Objective To investigate the predictive factors of clinical progression and short-term prognosis of cerebral infarction caused by large artery atherosclerosis (LAA). MethodsPatients with acute LAA cerebral infarction who were hospitalized in the Department of Neurology, Lianyungang Hospital of Traditional Chinese Medicine between January 2016 and May 2019 were included. On admission, the patients’ medical history was collected. The degree of neurological deficit was assessed, blood pressure, blood glucose, blood lipids, plasma homocysteine, lipoprotein-associated phospholipase A2 (Lp-PLA2) were measured, and intracranial and extracranial blood vessels related test results were collected. Within 72 hours of onset, the Scandinavian Stroke Scale (SSS) was used to determine whether the patients’ condition progressed. The modified Rankin scale was used to evaluate the short-term prognosis at 30 days of onset. The related factors of clinical progression and short-term prognosis of LAA cerebral infarction were analyzed. Results Finally, 100 patients were included. According to the SSS assessment results within 72 hours of onset, 27 cases were divided into the progression group and 73 cases in the non-progression group. There was no significant difference in gender and age between the two groups (P>0.05). According to the evaluation results of the modified Rankin scale at 30 days of onset, they were divided into 31 cases in the poor prognosis group and 69 cases in the good prognosis group. There was no significant difference in gender and age between the two groups (P>0.05). Logistic regression analysis showed that plasma Lp-PLA2 [odds ratio (OR)=1.013, 95% confidence interval (CI) (1.007, 1.018), P<0.001], SSS score [OR=0.910, 95%CI (0.842, 0.985), P=0.019], and history of hypertension [OR=5.527, 95%CI (1.241, 24.613), P=0.025] were the predictors of disease progression within 72 hours. SSS score [OR=0.849, 95%CI (0.744, 0.930), P<0.001], carotid artery stenosis [OR=9.536, 95%CI (1.395, 65.169), P=0.021] and progressive stroke [OR=8.873, 95%CI (1.937, 40.640), P=0.005] were the predictors of short-term prognosis of LAA cerebral infarction. Conclusions History of hypertension and high levels of plasma Lp-PLA2 are predictors of early progression of cerebral infarction. Carotid artery stenosis and progressive stroke are predictors of adverse outcomes in the acute phase of cerebral infarction. Neurological scores on admission was a predictor for short-term adverse outcomes in the early and acute phases.
Objective To assess the effect of different thrombolytic agents, and different regimens in acute ischaemic stroke. Methods A systematic review of all the relevant randomized controlled trials (RCTs) was performed. RCTs were identified from the Cochrane Stroke Group trials register, Embase (1980 to 1997), handsearching Japanese and Chinese journals, and personal contact with pharmaceutical companies. We included randomised and quasi-randomised trials in patients with confirmed acute ischaemic stroke comparing different doses of a thrombolytic agent, or different thrombolytic agent, or the same agent given by different routes. Results Eight trials involving 1 334 patients were included. Concealment of allocation was generally adequate. All the trials were conducted in Japan. Different doses (of tissue plasminogen activator or urokinase) were compared in six trials. Different agents (tissue plasminogen activator versus urokinase,or tissue-cultured urokinase versus conventional urokinase) were compared in three trials. Few data were available for functional outcomes. A higher dose of thrombolytic therapy was associated with a five-fold increase in fatal intracranial haernorrhages (odds ratio 5.02, 95% confidence interval 1.56 to 16.18). There was a non-significant trend towards more early deaths or clinically significant intracranial haemorrhages in higher dose group. No difference in late deaths or extra-cranial haemorrhages was shown between low and higher doses. However, very few of these events occurred. No difference was shown between the different thrombolytic agents tested. Conclusions There is not enough evidence to conclude whether lower doses of thrombolytic agents might be safer or more effective than higher doses in acute ischaemic stroke. It is not possible to conclude whether one agent might be better than another, or which route of administration might be best.
