Atrial fibrillation (AF) is a common arrhythmia affecting 8–10% of the population older than 80 years old. The importance of early diagnosis of atrial fibrillation has been broadly recognized since arrhythmias significantly increase the risk of stroke, heart failure and tachycardia-induced cardiomyopathy with reduced cardiac function. However, the prevalence of atrial fibrillation is often underestimated due to the high frequency of clinically silent atrial fibrillation as well as paroxysmal atrial fibrillation, both of which are hard to catch by routine physical examination or 12-lead electrocardiogram (ECG). The development of wearable devices has provided a reliable way for healthcare providers to uncover undiagnosed atrial fibrillation in the population, especially those most at risk. Furthermore, with the advancement of artificial intelligence and machine learning, the technology is now able to utilize the database in assisting detection of arrhythmias from the data collected by the devices. In this review study, we compare the different wearable devices available on the market and review the current advancement in artificial intelligence in diagnosing atrial fibrillation. We believe that with the aid of the progressive development of technologies, the diagnosis of atrial fibrillation shall be made more effectively and accurately in the near future.

Cardiac amyloidosis is caused by the deposition of misfolded protein fibrils into the extracellular space of the heart. The diagnosis of cardiac amyloidosis remains challenging because of the heterogeneous manifestations of the disease. There are many different types of amyloidosis with light-chain (AL) amyloidosis and transthyretin (ATTR) amyloidosis being the most common types of cardiac amyloidosis. Endomyocardial biopsy is considered the gold standard for diagnosing cardiac amyloidosis and differentiating amyloid subtypes, but its use is limited because of the invasive nature of the procedure, with risks for complications and the need for specialized training and centers to perform the procedure. Radionuclide cardiac imaging has recently become the most commonly performed test for the diagnosis of ATTR amyloidosis but is of limited value for the diagnosis of AL amyloidosis. Positron emission tomography has been increasingly used for the diagnosis of cardiac amyloidosis and its applications are expected to expand in the future. Imaging protocols are under refinement to achieve better quantification of the disease burden and prediction of prognosis.

Introduction: Heart failure (HF) is a major public health issue associated with significantly increased risk of stroke. It remains uncertain whether oral anticoagulation (OAC) in patients with heart failure and sinus rhythm (HF-SR) could improve prognosis. Methods: We performed a systematic search of PubMed and Embase databases for randomized controlled clinical trials assessing oral anticoagulants versus antiplatelets or placebo in patients with HF or ventricular dysfunction/cardiomyopathy without clinical HF and SR. The outcomes assessed were stroke/systemic embolism, major bleeding, myocardial infarction, all-cause mortality, and HF hospitalization. Results: Seven trials of 15,794 patients were eligible for our analyses. The overall follow-up duration was 32,367 patient-years corresponding to a mean follow-up of 2.05 years per patient. Four trials included patients treated with warfarin and three included patients treated with rivaroxaban. OAC was associated with reduced rate of stroke or systemic embolism compared to control (odds ratio (OR): 0.57, 95% confidence interval (CI): 0.44, 0.73, number needed to treat (NNT): 71.9) but higher rate of major bleeding (OR: 1.92, 95% CI: 1.47, 2.50, number needed to harm (NNH): 57.1). In the subgroup analysis according to the type of OAC, rivaroxaban was associated with significantly reduced rate of stroke or systemic embolism (1.24 vs 1.97 events per 100 patient-years, respectively, OR: 0.63, 95% CI: 0.45, 0.88, NNT: 82) and higher risk of major bleeding (OR: 1.66, 95% CI: 1.26, 2.20) compared to antiplatelets or placebo. There was no significant differences between groups for the outcomes of myocardial infarction, all-cause mortality, and HF hospitalization. Conclusion: This analysis shows that any benefit of OAC for stroke prevention may be offset by an increased risk of major bleeding in HF-SR patients. A well-designed randomized controlled trial of newer safer OACs is needed in this population.

Background: A paradoxical association of obesity with lower risk of transcatheter aortic valve replacement (TAVR) outcomes has been reported. We aimed to systematically review the literature and compare TAVR-related morbidity and mortality among individuals with overweight or obesity and their peers with normal body mass index (BMI). Methods: PubMed and Embase databases were systematically searched for studies reporting TAVR outcomes in different BMI groups. Separate meta-analyses were conducted for studies reporting hazard ratios (HR) and odds ratios/relative risks. Short- and mid-/long-term outcomes were examined. Results: 26 studies with a total of 74,163 patients were included in our study. Overweight was associated with lower risk of short-term mortality (HR: 0.77; 95% CI: 0.60–0.98) and mid-/long-term mortality (HR: 0.79; 95% CI: 0.70–0.89). Obesity was associated with lower risk for mid-/long-term mortality (HR: 0.79; 95% CI: 0.73–0.86), but no difference was observed in short-term mortality, although a trend was noted (HR: 0.87l 95% CI: 0.74–1.01). Individuals with obesity demonstrated an association with higher odds of major vascular complications (OR: 1.33; 95% CI: 1.05–1.68). Both overweight (OR: 1.16; 95% CI: 1.03–1.30) and obesity (OR: 1.26; 95% CI: 1.06–1.50) were associated with higher likelihood for receiving permanent pacemakers after TAVR. Conclusion: Individuals with overweight and obesity were associated with lower mortality risk compared to those with normal BMI but with higher likelihood of major vascular complications and permanent pacemaker implantation after TAVR.

Background: Infectious diseases are more frequent and can be associated with worse outcomes in patients with diabetes. Our aim was to systematically review and synthesize with a meta-analysis the available observational studies reporting the effect of diabetes in mortality among hospitalized patients with COVID-19. Methods: Medline, Embase, Google Scholar, and medRxiv databases were reviewed. A random-effect model meta-analysis was used and I-square was utilized to assess the heterogeneity. In-hospital mortality was defined as the endpoint. Sensitivity, subgroup, and meta-regression analyses were performed. Results: 18,506 patients were included in this meta-analysis (3,713 diabetics and 14,793 non-diabetics). Patients with diabetes were associated with a higher risk of death compared to patients without diabetes (OR: 1.65; 95% CI: 1.35-1.96; I ² 77.4%). The heterogeneity was high. A study level meta-regression analysis was performed for all the important covariates and no significant interactions were found between the covariates and the outcome of mortality. Conclusion: This meta-analysis shows that that the likelihood of death is 65% higher in diabetic hospitalized patients with COVID-19 compared to non-diabetics. Further studies are needed to assess whether this association is independent or not, as well as to investigate to role of glucose control prior or during the disease.