Identification Of Stroke Research Articles

Robust classification model for identifying stroke patients utilising a machine learning-based ensemblestacking method

Abstract Purpose of the study: Cerebrovascular accident (CVA) occurs swiftly
and disrupts brain blood flow, causing neurological issues. Risk factors include
smoking, diabetes, hyperlipidemia, hypertension, and atrial fibrillation. Many brain
stroke prediction systems are computationally demanding, sluggish, and unreliable.
This study uses interpolation spline curves impute data and Principal Component
Analysis to identify the most important factors for stroke prediction. By using stack
ensemble machine learning methods, the goal is to improve the precision of stroke
disease identification.
Used method: Using interpolation spline curve data imputation and Principal
Component Analysis to preprocess and extract EHR data is unique. In addition, this
article also proposes a novel stacking ensemble technique using bagging and boosting
as foundation learners. Bagging and boosting enhance prediction by reducing variation
and bias. Stack-based ensemble learning classifies brain strokes. Build base and meta-
learners with Random Forest, XGBoost, Decision tree classifier, KNN, and Logistic
Regression. Evaluation of machine learning model efficiency determines the optimal
stacking ensemble meta-learner model.
Brief Description of Results: The results emphasize the stacking ensemble’s best
meta-learner model and imputation technique’s impact on electronic health record
representation. By using PCA, most important factors has identified for detecting
stroke. The analysis results showed that the Logistic Regression as a meta learner had
the best predictive performance which achieves 0.96 precision and 0.98 recall value
respectively with 97% accuracy and AUC curve value of 0.99.
Findings: The Decision Tree Classifier and K-Nearest Neighbour as meta learner got
95%, 96%accuracy, and both 0.95 AOC curve values on this dataset, showing that the
method works. Precision values of 0.93, and 0.95 for the Decision Tree Classifier and
KNN Classifiers as meta-learner confirm their robustness. These findings suggest that
interpolation spline curve and stacking based ensemble machine learning can improve
the identification of brain stroke from incomplete electronic health records.

Impact of stroke order on Chinese spelling in the Vietnamese Chinese as a second language context

This article aimed to assess the impact of stroke order on Chinese spelling among Chinese as a second language (CSL) learners in Vietnam, using a cross-sectional design and an error analysis. It seeked to distinguish between sequence errors and traditional form errors in Chinese spelling and explored their relationship. The study involved 94 CSL students from four Vietnamese universities who participated in a writing-to-dictation test, a stroke identification task and a character orthographic awareness task in Chinese. Eight types of sequence errors were identified, most of which occurred within the basic orthographic units for Chinese spelling, namely the logographeme or the radical. The results of the generalized linear mixed model indicated that character frequency, stroke identification scores, orthography awareness scores and Chinese proficiency all had a significant impact on the spelling accuracy of CSL learners. In addition, a significant negative association was found between sequence accuracy and form errors. A one-way analysis of variance further revealed that the sequence error across orthographic units was an indicator of learners’ Chinese level. These findings contribute to a deeper understanding of the impact of stroke order on Chinese spelling and highlight the importance of considering not only stroke-related knowledge but also orthographic units of varying grain sizes in stroke order writing. Drawing from the empirical findings, the article explored possible educational implications of stroke order in the teaching of spelling to CSL learners.

Association between cardiometabolic index and the incidence of stroke: a prospective nationwide cohort study in China.

Cardiometabolic index (CMI), based on triglycerides (TG) to high-density lipoprotein cholesterol (HDL-C) ratio and waist-to-height ratio (WHtR), has been recognized as a novel and practical marker for the assessment of cardiometabolic risk. However, the relationship between CMI and the incidence of stroke remains to be elucidated. This investigation aimed to explore the association between CMI and stroke incidence. The investigation included 6,633 individuals aged over 45 years from the China Health and Retirement Longitudinal Study. Logistic regressions and restricted cubic spline regression were uitilized to determine the relationship between CMI and the incidence of stroke. Weighted quantile sum regression was used to offer a comprehensive explanation of the CMI by calculating the weights of triglyceride-glucose (TG), high-density lipoprotein cholesterol (HDL-C), weight, and height. During the 9-year follow-up, 827 (12%) incident stroke participants were identified. With CMI as a continuous variable, the OR (95% CI) for the risk of incident stroke was 1.09 (1.01-1.19) (p = 0.047) after adjusting for potential confounders, indicating a significant link between increased CMI and an elevated incidence of stroke. Additionally, when CMI was categorized into quartiles, compared to the first quartile, the incident stroke was significantly higher in the fourth quartile (OR 1.57, 95%CI 1.22-2.04, p <0.001). The association between CMI and stroke incidence was nonlinear (p overall=0.002, p non-linear = 0.006). TG emerged as the primary contributor when the weights were assigned to the constituent elements of the CMI (weight = 0.645). The CMI was independently associated with stroke incidence in middle-aged and elderly Chinese populations. Long-term CMI monitoring is of great importance for early identification and prevention of stroke, with significant implications for clinical practice. The online version contains supplementary material available at 10.1007/s40200-024-01530-3.

