Early Stroke Research Articles

AI-Based Cerebral Vascular Accident (CVA) Analysis and Prediction

Early stroke detection significantly increases the prognosis for both survival and rehabilitation. Patients are more likely to receive appropriate therapy that minimizes brain damage and lowers the risk of consequences if a stroke is detected early on. Researchers are motivated to investigate the possibilities of artificial intelligence and machine learning technologies in creating new categorization systems that can identify and detect strokes more quickly and accurately due to their rapid development. This could potentially enhance the likelihood of surviving and recuperating. The support-vector machine (SVM), logistic regression, decision tree, random forest, Bayes nets, and K-nearest neighbor (KNN) algorithms are employed in this study's CRISP model technique. To enhance the final quality, the dataset was balanced using an oversampling technique, and the algorithms employed were subjected to principal components analysis (PCA). With an accuracy rate of 99%, the Random Forest algorithm is regarded as the optimum for prediction. Our study illustrates that the random forest classification model using the data balancing strategy outperforms the other strategies investigated, with a 99% classification accuracy and a 98% F1 score. The study also shows that the outcomes are unaffected by preprocessing with the PCA technique. The next objectives of the study are to use a larger dataset, various preprocessing methods, and machine learning models to enhance the framework models.

A Clinical-Radiomics Nomogram for Predicting Early Ischemic Stroke Risk in Patients with Transient Ischemic Attack

To develop and validate a clinical-radiomics nomogram for predicting early ischemic stroke risk in patients who sustain a transient ischemic attack (TIA). A retrospective training dataset (n= 76) and a prospective validation dataset (n= 34) of patients with TIA were studied. Image processing was performed using ITK-snap and Artificial Intelligent Kit. Radiomics features were selected in R. A nomogram predicting recurrent TIA/stroke in 90 days as a recurrent ischemic event was established. Model performance was assessed by computing the receiver operating characteristic curve and decision curve analysis (DCA). We found a higher proportion of diabetes and hypertension in the patients with recurrent TIA compared with the stable patients in both the training and validation datasets (P < 0.05). Recurrent patients had significantly higher ABCD2 scores and plaque scores compared to stable patients. ABCD2 score and necrotic/lipid core area were independent risk factors for recurrent ischemic events (odds ratio [OR], 2.75; 95% confidence interval [CI], 1.47-6.40; and OR, 1.20; 95% CI, 1.07-1.41, respectively). The radiomics model had area under the curve values of 0.737 (95% CI, 0.715-0.878) in the training dataset and 0.899 (95% CI, 0.706-0.936) in the validation dataset, which was superior to the ABCD2 score and plaque model for predicting stroke recurrence (P < 0.05). The nomogram predicting recurrent ischemic events was 0.923 (95% CI, 0.895-0.978) in the training dataset and 0.935 (95% CI, 0.830-0.959) in the validation dataset. DCA confirmed the clinical value of this nomogram. The nomogram, based on clinical ABCD2 score, carotid plaque components and radiomics score, shows good performance in predicting the risk of recurrent ischemic events in patients with TIA.

