Dufani, Jalal

Abstract Background Atrial high-rate episodes (AHRE) are commonly detected in individuals with cardiac implantable electronic devices. Outcomes of atrial high-rate episodes remain unclear in those without a prior history of atrial fibrillation. Purpose This study aims to evaluate clinical outcomes of atrial high-rate episodes in patients who have no prior history of atrial fibrillation. Methods PubMed, EMBASE, and Cochrane databases were searched through February 2024 for studies evaluating clinical outcomes in individuals with cardiac implantable electronic devices who experienced atrial high-rate episodes, compared to those who did not. Individuals who had a prior history of atrial fibrillation were excluded. Outcomes assessed were clinical atrial fibrillation, thromboembolic events, and all-cause mortality. A fixed-effects model was used to calculate risk ratios (RRs) with 95% confidence intervals (CI). A subgroup analysis of thromboembolic events was conducted based on the duration of atrial high-rate episodes. Results A total of 9 studies met inclusion criteria with a combined enrollment of 7440 patients. Compared to those who did not experience atrial high-rate episodes, those who did had significantly increased risks of clinical atrial fibrillation (RR 3.33; 95% CI 1.99 - 5.57; P < 0.0001) and thromboembolic events (RR 2.21; 95% CI 1.72 -2.85; P < 0.0001). The risk of all-cause mortality was similar between both groups (RR 1.19; 95% CI 0.95 – 1.49; P = 0.13). Subgroup analysis revealed an increased risk of thromboembolic events among atrial high-rate episodes ≥ 24 hours (RR 12.34; 95% CI 2.70 – 56.36) while atrial high-rate episodes lasting < 24 hours were insignificantly associated with thromboembolic events (RR 0.83; 95% CI 0.12 – 5.58). Conclusions Our study indicates a heightened risk of clinical atrial fibrillation and thromboembolic events among patients with atrial high-rate episodes, with no observed increase in mortality risk. Furthermore, our findings suggest that the duration of atrial high-rate episodes influences the risk of thromboembolism. Further studies are warranted to confirm our findings.

Abstract Background Transcatheter aortic valve replacement (TAVR) continues to be increasingly performed. While machine learning has demonstrated utility in predicting adverse outcomes in various cardiovascular procedures, its effectiveness in predicting in-hospital mortality in TAVR remains uncertain. Purpose We aimed to examine the accuracy of machine learning in predicting in-hospital mortality in TAVR. Methods We searched PubMed and Cochrane databases up to February 2024 for observational studies assessing the diagnostic accuracy of machine learning for predicting in-hospital mortality in TAVR. Extracted data included study characteristics, patient demographics, type of predictive model used, and area under the receiver operating characteristic curves (AUC). Predictive accuracy was assessed by pooling AUC of machine-learning models across studies. AUC values ranging from 0.80 to 0.90, 0.70 to 0.80, 0.60 to 0.70, and 0.50 to 0.60 corresponded to excellent, good, fair, and poor discrimination ability, respectively (1). We used a random effects model to estimate the overall effect size with a 95% confidence interval (CI). Heterogeneity was evaluated with the I2 test.We conducted subgroup analyses based on the type of machine-learning model utilized to assess for any potential differences in predictive accuracy. Results A total of three studies were identified, comprising a study population of 70,714 patients. Across studies, eight machine learning models were utilized, consisting of three artificial neural network models, one logistic regression model, one naive Bayes model, two random forest models, and one support vector machine model. Pooled AUC showed excellent accuracy of machine learning in predicting in-hospital mortality in TAVR (0.92; 95% CI 0.90 - 0.95). Subgroup analyses revealed similar yet excellent accuracy across all types of machine learning models utilized: artificial neural network (0.92; 95% CI 0.86-0.99), logistic regression (0.92; 95% CI 0.89-0.95), naive Bayes (0.90; 95% CI 0.88-0.92), random forest (0.94; 95% CI 0.87-1.00), and support vector machine (0.94; 95% CI 0.91-0.96). Conclusion Our study suggests that utilizing machine learning can accurately predict in-hospital mortality in TAVR. Operators of TAVR procedures may benefit from incorporating machine learning prediction models in their clinical practice. Further exploration of machine learning on outcomes in TAVR is needed to verify our findings.

Abstract Background Patients with heart failure possess an increased risk for adverse outcomes with noncardiac surgery (1). While bariatric surgery is frequently performed in patients with obesity, there remains a limited understanding of the impact of bariatric surgery in obese patients with heart failure. Purpose The purpose of this study was to compare outcomes of bariatric surgery versus nonsurgical management in obese patients with heart failure. Methods We searched PubMed, EMBASE, and the Cochrane Library, through February 2024, for studies evaluating outcomes in obese patients with heart failure who underwent bariatric surgery compared to nonsurgical management. Outcomes assessed were postoperative atrial fibrillation, all-cause mortality, and hospital readmission. We used a fixed-effect model to calculate hazard ratios (HR) with 95% confidence intervals (CI) in R studio for outcomes assessed. In outcomes where significant heterogeneity was found (P< 0.05 and I2 > 60%), a random effects model was used. Results A total of 3 studies were identified, consisting of 260,186 patients. Compared to nonsurgical management, bariatric surgery was associated with a significant reduction in postoperative atrial fibrillation (HR 0.72; 95% CI: 0.62-0.84), all-cause mortality (HR 0.50; 95%CI 0.35-0.72), and hospital readmission (HR 0.73; 95% CI 0.69-0.78) in obese patients with heart failure. Conclusion Our study suggests that bariatric surgery significantly reduces post-operative atrial fibrillation, mortality, and hospital readmission among obese patients with heart failure.Our findings demonstrate that bariatric surgery should be considered as a viable intervention in obese patients with heart failure. Further studies are warranted to confirm our findings.Forest Plots of Outcomes Assessed

Background: Outcomes of device-detected AF remain unclear in individuals without a prior history of AF. Methods: A meta-analysis was conducted to evaluate outcomes in individuals with no prior history of AF who experienced device-detected AF. Outcomes assessed were clinical AF, thromboembolism and all-cause mortality. A fixed-effects model was used to calculate RRs with 95% CI. Results: Compared to individuals who did not experience device-detected AF, those who did had increased risks of clinical AF (RR 3.33, 95% CI [1.99–5.57]; p<0.0001) and thromboembolic events (RR 2.21; 95% CI [1.72–2.85]; p<0.0001). The risk of all-cause mortality was similar between both groups (RR 1.19; 95% CI [0.95–1.49]; p=0.13). Subgroup analysis revealed an increased risk of thromboembolic events among device-detected AF ≥24 hours (RR 12.34; 95% CI [2.70–56.36]). Conclusion: While there is an increased risk of clinical AF and thromboembolism in individuals with device-detected AF, mortality was insignificant.