Future cardiology最新文献

The European Society of Cardiology (ESC) guidelines provide detailed, evidence-based recommendations for managing cardiovascular diseases. However, their complexity and frequent updates can make them challenging to apply consistently in clinical settings. Artificial intelligence (AI), particularly large language models (LLMs), offers a novel solution by assisting in the interpretation and application of these guidelines more effectively. A narrative review was conducted to assess the role of large language models (LLMs) and related artificial intelligence (AI) systems in supporting the interpretation of ESC guidelines. From 102 records screened, seven studies met the inclusion criteria. Clinical Decision Support Systems (CDSSs) built on ESC guidelines demonstrated improvements in diagnostic accuracy and standardization. Comparative studies revealed that large language models (LLMs), including ChatGPT-4, showed high concordance with expert clinical decisions (up to 86% accuracy for acute coronary syndrome-related questions). Emerging tools, such as MedDoc-Bot, have highlighted the feasibility of direct ESC guideline interpretation by LLMs. LLMs show promise in enhancing clinician understanding and application of ESC guidelines. Although performance is encouraging, further validation and thoughtful integration into clinical practice are necessary to maximize their utility and safety.

Background: Leadless ventricular pacemakers have been developed for single chamber VVIR and AV synchronous pacing applications.

Aim: To assess the device longevity impact of battery/electronics enhancements of next-generation Micra leadless pacemakers, Micra VR2 and AV2, compared to Micra VR and AV.

Methods: Real-world pacing data gathered from the Micra IDE study, Medtronic's CareLink database, and historical pacemaker patient survival data from Medtronic's Device Registry were used to project device longevity and estimate the proportion of patients requiring lifetime device replacements.

Results: Based on data from 644 patients, the median projected longevity of Micra VR was 12.3 years and Micra VR2 was 16.7 years, with 91% of patients requiring a single VR2 device over their lifetime. Based on data from 999 patients, the median projected longevity of Micra AV was 10.8 years and Micra AV2 was 15.6 years, with 80% of patients requiring one AV2 device. The longevity improvements with Micra VR2 projected 8 fewer device replacements would be required across 100 patients. Similarly, 15 devices would be avoided when considering Micra AV versus AV2.

Conclusions: Modeling of the Micra leadless pacemakers projected meaningful improvements in device longevity and an increase in the number of patients served with a single device.

With a significant impact on morbidity and mortality rates worldwide, arrhythmias are a growing global health concern. The most common sustained arrhythmia, atrial fibrillation (AF), affects approximately 2% of the general population, with its prevalence increasing with age. Although significant advancements have been made in non-pharmacological therapies, such as catheter ablation and implantable devices, the basis of arrhythmia management remains antiarrhythmic drugs (AADs). Yet the development of safer and more effective AADs has not kept pace with the increasing burden of arrhythmias. This article aims to briefly explore the current landscape of antiarrhythmic treatment, emerging pharmacological targets, and the potential for innovative drug therapies to reshape clinical practice.

Background: Aortic Stenosis (AS) is a valvular heart disease. Hypertension accelerates stenosis, increasing morbidity and mortality. This study investigates mortality trends in US older adults with AS and hypertension from 1999 to 2020.

Methods: Using the CDC WONDER database, age-adjusted mortality rates (AAMRs) per 100,000 individuals aged 65+ with AS and hypertension were analyzed. Changes in AAMRs were examined through annual percent change (APC) and average APC (AAPC) using Joinpoint regression.

Results: From 1999 to 2020, hypertension caused 99,250 deaths in AS patients in the US, demonstrating an increasing trend (AAPC: 5.51%). Males had higher AAMRs (11.51) than females (9.99). Non-Hispanic (NH) white people (11.32) had the highest AAMRs, followed by Hispanic (7.37), NH Black people (7.27), and NH Asians (6.12). Regionally, the West showed the highest AAMR (13.3), followed by the Midwest (11.38), the Northeast (10.62), and the South (8.53). The states with the highest AAMRs were Vermont and Oregon, while Alabama and Georgia had the lowest. Non-metropolitan areas (11.19) experienced higher mortality than metropolitan areas (10.49).

Conclusion: We report increasing mortality rates in patients with AS and hypertension, especially in males, NH white people, and the West. Target healthcare measures are needed to address the rising mortality.

