Citations

Introduction: The treatment options for high and extreme-risk patients with severe symptomatic aortic stenosis have changed significantly over the past decade because of advances in transcatheter aortic valve replacement (TAVR). However new permanent pacemaker implant (PPI) remains an impending concern after TAVR. Hypothesis: We sought to determine the frequency, predictors and outcomes of patients requiring PPI after TAVR. Methods: We queried the National Inpatient Sample (NIS) database using the ICD-9-CM procedure code of 35.05 for transfemoral/transaortic and 35.06 for transapical replacement of aortic valve .We identified TAVR procedures associated with new permanent pacemaker implants using appropriate validated ICD-9-CM codes. Using multivariate analysis we further identified predictors of PPM implants Results: An estimated 8828 TAVR procedures were performed in United States between 2011-2012. 773 (8.7%) TAVR procedures were associated with PPIs. PPI were higher in older (age>80 years), White race, diabetics, left bundle branch block (LBBB), right bundle branch block (RBBB), transfemoral approach. The inhospital mortality ( 5.2% vs.4.6% ) , mean length of stay ( 9.8 vs. 8.3 days ) disposition to short term nursing facility was higher (38.2% vs. 32.1%) and mean total costs (82217$ vs. 52283$) was higher in TAVR + PPI group . On multivariate analysis, Age in 5 year increment [Odds ratio (OR) 1.11; Confidence interval (CI) 1.05-1.17, p<0.001], diabetes [OR 1.26; CI 1.07-1.49, p<0.001], LBBB [OR 1.99; CI 1.55-2.56, p<0.001], RBBB [OR 5.36; CI 3.80-7.55, p<0.001] were independent predictors of PPI. Transfemoral approach was associated with more PPI implants [OR 1.41; CI 1.12 -1.78, p<0.001] Conclusion: Age, baseline conduction system disturbances, diabetes, transfemoral approach emerged as independent predictors of PPI after TAVR. This study provides useful tools to identify high-risk patients for PPI after TAVR.

Introduction: Electro-anatomic mapping (EAM) has been studied as a modality to improve the sensitivity and reduce false negative rates with endomyocardial biopsy (EMB). We systematically reviewed the literature describing EAM guided EMB. Methods: An experienced librarian developed search strategies for Ovid MEDLINE, Ovid Embase, Ovid CDR, Cochrane Central, Scopus and Web of Science. There were no restrictions on language or publication date. Animal studies were excluded. Search strategy included MESH headings and keywords including endomyocardial biopsy and electroanatomic mapping. Results: Out of 139 studies initially identified, 16 (9 case series, 7 case reports) were included in this systematic review. EAM guided EMB was performed in 152 patients and results of 241 biopsies were available for analysis. The most common indications for EAM guided EMB were suspected arrhythmogenic right ventricular cardiomyopathy (ARVC), myocarditis and cardiac sarcoidosis (CS). While fluoroscopic visualization of the bioptome close to mapping catheter was the most commonly utilized biopsy technique (n=7 studies), direct visualization of bioptome on CARTO (n=4) and on NavX (n=2) were used as well. Low voltage areas had abnormal myocardial histology more commonly (range 60-100%) than normal voltage areas (range 11-52%). Sensitivity of low voltage areas for abnormal myocardium ranged from 60% to 82%. EAM guided EMB appears safe and adverse events were noted in only one study (remote hemopericardium). Conclusions: EAM guided EMB is a safe and efficacious method to improve the sensitivity and reduce the false negative rate of conventional fluoroscopy guided biopsy.

Abstract Aims Mobile devices such as smartphones and watches can now record single-lead electrocardiograms (ECGs), making wearables a potential screening tool for cardiac and wellness monitoring outside of healthcare settings. Because friends and family often share their smart phones and devices, confirmation that a sample is from a given patient is important before it is added to the electronic health record. Methods and results We sought to determine whether the application of Siamese neural network would permit the diagnostic ECG sample to serve as both a medical test and biometric identifier. When using similarity scores to discriminate whether a pair of ECGs came from the same patient or different patients, inputs of single-lead and 12-lead medians produced an area under the curve of 0.94 and 0.97, respectively. Conclusion The similar performance of the single-lead and 12-lead configurations underscores the potential use of mobile devices to monitor cardiac health.

Abstract Aims Recently, deep learning artificial intelligence (AI) models have been trained to detect cardiovascular conditions, including hypertrophic cardiomyopathy (HCM), from the 12-lead electrocardiogram (ECG). In this external validation study, we sought to assess the performance of an AI-ECG algorithm for detecting HCM in diverse international cohorts. Methods and results A convolutional neural network-based AI-ECG algorithm was developed previously in a single-centre North American HCM cohort (Mayo Clinic). This algorithm was applied to the raw 12-lead ECG data of patients with HCM and non-HCM controls from three external cohorts (Bern, Switzerland; Oxford, UK; and Seoul, South Korea). The algorithm’s ability to distinguish HCM vs. non-HCM status from the ECG alone was examined. A total of 773 patients with HCM and 3867 non-HCM controls were included across three sites in the merged external validation cohort. The HCM study sample comprised 54.6% East Asian, 43.2% White, and 2.2% Black patients. Median AI-ECG probabilities of HCM were 85% for patients with HCM and 0.3% for controls (P &lt; 0.001). Overall, the AI-ECG algorithm had an area under the receiver operating characteristic curve (AUC) of 0.922 [95% confidence interval (CI) 0.910–0.934], with diagnostic accuracy 86.9%, sensitivity 82.8%, and specificity 87.7% for HCM detection. In age- and sex-matched analysis (case–control ratio 1:2), the AUC was 0.921 (95% CI 0.909–0.934) with accuracy 88.5%, sensitivity 82.8%, and specificity 90.4%. Conclusion The AI-ECG algorithm determined HCM status from the 12-lead ECG with high accuracy in diverse international cohorts, providing evidence for external validity. The value of this algorithm in improving HCM detection in clinical practice and screening settings requires prospective evaluation.

