Canadian Journal of Cardiology最新文献_第7页

CREATION OF A POST CARDIAC INTENSIVE CARE UNIT RECOVERY CLINIC 创建心脏重症监护室术后恢复诊所

IF 5.8 2区医学

Canadian Journal of Cardiology Pub Date : 2024-10-01 DOI: 10.1016/j.cjca.2024.08.020

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ACCEPTABILITY AND EFFECTIVENESS OF A NOVEL, VIDEO-BASED CPR AND AED EDUCATION PROGRAM IN ONTARIO SCHOOLS 安大略省学校对基于视频的新颖 CPR 和 AED 教育计划的接受度和有效性

IF 5.8 2区医学

Canadian Journal of Cardiology Pub Date : 2024-10-01 DOI: 10.1016/j.cjca.2024.08.093

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TAILORING FETAL ECHOCARDIOGRAPHY INDICATIONS TO THE LIMITED RESOURCES IN SASKATCHEWAN 根据萨斯喀彻温省有限的资源调整胎儿超声心动图适应症

IF 5.8 2区医学

Canadian Journal of Cardiology Pub Date : 2024-10-01 DOI: 10.1016/j.cjca.2024.08.077

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INFLUENCE OF ATRIAL FIBRILLATION ON MUSICAL RHYTHM: PROOF OF CONCEPT STUDY 心房颤动对音乐节奏的影响：概念验证研究

IF 5.8 2区医学

Canadian Journal of Cardiology Pub Date : 2024-10-01 DOI: 10.1016/j.cjca.2024.08.009

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EVALUATING THE IMPACT OF EXTENDED WARM ISCHEMIC TIME USING EX-VIVO HEART PERFUSION IN JUVENILE PORCINE MODELS OF CIRCULATORY DEATH 利用体外心脏灌流技术评估延长温热缺血时间对幼年猪循环死亡模型的影响

IF 5.8 2区医学

Canadian Journal of Cardiology Pub Date : 2024-10-01 DOI: 10.1016/j.cjca.2024.08.006

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PERCUTANEOUS CORONARY INTERVENTION VERSUS CORONARY ARTERY BYPASS GRAFTING FOR LEFT MAIN CORONARY ARTERY REVASCULARIZATION IN PATIENTS WITH CHRONIC KIDNEY DISEASE: A SYSTEMATIC REVIEW AND META-ANALYSIS 经皮冠状动脉介入治疗与冠状动脉旁路移植术对慢性肾脏病患者左冠状动脉主干再血管化治疗的比较：系统回顾和荟萃分析

IF 5.8 2区医学

Canadian Journal of Cardiology Pub Date : 2024-10-01 DOI: 10.1016/j.cjca.2024.08.030

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IMPLEMENTATION OF STAPHYLOCOCCUS AUREUS SCREENING AND DECOLONIZATION IN CARDIAC SURGERY 在心脏外科手术中实施金黄色葡萄球菌筛查和去菌处理

IF 5.8 2区医学

Canadian Journal of Cardiology Pub Date : 2024-10-01 DOI: 10.1016/j.cjca.2024.08.008

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Role of Artificial Intelligence in Improving Syncope Management 人工智能在改善晕厥管理中的作用。

IF 5.8 2区医学

Canadian Journal of Cardiology Pub Date : 2024-10-01 DOI: 10.1016/j.cjca.2024.05.027

{"title":"Role of Artificial Intelligence in Improving Syncope Management","authors":"","doi":"10.1016/j.cjca.2024.05.027","DOIUrl":"10.1016/j.cjca.2024.05.027","url":null,"abstract":"<div><div>Syncope is common in the general population and a common presenting symptom in acute care settings. Substantial costs are attributed to the care of patients with syncope. Current challenges include differentiating syncope from its mimickers, identifying serious underlying conditions that caused the syncope, and wide variations in current management. Although validated risk tools exist, especially for short-term prognosis, there is inconsistent application, and the current approach does not meet patient needs and expectations. Artificial intelligence (AI) techniques, such as machine learning methods including natural language processing, can potentially address the current challenges in syncope management. Preliminary evidence from published studies indicates that it is possible to accurately differentiate syncope from its mimickers and predict short-term prognosis and hospitalisation. More recently, AI analysis of electrocardiograms has shown promise in detection of serious structural and functional cardiac abnormalities, which has the potential to improve syncope care. Future AI studies have the potential to address current issues in syncope management. AI can automatically prognosticate risk in real time by accessing traditional and nontraditional data. However, steps to mitigate known problems such as generalisability, patient privacy, data protection, and liability will be needed. In the past AI has had limited impact due to underdeveloped analytical methods, lack of computing power, poor access to powerful computing systems, and availability of reliable high-quality data. All impediments except data have been solved. AI will live up to its promise to transform syncope care if the health care system can satisfy AI requirement of large scale, robust, accurate, and reliable data.</div></div>","PeriodicalId":9555,"journal":{"name":"Canadian Journal of Cardiology","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141261202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Nonfungible Tokens in Cardiovascular Medicine 心血管医学中的非风化代币。

