Friedman, Paul A.

Introduction: Left atrial appendage (LAA) flow depends largely on left ventricular compliance and may play a role in mediating the regulation of left atrial volume-pressure relationships. Hypothesis: We hypothesize that LAA emptying flow velocity (LAAEV) is a predictive factor of long-term outcomes (e.g. recurrent AF, stroke, and survival) after cardioversion for non-valvular AF. Method: We identified 3,251 consecutive patients with non-valvular AF who underwent successful TEE-guided electrical cardioversion (ECV) at our institution between May 2000 and March 2012. Successful ECV was defined as sinus rhythm at time of discharge from the cardioversion unit. Patients were monitored following their ECV procedure for first documentation of recurrent AF, stroke or death. Multivariate Cox proportional hazards models were used to identify independent predictors of long-term outcomes. Patients with >= moderate valvular regurgitation or stenosis were excluded. Results: Among the 3,251 patients who were successfully cardioverted to sinus rhythm, the mean (±SD) LAAEV was 38.43±23 cm/s and the median was 33 cm/s, (interquartile range [IQR], 20-50). Patients with LAAEV <=33 cm/s had higher CHA 2 DS 2 -VASc score (2.6±1.2 vs. 1.9±1.3, P =.009), larger LAVI (52.0±20.9 cc/m 2 vs. 43.3±13.6 cc/m 2 , P <.001) than those with LAAEV >33 cm/s. Pre or post-procedure antiarrhythmic drug use was similar between the two groups. During 1-year follow-up, patients with LAAEV <=33 cm/s had significantly higher rate of AF recurrence than those with LAAEV >33 cm/s (55% vs 45%, P <.001). Likewise, during a mean follow-up of 4.9±3.6 years, similar patterns in 5-year rates were observed for first recurrence of AF (81% vs 73%, P <.001), stroke (7% vs 4%, P =.003) and mortality (31% vs 23%, P <.001) for LAAEV <=33 vs > 33cm/s, respectively. Stepwise multivariate Cox regression analysis revealed that LAAEV <=33 cm/s, age, CHA 2 DS 2 -VASc score were independent predictors of AF recurrence, stroke and mortality. Conclusions: LAA emptying flow velocity is an effective and convenient method for risk stratification of patients undergoing cardioversion for AF. Our data showed that patients with reduced LAAEV have an increased risk for AF recurrence, stroke and death following electrical cardioversion.

Background: Data regarding ventricular tachycardia (VT) or premature ventricular complex (PVC) ablation following MVS is limited.) CA can be challenging given perivalvular substrate in the setting of mitral annuloplasty or prosthetic valves. Objective: To investigate the characteristics, safety, and outcomes of radiofrequency catheter ablation (CA) in patients with prior mitral valve surgery (MVS) and ventricular arrhythmias (VA). Methods: We identified consecutive patients with prior MVS who underwent CA for VT or PVC between January 2013- December 2018. We investigated the mechanism of arrhythmia, ablation approach, peri-operative complications, and outcomes. Results: In our cohort of 31 patients (77% men, mean age 62.3±10.8 years, left ventricular ejection fraction 39.2±13.9%) with prior MVS underwent CA (16 VT; 15 PVC). Access to the left ventricle was via transseptal approach in 17 patients, and a retrograde aortic approach was used in 13 patients. A combined transseptal and retrograde aortic approach was used in one patient, and a percutaneous epicardial approach was combined with trans-septal approach in 1patient. Heterogenous scar regions were present in 94% of VT patients and scar-related reentry was the dominant mechanism of VT. Clinical VA substrates involved the peri-mitral area in 6 patients with VT and 5 patients with PVC ablation. No procedure-related complications were reported. The overall recurrence-free rate at 1-year was 72.2%; 67% in the VT group and 78% in the PVC group. No arrhythmia-related death was documented on long-term follow-up. Conclusion: CA of VAs can be performed safely and effectively in patients with MVS

