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.

Introduction: Studies on leadless pacemakers (LP) are very limited and they often fail to reflect real population outcomes. Hypothesis: We sought to evaluate the effect of leadless pacemaker implantation on the echocardiographic parameters including ventricular function and valvular competence. Methods: A retrospective review including 162 patients (81 Micra Leadless pacemaker implant and 81 dual-chamber transvenous pacemakers) from 3 centers were included. Follow-up clinical, echocardiographic, and pacing characteristics were obtained over a median of 14+/-2 months. Results: baseline demographics and clinical characteristics were comparable between the 2 groups including age (78.6+/-9.9, Range 35-95) and gender (49 males, 69.5%). Compared to conventional pacemakers, LP patients had worsening tricuspid regurgitation noted on follow-up echo in 59.3% of patients compared to baseline(P<0.001). This difference was independent of the RV pacing percentage and device location. In addition, LP implantation resulted in a reduction of RV function (P=0.003) and left ventricular ejection fraction (P=0.03). The RV Pacing burden in LP patient patients was not associated with a change in LVEF during the follow-up period (95% CI -0.24, 0.20, p = 0.846). Conclusions: LP therapy is associated with worsening tricuspid valve regurgitation and biventricular dysfunction over a mean 1 year of follow-up. Additional prospective randomized studies are warranted to further evaluate these findings.

Abstract Funding Acknowledgements Type of funding sources: None. Background/Introduction Idiopathic ventricular fibrillation (IVF) is responsible for 5-7% of sudden cardiac arrests in the absence of substrate for ventricular arrhythmia, structural diseases, or primary arrhythmic syndromes. The characteristics, management, and prognosis of these patients are unclear. Purpose To investigate the clinical features, management, and prognosis of IVF in our hospital healthcare system Methods Mayo Database Explorer software was used to identify IVF patients from our hospital electronic medical records between 1990 to 2022 in 5 states. The medical records were manually reviewed. Results A total of 132 patients were initially diagnosed with IVF. During the 9.2±6.8 years mean follow-up, 36 [27.3%] patients were later diagnosed with structural diseases or primary arrhythmic syndromes (dilated cardiomyopathy [41.7%], Long QT syndrome [27.8%], catecholaminergic polymorphic ventricular tachycardia [8.3%], Brugada syndrome [5.6%], mitral valve-related arrhythmia [5.6%], and short QT syndrome [2.8%]). Among the rest of the 96 patients with true IVF (mean age 38.4±15.7 years, male 59.4%, Caucasian 80.2%, hypertension 28.1%, diabetes 8.3%), 91 (94.8%) patients presented with out-of-hospital cardiac arrest and 88 (91.7%) patients had a history of syncope. Early repolarization and fragmented QRS were noted in 32 (33.3%) and 28 (29.1%) patients, respectively. Defibrillators were implanted in 92 (95.8%) patients. Genetic testing was performed in 35 (36.5%) patients, and possible associated mutations were found in 13 (37.1%) patients. An Electrophysiology study was conducted in 55 (57.3%) patients, and ventricular fibrillation was induced in 17 (30.9%) patients. The medical management with antiarrhythmic drugs was selected in 60 (62.5%) patients, and the catheter ablation ± anti-arrhythmic drugs was selected in 36 (37.5%) patients. The recurrence of ventricular fibrillation during 9.2±6.8 years of mean follow-up was significantly higher in the ablation strategy group (p=0.0003) (Figure 1). Conclusions We reported the largest cohort of IVF patients from a single healthcare system. Early repolarization and fragmented QRS were common in IVF. The possible associated mutations were found in one-third of IVF who underwent genetic testing. The ventricular fibrillation recurrences were significantly higher in the catheter ablation strategy group.

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.

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.

