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To assess whether an electrocardiography-based artificial intelligence (AI) algorithm developed to detect severe ventricular dysfunction (left ventricular ejection fraction [LVEF] of 35% or below) independently predicts long-term mortality after cardiac surgery among patients without severe ventricular dysfunction (LVEF35%).Patients who underwent valve or coronary bypass surgery at Mayo Clinic (1993-2019) and had documented LVEF above 35% on baseline electrocardiography were included. We compared patients with an abnormal vs a normal AI-enhanced electrocardiogram (AI-ECG) screen for LVEF of 35% or below on preoperative electrocardiography. The primary end point was all-cause mortality.A total of 20,627 patients were included, of whom 17,125 (83.0%) had a normal AI-ECG screen and 3502 (17.0%) had an abnormal AI-ECG screen. Patients with an abnormal AI-ECG screen were older and had more comorbidities. Probability of survival at 5 and 10 years was 86.2% and 68.2% in patients with a normal AI-ECG screen vs 71.4% and 45.1% in those with an abnormal screen (log-rank, P.01). In the multivariate Cox survival analysis, the abnormal AI-ECG screen was independently associated with a higher all-cause mortality overall (hazard ratio [HR], 1.31; 95% CI, 1.24 to 1.37) and in subgroups of isolated valve surgery (HR, 1.30; 95% CI, 1.18 to 1.42), isolated coronary artery bypass grafting (HR, 1.29; 95% CI, 1.20 to 1.39), and combined coronary artery bypass grafting and valve surgery (HR, 1.19; 95% CI, 1.08 to 1.32). In a subgroup analysis, the association between abnormal AI-ECG screen and mortality was consistent in patients with LVEF of 35% to 55% and among those with LVEF above 55%.A novel electrocardiography-based AI algorithm that predicts severe ventricular dysfunction can predict long-term mortality among patients with LVEF above 35% undergoing valve and/or coronary bypass surgery.

Objective To develop an artificial intelligence (AI)–based tool to detect cardiac amyloidosis (CA) from a standard 12-lead electrocardiogram (ECG). Methods We collected 12-lead ECG data from 2541 patients with light chain or transthyretin CA seen at Mayo Clinic between 2000 and 2019. Cases were nearest neighbor matched for age and sex, with 2454 controls. A subset of 2997 (60%) cases and controls were used to train a deep neural network to predict the presence of CA with an internal validation set (n=999; 20%) and a randomly selected holdout testing set (n=999; 20%). We performed experiments using single-lead and 6-lead ECG subsets. Results The area under the receiver operating characteristic curve (AUC) was 0.91 (CI, 0.90 to 0.93), with a positive predictive value for detecting either type of CA of 0.86. By use of a cutoff probability of 0.485 determined by the Youden index, 426 (84%) of the holdout patients with CA were detected by the model. Of the patients with CA and prediagnosis electrocardiographic studies, the AI model successfully predicted the presence of CA more than 6 months before the clinical diagnosis in 59%. The best single-lead model was V5 with an AUC of 0.86 and a precision of 0.78, with other single leads performing similarly. The 6-lead (bipolar leads) model had an AUC of 0.90 and a precision of 0.85. Conclusion An AI-driven ECG model effectively detects CA and may promote early diagnosis of this life-threatening disease.

Undiagnosed dilated cardiomyopathy (DC) can be asymptomatic or present as sudden cardiac death, therefore pre-emptively identifying and treating patients may be beneficial. Screening for DC with echocardiography is expensive and labor intensive and standard electrocardiography (ECG) is insensitive and non-specific. The performance and applicability of artificial intelligence-enabled electrocardiography (AI-ECG) for detection of DC is unknown. Diagnostic performance of an AI algorithm in determining reduced left ventricular ejection fraction (LVEF) was evaluated in a cohort that comprised of DC and normal LVEF control patients. DC patients and controls with 12-lead ECGs and a reference LVEF measured by echocardiography performed within 30 and 180 days of the ECG respectively were enrolled. The model was tested for its sensitivity, specificity, negative predictive (NPV) and positive predictive values (PPV) based on the prevalence of DC at 1% and 5%. The cohort consisted of 421 DC cases (60% males, 57±15 years, LVEF 28±11%) and 16,025 controls (49% males, age 69 ±16 years, LVEF 62±5%). For detection of LVEF≤45%, the area under the curve (AUC) was 0.955 with a sensitivity of 98.8% and specificity 44.8%. The NPV and PPV were 100% and 1.8% at a DC prevalence of 1% and 99.9% and 8.6% at a prevalence of 5%, respectively. In conclusion AI-ECG demonstrated high sensitivity and negative predictive value for detection of DC and could be used as a simple and cost-effective screening tool with implications for screening first degree relatives of DC patients.