Optimizing Stroke Classification with Pre-Trained Deep Learning Models

Background/Objectives: Insufficient blood supply to the brain, whether due to blocked arteries (ischemic stroke) or bleeding (hemorrhagic stroke), leads to brain cell death and cognitive impairment. Ischemic strokes, which are more common, occur when blood flow to the brain is obstructed. Magnetic resonance imaging (MRI) scans are essential for distinguishing stroke types, but precise and timely identification of ischemic strokes is crucial for effective treatment. Manual diagnosis can be difficult due to high patient volumes and time constraints in hospitals. This study aims to investigate the use of deep learning techniques for predicting ischemic strokes with high accuracy, enabling earlier diagnosis and intervention. Methods: The study utilized advanced deep learning algorithms, specifically ConvNeXt Base, to analyze large datasets of medical imaging data, focusing on MRI scans. The model was trained and validated on a labeled dataset to identify critical indicators and patterns associated with stroke risk. The performance of the model was evaluated based on accuracy metrics to determine its predictive capabilities. Results: ConvNeXt Base achieved an overall accuracy of 84% on the validation set, demonstrating its effectiveness in identifying ischemic strokes. The model was able to detect key patterns linked to stroke risk, highlighting its potential for use in clinical settings to aid in early diagnosis and decision-making. Conclusions: ConvNeXt Base reveals promise in improving stroke prediction accuracy, enabling earlier diagnosis and personalized treatment, which could lead to faster, more effective medical interventions.

Multiphase CTA Collateral Score to Identify Intracranial Atherosclerotic Stenosis-Related Large Vessel Occlusion

Objective: Identification of acute ischemic stroke with large vessel occlusion (AIS-LVO) etiology is crucial for effective revascularization therapy. As collaterals are pivotal in maintaining cerebral perfusion in intracranial atherosclerotic stenosis (ICAS), we investigated whether multiphase CT angiography (mCTA) collateral score can be a diagnosis marker of ICAS-related LVO. Methods: We reviewed clinical and imaging data from 92 patients who presented with AIS-LVO and underwent mCTA (57 ICAS-related LVO and 35 embolic LVO). Logistic regression was used to identify ICAS-related LVO. The diagnostic accuracy of the mCTA collateral score for identifying ICAS-related LVO was determined using receiver operating characteristic (ROC) analysis. Results: Compared with patients with embolic LVO, those with ICAS-related LVO had a high median mCTA collateral score (4 vs. 3; P&lt;0.0001). The multinomial logistic regression analysis revealed a significant increase in the mCTA collateral score (OR: 3.717, 95% CI: 2.009-6.876, P&lt;0.0001) in patients with ICAS-related LVO. ROC analysis revealed that the optimal cutoff point of the mCTA collateral score to diagnosis the ICAS-related LVO was 3.5, the area under the curve (AUC) was 0.817 (95% CI: 0.736-0.899; P&lt;0.0001), sensitivity was 80.7%, and specificity was 74.3%. Further analysis revealed that patients with a 4 to 5 mCTA collateral score exhibited a significantly higher median modified Rankin Scale (mRS) at discharge compared with those with a 0 to 3 score (P=0.0464). Conclusions: The mCTA collateral score may be associated with ICAS-related LVO and could be beneficial in identifying the etiology of AIS-LVO.

