Arsanjani, Reza

Abstract Aims Doppler mean gradient (MG) can underestimate aortic stenosis (AS) severity in patients with atrial fibrillation (AF) compared with patients with sinus rhythm (SR), potentially delaying intervention in AF. This study compared outcomes in patients with AF and SR following transcatheter aortic valve replacement (TAVR) and investigated delay in TAVR based on computed tomography aortic valve calcium score (AVCS). Methods and results Patients who underwent TAVR from 2013 to 2017 for native valve severe AS were identified from an institutional database. Baseline characteristics and overall survival were compared between those with SR and AF. There were 820 patients (mean age 81 years; 41.6% females) included in this study. AF was present in 356 patients. Patients with AF were older (82.2 vs. 80.5, P = 0.003) and had a lower MG compared with patients with SR (42.0 vs. 44.9, P = 0.002) with similar indexed aortic valve area (0.4 vs. 0.4, P = 0.17). Median AVCS was higher in AF (males: AF 2850.0 vs. SR 2561.0, P = 0.044; females: AF 1942.0 vs. SR 1610.5, P = 0.025). Projected AVCS, assuming the same age of diagnosis, was similar between AF and SR. Median survival post-TAVR was worse in AF compared with SR (3.2 vs. 5.4 years, log rank P < 0.001). AF, lower MG, higher right ventricular systolic pressure, dialysis, diabetes, and significant tricuspid regurgitation were associated with higher mortality (P < 0.05 for all). Conclusion Older age and higher AVCS in patients with AF compared with those with SR suggest that AS was both underestimated and more advanced at TAVR referral.

Introduction: In the current study, we examined the temporal trends in mortality across gender, race, and 10-year age groups over last two-decades. Methods: We conducted a retrospective population-based study using Centers for Disease Control and Prevention database. Subjects with underlying cause of death from AMI were identified using validated ICD10 codes. Age adjusted mortality rates were calculated for gender and race whereas crude rates were calculated for 10-year age group. Using Poisson regression, we estimated the rate ratio between race/ethnicity pairs adjusting for age and sex in order to analyze temporal trend. Results: We identified 2,709,173 subjects with reported underlying cause of death as AMI between January-2000 to December-2019 of which 54.7% were males (n=1,483,163). The age adjusted mortality rate per 100,000 was higher in males compared to females (52.4% vs 30.7%, OR: 1.66 [1.45-1.89]). Among ethnicities African American (AA) population had the highest age adjusted mortality rate per 100,000 [Table 1]. As expected, age group >85 years had the highest mortality rate per 100,000 constituting 30% of total deaths (78% of total deaths were noted in >65yr age group). AMI age adjusted mortality rates/100,000 showed significant decline across gender and race with highest decline in female population (66.5% decline). Highest percentage decline in mortality was noted in AA female population (68.2%) whereas in males it was the white population (62%). In females, highest percentage decline in mortality was noted in 75-84 age-group (70%) while it was >85 age-group (67%) in males. Conclusion: Over last two decades there has been a significant decline in AMI mortality rates across gender and race. AA continue to have higher age adjusted mortality rate among both male and female population (38% and 22% respectively). Further studies are needed to identify and provide targeted prevention and intervention strategies in high-risk populations.

Introduction: Pectus excavatum (PE) is the most common anterior chest wall deformity with dorsal deviation into the thoracic cavity. Depending on the severity, PE can cause minor cosmetic problem to significant cardiac compression in severe cases. This compression most frequently affects right heart chambers causing symptoms such as dyspnea, palpitations, fatigue, exercise intolerance, and difficulty with keeping up with peers. Previously, we reported the change in these symptoms with positon associated with change in right heart hemodynamics. Further, we are comparing the changes in right heart hemodynamics with position in pectus patients to normal healthy adults. Methods: A prospective cohort of 40 pectus excavatum patients (mean Haller index: 5.4±2) undergoing evaluation at Mayo clinic underwent 2-D transthoracic echo (TTE) with Doppler assessment of Right Ventricular Outflow Tract (RVOT) Velocity Time Integral (VTI). A cohort of 8 healthy adults without chest wall abnormality who had clinically indicated echocardiogram was used as control (Table). Additional assessment of the RVOT VTI was performed in sitting position in either group. A two-sample t-test was performed to assess significance between cohorts. Results: There is a significant difference in RVOT VTI between case and control cohorts in both supine and sitting positions (p=0.03 & p=0.001 respectively). Also, there is a significant decrease in RVOT VTI from supine to sitting position in PE patients compared to healthy adults (Table). Conclusion: This is the 1 st prospective cohort study in PE patients vs healthy adults showing the feasibility and utility of dynamic echocardiography in assessing the right ventricular hemodynamics. This study demonstrates a significant decrease in right sided hemodynamic changes with change in position in patients with PE which might explain their symptoms.