To evaluate the cost-effectiveness of an artificial intelligence electrocardiogram (AI-ECG) algorithm under various clinical and cost scenarios when used for universal screening at age 65.We used decision analytic modeling to perform a cost-effectiveness analysis of the use of AI-ECG to screen for asymptomatic left ventricular dysfunction (ALVD) once at age 65 compared with no screening. This screening consisted of an initial screening decision tree and subsequent construction of a Markov model. One-way sensitivity analysis on various disease and cost parameters to evaluate cost-effectiveness at both $50,000 per quality-adjusted life year (QALY) and $100,000 per QALY willingness-to-pay threshold.We found that for universal screening at age 65, the novel AI-ECG algorithm would cost $43,351 per QALY gained, test performance, disease characteristics, and testing cost parameters significantly affect cost-effectiveness, and screening at ages 55 and 75 would cost $48,649 and $52,072 per QALY gained, respectively. Overall, under most of the clinical scenarios modeled, coupled with its robust test performance in both testing and validation cohorts, screening with the novel AI-ECG algorithm appears to be cost-effective at a willingness-to-pay threshold of $50,000.Universal screening for ALVD with the novel AI-ECG appears to be cost-effective under most clinical scenarios with a cost of$50,000 per QALY. Cost-effectiveness is particularly sensitive to both the probability of disease progression and the cost of screening and downstream testing. To improve cost-effectiveness modeling, further study of the natural progression and treatment of ALVD and external validation of AI-ECG should be undertaken.

Objectives This report describes a series of patients with neuroendocrine tumors with or without carcinoid heart disease undergoing catheter ablation at the authors’ institution. Backgroun...

Objective To model and compare effect of digital contact tracing versus shelter-in-place on severe acute respiratory syndrome – coronavirus 2 (SARS-CoV-2) spread. Methods Using a classical epidemiologic framework and parameters estimated from literature published between February 1, 2020, and May 25, 2020, we modeled two non-pharmacologic interventions — shelter-in-place and digital contact tracing — to curb spread of SARS-CoV-2. For contact tracing, we assumed an advanced automated contact tracing (AACT) application that sends alerts to individuals advising self-isolation based on individual exposure profile. Model parameters included percentage population ordered to shelter-in-place, adoption rate of AACT, and percentage individuals who appropriately follow recommendations. Under influence of these variables, the number of individuals infected, exposed, and isolated were estimated. Results Without any intervention, a high rate of infection (>10 million) with early peak is predicted. Shelter-in-place results in rapid decline in infection rate at the expense of impacting a large population segment. The AACT model achieves reduction in infected and exposed individuals similar to shelter-in-place without impacting a large number of individuals. For example, a 50% AACT adoption rate mimics a shelter-in-place order for 40% of the population and results in a greater than 90% decrease in peak number of infections. However, as compared to shelter-in-place, with AACT significantly fewer individuals would be isolated. Conclusion Wide adoption of digital contact tracing can mitigate infection spread similar to universal shelter-in-place, but with considerably fewer individuals isolated.

Background The combination of contact force (CF) and local impedance (LI) may improve tissue characterization and lesion prediction during radiofrequency (RF) ablation. Objective The purpose of this study was to evaluate the utility of LI combined with CF in assessing RF ablation efficacy. Methods An LI catheter with CF sensing was evaluated in swine (n = 11) and in vitro (n = 14). The relationship between LI and CF in different tissue types was evaluated in vivo. Discrete lesions were created in vitro and in vivo at a range of forces, powers, and durations. Finally, an intercaval line was created in 3 groups at 30 W: 30s, Δ20Ω, and Δ30Ω. In the Δ20Ω and Δ30Ω groups, the user ablated until a 20 or 30 Ω LI drop. In the 30s group, the user was blinded to LI. Results In vivo, distinction in LI was found between the blood pool and the myocardium (blood pool: 122 ± 7.02 Ω; perpendicular contact: 220 ± 29 Ω; parallel contact: 207 ± 31 Ω). LI drop correlated with lesion depth both in vitro (R = 0.84) and in vivo (R = 0.79), informing sufficient lesion creation (LI drop >20 Ω) and warning of excessive heating (LI drop >65 Ω). When creating an intercaval line, the total RF time was significantly reduced when using LI guidance (6.4 ± 2 minutes in Δ20Ω and 8.1 ± 1 minutes in Δ30Ω) compared with a standard 30-second workflow (18 ± 7 minutes). Acute conduction block was achieved in all Δ30Ω and 30s lines. Conclusion The addition of LI to CF provides feedback on both electrical and mechanical loads. This provides information on tissue type and catheter-tissue coupling; provides feedback on whether volumetric tissue heating is inadequate, sufficient, or excessive; and reduces ablation time.

