Background: Presently, transcatheter aortic valve replacement (TAVR) as an effective and convenient intervention has been adopted extensively for patients with severe aortic disease. However, after surgical aortic valve replacement (SAVR) and TAVR, the incidence of new-onset atrial fibrillation (NOAF) is prevalently found. This meta-analysis was designed to comprehensively compare the incidence of NOAF at different times after TAVR and SAVR for patients with severe aortic disease. Methods: A systematic search of PubMed, Embase, Cochrane Library, and Web of Science up to October 1, 2020 was conducted for relevant studies that comparing TAVR and SAVR in the treatment of severe aortic disease. The primary outcomes were the incidence of NOAF with early, midterm and long term follow-up. The secondary outcomes included permanent pacemaker (PM) implantation, myocardial infarction (MI), cardiogenic shock, as well as mortality and other complications. Two reviewers assessed trial quality and extracted the data independently. All statistical analyses were performed using the standard statistical procedures provided in Review Manager 5.2. Results: A total of 16 studies including 13,310 patients were identified. The pooled results indicated that, compared with SAVR, TAVR experienced a significantly lower incidence of 30-day/in-hospital, 1-year, 2-year, and 5-year NOAF, with pooled risk ratios (RRs) of 0.31 (95% confidence interval [CI] 0.23–0.41; 5725 pts), 0.30 (95% CI 0.24–0.39; 6321 pts), 0.48 (95% CI 0.38–0.61; 3441 pts), and 0.45 (95% CI 0.37–0.55; 2268 pts) respectively. In addition, TAVR showed lower incidence of MI (RR 0.62; 95% CI 0.40–0.97) and cardiogenic shock (RR 0.34; 95% CI 0.19–0.59), but higher incidence of permanent PM (RR 3.16; 95% CI 1.61–6.21) and major vascular complications (RR 2.22; 95% CI 1.14–4.32) at 30-day/in-hospital. At 1- and 2-year after procedure, compared with SAVR, TAVR experienced a significantly higher incidence of neurological events, transient ischemic attacks (TIA), permanent PM, and major vascular complications, respectively. At 5-year after procedure, compared with SAVR, TAVR experienced a significantly higher incidence of TIA and re-intervention respectively. There was no difference in 30-day, 1-year, 2-year, and 5-year all-cause or cardiovascular mortality as well as stroke between TAVR and SAVR. Conclusions: Our analysis showed that TAVR was superior to SAVR in decreasing the both short and long term postprocedural NOAF. TAVR was equal to SAVR in early, midterm and long term mortality. In addition, TAVR showed lower incidence of 30-day/in-hospital MI and cardiogenic shock after procedure. However, pooled results showed that TAVR was inferior to SAVR in reducing permanent pacemaker implantation, neurological events, TIA, major vascular complications, and re-intervention.

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Background: Transesophageal echocardiography (TEE) is the first technique of choice for evaluating the left atrial appendage flow velocity (LAAV) in clinical practice, which may cause some complications. Therefore, clinicians require a simple applicable method to screen patients with decreased LAAV. Therefore, we investigated the feasibility and accuracy of a machine learning (ML) model to predict LAAV. Method: The analysis included patients with atrial fibrillation who visited the general hospital of PLA and underwent transesophageal echocardiography (TEE) between January 2017 and December 2020. Three machine learning algorithms were used to predict LAAV. The area under the receiver operating characteristic curve (AUC) was measured to evaluate diagnostic accuracy. Results: Of the 1039 subjects, 125 patients (12%) were determined as having decreased LAAV (LAAV < 25 cm/s). Patients with decreased LAAV were fatter and showed a higher prevalence of persistent AF, heart failure, hypertension, diabetes and stroke, and the decreased LAAV group had a larger left atrium diameter and a higher serum level of NT-pro BNP than the control group (p < 0.05). Three machine-learning models (SVM model, RF model, and KNN model) were developed to predict LAAV. In the test data, the RF model performs best (R = 0.608, AUC = 0.89) among the three models. A fivefold cross-validation scheme further verified the predictive ability of the RF model. In the RF model, NT-proBNP was the factor with the strongest impact. Conclusions: A machine learning model (Random Forest model)-based simple clinical information showed good performance in predicting LAAV. The tool for the screening of decreased LAAV patients may be very helpful in the risk classification of patients with a high risk of LAA thrombosis.

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