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1. Rethinking Risk in Hypertrophic Cardiomyopathy: Assessing the Role of Myocardial Fibrosis and Left Ventricular Hypertrophy in Sudden Cardiac Death

Author: Amro Badr, MD, Juan Farina, MD, Reza Arsanjani, MD, Srekar Ravi, MD, Michael O'Shea, MD, Omar Baqal, MD, Olubadewa Fatunde, MD, Jeffrey B. Geske, MD, Konstantinos C. Siontis, MD, and Said Alsidawi, MD
Subjects: Medicine (General), R5-920
Abstract: The American College of Cardiology/American Heart Association guidelines recommend implantable cardioverter-defibrillator (ICD) implantation for patients with hypertrophic cardiomyopathy (HCM) with a wall thickness of ≥30 mm (class IIA), whereas they give a class IIB recommendation for the implantation of an ICD on the basis of extensive late gadolinium enhancement alone. In this analysis, we show that in a high-risk population with ICD implanted for primary prevention of sudden cardiac death (SCD) in the setting of HCM, the presence of massive left ventricular hypertrophy predicts a higher incidence of ICD therapy than other traditional SCD risk factors. The presence of extensive myocardial fibrosis, however, identifies a subgroup of patients without massive left ventricular hypertrophy who have an equally high incidence of receiving appropriate device therapy. These findings suggest that the presence of extensive late gadolinium enhancement on cardiac magnetic resonance can be used as a risk modifier for traditional SCD risk factors in patients with HCM to better understand their overall risk of ventricular arrhythmias.
Published: 2024
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2. Assessment of Positive Cardiac Remodeling in Hypertrophic Obstructive Cardiomyopathy Using an Artificial Intelligence–Based Electrocardiographic Platform in Patients Treated With Mavacamten

Author: Mustafa Suppah, MD, Kaitlin Roehl, PA-C, Kathryn Lew, APRN, NP, MSN, Reza Arsanjani, MD, Steven Lester, MD, Steve Ommen, MD, Jeffrey Geske, MD, Konstantinos C. Siontis, MD, Hartzell Schaff, MD, and Said Alsidawi, MD
Subjects: Computer applications to medicine. Medical informatics, R858-859.7
Published: 2024
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3. Association between echocardiographic velocity time integral ratio of mitral valve and left ventricular outflow tract and clinical outcomes post transcatheter edge-to-edge mitral valve repair

Author: Isabel G. Scalia, Juan M. Farina, Rachel Wraith, Lisa Brown, Mohammed Tiseer Abbas, Milagros Pereyra, Mohamed Allam, Ahmed K. Mahmoud, Moaz A. Kamel, Timothy Barry, F. David Fortuin, Steven J. Lester, John Sweeney, Kristen A. Sell-Dottin, Mohamad Alkhouli, David R. Holmes, Chieh-Ju Chao, Said Alsidawi, Chadi Ayoub, and Reza Arsanjani
Subjects: Mitral regurgitation, Transcatheter edge-to-edge repair, Prognosis, Velocity time integral, Science (General), Q1-390, Social sciences (General), H1-99
Abstract: Background: Residual mitral regurgitation (MR) is frequent after transcatheter edge-to-edge repair (TEER). There is controversy regarding the clinical impact of residual MR and its quantitative assessment by transthoracic echocardiography (TTE), which is often challenging with multiple eccentric jets and artifact from the clip. The utility of the velocity time integral (VTI) ratio between the mitral valve (MV) and left ventricular outflow tract (LVOT), (VTIMV/LVOT), a simple Doppler measurement that increases with MR, has not been assessed post TEER. Methods: Baseline characteristics, clinical outcomes, and TTE data from patients who underwent TEER between 2014 and 2021 across three academic centers were retrospectively analyzed. Post-procedure TTEs were evaluated for VTIMV/LVOT in the first three months after TEER. One-year outcomes including all-cause and cardiac mortality, major adverse cardiac events, and MV reintervention were compared between patients with high VTIMV/LVOT (≥2.5) and low (
Published: 2024
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4. Artificial intelligence-enabled electrocardiogram (AI-ECG) does not predict atrial fibrillation following patent foramen ovale closure

Author: Omar Baqal, Eiad A. Habib, Elfatih A. Hasabo, Francesca Galasso, Timothy Barry, Reza Arsanjani, John P. Sweeney, Peter Noseworthy, and F. David Fortuin
Subjects: Artificial intelligence, PFO closure, Atrial fibrillation, Machine learning, Electrocardiography, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract: Background: Atrial fibrillation (AF) is a known complication following patent foramen ovale (PFO) closure. AI-enabled ECG (AI-ECG) acquired during normal sinus rhythm has been shown to identify individuals with AF by noting high-risk ECG features invisible to the human eye. We sought to characterize the value of AI-ECG in predicting AF development following PFO closure and investigate key clinical and procedural characteristics possibly associated with post-procedural AF. Methods: We performed a retrospective analysis of patients who underwent PFO closure at our hospital from January 2011 to December 2022. We recorded the probability (%) of AF using the Mayo Clinic AI-ECG dashboard from pre- and post-procedure ECGs. The cut-off point of ≥ 11 %, which was found to optimally balance sensitivity and specificity in the original derivation paper (the Youden index) was used to label an AI-ECG “positive” for AF. Pre-procedural transesophageal echocardiography (TEE) and pre- and post-procedure transcranial doppler (TCD) data was also recorded. Results: Out of 93 patients, 49 (53 %) were male, mean age was 55 ± 15 years with mean post-procedure follow up of 29 ± 3 months. Indication for PFO closure in 69 (74 %) patients was for secondary prevention of transient ischemic attack (TIA) and/or stroke. Twenty patients (22 %) developed paroxysmal AF post-procedure, with the majority within the first month post-procedure (15 patients, 75 %). Patients who developed AF were not significantly more likely to have a positive post-procedure AI-ECG than those who did not develop AF (30 % AF vs 27 % no AF, p = 0.8).Based on the PFO-Associated Stroke Causal Likelihood (PASCAL) classification, patients who had PFO closure for secondary prevention of TIA and/or stroke in the “possible” group were significantly more likely to develop AF than patients in “probable” and “unlikely” groups (p = 0.034). AF-developing patients were more likely to have post-procedure implantable loop recorder (ILR) (55 % vs 9.6 %, p
Published: 2024
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5. Computed Tomography Evaluation of Coronary Atherosclerosis: The Road Travelled, and What Lies Ahead

Author: Chadi Ayoub, Isabel G. Scalia, Nandan S. Anavekar, Reza Arsanjani, Clinton E. Jokerst, Benjamin J. W. Chow, and Leonard Kritharides
Subjects: coronary artery disease, coronary computed tomography angiography, coronary artery calcium scoring, artificial intelligence, Medicine (General), R5-920
Abstract: Coronary CT angiography (CCTA) is now endorsed by all major cardiology guidelines for the investigation of chest pain and assessment for coronary artery disease (CAD) in appropriately selected patients. CAD is a leading cause of morbidity and mortality. There is extensive literature to support CCTA diagnostic and prognostic value both for stable and acute symptoms. It enables rapid and cost-effective rule-out of CAD, and permits quantification and characterization of coronary plaque and associated significance. In this comprehensive review, we detail the road traveled as CCTA evolved to include quantitative assessment of plaque stenosis and extent, characterization of plaque characteristics including high-risk features, functional assessment including fractional flow reserve-CT (FFR-CT), and CT perfusion techniques. The state of current guideline recommendations and clinical applications are reviewed, as well as future directions in the rapidly advancing field of CT technology, including photon counting and applications of artificial intelligence (AI).
Published: 2024
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6. The Role of Cardiovascular Imaging in the Diagnosis of Athlete’s Heart: Navigating the Shades of Grey

