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Survivor gives us a lens into regional systems of care for acute ischemic stroke in North Dakota
I just got back from the North Dakota Mission: Lifeline STEMI and Acute Stroke Conference in Bismark, ND. I had a great time and I learned a lot. I often get asked to speak in various venues about acute STEMI and 12-lead ECG interpretation, but for this conference they wanted me to talk mostly about stroke. That turned out to be a good thing because it forced me to read the 2013 AHA / ASA Guildelines for the Early Management of Patients With Acute Ischemic Stroke to make sure I was asking intelligent questions during the panel discussion (which I moderated) and also giving accurate information for the class I taught about ...
Source: EMS 12-Lead - May 31, 2013 Category: Cardiology Authors: Tom Bouthillet Tags: ems-topics patient-management North Dakota Mission Lifeline Stroke Source Type: research

Prediction of incident atrial fibrillation in post-stroke patients using machine learning: a French nationwide study
ConclusionsML algorithms predict incident AF post-stroke with a better ability than previously developed clinical scores.Graphic AbstractAF: atrial fibrillation; DNN: deep neural network; IS: ischemic stroke; KNN: K-nearest neighbors; LR: logistic regression; RFC: random forest classifier; XGBoost: extreme gradient boosting
Source: Clinical Research in Cardiology - December 17, 2022 Category: Cardiology Source Type: research

Causative Classification of Ischemic Stroke by the Machine Learning Algorithm Random Forests
CONCLUSION: The proposed RF model could be a useful diagnostic tool to help neurologists categorize etiologies of stroke.CLINICAL TRIAL REGISTRATION: [www.ClinicalTrials.gov], identifier [NCT01274117].PMID:35493925 | PMC:PMC9051333 | DOI:10.3389/fnagi.2022.788637
Source: Atherosclerosis - May 2, 2022 Category: Cardiology Authors: Jianan Wang Xiaoxian Gong Hongfang Chen Wansi Zhong Yi Chen Ying Zhou Wenhua Zhang Yaode He Min Lou Source Type: research

The Role of the Left Atrial Appendage in Stroke and Arrhythmia Provocation
AbstractPurpose of the reviewThe review will provide an overview of the potential use of left atrial appendage (LAA) exclusion in patients with atrial fibrillation (AF), highlighting the benefits and risks involved with LAA exclusion.Recent findingsLAA ligation leads to electrical isolation of the LAA and a decrease in LA mass which is associated with a decrease in AF burden and occasional termination of AF in patients with persistent AF. This potential new indication will further expand the use of LAA exclusion and necessitate planning of combining LAA exclusion with catheter ablation for both stroke prevention and antiar...
Source: Current Cardiovascular Risk Reports - March 19, 2018 Category: Cardiology Source Type: research

Relation of Atrial Fibrillation to Cognitive Decline (from the REasons for Geographic and Racial Differences in Stroke REGARDS Study)
The association of atrial fibrillation (AF) with cognitive function remains unclear, especially among racially/geographically diverse populations. This analysis included 25,980 black and white adults, aged 48+, from the national REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort, free from cognitive impairment and stroke at baseline. Baseline AF was identified by self-reported medical history or electrocardiogram (ECG). Cognitive testing was conducted yearly with the Six Item Screener (SIS) to define impairment and at 2-year intervals to assess decline on: animal naming and letter fluency, Montreal Co...
Source: The American Journal of Cardiology - March 5, 2021 Category: Cardiology Authors: Margie J. Bailey, Elsayed Z. Soliman, Leslie A. McClure, George Howard, Virginia J. Howard, Suzanne E. Judd, Fred Unverzagt, Virginia Wadley, Bonnie C. Sachs, Timothy Hughes Source Type: research

Preprocedural determination of an occlusion pathomechanism in endovascular treatment of acute stroke: a machine learning-based decision
CONCLUSIONS: An ML-PM could accurately determine an occlusion pathomechanism with common preprocedural findings. A decision flowchart consisting of the four most influential findings was clinically applicable and superior to single common preprocedural findings for determining an occlusion pathomechanism.PMID:35710314 | DOI:10.1136/neurintsurg-2022-018946
Source: Atherosclerosis - June 16, 2022 Category: Cardiology Authors: Jang-Hyun Baek Byung Moon Kim Dong Joon Kim Ji Hoe Heo Hyo Suk Nam Young Dae Kim Myung Ho Rho Pil-Wook Chung Yu Sam Won Yeongu Chung Source Type: research

230 Machine Learning Models for Predicting Ischemic Stroke and Major Bleeding Risk in Patients with Atrial Fibrillation
Risk scores such as CHA2DS2-VASc and HAS-BLED are used to assess stroke and bleeding risk respectively and choose appropriate antithrombotic therapy in patients with atrial fibrillation (AF). The application of ML models may improve risk prediction and identification of potential risk factors.
Source: Heart, Lung and Circulation - November 8, 2020 Category: Cardiology Authors: J. Lu, G. Dwivedi, F. Sanfilippo, M. Bennamoun, J. Hung, T. Briffa, F. Sohel, R. Hutchens, J. Stewart, B. Chow, B. McQuillan Source Type: research

