Time-Dependent Risk for Recurrence in Survivors of Major Adverse Cardiovascular Events.

INTRODUCTION: The prevalence of the population with a history of an occlusive cardiovascular event has been increasing in recent years, which means that a large number of patients will have a higher risk of presenting a fatal recurrence. The aim is to determine variables associated with time-to-recurrent cardiovascular events and analyze how changes in low-density lipoprotein cholesterol (LDL-C) levels during follow-up may be associated with this time-to-event. MATERIALS AND METHODS: This is a prospective observational cohort study of 727 adults with a history of at least one occlusive cardiovascular event recruited at a referral hospital in northeastern Colombia. Data from a follow-up period of a maximum of 33 months (median 26 months) (one death) were used to define how clinical and sociodemographic variables impact the recurrence of major adverse cardiovascular events (MACE). Analyses were performed based on proportional hazard models and time-dependent hazard models. RESULTS: Upon enrollment, 215 (30%) of the participants reported experiencing their most recent cardiovascular event within the preceding year. After two years, the recurrence rate was 12.38% (90/727). The risk of recurrence before two years was 3.9% (95% CI 2.7-5.6). In the multiple models, the presence of severe depression gives a Hazard Ratio of 8.25 (95% CI 2.98-22.86) and LDL ≥120 md/dl Hazard Ratio of 2.12 (95% CI 1.2 -3.9). It was found that LDL >120 mg/dl maintained over time increases the chances of recurrence by 1.7% (Hazard Ratio: 1.017, 95% CI 0.008-0.025). CONCLUSIONS: The present study allows us to identify a profile of patients who should be treated promptly in an interdisciplinary manner to avoid recurrences of coronary events.

A Systematic Review: Do the Use of Machine Learning, Deep Learning, and Artificial Intelligence Improve Patient Outcomes in Acute Myocardial Ischemia Compared to Clinician-Only Approaches?

Cardiovascular diseases (CVDs) present a significant global health challenge and remain a primary cause of death. Early detection and intervention are crucial for improved outcomes in acute coronary syndrome (ACS), particularly acute myocardial infarction (AMI) cases. Artificial intelligence (AI) can detect heart disease early by analyzing patient information and electrocardiogram (ECG) data, providing invaluable insights into this critical health issue. However, the imbalanced nature of ECG and patient data presents challenges for traditional machine learning (ML) algorithms in performing unbiasedly. Investigators have proposed various data-level and algorithm-level solutions to overcome these challenges. In this study, we used a systematic literature review (SLR) approach to give an overview of the current literature and to highlight the difficulties of utilizing ML, deep learning (DL), and AI algorithms in predicting, diagnosing, and prognosis of heart diseases. We reviewed 181 articles from reputable journals published between 2013 and June 15, 2023, focusing on eight selected papers for in-depth analysis. The analysis considered factors such as heart disease type, algorithms used, applications, and proposed solutions and compared the benefits of algorithms combined with clinicians versus clinicians alone. This systematic review revealed that the current ML-based diagnostic approaches face several open problems and issues when implementing ML, DL, and AI in real-life settings. Although these algorithms show higher sensitivities, specificities, and accuracies in detecting heart disease, we must address the ethical concerns while implementing these models into clinical practice. The transparency of how these algorithms operate remains a challenge. Nevertheless, further exploration and research in ML, DL, and AI are necessary to overcome these challenges and fully harness their potential to improve health outcomes for patients with AMI.

COVID-19 Vaccine-Induced Thrombotic Thrombocytopaenia With Venous and Arterial Thrombosis: A Case Report.

Coronavirus disease 19 pandemic has put tremendous pressure on health systems and has caused significant morbidity and mortality throughout the world. Vaccination program against COVID-19 has been effective despite repeated outbreaks across the globe. It was however reported that COVID-19 vaccines in particular, the Oxford-AstraZeneca COVID-19 vaccine (AZD1222) was temporarily suspended by some European countries due to risk of thrombosis. COVID-19 is a prothrombotic condition and is associated with venous thromboembolism mainly. The condition can be challenging to diagnose due to its diagnostic variation. Cases of vaccine-induced thrombotic thrombocytopaenia has been reported in several countries. COVID-19 can also cause vaccine-induced thrombosis without thrombocytopaenia. The thrombotic events can affect different parts of the body including brain, heart, and peripheral vessels. We present a case of 54-year-old patient who presented with chest and abdominal pain for 12 hours and evidence of infero-lateral ST segment elevation on electrocardiogram. Patient received COVID-19 AstraZeneca vaccine 10 days prior to admission. Coronary angiography (CAG) showed occlusion of the proximal to mid part of the right coronary artery (RCA) distal to a large Right Ventricular branch with high thrombotic burden and multiple attempts at aspiration of the thrombus resulted in partial restoration of the flow to right coronary artery.