Energy sources used for catheter ablation of atrial fibrillation (AF) ablation have undergone an exceptional journey over the past 50 years. Traditional energy sources, such as radiofrequency and cryoablation, have been the mainstay of AF ablation. Novel investigations have led to inclusion of other techniques, such as laser, high-frequency ultrasound, and microwave energy, in the armamentarium of electrophysiologists. Despite these modalities, AF has remained one of the most challenging arrhythmias. Advances in the understanding of electroporation promise to overcome the shortcomings of conventional energy sources. A thorough understanding of the biophysics and practical implications of the existing energy sources is paramount.

Coronavirus disease 2019 (COVID-19) can result in deterioration of cardiac function, which is associated with high mortality. A simple point-of-care diagnostic test to screen for ventricular dysfunction would be clinically useful to guide management. We sought to review the clinical experience with an artificial intelligence electrocardiogram (AI ECG) to screen for ventricular dysfunction in patients with documented COVID-19. We examined all patients in the Mayo Clinic system who underwent clinically indicated electrocardiography and echocardiography within 2 weeks following a positive COVID-19 test and had permitted use of their data for research were included. Of the 27 patients who met the inclusion criteria, one had a history of normal ventricular function who developed COVID-19 myocarditis with rapid clinical decline. The initial AI ECG in this patient indicated normal ventricular function. Repeat AI ECG showed a probability of ejection fraction (EF) less than or equal to 40% of 90.2%, corroborated with an echocardiographic EF of 35%. One other patient had a pre-existing EF less than or equal to 40%, accurately detected by the algorithm before and after COVID-19 diagnosis, and another was found to have a low EF by AI ECG and echocardiography with the COVID-19 diagnosis. The area under the curve for detection of EF less than or equal to 40% was 0.95. This case series suggests that the AI ECG, previously shown to detect ventricular dysfunction in a large general population, may be useful as a screening tool for the detection of cardiac dysfunction in patients with COVID-19.

Objective This study sought to investigate incidence of left atrial appendage (LAA) triggers of atrial fibrillation (AF) and/or organized atrial tachycardias (OAT) in patients undergoing AF ablation and to evaluate outcomes after ablation. Background Although LAA isolation is being increasingly performed during AF ablation, the true incidence of LAA triggers for AF remains unclear. Methods All patients with LAA triggers of AF and/or OAT during AF ablation from 2001 to 2017 were included. LAA triggers were defined as atrial premature depolarizations from the LAA, which initiated sustained AF and/or OAT. Results Out of 7,129 patients undergoing AF ablation over 16 years, LAA triggers were observed in 21 (0.3%) subjects (age 60 ± 9 years; 57% males; 52% persistent AF). Twenty (95%) patients were undergoing repeat ablation. The LAA was the only nonpulmonary vein trigger in 3 patients; the remaining 18 patients had both LAA and other nonpulmonary vein triggers. LAA triggers were eliminated in all patients (focal ablation in 19 patients; LAA isolation in 2 patients). Twelve months after ablation, 47.6% remained free from recurrent arrhythmia. After overall follow-up of 5.0 ± 3.6 years (median: 3.7 years; interquartile range: 1.4 to 8.9 years), 38.1% were arrhythmia-free. All 3 patients with triggers limited to the LAA remained free of AF recurrence. One patient undergoing LAA isolation developed LAA thrombus during follow-up. Conclusions The incidence of true LAA triggers is very low (0.3%). Most patients with LAA triggers have additional nonpulmonary vein triggers, and despite elimination of LAA triggers, long-term arrhythmia recurrence rates remain high. Potential risks of empiric LAA isolation during AF ablation (especially first-time AF ablation) may outweigh benefits.

Atrial arrhythmias, including atrial tachycardia and atrial flutter, are not uncommon after prior ablation. Mechanisms for arrhythmogenesis may vary and include recurrent conduction through sites of ablation, leading to recurrence of prior ablated arrhythmias and creation of new substrate. Incidence of postablation atrial arrhythmias varies across studies and may relate to the approach to ablation, including extent of ablation performed, or to extent of substrate identified at the time of prior ablation and how that relates to the lesion set. In addition, postablation atrial arrhythmias may be more common in certain types of cardiomyopathy, including hypertrophic cardiomyopathy.