A Practical Guide to Setting up an ICM Service: Improving Detection of Atrial Fibrillation After Cryptogenic Stroke With Implantable Cardiac Monitors (ICMs)
*/ JOIN VIRTUAL MEETING BELOW TO LEARN MORE: THE RIGHT APPROACH FOR AF DETECTION   AND SECONDARY PREVENTION 12 May, 2-5pm GMT   Click here to register   Chapter 1: Making the case for change Chapter 2: Engaging the right stakeholders to set up the service Chapter 3: Developing th...
Source: Radcliffe Cardiology - March 24, 2021 Category: Cardiology Authors: c242508f1d9059bc0f2aa9bdd5421ba2 Source Type: research

Using machine learning to predict atrial fibrillation diagnosed after ischemic stroke
Selecting best candidates for prolonged poststroke cardiac monitoring in acute ischemic stroke (AIS) patients is still challenging. We aimed to develop a machine learning (ML) model to select AIS patients at high risk of poststroke atrial fibrillation (AF) for prolonged cardiac monitoring and then to compare ML model with traditional risk scores and classic statistical logistic regression (classic-LR) model.
Source: International Journal of Cardiology - November 11, 2021 Category: Cardiology Authors: Xiaohan Zheng, Fusang Wang, Juan Zhang, Xiaoli Cui, Fuping Jiang, Nihong Chen, Junshan Zhou, Jinsong Chen, Song Lin, Jianjun Zou Source Type: research

Prediction of incident atrial fibrillation in community-based electronic health records: a systematic review with meta-analysis
Conclusions Models externally validated for prediction of incident AF in community-based EHR demonstrate moderate predictive ability and high risk of bias. Novel methods may provide stronger discriminative performance. Systematic review registration PROSPERO CRD42021245093.
Source: Heart - June 10, 2022 Category: Cardiology Authors: Nadarajah, R., Alsaeed, E., Hurdus, B., Aktaa, S., Hogg, D., Bates, M. G. D., Cowan, C., Wu, J., Gale, C. P. Tags: Open access Arrhythmias and sudden death Source Type: research

First results of the Brazilian Registry of Percutaneous Left Atrial Appendage Closure
Conclusions: In this multicenter, real world registry, that included patients with NVAF and high thromboembol ic and bleeding risks, LAAC effectively prevented stroke and bleeding when compared to the expected rates based on CHA2DS2VASc and HASBLED scores for this population. Complications rate of the procedure was acceptable considering the beginning of the learning curve of most of the involved operators.Resumo Fundamento: A oclus ão percutânea do apêndice atrial esquerdo (OAAE) é uma alternativa eficaz à anticoagulação oral (ACO) para a prevenção de acidente vascular cerebral (AVC) em pacientes com fibrilação...
Source: Arquivos Brasileiros de Cardiologia - December 18, 2017 Category: Cardiology Source Type: research

Multi ‐modality machine learning approach for risk stratification in heart failure with left ventricular ejection fraction ≤ 45%
ConclusionsMulti ‐modality assessment is important for risk stratification in HF. A machine learning approach provides additional value for improving outcome prediction.
Source: ESC Heart Failure - October 23, 2020 Category: Cardiology Authors: Gary Tse, Jiandong Zhou, Samuel Won Dong Woo, Ching Ho Ko, Rachel Wing Chuen Lai, Tong Liu, Yingzhi Liu, Keith Sai Kit Leung, Andrew Li, Sharen Lee, Ka Hou Christien Li, Ishan Lakhani, Qingpeng Zhang Tags: Original Research Article Source Type: research

European Heart Rhythm Association Practical Guide on the use of new oral anticoagulants in patients with non-valvular atrial fibrillation
New oral anticoagulants (NOACs) are an alternative for vitamin K antagonists (VKAs) to prevent stroke in patients with non-valvular atrial fibrillation (AF). Both physicians and patients will have to learn how to use these drugs effectively and safely in clinical practice. Many unresolved questions on how to optimally use these drugs in specific clinical situations remain. The European Heart Rhythm Association set out to coordinate a unified way of informing physicians on the use of the different NOACs. A writing group listed 15 topics of concrete clinical scenarios and formulated as practical answers as possible based on ...
Source: Europace - April 26, 2013 Category: Cardiology Authors: Heidbuchel, H., Verhamme, P., Alings, M., Antz, M., Hacke, W., Oldgren, J., Sinnaeve, P., Camm, A. J., Kirchhof, P. Tags: EHRA PRACTICAL GUIDE Source Type: research

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