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Background Early endovascular intervention team mobilization may reduce reperfusion times and improve clinical outcomes for patients with acute ischemic stroke (AIS) with a possible intervenable vessel occlusion (IVO). In an emergency department or mobile stroke unit, incorporating rapidly available non-contrast CT (NCCT) information with examination findings may improve the accuracy of arterial occlusion prediction scales. For this purpose, we developed a rapid and straightforward IVO predictive instrument--the T³AM²PA¹ scale. Methods The T³AM²PA¹ scale was retrospectively derived from our 'Get with the Guidelines' database. We included all patients with acute stroke alert between January 2017 and August 2018 with a National Institutes of Health Stroke Scale (NIHSS) score between 5 and 25 inclusive. Different pre-intervention variables were collected, including itemized NIHSS and NCCT information. The T³AM²PA¹ scale was also compared with other commonly used scales and was validated in a separate sequential retrospective cohort of patients with a full range of NIHSS scores. Results 574 eligible patients from 2115 acute stroke alerts were identified. The scale was established with five items (CT hyperdense sign, parenchymal hypodensity, lateralizing hemiparesis, gaze deviation, and language disturbance), with a total score of 9. To minimize unnecessary angiography, a cut-off of ≥5 for IVO detection yielded a sensitivity of 52%, a specificity of 90%, and a positive predictive value of 76%. Conclusions The T³AM²PA¹ scale accurately predicts the presence of clinical IVO in patients with AIS. Adopting the T³AM²PA¹ scale could reduce revascularization times, improve treatment outcomes, and potentially reduce disability. [ABSTRACT FROM AUTHOR]

Aim: Globally, stroke is considered as one of the most important causes of mortality and morbidity. The most important steps for the diagnosis of stroke include rapid and focused physical examination followed by determination of the stroke's type. The study was conducted to determine the relationship between laboratory parameters frequently requested in the emergency department and stroke type. Material and Method: The study is a retrospective cross-sectional study. Patients' age, gender, comorbidities, stroke type, hospitalization, in-hospital mortality and laboratory parameters were recorded and compared. p value of <0.05 was considered statistically significant. Results: 251 patients were included in the study. The median age of the patients included in the study was 74 [64-81]. 137 (54.6%) of the patients were women. The most common comorbidity in the patients included in the study was hypertension. Ischemic stroke was seen in 236 (94%) patients and 236 (94%) were hospitalized. the platelet count in the hemorrhagic stroke and ischemic stroke group was 173 (153-204) 109/L and 218 (180-2262) 109/L, respectively. Platelet count in patients in the hemorrhagic stroke group was found to be significantly lower than in the ischemic stroke group (p=0.013). There was no significant difference in white blood cell, lymphocyte, sodium, potassium, C-reactive protein levels between both groups (p=0.318, 0.245, 0.461, 0.202, 0.322; respectively). Conclusion: Platelet count may be used as a biomarker to differentiate between ischemic and hemorrhagic stroke in patients who are examined for stroke. [ABSTRACT FROM AUTHOR]

This systematic review aims to identify the diagnostic accuracy of posterior circulation stroke (PCS) by paramedics and the causes and duration of delay in its recognition. A systematic search using CINAHL Plus, MEDLINE, Scopus, and PubMed was performed. All databases were searched up to May 25, 2022. Studies were included where patients were adults, assessed by paramedics, and PCS was the primary diagnosis. Bias was assessed using the Newcastle-Ottawa Scale and the Effective Practice and Organization of Care tool. Results have been described by proportions, and both sensitivity calculations and subgroup analysis were performed utilizing MedCalc. A total of 797 titles/abstracts and a subsequent 87 full texts were screened, of which 15 were included. There were 5395 patients who were assessed by paramedics and had a confirmed diagnosis of PCS. Among five studies containing both true positive and false negative data, there were 98 (45.8%) true positives. PCS patients lost an average of 27 min (p < 0.001) compared to anterior stroke patients in the prehospital setting. One study revealed that educational intervention, including implementing the finger-to-nose test, increased the sensitivity for diagnosis from 45.8 to 74.1% (p = 0.039) and decreased the time from door to computed tomography from 62 to 41 min (p = 0.037). There is a substantial lack of evidence regarding the diagnosis of PCS by paramedics. Despite the low quality of evidence available, overall, the sensitivity for paramedic PCS diagnosis appears to be poor. Further investigation is required into paramedics' diagnosis of PCS and the use of educational interventions. Prospective Register of Systematic Reviews Registration Number: CRD42022324675. [ABSTRACT FROM AUTHOR]

Background: Administrative healthcare databases, such as Medicare, are increasingly used to identify groups at risk of a crash. However, they only contain information on crash-related injuries, not all crashes. If the driver characteristics associated with crash and crash-related injury differ, conflating the two may result in ineffective or imprecise policy interventions. Methods: We linked 10 years (2008–2017) of Medicare claims to New Jersey police crash reports to compare the demographics, clinical diagnoses, and prescription drug dispensings for crash-involved drivers ≥ 68 years with a police-reported crash to those with a claim for a crash-related injury. We calculated standardized mean differences to compare characteristics between groups. Results: Crash-involved drivers with a Medicare claim for an injury were more likely than those with a police-reported crash to be female (62.4% vs. 51.8%, standardized mean difference [SMD] = 0.30), had more clinical diagnoses including Alzheimer's disease and related dementias (13.0% vs. 9.2%, SMD = 0.20) and rheumatoid arthritis/osteoarthritis (69.5% vs 61.4%, SMD = 0.20), and a higher rate of dispensing for opioids (33.8% vs 27.6%, SMD = 0.18) and antiepileptics (12.9% vs 9.6%, SMD = 0.14) prior to the crash. Despite documented inconsistencies in coding practices, findings were robust when restricted to claims indicating the injured party was the driver or was left unspecified. Conclusions: To identify effective mechanisms for reducing morbidity and mortality from crashes, researchers should consider augmenting administrative datasets with information from police crash reports, and vice versa. When those data are not available, we caution researchers and policymakers against the tendency to conflate crash and crash-related injury when interpreting their findings. [ABSTRACT FROM AUTHOR]

