This article explores the application and research progress of shared decision-making (SDM) tools in ultra-early vascular recanalization therapy for ischemic stroke, focusing on analyzing the functional characteristics and advantages and disadvantages of various tools. Based on functional goals, SDM tools can be divided into four categories: brief decision aids, risk communication tools, patient information tools, and prognosis assessment tools. These tools can assist patients and doctors in making informed treatment decisions quickly in time-sensitive situations, providing a reference for optimizing stroke revascularization treatment. Additionally, SDM tools can facilitate communication between doctors and patients, enabling patients to better understand the risks and benefits of treatment options, leading to choices more aligned with personal preferences and values. Through an in-depth study of these SDM tools, it is expected to improve the diagnostic and treatment efficiency for stroke patients, reduce decision conflicts, promote collaboration between doctors and patients, and provide new ideas and methods for stroke treatment and management.
ObjectiveTo investigate the technique and efficacy of left atrial appendage (LAA) occlusion during off-pump coronary artery bypass grafting (OPCABG) in elderly patients with coronary artery disease (CAD) and atrial fibrillation (AF).MethodsFrom 2013 to 2018, 84 elderly patients with CAD and AF with reduced left ventricular ejection fraction (LVEF< 50%) underwent OPCABG in our department. There were 54 males and 30 females at age of 70-82 years. They were divided into a left atrial appendage (LAA) occlusion group (n=56) and a non-LAA occlusion group (n=28). Postoperative antithrombotic therapy: the LAA occlusion group was given warfarin + aspirin + clopidogrel “triple antithrombotic therapy” for 3 months after operation, then was changed to aspirin + clopidogrel “dual antiplatelet” for long-term antithrombotic; the non-LAA occlusion group was given warfarin + aspirin + clopidogrel “triple antithrombotic” for long-term antithrombotic after operation. The clinical effectiveness of the two groups was compared.ResultsAll patients underwent the surgery successfully. There were 56 patients in the LAA occlusion group, including 44 patients of LAA exclusion and 12 patients of LAA clip. The time of LAA occlusion was 3 to 8 minutes. There was no injury of graft vessels and anastomotic stoma. Early postoperative death occurred in 2 patients (2.4%). There was no statistical difference between the two groups in postoperative hospital stay (P=0.115). Postoperative LVEF of the two groups significantly improved compared with that before operation (P<0.05). There was no stroke or bleeding in important organs during hospitalization. During follow-up of 1 year, no cerebral infarction occurred in both groups, but the incidence of bleeding related complications in the LAA occlusion group was significantly lower than that in the non-LAA occlusion group (3.6% vs. 18.5%, P=0.036).ConclusionFor elderly patients with CAD and AF with reduced LVEF, LAA occlusion during OPCABG can effectively reduce the risk of stroke and bleeding related complications, and without increasing the risk of surgery.
This paper describes a simulation of microwave brain imaging for the detection of hemorrhagic stroke. Firstly, in the research process, the formula of DebyeⅡwas used to study tissues of brain and blood clot so that microwave frequency band was confirmed for imaging. Then a model with electromagnetic characteristics of brain was built on this basis. In addition, an ultra-wideband (UWB) Vivaldi antenna is designed to use for transmitting and receiving microwave signals of widths 1.7 GHz to 4 GHz. Microwave signals were transmitted and received when the antenna revolved around the brain model. Symmetric position de-noising method was used to eliminate the strong background noise signals, and finally confocal imaging method was applied to get brain imaging. Blood clot was distinguished clearly from result of imaging and position error was less than 1 cm.
Clinically, non-contrastive computed tomography (NCCT) is used to quickly diagnose the type and area of stroke, and the Alberta stroke program early computer tomography score (ASPECTS) is used to guide the next treatment. However, in the early stage of acute ischemic stroke (AIS), it’s difficult to distinguish the mild cerebral infarction on NCCT with the naked eye, and there is no obvious boundary between brain regions, which makes clinical ASPECTS difficult to conduct. The method based on machine learning and deep learning can help physicians quickly and accurately identify cerebral infarction areas, segment brain areas, and operate ASPECTS quantitative scoring, which is of great significance for improving the inconsistency in clinical ASPECTS. This article describes current challenges in the field of AIS ASPECTS, and then summarizes the application of computer-aided technology in ASPECTS from two aspects including machine learning and deep learning. Finally, this article summarizes and prospects the research direction of AIS-assisted assessment, and proposes that the computer-aided system based on multi-modal images is of great value to improve the comprehensiveness and accuracy of AIS assessment, which has the potential to open up a new research field for AIS-assisted assessment.
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.
In order to accurately evaluate the similarity of motions during daily rehabilitation training for stroke patients, this paper proposed a novel quantitative assessment method based on dynamic time warping (DTW) algorithm. Firstly, the raw accelerometer signals were preprocessed to eliminate the noise. Secondly, the similarity between the accelerometer signals and four standard task templates was calculated respectively, and then the motion was recognized based on the similarity measurements. Finally, the corresponding quantitative assessment model was used to compute the result. The clinical experimental results showed that there were significant differences in the shortest path distance (R value) of DTW between different tasks, and the classification accuracy could be up to 91% when the R value was selected as the classification feature. Additionally, with the process of rehabilitation, the R value decreased gradually, which means that the R value can be taken as the assessment index to evaluate the quality of designated tasks for stroke patients. It also indicated that the R value could be applied into the scene of automatic prescription generation and interactive gaming to determine whether it is needed to change the rehabilitation plan or adjust the game difficulty level, so as to implement the individualized rehabilitation services.