Cross-Frequency Coupling as a Biomarker for Early Stroke Recovery

Background The application of neuroimaging-based biomarkers in stroke has enriched our understanding of post-stroke recovery mechanisms, including alterations in functional connectivity based on synchronous oscillatory activity across various cortical regions. Phase-amplitude coupling, a type of cross-frequency coupling, may provide additional mechanistic insight. Objective To determine how the phase of prefrontal cortex delta (1-3 Hz) oscillatory activity mediates the amplitude of motor cortex beta (13-20 Hz) oscillations in individual’s early post-stroke. Methods Participants admitted to an inpatient rehabilitation facility completed resting and task-based EEG recordings and motor assessments around the time of admission and discharge along with structural neuroimaging. Unimpaired controls completed EEG procedures during a single visit. Mixed-effects linear models were performed to assess within- and between-group differences in delta-beta prefrontomotor coupling. Associations between coupling and motor status and injury were also determined. Results Thirty individuals with stroke and 17 unimpaired controls participated. Coupling was greater during task versus rest conditions for all participants. Though coupling during affected extremity task performance decreased during hospitalization, coupling remained elevated at discharge compared to controls. Greater baseline coupling was associated with better motor status at admission and discharge and positively related to motor recovery. Coupling demonstrated both positive and negative associations with injury involving measures of lesion volume and overlap injury to anterior thalamic radiation, respectively. Conclusions This work highlights the utility of prefrontomotor cross-frequency coupling as a potential motor status and recovery biomarker in stroke. The frequency- and region-specific neurocircuitry featured in this work may also facilitate novel treatment strategies in stroke.

Improving acute stroke assessment in non-enhanced computed tomography: automated tool for early ischemic lesion volume detection.

ASPECTs is a widely used marker to identify early stroke signs on non-enhanced computed tomography (NECT), yet it presents interindividual variability and it may be hard to use for non-experts. We introduce an algorithm capable of automatically estimating the NECT volumetric extension of early acute ischemic changes in the 3D space. We compared the power of this marker with ASPECTs evaluated by experienced practitioner in predicting the clinical outcome. We analyzed and processed neuroimaging data of 153 patients admitted with acute ischemic stroke. All patients underwent a NECT at admission and on follow-up. The developed algorithm identifies the early ischemic hypodense region based on an automatic comparison of the gray level in the images of the two hemispheres, assumed to be an approximate mirror image of each other in healthy patients. In the two standard axial slices used to estimate the ASPECTs, the regions identified by the algorithm overlap significantly with those identified by experienced practitioners. However, in many patients, the regions identified automatically extend significantly to other slices. In these cases,the volume marker provides supplementary and independent information.Indeed,theclinical outcome of patients with volume marker = 0 can be distinguished with higher statistical confidence than the outcome of patients with ASPECTs = 10. The volumetric extension and the location of acute ischemic region in the 3D-space, automatically identified by our algorithm, provide data that are mostly in agreement with the ASPECTs value estimated by expert practitioners, and in some cases complementary and independent.

Effects of Vestibular vs. Somatosensory Rehabilitation on Balance and Quality of Life of Sub-acute Stroke Patients with Vestibular Syndrome

Background: Stroke is one of the most common causes of disability worldwide, affecting millions of people per year, and it has a large burden on health care systems. In the early subacute phase, patients who survive a stroke usually show impairments in balance, often due to sensory and vestibular deficits; this influences their quality of life. Methods: A double-blind, randomized controlled trial was conducted to assess the effectiveness of vestibular and somatosensory interventions on balance and quality of life in early subacute stroke patients with vestibular syndrome. Fifty-two patients were randomly divided into a vestibular or somatosensory rehabilitation group or a control group receiving usual care. The trial was completed by 49 patients who were evaluated onon the Berg Balance Scale and the Vestibular Quality of Life Questionnaire at baseline and 12 weeks post-intervention. Results: The study found that both vestibular and somatosensory rehabilitation interventions led to significant improvements in balance, as indicated by the Berg Balance Scale scores, and quality of life, based on vestibular Quality of Life Questionnaire scores, compared to the control group (p&lt;0.05). There was no significant difference between the two intervention groups in terms of their impact on either balance or quality of life outcomes. Conclusion: Vestibular and somatosensory rehabilitation interventions represent effective strategies for improving balance and quality of life in early subacute stroke patients with vestibular syndrome. These findings underline the importance of including targeted rehabilitation approaches within stroke care protocols to achieve optimal functional outcomes and long-term recovery. Keywords: Balance, Stroke, Quality of Life, Vestibular Symptoms.

