ABSTRACT
A precarious complication of thyrotoxicosis, or thyroid storm, is the increased risk of cardiomyopathy, which leads to circulatory collapse and cardiopulmonary arrest. It is crucial to promptly identify this condition to prevent significant deterioration of the left ventricular function and cardiogenic shock. This article seeks to examine published research that emphasizes the connection between thyroid storm and beta-blocker usage in relation to cardiogenic collapse and provides management recommendations. The search was performed on September 9, 2022, using PubMed, Science Direct, and Google Scholar libraries. A systematic exploration was carried out using the keywords Thyroid Storm AND cardiogenic Shock AND cardiac arrest AND beta blocker. The use of beta blockers as part of thyroid storm management was linked to the development of cardiogenic collapse and cardiac arrest. Ultra-short-acting beta-blockers like esmolol were a safer option than propranolol in treating patients with a thyrotoxic storm.
ABSTRACT
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.
ABSTRACT
Internet gaming and social media usage (SMU), particularly among children and teenagers, have witnessed a remarkable surge over the past decade. However, it remains uncertain whether this widespread usage has a positive or negative impact. The primary objective of this systematic review was to investigate the diverse effects of excessive video game playing and extensive SMU, both favorable and detrimental, on the psychological and mental well-being of children and adolescents. To assess the influence of internet gaming disorder (IGD) and disordered SMU on the mental health of children aged 6-12 years and adolescents aged 13-18 years, we conducted a systematic review of 20 studies on the subject. These studies utilized a substantial sample size of 48,652 participants, encompassing online and in-person questionnaires administered to children, teenagers, and their parents in educational institutions, healthcare facilities, and online platforms. Our findings suggest that multiple factors contribute to the intricate relationship between SMU, video game playing, and mental health outcomes. The majority of research indicates that excessive gaming or SMU among children and teenagers leads to adverse consequences on their mental well-being. Furthermore, certain studies have even reported fatal consequences, while others have identified a worsening of preexisting mental health issues. A few studies have explored the potential positive impacts of SMU and gaming on individuals and society at large. In light of this, we have concluded that it is inappropriate to categorize internet gaming and SMU as solely beneficial or detrimental without considering the broader context and the interplay of various factors.
ABSTRACT
Chronic kidney disease (CKD) causes various complications that significantly impact a patient's overall well-being and quality of life. Sleep disorders are a particularly common issue, especially in patients with advanced disease. This systematic review aims to explore the distinguishing features, prevalence rates, underlying causes, and associated factors related to the most frequent sleep disorders in these patients and present the latest treatment methods for them. It also investigates the link between CKD and sleep disorders and presents the results of the most common sleep disorders found in patients with CKD. Four major sleep disorders have been identified: sleep apnea, restless leg syndrome, excessive drowsiness, and insomnia. These sleep disorders have been discovered to be highly common in CKD patients and have a major influence on their quality of life and morbidity.
