Citations

ABSTRACT Cardiogenic shock (CS) remains a high morbidity and mortality condition worldwide frequently complicating acute myocardial infarction (AMI) and decompensated heart failure (HF). Within the management of CS, mechanical circulatory support (MCS) devices play a critical role in maintaining hemodynamic stability, preserving end‐organ perfusion and bridging patients through to recovery, implantation of durable support or transplantation. Despite their use, optimal timing of initiation, as well as patient and device selection remain unclear. This review explores the current landscape of MCS devices, surrounding evidence and key distinctions between devices. With increasing acknowledgment for the heterogeneity of CS, understanding the strengths and limitations of each device remains crucial to improving outcomes in this high‐risk population.

Managing persistent atrial fibrillation (AF) remains challenging due to its complex arrhythmic substrate, and pulmonary vein isolation through catheter ablation remains the primary treatment. This meta-analysis aims to compare the efficacy and safety outcomes of cryoballoon ablation (CBA) and radiofrequency ablation (RFA) in patients with persistent AF to provide evidence-based insights for clinical decision-making. A systematic search of PubMed, Embase, and Cochrane Central was conducted following PRISMA guidelines. Randomized controlled trials (RCTs) comparing CBA and RFA in persistent AF were included. Data on efficacy, safety, and procedural outcomes were pooled in the form of odds ratios (ORs) or mean differences (MDs) with corresponding 95% confidence intervals (CIs) using the random effects model in the Review Manager (RevMan) software. Four RCTs comprising 899 patients were included in our analysis. There were no significant differences between CBA and RFA in terms of AF recurrence, atrial tachycardia/flutter recurrence, or any arrhythmia recurrence during a mean follow-up period of 18 months after ablation. Procedure time was significantly shorter with CBA (MD: −44.85 minutes, 95% CI: −56.46 to −32.24, P < 0.01), while fluoroscopy time was comparable between the 2 groups (MD: 4.31 minutes, 95% CI: −1.79 to 10.40, P = 0.17). Safety outcomes, including overall complications, phrenic nerve palsy, access-site complications, and pericardial effusion, showed no significant differences between the 2 groups. CBA and RFA show similar rates of atrial arrhythmia recurrence and periprocedural complications in the treatment of persistent AF, while CBA offers the advantage of a shorter procedure time.

Introduction: Data on outcomes between unfractionated heparin and bivalirudin anticoagulation during percutaneous coronary intervention (PCI) in acute coronary syndromes remain inconclusive. We aimed to systematically analyze PCI outcomes by comparing unfractionated heparin and bivalirudin. Methods: We systematically searched Ovid MEDLINE, Ovid Embase, Ovid Cochrane Database of Systematic Reviews, Scopus, and Web of Science from database inception in 1966 through January 2024 for studies evaluating PCI outcomes comparing unfractionated heparin and bivalirudin. Two investigators independently reviewed the data. Conflicts were resolved through consensus. Random-effects meta-analyses were used. Results: A total of 10 prospective trials were identified that enrolled 42,253 individuals who presented with an acute coronary syndrome. Our analysis found that heparin when compared to bivalirudin was associated with an increased risk of trial-based definition of major bleeding [relative risk (RR): 1.68, 95% confidence interval (CI): 1.29–2.20], nonaccess site complications (RR: 4.6, 95% CI: 1.75–12.09), thrombolysis in myocardial infarction major bleeding (RR: 1.70, 95% CI: 1.20–2.41), major bleeding risks (RR: 1.87, 95% CI: 1.49–2.36), cardiovascular disease death (RR: 1.26, 95% CI: 1.02–1.57), and thrombocytopenia (RR: 1.67, 95% CI: 1.07–2.62). There were no statistically significant differences between heparin and bivalirudin for all-cause mortality, major adverse cardiovascular event, stroke, reinfarction, target vessel revascularization, and acute or stent thrombosis. Conclusions: The present meta-analysis demonstrates bivalirudin reduces major bleeding when used for anticoagulation during PCI in patients with acute coronary syndromes and is not associated with an increased risk of stent thrombosis or major adverse cardiovascular event.

Artificial intelligence, specifically advanced language models such as ChatGPT, have the potential to revolutionize various aspects of healthcare, medical education, and research. In this review, we evaluate the myriad applications of artificial intelligence in diverse healthcare domains. We discuss its potential role in clinical decision-making, exploring how it can assist physicians by providing rapid, data-driven insights for diagnosis and treatment. We review the benefits of artificial intelligence such as ChatGPT in personalized patient care, particularly in geriatric care, medication management, weight loss and nutrition, and physical activity guidance. We further delve into its potential to enhance medical research, through the analysis of large datasets, and the development of novel methodologies. In the realm of medical education, we investigate the utility of artificial intelligence as an information retrieval tool and personalized learning resource for medical students and professionals.

Bifurcation lesions account for 20% of all percutaneous coronary interventions and represent a complex subset which are associated with lower procedural success and higher rates of restenosis [...]

The utilization of transcatheter aortic valve implantation (TAVI) has become the treatment of choice in patients with severe aortic stenosis (AS) with intermediate-to-high surgical risk for surgical aortic valve replacement (SAVR) [...]

Primary care has the potential to be transformed by artificial intelligence (AI) and, in particular, machine learning (ML). This review summarizes the potential of ML and its subsets in influencing two domains of primary care: pre-operative care and screening. ML can be utilized in preoperative treatment to forecast postoperative results and assist physicians in selecting surgical interventions. Clinicians can modify their strategy to reduce risk and enhance outcomes using ML algorithms to examine patient data and discover factors that increase the risk of worsened health outcomes. ML can also enhance the precision and effectiveness of screening tests. Healthcare professionals can identify diseases at an early and curable stage by using ML models to examine medical pictures, diagnostic modalities, and spot patterns that may suggest disease or anomalies. Before the onset of symptoms, ML can be used to identify people at an increased risk of developing specific disorders or diseases. ML algorithms can assess patient data such as medical history, genetics, and lifestyle factors to identify those at higher risk. This enables targeted interventions such as lifestyle adjustments or early screening. In general, using ML in primary care offers the potential to enhance patient outcomes, reduce healthcare costs, and boost productivity.