Author: Nima Baba Ali, Sogol Attaripour Esfahani, Isabel G. Scalia, Juan M. Farina, Milagros Pereyra, Timothy Barry, Steven J. Lester, Said Alsidawi, David E. Steidley, Chadi Ayoub, Stefano Palermi, and Reza Arsanjani
Subjects: athlete’s heart, cardiac computed tomography, cardiac magnetic resonance, cardiomyopathies, echocardiography, strain, Photography, TR1-1050, Computer applications to medicine. Medical informatics, R858-859.7, Electronic computers. Computer science, QA75.5-76.95
Abstract: Athlete’s heart (AH) represents the heart’s remarkable ability to adapt structurally and functionally to prolonged and intensive athletic training. Characterized by increased left ventricular (LV) wall thickness, enlarged cardiac chambers, and augmented cardiac mass, AH typically maintains or enhances systolic and diastolic functions. Despite the positive health implications, these adaptations can obscure the difference between benign physiological changes and early manifestations of cardiac pathologies such as dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), and arrhythmogenic cardiomyopathy (ACM). This article reviews the imaging characteristics of AH across various modalities, emphasizing echocardiography, cardiac magnetic resonance (CMR), and cardiac computed tomography as primary tools for evaluating cardiac function and distinguishing physiological adaptations from pathological conditions. The findings highlight the need for precise diagnostic criteria and advanced imaging techniques to ensure accurate differentiation, preventing misdiagnosis and its associated risks, such as sudden cardiac death (SCD). Understanding these adaptations and employing the appropriate imaging methods are crucial for athletes’ effective management and health optimization.
Published: 2024
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7. 'Unkinking' the 'Kink' Normalizes the Doppler Pattern

Author: Elaina A. Blickenstaff, Michael O’Shea, Timothy Barry, Reza Arsanjani, John P. Fasolino, Donald J. Hagler, Francois Marcotte, and David S. Majdalany
Subjects: coarctation of aorta, stent, echocardiography, Doppler pattern, Medicine (General), R5-920
Abstract: Coarctation of the aorta (CoA) comprises 5–7% of congenital heart disease and can present as an isolated narrowing in the aortic arch just distal to the left subclavian artery or can be associated with cardiac abnormalities such as a bicuspid aortic valve, aortopathy, or ventricular septal defects. With the advances in the medical field, intervention on CoA can either be via surgical repair or endovascular stenting. Echocardiography is the mainstay in diagnosing CoA, with tomographic imaging such as magnetic resonance imaging (MRI) or computed tomography providing supplementary assessment of the aorta, valves, and collateral vessels. We present a case of a young hypertensive male who was noted to have a continuous cardiac murmur with diagnostic Doppler pattern of CoA on echocardiography that normalized soon after percutaneous stenting.
Published: 2024
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8. Topological Data Analysis Identified Prognostically-Distinct Phenotypes in Transcatheter Edge-to-Edge Repair Patients

Author: Chieh-Ju Chao, MD, Timothy Barry, MB, BCh, BAO, Amith Seri, MBBS, Ahmed El Shaer, MBBS, Nadia Chavez Ponce, MD, Soham Chakraborty, BS, Sean Smith, MD, Mohamad Alkhouli, MD, Jeremy Thaden, MD, David Fortuin, MD, John P. Sweeney, MD, Mackram Eleid, MD, Charanjit S. Rihal, MD, David R. Holmes, MD, Peter M. Pollak, MD, Abdallah El Sabbagh, MD, Steven J. Lester, MD, Jae K. Oh, MD, Win-Kuang Shen, MD, Imon Banerjee, PhD, and Reza Arsanjani, MD
Subjects: Computer applications to medicine. Medical informatics, R858-859.7
Abstract: Objective: To identify prognostically distinct phenotype clusters in transcatheter edge-to-edge repair (TEER) patients based on topological data analysis (TDA), which was never used to assess the heterogeneous TEER population. Patients and Methods: Patients who underwent TEER (June 2014-September 2020) at Mayo Clinic sites were identified from the institutional database. 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 of each cluster. Result: A total of 389 consecutive patients were included in the final analysis, and 2 major clusters were identified. The mean age was 80.3 ± 8.7 years; 256 (65.8%) were male. Fifty-five (14.5%) patients died during the mean follow-up duration (185 days). Kaplan-Meier analysis showed significant survival differences among the 2 clusters (hazard ratio, 2.70; 95% CI, 1.50-4.87; P=.0005). Cluster 1 (n=227) had moderate or increased residual mitral regurgitation (cluster 0 vs cluster 1: 19.4% vs 41.9%; P
Published: 2023
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9. Multi-Modality Imaging in Vasculitis

Author: Mohamed N. Allam, Nima Baba Ali, Ahmed K. Mahmoud, Isabel G. Scalia, Juan M. Farina, Mohammed Tiseer Abbas, Milagros Pereyra, Moaz A. Kamel, Kamal A. Awad, Yuxiang Wang, Timothy Barry, Steve S. Huang, Ba D. Nguyen, Ming Yang, Clinton E. Jokerst, Felipe Martinez, Chadi Ayoub, and Reza Arsanjani
Subjects: vasculitis, giant cell arteritis, Takayasu’s arteritis, polyarteritis nodosa, Kawasaki disease, granulomatosis with polyangiitis, Medicine (General), R5-920
Abstract: 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.
Published: 2024
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10. How Artificial Intelligence Can Enhance the Diagnosis of Cardiac Amyloidosis: A Review of Recent Advances and Challenges

Author: Moaz A. Kamel, Mohammed Tiseer Abbas, Christopher N. Kanaan, Kamal A. Awad, Nima Baba Ali, Isabel G. Scalia, Juan M. Farina, Milagros Pereyra, Ahmed K. Mahmoud, D. Eric Steidley, Julie L. Rosenthal, Chadi Ayoub, and Reza Arsanjani
Subjects: cardiac amyloidosis, artificial intelligence, deep-learning, convolutional neural networks, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract: 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.
Published: 2024
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11. Role of Genetics in Diagnosis and Management of Hypertrophic Cardiomyopathy: A Glimpse into the Future

Author: Mohammed Tiseer Abbas, Nima Baba Ali, Juan M. Farina, Ahmed K. Mahmoud, Milagros Pereyra, Isabel G. Scalia, Moaz A. Kamel, Timothy Barry, Steven J. Lester, Charles R. Cannan, Rohit Mital, Susan Wilansky, William K. Freeman, Chieh-Ju Chao, Said Alsidawi, Chadi Ayoub, and Reza Arsanjani
Subjects: hypertrophic cardiomyopathy, sarcomeric genes, next-generation sequencing, whole exome sequencing, phenocopies, cascade testing, Biology (General), QH301-705.5
Abstract: 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.
Published: 2024
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12. Cardiac Allograft Vasculopathy: Challenges and Advances in Invasive and Non-Invasive Diagnostic Modalities

Author: Moaz A. Kamel, Isabel G. Scalia, Amro T. Badr, Nima Baba Ali, Juan M. Farina, Milagros Pereyra, Mohammed Tiseer Abbas, Ahmed K. Mahmoud, Robert L. Scott, David E. Steidley, Julie L. Rosenthal, Lisa M. Lemond, Kristen A. Sell-Dottin, Brian W. Hardaway, Timothy Barry, Ming Yang, Chieh-Ju Chao, Clinton E. Jokerst, Chadi Ayoub, and Reza Arsanjani
Subjects: cardiac allograft vasculopathy, angiography, echocardiography, nuclear imaging, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract: 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.
Published: 2024
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13. Identifying the Causes of Unexplained Dyspnea at High Altitude Using Normobaric Hypoxia with Echocardiography

Author: Jan Stepanek, Juan M. Farina, Ahmed K. Mahmoud, Chieh-Ju Chao, Said Alsidawi, Chadi Ayoub, Timothy Barry, Milagros Pereyra, Isabel G. Scalia, Mohammed Tiseer Abbas, Rachel E. Wraith, Lisa S. Brown, Michael S. Radavich, Pamela J. Curtisi, Patricia C. Hartzendorf, Elizabeth M. Lasota, Kyley N. Umetsu, Jill M. Peterson, Kristin E. Karlson, Karen Breznak, David F. Fortuin, Steven J. Lester, and Reza Arsanjani
Subjects: echocardiography, stress test, high altitude, dyspnea, hypoxic simulation testing, Photography, TR1-1050, Computer applications to medicine. Medical informatics, R858-859.7, Electronic computers. Computer science, QA75.5-76.95
Abstract: 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.
Published: 2024
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14. Aortic Valve Calcium Score by Computed Tomography as an Adjunct to Echocardiographic Assessment—A Review of Clinical Utility and Applications