Summary Background Atrial fibrillation is frequently asymptomatic and thus underdetected but is associated with stroke, heart failure, and death. Existing screening methods require prolonged monitoring and are limited by cost and low yield. We aimed to develop a rapid, inexpensive, point-of-care means of identifying patients with atrial fibrillation using machine learning. Methods We developed an artificial intelligence (AI)-enabled electrocardiograph (ECG) using a convolutional neural network to detect the electrocardiographic signature of atrial fibrillation present during normal sinus rhythm using standard 10-second, 12-lead ECGs. We included all patients aged 18 years or older with at least one digital, normal sinus rhythm, standard 10-second, 12-lead ECG acquired in the supine position at the Mayo Clinic ECG laboratory between Dec 31, 1993, and July 21, 2017, with rhythm labels validated by trained personnel under cardiologist supervision. We classified patients with at least one ECG with a rhythm of atrial fibrillation or atrial flutter as positive for atrial fibrillation. We allocated ECGs to the training, internal validation, and testing datasets in a 7:1:2 ratio. We calculated the area under the curve (AUC) of the receiver operatoring characteristic curve for the internal validation dataset to select a probability threshold, which we applied to the testing dataset. We evaluated model performance on the testing dataset by calculating the AUC and the accuracy, sensitivity, specificity, and F1 score with two-sided 95% CIs. Findings We included 180 922 patients with 649 931 normal sinus rhythm ECGs for analysis: 454 789 ECGs recorded from 126 526 patients in the training dataset, 64 340 ECGs from 18 116 patients in the internal validation dataset, and 130 802 ECGs from 36 280 patients in the testing dataset. 3051 (8·4%) patients in the testing dataset had verified atrial fibrillation before the normal sinus rhythm ECG tested by the model. A single AI-enabled ECG identified atrial fibrillation with an AUC of 0·87 (95% CI 0·86–0·88), sensitivity of 79·0% (77·5–80·4), specificity of 79·5% (79·0–79·9), F1 score of 39·2% (38·1–40·3), and overall accuracy of 79·4% (79·0–79·9). Including all ECGs acquired during the first month of each patient's window of interest (ie, the study start date or 31 days before the first recorded atrial fibrillation ECG) increased the AUC to 0·90 (0·90–0·91), sensitivity to 82·3% (80·9–83·6), specificity to 83·4% (83·0–83·8), F1 score to 45·4% (44·2–46·5), and overall accuracy to 83·3% (83·0–83·7). Interpretation An AI-enabled ECG acquired during normal sinus rhythm permits identification at point of care of individuals with atrial fibrillation. Funding None.

The accurate differentiation of wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular wide complex tachycardia (SWCT) remains problematic despite numerous manually-operated electrocardiogram (ECG) interpretation methods. We sought to create a new WCT differentiation method that could be automatically implemented by computerized ECG interpretation (CEI) software.In a two-part study, we developed and validated a logistic regression model (i.e. WCT Formula) that utilizes computerized measurements and computations derived from patients' paired WCT and subsequent baseline ECGs. In Part 1, a derivation cohort of paired WCT and baseline ECGs was examined to identify independent VT predictors to be incorporated into the WCT Formula. In Part 2, a separate validation cohort of paired WCT and baseline ECGs was used to prospectively evaluate the WCT Formula's diagnostic performance.The derivation cohort was comprised of 317 paired WCT (157 VT, 160 SWCT) and baseline ECGs. A logistic regression model (i.e. WCT Formula) incorporating WCT QRS duration (ms) (p 0.001), frontal percent amplitude change (%) (p 0.001), and horizontal percent amplitude change (%) (p 0.001) yielded effective WCT differentiation (AUC of 0.96). The validation cohort consisted of 284 paired WCT (116 VT, 168 SWCT) and baseline ECGs. The WCT Formula achieved favorable accuracy (91.5%) with strong sensitivity (89.7%) and specificity (92.9%) for VT.The WCT Formula is an example of how contemporary CEI software could be used to successfully differentiate WCTs. The incorporation of similar automated methods into CEI software may improve clinicians' ability to accurately distinguish VT and SWCT.