Systemic vasculitides are a rare and complex group of diseases that can affect multiple organ systems. Clinically, presentation may be vague and non-specific and as such, diagnosis and subsequent management are challenging. These entities are typically classified by the size of vessel involved, including large-vessel vasculitis (giant cell arteritis, Takayasu’s arteritis, and clinically isolated aortitis), medium-vessel vasculitis (including polyarteritis nodosa and Kawasaki disease), and small-vessel vasculitis (granulomatosis with polyangiitis and eosinophilic granulomatosis with polyangiitis). There are also other systemic vasculitides that do not fit in to these categories, such as Behcet’s disease, Cogan syndrome, and IgG4-related disease. Advances in medical imaging modalities have revolutionized the approach to diagnosis of these diseases. Specifically, color Doppler ultrasound, computed tomography and angiography, magnetic resonance imaging, positron emission tomography, or invasive catheterization as indicated have become fundamental in the work up of any patient with suspected systemic or localized vasculitis. This review presents the key diagnostic imaging modalities and their clinical utility in the evaluation of systemic vasculitis.

Cardiac amyloidosis (CA) is an underdiagnosed form of infiltrative cardiomyopathy caused by abnormal amyloid fibrils deposited extracellularly in the myocardium and cardiac structures. There can be high variability in its clinical manifestations, and diagnosing CA requires expertise and often thorough evaluation; as such, the diagnosis of CA can be challenging and is often delayed. The application of artificial intelligence (AI) to different diagnostic modalities is rapidly expanding and transforming cardiovascular medicine. Advanced AI methods such as deep-learning convolutional neural networks (CNNs) may enhance the diagnostic process for CA by identifying patients at higher risk and potentially expediting the diagnosis of CA. In this review, we summarize the current state of AI applications to different diagnostic modalities used for the evaluation of CA, including their diagnostic and prognostic potential, and current challenges and limitations.

Cardiac allograft vasculopathy (CAV) is a distinct form of coronary artery disease that represents a major cause of death beyond the first year after heart transplantation. The pathophysiology of CAV is still not completely elucidated; it involves progressive circumferential wall thickening of both the epicardial and intramyocardial coronary arteries. Coronary angiography is still considered the gold-standard test for the diagnosis of CAV, and intravascular ultrasound (IVUS) can detect early intimal thickening with improved sensitivity. However, these tests are invasive and are unable to visualize and evaluate coronary microcirculation. Increasing evidence for non-invasive surveillance techniques assessing both epicardial and microvascular components of CAV may help improve early detection. These include computed tomography coronary angiography (CTCA), single-photon emission computed tomography (SPECT), positron emission tomography (PET), and vasodilator stress myocardial contrast echocardiography perfusion imaging. This review summarizes the current state of diagnostic modalities and their utility and prognostic value for CAV and also evaluates emerging tools that may improve the early detection of this complex disease.

Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiomyopathy. It follows an autosomal dominant inheritance pattern in most cases, with incomplete penetrance and heterogeneity. It is familial in 60% of cases and most of these are caused by pathogenic variants in the core sarcomeric genes (MYH7, MYBPC3, TNNT2, TNNI3, MYL2, MYL3, TPM1, ACTC1). Genetic testing using targeted disease-specific panels that utilize next-generation sequencing (NGS) and include sarcomeric genes with the strongest evidence of association and syndrome-associated genes is highly recommended for every HCM patient to confirm the diagnosis, identify the molecular etiology, and guide screening and management. The yield of genetic testing for a disease-causing variant is 30% in sporadic cases and up to 60% in familial cases and in younger patients with typical asymmetrical septal hypertrophy. Genetic testing remains challenging in the interpretation of results and classification of variants. Therefore, in 2015 the American College of Medical Genetics and Genomics (ACMG) established guidelines to classify and interpret the variants with an emphasis on the necessity of periodic reassessment of variant classification as genetic knowledge rapidly expands. The current guidelines recommend focused cascade genetic testing regardless of age in phenotype-negative first-degree relatives if a variant with decisive evidence of pathogenicity has been identified in the proband. Genetic test results in family members guide longitudinal clinical surveillance. At present, there is emerging evidence for genetic test application in risk stratification and management but its implementation into clinical practice needs further study. Promising fields such as gene therapy and implementation of artificial intelligence in the diagnosis of HCM are emerging and paving the way for more effective screening and management, but many challenges and obstacles need to be overcome before establishing the practical implications of these new methods.