Introduction: An artificial intelligence (AI) algorithm applied to electrocardiography (ECG) during normal sinus rhythm (NSR) has been shown to predict concomitant atrial fibrillation (AF). We sought to characterize the value of AI-ECG as a predictor of future AF and assess its performance compared to other clinical prediction scores in a population-based sample. Methods: We calculated the AI-ECG probability during NSR in patients who enrolled in the population-based Mayo Clinic Study of Aging with at least one ECG in NSR within two years prior and no history of AF at the time of the baseline study visit. The cumulative incidence of AF was estimated for strata defined by AI-ECG probability and CHARGE-AF score. Cox proportional hazards were fit to assess the independent prognostic value and interaction of AI-ECG probability and CHARGE-AF score. Concordance (c) statistics were calculated for AI-ECG probability, CHARGE-AF score and combined AI-ECG and CHARGE-AF score. Results: A total of 1,936 patients with a median age 75.8 (quartile range [QR] 70.4, 81.8) years, median CHARGE-AF score 14.0 (QR 13.2, 14.7) and median CHADS2VASC score 3 (QR 2, 4) were included in the analysis. The cumulative incidence of AF increased in a stepwise fashion across quartiles of AI-ECG probability and CHARGE-AF score (Figure 1). When compared in the same model, both AI-ECG probability (hazard ratio [HR] 1.76, 95% confidence interval [CI] 1.51-2.04) and CHARGE-AF score (HR 1.90, 95% CI 1.58-2.28) independently predicted AF without significant interaction (p=0.54). C statistics were 0.69 (95% CI 0.66-0.72) for AI-ECG probability, 0.69 (95% CI 0.66-0.71) for CHARGE-AF and 0.72 (95% CI 0.69-0.75) for combined AI-ECG and CHARGE-AF score. Conclusions: In the Mayo Clinic Study of Aging, both the AI-ECG probability and CHARGE-AF score independently predicted time to AF. The AI-ECG may offer a means to assess risk with a single test without requiring manual or automated clinical data abstraction.

Background: Left atrial appendage occlusion (LAAO) devices have successfully provided an alternative therapeutic option in patients with atrial fibrillation and high bleeding risk. However, patients with thrombocytopenia (TP) were underrepresented in the milestone trials, and the optimal strategy of postprocedural antithrombotic therapy in this group of patients remains unclear. Methods: Patients were identified from the National Cardiac Database Registry - LAAO database at all three Mayo Clinic sites in the period from 1/2016-10/2019. Patients were further divided into TP (platelet &lt; 150 x 10^9/L) and control (platelet &gt; 150 x 10^9 /L) groups for clinical outcome analysis. Outcome endpoints include composite of bleeding (GI bleeding, hemorrhagic stroke, and other non-intracranial hemorrhage) and composite thrombotic events (device thrombus, ischemic stroke, TIA and undetermined stroke). Results: A total of 249 consecutive patients were included in the final analysis. The mean age was 76.7 ± 7.2 years, and 169 (67.9%) were male; 242 (97.2%) were white. The mean CHA2DS2-VASc score was 5.3 ±1.5, and the HAS-BLED score was 3.0 ± 1.1. TP at baseline was present in 74 (29.7%) patients. The median follow-up time was 6.8 months. The use of post-procedural antithrombotic regimens was similar between the two groups (Table 1). When comparing the TP group to control, there was no significant difference in composite bleeding events (10.8% vs. 8.0%, P = 0.069) and thrombotic events (2.7% vs. 2.3%, P= 0.170). Conclusions: The presence of baseline TP does not significantly increase post-procedural bleeding risk in atrial fibrillation/atrial flutter patients who underwent an LAAO occlusion procedure with the Watchman device (Boston Scientific). This group of patients can be treated with the same antithrombotic regimen as patients with normal platelet levels. Future studies are still warranted to determine the optimal antithrombotic regimen.

