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.
RESUMO
PURPOSE: Chemotherapy is the cornerstone of adjuvant therapy in ovarian cancer. Its impact on the quality of life (QoL) has been addressed in several studies; however, several misperceptions concerning this affect patient counseling and physicians' ability to overcome patient fears. In the present systematic review, we sought to accumulate current evidence in the field in order to help establish robust information that will help physicians answer patients' questions. METHODS: The present systematic review is based on the PRISMA guidelines. Studies that evaluated patient QoL pre-, during, and post-chemotherapy with the use of the QLQC-30 were selected for inclusion. Their methodological quality was assessed with the before-after studies tool that is proposed by the National Institute of Health (NIH). RESULTS: Ten studies that involved 5181 patients were included in the present systematic review. The risk of bias and methodological quality of included studies was of good and fair overall quality. Retrieved data suggest there is substantial evidence that points toward improved global QoL among ovarian cancer patients treated with taxanes-platinum combination therapy. Individual outcomes evaluated with the QLQ-C30 also provide positive results, although underreporting was noted. CONCLUSION: Despite the significant heterogeneity in outcome reporting, the findings of this study reveal the significant benefit of combined platinum taxane chemotherapy on the QoL of ovarian cancer patients and can be used for patients counseling in order to reduce refusals that arise from fear of adverse effects that may negatively affect QoL. Graphical abstract.