Author: Isabel G. Scalia, Juan M. Farina, Ratnasari Padang, Clinton E. Jokerst, Milagros Pereyra, Ahmed K. Mahmoud, Tasneem Z. Naqvi, Chieh-Ju Chao, Jae K. Oh, Reza Arsanjani, and Chadi Ayoub
Subjects: aortic stenosis, aortic valve calcium score, computed tomography, Photography, TR1-1050, Computer applications to medicine. Medical informatics, R858-859.7, Electronic computers. Computer science, QA75.5-76.95
Abstract: Aortic valve stenosis (AS) is increasing in prevalence due to the aging population, and severe AS is associated with significant morbidity and mortality. Echocardiography remains the mainstay for the initial detection and diagnosis of AS, as well as for grading of severity. However, there are important subgroups of patients, for example, patients with low-flow low-gradient or paradoxical low-gradient AS, where quantification of severity of AS is challenging by echocardiography and underestimation of severity may delay appropriate management and impart a worse prognosis. Aortic valve calcium score by computed tomography has emerged as a useful clinical diagnostic test that is complimentary to echocardiography, particularly in cases where there may be conflicting data or clinical uncertainty about the degree of AS. In these situations, aortic valve calcium scoring may help re-stratify grading of severity and, therefore, further direct clinical management. This review presents the evolution of aortic valve calcium score by computed tomography, its diagnostic and prognostic value, as well as its utility in clinical care.
Published: 2023
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15. Artificial Intelligence-Based Prediction of Cardiovascular Diseases from Chest Radiography

Author: Juan M. Farina, Milagros Pereyra, Ahmed K. Mahmoud, Isabel G. Scalia, Mohammed Tiseer Abbas, Chieh-Ju Chao, Timothy Barry, Chadi Ayoub, Imon Banerjee, and Reza Arsanjani
Subjects: artificial intelligence, chest radiography, cardiovascular diseases, deep learning, Photography, TR1-1050, Computer applications to medicine. Medical informatics, R858-859.7, Electronic computers. Computer science, QA75.5-76.95
Abstract: Chest radiography (CXR) is the most frequently performed radiological test worldwide because of its wide availability, non-invasive nature, and low cost. The ability of CXR to diagnose cardiovascular diseases, give insight into cardiac function, and predict cardiovascular events is often underutilized, not clearly understood, and affected by inter- and intra-observer variability. Therefore, more sophisticated tests are generally needed to assess cardiovascular diseases. Considering the sustained increase in the incidence of cardiovascular diseases, it is critical to find accessible, fast, and reproducible tests to help diagnose these frequent conditions. The expanded focus on the application of artificial intelligence (AI) with respect to diagnostic cardiovascular imaging has also been applied to CXR, with several publications suggesting that AI models can be trained to detect cardiovascular conditions by identifying features in the CXR. Multiple models have been developed to predict mortality, cardiovascular morphology and function, coronary artery disease, valvular heart diseases, aortic diseases, arrhythmias, pulmonary hypertension, and heart failure. The available evidence demonstrates that the use of AI-based tools applied to CXR for the diagnosis of cardiovascular conditions and prognostication has the potential to transform clinical care. AI-analyzed CXRs could be utilized in the future as a complimentary, easy-to-apply technology to improve diagnosis and risk stratification for cardiovascular diseases. Such advances will likely help better target more advanced investigations, which may reduce the burden of testing in some cases, as well as better identify higher-risk patients who would benefit from earlier, dedicated, and comprehensive cardiovascular evaluation.
Published: 2023
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16. Current Management and Future Perspectives in the Treatment of Lp(a) with a Focus on the Prevention of Cardiovascular Diseases

Author: Juan M. Farina, Milagros Pereyra, Ahmed K. Mahmoud, Chieh-Ju Chao, Timothy Barry, Susan M. Halli Demeter, Chadi Ayoub, and Reza Arsanjani
Subjects: lipoprotein(a), cardiovascular diseases, treatment, RNA interference, Medicine, Pharmacy and materia medica, RS1-441
Abstract: 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.
Published: 2023
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17. Transcatheter Aortic Valve Replacement Prognostication with Augmented Mean Arterial Pressure

Author: Chieh-Ju Chao, Pradyumna Agasthi, Amith R. Seri, Timothy Barry, Anusha Shanbhag, Yuxiang Wang, Mackram F. Eleid, David Fortuin, John P. Sweeney, Peter Pollak, Abdallah El Sabbagh, Steven J. Lester, William K. Freeman, Tasneem Z. Naqvi, David R. Holmes, Christopher P. Appleton, and Reza Arsanjani
Subjects: aortic valve stenosis, transcatheter aortic valve replacement (TAVR), STS risk score, augmented mean arterial pressure, mortality, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract: 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.
Published: 2023
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18. Natural history and clinical significance of isolated complete left bundle branch block without associated structural heart disease

Author: Hasan Ashraf, Pradyumna Agasthi, Robert J. Siegel, Sai Harika Pujari, Mohamed Allam, Win Kuang Shen, Komandoor Srivathsan, Dan Sorajja, Hicham El Masry, William K. Freeman, Farouk Mookadam, Siva Mulpuru, and Reza Arsanjani
Subjects: Diseases of the circulatory (Cardiovascular) system, RC666-701
Published: 2021
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19. Cardiopulmonary Outcomes After the Nuss Procedure in Pectus Excavatum

Author: Dawn E. Jaroszewski, Juan M. Farina, Michael B. Gotway, Joshua D. Stearns, Michelle A. Peterson, Venkata S. K. K. Pulivarthi, Peter Bostoros, Ahmad S. Abdelrazek, Ashwini Gotimukul, David S. Majdalany, Courtney M. Wheatley‐Guy, and Reza Arsanjani
Subjects: cardiothoracic surgery, echocardiography, exercise testing, pectus excavatum, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract: Background Pectus excavatum is the most common chest wall deformity. There is still controversy about cardiopulmonary limitations of this disease and benefits of surgical repair. This study evaluates the impact of pectus excavatum on the cardiopulmonary function of adult patients before and after a modified minimally invasive repair. Methods and Results In this retrospective cohort study, an electronic database was used to identify consecutive adult (aged ≥18 years) patients who underwent cardiopulmonary exercise testing before and after primary pectus excavatum repair at Mayo Clinic Arizona from 2011 to 2020. In total, 392 patients underwent preoperative cardiopulmonary exercise testing; abnormal oxygen consumption results were present in 68% of patients. Among them, 130 patients (68% men, mean age, 32.4±10.0 years) had post‐repair evaluations. Post‐repair tests were performed immediately before bar removal with a mean time between repair and post‐repair testing of 3.4±0.7 years (range, 2.5–7.0). A significant improvement in cardiopulmonary outcomes (P
Published: 2022
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20. Safety and efficacy of direct oral anticoagulants compared to Vitamin K antagonists postpercutaneous coronary interventions in patients with atrial fibrillation: A systematic review and meta‐analysis

Author: Pradyumna Agasthi, Justin Z. Lee, Sai Harika Pujari, Andrew S. Tseng, Justin Shipman, Diana Almader‐Douglas, Hasan Ashraf, Farouk Mookadam, Floyd David Fortuin, Nirat Beohar, Reza Arsanjani, and Siva Mulpuru
Subjects: atrial fibrillation, direct oral anticoagulants, percutaneous coronary intervention, vitamin K antagonists, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract: Abstract Background Atrial fibrillation (AF) and coronary artery disease (CAD) are commonly associated. Cotreatment with multiple antithrombotic agents can increase the risk of bleeding. We sought to evaluate patient‐centered outcomes in patients with AF on double therapy with direct oral anticoagulants (DOACs) compared to patients with standard triple therapy, [a vitamin K antagonist (VKA) plus dual antiplatelet therapy]. Methods We performed a literature search of randomized controlled trials (RCTs) reporting outcomes of patients receiving double therapy with DOACs compared to triple therapy with VKAs in patients with AF undergoing percutaneous coronary intervention (PCI). Patient‐centered outcomes were the International Society of Thrombosis and Hemostasis (ISTH) major or clinically relevant nonmajor bleeding (CRNB), all‐cause mortality, major adverse cardiovascular events (MACE), stent thrombosis, myocardial infarction, and stroke. Results Four RCTs (9602 patients) met our inclusion criteria. Compared to VKAs, DOACs were associated with significantly lower ISTH major bleeding/ CRNB (RR: 0.75, 95% CI: 0.67‐0.82, P
Published: 2020
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21. Developing an Echocardiography-Based, Automatic Deep Learning Framework for the Differentiation of Increased Left Ventricular Wall Thickness Etiologies