This study was conducted to determine the relationship between preparedness for caregiving and spiritual well-being in the carers of stroke patients. This descriptive and cross-sectional study was conducted with 130 primary carers of patients hospitalized with the diagnosis of stroke at the neurology clinic of a hospital. It was determined that the participants had moderate levels of preparedness for caregiving, they had high levels of spiritual well-being, and there was a positive significant relationship between these two variables. According to the regression analysis results, the spiritual well-being level of the participants was a predictor of their preparedness for caregiving (B: 0.144, p < 0.001). The results of this study demonstrated that the spiritual well-being of the carers of stroke patients is important in increasing preparedness for caregiving. In this context, to ensure that the carers of all stroke patients feel prepared for the caregiving process, it is recommended to evaluate their preparedness levels, support them in their preparation for their caregiving roles, and identify their spiritual needs. [ABSTRACT FROM AUTHOR]

Microwaves can safely and non-destructively illuminate and penetrate dielectric materials, making them an attractive solution for various medical tasks, including detection, diagnosis, classification, and monitoring. Their inherent electromagnetic properties, portability, cost-effectiveness, and the growth in computing capabilities have encouraged the development of numerous microwave sensing and imaging systems in the medical field, with the potential to complement or even replace current gold-standard methods. This review aims to provide a comprehensive update on the latest advances in medical applications of microwaves, particularly focusing on the near-field ones working within the 1–15 GHz frequency range. It specifically examines significant strides in the development of clinical devices for brain stroke diagnosis and classification, breast cancer screening, and continuous blood glucose monitoring. The technical implementation and algorithmic aspects of prototypes and devices are discussed in detail, including the transceiver systems, radiating elements (such as antennas and sensors), and the imaging algorithms. Additionally, it provides an overview of other promising cutting-edge microwave medical applications, such as knee injuries and colon polyps detection, torso scanning and image-based monitoring of thermal therapy intervention. Finally, the review discusses the challenges of achieving clinical engagement with microwave-based technologies and explores future perspectives. [ABSTRACT FROM AUTHOR]

An estimation error is the difference between motor imagery and actual motor time. Previous studies have reported that overestimation (motor imagery time < actual motor time) is related to physical functions in healthy individuals. However, this finding is unclear among individuals diagnosed with a stroke. We investigated whether overestimation is related to physical function in individuals diagnosed with a stroke. This study included 71 individuals diagnosed with a stroke (mean age, 67.2 ± 13.4 years; mean time since stroke, 68.4 ± 44.7 days). Imagined timed up and go test (iTUGT) was performed to assess the estimation error. First, the iTUGT was performed; subsequently, the TUGT was performed. The estimation error was calculated by subtracting the TUGT from the iTUGT, with two standard deviations (2 SDs) being calculated. Furthermore, patients were classified into appropriate estimation (AE, within ±2 SD) and overestimation (OE, over −2 SD) groups. Both groups were tested using the estimation error, iTUGT, TUGT, Berg Balance Scale (BBS), and Brunnstrom Recovery Stage (BRS). Subsequently, a correlation analysis was performed. The OE group had a significantly higher estimation error than the AE group (OE: −7.08 ± 6.87 s, AE: −0.29 ± 1.53 s, P <.001). Moreover, the OE group had significantly lower TUGT and BBS than the AE group. The estimation error was correlated with the TUGT, BBS, and lower-limb BRS (ρ = −0.454, 0.431, 0.291, respectively; P <.05). Overestimation was associated with TUGT and balance function in individuals diagnosed with a stroke. [ABSTRACT FROM AUTHOR]

Aim: To determine the reliability of hospital discharge codes for heart failure (HF), acute myocardial infarction (AMI) and stroke compared with adjudicated diagnosis, and to pilot a scalable approach to adjudicate records on a population‐based sample. Methods: A population‐based sample of 685 people with diabetes admitted (1274 admissions) to one of three Australian hospitals during 2018–2020 were randomly selected for this study. All medical records were reviewed and adjudicated. Results: Cardiovascular diseases were the most common primary reason for hospitalisation in people with diabetes, accounting for ~17% (215/1274) of all hospitalisations, with HF as the leading cause. ICD‐10 codes substantially underestimated HF prevalence and had the lowest agreement with the adjudicated diagnosis of HF (Kappa = 0.81), compared with AMI and stroke (Kappa ≥ 0.91). While ICD‐10 codes provided suboptimal sensitivity (72%) for HF, the performance was better for AMI (sensitivity 84%; specificity 100%) and stroke (sensitivity 85%; specificity 100%). A novel approach to screen possible HF cases only required adjudicating 8% (105/1274) of records, correctly identified 78/81 of HF admissions and yielded 96% sensitivity and 98% specificity. Conclusions: While ICD‐10 codes appear reliable for AMI or stroke, a more complex diagnosis such as HF benefits from a two‐stage process to screen for suspected HF cases that need adjudicating. The next step is to validate this novel approach on large multi‐centre studies in diabetes. [ABSTRACT FROM AUTHOR]

Copyright of SCENARIO: Official Italian Journal of ANIARTI is the property of ANIARTI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: Shoalhaven District Memorial Hospital is a rural (MM3) secondary hospital which is over an hour travel time from the nearest tertiary centre. The objective of the present study was to pilot the implementation of the BEFAST (Balance, Eyes, Face, Arms, Speech and Time) stroke screening tool at the ED, and determine whether its usage improved timely stroke detection. Methods: During initial implementation and training (October–December 2019), triage nurses consulted with senior medical officers before activating stroke calls. Data were collected for the subsequent 24 months (January 2020–2022), and retrospective records for confirmed strokes during a 24‐month period prior to BEFAST implementation (October 2017–2019) were also collected. The main outcome measures were triage category, CT scan result time, discharge destination, length of stay (LOS) and Modified Rankin Score (MRS). Results: After BEFAST implementation, patients (n = 268) were three times more likely to be triaged at category 1 or 2, and door‐to‐CT scan time was reduced by 20.7 min on average. More patients were discharged to their usual residence and more quickly (LOS 7.9 vs 11.1 days). MRS 90 days after stroke was less, and patients were nearly twice as likely to experience an improvement in neurological symptoms. Conclusions: Patient outcomes were improved after implementation of the BEFAST stroke triage tool. More stroke patients were identified upon presentation to the ED, and in a timely fashion. For those with a stroke diagnosis, time‐critical interventions can take place earlier, allowing patients to return home sooner, and with less disability. [ABSTRACT FROM AUTHOR]