IF 5.8 2区医学

Canadian Journal of Cardiology Pub Date : 2024-10-01 DOI: 10.1016/j.cjca.2024.07.010

{"title":"Nonfungible Tokens in Cardiovascular Medicine","authors":"","doi":"10.1016/j.cjca.2024.07.010","DOIUrl":"10.1016/j.cjca.2024.07.010","url":null,"abstract":"<div><div>The integration of nonfungible tokens (NFTs) in health care, particularly in cardiovascular medicine, represents a disruptive shift toward enhancing the security and interconnection of electronic health data around the patient. NFTs, unique digital certificates stored on a blockchain network, bind various sources of health data to their owner (the patient) and delineate the access rights for stakeholders (providers, researchers) using smart contracts. Data sources might include electronic medical records from different hospitals, clinics, pharmacies, test centres, and mHealth devices. Accordingly, patients and their providers benefit from seamless visibility of diagnoses, medications, electrocardiograms, imaging, home blood pressure logs, and artificial intelligence-enabled insights from these aggregated data. Rather than being stored on proprietary servers, data are encrypted and stored on decentralized networks with a unified point of access and immutable proof of ownership, making them more robust to theft or tampering. As custodians of their NFTs, patients are incentivized to actively partake in their health monitoring and self-driven research that aligns with their needs using innovative marketplaces that allow them to browse studies, document their informed consent, and monetize their contributions. Furthermore, they are empowered to educate themselves and seek care across siloes in traditional settings or virtual platforms such as the metaverse, where NFTs serve as digital passports. Despite these exciting prospects, adoption within the health care sector remains in its infancy, with ethical and technical limitations still being addressed. In this article we explore the multifaceted applications and key players in the field, and outline use-cases for patient-centred cardiovascular care featuring NFTs.</div></div>","PeriodicalId":9555,"journal":{"name":"Canadian Journal of Cardiology","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141733575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Emerging Analytical Approaches for Personalized Medicine Using Machine Learning In Pediatric and Congenital Heart Disease 利用机器学习对儿科和先天性心脏病进行个性化医疗的新兴分析方法。

IF 5.8 2区医学

Canadian Journal of Cardiology Pub Date : 2024-10-01 DOI: 10.1016/j.cjca.2024.07.026

{"title":"Emerging Analytical Approaches for Personalized Medicine Using Machine Learning In Pediatric and Congenital Heart Disease","authors":"","doi":"10.1016/j.cjca.2024.07.026","DOIUrl":"10.1016/j.cjca.2024.07.026","url":null,"abstract":"<div><div>Precision and personalized medicine, the process by which patient management is tailored to individual circumstances, are now terms that are familiar to cardiologists, despite it still being an emerging field. Although precision medicine relies most often on the underlying biology and pathophysiology of a patient’s condition, personalized medicine relies on digital biomarkers generated through algorithms. Given the complexity of the underlying data, these digital biomarkers are most often generated through machine-learning algorithms. There are a number of analytic considerations regarding the creation of digital biomarkers that are discussed in this review, including data preprocessing, time dependency and gating, dimensionality reduction, and novel methods, both in the realm of supervised and unsupervised machine learning. Some of these considerations, such as sample size requirements and measurements of model performance, are particularly challenging in small and heterogeneous populations with rare outcomes such as children with congenital heart disease. Finally, we review analytic considerations for the deployment of digital biomarkers in clinical settings, including the emerging field of clinical artificial intelligence (AI) operations, computational needs for deployment, efforts to increase the explainability of AI, algorithmic drift, and the needs for distributed surveillance and federated learning. We conclude this review by discussing a recent simulation study that shows that, despite these analytic challenges and complications, the use of digital biomarkers in managing clinical care might have substantial benefits regarding individual patient outcomes.</div></div>","PeriodicalId":9555,"journal":{"name":"Canadian Journal of Cardiology","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141888510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0