Author: James Li, Chieh-Ju Chao, Jiwoong Jason Jeong, Juan Maria Farina, Amith R. Seri, Timothy Barry, Hana Newman, Megan Campany, Merna Abdou, Michael O’Shea, Sean Smith, Bishoy Abraham, Seyedeh Maryam Hosseini, Yuxiang Wang, Steven Lester, Said Alsidawi, Susan Wilansky, Eric Steidley, Julie Rosenthal, Chadi Ayoub, Christopher P. Appleton, Win-Kuang Shen, Martha Grogan, Garvan C. Kane, Jae K. Oh, Bhavik N. Patel, Reza Arsanjani, and Imon Banerjee
Subjects: deep learning, LV wall thickness, echocardiography, Photography, TR1-1050, Computer applications to medicine. Medical informatics, R858-859.7, Electronic computers. Computer science, QA75.5-76.95
Abstract: 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.
Published: 2023
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22. The Role of Artificial Intelligence in Echocardiography

Author: Timothy Barry, Juan Maria Farina, Chieh-Ju Chao, Chadi Ayoub, Jiwoong Jeong, Bhavik N. Patel, Imon Banerjee, and Reza Arsanjani
Subjects: echocardiography, machine learning, artificial intelligence, Photography, TR1-1050, Computer applications to medicine. Medical informatics, R858-859.7, Electronic computers. Computer science, QA75.5-76.95
Abstract: 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.
Published: 2023
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23. Impact of Persistent Iatrogenic Atrial Septal Defect following MitraClip

Author: Chieh-Ju Chao, Amith Seri, Bishoy Abraham, Juan M. Farina, Evelyn Fennelly, Megan Campany, Milagros Pereyra, Ebram F. Said, Courtney Kenyon, Ayman R. Fath, Sean Smith, Skye Buckner-Petty, Corbin A. Rayfield, David Fortuin, John P. Sweeney, Eric H. Yang, Chadi Ayoub, Mackram F. Eleid, Mohamad Alkhouli, Charanjit S. Rihal, David R. Holmes, Peter M. Pollak, Abdallah El Sabbagh, Jae K. Oh, and Reza Arsanjani
Subjects: mitral valve insufficiency, endovascular procedures, echocardiography, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract: 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.
Published: 2022
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24. Near complete resolution of nonbacterial thrombotic endocarditis in a patient with antiphospholipid antibody syndrome

Author: Justin Shipman, Pradyumna Agasthi, David Majdalany, Farouk Mookadam, and Reza Arsanjani
Subjects: echocardiography, nonbacterial thrombotic endocarditis, antiphospholipid antibody syndrome, Diseases of the circulatory (Cardiovascular) system, RC666-701
Published: 2020
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25. Echocardiography in Cardiac Assist Devices

Author: Senthil Anand, Timothy Barry, Reza Arsanjani, and Lisa LeMond
Subjects: heart failure, echocardiography, cardiac assist devices, mechanical circulatory support, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract: 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.
Published: 2022
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26. Clinical implications of inducible left ventricular outflow tract obstruction among patients undergoing liver transplant evaluation

Author: Antoine Addoumieh, Mouin S. Abdallah, Jad A. Ballout, Lucy Thuita, Allan Klein, Wael A. Jaber, Reza Arsanjani, William Carey, and David Majdalany
Subjects: End stage liver disease, Dobutamine stress echocardiography, Left ventricular outflow tract obstruction, Liver transplant evaluation, Cirrhosis of the liver, Echocardiography, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract: Introduction: Patients with end stage liver disease (ESLD) have a hyperdynamic state due to decreased systemic vascular resistance and increased cardiac output. Preoperative evaluation with dobutamine stress echocardiography (DSE) is used to risk-stratify patients prior to liver transplant. We sought to identify the impact of inducible left ventricular outflow tract obstruction (LVOTO) on DSE on post-operative liver transplant outcomes. Methods: Patients with ESLD who underwent liver transplant at Cleveland Clinic between January 2007 and August 2016 were identified. Pre-operative DSE data, and post-operative intensive care unit (ICU) data were extracted. Patients with inducible LVOTO were compared to those without LVOTO. Results: Of the 515 patients identified who underwent DSE prior to liver transplant, 165 (30%) were female, and 95 (18%) had LVOTO. There were no major differences in baseline characteristics between the two groups. In the LVOTO group, rest gradients were 10.8 ± 3 mm Hg while peak gradients were 90 ± 48.2 mm Hg. No significant differences in ICU length of stay or duration of mechanical ventilation between both groups were noted. There were 21 deaths at 30 days. There were 2 (2.1%) deaths in the LVOTO group, versus 19 (4.5%) deaths in the non LVOTO group (p = 0.28). Higher Model for End Stage Liver Disease (MELD) scores predicted longer duration of mechanical ventilation and ICU length of stay. Conclusion: Inducible LVOTO on DSE does not adversely affect the short-term outcomes post liver transplant. Presence of inducible LVOTO should not be the mere reason to deny liver transplant among patients with ESLD.
Published: 2021
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27. Deep Neural Network for Cardiac Magnetic Resonance Image Segmentation

Author: David Chen, Huzefa Bhopalwala, Nakeya Dewaswala, Shivaram P. Arunachalam, Moein Enayati, Nasibeh Zanjirani Farahani, Kalyan Pasupathy, Sravani Lokineni, J. Martijn Bos, Peter A. Noseworthy, Reza Arsanjani, Bradley J. Erickson, Jeffrey B. Geske, Michael J. Ackerman, Philip A. Araoz, and Adelaide M. Arruda-Olson
Subjects: cardiac magnetic resonance imaging, deep learning, image segmentation, hypertrophic cardiomyopathy, Photography, TR1-1050, Computer applications to medicine. Medical informatics, R858-859.7, Electronic computers. Computer science, QA75.5-76.95
Abstract: The analysis and interpretation of cardiac magnetic resonance (CMR) images are often time-consuming. The automated segmentation of cardiac structures can reduce the time required for image analysis. Spatial similarities between different CMR image types were leveraged to jointly segment multiple sequences using a segmentation model termed a multi-image type UNet (MI-UNet). This model was developed from 72 exams (46% female, mean age 63 ± 11 years) performed on patients with hypertrophic cardiomyopathy. The MI-UNet for steady-state free precession (SSFP) images achieved a superior Dice similarity coefficient (DSC) of 0.92 ± 0.06 compared to 0.87 ± 0.08 for a single-image type UNet (p < 0.001). The MI-UNet for late gadolinium enhancement (LGE) images also had a superior DSC of 0.86 ± 0.11 compared to 0.78 ± 0.11 for a single-image type UNet (p = 0.001). The difference across image types was most evident for the left ventricular myocardium in SSFP images and for both the left ventricular cavity and the left ventricular myocardium in LGE images. For the right ventricle, there were no differences in DCS when comparing the MI-UNet with single-image type UNets. The joint segmentation of multiple image types increases segmentation accuracy for CMR images of the left ventricle compared to single-image models. In clinical practice, the MI-UNet model may expedite the analysis and interpretation of CMR images of multiple types.
Published: 2022
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28. A Unique Compensatory Mechanism for Total Pulmonary Vein Occlusion Post Atrial Fibrillation Catheter Ablation Visualized by Multimodality Imaging

