Mechanisms, Risk Factors, and Management of Acquired Long QT Syndrome: A Comprehensive Review

Long QT syndrome is characterized by prolongation of the corrected QT (QTc) interval on the surface electrocardiogram and is associated with precipitation of torsade de pointes (TdP), a polymorphic ventricular tachycardia that may cause sudden death. Acquired long QT syndrome describes pathologic excessive prolongation of the QT interval, upon exposure to an environmental stressor, with reversion back to normal following removal of the stressor. The most common environmental stressor in acquired long QT syndrome is drug therapy. Acquired long QT syndrome is an important issue for clinicians and a significant public health problem concerning the large number of drugs with this adverse effect with a potentially fatal outcome, the large number of patients exposed to these drugs, and our inability to predict the risk for a given individual. In this paper, we focus on mechanisms underlying QT prolongation, risk factors for torsades de pointes and describe the short- and long-term treatment of acquired long QT syndrome

Critical appraisal of artificial intelligence-based prediction models for cardiovascular disease

The medical field has seen a rapid increase in the development of artificial intelligence (AI)-based prediction models. With the introduction of such AI-based prediction model tools and software in cardiovascular patient care, the cardiovascular researcher and healthcare professional are challenged to understand the opportunities as well as the limitations of the AI-based predictions. In this article, we present 12 critical questions for cardiovascular health professionals to ask when confronted with an AI-based prediction model. We aim to support medical professionals to distinguish the AI-based prediction models that can add value to patient care from the AI that does not

The role of intravascular imaging in chronic total occlusion percutaneous coronary intervention

Chronic total occlusions (CTOs) represent the most complex subset of coronary artery disease and therefore careful planning of CTO percutaneous coronary recanalization (PCI) strategy is of paramount importance aiming to achieve procedural success, and improve patient's safety and post CTO PCI outcomes. Intravascular imaging has an essential role in facilitating CTO PCΙ. First, intravascular ultrasound (IVUS), due to its higher penetration depth compared to optical coherence tomography (OCT), and the additional capacity of real-time imaging without need for contrast injection is considered the preferred imaging modality for CTO PCI. Secondly, IVUS can be used to resolve proximal cap ambiguity, facilitate wire re-entry when dissection and re-entry strategies are applied and most importantly to guide stent deployment and optimization post implantation. The role of OCT during CTO PCI is currently limited to stent sizing and optimization, however, due to its high spatial resolution, OCT is ideal for detecting stent edge dissections and strut malapposition. In this review, we describe the use of intravascular imaging for lesion crossing, plaque characterization and wire tracking, extra- or intra-plaque, and stent sizing and optimization during CTO PCI and summarize the findings of the major studies in this field

Big data from electronic health records for early and late translational cardiovascular research: challenges and potential.

AIMS: Cohorts of millions of people's health records, whole genome sequencing, imaging, sensor, societal and publicly available data present a rapidly expanding digital trace of health. We aimed to critically review, for the first time, the challenges and potential of big data across early and late stages of translational cardiovascular disease research. METHODS AND RESULTS: We sought exemplars based on literature reviews and expertise across the BigData@Heart Consortium. We identified formidable challenges including: data quality, knowing what data exist, the legal and ethical framework for their use, data sharing, building and maintaining public trust, developing standards for defining disease, developing tools for scalable, replicable science and equipping the clinical and scientific work force with new inter-disciplinary skills. Opportunities claimed for big health record data include: richer profiles of health and disease from birth to death and from the molecular to the societal scale; accelerated understanding of disease causation and progression, discovery of new mechanisms and treatment-relevant disease sub-phenotypes, understanding health and diseases in whole populations and whole health systems and returning actionable feedback loops to improve (and potentially disrupt) existing models of research and care, with greater efficiency. In early translational research we identified exemplars including: discovery of fundamental biological processes e.g. linking exome sequences to lifelong electronic health records (EHR) (e.g. human knockout experiments); drug development: genomic approaches to drug target validation; precision medicine: e.g. DNA integrated into hospital EHR for pre-emptive pharmacogenomics. In late translational research we identified exemplars including: learning health systems with outcome trials integrated into clinical care; citizen driven health with 24/7 multi-parameter patient monitoring to improve outcomes and population-based linkages of multiple EHR sources for higher resolution clinical epidemiology and public health. CONCLUSION: High volumes of inherently diverse ('big') EHR data are beginning to disrupt the nature of cardiovascular research and care. Such big data have the potential to improve our understanding of disease causation and classification relevant for early translation and to contribute actionable analytics to improve health and healthcare

Risk factors for ischaemic heart disease in a Cretan rural population: a twelve year follow-up study

<p>Abstract</p> <p>Background</p> <p>Crete has been of great epidemiological interest ever since the publication of the Seven Countries Study. In 1988 a well-defined area of rural Crete was studied, with only scarce signs of coronary heart disease (CHD) despite the unfavorable risk profile. The same population was re-examined twelve years later aiming to describe the trends of CHD risk factors over time and discuss some key points on the natural course of coronary heart disease in a rural population of Crete.</p> <p>Methods and Results</p> <p>We re-examined 200 subjects (80.7% of those still living in the area, 62.4 ± 17.0 years old). The prevalence of risk factors for CHD was high with 65.9% of men and 65.1% of women being hypertensive, 14.3% of men and 16.5% of women being diabetic, 44% of men being active smokers and more than 40% of both sexes having hyperlipidaemia. Accordingly, 77.5% of the population had a calculated Framingham Risk Score (FRS) ≥ 15%, significantly higher compared to baseline (p < 0.001). The overall occurrence rate for CHD events was calculated at 7.1 per 1000 person-years (95% confidence interval: 6.8–7.3).</p> <p>Conclusion</p> <p>The study confirms the unfavorable risk factor profile of a well defined rural population in Crete. Its actual effect on the observed incidence of coronary events in Cretans remains yet to be defined.</p

CODE-EHR best practice framework for the use of structured electronic healthcare records in clinical research.

Big data is central to new developments in global clinical science aiming to improve the lives of patients. Technological advances have led to the routine use of structured electronic healthcare records with the potential to address key gaps in clinical evidence. The covid-19 pandemic has demonstrated the potential of big data and related analytics, but also important pitfalls. Verification, validation, and data privacy, as well as the social mandate to undertake research are key challenges. The European Society of Cardiology and the BigData@Heart consortium have brought together a range of international stakeholders, including patient representatives, clinicians, scientists, regulators, journal editors and industry. We propose the CODE-EHR Minimum Standards Framework as a means to improve the design of studies, enhance transparency and develop a roadmap towards more robust and effective utilisation of healthcare data for research purposes