RESUMO
BACKGROUND: This meta-analysis and systematic review aim to consolidate evidence on cardiotoxicity prevention and treatment strategies in patients receiving anthracyclines or HER2 receptor inhibitors, vital treatments for breast cancer and hematologic malignancies. By synthesizing existing research, the goal is to provide impactful insights that enhance patient care and outcomes. METHODS: Comprehensive research across PubMed, Scopus, EMBASE, and the Cochrane Central Register for Controlled Trials was conducted, selecting clinical trials focusing on cardioprotection in anthracyclines or HER2 inhibitor-treated individuals. Effect sizes were computed using OpenMeta (Analyst), with leave-out meta-analysis to assess potential small study effects. Meta-regression explored treatment duration and sample size effects. Evidence quality for primary outcomes was evaluated using ROB, Robins 2, and Newcastle-Ottawa tools. RESULTS: Twenty -three studies involving a total of 14,652 patients (13,221 adults and 1431 kids) were included in the current systematic review and meta-analysis. The risk of bias and methodological quality of the included studies suggested good and moderate quality. Patients prescribed ß-blockers demonstrated a 74% lower likelihood of exhibiting cardiotoxicity symptoms (OR 1.736). Similarly, the use of dexrazoxane was linked to a threefold decrease in cardiac abnormalities risk (OR 2.989), and ACE inhibitor administration showed half the risk compared with the control group (OR 1.956). CONCLUSIONS: Through this systematic review and meta-analysis, it was shown that there is a reduction in cardiotoxicity from either anthracyclines or HER2 inhibitors in patients receiving pharmacoprophylaxis.
RESUMO
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.