Background: Prior studies have reported conflicting results of persistent iatrogenic atrial septal defect (iASD) and its impact following a transcatheter edge-to-edge repair (TEER) procedure. This study aims to evaluate the incidence of iASD and its clinical and hemodynamic impact after a TEER. Methods: Consecutive patients who underwent a TEER procedure from June 2014 to September 2020 at the Mayo Clinic were identified. The presence of iASD was retrospectively identified on post-procedure transthoracic echocardiography (TTE) to group patients into an iASD+ group and an iASD− group for comparison of prognosis and cardiac function. Results: A total of 316 patients were included; the mean age was 79.1 ± 9.1 years, and 67.7% were male. Persistent iASD was diagnosed in 108 (34.2%) patients. There was no difference concerning all-cause mortality, heart failure hospitalization, and stroke/TIA between groups at follow-up (median follow-up of 9 months). Post-procedure TTE demonstrated no differences regarding right ventricle (RV) and left ventricle (LV) dimensions and function, including TAPSE (15.2 ± 4.6 vs. 15.4 ± 5.5 mm, p = 0.875), and LV ejection fraction (51.1 ± 14.0% vs. 51.3 ± 13.9%, p = 0.933). However, patients with iASD had higher RV systolic pressure (48.7 ± 14.4 vs. 45.5 ± 14.5 mmHg, p = 0.042) compared with patients without iASD. Conclusion: Notwithstanding higher RV pressures, patients with persistent iASD had no hemodynamic compromise and an equal prognosis compared with those without a residual atrial defect after a TEER procedure. These findings support the mid-term safety of procedures in which an interatrial defect needs to be created and would argue against the need for interventional closure.

Abstract Background Topological data analysis (TDA) is the state-of-the-art unsupervised machine learning framework that can provide insight into the dataset and visualize condensed information via the topological network graph. This robust approach was never used to assess the heterogeneous MitraClip population. Purpose We aim to develop a TDA model that will identify prognostically-distinct phenogroups in MitraClip patients without a priori knowledge of the population and their outcomes. Method Patients who underwent MitraClip (June 2014-September 2020) at Mayo Clinic sites were identified from the institutional database for baseline and follow-up data. Thirteen variables were used for TDA. The topological network graph was created using the Python Scikit-TDA Kepler-Mapper package (v. 2.0.1), and clustering was performed at the graph level with Louvain's modularity method. Kaplan-Meier survival analysis was used to assess the all-cause mortality endpoint of each cluster identified in an unsupervised manner. The dataset with cluster labels was also used to train a Light Gradient Boosted Machine model, and SHapley Additive exPlanations (SHAP) analysis was applied to determine the feature importance. Results A total of 389 consecutive patients were included in the final analysis and two major clusters consisting of 384 patients were identified. The mean age was 80.3±8.7 years; 256 (65.8%) were male. The mean Society of Thoracic Surgeons Mitral Valve Replacement risk score was 9.6±6.9%. Fifty-five (14.5%) patients died during the mean follow duration (185 days). Kaplan-Meier analysis showed significant survival differences among the two clusters (HR: 2.38; 95% CI: 1.39–4.06, p=0.001; Figure 1). Clusters 1 (n=195) was associated with &gt; mild residual mitral regurgitation and worse survival performance and was characterized with worse tricuspid regurgitation severity, a higher proportion of patients with atrial fibrillation/flutter, anterior leaflet prolapse, and mitral annular/leaflet calcification, as summarized in Table 1. Conclusion TDA can identify distinct phenotype clusters with prognostic significance in MitraClip patients based on mitral valve morphology and clinical risk factors. This simple model can facilitate clinical risk stratification for MitraClip patients regarding procedural success and survival performance. Funding Acknowledgement Type of funding sources: None.

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.

Abstract Background Fever, alcohol, and sodium channel blockers can unmask Brugada pattern and may also induce arrhythmias in Brugada syndrome. We report a case of unmasked Type-1 Brugada pattern presenting with ventricular fibrillation that was induced by a tetrahydrocannabinol vaping. Case summary A 48-year-old male with a past medical history of hypertension treated with hydrochlorothiazide and back pain controlled with tetrahydrocannabinol vaping presented with sudden cardiac arrest from ventricular fibrillation, which was terminated with defibrillation. Electrocardiogram after resuscitation showed a new Type-1 Brugada pattern compared to a previous normal baseline electrocardiogram. Echocardiography and coronary angiogram were unremarkable. Complete blood count and chemistries were unremarkable except for mild hypokalaemia (K = 3.3 mmol/L). After correction of the hypokalaemia, the Type-1 Brugada pattern persisted. Urine drug screen was positive for tetrahydrocannabinol (60 ng/mL). Genetic testing was negative for inherited arrhythmic disease and cardiomyopathy gene panels. Discussion The patient’s type-1 Brugada pattern and ventricular fibrillation were likely induced by vaping tetrahydrocannabinol. He underwent secondary prevention with an implantable cardioverter-defibrillator. He abstains from cannabis and Type-1 Brugada pattern is normalized. There was no arrhythmic event at his 18-month follow-up appointment with abstinence from tetrahydrocannabinol.

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.