Objectives This study hypothesized that atrial fibrillation was associated with heart failure (HF) hospitalization, and that patients who received rhythm control therapy had a lower incidence of HF hospitalization and mortality. Background Atrial fibrillation is a known risk factor for HF hospitalization and mortality in patients with acquired heart disease. Although atrial arrhythmias are common in adults with tetralogy of Fallot (TOF), data about prevalence and outcomes of therapy for atrial fibrillation are very limited. Methods The MACHD (Mayo Adult Congenital Heart Disease) database was queried for adults with repaired TOF and documented atrial fibrillation from 1990 to 2017. Primary endpoint was HF hospitalization defined as admission for volume overload (pulmonary congestion and/or peripheral edema) requiring intravenous diuretics. Secondary endpoint was the effect of rhythm control therapy on HF hospitalization and all-cause mortality. Patients were divided into rhythm control and rate control groups based on the therapy initiated at the time of arrhythmia diagnosis. Results Of 415 patients, 27 (7%) had 42 HF hospitalizations. Of these 415 patients, 88 (21%) had atrial fibrillation at age 49 ± 13 years. Atrial fibrillation was an independent risk factor for HF hospitalization (adjusted hazard ratio: 2.67; 95% confidence interval: 1.04 to 7.34; p = 0.045). The 88 patients were divided into the rhythm control group (n = 61, 69%) and the rate control group (n = 27, 31%). The rate control group had higher unadjusted annual incidence of HF hospitalization (13% vs. 3%; p = 0.001) and all-cause mortality (11% vs. 4%; p = 0.002). Conclusions Atrial fibrillation was a risk factor for HF hospitalization and mortality in TOF patients, and rhythm control therapy was protective against these adverse events.

Background An artificial intelligence-augmented electrocardiogram (AI-ECG) can identify left ventricular systolic dysfunction (LVSD). We examined the accuracy of AI ECG for identification of LVSD (defined as LVEF ≤40% by transthoracic echocardiogram [TTE]) in cardiac intensive care unit (CICU) patients. Method We included unique Mayo Clinic CICU patients admitted from 2007 to 2018 who underwent AI-ECG and TTE within 7 days, at least one of which was during hospitalization. Discrimination of the AI-ECG for LVSD was determined using receiver-operator characteristic curve (AUC) values. Results We included 5680 patients with a mean age of 68 ± 15 years (37% females). Acute coronary syndrome (ACS) was present in 55%. LVSD was present in 34% of patients (mean LVEF 48 ± 16%). The AI-ECG had an AUC of 0.83 (95% confidence interval 0.82–0.84) for discrimination of LVSD. Using the optimal cut-off, the AI-ECG had 73%, specificity 78%, negative predictive value 85% and overall accuracy 76% for LVSD. AUC values were higher for patients aged Conclusions The AI-ECG algorithm had very good discrimination for LVSD in this critically-ill CICU cohort with a high prevalence of LVSD. Performance was better in younger male patients and those without ACS, highlighting those CICU patients in whom screening for LVSD using AI ECG may be more effective. The AI-ECG might potentially be useful for identification of LVSD in resource-limited settings when TTE is unavailable.

The presence of left ventricular systolic dysfunction (LVSD) alters clinical management and prognosis in most acute and chronic cardiovascular conditions. While transthoracic echocardiography (TTE) remains the most common diagnostic tool to screen for LVSD, it is operator-dependent, time-consuming, effort-intensive, and relatively expensive. Recent work has demonstrated the ability of an artificial intelligence-augment ECG (AI-ECG) model to accurately predict LVSD in critical intensive care unit (CICU) patients. We demonstrate that the AI-ECG algorithm can maintain its performance in these patients with and without AF despite their clinical differences. An AI-ECG algorithm can serve as a non-invasive, inexpensive, and rapid screening tool for early detection of LVSD in resource-limited settings, and potentially expedite clinical decision making and guideline-directed therapies in the acute care setting.

In patients with unexplained cardiomyopathy, electroanatomical mapping can identify abnormal tissue to target during electrophysiology-guided endomyocardial biopsy (EP-guided EMB). The objective of this study is to determine whether catheter ablation performed in the same setting as EP-guided EMB increases procedural risk. Sixty-seven patients (mean age 54.4 ± 13.8, 57% male) undergoing EP-guided EMB were included. Radiofrequency catheter ablation was performed in 17 patients (25%) for ventricular arrhythmias and in 2 (3%) for typical atrial flutter. Femoral arterial access was obtained in 90% ablation patients and 40% biopsy-only patients; vascular access complications were more common in the ablation group than in the EMB-only group (p = 0.02). There were no significant differences in rate of tricuspid regurgitation, thromboembolism, or pericardial effusion, whether procedural anticoagulation was used. In conclusion, catheter ablation and procedural anticoagulation can be combined with EP-guided EMB with an increased risk of vascular access complications, but no significant increase in intracardiac complications.