ObjectiveTo investigate the relationship between the level of homocysteine (HCY) and the overall burden of cerebral small vessel disease (CSVD) in patients with ischemic stroke.MethodsA total of 322 patients with first-ever ischemic stroke admitted to the People’s Hospital of Deyang City between January 2016 and December 2017 were enrolled. The patients’ demographic information, clinical information, and serum HCY concentration were collected after admission. The presence or absence of a CSVD was assessed by MRI and the overall burden score for the CSVD was determined. Multivariate logistic regression analysis was used to assess whether serum HCY level was associated with the overall burden of CSVD.ResultsThe median level of HCY was 13.2 μmol/L (inter-quartile range: 4.3 to 22.6 μmol/L). Univariate analysis showed that the difference of HCY levels among patients with different total CSVD scores was statistically significant (F=6.874, P=0.001); Spearman correlation analyses showed that the HCY level grouped by quartiles was correlated to the number of lacunar infarctions (rs=0.267, P=0.001), Fazekas score of white matter lesions (rs=0.122, P=0.042), and enlarged perivascular space (EPV) score (rs=0.319, P=0.001), but was not correlated to cerebral microhemorrhage (rs=−0.010, P=0.869). After multivariate regression analysis to adjust the effects of other factors, compared with the patients with HCY levels in the lowest quartile group, the patients with HCY levels in the highest quartile group were more likely to develop lacunar infarction [odds ratio (OR)=1.892, 95% confidence interval (CI) (1.012, 2.987)], white matter lesions [OR=1.548, 95%CI (1.018, 1.654)], severe EPV [OR=6.347, 95%CI (3.592, 13.978)], and the increase in the CSVD score [OR=2.981, 95%CI (1.974, 5.398)].ConclusionIn patients with ischemic stroke, elevated HCY levels may be associated with the overall burden of the CSVD.
Objective To explore the risk factors of carotid artery atherosclerotic plaque in ischemic stroke patients. Methods One hundred and forty-eight patients with ischemic stoke were allocated into two groups by ultrasonographic testing (80 with plaque and 68 without plaque). The carotid artery acoustic densitometry (IMT), blood pressure, blood glucose , blood lipid, fibriongen (FIB), c-reactive protein (CRP) were tested. First, single variable analysis was conducted and then multivariate non-condition stepwise logistic model analysis was conducted. Results Carotid IMT, age , total cholesterol (TC), low density lipoprotein (LDL)-CH, FIB, CRP level and the incidence of hypertension and diabetes were significantly higher in ischemic stroke patients with carotid artery plaques than patients without plaques (P≤0.05); Multiple logistic regression analysis showed the most important risk factors of plaques were CRP (OR=3.546, P=0.035) and FIB (OR=1.074, P=0.012) level. Conclusion The main risk factors of carotid atherosclerosis plaque are almost the same as atherosclerosis, such as age , hypertension ,diabetes, hyperlipidemia , high FIB and CRP level and increase in carotid IMT. CRP and FIB may play a crucial role in the development of carotid artery atherosclerosis plaque.
Objective To explore the correlation between interleukin-6 (IL-6) 174G/C polymorphism and ischemic stroke risks. Methods Systematic searches of electronic databases as CBM, CNKI, PubMed, MEDLINE and EMbase were performed. Meta-analysis was conducted by using RevMan 5.1.2 and Stata 11.0 software. The pooled odds ratios (ORs) with 95% confidence intervals (95%CIs) were performed. Publication bias was tested by funnel plot, Egger’s regression test and Begg’s test. Sensitivity analysis was made by repeating the fixed effects model or random effects model Meta-analysis with each of the studies individually removed. Results A total of 11 publications with 12 studies were identified. The results of meta-analyses showed no significant difference was found in the correlation between IL-6 174G/C polymorphism and ischemic stroke risks (for G/C vs. G/G: OR=0.98, 95%CI 0.78 to 1.24; for C/C vs. G/G: OR=0.75, 95%CI 0.38 to 1.50; for dominant inheritance model: OR=0.93, 95%CI 0.68 to 1.28; for recessive inheritance model: OR=0.80, 95%CI 0.45 to 1.42). In the subgroup analyses on ethnicity, no significant correlation was found. But in the subgroup analyses on source of control population, the hospital-based subgroup showed IL-6 174G/C polymorphism was the protective factor of ischemic stroke (for G/C vs. G/G: OR=0.56, 95%CI 0.40 to 0.79; for C/C vs. G/G: OR=0.17, 95%CI 0.11 to 0.27; for dominant inheritance model: OR=0.40, 95%CI 0.29 to 0.55; for recessive inheritance model: OR=0.24, 95%CI 0.16 to 0.37). Conclusion Meta-analysis bly suggests that the correlation between IL-6 174 G/C polymorphism and ischemic stroke is not significantly different.