Abstract Introduction Transsubclavian venous implantation of the Aveir leadless cardiac pacemaker (LCP) has not been previously reported. Methods and Results Three cases of transsubclavian implantation of the Aveir LCP are reported. Two cases were postbilateral orthotopic lung transplant, without appropriate femoral or jugular access due to recent ECMO cannulation and jugular central venous catheters. In one case, there was strong patient preference for same‐day discharge. Stability testing confirmed adequate fixation and electrical testing confirmed stable parameters in all cases. All patients tolerated the procedure well without significant immediate complications. Conclusions We demonstrate the feasibility of transsubclavian implantation of the Aveir LCP.

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.

Introduction: Patients diagnosed with ischemic stroke routinely undergo prolonged cardiac telemetry (PCT) to evaluate for occult atrial fibrillation (AF), but this approach is limited by high cost and low yield. Clinical scores including CHA2DS2-VASc and CHARGE-AF can risk stratify patients but lack desired accuracy. Therefore, we aimed to evaluate an artificial intelligence (AI)-enabled ECG algorithm to predict AF detected by PCT after index stroke. Methods: This retrospective study included all adult patients with ischemic stroke evaluated at Mayo Clinic with baseline electrocardiogram (ECG) and PCT between 2018-2020. We recorded demographics, CHA2DS2-VASc score, CHARGE-AF score, stroke etiology, duration of PCT, and if PCT detected AF. ECGs were analyzed using the AI-ECG algorithm to determine AF likelihood. Stroke etiology was adjudicated using TOAST and embolic stroke of undetermined source (ESUS) definitions. The ability of the AI-ECG algorithm, CHA2DS2-VASc, and CHARGE-AF to predict AF detected by PCT was assessed via receiver operating characteristics analysis, calculating the area under curve (AUC) (c-statistic). Sensitivity and specificity analyses were performed for each tool using optimal cutoffs (using maximum Youden indices). Results: 863 patients were included; median age was 69 years, 496 (57.5%) were male, and 561 (65%) patients were categorized as ESUS. PCT detected AF in 85 (9.8%) patients. Median duration of cardiac telemetry was 30 (IQR 25-30) days. Sensitivity and specificity analyses are outlined in Table 1. The AI-ECG algorithm outperformed the CHA2DS2-VASc score (AUC 0.690 vs. 0.597, p=0.013), but was similar to the CHARGE-AF score (AUC 0.690 vs 0.651, p=0.233). Conclusions: Our AI-ECG algorithm demonstrated improved ability to predict AF detection on PCT after index stroke when compared to the CHA2DS2-VASc score, but was similar in performance to the CHARGE-AF score. All three methods had suboptimal accuracy.

Background: Atrial septal defects (ASD) and ventricular septal defects (VSD) are among the most common forms of congenital heart disease (CHD). Early detection and intervention for these defects are crucial to prevent complications. Given advancements in AI technology, the development of AI-enabled ECG models for detecting these defects is of significant interest. Aims: This study aimed to create AI models using pediatric ECG data for the detection of ASD, VSD, and a combined model detecting either ASD or VSD. Methods: We analyzed a dataset of 7,795 pediatric patients from Mayo Clinic who had undergone echocardiography between 2002 and 2022, with corresponding 10-second, 12-lead surface ECGs taken within 14 days of echocardiography. This dataset included 259 patients with ASDs and 139 patients with VSDs (single or multiple). The control group was comprised of 7,397 patients without significant or complex CHD. For cases and controls, we included patients with all forms of electrocardiographic abnormalities and those with valvular lesions who had less than moderate stenosis or regurgitation. The dataset was divided into training, validation, and testing subsets to develop convolutional neural network (CNN) models for ASD, VSD, and either ASD or VSD detection. Results: Our ASD and VSD detection models achieved test AUCs of 0.82 (95% CI 0.76, 0.88) and 0.80 (95% CI 0.70, 0.90), respectively. Meanwhile, a combined model that detected either ASD or VSD demonstrated a test AUC of 0.81 (95% CI, 0.76, 0.86), sensitivity of 0.73, specificity of 0.76, PPV of 0.14, and NPV of 0.98 (Figure 1). Conclusions: AI-enabled ECG models demonstrate promising performance in detecting ASD and VSD in pediatric patients. A combined model that detects either ASD or VSD also shows robust results. Future research should focus on multi-center validation of these models, refining their diagnostic capacities, and investigating their potential for early intervention in CHDs.