Background: Cardiovascular diseases (CVDs) remain the leading cause of mortality globally, with mitochondrial dysfunction playing a key role in their pathogenesis. SIRT3 (Sirtuin 3), a mitochondrial deacetylase, has emerged as a critical regulator of mitochondrial function and oxidative stress, with evidence linking its reduction to the progression of CVDs. This meta-analysis aimed to evaluate the association between SIRT3 levels and CVDs in order to elucidate its role in CVD pathogenesis and its potential as a biomarker.

Methods: A systematic search of MEDLINE and Embase databases was conducted up to August 2024, adhering to PRISMA guidelines. Observational studies evaluating SIRT3 levels in human patients with CVDs as compared to healthy controls were included.

Results: 8 studies comprising 397 participants were included in this meta-analysis. Overall, SIRT3 levels were found to be significantly lower in individuals with CVDs compared to healthy controls (SMD: 1.08, 95% CI: 0.495-1.662, p = 0.0032). The reduction in SIRT3 levels was most pronounced in hypertension (SMD: 1.82) and dilated cardiomyopathy (SMD: 1.08).

Conclusion: This meta-analysis provides compelling evidence of gsignificantly reduced SIRT3 levels in CVD patients, highlighting its critical role in cardiovascular diseases. These findings underscore the potential of SIRT3 as a biomarker and therapeutic target in CVDs.

Systemic light-chain (AL) amyloidosis is a rare clonal plasma cell disorder characterized by the production of abnormal immunoglobulin free light-chains that misfold into amyloid fibrils and deposit in the extracellular matrix of tissues. Despite being a multisystemic disease process, the presence and severity of cardiac involvement remains the main determinant of prognosis. Improved understanding of the underlying pathophysiology has resulted in transformative changes in both diagnostics and treatment. Improvements in multimodality cardiac imaging have enabled accurate diagnosis, facilitated rapid initiation of treatment and allowed the direct visualization changes in the myocardial substrate in response to chemotherapy. Significant progress has also been made through leveraging treatments that directly target the underlying abnormal plasma cell clone responsible the production of the amyloidogenic immunoglobulin free light-chains. Current treatment options successfully target amyloid production, but novel anti-amyloid therapies seek to target amyloid fibrils that have already deposited in the organs and facilitate their removal through an immune-mediated degradation process are at advanced stages of development. These treatments have the potential to induce disease regression by depleting amyloid deposits and if successful will represent a significant step forward in the treatment of systemic AL amyloidosis, especially for patients with advanced cardiac disease.

Bedside assessment of venous congestion is key to managing hospitalized adults with cardiovascular disease and guiding fluid management to achieve euvolemia. Current methods include physical examination, radiographic imaging, and point-of-care ultrasound (POCUS) of the inferior vena cava (IVC). While accessible, physical exam and IVC measurements are prone to errors and variability. Right heart catheterization (RHC), considered the gold standard, is invasive, costly, and often unavailable. A novel ultrasound technique, Venous Excess Ultrasound (VExUS), offers a noninvasive alternative for assessing venous congestion by characterizing the IVC and hepatic, portal, and intrarenal veins. Rapidly evolving literature on VExUS has created controversy and uncertainty around its application to patients with cardiovascular disease. This review summarizes the utility of the VExUS scoring system as a bedside tool for assessing venous congestion in adults with cardiovascular disease. It will discuss the methodology of VExUS, its performance compared to traditional IVC measurements and RHC, and its applications in various cardiovascular conditions including acute decompensated heart failure (ADHF), acute coronary syndromes (ACS), tricuspid regurgitation (TR), cardiorenal syndromes (CRS), cardiogenic shock (CS), and patients undergoing cardiac surgery.

Heart Failure is a prevalent disease with significant impacts on morbidity and mortality. Heart failure patients have a large volume of healthcare data which is digitized and can be collated. Artificial intelligence (AI) can then be used to assess the data for underlying patterns. AI systems can be trained to analyze readily available data, such as ECGs and heart sounds, and assess likelihood of heart failure. AI can also risk stratify heart failure patients by analyzing available healthcare data. AI can allow rapid assignment of heart failure patients to specific groups via automated echo analysis, but can also provide information regarding novel imaging bio-markers that may be more useful than left ventricular ejection fraction, such as first phase ejection fraction. AI can be used to assess patients' suitability for existing drugs, whilst also enabling development of novel drugs for known or newly discovered drug targets. Heart Failure as a field, with its multi-modal data set and variability in outcomes, will greatly benefit from the expansion and improvement of AI technology over the next 20 years.