It is unknown whether systemic right ventricular (SRV) dysfunction confers increased risk for sudden death in congenitally corrected transposition of the great arteries (ccTGA). We sought to define risk of ventricular arrhythmias and sudden death attributable to systemic right ventricular dysfunction in ccTGA.The study cohort is comprised of adult patients with a diagnosis of ccTGA. Clinical information and clinical outcomes including ICD therapy, incidence of VT/VF, and cause of death were reviewed.129 patients with ccTGA were followed for 7.2 ± 3.4 years. Mean SRV ejection fraction (SRVEF) was 39% (n = 56 with an EF35%). Significant documented arrhythmia (sustained VT requiring defibrillation or cardiac arrest due to VT/VF) occurred in 13/56 patients (23%) with SRV EF35% compared with 2/73 (3%) with SRV EF35% (p 0.001). There was no significant difference in age at death, death from documented arrhythmia, or death from all cause between patients with SRV EF35% and35%. However, risk of sudden death was higher in patients with SRV EF35% (16% vs 1%, p = 0.002). The combined clinical endpoint of ICD therapy, clinically significant VT/VF, and sudden death was significantly higher in patients with SRV EF35% than in patients with SRV EF35%, independent of the presence of LV EF35% (p 0.001).Dysfunction of the systemic RV in ccTGA increases the risk of sudden death and clinically significant ventricular tachyarrhythmias. Further study is needed to determine if ventricular tachyarrhythmias comprise the primary cause of sudden death in these patients and if ICDs offer any significant mortality benefit.

Objectives This study aimed to assess the outcome of cryoablation in patients with ventricular arrhythmias (VAs) originating from the para-Hisian region. Background There are few data regarding the outcome of cryoablation in patients with VAs originating from the para-Hisian region, where there is the risk of injury to the conduction system. Methods The study analyzed all patients undergoing cryoablation at the Mayo Clinic (Rochester, Minnesota) as part of an ablation for VAs originating from the para-Hisian region. Results The study population consisted of 10 patients (64 ± 15 years of age, 7 men). Cryoenergy was applied after an unsuccessful radiofrequency (RF) ablation in 8 (80%) patients. The VAs were successfully ablated with cryoablation in 7 (70%) patients; RF ablation after an unsuccessful cryoablation eliminated the VAs at almost the same location with careful monitoring in 1 patient. The authors could not ablate the actual focus because a transient atrioventricular block developed during cryo- and RF energy applications, which led to an unsuccessful ablation in the remaining 2 patients. A complete atrioventricular block occurred during the cryoenergy application in 1 patient, who needed a permanent pacemaker implantation. There were no VA recurrences in 4 of 8 (50%) patients with procedural success during a median follow-up period of 122 days (interquartile range: 43 to 574 days). Conclusions Cryoablation is clinically effective in some patients with VAs originating from the para-Hisian region, where there is the risk of injury to the conduction system, and therefore should be considered as an alternative to or in addition to RF ablation in these cases. Cryoablation requires care because it can also lead to major complications.

We examined the effect of AF a commonly encountered arrhythmia with significant morbidity on mortality following a motor vehicle accident (MVA) related hospitalization.The Nationwide Inpatient Sample (NIS) was queried to identify patients with AF (ICD-9 CM 427.31) and MVA (ICD-9 CM E810.0-E819.9), considered separately and together, from 2003 through 2012. Baseline characteristics were identified and multilevel mixed model multivariate analysis was employed to verify the impact of AF on in-patient mortality in survivors.Of an estimated 2,978,630 MVA admissions reported, 79,687 (2.6%) hospitalizations also had a diagnosis of AF. The in-hospital mortality was 2.6% in MVA alone and 7.6% in MVA and AF. In multivariate analysis, after adjustment for age, gender, Charlson Comorbidity Index (CCI), the Trauma Mortality Prediction Model (TMPM), and hospital characteristics, AF was independently associated with in-hospital mortality [Odds ratio (OR) 1.52, confidence interval (CI) 1.41-1.69, P value0.0001]. In patients with MVA and AF, increasing age, CCI, and TMPM were associated with higher mortality. Female gender is associated with lower mortality (OR 0.84, CI 0.81-0.88, P -0.0016). Most patients with MVA and AF had a CHADS2 score of 2 (34.6%). Mortality and transfusion rates were higher in MVA and AF patients compared to patients with MVA alone across all CHADS2 scores.In patients with a MVA, the presence of AF is an independent risk factor for in-hospital mortality.