Aims: (a) To investigate support for caregivers of people diagnosed with stroke, cancer, chronic obstructive pulmonary disease (COPD), dementia, or heart disease provided across healthcare settings in Denmark; (b) to assess differences in caregiver support across diagnoses and settings. Methods: A cross-sectional nationwide survey among professionals representing healthcare settings at municipalities (n = 479) and hospital wards and outpatient clinics (n = 425). The survey assessed identification of caregivers and support initiatives. Results: The response rate was 81% for municipalities and 49% for hospitals. Identification of caregivers was frequent in dementia care (81% and 100%) and less frequent in COPD care (58% and 64%) in municipalities and hospitals, respectively. Caregiver support differed significantly across diagnoses within municipalities (p = 0.009) and hospitals (p < 0.001). Systematic identification of vulnerable caregivers was <25% for all diagnoses except dementia. The most common support initiatives involving caregivers were primarily aimed at the ill person and included guidance about the disease and consequences for everyday life and lifestyle changes. Caregivers were least involved in support initiatives addressing physical training, work retention, sexuality, or cohabitation. Conclusions: Disparities and significant differences across diagnoses exist in the identification of caregivers and the provision of support initiatives. Support initiatives involving caregivers primarily targeted patients. Future studies should investigate how caregivers' needs can be met across different diagnoses and healthcare settings and investigate potential changes in caregivers' needs during disease trajectories. In clinical practice, identification of vulnerable caregivers should be a major focus, and disease-specific clinical guidelines may be required to ensure sufficient support for caregivers. [ABSTRACT FROM AUTHOR]

Background Endovascular thrombectomy improves outcomes and reduces mortality for large vessel occlusion (LVO) and is time-sensitive. Computer automation may aid in the early detection of LVOs, but false values may lead to alarm desensitization. We compared Viz LVO and Rapid LVO for automated LVO detection. Methods Data were retrospectively extracted from Rapid LVO and Viz LVO running concurrently from January 2022 to January 2023 on CT angiography (CTA) images compared with a radiologist interpretation. We calculated diagnostic accuracy measures and performed a McNemar test to look for a difference between the algorithms' errors. We collected demographic data, comorbidities, ejection fraction (EF), and imaging features and performed a multiple logistic regression to determine if any of these variables predicted the incorrect classification of LVO on CTA. Results 360 participants were included, with 47 large vessel occlusions. Viz LVO and Rapid LVO had a specificity of 0.96 and 0.85, a sensitivity of 0.87 and 0.87, a positive predictive value of 0.75 and 0.46, and a negative predictive value of 0.98 and 0.97, respectively. A McNemar test on correct and incorrect classifications showed a statistically significant difference between the two algorithms' errors (P=0.00000031). A multiple logistic regression showed that low EF (Viz P=0.00125, Rapid P=0.0286) and Modified Woodcock Score >1 (Viz P=0.000198, Rapid P=0.000000975) were significant predictors of incorrect classification. Conclusion Rapid LVO produced a significantly larger number of false positive values that may contribute to alarm desensitization, leading to missed alarms or delayed responses. EF and intracranial atherosclerosis were significant predictors of incorrect predictions. [ABSTRACT FROM AUTHOR]

Urinary tract infection is a frequent problem after stroke. Although prior scoring systems for UTI after stroke have been developed, we developed a simple scoring system for all types of stroke in our own. The study was designed on retrospective data. The population includes 1496 patients with stroke who had been admitted at the Neurology Department of Pyongyang Medical College Hospital between January 2010 and August 2019. The patients were diagnosed with confirmed CT and MRI. Urinary tract infection (UTI) was diagnosed through urine culture: more than 100,100 colony-forming units per millimeter in patients with signs and symptoms. The UTI prediction scoring system was developed by means of the variables available on admission. The variables with significant difference between the non-UTI group and the UTI group were age (non-UTI versus UTI, 56.4 ± 7.2 vs. 59.0 ± 12.8; p < 0.001), female (244 (24.2) vs. 176 (36.1), p < 0.001), 300 ≦ SI (smoking index) (16 (2.4) vs. 48 (12.0), p < 0.001), alcohol > 25 g/d (292 (29.0) vs. 184 (37.7), p < 0.001), poststroke hyperglycemia (120 (10.3) vs. 163 (33.4), p < 0.001), indwelling of urinary catheter (157 (15.6) vs. 351 (72.0), p < 0.001), GCS (Glasgow Coma Scale) on admission (11.2 ± 3.9 vs. 8.5 ± 4.0, p = 0.038), and WFNS (World Federation of Neurosurgeons) (in subarachnoid hemorrhage) on admission (2.9 ± 1.7 vs. 3.5 ± 1.5, p < 0.001). The UTI prediction score ranged from 0 to 8 and produced an AUC (area under curve) of 0.800. The optimal cutoff point was 2.5 (sensitivity 64.3% and specificity 79.9%). So, the score ≧ 3 was the optimal score for the prediction of UTI after stroke. [ABSTRACT FROM AUTHOR]

Stroke is a high morbidity and mortality disease that poses a serious threat to people's health. Early recognition of the various warning signs of stroke is necessary so that timely clinical intervention can help reduce the severity of stroke. Deep neural networks have powerful feature representation capabilities and can automatically learn discriminant features from large amounts of data. This paper uses a range of physiological characteristic parameters and collaborates with deep neural networks, such as the Wasserstein generative adversarial networks with gradient penalty and regression network, to construct a stroke prediction model. Firstly, to address the problem of imbalance between positive and negative samples in the stroke public data set, we performed positive sample data augmentation and utilized WGAN‐GP to generate stroke data with high fidelity and used it for the training of the prediction network model. Then, the relationship between observable physiological characteristic parameters and the predicted risk of suffering a stroke was modeled as a nonlinear mapping transformation, and a stroke prediction model based on a deep regression network was designed. Finally, the proposed method is compared with commonly used machine learning‐based classification algorithms such as decision tree, random forest, support vector machine, and artificial neural networks. The prediction results of the proposed method are optimal in the comprehensive measurement index F. Further ablation experiments also show that the designed prediction model has certain robustness and can effectively predict stroke diseases. [ABSTRACT FROM AUTHOR]

