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Atrial fibrillation (AF) is the most common arrhythmia with a high burden of morbidity including impaired quality of life and increased risk of thromboembolism. Early detection and management of AF could prevent thromboembolic events. Artificial intelligence (AI)--based methods in healthcare are developing quickly and can be proved as valuable for the detection of atrial fibrillation. In this metanalysis, we aim to review the diagnostic accuracy of AI-based methods for the diagnosis of atrial fibrillation. A predetermined search strategy was applied on four databases, the PubMed on 31 August 2022, the Google Scholar and Cochrane Library on 3 September 2022, and the Embase on 15 October 2022. The identified studies were screened by two independent investigators. Studies assessing the diagnostic accuracy of AI-based devices for the detection of AF in adults against a gold standard were selected. Qualitative and quantitative synthesis to calculate the pooled sensitivity and specificity was performed, and the QUADAS-2 tool was used for the risk of bias and applicability assessment. We screened 14,770 studies, from which 31 were eligible and included. All were diagnostic accuracy studies with case-control or cohort design. The main technologies used were: (a) photoplethysmography (PPG) with pooled sensitivity 95.1% and specificity 96.2%, and (b) single-lead ECG with pooled sensitivity 92.3% and specificity 96.2%. In the PPG group, 0% to 43.2% of the tracings could not be classified using the AI algorithm as AF or not, and in the single-lead ECG group, this figure fluctuated between 0% and 38%. Our analysis showed that AI-based methods for the diagnosis of atrial fibrillation have high sensitivity and specificity for the detection of AF. Further studies should examine whether utilization of these methods could improve clinical outcomes.

Diabetes mellitus (DM) and heart failure (HF) are two chronic disorders that affect millions worldwide. Hyperglycemia can induce excessive generation of highly reactive free radicals that promote oxidative stress and further exacerbate diabetes progression and its complications. Vascular dysfunction and damage to cellular proteins, membrane lipids and nucleic acids can stem from overproduction and/or insufficient removal of free radicals. The aim of this article is to review the literature regarding the use of antidiabetic drugs and their role in glycemic control in patients with heart failure and oxidative stress. Metformin exerts a minor benefit to these patients. Thiazolidinediones are not recommended in diabetic patients, as they increase the risk of HF. There is a lack of robust evidence on the use of meglinitides and acarbose. Insulin and dipeptidyl peptidase-4 (DPP-4) inhibitors may have a neutral cardiovascular effect on diabetic patients. The majority of current research focuses on sodium glucose cotransporter 2 (SGLT2) inhibitors and glucagon-like peptide 1 (GLP-1) receptor agonists. SGLT2 inhibitors induce positive cardiovascular effects in diabetic patients, leading to a reduction in cardiovascular mortality and HF hospitalization. GLP-1 receptor agonists may also be used in HF patients, but in the case of chronic kidney disease, SLGT2 inhibitors should be preferred.