Digoxin is a commonly prescribed drug in the management of heart failure and atrial fibrillation. Despite its widespread use, most clinicians have little experience with recognising clinical signs and symptoms that might suggest a potentially lethal drug toxicity. We herein describe two cases with specific reference to the electrocardiographic changes induced by digoxin and discuss the predisposing factors for toxicity, recognition of possible toxicity and treatment approaches.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7900815 | PMC |
| http://dx.doi.org/10.11622/smedj.2020005 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Hospital-Based Electrocardiographic Monitoring: The Good, the Not So Good, and Untapped Potential.
Am J Crit Care
July 2024
Michele M. Pelter is an associate professor, director of the ECG Monitoring Research Lab, and an associate translational scientist, Center for Physiologic Research, Department of Physiological Nursing, School of Nursing, University of California San Francisco.
Continuous electrocardiographic (ECG) monitoring was first introduced into hospitals in the 1960s, initially into critical care, as bedside monitors, and eventually into step-down units with telemetry capabilities. Although the initial use was rather simplistic (ie, heart rate and rhythm assessment), the capabilities of these devices and associated physiologic (vital sign) monitors have expanded considerably. Current bedside monitors now include sophisticated ECG software designed to identify myocardial ischemia (ie, ST-segment monitoring), QT-interval prolongation, and a myriad of other cardiac arrhythmia types.
View Article and Find Full Text PDFPredicting Out-of-Hospital Cardiac Arrest in the General Population Using Electronic Health Records.
Circulation
July 2024
Division of Cardiology (N.A.C.), University of Washington, Seattle.
Background: The majority of out-of-hospital cardiac arrests (OHCAs) occur among individuals in the general population, for whom there is no established strategy to identify risk. In this study, we assess the use of electronic health record (EHR) data to identify OHCA in the general population and define salient factors contributing to OHCA risk.
Methods: The analytical cohort included 2366 individuals with OHCA and 23 660 age- and sex-matched controls receiving health care at the University of Washington.
Addressing comprehensive complexities a striking familial hypercholesterolemia case study.
Egypt Heart J
April 2024
Non-Invasive Cardiology at National Institute of Cardiovascular Diseases (NICVD), Rafiqui (H.J.) Shaheed Road, Karachi, 75510, Pakistan.
Background: Premature aortic involvement and comprehensive management strategies in familial hypercholesterolemia familial hypercholesterolemia (FH), a rare autosomal dominant genetic disorder, poses significant challenges due to its propensity for elevated low-density lipoprotein cholesterol, premature coronary heart disease, and vascular atherosclerosis.
Case Presentation: Unraveling Cardiovascular Complexities: A Striking Familial Hypercholesterolemia. This case study delves into a remarkable instance of FH in a 16-year-old female who presented with chest pain and worsening dyspnea.
Wearables in Cardiovascular Disease.
J Cardiovasc Transl Res
June 2023
Clinical and Translational Research Accelerator, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, 06517, USA.
Wearable devices stand to revolutionize the way healthcare is delivered. From consumer devices that provide general health information and screen for medical conditions to medical-grade devices that allow collection of larger datasets that include multiple modalities, wearables have a myriad of potential uses, especially in cardiovascular disorders. In this review, we summarize the underlying technologies employed in these devices and discuss the regulatory and economic aspects of such devices as well as the future implications of their use.
View Article and Find Full Text PDFFeature Selection Techniques for a Machine Learning Model to Detect Autonomic Dysreflexia.
Front Neuroinform
August 2022
Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States.
Feature selection plays a crucial role in the development of machine learning algorithms. Understanding the impact of the features on a model, and their physiological relevance can improve the performance. This is particularly helpful in the healthcare domain wherein disease states need to be identified with relatively small quantities of data.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!