Citations

Introduction: Post-transcatheter aortic valve replacement (TAVR) outcome of patients is an important research topic. To accurately assess post-TAVR mortality, we examined a family of new echo-parameters (augmented systolic blood pressure (AugSBP) and arterial mean pressure parameters (AugMAP)) derived from blood pressure and aortic valve gradient ( Figure 1 ). Methods: Patients in the Mayo Clinic National Cardiovascular Diseases Registry-TAVR database who underwent TAVR between January 1, 2012, and June 30, 2017, were identified to retrieve baseline clinical, echocardiographic, and mortality data. The median of AugSBP and AugMAP were used to stratify patients in Kaplan-Meier analysis. Results: The final cohort contained 974 patients with a mean age of 81.4±8.3 years old, and 56.6% were male. The mean Society of Thoracic Surgeons risk score was 8.2±5.2. The median follow-up duration was 354 days, and the one-year all-cause mortality rate was 14.2%. Detailed characteristics are summarized in Table 1. AugMAP1 < 102.5 mmHg (median) was associated with a 3-fold risk of all-cause mortality post-TAVR at 1 year (hazard ratio 3.0, 95%confidence interval 2.0-4.5, p<0.0001)( Figure 1 ). Conclusions: Augmented mean arterial pressure provides clinicians with a simple but effective approach to quickly identify patients at risk and potentially improve post-TAVR prognosis.

Abstract Funding Acknowledgements Type of funding sources: Private hospital(s). Main funding source(s): Mayo Clinic Background/Introduction Syncope is a common presentation in heart failure (HF) patients. However, the associations between syncope and prognosis in HF patients with the cardiac implantable electronic device (CIED) have not been demonstrated. It is unclear whether syncope increases all-cause mortality in HF patients with CIED. Purpose We aimed to examine the association between syncope and all-cause mortality in the largest cohort of HF with reduced ejection fraction (HFrEF), mid-ranged ejection fraction (HFmEF), and preserved ejection fraction (HFpEF) patients with and without CIED. Methods A retrospective cohort of patients with HF and syncope at our hospital between 2010 and 2015 were identified, and 1:1 propensity was matched with a control group of HF patients without syncope. A chart review was conducted. The multivariate Cox regression model was applied to estimate the association between syncope and the all-cause mortality endpoint. Results 3,449 HF patients were diagnosed with syncope (mean age, 72.8±14.5 years; 41.5% women), and the propensity-matched control HF patients without syncope (n=3,449) aged 72.8±14.3 years, and 42.5% women were selected. Cardiac implantable electronic devices were implanted more in HF patients with syncope (32.5% versus 13.7%, p&lt;0.001). Implantable cardioverter defibrillators were implanted more in HF patients with syncope (15.6% versus 8.3%, p&lt;0.001) as well as pacemakers (16.9% versus 7.5%, p&lt;0.001) and cardiac resynchronization therapy devices (6.9% versus 4.8%, p=0.015). Among HF patients with CIED, syncope significantly increased the risk of all-cause mortality in HFrEF patients (hazard ratio [HR]=1.479, 95% confidence interval [CI]: 1.197-1.828, p&lt;0.001) but not in HFpEF (HR=0.980, 95%CI: 0.793-1.210, p=0.849)(Figure 1). Syncope also tended to increase the risk of all-cause mortality in HFmEF patients with CIED but was not significant (HR=1.193, 95%CI: 0.762-1.865, p=0.762)(Figure 1). Among HF patients without CIED, syncope did not increase the risk of all-cause mortality in HFrEF (HR=0.923, 95%CI: 0.806-1.057, p=0.247), HFmEF (HR=1.074, 95%CI: 0.848-1.360, p=0.554), or HFpEF (HR=0.964, 95%CI: 0.883-1.052, p=0.406). Conclusions Syncope significantly increased the risk of all-cause mortality in HFrEF patients but not in HFmEF or HFpEF patients with CIED. The association of syncope and increased mortality in HFrEF patients with CIED suggests that syncope may be a marker and a factor of mortality and warrants further investigation.