BACKGROUND: Individuals diagnosed with stroke have a poor return to work rate, particularly in rural communities. OBJECTIVE: The aim of the study is to describe the experiences and perceptions of individuals diagnosed with stroke about the usefulness of the Model of Occupational Self Efficacy (MOOSE) in assisting them in returning to their worker role. METHODS: The study utilized an explorative, descriptive, qualitative research design, furthermore the data was analyzed using thematic analysis, resulting in three themes. Seven stroke survivors and one key informant participated in the study. RESULTS: Theme one: Obstacles that affect the return to work of stroke survivors in a rural community. Theme two: Re-establishing a strong belief in functional ability through participation in occupation. Finally, theme three: The MOOSE enables transition to the worker role in a rural context. CONCLUSION: The findings indicate that the MOOSE is a useful model in facilitating the return to work of individuals diagnosed with a stroke. The participants struggled to return to work not only due to their loss of abilities but also their lack of knowledge with regard to how to return to work and their diagnosis of a stroke. [ABSTRACT FROM AUTHOR]

Stroke is one of the leading causes of death and disability in adults. It can present itself in many different ways beginning with focal neurological symptoms and ending with sudden cardiac arrest. Stroke predisposes to ventricular arrhythmias due to prolongation of the QT interval. This problem mainly affects patients with additional risk factors that affect QT interval prolongation. The following case report presents the story of a 68-year-old patient with diabetes mellitus, hypertension, heart failure and rheumatoid arthritis taking chronic chloroquine diagnosed in the ED and subsequently admitted to the Cardiac Intensive Care Clinic for out-of-hospital ventricular fibrillation cardiac arrest in the course of recurrent ventricular tachyarrhythmias, who was diagnosed with ischaemic stroke. Coordinated management is presented to achieve a stable outcome. [ABSTRACT FROM AUTHOR]

Background/Aim: Stroke is a significant cause of death along with malignant neoplasm and cardiovascular disease. Comorbidities and laboratory abnormalities are common in stroke patients. Imaging methods are the gold standard in the differential diagnoses of stroke, but they are not used sufficiently to diagnose stroke, especially in underdeveloped countries. In this study, we aimed to examine the association between electrolytes and clinical outcomes in patients with hemorrhagic and ischemic stroke. Methods: Patients diagnosed with a stroke in the emergency department for one year were reviewed for this retrospective cohort study. We separated the patients into two groups, hemorrhagic and ischemic stroke, according to their diagnosis. Demographic, clinical features, laboratory, and imaging results were compared for the two groups. Potassium and sodium variables and receiver operating characteristic (ROC) analysis were used to predict the stroke status of individuals. Results: In total, we included 321 patients in our study; 114 (35.5%) patients had experienced a hemorrhagic stroke, and 207 (64.5%) patients had had an ischemic stroke. In the hemorrhagic stroke group, 64% were males, while 50.2% of the ischemic stroke group were males. The most common chronic disease was found to be hypertension in both groups (42.1% (hemorrhagic) and 33.3% (ischemic)). There was a statistically significant difference in the comparison of potassium and sodium parameters and diagnostic groups (P=0.021 and P=0.036). In addition, hypokalemia was found to be significant in the diagnosis of hemorrhagic stroke (P<0.001). Conclusion: Using potassium levels in the differential diagnosis of ischemic and hemorrhagic stroke is especially useful in the management of patients who cannot undergo imaging. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Physical Medicine & Rehabilitation Sciences is the property of Turkiye Klinikleri and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: The aim of the article is to outline the frequency of the occurrence, course, diagnosis and possible treatment of spinal cord ischemia (SCI) on the basis of a literature overview and to raise awareness of this rare yet devastating condition. Views: SCI, when compared to cerebral stroke, is a relatively rare disease, being diagnosed 100 times less often. Our knowledge as to its root causes, proper treatment for it and long-term prognosis is still inconclusive. Magnetic resonance imaging with diffusionweighted contrast is a main tool with which to confirm SCI and rule out a broad spectrum of possible alternative diagnoses. SCI is a condition in which prompt recognition, accurate diagnostic steps, and reperfusion therapy are essential to ensure a desirable functional outcome and reduce mortality and disability. Although there are no specific guidelines regarding treatment, the administration of rt-PA might be an effective therapy for acute ischemic stroke, preventing permanent spinal dysfunction. In surgical causes close cooperation between a neurologist and a neurosurgeon is necessary to provide combined appropriate management promptly. Conclusions: Due to the relative rarity of SCI, multi-center studies of ischemia of the spinal cord and its treatment would be advisable in neurological practice to enhance current knowledge. A rapid diagnosis is crucial for appropriate care and desirable long-term outcomes. [ABSTRACT FROM AUTHOR]

The link between stressful life events (SLE) and cardiovascular diseases (CVD) remains underexplored. This study aimed to examine the association between SLE and the diagnosis of heart disease or stroke, among older adults. Data from 678 participants from the population‐based cohort EPIPorto, with ≥60 years and complete information regarding SLE and heart disease or stroke, were analysed. Stressful life events were measured through the 'Stressful Life Events Screening Questionnaire'. A previous diagnosis of heart disease or stroke was self‐reported. Adjusted odds ratios (OR) with the respective 95% confidence intervals were computed through logistic regression. Almost a fourth of the participants never experienced any SLE throughout life, 30.0% experienced at least one event, 17.5% experienced two and 27.7% had experienced three or more SLE. A dose‐effect association between SLE and the diagnosis of heart disease or stroke was observed, statistically significant for those who had at least 3 types of SLE, independently of confounders (≥3SLE vs. 0SLE: OR = 2.00; 95% CI: 1.12–3.57). This cross‐sectional study suggests that cumulative exposure to different types of SLE during the life course was associated with a higher likelihood of having a diagnosis of heart disease or a stroke at a later age. Future longitudinal studies should better deepen this association, particularly by evaluating which type of SLE is more related to a higher prevalence of heart disease and stroke, and how the timing of the SLE influence this relation. [ABSTRACT FROM AUTHOR]

Telestroke systems have transformed the way stroke patients are diagnosed and treated in remote settings. However, there is still room for improvement to optimize stroke care delivery. The integration of advanced technologies into telestroke systems can enhance stroke diagnosis and treatment, leading to better patient outcomes and reduced healthcare costs. In this discussion, we explored various technologies, such as artificial intelligence, remote monitoring systems, telemedicine, and mobile applications, that can be integrated into telestroke systems to improve stroke care delivery. These technologies can enable accurate and timely diagnosis, facilitate remote consultations, monitor patients' conditions, and improve communication among healthcare providers. By integrating advanced technologies into telestroke systems, healthcare providers can improve stroke care delivery, particularly in underserved areas, and increase access to specialized stroke care, resulting in better patient outcomes. [ABSTRACT FROM AUTHOR]