Hemorrhagic stroke code the transition to comprehensive stroke code

As recommended by the working group of vascular and interventional neurology of the Colombian Association of Neurology (ACN), stroke is defined as a neurological emergency characterized by a sudden neurological deficit potentially of arterial origin. Upon confirmation via neuroimaging, the diagnosis is rendered as cerebral infarction (CI) or hemorrhage, either intracerebral (ICH) or subarachnoid (SAH). Additionally, it can be of venous origin, such as cerebral venous thrombosis (CVT). The stroke code is currently widely recognized as a time-is-brain strategy that seeks to meet strict time goals for treating ischemic stroke (iS), which have substantially improved the outcomes of stroke patients. However, this has not been the case for hemorrhagic stroke (hS), which, given the conditions, is also admitted through the emergency department and clearly benefits from having a standardized protocol of care and time goals, like iS, to improve patient outcomes. The strategy recommended by the ACN for the early identification of a stroke is CORRE+. This acronym was created by neurologist Luis Roa and is designed to facilitate the identification of both ischemic and hemorrhagic strokes of anterior or posterior circulation. It comprises the following criteria: C: twisted face and/or worst headache, O: eye sudden alteration of vision, R: rapid weakness of an arm and/or leg, R: rare when speaking, E: altered balance + emergencies attend. The acronym is intended to be used by the general community and in different extra- and intra-hospital scenarios for immediate consultation.

Neuroimage-Based Stroke Identification: A Machine Learning Approach

Stroke diagnosis is a time-critical process that requires rapid and accurate identification to ensure timely treatment. This study proposes a machine learning-based diagnostic model for stroke identification using neuro images. Early identification and timely intervention are critical to improving outcomes for stroke patients, but current diagnostic techniques, such as CT and MRI scans, often require time-consuming expert analysis. These delays can limit the effectiveness of treatment, particularly in acute cases where every minute counts. The problem lies in the need for faster, more reliable diagnostic tools that can analyze neuroimaging data with high accuracy and minimal human intervention. Machine learning, specifically deep learning, offers a promising solution to address this gap by automating the process of stroke detection. We employed a comprehensive approach, utilizing Inceptionv3, MobileNet, Convolutional Neural Network (CNN) algorithms to analyze neuroimages and predict stroke occurrence. This research proposes a machine learning-based diagnostic model for stroke identification using neuroimages, leveraging the power of Convolutional Neural Networks (CNN), with Inception V3 and MobileNet architectures. Inception V3, known for its ability to capture intricate image features through deep convolutional layers, and MobileNet, optimized for efficiency and speed, were employed to process large datasets of brain scans. The model was trained on these neuroimaging datasets to distinguish between healthy brain tissues and those affected by stroke. The combination of these two architectures allows for both detailed analysis and fast processing, making the model adaptable to clinical settings. The results showed that the model achieved a high accuracy rate in stroke identification, demonstrating its potential to assist healthcare professionals in diagnosing stroke faster and more accurately. By integrating this machine learning model into existing diagnostic workflows, it could significantly reduce the time to diagnosis, enabling earlier treatment and ultimately improving patient outcomes. Our model has the potential to enhance patient outcomes and reduce the economic burden of stroke. By leveraging the power of these advanced machine learning techniques, the model aims to enhance the efficiency and accuracy of stroke diagnosis compared to traditional methods

SDS-Net: A Synchronized Dual-Stage Network for Predicting Patients Within 4.5-h Thrombolytic Treatment Window Using MRI.

Timely and precise identification of acute ischemic stroke (AIS) within 4.5h is imperative for effective treatment decision-making. This study aims to construct a novel network that utilizes limited datasets to recognize AIS patients within this critical window. We conducted a retrospective analysis of 265 AIS patients who underwent both fluid attenuation inversion recovery (FLAIR) and diffusion-weighted imaging (DWI) within 24h of symptom onset. Patients were categorized based on the time since stroke onset (TSS) into two groups: TSS ≤ 4.5 h and TSS > 4.5 h. The TSS was calculated as the time from stroke onset to MRI completion. We proposed a synchronized dual-stage network (SDS-Net) and a sequential dual-stage network (Dual-stage Net), which were comprised of infarct voxel identification and TSS classification stages. The models were trained on 181 patients and validated on an independent external cohort of 84 patients using metrics of area under the curve (AUC), sensitivity, specificity, and accuracy. A DeLong test was used to statistically compare the performance of the two models. SDS-Net achieved an accuracy of 0.844 with an AUC of 0.914 in the validation dataset, outperforming the Dual-stage Net, which had an accuracy of 0.822 and an AUC of 0.846. In the external test dataset, SDS-Net further demonstrated superior performance with an accuracy of 0.800 and an AUC of 0.879, compared to the accuracy of 0.694 and AUC of 0.744 of Dual-stage Net (P = 0.049). SDS-Net is a robust and reliable tool for identifying AIS patients within a 4.5-h treatment window using MRI. This model can assist clinicians in making timely treatment decisions, potentially improving patient outcomes.