Abstract Funding Acknowledgements Type of funding sources: None. Background/Introduction Immune checkpoint inhibitor (ICI) myocarditis is a life-threatening condition characterized by lymphocytic myocardial infiltration[1]. Complete heart block (CHB) occurs in 15% of cases and is often fatal[2-4]. The nature and optimal management of this often-malignant condition is poorly understood[5]. Purpose This case series assesses prognostic factors associated with survival at 90 days among people with ICI myocarditis and CHB. Methods The electronic patient record system was used to identify patients admitted with ICI myocarditis. A two-physician review was used to assess the presence of ICI-associated CHB. Wilcoxon rank sum and Spearman’s rank correlation were used for categorical and quantitative variables respectively. Data was analysed using STATA/IC 16.1. The primary outcome was person-day survival 90 days after presenting symptom onset. Results Six patients were included in the final analysis. Mean survival was 47 person days. All had metastatic disease and presented 15 to 32 days after the first ICI infusion. All had muscle weakness. Other common self-symptoms were shortness of breath (4), blurred vision (4), double vision (3), collapse (3), and leg swelling (1). Proximal myopathy (2), fatigable myopathy (1), ptosis (3), and peripheral oedema (1) were present on examination. Raised troponin T and white blood cell count (WBC) were associated with 90-day survival (see Table 1). Nt-pro BNP, AST, and CK levels were raised in all patients. Two patients were tested for anti-striated muscle antibody titres, and both were strongly positive. Increase in QRS duration compared to baseline ECG was associated with lower survival at 90 days. Only one patient had CHB on admission. Steroid dose, IVIG, and plasmapheresis weren’t associated with survival. One patient declined device therapy. One declined permanent pacemaker (PPM) after insertion of transvenous pacemaker (TVP). Both died within 2 weeks. Three people underwent insertion of a transvenous pacemaker (TVP) followed by PPM insertion (table 2). All three eventually died within 100 days. One person received isoproterenol and immunosuppression as a bridge to PPM and survived. One patient was managed initially with TVP but subsequently declined PPM insertion. TVP removal resulted in CHB recurrence, and they died shortly after discharge. One person declined device therapy and died while receiving comfort care. Lead malfunction was frequent. One patient experienced bradycardia arrest with failure of a TVP lead to capture, while another experienced lead dislodgement. Conclusion CHB secondary to ICI is often fatal. It should be considered in patients presenting with symptoms of myositis, raised troponin and increased QRS duration. Despite a small sample size, this study identifies several prognostic factors which can guide both patients and physicians with decision-making. Larger studies are needed to better understand the role of device therapy in this population.

Background: Post-transcatheter aortic valve replacement (TAVR) patient outcome is an important research topic. To accurately assess post-TAVR mortality, we examined a family of new echo parameters (augmented systolic blood pressure (AugSBP) and arterial mean pressure (AugMAP)) derived from blood pressure and aortic valve gradients. Methods: Patients in the Mayo Clinic National Cardiovascular Diseases Registry-TAVR database who underwent TAVR between 1 January 2012 and 30 June 2017 were identified to retrieve baseline clinical, echocardiographic and mortality data. AugSBP, AugMAP and valvulo-arterial impedance (Zva) (Zva) were evaluated using Cox regression. Receiver operating characteristic curve analysis and the c-index were used to assess the model performance against the Society of Thoracic Surgeons (STS) risk score. Results: The final cohort contained 974 patients with a mean age of 81.4 ± 8.3 years old, and 56.6% were male. The mean STS risk score was 8.2 ± 5.2. The median follow-up duration was 354 days, and the one-year all-cause mortality rate was 14.2%. Both univariate and multivariate Cox regression showed that AugSBP and AugMAP parameters were independent predictors for intermediate-term post-TAVR mortality (all p < 0.0001). AugMAP1 < 102.5 mmHg was associated with a 3-fold-increased risk of all-cause mortality 1-year post-TAVR (hazard ratio 3.0, 95%confidence interval 2.0–4.5, p < 0.0001). A univariate model of AugMAP1 surpassed the STS score model in predicting intermediate-term post-TAVR mortality (area under the curve: 0.700 vs. 0.587, p = 0.005; c-index: 0.681 vs. 0.585, p = 0.001). Conclusions: Augmented mean arterial pressure provides clinicians with a simple but effective approach to quickly identify patients at risk and potentially improve post-TAVR prognosis.