Background The temporal incidence of high‐grade atrioventricular block (HAVB) after transcatheter aortic valve replacement (TAVR) is uncertain. As a result, periprocedural monitoring and pacing strategies remain controversial. This study aimed to describe the temporal incidence of initial episode of HAVB stratified by pre‐ and post‐TAVR conduction and identify predictors of delayed events. Methods and Results Consecutive patients undergoing TAVR at a single center between February 2012 and June 2019 were retrospectively assessed for HAVB within 30 days. Patients with prior aortic valve replacement, permanent pacemaker (PPM), or conversion to surgical replacement were excluded. Multivariable logistic regression was performed to assess predictors of delayed HAVB (initial event >24 hours post‐TAVR). A total of 953 patients were included in this study. HAVB occurred in 153 (16.1%). After exclusion of those with prophylactic PPM placed post‐TAVR, the incidence of delayed HAVB was 33/882 (3.7%). Variables independently associated with delayed HAVB included baseline first‐degree atrioventricular block or right bundle‐branch block, self‐expanding valve, and new left bundle‐branch block. Forty patients had intraprocedural transient HAVB, including 16 who developed HAVB recurrence and 6 who had PPM implantation without recurrence. PPM was placed for HAVB in 130 (13.6%) (self‐expanding valve, 23.7% versus balloon‐expandable valve, 11.9%; P <0.001). Eight (0.8%) patients died by 30 days, including 1 unexplained without PPM present. Conclusions Delayed HAVB occurs with higher frequency in patients with baseline first‐degree atrioventricular block or right bundle‐branch block, new left bundle‐branch block, and self‐expanding valve. These findings provide insight into optimal monitoring and pacing strategies based on periprocedural ECG findings.

Abstract Background We have demonstrated that a neural network is able to predict a person’s age from the electrocardiogram (ECG) [artificial intelligence (AI) ECG age]. However, some discrepancies were observed between ECG-derived and chronological ages. We assessed whether the difference between AI ECG and chronological age (Age-Gap) represents biological ageing and predicts long-term outcomes. Methods and results We previously developed a convolutional neural network to predict chronological age from ECGs. In this study, we used the network to analyse standard digital 12-lead ECGs in a cohort of 25 144 subjects ≥30 years who had primary care outpatient visits from 1997 to 2003. Subjects with coronary artery disease, stroke, and atrial fibrillation were excluded. We tested whether Age-Gap was correlated with total and cardiovascular mortality. Of 25 144 subjects tested (54% females, 95% Caucasian) followed for 12.4 ± 5.3 years, the mean chronological age was 53.7 ± 11.6 years and ECG-derived age was 54.6 ± 11 years (R2 = 0.79, P &lt; 0.0001). The mean Age-Gap was small at 0.88 ± 7.4 years. Compared to those whose ECG-derived age was within 1 standard deviation (SD) of their chronological age, patients with Age-Gap ≥1 SD had higher all-cause and cardiovascular disease (CVD) mortality. Conversely, subjects whose Age-Gap was ≤1 SD had lower all-cause and CVD mortality. Results were unchanged after adjusting for CVD risk factors and other survival influencing factors. Conclusion The difference between AI ECG and chronological age is an independent predictor of all-cause and cardiovascular mortality. Discrepancies between these possibly reflect disease independent biological ageing.

Echocardiography (Echo), a widely available, noninvasive, and portable bedside imaging tool, is the most frequently used imaging modality in assessing cardiac anatomy and function in clinical practice. On the other hand, its operator dependability introduces variability in image acquisition, measurements, and interpretation. To reduce these variabilities, there is an increasing demand for an operator- and interpreter-independent Echo system empowered with artificial intelligence (AI), which has been incorporated into diverse areas of clinical medicine. Recent advances in AI applications in computer vision have enabled us to identify conceptual and complex imaging features with the self-learning ability of AI models and efficient parallel computing power. This has resulted in vast opportunities such as providing AI models that are robust to variations with generalizability for instantaneous image quality control, aiding in the acquisition of optimal images and diagnosis of complex diseases, and improving the clinical workflow of cardiac ultrasound. In this review, we provide a state-of-the art overview of AI-empowered Echo applications in cardiology and future trends for AI-powered Echo technology that standardize measurements, aid physicians in diagnosing cardiac diseases, optimize Echo workflow in clinics, and ultimately, reduce healthcare costs.