Author: Ayman R. Fath, Amro Aglan, Luis R. Scott, Clinton E. Jokerst, Hemalatha Narayanasamy, Farouk Mookadam, Nawfal Mihyawi, Nithin R. Venepally, Sudheer Konduru, and Reza Arsanjani
Subjects: Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract: Pulmonary vein (PV) stenosis is a rare and serious complication of radiofrequency catheter ablation (RFCA) for atrial fibrillation. However, it can be asymptomatic or mildly symptomatic depending on the severity of the stenosis and the development of compensatory mechanisms. This study provides a detailed description and visualization of a unique type of venous collaterals that bypass the PV stenosis and drain directly in the left atrium alleviating PV stenosis sequelae. This study reports a case of a 61-year-old male who presented with mild dyspnea and fatigue 3 years post atrial fibrillation RFCA. After a thorough evaluation of the case, a redo-ablation was planned. As a part of the preablation workup, a transesophageal echocardiography (TEE), a ventilation-perfusion (V/Q) scan of the lungs, and a chest computed tomography angiogram (CTA) were performed. The TEE revealed total obstruction of the left superior PV, with no color Doppler flow detected. It also showed evidence of multiple collateral flows at the os of the left superior PV. The V/Q scan showed a large perfusion defect involving the entire left upper lobe consistent with a compromised left upper PV flow. The CTA with 3D volume rendering revealed the total occlusion of the left superior PV at its ostium. Moreover, the scan confirmed the pulmonary venous drainage via small collateral channels that was suggested by the TEE.
Published: 2020
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29. Left ventricular outflow tract obstruction due to residual native valve following mitral valve replacement

Author: Justin Shipman, Pradyumna Agasthi, Patrick DeValeria, Farouk Mookadam, and Reza Arsanjani
Subjects: left ventricular outflow tract obstruction, mitral valve replacement, systolic anterior motion, Diseases of the circulatory (Cardiovascular) system, RC666-701
Published: 2020
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30. A Coronary Conundrum: Papillary Muscle Rupture and Ischemic Mitral Regurgitation Secondary to Coronary Thromboembolism in Antiphospholipid Syndrome

Author: Amar Shere MD, Pradyumna Agasthi MD, Farouk Mookadam MBBch, Sudheer Konduru MBBS, and Reza Arsanjani MD
Subjects: Medicine (General), R5-920, Pathology, RB1-214
Abstract: Antiphospholipid syndrome (APS) is an autoimmune disorder that has a strong propensity for a hypercoagulable state and is known to be associated with venous and arterial thromboembolism. We describe an uncommon case of APS in the setting of non-Hodgkin’s lymphoma, with thromboembolism, and a rare complication after an uncommon etiology of myocardial infarction. This case highlights the importance of early and appropriate type of anticoagulation to reduce the morbidity and mortality in patients with APS.
Published: 2019
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31. Are we there yet with patent foramen ovale closure for secondary prevention in cryptogenic stroke? A systematic review and meta-analysis of randomized trials

Author: Pradyumna Agasthi, Kantha Ratnam Kolla, Charan Yerasi, Sibghat Tullah, Venkata Siva Pulivarthi, Boshra Louka, Reza Arsanjani, Eric H Yang, Farouk Mookadam, and F David Fortuin
Subjects: Medicine (General), R5-920
Abstract: Background: We performed a meta-analysis to evaluate the benefit of patent foramen ovale closure in stroke prevention. Methods: We searched Medline/PubMed, EMBASE, Web of Science and Cochrane central database for randomized control trials assessing the incidence of recurrent stroke after patent foramen ovale closure when compared to medical therapy. Pooled odds ratio and 95% confidence intervals were calculated using a random effects model. The heterogeneity among studies was tested using the χ 2 test and inconsistency was quantified using the I 2 statistic. Results: Our search strategy yielded 71 articles. We included five studies with a total of 3440 patients. Median age in the device group was 45 (43, 5.5) years and in the medical group was 45 (44.5, 46) years; 52% were male, 27.7% of patients had an atrial septal aneurysm, 25% had hypertension, and 20.5% had diabetes mellitus. The median follow-up time was 44 (34.5–50) months. The pooled odds ratio of recurrent stroke, transient ischemic attack and composite end point of stroke + transient ischemic attack + peripheral embolism in the patent foramen ovale closure versus medical therapy group were 0.4 (95% confidence interval 0.25–0.63, I 2 = 57.5%), 0.93 (95% confidence interval 0.61–1.42, I 2 = 0%), and 0.6 (95% confidence interval 0.44–0.82, I 2 = 0%), respectively. The incidence of atrial fibrillation was found to be significantly higher in the patent foramen ovale closure group with odds ratio of 6 (95% confidence interval 3.13–11.4, I 2 = 33.5%). On subgroup analysis, patent foramen ovale closure appeared to benefit males and patients with a large shunt. Number needed to treat to prevent one recurrent stroke with patent foramen ovale closure is 42. Number needed to harm to cause one atrial fibrillation with patent foramen ovale closure is 39. Conclusion: This meta-analysis of randomized trials concludes that percutaneous patent foramen ovale closure is effective in recurrent stroke prevention especially in males and in those with a large shunt.
Published: 2019
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32. Improved Right Ventricular Diastolic Function Assessed by Hepatic Vein Flow After Pectus Excavatum Repair

Author: Juan M. Farina, Dawn E. Jaroszewski, Reza Arsanjani, Tasneem Z. Naqvi, Mohamed R. Aly, William K. Freeman, Carolyn M. Larsen, Francois Marcotte, Steven J. Lester, Hari P. Chaliki, Hemalatha Narayanasamy, Susan Wilansky, and Christopher P. Appleton
Published: 2023
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33. Prediction of permanent pacemaker implantation after transcatheter aortic valve replacement: The role of machine learning

Author: Pradyumna Agasthi, Hasan Ashraf, Sai Harika Pujari, Marlene Girardo, Andrew Tseng, Farouk Mookadam, Nithin Venepally, Matthew R Buras, Bishoy Abraham, Banveet K Khetarpal, Mohamed Allam, Siva K Mulpuru MD, Mackram F Eleid, Kevin L Greason, Nirat Beohar, John Sweeney, David Fortuin, David R Jr Holmes, and Reza Arsanjani
Subjects: Cardiology and Cardiovascular Medicine
Published: 2023
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34. Safety and efficacy of balloon angioplasty compared to stent-based-strategies with pulmonary vein stenosis: A systematic review and meta-analysis

Author: Pradyumna Agasthi, Srilekha Sridhara, Pattara Rattanawong, Nithin Venepally, Chieh-Ju Chao, Hasan Ashraf, Sai Harika Pujari, Mohamed Allam, Diana Almader-Douglas, Yamini Alla, Amit Kumar, Farouk Mookadam, Douglas L Packer, David R Holmes Jr, Donald J Hagler, Floyd David Fortuin, and Reza Arsanjani
Subjects: Cardiology and Cardiovascular Medicine
Published: 2023
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35. Non-invasive detection of cardiac allograft rejection among heart transplant recipients using an electrocardiogram based deep learning model

Author: Demilade Adedinsewo, Heather D Hardway, Andrea Carolina Morales-Lara, Mikolaj A Wieczorek, Patrick W Johnson, Erika J Douglass, Bryan J Dangott, Raouf E Nakhleh, Tathagat Narula, Parag C Patel, Rohan M Goswami, Melissa A Lyle, Alexander J Heckman, Juan C Leoni-Moreno, D Eric Steidley, Reza Arsanjani, Brian Hardaway, Mohsin Abbas, Atta Behfar, Zachi I Attia, Francisco Lopez-Jimenez, Peter A Noseworthy, Paul Friedman, Rickey E Carter, and Mohamad Yamani
Subjects: General Engineering, General Earth and Planetary Sciences, General Environmental Science
Abstract: Aims Current non-invasive screening methods for cardiac allograft rejection have shown limited discrimination and are yet to be broadly integrated into heart transplant care. Given electrocardiogram (ECG) changes have been reported with severe cardiac allograft rejection, this study aimed to develop a deep-learning model, a form of artificial intelligence, to detect allograft rejection using the 12-lead ECG (AI-ECG). Methods and results Heart transplant recipients were identified across three Mayo Clinic sites between 1998 and 2021. Twelve-lead digital ECG data and endomyocardial biopsy results were extracted from medical records. Allograft rejection was defined as moderate or severe acute cellular rejection (ACR) based on International Society for Heart and Lung Transplantation guidelines. The extracted data (7590 unique ECG-biopsy pairs, belonging to 1427 patients) was partitioned into training (80%), validation (10%), and test sets (10%) such that each patient was included in only one partition. Model performance metrics were based on the test set (n = 140 patients; 758 ECG-biopsy pairs). The AI-ECG detected ACR with an area under the receiver operating curve (AUC) of 0.84 [95% confidence interval (CI): 0.78–0.90] and 95% (19/20; 95% CI: 75–100%) sensitivity. A prospective proof-of-concept screening study (n = 56; 97 ECG-biopsy pairs) showed the AI-ECG detected ACR with AUC = 0.78 (95% CI: 0.61–0.96) and 100% (2/2; 95% CI: 16–100%) sensitivity. Conclusion An AI-ECG model is effective for detection of moderate-to-severe ACR in heart transplant recipients. Our findings could improve transplant care by providing a rapid, non-invasive, and potentially remote screening option for cardiac allograft function.
Published: 2023
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36. Impact of statins based on high-risk plaque features on coronary plaque progression in mild stenosis lesions: results from the PARADIGM study