Abstract Aims Cardiac amyloidosis (CA) is common in patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR). Cardiac amyloidosis has poor outcomes, and its assessment in all TAVR patients is costly and challenging. Electrocardiogram (ECG) artificial intelligence (AI) algorithms that screen for CA may be useful to identify at-risk patients. Methods and results In this retrospective analysis of our institutional National Cardiovascular Disease Registry (NCDR)-TAVR database, patients undergoing TAVR between January 2012 and December 2018 were included. Pre-TAVR CA probability was analysed by an ECG AI predictive model, with >50% risk defined as high probability for CA. Univariable and propensity score covariate adjustment analyses using Cox regression were performed to compare clinical outcomes between patients with high CA probability vs. those with low probability at 1-year follow-up after TAVR. Of 1426 patients who underwent TAVR (mean age 81.0 ± 8.5 years, 57.6% male), 349 (24.4%) had high CA probability on pre-procedure ECG. Only 17 (1.2%) had a clinical diagnosis of CA. After multivariable adjustment, high probability of CA by ECG AI algorithm was significantly associated with increased all-cause mortality [hazard ratio (HR) 1.40, 95% confidence interval (CI) 1.01–1.96, P = 0.046] and higher rates of major adverse cardiovascular events (transient ischaemic attack (TIA)/stroke, myocardial infarction, and heart failure hospitalizations] (HR 1.36, 95% CI 1.01–1.82, P = 0.041), driven primarily by heart failure hospitalizations (HR 1.58, 95% CI 1.13–2.20, P = 0.008) at 1-year follow-up. There were no significant differences in TIA/stroke or myocardial infarction. Conclusion Artificial intelligence applied to pre-TAVR ECGs identifies a subgroup at higher risk of clinical events. These targeted patients may benefit from further diagnostic evaluation for CA.

Exposure to high altitude results in hypobaric hypoxia, leading to physiological changes in the cardiovascular system that may result in limiting symptoms, including dyspnea, fatigue, and exercise intolerance. However, it is still unclear why some patients are more susceptible to high-altitude symptoms than others. Hypoxic simulation testing (HST) simulates changes in physiology that occur at a specific altitude by asking the patients to breathe a mixture of gases with decreased oxygen content. This study aimed to determine whether the use of transthoracic echocardiography (TTE) during HST can detect the rise in right-sided pressures and the impact of hypoxia on right ventricle (RV) hemodynamics and right to left shunts, thus revealing the underlying causes of high-altitude signs and symptoms. A retrospective study was performed including consecutive patients with unexplained dyspnea at high altitude. HSTs were performed by administrating reduced FiO2 to simulate altitude levels specific to patients’ history. Echocardiography images were obtained at baseline and during hypoxia. The study included 27 patients, with a mean age of 65 years, 14 patients (51.9%) were female. RV systolic pressure increased at peak hypoxia, while RV systolic function declined as shown by a significant decrease in the tricuspid annular plane systolic excursion (TAPSE), the maximum velocity achieved by the lateral tricuspid annulus during systole (S’ wave), and the RV free wall longitudinal strain. Additionally, right-to-left shunt was present in 19 (70.4%) patients as identified by bubble contrast injections. Among these, the severity of the shunt increased at peak hypoxia in eight cases (42.1%), and the shunt was only evident during hypoxia in seven patients (36.8%). In conclusion, the use of TTE during HST provides valuable information by revealing the presence of symptomatic, sustained shunts and confirming the decline in RV hemodynamics, thus potentially explaining dyspnea at high altitude. Further studies are needed to establish the optimal clinical role of this physiologic method.

Abstract Percutaneous Coronary Intervention (PCI) is a widely utilized standard ofcare and efficacious intervention in the management of coronary artery disease,improving prognosis and symptoms for many patients. Prediction of post-PCIoutcomes contributes to effective patient management and quality improvementinitiatives in healthcare. Our study focuses on comparative performance analysisfor state-of-the-art vision transformer (ViT) and convolutional neural network(CNN) on multimodal data analysis in a joint fusion framework. We integrateimages of electrocardiogram (ECG) data and tabular electronic health records(EHR) to predict 3 clinically relevant post-PCI (6 months) endpoints - heart failure hospitalization, all-cause mortality, and ischemic stroke. To design a comparativemodel for ViT, we proposed a new joint fusion architecture that consistsof a convolutional neural network (CNN) with a convolutional block attentionmodule (CBAM). The learned representations are combined at an intermediatelayer followed by processing these representations using a fully connectedlayer. The proposed model demonstrates excellent performance with the highestAUROC scores of 0.849, 0.913, and 0.794 for predicting heart failure hospitalization,all-cause mortality, and stroke, respectively. Surpassing the baseline EHRmodel and ViT, the proposed CNN+CBAM fusion model showcases superiorpredictive capabilities for heart failure prediction.