Background: Stroke readmissions are considered a marker of health quality and may pose a burden to healthcare systems. However, information on the costs of post-stroke readmissions has not been systematically reviewed. Objectives: To systematically review information about the costs of hospital readmissions of patients whose primary diagnosis in the index admission was a stroke. Methods: A rapid systematic review was performed on studies reporting post-stroke readmission costs in EMBASE, MEDLINE, and Web of Science up to June 2021. Relevant data were extracted and presented by readmission and stroke type. The original study's currency values were converted to 2021 US dollars based on the purchasing power parity for gross domestic product. The reporting quality of each of the included studies was assessed using the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist. Results: Forty-four studies were identified. Considerable variability in readmission costs was observed among countries, readmissions, stroke types, and durations of the follow-up period. The UK and the USA were the countries reporting the highest readmission costs. In the first year of follow-up, stroke readmission costs accounted for 2.1–23.4%, of direct costs and 3.3–21% of total costs. Among the included studies, only one identified predictors of readmission costs. Conclusion: Our review showed great variability in readmission costs, mainly due to differences in study design, countries and health services, follow-up duration, and reported readmission data. The results of this study can be used to inform policymakers and healthcare providers about the burden of stroke readmissions. Future studies should not solely focus on improving data standardization but should also prioritize the identification of stroke readmission cost predictors. [ABSTRACT FROM AUTHOR]

Atherosclerosis is the most common cause of heart disease and stroke. Plaque thickness ≥4 mm in the ascending aorta or aortic arch is strongly correlated with cerebral embolic events and ischemic stroke. However, despite imaging workup, the cause of embolic stroke remains unidentified in many patients. Transesophageal echocardiography (TEE) is the preferred echocardiographic method for the evaluation of cardiac source of emboli. 2D TEE imaging evaluates aortic root and aortic arch in a single plane or two planes with biplane imaging. However, 2D TEE often fails to detect mobile or complex components in the ascending aorta and aortic arch plaques. The routine availability of 3D TEE in current ultrasound systems may significantly improve the assessment of aortic plaques as a potential embolic source. In this case series, we present four consecutive patients with stroke who underwent TEE by a single cardiologist for possible cardioembolic source. Some of these patients may have been labelled as "cryptogenic stroke" or "embolic stroke of undetermined source" (ESUS) due to the presence of insignificant or nonmobile ascending aortic or aortic arch plaques on 2D TEE imaging. In our four consecutive patients with ESUS who underwent TEE by a single operator, 3D TEE showed complex aortic arch plaques with ulceration with mobile components and established these plaques as the likely source of embolic stroke. [ABSTRACT FROM AUTHOR]

Stroke and TIAs are amongst the common neurological presentations encountered by specialists and non-specialist health care providers. Despite the advances of neuroimaging techniques, clinicians are frequently faced with diagnostic challenges on evaluation of patients with suspected stroke. In this review, we discuss the characteristic features of cerebrovascular diseases and how to identify them. We also aim to provide a resource for non-stroke specialist clinicians to help them to correctly identify the symptoms and signs of disorders that may masquerade as stroke such as migraine, seizure, and functional disorder, and at the same time we explore how we can identify strokes that present atypically. [ABSTRACT FROM AUTHOR]

The advancement of machine learning in healthcare offers significant potential for enhancing disease prediction and management. This study harnesses the PyCaret library—a Python-based machine learning toolkit—to construct and refine predictive models for diagnosing diabetes mellitus and forecasting hospital readmission rates. By analyzing a rich dataset featuring a variety of clinical and demographic variables, we endeavored to identify patients at heightened risk for diabetes complications leading to readmissions. Our methodology incorporates an evaluation of numerous machine learning algorithms, emphasizing their predictive accuracy and generalizability to improve patient care. We scrutinized the predictive strength of each model concerning crucial metrics like accuracy, precision, recall, and the area under the curve, underlining the imperative to eliminate false diagnostics in the field. Special attention is given to the use of the light gradient boosting machine classifier among other advanced modeling techniques, which emerge as particularly effective in terms of the Kappa statistic and Matthews correlation coefficient, suggesting robustness in prediction. The paper discusses the implications of diabetes management, underscoring interventions like lifestyle changes and pharmacological treatments to avert long-term complications. Through exploring the intersection of machine learning and health informatics, the study reveals pivotal insights into algorithmic predictions of diabetes readmission. It also emphasizes the necessity for further research and development to fully incorporate machine learning into modern diabetes care to prompt timely interventions and achieve better overall health outcomes. The outcome of this research is a testament to the transformative impact of automated machine learning in the realm of healthcare analytics. [ABSTRACT FROM AUTHOR]

Disorders of the nervous system, encompassing the brain, spinal cord and peripheral nerves, have emerged as a significant public health issue, with profound implications for individuals worldwide. These conditions result in significant morbidity and mortality. Many patients with neurological disorders often have comorbidities, further complicating their clinical presentation. Therefore, nurses must possess a comprehensive understanding of the nervous system and its associated disorders to formulate detailed care plans that address the unique needs of each patient. This article aims to explore the underlying pathophysiology of some of the most prevalent neurological disorders and how this informs effective patient assessment and diagnostic strategies. A further article will build on this to consider patient assessment and formulating a care plan in more detail. [ABSTRACT FROM AUTHOR]

Early identification of stroke symptoms is essential. The rate of stroke identification by call-takers at emergency medical communication centres (EMCCs) varies, and patients who are found in a lying down position are often not identified as having an ongoing stroke. Objectives: this study aimed to explore signs and symptoms of stroke in patients who had fallen or were found in a lying position. Design: a retrospective exploratory qualitative study design was used. Method: a total of 29 emergency calls to EMCCs regarding patients discharged with a stroke diagnosis from a large teaching hospital in Stockholm, Sweden, in January–June 2011, were analysed using qualitative content analysis. Results: during the emergency calls, the callers described a sudden change in the patient's health status including signs such as the patient's loss of bodily control, the patient's perception of a change in sensory perception, and the callers' inability to communicate with the patient. Conclusions: The callers' descriptions of stroke in a person found in a lying position are not always as described in assessment protocols describing the onset of a stroke. Instead, the symptom descriptions are much vaguer. Therefore, to increase identification of stroke during emergency calls, there is a need for an increased understanding of how callers describe stroke symptoms and communicate with the call-takers. [ABSTRACT FROM AUTHOR]

