Barry, Timothy

Objectives Lipoprotein(a) (Lp(a)) is associated with an increased incidence of native aortic stenosis, which shares similar pathological mechanisms with bioprosthetic aortic valve (bAV) degeneration. However, evidence regarding the role of Lp(a) concentrations in bAV degeneration is lacking. This study aims to evaluate the association between Lp(a) concentrations and bAV degeneration. Methods In this retrospective multicentre study, patients who underwent a bAV replacement between 1 January 2010 and 31 December 2020 and had a Lp(a) measurement were included. Echocardiography follow-up was performed to determine the presence of bioprosthetic valve degeneration, which was defined as an increase >10 mm Hg in mean gradient from baseline with concomitant decrease in effective orifice area and Doppler Velocity Index, or new moderate/severe prosthetic regurgitation. Levels of Lp(a) were compared between patients with and without degeneration and Cox regression analysis was performed to investigate the association between Lp(a) levels and bioprosthetic valve degeneration. Results In total, 210 cases were included (mean age 74.1±9.4 years, 72.4% males). Median time between baseline and follow-up echocardiography was 4.4 (IQR 3.7) years. Bioprostheses degeneration was observed in 33 (15.7%) patients at follow-up. Median serum levels of Lp(a) were significantly higher in patients affected by degeneration versus non-affected cases: 50.0 (IQR 72.0) vs 15.6 (IQR 48.6) mg/dL, p=0.002. In the regression analysis, high Lp(a) levels (≥30 mg/dL) were associated with degeneration both in a univariable analysis (HR 3.6, 95% CI 1.7 to 7.6, p=0.001) and multivariable analysis adjusted by other risk factors for bioprostheses degeneration (HR 4.4, 95% CI 1.9 to 10.4, p=0.001). Conclusions High serum Lp(a) is associated with bAV degeneration. Prospective studies are needed to confirm these findings and to investigate whether lowering Lp(a) levels could slow bioprostheses degradation.

Lipoprotein(a) [Lp(a)] is a lipid molecule with atherogenic, inflammatory, thrombotic, and antifibrinolytic effects, whose concentrations are predominantly genetically determined. The association between Lp(a) and cardiovascular diseases (CVDs) has been well-established in numerous studies, and the ability to measure Lp(a) levels is widely available in the community. As such, there has been increasing interest in Lp(a) as a therapeutic target for the prevention of CVD. The impact of the currently available lipid-modifying agents on Lp(a) is modest and heterogeneous, except for the monoclonal antibody proprotein convertase subtilisin/kexin type 9 inhibitors (PCSK9i), which demonstrated a significant reduction in Lp(a) levels. However, the absolute reduction in Lp(a) to significantly decrease CVD outcomes has not been definitely established, and the magnitude of the effect of PCSK9i seems insufficient to directly reduce the Lp(a)-related CVD risk. Therefore, emerging therapies are being developed that specifically aim to lower Lp(a) levels and the risk of CVD, including RNA interference (RNAi) agents, which have the capacity for temporary and reversible downregulation of gene expression. This review article aims to summarize the effects of Lp(a) on CVD and to evaluate the available evidence on established and emerging therapies targeting Lp(a) levels, focusing on the potential reduction of CVD risk attributable to Lp(a) concentrations.

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.

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.

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.

Introduction: Syncope was reported to be associated with sudden cardiac death in heart failure patients regardless of the etiology of syncope. We attempted to use unsupervised machine learning to identify the prognostically distinct phenotypes in a large population of heart failure patients presenting with syncope. Hypothesis: Unsupervised machine learning can identify prognostically distinct phenotypes in a large population of heart failure patients presenting with syncope. Methods: Patients with a diagnosis of systolic or diastolic heart failure and syncope at Mayo Clinic sites were identified for baseline and follow-up data. Variables with ≥ 20% missing data were excluded; a total of 57 variables were used for k-means clustering analysis. Patients were grouped according to 4 distinct k-means determined clusters for statistical analysis. Results: A total of 3,557 patients with proper follow-up and echocardiography data were included in the final analysis. The median follow-up duration was 36.3 months, and 2908 (81.7%) patients died. Cluster 2 (n= 680) was identified as the cluster with the highest mortality (92%), which contained patients with high proportions of cardiovascular comorbidities, especially chronic kidney disease. The Kaplan-Meier survival curves are shown in Figure 1. In multivariate Cox regression analysis, the presence of CKD independently predicts mortality (HR 1.62, 95%CI 1.50-1.76, p <0.0001) after adjusting for age and sex. Conclusions: Unsupervised machine learning identified patients with CKD, CAD and DM are high-risk phenotypes in heart failure patients with syncope, and can be used to guide future studies in this highly heterogeneous, unexplored population.