Objective To investigate the relationship between age-adjusted Charlson comorbidity index (aCCI) and ischemic stroke in patients with ophthalmic artery occlusion (OAO) or retinal artery occlusion (RAO). MethodsA single center retrospective cohort study. Seventy-four patients with OAO or RAO diagnosed by ophthalmology examination in Shenzhen Second People's Hospital from June 2004 to December 2020 were included in the study. The baseline information of patients were collected and aCCI was used to score the patients’ comorbidity. The outcome was ischemic stroke. The median duration of follow-up was 1 796.5 days. According to the maximum likelihood ratio of the two-piecewise COX regression model and the recursive algorithm, the aCCI inflection point value was determined to be 6, and the patients were divided into low aCCI group (＜6 points) and high aCCI group (≥6 points). A Cox regression model was used to quantify the association between baseline aCCI and ischemic stroke. ResultsAmong the 74 patients, 53 were males and 21 were females, with the mean age of (55.22±14.18) (19-84) years. There were 9 patients of OAO and 65 patients of RAO. The aCCI value ranges from 1 to 10 points, with a median of 3 points. There were 63 patients (85.14%, 63/74) in the low aCCI group and 11 patients (14.86%, 11/74) in the high aCCI group. Since 2 patients could not determine the time from baseline to the occurrence of outcome events, 72 patients were included for Cox regression analysis. The results showed that 16 patients (22.22%, 16/72) had ischemic stroke in the future. The baseline aCCI in the low aCCI group was significantly associated with ischemic stroke [hazard ratio (HR)=1.76, 95% confidence interval (CI) 1.21-2.56, P=0.003], and for every 1 point increase in baseline aCCI, the risk of future ischemic stroke increased by 76% on average. The baseline aCCI in the high aCCI group had no significant correlation with the ischemic stroke (HR=0.66, 95%CI 0.33-1.33, P=0.247). ConclusionsaCCI score is an important prognostic information for patients with OAO or RAO. A higher baseline aCCI score predicts a higher risk of ischemic stroke, and the association has a saturation effect.
ObjectivesTo investigate risk factors for unplanned readmission in ischemic stroke patients within 31 days by using random forest algorithm.MethodsThe record of readmission patients with ischemic stroke within 31 days from 24 hospitals in Beijing between between 2015 and 2016 were collected. Patients were divided into two groups according to the occurrence of readmission within 31 days or not. Chi-squared or Mann-Whitney U test was used to select variables into the random forest algorithm. The precision coefficient and the Gini coefficient were used to comprehensively assess the importance of all variables, and select the more important variables and use the margind effect to assess relative risk of different levels.ResultsA total of 3 473 patients were included, among them 960 (27.64%) were readmitted within 31 days after stroke hospitalization. Based on the result of random forest, the most important variables affecting the risk of unplanned readmission within 31 days included the length of hospital stay, age, medical expense payment, rank of hospital, and occupation. When hospitalization was within 1 month, 10-day-hospitalization-stay patients had the lowest risk of rehospitalization; the younger the patients was, the higher the risk of readmission was. For ranks of hospital, patients from tertiary hospital had higher risk than secondary hospital. Furthermore, patients whose medical expenses were paid by free medical service and whose occupations were managers or staffs had higher risk of readmission within 31 days.ConclusionsThe unplanned readmission risk within 31 days of discharged ischemic stroke patients was connected not only with disease, but also with personal social and economic factors. Thus, more attention should be paid to both the medical process and the personal and family factors of stroke patients.