Introduction: Monitoring upper limb function is crucial for tracking progress, assessing treatment effectiveness, and identifying potential problems or complications. Hand goal-directed movements (GDMs) are a crucial aspect of daily life, reflecting planned motor commands with hand trajectories towards specific target locations. Previous studies have shown that GDM tasks can detect early changes in upper limb function in neurodegenerative diseases and can be used to track disease progression over time. Methods: In this study, we used accelerometer data from stroke survivor participants and controls doing activities of daily living to develop an automated deep learning approach to detect GDMs. The model performance for detecting GDM or non-GDM from windowed data achieved an AUC of 0.9, accuracy 0.83, sensitivity 0.81, specificity 0.84 and F1 0.82. Results: We further validated the utility of detecting GDM by extracting features from GDM periods and using these features to classify whether the measurements are collected from a stroke survivor or a control participant, and to predict the Fugl-Meyer assessment score from stroke survivors. Discussion: This study presents a promising and reliable tool for monitoring upper limb function in a real-world setting, and assessing biomarkers related to upper limb health in neurological, neuromuscular and muscles disorders. [ABSTRACT FROM AUTHOR]

Stroke is the second leading cause of death and a major cause of disability worldwide. Stroke severity scales serve as reliable means to track a patient's neurological deficit, predict outcome, and guide treatment decisions in clinical practice. The National Institute of Health Stroke Scale (NIHSS) was introduced over 30 years ago, marking a significant milestone in the field of stroke. Over the years, there have been notable advancements in acute stroke care. Despite several modifications made to NIHSS, none has yet succeeded in effectively capturing all the complex effects of a stroke. This review focuses on the pitfalls of NIHSS and emphasizes the need for a quick and comprehensive clinical and upgraded version of the stroke severity rating scale. [ABSTRACT FROM AUTHOR]

Introduction: Stroke is common cerebrovascular disease in the elderly, which is characterized by neurological defects caused by cerebral vessels. Multiple studies have shown that miRNAs play important roles in stroke. In addition, a large number of evidence suggest that stroke increases the risk and severity of cognitive impairments. Methods: miR-511-3p expression levels were detected by real-time PCR. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic value of miR-511-3p in distinguishing stroke patients from healthy controls and to assess risk of post-stroke cognitive impairment (PSCI) in stroke patients. Pearson correlation coefficient was used to determine the relationship between miR-511-3p expression level and Montreal Cognitive Assessment Scale (MoCA) scores. Results: Serum miR-511-3p expression levels were decreased in stroke patients, and the decrease was more significant in PSCI patients. ROC curve results showed that miR-511-3p had high diagnostic accuracy in distinguishing healthy controls from stroke patients. Moreover, the expression level of miR-511-3p can be used as an independent predictor for the occurrence of PSCI and is positively correlated with MoCA scores of PSCI patients. Conclusion: miR-511-3p may be involved in the occurrence and development of stroke. In addition, miR-511-3p may be a novel biomarker for predicting PSCI occurred in stroke patients. These results may help improve the quality of prognosis of stroke. [ABSTRACT FROM AUTHOR]

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Stroke is a global leading cause of death and disability. Knowledge of related guidelines is crucial for emergency department (ED) staff, influencing early diagnosis and timely treatment. We investigated Greek Cypriot ED healthcare professionals' (nurses and physicians) knowledge in recognizing and managing stroke. A descriptive cross-sectional study spanned November 2019 to April 2020, encompassing four private and seven public EDs in the Republic of Cyprus. The data were collected through a self-reported questionnaire developed by the research team, consisting of 37 questions. Eight questions focused on sociodemographic and employment characteristics, twenty-eight assessed knowledge in stroke recognition and management (each item was equally weighted without deliberate prioritization), and one question addressed self-assessment of knowledge in stroke care. A total of 255 nurses (response rate (RR): 74.1%) and 26 physicians (RR: 47.3%) completed the questionnaire. The average correct response rate was 12.9 out of 28 statements (SD: 4.2), with nurses and physicians scoring 12.6 (SD: 4.1) and 15.7 (SD: 4), respectively. Work experience significantly influenced stroke knowledge, with all groups demonstrating superiority over those with less than one year of experience. Participants with previous training scored an average of 1.45 additional correct answers while educational attainment did not significantly influence stroke knowledge. Investigating stroke knowledge among emergency department nurses and physicians in the Republic of Cyprus revealed significant deficits. This study stresses targeted interventions, including education, yearly examinations, workshops with hands-on training, and repeated training, to address these gaps and enhance the overall stroke care capabilities of the healthcare professionals. [ABSTRACT FROM AUTHOR]

Advanced stroke treatment is time-dependent and, therefore, relies on recognition by call-takers at prehospital telehealth services to ensure fast hospitalisation. This study aims to develop and assess the potential of machine learning in improving prehospital stroke recognition during medical helpline calls. We used calls from 1 January 2015 to 31 December 2020 in Copenhagen to develop a machine learning-based classification pipeline. Calls from 2021 are used for testing. Calls are first transcribed using an automatic speech recognition model and then categorised as stroke or non-stroke using a text classification model. Call-takers achieve a sensitivity of 52.7% (95% confidence interval 49.2–56.4%) with a positive predictive value (PPV) of 17.1% (15.5–18.6%). The machine learning framework performs significantly better (p < 0.0001) with a sensitivity of 63.0% (62.0–64.1%) and a PPV of 24.9% (24.3–25.5%). Thus, a machine learning framework for recognising stroke in prehospital medical helpline calls may become a supportive tool for call-takers, aiding in early and accurate stroke recognition. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is routinely mentioned in journals and newspapers, and non-technical outsiders may have difficulty in distinguishing hyperbole from reality. We present a practical guide to help non-technical neurologists to understand healthcare AI. AI is being used to support clinical decisions in treating neurological disorders. We introduce basic concepts of AI, such as machine learning and natural language processing, and explain how AI is being used in healthcare, giving examples its benefits and challenges. We also cover how AI performance is measured, and its regulatory aspects in healthcare. An important theme is that AI is a general-purpose technology like medical statistics, with broad utility applicable in various scenarios, such that niche approaches are outpaced by approaches that are broadly applicable in many disease areas and specialties. By understanding AI basics and its potential applications, neurologists can make informed decisions when evaluating AI used in their clinical practice. This article was written by four humans, with generative AI helping with formatting and image generation. [ABSTRACT FROM AUTHOR]