Background Cardiac pacing can be challenging after a Fontan operation, and limited data exist regarding pacing in adult Fontan patients. The objectives of our study were to determine risk factors for pacing and occurrence of device-related complications (DRCs) and pacemaker reinterventions. Methods We performed a retrospective review of Fontan patients from 1994 through 2014. We defined DRCs as lead failure, lead recall, cardiac perforation, lead thrombus/vegetation, or device-related infection, and cardiovascular adverse events (CAEs) as venous thrombosis, stroke, death, or heart transplant. Pacemaker reintervention was defined as lead failure or recall. Results Of 439 patients, 166 (38%) had pacemakers implanted (79 during childhood; 87, adulthood); 114 patients (69%) received epicardial leads initially, and 52 (31%), endocardial leads. Pacing was initially atrial in 52 patients (31%); ventricular, 30 (18%); or dual chamber, 84 (51%). There were 37 reinterventions (1.9% per year) and 48 DRCs (2.4% per year). Pacemaker implantation during childhood was a risk factor for DRCs (hazard ratio, 2.01 [CI, 1.22-5.63]; P = .03). There were 70 CAEs (venous thrombosis, 5; stroke, 11; transplant, 8; and death, 46), yielding a rate of 3.5% per year. DRCs, CAEs, and reintervention rates were comparable for patients with epicardial or endocardial leads. Conclusions More than one-third of adult Fontan patients referred to Mayo Clinic had pacemaker implantation. Epicardial leads were associated with high rate of pacemaker reinterventions but similar DRC rates in comparison to endocardial leads.

The optimal management strategy for atrial tachyarrhythmia in the Fontan population is unknown.Retrospective review of 264 adult Fontan patients with atrial tachyarrhythmia evaluating 3 clinically adopted scenarios: antiarrhythmic drug (AAD) therapy, catheter ablation (CA), and Fontan conversion (FC). These patients were followed up at Mayo Clinic from 1994 to 2014. The study objective was to compare freedom from atrial tachyarrhythmia recurrence (AR) and occurrence of composite adverse events (stroke, heart failure hospitalization, death, or heart transplant) between treatment groups.The age of atrial tachyarrhythmia onset was 25 ± 4 years, time from Fontan operation was 13 ± 6 years, follow-up was 74 ± 18 months, atriopulmonary Fontan was 215 (81%), and atrial flutter/intra-atrial reentry tachycardia was 173 (65%). In those managed with AAD (n = 110), freedom from AR was 7% at 60 months. Catheter ablation (n = 31) was associated with an acute procedural success of 94%, and freedom from AR was 41% at 60 months. Fontan conversion (n = 33) resulted in a perioperative mortality of 3%, and freedom from AR was 51% at 60 months. Fontan conversion and CA were similar with regard to AR (P = .14) and significantly better compared with AAD (P.0001). Adverse events were found to occur more frequently in the patients with AR (P.0001) and the patients treated with AAD only (P.0001).Catheter ablation and FC operations are associated with less recurrence of atrial tachyarrhythmia compared with AAD. Atrial tachyarrhythmias are more likely to recur in patients with a longer history of the arrhythmia and are associated with more adverse events. Early referral to a specialty center for these interventions should be considered.

Introduction Obesity and aging are prominent risk factors for HFpEF. Obese versus non-obese patients are typically younger, but the effects of obesity on premature cardiac senescence remain insufficiently explained. Recent data suggest that cardiac senescence might be estimated from a 12-lead ECG by artificial intelligence (AI). Hypothesis Obese versus non-obese patients with HFpEF have true premature adverse cardiac remodeling, associated with diastolic dysfunction and a higher risk of atrial fibrillation (AF), which is reflected by faster ECG aging. Methods This retrospective cohort study of 408 patients with HFpEF, hospitalized for decompensation and treatment with intravenous loop diuretics, assessed ECG age upon admission with a previously validated AI algorithm. Differences in the relationship between chronological versus ECG age and underlying cardiac remodeling and function on echocardiography, as well AF risk, were evaluated and compared between obese versus non-obese patients. Age- and gender normalized echocardiography parameters were calculated according to regression equations determined from the PAVD study in Olmsted County (MN, US) Results Obese (n = 255) versus non-obese (n = 153) HFpEF patients were on average 9 years younger. ECG minus chronological age as surrogate for premature cardiac senescence was higher in the obese (P-value Conclusions Obesity is associated with older ECG compared to chronological age in HFpEF, which probably reflects premature cardiac senescence given its association with structural remodeling, more pronounced diastolic dysfunction, and a higher AF risk.