Author: Hyung-Bok Park, Reza Arsanjani, Ji Min Sung, Ran Heo, Byoung Kwon Lee, Fay Y Lin, Martin Hadamitzky, Yong-Jin Kim, Edoardo Conte, Daniele Andreini, Gianluca Pontone, Matthew J Budoff, Ilan Gottlieb, Eun Ju Chun, Filippo Cademartiri, Erica Maffei, Hugo Marques, Pedro de Araújo Gonçalves, Jonathon A Leipsic, Sang-Eun Lee, Sanghoon Shin, Jung Hyun Choi, Renu Virmani, Habib Samady, Kavitha Chinnaiyan, Peter H Stone, Daniel S Berman, Jagat Narula, Leslee J Shaw, Jeroen J Bax, James K Min, and Hyuk-Jae Chang
Subjects: Radiology, Nuclear Medicine and imaging, General Medicine, Cardiology and Cardiovascular Medicine
Abstract: Aims To investigate the impact of statins on plaque progression according to high-risk coronary atherosclerotic plaque (HRP) features and to identify predictive factors for rapid plaque progression in mild coronary artery disease (CAD) using serial coronary computed tomography angiography (CCTA). Methods and results We analyzed mild stenosis (25–49%) CAD, totaling 1432 lesions from 613 patients (mean age, 62.2 years, 63.9% male) and who underwent serial CCTA at a ≥2 year inter-scan interval using the Progression of AtheRosclerotic PlAque DetermIned by Computed TomoGraphic Angiography Imaging (NCT02803411) registry. The median inter-scan period was 3.5 ± 1.4 years; plaques were quantitatively assessed for annualized percent atheroma volume (PAV) and compositional plaque volume changes according to HRP features, and the rapid plaque progression was defined by the ≥90th percentile annual PAV. In mild stenotic lesions with ≥2 HRPs, statin therapy showed a 37% reduction in annual PAV (0.97 ± 2.02 vs. 1.55 ± 2.22, P = 0.038) with decreased necrotic core volume and increased dense calcium volume compared to non-statin recipient mild lesions. The key factors for rapid plaque progression were ≥2 HRPs [hazard ratio (HR), 1.89; 95% confidence interval (CI), 1.02–3.49; P = 0.042], current smoking (HR, 1.69; 95% CI 1.09–2.57; P = 0.017), and diabetes (HR, 1.55; 95% CI, 1.07–2.22; P = 0.020). Conclusion In mild CAD, statin treatment reduced plaque progression, particularly in lesions with a higher number of HRP features, which was also a strong predictor of rapid plaque progression. Therefore, aggressive statin therapy might be needed even in mild CAD with higher HRPs. Clinical trial registration ClinicalTrials.gov NCT02803411
Published: 2023
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37. The impact of moderate aortic stenosis in acute myocardial infarction: A multicenter retrospective study

Author: Bishoy Abraham, Juan M. Farina, Ayman Fath, Merna Abdou, Mostafa Elbanna, Mustafa Suppah, Mohamed Sleem, Abdullah Eldaly, Mohamed Aly, Michael Megaly, Pradyumna Agasthi, Chieh‐Ju Chao, David Fortuin, Said Alsidawi, Chadi Ayoub, Mohamad Alkhouli, Abdallah El Sabbagh, David Holmes, Emmanouil S. Brilakis, and Reza Arsanjani
Subjects: Radiology, Nuclear Medicine and imaging, General Medicine, Cardiology and Cardiovascular Medicine
Published: 2023
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38. Quantitative analysis of myocardial perfusion images.

Author: Piotr J. Slomka, Reza Arsanjani, Yuan Xu, Daniel S. Berman, and Guido Germano 0002
Published: 2012

39. Global and Regional Variations in Transthyretin Cardiac Amyloidosis: A Comparison of Longitudinal Strain and 99mTc-Pyrophosphate Imaging

Author: Christopher Lee, Chieh-Ju Chao, Pradyumna Agasthi, Amith R. Seri, Amar Shere, Lanyu Mi, Lisa Brown, Chance Marostica, Timothy Barry, Ming Yang, Julie Rosenthal, Samuel Unzek, Farouk Mookadam, and Reza Arsanjani
Subjects: Radiological and Ultrasound Technology, Radiology, Nuclear Medicine and imaging, General Medicine
Published: 2021
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40. Successful Treatment of Steroid-Refractory Checkpoint Inhibitor Myocarditis with Globulin Derived-Therapy: A Case Report and Literature Review

Author: Dawn M. Pedrotty, Ming Yang, Farouk Mookadam, Clinton Jokerst, Carolyn M. Larsen, Pradyumna Agasthi, Timothy Barry, Rory Gallen, Lisa LeMond, Catherine M. Freeman, Reza Arsanjani, and Brian W. Hardaway
Subjects: Male, Myocarditis, Fulminant, 030204 cardiovascular system & hematology, 03 medical and health sciences, 0302 clinical medicine, medicine, Humans, 030212 general & internal medicine, Adverse effect, Immune Checkpoint Inhibitors, Aged, 80 and over, Cardiotoxicity, biology, Thymoglobulin, business.industry, Cancer, Globulins, General Medicine, medicine.disease, Treatment Outcome, Adjunctive treatment, Immunology, biology.protein, Steroids, Antibody, business
Abstract: Immune checkpoint inhibitor (ICI) monoclonal antibody drugs are an important interface of immunology and cancer biology with the intended goal to create cancer specific treatments with less systemic toxicity. Recognition of immune-related adverse events is critical and these include significant cardiovascular toxicity and myocarditis. Compared with other immune-related events, ICI associated myocarditis is rare but is associated with high mortality. The majority of cases present early in the course of therapy and patients can rapidly progress to fulminant myocarditis. Initially, the mainstay of treatment in patients with ICI-associated myocarditis is immunosuppressive therapy with glucocorticoids. For those who do not respond to steroids, the optimal treatment is unclear. This review summarizes the potential adjunctive treatment options for patients with steroid-refractory myocarditis by illustrating a case of myocarditis that was treated with Thymoglobulin and immunoglobulin.
Published: 2021
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41. Echocardiography-Based Deep Learning Model to Differentiate Constrictive Pericarditis and Restrictive Cardiomyopathy

Author: Chieh-Ju Chao, Jiwoong Jeong, Reza Arsanjani, Kihong Kim, Chadi Ayoub, Martha Grogan, Garvan Kane, Imon Banerjee, and Jae K Oh
Abstract: BackgroundConstrictive pericarditis (CP) is an uncommon but reversible cause of diastolic heart failure if appropriately identified and treated. Although echocardiography can detect CP based on characteristic cardiac motion and Doppler findings, its diagnosis remains a challenge for clinicians. Artificial intelligence (AI) may enhance identification of CP. We proposed a deep learning approach based on transthoracic echocardiography (TTE) to differentiate CP from restrictive cardiomyopathy (RCM).MethodsPatients with a confirmed diagnosis of CP and cardiac amyloidosis (CA, as the representative disease of RCM) at Mayo Clinic Rochester from 1/2003-12/2021 were identified to extract baseline demographics and the apical 4 chamber (A4C) view from TTE studies. The cases were split into a 60:20:20 ratio for training, validation, and held-out test sets of the ResNet50 deep learning model. The model performance (differentiating CP and CA) was evaluated in the test set with the area under the curve (AUC). GradCAM was used for model interpretation.ResultsA total of 381 patients were identified, including 184 (48.3%) CP, and 197 (51.7%) CA cases. The mean age was 68.7±11.4, and 72.8% were male. ResNet50 had a performance with an AUC to differentiate the 2-class classification task (CP vs. CA, AUC 0.97). The GradCAM heatmap showed activation around the ventricular septal area.ConclusionWith a standard A4C view, our AI model provides a platform for the early and accurate detection of CP, allowing for improved workflow efficiency and prompt referral for more advanced evaluation and intervention of CP.
Published: 2022
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42. Gastroenteropancreatic Neuroendocrine Tumor Metastasis to the Heart: Evaluation of Imaging Manifestations