Julie Brix Bindslev,1,2 Soeren Paaske Johnsen,3 Klaus Hansen,1,4 Jan Brink Valentin,3 Christina Engel Hoei-Hansen,2,4 Thomas Truelsen1,4 1Department of Neurology, University Hospital of Copenhagen, Rigshospitalet, Copenhagen, Denmark; 2Department of Pediatrics, University Hospital of Copenhagen, Rigshospitalet, Copenhagen, Denmark; 3Danish Center for Clinical Health Services Research, Department of Clinical Medicine, Aalborg University, Aalborg, Denmark; 4Department of Clinical Medicine, University of Copenhagen, Copenhagen, DenmarkCorrespondence: Julie Brix Bindslev, Department of Neurology, University Hospital of Copenhagen, Rigshospitalet, Blegdamsvej 9, Copenhagen, 2100, Denmark, Tel +45 29 92 89 10, Email JBIN0007@regionh.dkBackground: This retrospective cohort study aimed to examine the positive predictive value (PPV) of pediatric stroke diagnoses in the Danish National Registry of Patients (DNRP) and the impact of different stroke definitions on the PPV.Methods: We included children registered with a stroke or stroke-related diagnosis in the DNRP between January 2017 through December 2020. Two assessors reviewed medical records and validated cases according to the American Heart and American Stroke Association (AHA/ASA) stroke definition. The level of interrater agreement was examined using kappa statistics. Validation by the AHA/ASA definition was compared with validation according to the definition in the International Classification of Disease 11th version (ICD-11) and the World Health Organization’s definition.Results: Stroke was confirmed in 120 of 309 included children, yielding an overall PPV of 0.39 (95% CI: 0.33– 0.45). PPV varied across stroke subtypes from 0.83 (95% CI: 0.71– 0.92) for ischemic stroke (AIS), 0.57 (95% CI: 0.37– 0.76) for unspecified stroke, 0.42 (95% CI: 0.33– 0.52) for intracerebral hemorrhage (ICH) to 0.31 (95% CI: 0.55– 0.98) and 0.07 (95% CI: 0.01– 0.22) for cerebral venous thrombosis and subarachnoid hemorrhage (SAH), respectively. Most non-confirmed ICH and SAH diagnoses were in children with traumatic intracranial hemorrhages (36 and 66% respectively). Among 70 confirmed AIS cases, 25 (36%) were identified in non-AIS code groups. PPV varied significantly across stroke definitions with the highest for the AHA/ASA definition (PPV = 0.39, 95% CI: 0.34– 0.45) and the lowest for the WHO definition (PPV = 0.29, 95% CI: 0.24– 0.34). Correspondingly, the incidence of pediatric AIS per 100.000 person-years changed from 1.5 for the AHA/ASA definition to 1.2 for ICD-11 and 1.0 for the WHO-definition. The overall interrater agreement was considered excellent (κ=0.85).Conclusion: After validation, stroke was confirmed in only half of the children registered in the DNRP with a stroke-specific diagnosis. Non-validated administrative data should be used with caution in pediatric stroke research. Pediatric stroke incidence rates may vary markedly depending on which stroke definition is used.Keywords: positive predictive value, stroke diagnosis, stroke definition, children, interrater reliability, Danish National Registry of Patients

IntroductionMonitoring upper limb function is crucial for tracking progress, assessing treatment effectiveness, and identifying potential problems or complications. Hand goal-directed movements (GDMs) are a crucial aspect of daily life, reflecting planned motor commands with hand trajectories towards specific target locations. Previous studies have shown that GDM tasks can detect early changes in upper limb function in neurodegenerative diseases and can be used to track disease progression over time.MethodsIn this study, we used accelerometer data from stroke survivor participants and controls doing activities of daily living to develop an automated deep learning approach to detect GDMs. The model performance for detecting GDM or non-GDM from windowed data achieved an AUC of 0.9, accuracy 0.83, sensitivity 0.81, specificity 0.84 and F1 0.82.ResultsWe further validated the utility of detecting GDM by extracting features from GDM periods and using these features to classify whether the measurements are collected from a stroke survivor or a control participant, and to predict the Fugl-Meyer assessment score from stroke survivors.DiscussionThis study presents a promising and reliable tool for monitoring upper limb function in a real-world setting, and assessing biomarkers related to upper limb health in neurological, neuromuscular and muscles disorders.

Background: Stroke mimics are common in the emergency department (ED) and early detection is important to initiate appropriate treatment and withhold unnecessary procedures. We aimed to compare the frequency, clinical characteristics and predictors of non-neurological and neurological stroke mimics transferred to our ED for suspected stroke. Methods: This was a cross-sectional study of consecutive patients with suspected stroke transported to the ED of the University Hospital Essen between January 2017 and December 2021 by the city's Emergency Medical Service. We investigated patient characteristics, preclinical data, symptoms and final diagnoses in patients with non-neurological and neurological stroke mimics. Multinominal logistic regression analysis was performed to assess predictors of both etiologic groups. Results: Of 2167 patients with suspected stroke, 762 (35.2%) were diagnosed with a stroke mimic. Etiology was non-neurological in 369 (48.4%) and neurological in 393 (51.6%) cases. The most common diagnoses were seizures (23.2%) and infections (14.7%). Patients with non-neurological mimics were older (78.0 vs. 72.0 y, p < 0.001) and more likely to have chronic kidney disease (17.3% vs. 9.2%, p < 0.001) or heart failure (12.5% vs. 7.1%, p = 0.014). Prevalence of malignancy (8.7% vs. 13.7%, p = 0.031) and focal symptoms (38.8 vs. 57.3%, p < 0.001) was lower in this group. More than two-fifths required hospitalization (39.3 vs. 47.1%, p = 0.034). Adjusted multinominal logistic regression revealed chronic kidney and liver disease as independent positive predictors of stroke mimics regardless of etiology, while atrial fibrillation and hypertension were negative predictors in both groups. Prehospital vital signs were independently associated with non-neurological stroke mimics only, while age was exclusively associated with neurological mimics. Conclusions: Up to half of stroke mimics in the neurological ED are of non-neurological origin. Preclinical identification is challenging and a high proportion requires hospitalization. Awareness of underlying etiologies and differences in clinical characteristics is important to provide optimal care. [ABSTRACT FROM AUTHOR]