Incidence and risk factors associated with stroke when utilizing peripheral VA-ECMO

BackgroundMechanical circulatory support with veno-arterial extracorporeal membrane oxygenation (VA-ECMO) has brought forward a paradigm shift in the management of cardiogenic shock. Neurological complications associated with VA-ECMO represent a significant source of morbidity and mortality and serve as a limiting factor in its application and duration of use. MethodsWe performed a single-center retrospective case-control study of patients who developed stroke while managed with peripheral VA-ECMO from January 2018 to September 2022 at a quaternary center. We included consecutive patients above the age of 18 who were admitted to the cardiac intensive care unit and were managed with peripheral VA-ECMO. All patients who developed a stroke while on VA-ECMO were included in the case cohort, and compared to those who did not suffer stroke. Multivariable logistic regression was performed to identify risk factors associated with stroke on VA-ECMO. In-hospital outcomes were assessed out to 30 days. ResultsA total 244 patients were included in the final analysis, 36 (14.7 %) of whom developed stroke on VA-ECMO. Ischemic stroke was seen in 20 patients (55.6 %) whereas hemorrhagic stroke was seen in 16 patients (44.4 %). The use of P2Y12 antagonists (aOR 2.70, p = 0.019), limb ischemia (aOR 4.41, p = 0.002), and blood transfusion requirement (aOR 8.55, p = 0.041) were independently associated with development of stroke on VA-ECMO. Female sex trended towards statistical significance (aOR 2.19, p = 0.053) while age was not independently associated with development of stroke on VA-ECMO. There was no significant association between stroke development and outcomes of VA-ECMO duration, hospital length of stay, and all-cause mortality out to 30-days. ConclusionsVA-ECMO carried a considerable risk of neurological complications. Mortality and duration of hemodynamic support was not associated with stroke risk. Awareness regarding stroke risk is imperative in facilitating early identification and management of ischemic and hemorrhagic stroke. Research involving clinical trials and multicenter studies are necessary to empower centers in mitigating this source of significant morbidity and mortality in patients on mechanical circulatory support.

Prevalence and Determinants of Prehospital Impression of Stroke in Ischemic Stroke Patients.

Emergency Medical Services (EMS) clinicians are front-line in evaluating patients with stroke in the community. Their ability to correctly identify stroke influences downstream management decisions. We sought to use a large national database of prehospital clinical data to determine risk factors associated with missed EMS stroke identification. Retrospective study examining EMS evaluation of adults with Emergency Department (ED) stroke diagnosis. We leveraged the ESO Data Collaborative research dataset containing EHR data from 2019-2022 that has a subset of encounters with linked hospital diagnostic codes. Our primary outcome was the presence of an EMS diagnosis of stroke. We evaluated the association between demographic and clinical variables with EMS stroke identification using Pearson χ2 test for demographic variables and multivariable GLM for clinical variables with adjustment for demographic variables. We identified 34,504 EMS encounters for patients with ED stroke diagnosis. Of these, 11,077 (32.1%) strokes had missed EMS stroke identification and instead had an EMS impression of "Generalized Weakness" (25.9%), "Altered Level of Consciousness" (24.9%), and "Dizziness" (7.2%). Patients more likely to have missed prehospital stroke identification were of Black race (p=0.0001) and Hispanic ethnicity (p=0.0001). Clinical variables associated with higher risk of missed EMS stroke identification were suspected alcohol or drug use (RR 1.48, 95% CI 1.37-1.59), low GCS (RR 1.17, 95% CI 1.10-1.24), tachycardia (RR 1.05, 95% CI 1.01-1.09), and hypotension (RR 1.47, 95% CI 1.34-1.61). Approximately 1-in-3 patients transported by EMS did not have their stroke identified in the prehospital setting. Factors associated with lower odds of missed EMS stroke identification provide a starting point for future performance improvement initiatives.