Sickle cell anemia and early stroke detection and prevention in Nigeria

Sickle cell disease (SCD) is the most common hereditary blood disorder worldwide, and sickle cell anemia (SCA), the homozygous state of SCD, is the most common and severe variant of the disease. Nigeria has the highest burden of SCA in the world. Hemolysis and vaso-occlusion can lead to a wide range of complications, including stroke which is one of the most devastating manifestations of SCA with significant morbidity and mortality. SCA remains the leading cause of stroke in black children. Without any intervention, strokes occur in approximately 11% of children with SCA before their 20th birthday, with the greatest risk in very young children between 2 and 5 years of age. In resource-constrained countries, where the burden of SCA is highest, stroke is underreported, hence the need to develop strategies for stroke prevention and early detection. Improving awareness among healthcare providers and the community can significantly reduce stroke rates and improve stroke detection. The goal of this manuscript is to discuss the progress that has been made in stroke prevention and detection in children with SCA in Nigeria and outline current challenges and future goals. We believe that our experience will be valuable not only in Nigeria which has the highest burden of SCA globally, but also in other low- and middle-income countries.

Orthotist involvement in early gait rehabilitation after stroke: A cross-sectional survey of orthotists in the United Kingdom.

The role of the orthotist in early gait rehabilitation after stroke in the United Kingdom is currently poorly understood. The objective of this study was to capture current opinion and practice of orthotists on their role in early stroke gait rehabilitation in the United Kingdom. An anonymous web-based cross-sectional survey was developed and distributed to Health and Care Professions Council-registered orthotists by the mailing list of the British Association of Prosthetists and Orthotists professional body in the United Kingdom. Survey items were multiple-choice, Likert scale, or open-ended questions to explore the experiences and opinions of orthotists in early poststroke gait rehabilitation. Responses were received from 56 participants. Orthotists reported having regular contact with patients who had a stroke within their usual clinical caseload. Multidisciplinary care was not typical with 46% respondents "rarely" seeing patients who had a stroke in joint assessment with another clinician. Confidence in managing lower limb gait difficulties was high, with 89% of orthotists feeling able to recommend a lower limb orthotic treatment. Ninety-eight percent (n = 55) of respondents agreed that orthotic assessment should be an essential element of gait re-education after stroke; however, orthotists reported limited involvement within early stroke rehabilitation. UK orthotists surveyed in this study report that orthotic treatment is an important aspect of early gait rehabilitation after stroke. Respondents report barriers to multidisciplinary working, which may affect outcomes in this patient group.

The brief executive language screen: sensitivity and specificity in acute to early sub-acute stroke

ABSTRACT Introduction Propositional language and underlying executive functions can be impaired post-stroke and affect communication and quality of life. Current stroke screening tools are largely tailored to patients with aphasia, being either non-verbal or focussed on core language skills such as naming and repetition. The Brief Executive Language Screening Test (BELS) is a newly developed cognitive screening tool that assesses memory, oral apraxia, core language, as well as propositional language and associated executive functions that can be impacted and overlooked in stroke patients without aphasia. This study examines BELS sensitivity and specificity, and performance in acute to early sub-acute stroke relative to controls. Method Cross-sectional BELS data from 88 acute left and right hemisphere stroke patients (within 7 weeks of stroke) and 116 age-matched healthy controls were compared using independent samples t-tests. ROC Curve Analysis was performed to determine a cutoff score for the BELS. Results Left and right stroke patients were reduced on all propositional language subtests, and executive function subtests of inhibition, strategy, and selection. Differences were also observed for Oral Apraxia, Naming, and Memory. By contrast, Word Comprehension and Repetition, and Sentence Completion Initiation (after corrections applied) did not differ between groups. A total BELS score of 79.25/100 was highly sensitive (.89) and specific (.89) when classifying stroke patients and healthy controls. Conclusion The BELS is brief, sensitive, suitable for bedside administration, and can aid in detection and rehabilitation of subtle executive language impairments. This in turn will help improve relationships and quality of life post-stroke.