Percutaneous epicardial access (EpiAcc) is used in an attempt to improve outcomes of ablation. We aim to report our experience in EpiAcc for management of symptomatic ventricular premature complexes (VPC) and ventricular tachycardia (VT). All patients from January 2004 to July 2014 who underwent EpiAcc as part of a VPC or VT ablation procedure were included. Outcomes between those with endocardial-only (Gp1) and endocardial/epicardial (Gp2) ablation and those for VPC and VT ablation were compared. EpiAcc for VPC or VT ablation was attempted in 173 patients; 10 patients were excluded because of failure of access (n = 7) or no ablation performed (n = 3). Of the remaining 163, 131 patients (80.4%) had undergone previous endocardial ablation. Mean age was 53.7 ± 15.7 years; 115 (71%) were men. VT ablation was the indication in 105 patients (64%). The underlying substrate was predominately nonischemic cardiomyopathy (49.1%). Epicardial ablation was performed in 115 (70.6%). Procedural and clinical success was obtained in 92.0% and 69.9% of patients, respectively, with no difference between Gp1 and Gp2. Those who underwent VPC ablation had superior clinical outcomes at 1-year follow-up. EpiAcc is feasible in almost all patients with no previous cardiac surgery and permits acute procedural success in >90% of patients, most of whom had failed previous ablation. However, epicardial ablation was not delivered in 1/3 of patients. Epicardial mapping may be helpful as in the absence of an appropriate epicardial site for ablation, and focus can be shifted to more detailed endocardial mapping and ablation.

Artificial-intelligence enabled electrocardiography (AI-ECG) has demonstrated high accuracy in identifying reduced left ventricular ejection fraction (LVEF) and could be instituted upstream of echocardiography with associated cost savings. However, the accuracy of AI-ECG for diagnosis and

The ACC/AHA Pooled Cohort Equation (PCE) for Atherosclerotic cardiovascular disease (ASCVD) has shown modest accuracy. We assessed if machine learning algorithms (MLA) could improve PCE performance with traditional and selected enriched clinical features. We tested MLA in a community cohort of

Cardiac sarcoidosis (CS) is an uncommon inflammatory cardiomyopathy for which sex differences have not been well characterized. Patients meeting Heart Rhythm Society criteria for CS were evaluated at a single institution (n=283). Presenting characteristics including demographics, symptoms

Artificial intelligence interpretation of ECGs can estimate ECG Age. This can vary from chronologic age (AGE) and may reflect random error, comorbidities or physiologic age. We assessed if ECG Age minus AGE (Age-Gap) correlates with mortality, an outcome measure of physiologic aging. We developed a

Dilated cardiomyopathy (DCM) can be inherited and guidelines recommend screening first-degree relatives. However, screening with echocardiography is limited due to availability and cost. Recently, artificial intelligence-enabled electrocardiography (AI-ECG) has shown promise in detecting left

Purpose Clinical findings of cardiac sarcoidosis are evident in only about 5% of patients with sarcoidosis however approximately 20% of autopsies demonstrate myocardial involvement. The epidemiology and prognosis of cardiac sarcoidosis remains poorly understood. Methods Tissue archives of Mayo Clinic were queried for autopsy cases diagnosed with sarcoidosis between 1994-2017. Tissue slides were re-reviewed by a cardiovascular pathologist (JJM) to confirm the histologic diagnosis. Granulomatous infiltrate was described in terms of character and distribution within the heart. Results Thirty-six cases of sarcoidosis were identified in the autopsy archives; 12 of these decedents (5 women) were found to have cardiac sarcoidosis. Left ventricular (LV) and right ventricular hypertrophy or dilation was assessed (expected and actual weight of heart shown in Fig). Two-thirds of the patients experienced electrophysiology disturbances prior to death (Fig). Four patients were diagnosed with heart failure (HF) with a median LV ejection fraction of 45% (range: 20% - 60%). Three patients were diagnosed with non-ischemic HF (2 presumed from alcohol use and 1 idiopathic) and 1 patient was diagnosed with ischemic HF due to concurrent coronary artery disease . Conclusion Prior autopsy data demonstrate that while death from sarcoidosis is rare, cardiac involvement is the most common cause of sarcoidosis fatalities. Our data support prior studies over the last decade, demonstrating a low rate of pre-mortem diagnosis and that sudden cardiac death is common These data highlight a need to identify better screening tools for cardiac sarcoidosis.