Echocardiography is an integral part of the diagnosis and management of cardiovascular disease. The use and application of artificial intelligence (AI) is a rapidly expanding field in medicine to improve consistency and reduce interobserver variability. AI can be successfully applied to echocardiography in addressing variance during image acquisition and interpretation. Furthermore, AI and machine learning can aid in the diagnosis and management of cardiovascular disease. In the realm of echocardiography, accurate interpretation is largely dependent on the subjective knowledge of the operator. Echocardiography is burdened by the high dependence on the level of experience of the operator, to a greater extent than other imaging modalities like computed tomography, nuclear imaging, and magnetic resonance imaging. AI technologies offer new opportunities for echocardiography to produce accurate, automated, and more consistent interpretations. This review discusses machine learning as a subfield within AI in relation to image interpretation and how machine learning can improve the diagnostic performance of echocardiography. This review also explores the published literature outlining the value of AI and its potential to improve patient care.

Aims:Increased left ventricular (LV) wall thickness is frequently encountered in transthoracic echocardiography (TTE). While accurate and early diagnosis is clinically important, given the differences in available therapeutic options and prognosis, an extensive workup is often required to establish the diagnosis. We propose the first echo-based, automated deep learning model with a fusion architecture to facilitate the evaluation and diagnosis of increased left ventricular (LV) wall thickness. Methods and Results: Patients with an established diagnosis of increased LV wall thickness (hypertrophic cardiomyopathy (HCM), cardiac amyloidosis (CA), and hypertensive heart disease (HTN)/others) between 1/2015 and 11/2019 at Mayo Clinic Arizona were identified. The cohort was divided into 80%/10%/10% for training, validation, and testing sets, respectively. Six baseline TTE views were used to optimize a pre-trained InceptionResnetV2 model. Each model output was used to train a meta-learner under a fusion architecture. Model performance was assessed by multiclass area under the receiver operating characteristic curve (AUROC). A total of 586 patients were used for the final analysis (194 HCM, 201 CA, and 191 HTN/others). The mean age was 55.0 years, and 57.8% were male. Among the individual view-dependent models, the apical 4-chamber model had the best performance (AUROC: HCM: 0.94, CA: 0.73, and HTN/other: 0.87). The final fusion model outperformed all the view-dependent models (AUROC: HCM: 0.93, CA: 0.90, and HTN/other: 0.92). Conclusion: The echo-based InceptionResnetV2 fusion model can accurately classify the main etiologies of increased LV wall thickness and can facilitate the process of diagnosis and workup.

Aims Increased LV wall thickness is frequently encountered in transthoracic echocardiography (TTE). While accurate and early diagnosis is clinically important, given the differences in available therapeutic options and prognosis, an extensive workup is often required to establish the diagnosis. We propose the first echo-based, automated deep learning model with a fusion architecture to facilitate the evaluation and diagnosis of increased left ventricular (LV) wall thickness. Methods and Results Patients with an established diagnosis of increased LV wall thickness (hypertrophic cardiomyopathy (HCM), cardiac amyloidosis (CA), and hypertensive heart disease (HTN)/others) between 1/2015 to 11/2019 at Mayo Clinic Arizona were identified. The cohort was divided into 80%/10%/10% for training, validation, and testing sets, respectively. Six baseline TTE views were used to optimize a pre-trained InceptionResnetV2 model. Each model output was used to train a meta-learner under a fusion architecture. Model performance was assessed by multiclass area under the receiver operating characteristic curve (AUROC). A total of 586 patients were used for the final analysis (194 HCM, 201 CA, and 191 HTN/others). The mean age was 55.0 years, and 57.8% were male. Among the individual view-dependent models, the apical 4 chamber model had the best performance (AUROC: HCM: 0.94, CA: 0.73, and HTN/other: 0.87). The final fusion model outperformed all the view-dependent models (AUROC: CA: 0.90, HCM: 0.93, and HTN/other: 0.92). Conclusion The echo-based InceptionResnetV2 fusion model can accurately classify the main etiologies of increased LV wall thickness and can facilitate the process of diagnosis and workup.