Prognosis of ischemic stroke patients with both aortic atheroma and cardioembolic sources

This study aimed to investigate the relationship between complex aortic plaque (CAP) and short-term as well as long-term outcomes following cardioembolic stroke. CAP is a known risk factor for occurrence and recurrence of ischemic stroke. However, the association of CAP on cardioembolic stroke remains unclear. This was retrospective study using prospective cohort of consecutive patients with cardioembolic stroke who underwent transesophageal echocardiography. The functional outcome was evaluated using the modified Rankin Scale score at 3 months, and long-term outcomes were assessed by recurrence of ischemic stroke and occurrence of major adverse cardiovascular events (MACE). Among 759 patients with cardioembolic stroke, 91 (12.0%) had CAP. Early ischemic stroke recurrence within 3 months was associated with CAP (p = 0.025), whereas CAP was not associated with functional outcome at 3 months (odd ratio 1.01, 95% confidence interval [CI] 0.57–1.84, p = 0.973). During a median follow-up of 3.02 years, CAP was significantly associated with ischemic stroke recurrence (hazard ratio = 2.68, 95% CI 1.48–4.88, p = 0.001) and MACE occurrence (hazard ratio = 1.61, 95% CI 1.03–2.51, p = 0.039). In conclusion, CAP was associated with early ischemic stroke recurrence and poor long-term outcomes in patients with cardioembolic stroke. It might be helpful to consider transesophageal echocardiography for patients with cardioembolic stroke to identify CAP.

High-intensity interval training is feasible, credible and clinically effective in the early subacute stroke stage in the low-income country of Benin

ABSTRACT High-intensity interval training (HIIT) has been shown to benefit stroke patients when implemented three months post-stroke. This study examined HIIT’s feasibility and clinical effectiveness in the early post-stroke stage in Benin. This was a prospective interventional study comprising an HIIT programme executed on a recumbent bike, three times/week, 20–30 min/session for 6 weeks, added to a conventional physiotherapy. The primary outcomes were feasibility, credibility and expectancy assessed with credibility and expectancy questionnaire. A maximal exercise test, 6-min walking test (6MWT), 10-m walking test (10mWT), Berg balance scale (BBS) and five repetitions sit-to-stand test (5 R-STS) were performed before and after the training programme. Ten outpatients, with a median age [P25-P75]: 63.5[56.7–71.2] years; time since stroke: 15.0[9.7–21.0] days, started and completed all training sessions without serious adverse events. High scores were observed on the Credibility subscale at admission (27.0[25.7–27.0]), which remained so after intervention (26.5[25.7–27.0]). Expectancy subscale scores were high at admission (25.5[24.0–27.0]) and post-training (25.5[24.5–27.0]). Peak workload (p < 0.001), BBS (p < 0.001), 6MWT (p < 0.001), 10mWT (p < 0.001) and 5 R-STS (p = 0.004) were all improved. HIIT is feasible and safe in the early subacute post-stroke stage and is perceived by patients as highly credible, meeting their expectations of recovery.

Predictive Modeling for Brain Stroke Detection Using Machine Learning M. Asan Nainar

Brain strokes often resulting in severe health complications and mortality are a significant global health concern. Early detection and brain stroke prediction involves assessing risk factors, medical history, diagnostic tests, and predictive models. Aims to identify individuals at risk before stroke occurrence, enabling timely interventions and lifestyle modifications to mitigate the risk. In this research, an in-depth exploration of predictive modeling for brain stroke detection utilizing machine learning algorithms specifically XG Boost, Decision Tree, and K-Nearest Neighbors (KNN) is presented. The proposed methodology encompasses data preprocessing, feature engineering, model selection, and accuracy evaluation. Through extensive experimentation, cross-validation, prediction of the performance of each algorithm focuses on metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC). Keywords—Machine learning, XG Boost, Decision Tree, K-Nearest Neighbor (KNN), Accuracy, Brain Stroke.