Aim: To assess whether stroke diagnoses in national health registers are sufficiently correct and complete to replace manual collection of endpoint data for the Tromsø Study, a population-based epidemiological study. Method: Using the Tromsø Study Cardiovascular Disease Register for 2013–2014 as the gold standard, we calculated correctness (defined as positive predictive value, PPV) and completeness (defined as sensitivity) of stroke cases in four different data subsets derived from the Norwegian Patient Register and the Norwegian Stroke Register. We calculated the sensitivity and PPV with 95% confidence intervals (CIs) assuming a normal approximation of the binomial distribution. Results: In the Norwegian Stroke Register we found a sensitivity of 79.8% (95% CI 74.2–85.4) and a PPV of 97.5% (95% CI 95.1–99.9). In the Norwegian Patient Register the sensitivity was 86.4% (95% CI 81.6–91.1) and the PPV was 84.2% (95% CI 79.2–89.2). The overall highest levels were found in a subset based on a linkage between the Norwegian Stroke Register and the Norwegian Patient Register, with a sensitivity of 88.9% (95% CI 84.5–93.3), and a PPV of 89.3% (95% CI 85.0–93.6). Conclusions: Data from the Norwegian Patient Register and from the linked data set between the Norwegian Patient Register and the Norwegian Stroke Register had acceptable levels of correctness and completeness to be considered as endpoint sources for the Tromsø Study Cardiovascular Disease Register. The benefits of using data from national registers as endpoints in epidemiological studies must be weighed against the impact of potentially decreased data quality. [ABSTRACT FROM AUTHOR]

There is mounting evidence that the vascular manifestations associated with COVID-19 are linked to ischemic strokes. The emergence of patients with both COVID-19 and cerebrovascular accidents (COV-CVA) has created a need to adjust occupational therapy service delivery for inpatients with this dual diagnosis. This clinical perspective paper conceptualizes COV-CVA as a health condition using the International Classification of Functioning, Disability and Health Framework (ICF). The Occupational Therapy Practice Framework-4 was used to guide clinical considerations and recommendations for the evaluation, intervention, and discharge planning of patients with COV-CVA. [ABSTRACT FROM AUTHOR]

Background: Cerebrovascular accident, or stroke, is a common cause of death or disability. Timely diagnosis and intervention are critical for improving survival rates and reducing the long-term effects of stroke. For patients with ischemic stroke, thrombolytic drugs and endovascular intervention are time-sensitive treatment options. Local Problem: Patients living in rural areas often do not have access to rapid consultation with specialized neurologic teams for diagnosis and treatment of stroke. The use of telemedicine in the form of a telestroke consultation can improve timely diagnosis and treatment for rural patients exhibiting stroke symptoms. Methods: A telestroke program was implemented in the upper Midwest. A team of 4 interventional neurologists provided telestroke consultation to a comprehensive stroke center and 5 other acute stroke–ready rural hospitals. Results: A tiered stroke alert algorithm and telestroke workflow chart were developed to help health care professionals at rural sites determine eligibility for telestroke consultation. A teleneurologist connected with the originating site, and the National Institutes of Health Stroke Scale could be completed remotely with assistance from the originating site. Telestroke has increased the percentage of patients receiving thrombolytics in less than 60 minutes, and door-to-needle time has decreased. Conclusion: Rural patients with stroke symptoms may experience a delay in care or stroke diagnosis due to distance to specialized neurologic services. Telestroke consultation is a successful method for timely diagnosis of stroke and recommendation for treatment. [ABSTRACT FROM AUTHOR]

Aims and Objectives: To describe dyadic psycho‐social intervention measures and to evaluate their influence on stroke survivors and caregiver's functional independence, quality of life, depression, anxiety, self‐efficacy and coping ability. Background: Because of the importance of dyadic intervention and the seriousness of the psycho‐social problems of stroke survivors and caregivers, understanding the influence of dyadic psycho‐social interventions is vital. Design: A systematic review and meta‐analysis based on PRISMA guidelines. Data Sources: Nine databases were systematically searched for randomized controlled trials submitted from 1910 to 4 July 2022. Methods: The included papers were evaluated for quality, and quantitative data were standardly extracted and analysed by meta‐analysis, followed by synthesis. The meta‐analysis was carried out using Review Manager 5.4 software. Results: Fifteen randomized controlled trials were included (n = 2190 for patients, and n = 1933 for caregivers). Study results showed that dyadic psycho‐social interventions significantly alleviated the depressive symptoms of patients, obviously improved the ability to function independently of patients and more quickly alleviated the care burden of caregivers. Conclusions: This study provided moderate support for the benefits of dyadic psycho‐social intervention in improving survivor and caregiver's functional independence, quality of life, depression, anxiety, self‐efficacy and care burden. Nevertheless, due to limitations of the study, it was deemed necessary that this topic is studied further. Relevance to Clinical Practice: This review suggests that dyadic psycho‐social interventions should be considered as effective strategies for decreasing psycho‐social problems of stroke survivors and caregivers, and provides evidence for the formulation of targeted intervention programs. The personalized implementation of such interventions should be the focus of clinical practice. No Patient or Public Contribution: There was no patient or public contribution. [ABSTRACT FROM AUTHOR]

Stroke is a life-threatening disease usually due to blockage of blood or insufficient blood flow to the brain. It has a tremendous impact on every aspect of life since it is the leading global factor of disability and morbidity. Strokes can range from minor to severe (extensive). Thus, early stroke assessment and treatment can enhance survival rates. Manual prediction is extremely time and resource intensive. Automated prediction methods such as Modern Information and Communication Technologies (ICTs), particularly those inMachine Learning (ML) area, are crucial for the early diagnosis and prognosis of stroke. Therefore, this research proposed an ensemble voting model based on three Machine Learning (ML) algorithms: Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LGBM). We apply data preprocessing to manage the outliers and useless instances in the dataset. Furthermore, to address the problem of imbalanced data, we enhance the minority class's representation using the Synthetic Minority Over-Sampling Technique (SMOTE), allowing it to engage in the learning process actively. Results reveal that the suggested model outperforms existing studies and other classifiers with 0.96% accuracy, 0.97% precision, 0.97% recall, and 0.96% F1-score. The experiment demonstrates that the proposed ensemble voting model outperforms state-of-the-art and other traditional approaches. [ABSTRACT FROM AUTHOR]