In patients with medically refractory heart failure or cardiogenic shock, both temporary and durable mechanical circulatory support devices can be used to support cardiac circulation. Both transthoracic echocardiography (TTE) and transesophageal echocardiography (TEE) are widely available, relatively noninvasive, and avoid radiation exposure. Thus, echocardiography is an invaluable tool that provides vital information aiding in preprocedure evaluation, placement, management, and weaning of cardiac assist devices. The purpose of this article is to review the utility of both TTE and TEE in managing patients with cardiac assist devices.

AbstractIntroductionAtrial fibrillation (AF) is the most common cardiac arrhythmia with a high stroke and mortality rate. The video‐assisted thoracoscopic radiofrequency pulmonary vein ablation is a treatment option for patients who fail catheter ablation. Randomized data comparing surgical versus catheter ablation are limited. We performed a meta‐analysis of randomized control trials to explore the outcome efficacy between surgical and catheter radiofrequency pulmonary vein ablation in patients with AF.MethodsWe comprehensively searched the databases of MEDLINE and EMBASE from inception to December 2020. Included studies were published randomized control trials that compared video‐assisted thoracoscopic and catheter radiofrequency pulmonary vein ablation. Data from each study were combined using the fixed‐effects, generic inverse variance method of DerSimonian, and Laird to calculate odds ratios and 95% confidence intervals.ResultsSix studies from November 2013 to 2020 were included in this meta‐analysis involving 511 AF patients (79% paroxysmal) with 263 catheter ablation (mean age 56 ± 3 years) and 248 surgical ablations (mean age 52 ± 4 years). Catheter ablation was associated with increased atrial arrhythmias recurrence when compared to surgical ablation (pooled relative risk = 1.85, 95% confidence interval: 1.44−2.39, p < .001, I2 = 0.0%) but associated with less total major adverse events (pooled relative risk = 0.29, 95% confidence interval: 0.16−0.53, p < .001, I2 = 0.0%). In subgroup analysis, catheter ablation was associated with increased AF recurrence in refractory paroxysmal AF when compared to surgical ablation (pooled relative risk = 2.47, 95% confidence interval: 1.31−4.65, p = .005, I2 = 0.0%) but not in persistent AF (relative risk = 1.09, 95% confidence interval: 0.60−2.0, p = .773).ConclusionCatheter ablation was associated with higher atrial arrhythmia recurrence when compared with surgical ablation. However, our study suggests that the benefit of surgical ablation in patients with persistent AF is unclear. More studies and alternative ablation strategies investigation in persistent AF are warranted.

Introduction: Multiple supervised machine learning (ML) models have been used to assess the outcomes in patients who underwent transcatheter aortic valve replacement (TAVR) procedure, however, supervised ML requires significant expert time for annotation and labeling. Unsupervised ML can provide insight into the dataset without this process. Hypothesis: We hypothesized that unsupervised k-means ML can identify distinct phenogroups in TAVR patients that can predict prognosis without a priori knowledge. Methods: A retrospective study of patients who underwent TAVR (Jan 2012-Dec 2017) at Mayo Clinic sites was performed. Baseline demographics, EKG, cardiac computed tomography, and echocardiographic data were abstracted. Variables with ≥ 20% missing data were excluded. Prior to the k-means clustering algorithm, principal component analysis was used for dimensionality reduction. Input variables were analyzed according to k-means determined clusters. Kaplan-Meier survival analysis was used to assess the survival of each cluster. Results: A total of 1071 consecutive patients were included in the final analysis. A total of 141 unique variables were used. The mean age was 80.7±8.6 years, 58.2% were male. Patients were divided into 2 distinct phenogroups. In the two phenogroups, cluster 0 (n=639) has significantly better survival than cluster 1 (n= 432) (log-rank p=0.0002) (Figure 1). Cluster 1 has features of low flow-low gradient (LFLG) hemodynamic profile, including lower systolic blood pressure, mean aortic valve gradient, peak aortic valve velocity, and worse LV ejection fraction. Conclusions: Unsupervised ML can identify distinct phenotype features with prognostic significance in TAVR patients. Our results suggest that patients with LFLG hemodynamic features are intrinsically different from other TAVR patients and require more attention to avoid adverse outcomes.