Machine Learning-Driven Stroke Prediction Using Independent Dataset

The incidence of stroke cases has witnessed a rapid global rise, affecting not only the elderly but also individuals across all age groups. Accurate prediction of stroke occurrence demands the utilization of extensive data pre-processing techniques. Moreover, the automation of early stroke forecasting is crucial to prevent its onset at the initial stage. In this study, stroke prediction models are evaluated to estimate the likelihood of stroke based on various symptoms such as age, gender, pre-existing medical conditions, and social variables. The machine learning techniques employed include Linear Support Vector Classifier, Extreme Gradient Boosting Classifier, Multilayer Perceptron, Adaptive Boosting Classifier, Bootstrap Aggregating Classifier, and Light Gradient-Boosting Machine. The purpose of this paper is to optimize the hyperparameters of machine learning approaches in developing stroke prediction models. The goal was achieved through a comprehensive comparison of three different sampling techniques for handling imbalanced datasets and evaluating their performance by using various metrics. The most effective model is identified, which is the Adaptive Boosting Classifier utilizing the Tomek Links, with a cross-dataset accuracy of 99% which demonstrated a reliable performance and generalization as evidenced by high cross-validation scores and accuracy on an independent dataset. The next stage of this endeavor entails looking into multiple ways to forecast the development of new dangerous diseases such as breast cancer and skin disorders. In the long run, the aim of subsequent work is to build a powerful toolset that is obtainable to all medical practitioners, allowing for the pre-emptive diagnosis of all potentially hazardous illnesses.

ABCD2-I Score Predicts Unplanned Emergency Department Revisits within 72 Hours Due to Recurrent Acute Ischemic Stroke.

the ABCD2 score is valuable for predicting early stroke recurrence after a transient ischemic attack (TIA), and Doppler ultrasound can aid in expediting stroke triage. The study aimed to investigate whether combining the ABCD2 score with carotid duplex results can enhance the identification of early acute ischemic stroke after TIA. we employed a retrospective cohort design for this study, enrolling patients diagnosed with TIA who were discharged from the emergency department (ED). The modified ABCD2-I (c50) score, which incorporates a Doppler ultrasound assessment of internal carotid artery stenosis > 50%, was used to evaluate the risk of acute ischemic stroke within 72 h. Patients were categorized into three risk groups: low risk (with ABCD2 and ABCD2-I scores = 0-4), moderate risk (ABCD2 score = 4-5 and ABCD2-I score = 5-7), and high risk (ABCD2 score = 6-7 and ABCD2-I score = 8-9). between 1 January 2014, and 31 December 2019, 1124 patients with new neurological deficits were screened, with 151 TIA patients discharged from the ED and included in the analysis. Cox proportional hazards analysis showed that patients in the high-risk group, as per the ABCD2-I (c50) score, were significantly associated with revisiting the ED within 72 h due to acute ischemic stroke (HR: 3.12, 95% CI: 1.31-7.41, p = 0.0102), while the ABCD2 alone did not show significant association (HR: 1.12, 95% CI: 0.57-2.22, p = 0.7427). ABCD2-I (c50) scores effectively predict early acute ischemic stroke presentations to the ED within 72 h after TIA.

Stroke outcomes following durable left ventricular assist device implant in patients bridged with micro-axial flow pump: Insights from a large registry.