Endogenous tPA levels: A biomarker for discriminating hemorrhagic stroke from ischemic stroke and stroke mimics.

Stroke is the leading cause of death and disability. Timely differentiation between ischemic stroke, hemorrhagic stroke, and stroke mimics is critical for tailored treatment and triage. To accelerate the identification of stroke's subtype, we propose to use the levels of circulating tPA as a biomarker. Biostroke is an observational study performed at the Caen Hospital. We quantified tPA levels in 110 patients with ischemic strokes, 30 patients with hemorrhagic strokes, and 67 stroke mimic patients upon their arrival at the emergency. Two logistic regression models were formulated: one with parameters measurable in an ambulance (Model A) and one with parameters measurable at the hospital (Model H). These models were both tested with or without plasma tPA measurements. Our initial assessment involved evaluating the effectiveness of both models in distinguishing between hemorrhagic strokes, ischemic strokes, and stroke mimics within our study cohort. Plasmatic tPA levels exhibit significant distinctions between hemorrhagic, ischemic, and mimic stroke patients (1.8; 2.5; 2.4 ng/mL, respectively). The inclusion of tPA in model A significantly enhances the classification accuracy of hemorrhagic patients only, increasing identification from 0.67 (95% CI, 0.59 to 0.75) to 0.78 (95% CI, 0.7 to 0.85) (p = 0.0098). Similarly, in model H, classification accuracy of hemorrhagic patients significantly increased with the addition of tPA, rising from 0.75 (95% CI, 0.67 to 0.83) without tPA to 0.86 (95% CI, 0.81 to 0.91) with tPA (p = 0.024). Our findings underscore the valuable role of tPA levels in distinguishing between stroke subtypes.

Retraction Note: Healthcare framework for identification of strokes based on versatile distributed computing and machine learning

Retraction Note: Healthcare framework for identification of strokes based on versatile distributed computing and machine learning

Forensic differentiation of blue pen inks using time-of-flight secondary ion mass spectrometry and multivariate statistical methods

This study investigates the use of time-of-flight secondary ion mass spectrometry (ToF-SIMS) combined with multivariate statistical analysis for the identification of blue pen strokes in document forgery cases. Strokes of six commercially available blue pens were analysed using ToF-SIMS, and the data were interpreted using principal component analysis (PCA) and multivariate curve resolution (MCR). PCA effectively reduced the dimensionality of the complex ToF-SIMS data, enabling clear differentiation of the blue pen inks based on the obtained PCA scores. The first three principal components accounted for most of the variance, with PCA score plots showing distinct clusters of samples corresponding to the spectra of each pen. PCA analysis of the large-area ToF-SIMS images further revealed some characteristic spatial patterns and variations across the pen strokes; however, this analysis was less useful for the identification process. On the other hand, MCR provided a more chemically intuitive analysis by extracting pure component spectra from the complex mixtures. Each MCR factor was individually associated with a specific pen, allowing for exact identification of the pen stroke made. MCR score images displayed clear correlations with individual pen strokes, and the associated loadings identified signals at particular m/z values that were characteristic of each pen. This study shows that an efficient method for non-destructive forensic analysis of blue pen strokes can be achieved by combining ToF-SIMS with PCA and MCR. This analytical method provides reliable identification, which is crucial for forgery detection without requiring any sample pretreatment, i.e. the samples (pen strokes) were used just as they were made on paper.

Rapid Assay Diagnostic for Acute Stroke Recognition (RADAR): study protocol for a diagnostic accuracy study

IntroductionLarge-vessel occlusion (LVO) stroke is effectively treated by time-critical thrombectomy, a highly specialised procedure only available in a limited number of centres. Many patients with suspected stroke are admitted to...

Data-Driven Identification of Stroke through Machine Learning Applied to Complexity Metrics in Multimodal Electromyography and Kinematics.

