Friedman, Paul A

Background Recent studies have indicated high rates of future major adverse cardiovascular events in patients with Takotsubo cardiomyopathy (TC), but there is no well‐established tool for risk stratification. This study sought to evaluate the prognostic value of several artificial intelligence‐augmented ECG (AI‐ECG) algorithms in patients with TC. Methods and Results This study examined consecutive patients in the prospective and observational Mayo Clinic Takotsubo syndrome registry. Several previously validated AI‐ECG algorithms were used for the estimation of ECG‐ age, probability of low ejection fraction, and probability of atrial fibrillation. Multivariable models were constructed to evaluate the association of AI‐ECG and other clinical characteristics with major adverse cardiac events, defined as cardiovascular death, recurrence of TC, nonfatal myocardial infarction, hospitalization for congestive heart failure, and stroke. In the final analysis, 305 patients with TC were studied over a median follow‐up of 4.8 years. Patients with future major adverse cardiac events were more likely to be older, have a history of hypertension, congestive heart failure, worse renal function, as well as high‐risk AI‐ECG findings compared with those without. Multivariable Cox proportional hazards analysis indicated that the presence of 2 or 3 high‐risk findings detected by AI‐ECG remained a significant predictor of major adverse cardiac events in patients with TC after adjustment by conventional risk factors (hazard ratio, 4.419 [95% CI, 1.833–10.66], P =0.001). Conclusions The combined use of AI‐ECG algorithms derived from a single 12‐lead ECG might detect subtle underlying patterns associated with worse outcomes in patients with TC. This approach might be beneficial for stratifying high‐risk patients with TC.

Abstract Introduction The pivotal study of the extravascular implantable cardioverter‐defibrillator (EV ICD) recently demonstrated primary efficacy and safety endpoints comparable to previous ICD systems. Patient experience with this novel device has not been reported. The current study examined the standardized patient‐reported outcome (PRO) metrics of quality of life (QOL) and patient acceptance of the device. Methods The EV ICD Pivotal Study was a prospective, single‐arm, nonrandomized, global, premarket approval trial. Patients completed the 12‐Item Short Form Survey (SF‐12) QOL surveys at baseline and at 6 months following implant. Additionally, patients completed the Florida Patient Acceptance Survey (FPAS) QOL survey at 6 months. Results From baseline to 6 months, patients within the EV ICD Pivotal Study ( n = 247) reported statistically significant SF‐12 improvements in physical QOL (45.4 ± 9.4 vs. 46.8 ± 9.1 respectively, p = .020) and no changes in mental QOL (49.3 ± 10.4 vs. 50.5 ± 9.7, p = .061). No differences were noted by sex, atrial fibrillation, or the experience of ICD shock. EV ICD patients reported better total FPAS patient acceptance of their ICD than TV‐ICD or S‐ICD patients using historical norms comparisons (80.4 ± 15.7 vs. 70.2 ± 17.8, p < .0001 for S‐ICD and 73.0 ± 17.4, p = .004 for TV‐ICD). Conclusion The initial PROs for EV ICD patients indicated that patients had improvements in physical QOL from baseline to 6‐month follow‐up and markedly better overall acceptance of their ICD compared to a previous study with S‐ICD and TV‐ICD data. These initial results suggest that the EV ICD is evaluated positively by patients.

AbstractIntroductionData regarding ventricular tachycardia (VT) or premature ventricular complex (PVC) ablation in patients with aortic valve (AV) intervention (AVI) is limited. Catheter ablation (CA) can be challenging given perivalvular substrate in the setting of prosthetic valves. We sought to investigate the characteristics, safety, and outcomes of CA in patients with prior AVI and ventricular arrhythmias (VA).MethodsWe identified consecutive patients with prior AVI (replacement or repair) who underwent CA for VT or PVC between 2013 and 2018. We investigated the mechanism of arrhythmia, ablation approach, perioperative complications, and outcomes.ResultsWe included 34 patients (88% men, mean age 64 ± 10.4 years, left ventricular (LV) ejection fraction 35.2 ± 15.0%) with prior AVI who underwent CA (22 VT; 12 PVC). LV access was obtained through trans‐septal approach in all patients except one patient who had percutaneous transapical access. One patient had combined retrograde aortic and trans‐septal approach. Scar‐related reentry was the dominant mechanism of induced VTs. Two patients had bundle branch reentry VTs. In the VT group, substrate mapping demonstrated heterogeneous scar that involved the peri‐AV area in 95%. Despite that, the site of successful ablation included the periaortic region only in 6 (27%) patients. In the PVC group, signal abnormalities consistent with scar in the periaortic area were noted in 4 (33%) patients. In 8 (67%) patients, the successful site of ablation was unrelated to the periaortic area. No procedure‐related complications occurred. The survival and recurrence‐free survival rate at 1 year tended to be lower in VT group than in PVC group (p = .06 and p = .05, respectively) with a 1‐year recurrence‐free survival rate of 52.8% and 91.7%, respectively. No arrhythmia‐related death was documented on long‐term follow‐up.ConclusionCA of VAs can be performed safely and effectively in patients with prior AVI.