Stroke after durable left ventricular assist device (d-LVAD) implantation portends high mortality. The incidence of ischemic and hemorrhagic stroke and the impact on stroke outcomes of temporary mechanical circulatory support (tMCS) management among patients requiring bridge to d-LVAD with micro-axial flow-pump (mAFP, Abiomed) is unsettled. Consecutive patients, who underwent d-LVAD implantation after being bridged with mAFP at 19 institutions, were retrospectively included. The incidence of early ischemic and hemorrhagic stroke after d-LVAD implantation (<60 days) and association of pre-d-LVAD characteristics and peri-procedural management with a specific focus on tMCS strategies were studied. Among 341 patients, who underwent d-LVAD implantation after mAFP implantation (male gender 83.6%, age 58 [48-65] years, mAFP 5.0/5.5 72.4%), the early ischemic stroke incidence was 10.8% and early hemorrhagic stroke 2.9%. The tMCS characteristics (type of mAFP device and access, support duration, upgrade from intra-aortic balloon pump, ECMELLA, ECMELLA at d-LVAD implantation, hemolysis, and bleeding) were not associated with ischemic stroke after d-LVAD implant. Conversely, the device model (mAFP 2.5/CP vs. mAFP 5.0/5.5: HR 5.6, 95%CI 1.4-22.7, p = 0.015), hemolysis on mAFP support (HR 10.5, 95% CI 1.3-85.3, p = 0.028) and ECMELLA at d-LVAD implantation (HR 5.0, 95% CI 1.4-18.7, p = 0.016) were associated with increased risk of hemorrhagic stroke after d-LVAD implantation. Both early ischemic (HR 2.7, 95% CI 1.9-4.5, p < 0.001) and hemorrhagic (HR 3.43, 95% CI 1.49-7.88, p = 0.004) stroke were associated with increased 1-year mortality. Among patients undergoing d-LVAD implantation following mAFP support, tMCS characteristics do not impact ischemic stroke occurrence, while several factors are associated with hemorrhagic stroke suggesting a proactive treatment target to reduce this complication.

Long-term outcomes in hybrid versus standalone thoracoscopic ablation in patients with atrial fibrillation: a systematic review and reconstructed individual patient data meta-analysis

Abstract Background Both hybrid and standalone thoracoscopic atrial fibrillation (AF) ablation techniques have demonstrated favorable outcomes in the management of (long-standing) persistent AF patients, as compared to catheter ablation. However, it is currently unknown whether there is a difference in long-term freedom from AF when comparing these two procedures. Purpose To compare long-term freedom from atrial tachyarrhythmias (ATA) in patients undergoing hybrid and standalone thoracoscopic AF ablation. Methods A systematic search encompassing PubMed, EMBASE, and the Cochrane Library databases from January 1, 2012, to September 6, 2023, was performed using the following search terms: 'thoracoscopic ablation,' 'hybrid ablation,' 'atrial fibrillation,' and various alternative spellings. Articles were included for pooled analysis of freedom from ATA after meeting the criteria of reporting on consecutive patients and presenting Kaplan-Meier (KM) curves. Individual time-to-event data were reconstructed from the KM curves and included in a multivariable Cox and frailty Cox model with adjustments for age, sex, type of AF, AF duration, and study (frailty term in the frailty Cox model). Results In total, 18 studies were included in the meta-analysis for long-term freedom from ATA, comprising 2215 patients. Patients undergoing hybrid thoracoscopic ablation were older (62.0 vs. 58.8 years old, p=0.046), had less frequently paroxysmal AF-history (9.6% vs. 32.1%, p&amp;lt;0.01), and had a longer duration of AF-history (7.0 vs. 5.3 years, p=0.04), as compared to patients undergoing standalone thoracoscopic ablation. There were no significant differences between the two ablation procedures regarding short-term outcomes including early mortality (0.6% vs. 1.0%, p=0.07) and early stroke (1.9% vs 0.9%, p=0.11). Adjusted analysis revealed that hybrid thoracoscopic ablation was significantly associated with greater freedom from ATA (Hazard Ratio [HR]=0.59, 95%CI: 0.43-0.83, p&amp;lt;0.001) compared to standalone thoracoscopic ablation (see Figure 1A). Additionally, older age (HR=1.07, 95%CI: 1.03-1.12, p=0.002) and a higher percentage of male patients (HR=1.02, 95% CI: 1.01-1.03, p&amp;lt;0.001) were significantly associated with ATA recurrence. Adjusted freedom from ATA at 1 year, 3 years, and 5 years was 71.6%, 55.1%, and 46.8% for thoracoscopic ablation, and 82.0%, 69.9%, and 63.6% for hybrid ablation (see Figure 1B). Conclusion Hybrid thoracoscopic AF-ablation is associated with a higher level of freedom from ATA when compared to standalone thoracoscopic ablation.Figure 1

