RESUMO
Atrial fibrillation (AF) poses a substantial risk of stroke, necessitating effective anticoagulation therapy. This systematic review and meta-analysis (SRMA) evaluates the efficacy and safety of different dosing regimens of rivaroxaban in patients with AF. A comprehensive search of relevant databases, focusing on studies published from 2017 onward, was conducted. Inclusion criteria comprised randomized controlled trials (RCTs) and observational studies comparing standard and reduced dosing of rivaroxaban in AF. Data extraction and risk of bias (ROB) assessment were performed, and a meta-analysis was conducted for relevant outcomes. A total of 21 studies fulfilled the inclusion criteria. Standard dosing demonstrates a slightly lower risk of composite effectiveness outcomes and safety outcomes (HR: 0.79, 95% CI: 0.66-0.94, P=0.01) compared to reduced dosing (HR: 0.83, 95% CI: 0.71-0.97, P=0.02). Notable differences in major bleeding, gastrointestinal bleeding (GIB), and intracranial bleeding favored standard dosing. Hemorrhagic stroke and all-cause stroke rates differed significantly, with standard dosing showing a more favorable profile for ischemic stroke prevention. This study highlights the pivotal role of personalized anticoagulation therapy in AF. Standard dosing of rivaroxaban emerges as a preferred strategy for stroke prevention, balancing efficacy and safety. Clinical decision-making should consider individual patient characteristics and future research should delve into specific subpopulations and long-term outcomes to further refine treatment guidelines. The study bridges evidence from clinical trials to real-world practice, offering insights into the evolving landscape of AF management.
RESUMO
Beta-blockers are a class of medications that act on beta-adrenergic receptors and are categorized as cardio-selective and non-selective. They are principally used to treat cardiovascular conditions such as hypertension and arrhythmias. Beta-blockers have also been used to treat non-cardiogenic indications in non-pregnant individuals and the pediatric population. In pregnancy, labetalol is the mainstay treatment for hypertension and other cardiovascular indications. However, contraindications to certain sub-types of beta-blockers include bradycardia, heart failure, obstructive lung diseases, and hemodynamic instability. There is conflicting evidence of the adverse effects on fetal and neonatal health due to a scarce safety and efficacy profile, and further studies are necessary to understand the pharmacokinetics of the different classes of beta-blockers in pregnancy and fetal health. Understanding the hemodynamic changes during the stages of pregnancy is important to target a more beneficial therapy for both mother and fetus as well as better neonatal outcomes. Beta-blocker use in the pediatric population is less documented in studies but does have the potential to treat various cardiogenic and non-cardiogenic conditions. Future comprehensive studies would further benefit the direction of beta-blocker treatment during pregnancy in neonates and pediatrics.
RESUMO
Cardiogenic shock (CS) may have a negative impact on mortality in patients with heart failure (HF) or acute myocardial infarction (AMI). Early prediction of CS can result in improved survival. Artificial intelligence (AI) through machine learning (ML) models have shown promise in predictive medicine. Here, we conduct a systematic review and meta-analysis to assess the effectiveness of these models in the early prediction of CS. A thorough search of the PubMed, Web of Science, Cochrane, and Scopus databases was conducted from the time of inception until November 2, 2023, to find relevant studies. Our outcomes were area under the curve (AUC), the sensitivity and specificity of the ML model, the accuracy of the ML model, and the predictor variables that had the most impact in predicting CS. Comprehensive Meta-Analysis (CMA) Version 3.0 was used to conduct the meta-analysis. Six studies were considered in our study. The pooled mean AUC was 0.808 (95% confidence interval: 0.727, 0.890). The AUC in the included studies ranged from 0.77 to 0.91. ML models performed well, with accuracy ranging from 0.88 to 0.93 and sensitivity and specificity of 58%-78% and 88%-93%, respectively. Age, blood pressure, heart rate, oxygen saturation, and blood glucose were the most significant variables required by ML models to acquire their outputs. In conclusion, AI has the potential for early prediction of CS, which may lead to a decrease in the high mortality rate associated with it. Future studies are needed to confirm the results.