Background: Transvenous implantable cardiac defibrillators (TV ICD) provide life-saving therapy for millions of patients worldwide. However, they are susceptible to several potential short- and long- term complications including cardiac perforation and pneumothorax, lead dislodgement, venous obstruction, and infection. The extravascular ICD (EV ICD) system’s novel design and substernal implant approach avoids the risks associated with TV ICDs while still providing pacing features and similar generator size to TV ICDs. Study Design: The EV ICD pivotal study is a prospective, multi-center, single-arm, non-randomized, pre-market clinical study designed to examine the safety and acute efficacy of the system. This study will enroll up to 400 patients with a Class I or IIa indication for implantation of an ICD. Implanted subjects will be followed up to approximately 3.5 years, depending on when the patient is enrolled. Objective: The clinical trial is designed to demonstrate safety and effectiveness of the EV ICD system in human use. The safety endpoint is freedom from major complications, while the efficacy endpoint is defibrillation success. Both endpoints will be assessed against prespecified criteria. Additionally, this study will evaluate antitachycardia pacing (ATP) performance, electrical performance, extracardiac pacing sensation, asystole pacing, appropriate and inappropriate shocks, as well as a summary of adverse events. Conclusion: The EV ICD pivotal study is designed to provide clear evidence addressing the safety and efficacy performance of the EV ICD System.

Background: Atrial fibrillation (AF) is an established risk factor for ischemic stroke, but it can be paroxysmal and may go undiagnosed. An artificial intelligence (AI)-enabled ECG acquired during normal sinus rhythm was recently shown to detect silent AF. The objective of this study was to determine if AI-ECG AF score is associated with presence of cerebral infarcts. Methods: Participants from a population-based study ages 30 to 95 years with T2 fluid attenuation inversion recovery (FLAIR) MRI obtained between October 10, 2011, and November 2, 2017 were considered for inclusion. Participants without ECG were excluded. AI-ECG score was calculated using most recent ECG with normal sinus rhythm at the time of MRI. Presence of infarcts was determined on FLAIR MRI scans. Logistic regression was run to evaluate the relationship between AI-ECG AF score and presence of cerebral infarcts. Similar analyses were performed using history of AF rather than AI-ECG AF score as predictor. Age and sex were included as covariates. We also examined whether a high-threshold AI-ECG score was associated with infarcts. In a prior study, an AI-ECG AF score > 0.5 was associated with a cumulative incidence of AF of 21.5% at 2 years and 52.2% at 10 years. Results: This study included 1,373 individuals. Average age was 69.6 years and 53% of participants were male. There were 136 (10%) individuals with ECG-confirmed AF; 1237 (90%) participants had no AF history. Of participants with AF, 23% (n=31) were on anticoagulation, 47% (n=64) were on antiplatelet and 18% (n=24) were on dual therapy. Only 1.3% (n=16) of patients without AF were on anticoagulation and 47% (n=578) were on antiplatelet therapy. Ischemic infarcts were detected in 214 (15.6%) patients. As a continuous measure AI-ECG was associated with infarcts but not after adjusting for age and sex (p=0.46). AI-ECG AF score > 0.5 was associated with infarcts ( p < 0.001); even after adjusting for age and sex ( p = 0.03). History of AF was also associated with infarcts after adjusting for age and sex ( p = 0.018). Conclusion: AI-ECG AF score and history of AF were associated with presence of cerebral infarcts after adjusting for age and sex. This tool could be useful in select patients with cryptogenic stroke but further investigation would be required.