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Needs Analysis for Educating Community Pharmacists to Interface with Prehospital Stroke Chain of Survival
Conclusions: Community pharmacists surveyed were willing to interface with the prehospital phase of the Stroke Chain of Survival; nearly 75% of them required education to do so. Community pharmacies are potentially a venue for educating the public on the Stroke Chain of Survival. It may be necessary to approach community pharmacy corporate leadership to partner with such efforts.
Source: Journal of Stroke and Cerebrovascular Diseases - December 17, 2012 Category: Neurology Authors: Tina Harrach Denetclaw, Patricia Cefalu, Louis L. Manila, John J. Panagotacos Tags: Original Articles Source Type: research

Initial Experience with Upfront Arterial and Perfusion Imaging among Ischemic Stroke Patients Presenting within the 4.5-hour Time Window
Conclusions: An upfront CTA/CTP protocol aided stroke team decision-making in nearly half of cases. Implementation of a CTA/CTP protocol was associated with a learning curve of 6 months before door to needle time ≤60 minutes returned to similar rates as the pre-CTA/CTP protocol.
Source: Journal of Stroke and Cerebrovascular Diseases - January 24, 2013 Category: Neurology Authors: Ali Reza Noorian, Katja Bryant, Ashley Aiken, Andrew D. Nicholson, Adam B. Edwards, Mason P. Markowski, Seena Dehkharghani, Jemisha C. Bouloute, Jacquelyn Abney, Fadi Nahab Tags: Original Articles Source Type: research

Decision-Making on Referral to Primary Care Physiotherapy After Inpatient Stroke Rehabilitation
Worldwide, stroke is a leading cause of death and disability.1 Although incidence rates are expected to increase over the next few decades, survival rates are expected to improve. Consequently, more stroke survivors will have to learn to live with the consequences. After acute stroke care or rehabilitation, returning home is one of the primary goals for stroke survivors.2 In the Netherlands, 65 % of stroke survivors return home immediately after acute hospital care.3 The remaining 35% continue inpatient rehabilitation in a rehabilitation center (RC) or geriatric rehabilitation center (GRC) before returning home.
Source: Journal of Stroke and Cerebrovascular Diseases - February 23, 2021 Category: Neurology Authors: Marieke Geerars, Roderick Wondergem, Martijn F. Pisters Source Type: research

Clinical Features for Identifying the Possibility of Toileting Independence after Convalescent Inpatient Rehabilitation in Severe Stroke Patients: A Decision Tree Analysis Based on a Nationwide Japan Rehabilitation Database
This study aimed to identify the factors affecting toileting independence in severe stroke patients using ML.
Source: Journal of Stroke and Cerebrovascular Diseases - November 27, 2020 Category: Neurology Authors: Takeshi Imura, Yu Inoue, Ryo Tanaka, Junji Matsuba, Yasutaka Umayahara Source Type: research

Time metrics in acute ischemic stroke care during the second and first wave of COVID 19 Pandemic: A tertiary care center experience from South India.
This study shares our experience in stroke time metrics during the second wave of pandemic compared to the first wave.
Source: Journal of Stroke and Cerebrovascular Diseases - January 13, 2022 Category: Neurology Authors: Dileep Ramachandran, Praveen Panicker, P Chitra, Thomas Iype Source Type: research

Can We Learn from Our Children About stroke? Effectiveness of a School-Based Educational Programme in Greece
Stroke is the second most common cause of death worldwide and the leading cause of chronic functional limitations.1 Without appropriate and timely care between the stroke symptom appearance and the treatment in acute stroke incidents, the ischemic brain ages 3.6 years for every hour of blood deprivation.2 Yet patients repeatedly arrive late to the hospital.3,4 The lack of public awareness about stroke symptoms has been reported in the literature as one of the main factors, causing this belated arrival to the hospital and hence the belated medical treatment.
Source: Journal of Stroke and Cerebrovascular Diseases - May 13, 2022 Category: Neurology Authors: Hariklia Proios, Maria Baskini, Christos Keramydas, Tatiana Pourliaka, Kalliopi Tsakpounidou Source Type: research

Machine learning based reanalysis of clinical scores for distinguishing between ischemic and hemorrhagic stroke in low resource setting
Identifying ischemic or hemorrhagic strokes clinically may help in situations where neuroimaging is unavailable to provide primary-care prior to referring to stroke-ready facility. Stroke classification-based solely on clinical scores faces two unresolved issues. One pertains to overestimation of score performance, while other is biased performance due to class-imbalance inherent in stroke datasets. After correcting the issues using Machine Learning theory, we quantitatively compared existing scores to study the capabilities of clinical attributes for stroke classification.
Source: Journal of Stroke and Cerebrovascular Diseases - August 1, 2022 Category: Neurology Authors: Aman Bhardwaj, MV Padma Srivastava, Pulikottil Vinny Wilson, Amit Mehndiratta, Venugopalan Y Vishnu, Rahul Garg Source Type: research