Author: Yuxiang Wang, Chadi Ayoub, Aaron F. Yang, Mohamad B. Sonbol, Richard Butterfield, Thorvardur R. Halfdanarson, Reza Arsanjani, Wuqiang Zhu, and Ming Yang
Subjects: Radiology, Nuclear Medicine and imaging
Abstract: Neuroendocrine tumors (NET) may affect the heart by cardiac metastasis or carcinoid heart disease. NET metastasis to the heart is rare, with limited data characterizing it. We sought to evaluate
Published: 2022
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43. Initial Invasive or Conservative Strategy for Stable Coronary Disease

Author: Maron D. J., Hochman J. S., Reynolds H. R., Bangalore S., O'Brien S. M., Boden W. E., Chaitman B. R., Senior R., Lopez-Sendon J., Alexander K. P., Lopes R. D., Shaw L. J., Berger J. S., Newman J. D., Sidhu M. S., Goodman S. G., Ruzyllo W., Gosselin G., Maggioni A. P., White H. D., Bhargava B., Min J. K., John Mancini G. B., Berman D. S., Picard M. H., Kwong R. Y., Ali Z. A., Mark D. B., Spertus J. A., Krishnan M. N., Elghamaz A., Moorthy N., Hueb W. A., Demkow M., Mavromatis K., Bockeria O., Peteiro J., Miller T. D., Szwed H., Doerr R., Keltai M., Selvanayagam J. B., Gabriel Steg P., Held C., Kohsaka S., Mavromichalis S., Kirby R., Jeffries N. O., Harrell F. E., Rockhold F. W., Broderick S., Bruce Ferguson T., Williams D. O., Harrington R. A., Stone G. W., Rosenberg Y, ISCHEMIA Research Group: Joseph Ricci, A Tello Montoliu, A I Robero Aniorte, Abbey Mulder, Abhay A Laddu, Abhinav Goyal, Abhishek Dubey, Abhishek Goyal, Abigail Knighton, Abraham Oomman, Adam J Jaskowiak, Adam Kolodziej, Adam Witkowski, Adnan Hameed, Adriana Anesini, Afshan Hussain, Agne Juceviciene, Agne Urboniene, Agnes Jakal, Agnieszka Szramowska, Ahmad Khairuddin, Ahmed Abdel-Latif, Ahmed Adel, Ahmed Aljzeeri, Ahmed Kamal, Ahmed Talaat, Aimee Mann, Aira Contreras, Ajit Kumar, V K Kumar, Akemi Furukawa, Akshay Bagai, Akvile Smigelskaite, Alain Furber, Alain Rheault, Alaine Melanie Loehr, Alan Rosen, Albert Varga, Albertina Qelaj, Alberto Barioli, Aldo Russo, Alec Moorman, Alejandro Gisbert, Aleksandra Fratczak, Aleksandras Laucevicius, Alena Kuleshova, Alessandro Sionis, Alexander A Sirker, Alexander M Chernyavskiy, Alexandra Craft, Alexandra Vazquez, Alexandre Ciappina Hueb, Alexandre S Colafranseschi, Alexandre Schaan de Quadros, Alexandre Tognon, Ali Alghamdi, Alice Manica Muller, Aline Nogueira Rabaça, Aline Peixoto Deiro, Alison Hallam, Allegra Stone, Allison Schley, Almudena Castro, Alvaro Rabelo Ales, Amanda Germann, Amanda O'Malley, Amar Uxa, Amarachi Ojajuni, Amarino C Oliveira Jr, Amber B Hull, Ambuj Roy, Amer Zarka, Amir Janmohamed, Ammani Brown, Ammy Malinay, Amparo Martinez Monzonis, Amy J Richards, Amy Iskandrian, Amy Ollinger, Ana D Djordjevic-Dikic, Ana Fernández Martínez, Ana Gomes Almeida, Ana Paula Batista, Ana Rita Francisco, Ana S Mladenovic, Ana Santana, Anam Siddiqui, Anastasia M Kuzmina-Krutetskaya, Andras Vertes, Andre S Sousa, Andre Gabriel, André Schmidt, Andrea M Lundeen, Andrea Bartykowszki, Andrea Lorimer, Andrea Mortara, Andrea Pascual, Andreia Coelho, Andreia Rocha, Andrés García-Rincón, Andrew G Howarth, Andrew J Moriarty, Andrew Docherty, Andrew Starovoytov, Andrew Zurick, Andrzej Łabyk, Andrzej Swiatkowski, Andy Lam, Anelise Kawakami, Angela Hoye, Angela Kim, Angelique Smit, Angelo Nobre, Anil V Shah, Anja Ljubez, Anjali Anand, Ankush Sachdeva, Ann Greenberg, Ann Luyten, Ann Ostrander, Anna Di Donato, Anna Cichocka-Radwan, Anna Fojt, Anna Plachcinska, Anna Proietti, Anna Teresinska, Anne Marie Webb, Anne Cartwright, Anne Heath, Anne Mackin, Anong Amaritakomol, Anong Chaiyasri, Anoop Chauhan, Anoop Mathew, Anthony Gemignani, Anto Luigi Andres, Antonia Vega, Antonietta Hansen, Antonino Ginel Iglesias, Antonio Carlos Carvalho, Antonio Di Chiara, Antonio Serra Peñaranda, Antonio Carvalho, Antonio Colombo, Antonio Fiarresga, Anupama Rao, Aquiles Valdespino-Estrada, Araceli Boan, Areef Ishani, Ariel Diaz, Arijit Ghosh, Arintaya Prommintikul, Arline Roberts, Arnold H Seto, Arnold P Good, Arshed Quyyumi, Arthur J Labovitz, Arthur Kerner, Arturo S Campos-Santaolalla, Arunima Misra, Ashok Mukherjee, Ashok Seth, Ashraf Seedhom, Asim N Cheema, Asker Ahmed, Atul Mathur, Atul Verma, Audrey W Leong, Axel Åkerblom, Axelle Fuentes, Aynun Naher, Badhma Valaiyapathi, Baljeet Kaur, Bandula Guruge, Barbara Brzezińska, Barbara Nardi, Bartosz Czarniak, Bebek Singh, Begoña Igual, Bela Merkely, Belen Cid Alvarez, Benjamin J Spooner, Benjamin J W Chow, Benjamin Cheong, Benoy N Shah, Bernard de Bruyne, Bernardas Valecka, Bernhard Jäger, Beth A Archer, Beth Abramson, Beth Jorgenson, Bethany Harvey, Betsy O'Neal, Bev Atkinson, Bev Bozek, Bevin Lang, Bijulal Sasidharan, Bin Yang, Bin Zhang, Binoy Mannekkattukudy Kurian, Bjoern Goebel, Bob Hu, Bogdan A Popescu, Bogdan Crnokrak, Bolin Zhu, Bonnie J Kirby, Brandi D Zimbelman, Brandy Starks, Branko D Beleslin, Brenda Hart, Brian P Shapiro, Brian McCandless, Brianna Wisniewski, Brigham R Smith, Brooks Mirrer, Bruce McManus, Bruce Rutkin, Bruna Edilena Paulino, Bruna Maria Ascoli, Bryn Smith, Byron J Allen, C Michael Gibson, C Noel Bairey Merz, Calin Pop, Cameron Hague, Camila Thais de Ormundo, Candace Gopaul, Candice P Edillo, Carísi A Polanczyk, Carita Krannila, Carla Vicente, Carl-Éric Gagné, Carlo Briguori, Carlos Peña Gil, Carlos Alvarez, Carly Ohmart, Carmen C Beladan, Carmen Ginghina, Carol M Kartje, Caroline Alsweiler, Caroline Brown, Caroline Callison, Caroline Pinheiro, Caroline Rodgers, Caroline Spindler, Carolyn Corbett, Carrie Drum, Casey Riedberger, Catherine Bone, Catherine Fleming, Catherine Gordon, Catherine Jahrsdorfer, Catherine Lemay, Catherine Weick, Cathrine Patten, Cecilia Goletto, Cezary Kepka, Chandini Suvarna, Chang