ObjectiveTo explore the association between glycosylated hemoglobin level and poor prognosis in acute ischemic stroke (AIS) patients treated with intravenous thrombolysis.MethodsThe AIS patients treated with recombinant tissue-type plasminogen activator who were hospitalized in the Department of Neurology of the First Affiliated Hospital of Henan University of Science and Technology from September to December 2020 were retrospectively included. According to different levels of glycosylated hemoglobin, they were divided into pre-diabetic group (5.7%≤glycated hemoglobin≤6.4%), diabetic group (previously diabetic or glycosylated hemoglobin≥6.5%), and non-diabetic group (glycated hemoglobin <5.7%). The relevant information of the patients was collected, and a telephone follow-up was conducted 90 days after discharge. According to the modified Rankin Scale (mRS) score, the patients were divided into the good prognosis group (mRS score≤2) and the poor prognosis group (mRS score>2). Logistic regression analysis was used to determine the risk factors for the poor prognosis of intravenous thrombolysis in patients with AIS.ResultEventually 101 patients were included, including 44 in the non-diabetic group, 24 in the pre-diabetic group, and 33 in the diabetic group. And 64 patients were in the good prognosis group and 37 patients were in the poor prognosis group. Regression analysis results showed that diabetes was associated with poor prognosis 3 months after intravenous thrombolysis in patients with AIS [odds ratio=6.518, 95% confidence interval (1.568, 27.096), P=0.010]; and the higher the National Institutesof Health Stroke Scale score at admission was, the higher the risk of poor prognosis would be [odds ratio=1.421, 95% confidence interval (1.231, 1.640), P<0.001].ConclusionIn AIS patients who received intravenous thrombolysis, diabetes is associated with poor prognosis after 3 months.
Objective To understand the quality of life of patients with acute mild to moderate ischemic stroke one year after stroke, analyze the factors affecting their quality of life, and provide a scientific basis for improving their health-related quality of life. Methods This study included patients who were diagnosed with acute mild to moderate ischemic stroke between March 2019 and March 2021 in four hospitals in Nanchang. Sociodemographic information and relevant clinical data were collected during hospitalization. The EQ-5D-5L questionnaire was administered to assess health-related quality of life one year after discharge. The Mann-Whitney U test (for two groups) and Kruskal-Wallis one-way ANOVA (for multiple groups) were used to analyze differences in utility scores among various factors. A Tobit regression model was built to investigate the factors influencing quality of life one-year post-stroke. Results A total of 1 181 patients participated in the study, including 791 males (66.98%) and 390 females (33.02%), with an average age of 63.7±10.9 years. Health-related quality of life data collected one year after the stroke revealed that 22.69% of patients experienced pain/discomfort, 17.27% suffered anxiety/depression, 15.66% had mobility issues, 10.33% had difficulties with daily activities, and 8.64% had trouble with self-care. Tobit regression results showed that age (β=−0.263, 95%CI −0.327 to −0.198), gender (β=−0.134, 95%CI −0.189 to −0.080), previous hypertension (β=−0.068, 95%CI −0.120 to −0.016), previous dyslipidemia (β=−0.068, 95%CI −0.126 to −0.011), admission NIHSS score (β=−0.158, 95%CI −0.198 to −0.118), and discharge mRS score (β=−0.193, 95%CI −0.250 to −0.136) were negatively associated with health utility values. Current employment status (β=0.141, 95%CI 0.102 to 0.181) and admission GCS score (β=0.209, 95%CI 0.142 to 0.276) were positively correlated with health utility values. Conclusion One year after an acute mild to moderate ischemic stroke, patients commonly face pain/discomfort and anxiety/depression. Factors affecting overall quality of life include age, sex, current employment status, previous hypertension, previous dyslipidemia, admission NIHSS score, admission GCS score, and discharge mRS score. Clinically, developing scientifically sound and reasonable rehabilitation plans post-discharge is crucial for improving long-term quality of life.
ObjectiveTo review the recent research progress of different types of stem cells in the treatment of ischemic stroke.MethodsBy searching the PubMed database, a systematic review had been carried out for the results of applying different types of stem cells in the treatment of ischemic stroke between 2000 and 2020.ResultsStem cells can be transplanted via intracranial, intravascular, cerebrospinal fluid, and intranasal route in the treatment of ischemic stroke. Paracrine and cell replacement are the two major mechanisms of the therapy. The researches have mainly focused on utilization of neural stem cells, embryonic stem cells, and mesenchymal stem cells. Each has its own advantages and disadvantages in terms of capability of migration, survival rate, and safety. Certain stem cell therapies have completed phase one clinical trial.ConclusionStem cells transplantation is feasible and has a great potential for the treatment of ischemic stroke, albeit that certain obstacles, including the selection of stem cells, transplantation strategy, migration ability, survival rate, still wait to be solved.
Post-stroke cognitive dysfunction is a common complication of stroke, and active rehabilitation therapy can effectively promote the recovery of patients. As a new treatment method, telecognitive rehabilitation is used in rehabilitation treatment of cognitive disorders. Its main technologies include computer-assisted cognitive rehabilitation, virtual reality technology, and artificial intelligence technology. It can use the Internet platform to provide homogeneous treatment, make patients more convenient for cognitive rehabilitation treatment, help to ensure the continuity of rehabilitation treatment, and save medical costs. This article describes the definition of cognitive telerehabilitation, the development and application of cognitive telerehabilitation technology, and summarizes the existing problems. The purpose is to provide a reference for the clinical application of cognitive telerehabilitation in China and future research directions.