Image level detection of large vessel occlusion on 4D-CTA perfusion data using deep learning in acute stroke
Acute ischemic stroke (AIS) secondary to LVOs represent approximately 30-40% of all stroke cases and are associated with disproportionately higher morbidity and mortality.1, 2 The importance of endovascular thrombectomy (EVT) in patients with acute ischemic stroke (AIS) has been well established in multiple randomized controlled trials for patients in both early and late stroke windows.3-6 A critical aspect of the patient triage is the accurate and timely detection of underlying large vessel occlusion (LVO).
Source: Journal of Stroke and Cerebrovascular Diseases - September 10, 2022 Category: Neurology Authors: Girish Bathla, Dhruba Durjoy, Sarv Priya, Edgar Samaniego, Colin P. Derdeyn Source Type: research

Development and Validation of Machine Learning-Based Prediction for Dependence in the Activities of Daily Living after Stroke Inpatient Rehabilitation: A Decision-Tree Analysis
This study aimed to develop and assess the CPRs using machine learning-based methods to identify ADL dependence in stroke patients.
Source: Journal of Stroke and Cerebrovascular Diseases - September 26, 2020 Category: Neurology Authors: Yuji Iwamoto, Takeshi Imura, Ryo Tanaka, Naoki Imada, Tetsuji Inagawa, Hayato Araki, Osamu Araki Source Type: research

Decision Tree Algorithm Identifies Stroke Patients Likely Discharge Home After Rehabilitation Using Functional and Environmental Predictors
The importance of environmental factors for stroke patients to achieve home discharge was not scientifically proven. There are limited studies on the application of the decision tree algorithm with various functional and environmental variables to identify stroke patients with a high possibility of home discharge. The present study aimed to identify the factors, including functional and environmental factors, affecting home discharge after stroke inpatient rehabilitation using the machine learning method.
Source: Journal of Stroke and Cerebrovascular Diseases - February 3, 2021 Category: Neurology Authors: Takeshi Imura, Yuji Iwamoto, Tetsuji Inagawa, Naoki Imada, Ryo Tanaka, Haruki Toda, Yu Inoue, Hayato Araki, Osamu Araki Source Type: research

Discrepancies in Stroke Distribution and Dataset Origin in Machine Learning for Stroke
Machine learning algorithms depend on accurate and representative datasets for training in order to become valuable clinical tools that are widely generalizable to a varied population. We aim to conduct a review of machine learning uses in stroke literature to assess the geographic distribution of datasets and patient cohorts used to train these models and compare them to stroke distribution to evaluate for disparities.
Source: Journal of Stroke and Cerebrovascular Diseases - April 30, 2021 Category: Neurology Authors: Lohit Velagapudi, Nikolaos Mouchtouris, Michael P. Baldassari, David Nauheim, Omaditya Khanna, Fadi Al Saiegh, Nabeel Herial, M. Reid Gooch, Stavropoula Tjoumakaris, Robert H. Rosenwasser, Pascal Jabbour Source Type: research

Methodology of the Stroke Self-Management Rehabilitation Trial: An International, Multisite Pilot Trial
Stroke is a major cause of long-term adult disability with many survivors living in the community relying on family members for on-going support. However, reports of inadequate understanding of rehabilitation techniques are common. A self-management DVD-based observational learning tool may help improve functional outcomes for survivors of stroke and reduce caregivers' burden.
Source: Journal of Stroke and Cerebrovascular Diseases - December 9, 2014 Category: Neurology Authors: Kelly M. Jones, Rohit Bhattacharjee, Rita Krishnamurthi, Sarah Blanton, Alice Theadom, Suzanne Barker-Collo, Amanda Thrift, Priya Parmar, Annick Maujean, Annemarei Ranta, Emmanuel Sanya, Valery L. Feigin, SMART Study Group Source Type: research

Measuring Functional Arm Movement after Stroke Using a Single Wrist-Worn Sensor and Machine Learning
We present a novel, inexpensive, and feasible method for separating UE functional use from nonfunctional movement after stroke using a single wrist-worn accelerometer.
Source: Journal of Stroke and Cerebrovascular Diseases - August 4, 2017 Category: Neurology Authors: Elaine M. Bochniewicz, Geoff Emmer, Adam McLeod, Jessica Barth, Alexander W. Dromerick, Peter Lum Source Type: research

Predicting Motor and Cognitive Improvement Through Machine Learning Algorithm in Human Subject that Underwent a Rehabilitation Treatment in the Early Stage of Stroke
The objective of this study was to investigate, in subject with stroke, the exact role as prognostic factor of common inflammatory biomarkers and other markers in predicting motor and/or cognitive improvement after rehabilitation treatment from early stage of stroke.
Source: Journal of Stroke and Cerebrovascular Diseases - August 2, 2018 Category: Neurology Authors: Patrizio Sale, Giorgio Ferriero, Lucio Ciabattoni, Anna Maria Cortese, Francesco Ferracuti, Luca Romeo, Francesco Piccione, Stefano Masiero Source Type: research

Machine Learning for Brain Stroke: A Review
Machine Learning (ML) delivers an accurate and quick prediction outcome and it has become a powerful tool in health settings, offering personalized clinical care for stroke patients. An application of ML and Deep Learning in health care is growing however, some research areas do not catch enough attention for scientific investigation though there is real need of research. Therefore, the aim of this work is to classify state-of-arts on ML techniques for brain stroke into 4 categories based on their functionalities or similarity, and then review studies of each category systematically.
Source: Journal of Stroke and Cerebrovascular Diseases - August 11, 2020 Category: Neurology Authors: Manisha Sanjay Sirsat, Eduardo Ferm é, Joana Câmara Tags: Review Article Source Type: research

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