Stroke Risk After Mitral Valve Interventions: A Systematic Review and Network Meta-Analysis

Previous research indicates varying stroke rates after mitral valve (MV) interventions. This study aimed to compare post-procedural stroke risks following transcatheter and surgical MV interventions. Electronic databases were searched from inception to February 2024 for studies comparing stroke rates after mitral transcatheter edge-to-edge repair (mTEER), surgical MV repair/replacement, or guideline-directed medical therapy (GDMT). Primary endpoints were all-time and early (<30 days) stroke. Secondary outcomes included new-onset atrial fibrillation and 1-year all-cause mortality. A frequentist network meta-analysis was employed to compare outcomes. The network meta-analysis included 18 studies (3 randomized clinical trials and 15 observational), with 51,703 patients. mTEER was associated with a decreased risk of all-time (odds ratio [OR] 0.61, 95% CI 0.41-0.89) and early stroke (OR 0.41, 95% CI 0.33-0.51) compared with surgery, and a similar risk of all-time (OR 1.54, 95% CI 0.76-3.12) and early stroke (OR 2.12, 95% CI 0.53-8.47) compared with GDMT. Conversely, surgery was associated with an increased risk of all-time (OR 2.55, 95% CI 1.17-5.57) and early stroke (OR 5.15, 95% CI 1.27-20.84) compared with GDMT. There were no statistically significant differences in the risk of new-onset atrial fibrillation (OR 0.38, 95% CI 0.11-1.31) and 1-year all-cause mortality (OR 1.43, 95% CI 0.91-2.24) between mTEER vs. surgery. In conclusion, mTEER was associated with a lower risk of stroke and similar risks of new-onset atrial fibrillation and 1-year mortality compared with surgical MV interventions. Further studies are needed to understand the mechanisms of stroke and to determine strategies to reduce stroke risk following MV interventions.

Predictive modelling and identification of key risk factors for stroke using machine learning

Strokes are a leading global cause of mortality, underscoring the need for early detection and prevention strategies. However, addressing hidden risk factors and achieving accurate prediction become particularly challenging in the presence of imbalanced and missing data. This study encompasses three imputation techniques to deal with missing data. To tackle data imbalance, it employs the synthetic minority oversampling technique (SMOTE). The study initiates with a baseline model and subsequently employs an extensive range of advanced models. This study thoroughly evaluates the performance of these models by employing k-fold cross-validation on various imbalanced and balanced datasets. The findings reveal that age, body mass index (BMI), average glucose level, heart disease, hypertension, and marital status are the most influential features in predicting strokes. Furthermore, a Dense Stacking Ensemble (DSE) model is built upon previous advanced models after fine-tuning, with the best-performing model as a meta-classifier. The DSE model demonstrated over 96% accuracy across diverse datasets, with an AUC score of 83.94% on imbalanced imputed dataset and 98.92% on balanced one. This research underscores the remarkable performance of the DSE model, compared to the previous research on the same dataset. It highlights the model's potential for early stroke detection to improve patient outcomes.

Early-life tobacco smoke exposure and stroke risk: a prospective study of 341,783 and 352,737 UK Biobank participants.