AbstractIntroductionA current limitation of single chamber implantable cardioverter defibrillators (ICDs) is the lack of an atrial lead to reliably detect atrial fibrillation (AF) episodes. A novel ventricular based atrial fibrillation (VBAF) detection algorithm was created for single chamber ICDs to assess R‐R variability for detection of AF.MethodsPatients implanted with Visia AF™ ICDs were prospectively enrolled in the Medtronic Product Surveillance Registry from December 15, 2015 to January 23, 2019 and followed with at least 30 days of monitoring with the algorithm. Time to device‐detected daily burden of AF ≥ 6 min, ≥6 h, and ≥23 h were reported. Clinical actions after device‐detected AF were recorded.ResultsA total of 291 patients were enrolled with a mean follow‐up of 22.5 ± 7.9 months. Of these, 212 (73%) had no prior history of AF at device implant. However, 38% of these individuals had AF detected with the VBAF algorithm with daily burden of ≥6 min within two years of implant. In these 80 patients with newly detected AF by their ICD, 23 (29%) had a confirmed clinical diagnosis of AF by their provider. Of patients with a clinical diagnosis of AF, nine (39%) were newly placed on anticoagulation, including five of five (100%) patients having a burden >23 h.ConclusionsContinuous AF monitoring with the new VBAF algorithm permits early identification and actionable treatment for patients with undiagnosed AF that may improve patient outcomes.

Abstract Aims Our goal was to evaluate a previously validated artificial intelligence–augmented electrocardiography (AI-ECG) screening tool for left ventricular systolic dysfunction (LVSD) in patients undergoing high-sensitivity-cardiac troponin T (hs-cTnT). Methods and results Retrospective application of AI-ECG for LVSD in emergency department (ED) patients undergoing hs-cTnT. AI-ECG scores (0–1) for probability of LVSD (left ventricular ejection fraction ≤ 35%) were obtained. An AI-ECG score ≥0.256 indicates a positive screen. The primary endpoint was a composite of post-discharge major adverse cardiovascular events (MACEs) at two years follow-up. Among 1977 patients, 248 (13%) had a positive AI-ECG. When compared with patients with a negative AI-ECG, those with a positive AI-ECG had a higher risk for MACE [48 vs. 21%, P &lt; 0.0001, adjusted hazard ratio (HR) 1.39, 95% confidence interval (CI) 1.11–1.75]. This was largely because of a higher rate of deaths (32 vs. 14%, P &lt; 0.0001; adjusted HR 1.26, 95% 0.95–1.66) and heart failure hospitalizations (26 vs. 6.1%, P &lt; 0.001; adjusted HR 1.75, 95% CI 1.25–2.45). Together, hs-cTnT and AI-ECG resulted in the following MACE rates and adjusted HRs: hs-cTnT &lt; 99th percentile and negative AI-ECG: 116/1176 (11%; reference), hs-cTnT &lt; 99th percentile and positive AI-ECG: 28/107 (26%; adjusted HR 1.54, 95% CI 1.01–2.36), hs-cTnT &gt; 99th percentile and negative AI-ECG: 233/553 (42%; adjusted HR 2.12, 95% CI 1.66, 2.70), and hs-cTnT &gt; 99th percentile and positive AI-ECG: 91/141 (65%; adjusted HR 2.83, 95% CI 2.06, 3.87). Conclusion Among ED patients evaluated with hs-cTnT, a positive AI-ECG for LVSD identifies patients at high risk for MACE. The conjoint use of hs-cTnT and AI-ECG facilitates risk stratification.

Abstract Aims Some artificial intelligence models applied in medical practice require ongoing retraining, introduce unintended racial bias, or have variable performance among different subgroups of patients. We assessed the real-world performance of the artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction with respect to multiple patient and electrocardiogram variables to determine the algorithm’s long-term efficacy and potential bias in the absence of retraining. Methods and results Electrocardiograms acquired in 2019 at Mayo Clinic in Minnesota, Arizona, and Florida with an echocardiogram performed within 14 days were analyzed (n = 44 986 unique patients). The area under the curve (AUC) was calculated to evaluate performance of the algorithm among age groups, racial and ethnic groups, patient encounter location, electrocardiogram features, and over time. The artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction had an AUC of 0.903 for the total cohort. Time series analysis of the model validated its temporal stability. Areas under the curve were similar for all racial and ethnic groups (0.90–0.92) with minimal performance difference between sexes. Patients with a ‘normal sinus rhythm’ electrocardiogram (n = 37 047) exhibited an AUC of 0.91. All other electrocardiogram features had areas under the curve between 0.79 and 0.91, with the lowest performance occurring in the left bundle branch block group (0.79). Conclusion The artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction is stable over time in the absence of retraining and robust with respect to multiple variables including time, patient race, and electrocardiogram features.