly increase the risk of hemorrhagic stroke.