A stroke represents a significant medical condition characterized by the sudden interruption of blood flow to the brain, leading to cellular damage or death. The impact of stroke on individuals can vary from mild impairments to severe disability. Treatment for stroke often focuses on gait rehabilitation. Notably, assessing muscle activation and kinematics patterns using electromyography (EMG) and stereophotogrammetry, respectively, during walking can provide information regarding pathological gait conditions. The concurrent measurement of EMG and kinematics can help in understanding disfunction in the contribution of specific muscles to different phases of gait. To this aim, complexity metrics (e.g., sample entropy; approximate entropy; spectral entropy) applied to EMG and kinematics have been demonstrated to be effective in identifying abnormal conditions. Moreover, the conditional entropy between EMG and kinematics can identify the relationship between gait data and muscle activation patterns. This study aims to utilize several machine learning classifiers to distinguish individuals with stroke from healthy controls based on kinematics and EMG complexity measures. The cubic support vector machine applied to EMG metrics delivered the best classification results reaching 99.85% of accuracy. This method could assist clinicians in monitoring the recovery of motor impairments for stroke patients.

Barriers to Effective Prehospital and Hyperacute Stroke Care in India: A Physician Perspective.

The incidence of stroke is increasing in India. Prehospital stroke care is crucial for reducing stroke morbidity and mortality, but its implementation in India faces several challenges. Limited original research exists on prehospital stroke care in India, making it essential to identify the problems in implementing effective prehospital stroke care. A web-based survey was conducted among registered medical practitioners in India who treat acute stroke. The survey questionnaire was developed in English and included 26 questions divided into five parts: questions about the physician's practice setup/hospital in India, perception of community awareness, existing prehospital care/systems, in-hospital stroke care availability, and specific issues faced. Eighty-three doctors in India participated in the survey (43% response rate). Most of the respondents worked in private hospitals (68%) and urban areas (76%). While 89% of hospitals had ambulance services, over 33% reported that patients had to pay for ambulance transport. Among respondents, 12% reported a community stroke care network, with infrequent prehospital procedures such as random blood glucose measurement (22%), stroke identification (15.7%), "last seen normal" documentation (14.5%), and low prehospital notification to hospitals (5%). Delays in referral from peripheral centers were reported by 73% of respondents. Most hospitals had standard operating procedures (SOPs) (84%), computed tomography (CT) (94%), magnetic resonance imaging (MRI) (85%), and offered intravenous thrombolysis (IVT) (77%). However, 24 h availability of CT was reported only by 6%, MRI by 19% and IVT by 12%. Nearly half (45%) reported treatment with thrombolysis was not covered by insurance. Mechanical thrombectomy was available in 34% of hospitals and 63% of hospitals conducted in-hospital audits for stroke patients. The capabilities of stroke-catering hospitals in urban settings are encouraging, with many having SOPs, imaging capabilities, and thrombolysis and mechanical thrombectomy services. However, there is much room for improvement, in making the essential stroke care services financially accessible to all and available around the clock.

Increased plasma levels of N-terminal pro-B-type natriuretic peptide as biomarker for the diagnosis of cardioembolic ischaemic stroke

BackgroundDespite comprehensive study, the aetiology of stroke is not identified in 35% of cases. AimsWe conducted a study to assess the diagnostic capacity of N-terminal pro-B-type natriuretic peptide (NT-proBNP) in the identification of ischaemic stroke of cardioembolic origin. The secondary purpose of the study was to evaluate the prognostic value of NT-proBNP for predicting 90-day all-cause mortality. MethodsWe designed a prospective observational study including patients hospitalised due to stroke between March 2019 and March 2020. Blood samples were collected on admission to the emergency department and serum NT-proBNP levels were determined. Statistical analysis was performed using a bivariate logistic regression model and receiver operating characteristic (ROC) and Kaplan–Meier curves. Statistical significance was established at p<.05. ResultsThe study included 207 patients with first ischaemic stroke. Plasma NT-proBNP levels were significantly higher (p<.001) in the cardioembolic stroke group (2069pg/mL±488.5). ROC curves showed that NT-proBNP>499pg/mL was the optimum value for diagnosing cardioembolic ischaemic stroke (sensitivity, 82%; specificity, 80%). Moreover, plasma NT-proBNP levels>499pg/mL were independently associated with cardioembolic stroke (OR: 9.881; p=.001). Finally, NT-proBNP>1500pg/mL was useful for predicting 90-day mortality (sensitivity, 70%; specificity, 93%). ConclusionsNT-proBNP was independently associated with cardioembolic stroke and should be quantified in blood tests within 24h of stroke onset. High plasma levels (>499pg/mL) may indicate an underlying cardioembolic cause, which should be further studied, while NT-proBNP >1500pg/mL was associated with increased 90-day mortality.