Acute ischemic stroke (AIS) is a clinical syndrome caused by blood supply disorder of brain tissue, which has the characteristics of high incidence rate and high disability rate, and seriously affects the quality of life of patients. Intravenous thrombolysis is currently an important treatment for AIS, with alteplase being the only thrombolytic drug recommended by all guidelines. However, some patients did not experience improvement or even experienced deterioration in their neurological function after undergoing thrombolysis. Therefore, this article reviews the current status of treatment research on promoting neurological function in patients with AIS after intravenous thrombolysis, in order to explore the importance of combined treatment strategies on further promoting neurological function improvement.
Objective The core indicator pool of ischemic stroke (IS) was constructed to provide a basis for the establishment of the core outcome set (COS), so as to improve the consistency of clinical research and evaluation results of traditional Chinese medicine (TCM) treatment for IS. Methods In this study, the mixed methods research (MMR) convergent parallel design was used to carry out qualitative research and quantitative research at the same time, and the two research results were integrated to reach a conclusion. Quantitative research comprehensively collected the multi-source efficacy evaluation indicators of TCM treatment of IS, and carried out descriptive statistical analysis based on frequency theory. Semi-structured interviews were used in the qualitative research, relevant interest groups were selected to understand the evaluation indicators of the IS efficacy of TCM treatment that they were concerned about, and NVivo software was used for in-depth analysis, coding, classification, and extraction of the efficacy indicators. Based on the principle of pillar integration, quantitative and qualitative research results were integrated to construct an element pool of evaluation indicators for the treatment of IS with traditional Chinese medicine. Results A total of 437 standard papers, 71 registered trial protocols, 100 real-world medical data cases and several guideline consensus policy documents were included in the quantitative study, and a total of 314 indicators in the acute phase of IS, 154 indicators in the recovery phase, and 104 indicators in the sequelae phase were extracted. In the qualitative research part, a total of 32 indicators in the acute stage of IS, 34 indicators in the recovery stage and 35 indicators in the sequelae stage were extracted through interviews. Through group discussion and the principle of pillar integration, an element pool of IS indicators was formed, including 279 IS indicators in the acute stage, 142 indicators in the recovery stage and 91 indicators in the sequelae stage. Conclusion Based on the MMR convergent parallel design, the element pool of the characteristic indicators of the therapeutic effect of IS in TCM is constructed to meet the needs, which provides the preliminary work basis for the construction of the core outcome set of IS in the next stage.
Objective To evaluate the predictive effect of three machine learning methods, namely support vector machine (SVM), K-nearest neighbor (KNN) and decision tree, on the daily number of new patients with ischemic stroke in Chengdu. Methods The numbers of daily new ischemic stroke patients from January 1st, 2019 to March 28th, 2021 were extracted from the Third People’s Hospital of Chengdu. The weather and meteorological data and air quality data of Chengdu came from China Weather Network in the same period. Correlation analyses, multinominal logistic regression, and principal component analysis were used to explore the influencing factors for the level of daily number of new ischemic stroke patients in this hospital. Then, using R 4.1.2 software, the data were randomly divided in a ratio of 7∶3 (70% into train set and 30% into validation set), and were respectively used to train and certify the three machine learning methods, SVM, KNN and decision tree, and logistic regression model was used as the benchmark model. F1 score, the area under the receiver operating characteristic curve (AUC) and accuracy of each model were calculated. The data dividing, training and validation were repeated for three times, and the average F1 scores, AUCs and accuracies of the three times were used to compare the prediction effects of the four models. Results According to the accuracies from high to low, the prediction effects of the four models were ranked as SVM (88.9%), logistic regression model (87.5%), decision tree (85.9%), and KNN (85.1%); according to the F1 scores, the models were ranked as SVM (66.9%), KNN (62.7%), decision tree (59.1%), and logistic regression model (57.7%); according to the AUCs, the order from high to low was SVM (88.5%), logistic regression model (87.7%), KNN (84.7%), and decision tree (71.5%). Conclusion The prediction result of SVM is better than the traditional logistic regression model and the other two machine learning models.