Stroke is a life-threatening condition that causes a major medical burden globally. The currently used methods for the prevention or prediction of stroke have certain limitations. Exposure to tobacco in early life, including smoking during adolescence and maternal smoking during pregnancy, can affect adolescent development and lead to several negative outcomes. However, the association between early-life tobacco exposure and stroke is not known. In this prospective cohort study, for the analyses involving exposure to maternal smoking during pregnancy and age of smoking initiation, we included 304,984 and 342,893 participants, respectively., respectively from the UK Biobank. Cox proportional hazard regression model and subgroup analyses were performed to investigate the association between early-life tobacco exposure and stroke. Mediation analyses were performed to identify the mediating role of biological aging in the association between early tobacco exposure and stroke. Compared with participants whose mothers did not smoke during pregnancy, participants whose mothers smoked during pregnancy showed an 11% increased risk of stroke (HR: 1.11, 95% CI: 1.05-1.18, P < 0.001). Compared with participants who never smoked, participants who smoked during adulthood, adolescence and childhood showed a 22%, 24%, and 38% increased risk of stroke during their adulthood, respectively. Mediation analysis indicated that early-life tobacco exposure can cause stroke by increasing biological aging. This study reveals that exposure to tobacco during early life is associated with an increased risk of experiencing a stroke, and increased biological aging can be the underlying mechanism.

Stroke Transition of Care Intervention with Stroke Nurse Navigators and Early Stroke Clinic Follow-up Reduces Readmissions for Stroke at 12 Months

Background One in four strokes occur in stroke victims, with hospital readmissions contributing to high-cost care. Transition of care programs have been successful in reducing hospital readmissions in other diseases, but the data on such programs for stroke are mixed. A transition of care program was implemented with the goal of reducing recurrent strokes and hospital readmissions. Methods We implemented a transition of care program using nurse navigators and early outpatient follow-up with a vascular neurologist. Data were obtained on: Rate of recurrent stroke admissions within one-year, all-cause readmission within one-year, all-cause readmission within 30 days, initial follow-up scheduled within 7-10 days, compliance with follow up, and compliance rates with provision of two-day post-hospital discharge phone calls. Results An improvement was seen in process measures reflecting adherence to the intervention across all 3 years. The rate of readmission for stroke at 12 months was 8.5%, 9.0%, 6.6%, and 4.2% for year 0, 1, 2, and 3, respectively, representing a 50% reduction from baseline year 0. All-cause readmission remained unchanged, at 38.9%, 42.6%, 36.6%, and 37.4% for year 0, 1, 2, and 3 respectively. Conclusions Our nurse navigator led stroke transition intervention was associated with significant reduction in readmissions for stroke but did not impact all cause readmission at one year or 30 days. Our focus on Centers for Medicare/Medicaid intervention compliance has produced a sustainable program capable of now expanding to support other important patient needs.

The mediating effects of hope on the relationships of social support and self-esteem with psychological resilience in patients with stroke

PurposeTo explore the mediating effect of hope in the relationships between social support and self-esteem with psychological resilience among patients with stroke survivors in early rehabilitation.MethodsA cross-sectional study design was adopted. Data from a cross-sectional survey of 210 patients undergoing early stroke rehabilitation were analyzed using structural equation modeling. The variables of interest were measured using the Connor Davidson Resilience Scale, the Social Support Rating Scale, the Herth Hope Index, and the Self-Esteem Scale. This article reports according to the STROBE checklist.ResultsA positive relationship was found between social support and psychological resilience (β1 = 0.548), which was mediated by hope (β2 = 0.114), and social support had significant direct effect on resilience (β3 = 0.434). A positive relationship was also found between self-esteem and psychological resilience (β4 = 0.380), which was mediated by hope (β5 = 0.200), and self-esteem had significant direct effect on resilience (β6 = 0.179).ConclusionAccording to the results of this study, some strategies can be incorporated into the rehabilitation process to enhance psychological resilience, such as cultivating individual personality characteristics and improving patients’ social relationships. In the future, we need to explore methods for improving psychological resilience among patients with stroke in combination with their risk factors to improve their quality of life and reduce the incidence of post-stroke depression.