Pay attention to the identification and management of perioperative stroke

Perioperative stroke is a serious complication that leads to perioperative death, prolonged hospitalization, and permanent or temporary neurological impairment. Early identification and diagnosis is the key of improving the prognosis of perioperative stroke patients, which requires surgeons to have a complete understanding of patient risk factors, early rapid identification of symptoms and timely diagnosis. The core of the prevention strategy is delaying the operating time, reducing known risk factors, drug intervention (such as the use of antiplatelet drugs or anticoagulants), optimizing the control of the basic disease, and identifying high-risk patients in the preoperative evaluation to implement a more personalized perioperative management plan, to reduce the incidence of perioperative stroke and related adverse consequences.

First stroke incidence, causes, treatments, and outcomes for Aboriginal Peoples in South Australia and the Northern Territory: a pilot prospective study.

We performed a pilot stroke incidence study, focused on feasibility and inclusion of the CONSIDER reporting guidelines, to model the design of a future population-based study aiming to definitively determine stroke incidence, antecedents, treatment, and outcomes. Prospective stroke incidence study (pilot study). All people aged 15 years or older who lived in postcode-defined areas of South Australia and Northern Territory (885 472 people, including 45 127 Aboriginal people [5.1%]) diagnosed with stroke for the first time during 1 October - 31 December 2015 and admitted to public hospitals or stroke and transient ischaemic attack clinics. Feasibility of a prospective population-based stroke incidence study. Of the 123 participants with first strokes, ten were Aboriginal (8%); the median age of Aboriginal people was 45 years (interquartile range [IQR], 33-55 years), of non-Indigenous people 73 years (IQR, 62-84 years). For Aboriginal people, the age-standardised incidence of stroke was 104 (95% confidence interval [CI], 84-124) per 100 000 person-years, for non-Indigenous people 33 (95% CI, 22-44) per 100 000 person-years. We found that a prospective population-based stroke incidence study in Aboriginal people was feasible, including with respect to establishing an adequate sample size, diagnostic confirmation, identification of incident stroke, confirming stroke subtypes, establishing a stable statistical population, standardising data reporting for comparison with other stroke incidence studies, and ethical research reporting that conforms to CONSIDER guidelines. A larger, population-based study of the incidence of stroke in Aboriginal people is both feasible and needed to provide robust estimates of stroke incidence, antecedents, treatments and outcomes to help guide strategies for reducing the risk of and outcomes of stroke in Aboriginal people.

MSE-VGG: A Novel Deep Learning Approach Based on EEG for Rapid Ischemic Stroke Detection.

Ischemic stroke is a type of brain dysfunction caused by pathological changes in the blood vessels of the brain which leads to brain tissue ischemia and hypoxia and ultimately results in cell necrosis. Without timely and effective treatment in the early time window, ischemic stroke can lead to long-term disability and even death. Therefore, rapid detection is crucial in patients with ischemic stroke. In this study, we developed a deep learning model based on fusion features extracted from electroencephalography (EEG) signals for the fast detection of ischemic stroke. Specifically, we recruited 20 ischemic stroke patients who underwent EEG examination during the acute phase of stroke and collected EEG signals from 19 adults with no history of stroke as a control group. Afterwards, we constructed correlation-weighted Phase Lag Index (cwPLI), a novel feature, to explore the synchronization information and functional connectivity between EEG channels. Moreover, the spatio-temporal information from functional connectivity and the nonlinear information from complexity were fused by combining the cwPLI matrix and Sample Entropy (SaEn) together to further improve the discriminative ability of the model. Finally, the novel MSE-VGG network was employed as a classifier to distinguish ischemic stroke from non-ischemic stroke data. Five-fold cross-validation experiments demonstrated that the proposed model possesses excellent performance, with accuracy, sensitivity, and specificity reaching 90.17%, 89.86%, and 90.44%, respectively. Experiments on time consumption verified that the proposed method is superior to other state-of-the-art examinations. This study contributes to the advancement of the rapid detection of ischemic stroke, shedding light on the untapped potential of EEG and demonstrating the efficacy of deep learning in ischemic stroke identification.