Xu, Chantale Mercure, Charle A Viljoen, Charlene Wiyarand, Charles Jia-Yin Hou, Charles Y Lui, Charles Cannan, Charles Cornet, Charlotte Pirro, Chataroon Rimsukcharoenchai, Chen Wang, Cheng-Ting Tsai, Chen-Yen Chien, Cheryl A Allardyce, Chester M Hedgepeth, Chetan Patel, Chiara Attanasio, Chih-Hsuan Yen, Chi-Ming Chow, Ching Min Er, Ching-Ching Ong, Cholenahally Nanjappa Manjunath, Chris Beck, Chris Buller, Christel Vassaliere, Christian Hamm, Christiano Caldeira, Christie Ballantyne, Christina Björklund, Christine R Hinton, Christine Bergeron, Christine Masson, Christine Roraff, Christine Shelley, Christophe Laure, Christophe Thuaire, Christopher Kinsey, Christopher McFarren, Christopher Spizzieri, Christopher Travill, Chun-Chieh Liu, Chung-Lieh Hung, Chunguang Li, Chun-Ho Yun, Chunli Xia, Ciarra Heard, Cidney Schultz, Clare Venn-Edmonds, Claudia P Hochberg, Claudia Wegmayr, Claudia Cortés, Claudia Escobar, Cláudia Freixo, Claudio T Mesquita, Clemens T Kadalie, Colin Berry, Constance Philander, Corine Thobois, Costantino Costantini, Courtney Page, Craig Atkinson, Craig Barr, Craig Paterson, Cristina Bare, Cynthia Baumann, Cynthia Burman, Dalisa Espinosa, Damien Collison, Dan Deleanu, Dan Elian, Dan Gao, Dana Oliver, Daniel P Vezina, Daniel O'Rourke, Daniele Komar, Danielle Schade, Darrel P Francis, Dastan Malaev, David A Bull, David E Winchester, David P Faxon, David Booth, David Cohen, David DeMets, David Foo, David Schlichting, David Taggart, David Waters, David Wohns, Davis Vo, Dawid Teodorczyk, Dawn Shelstad, Dawn Turnbull, Dayuan Li, Dean Kereiakes, Deborah O'Neill, Deborah Yip, Debra K Johnson, Debra Dees, Deepak L Bhatt, Deepika Gopal, Deepti Kumar, Deirdre Mattina, Deirdre Murphy, Delano R Small, Delsa K Rose, Dengke Jiang, Denis Carl Phaneuf, Denise Braganza, Denise Fine, Derek Cyr, Desiree Tobin, Diana Cukali, Diana Parra, Diane Camara, Diane Minshall Liu, Diego Adrián Vences, Diego Franca de Cunha, Dimitrios Stournaras, Dipti Patel, Dongze Li, Donna Exley, Dorit Grahl, Dragana Stanojevic, Duarte Cacela, Dwayne S G Conway, E Pinar Bermudez, Eapen Punnoose, Edgar L Tay, Edgar Karanjah, Edoardo Verna, Eduardo Hernandez-Rangel, Edward D Nicol, Edward O McFalls, Edward T Martin, Edyta Kaczmarska, Ekaterina I Lubinskaya, Elena A Demchenko, Elena Refoyo Salicio, Eli Feen, Elihú Durán-Cortés, Elisabeth M Janzen, Elise L Hannemann, Elise van Dongen, Elissa Restelli Piloto, Eliza Kaplan, Elizabeta Srbinovska Kostovska, Elizabeth Capasso-Gulve, Elizabeth Congdon, Elizabeth Ferguson, Elizaveta V Zbyshevskaya, Ellen Magedanz, Ellie Fridell, Ellis W Lader, Elvin Kedhi, Emanuela Racca, Emilie Tachot, Emily DeRosa, Encarnación Alonso-Álvarez, Eric Nicollet, Eric Peterson, Erick Alexánderson Rosas, Erick Donato Morales, Erin Orvis, Ermina Moga, Estelle Montpetit, Estevao Figueiredo, Eugene Passamani, Eugenia Nikolsky, Eunice Yeoh, Evgeniy I Kretov, Ewa Szczerba, Ewelina Wojtala, Expedito Eustáquio Ribeiro Silva, F Marin Ortuño, Fabio R Farias, Fabio Fimiani, Fabrizio Rolfo, Fa-Chang Yu, Fadi Hage, Fadi Matar, Fahim Haider Jafary, Fang Feng, Fang Liu, Fatima Ranjbaran, Fatima Rodriguez, Fausto J Pinto, Fauzia Rashid, Federica Ramani, Fei Wang, Fernanda Igansi, Filipa Silva, Filippo Ottani, Fiona Haines, Firas Al Solaiman, Flávia Egydio, Flavio Lyra, Florian Egger, Fran Farquharson, Frances Laube, Francesc Carreras Costa, Francesca de Micco, Francesca Bianchini, Francesca Pezzetta, Francesca Pietrucci, Francesco Orso, Francesco Pisano, Francis Burt, Francisca Patuleia Figueiras, Francisco Fernandez-Aviles, Francois Pierre Mongeon, Frans Van de Werf, Franziska Guenther, Fraser N Witherow, Fred Mohr, Frederico Dall'Orto, Fumiyuki Otsuka, G De La Morena, G Karthikeyan, Gabor Dekany, Gabor Kerecsen, Gabriel Galeote, Gabriel Grossmann, Gabriel Vorobiof, Gabriela Sanchez de Souza, Gabriela Guzman, Gabriela Zeballos, Gabriele Gabrielli, Gabriele Jakl-Kotauschek, Gail A Shammas, Gail Brandt, Gang Chen, Gary E Lane, Gary J Luckasen, Gautam Sharma, Gelmina Mikolaitiene, Gennie Yee, Georg Nickenig, George E Revtyak, George J Juang, Gerald Fletcher, Gerald Leonard, Gerard Patrick Devlin, Gerard Esposito, Gergely Ágoston, Gervasio Lamas, Geza Fontos, Ghada Mikhail, Gia Cobb, Gian Piero Perna, Gianpiero Leone, Giles Roditi, Gilles Barone-Rochette, Girish Mishra, Giuseppe Tarantini, Glenda Wong, Glenn S Hamroff, Glenn Rayos, Gong Cheng, Gonzalo Barge-Caballero, Goran Davidović, Goran Stankovic, Gordana Stevanovic, Grace Jingyan Wang, Grace M Young, Graceanne Wayser, Graciela Scaro, Graham S Hillis, Graham Wong, Grazyna Anna Szulczyk, Gregor Simonis, Gregory Kumkumian, Gretchen Ann Peichel, Grzegorz Gajos, Gudrun Steinmaurer, Guilherme G Rucatti, Guilherme Portugal, Guilhermina Cantinho Lopes, Guillem Pons Lladó, Gunnar Frostfelt, Gurpreet S Wander, Gurpreet Gulati, Gustavo Pucci, Hafidz Abd Hadi, Haibo Zhang, Haitao Wang, Halina Marciniak, Han Chen, Hanan Kerr, Hani Najm, Hanna Douglas, Hannah Phillips, Hao Dai, Haojian Dong, Haqeel Jamil, Harikrishnan Sivadasanpillai, Harry Suryapranata, Hassan Reda, Hayley Pomeroy, Heather Barrentine, Heather Golden, Heather Hurlburt, Heidi Wilson, Helen C Tucker, Helene Abergel, Hemalata Siddaram, Hermine Osseni, Herwig Schuchlenz, Hesong Zeng, Hicham Skali, Hilda Solomon, Hollie Horton, Holly Hetrick, Holly Little, Holly Park, Hongjie Chi, Hossam Mahrous, Howard A Levite, Hristo Pejkov, Huajun Li, Hugo Bloise-Adames, Hugo Marques, Hui Zhong, Hui-Min Zhang, Humayrah Hashim, Hung-I Yeh, Hussien El Fishawy, Ian Webb, Iftikhar Kullo, Igor O Grazhdankin, Ihab Hamzeh, Ikraam Hassan, Ikuko Ueda, Ileana L Pina, Ilona Tamasauskiene, Ilse Bouwhuis, Imran Arif, Ina Wenzelburger, Inês Zimbarra Cabrita, Ines Rodrigues, Inga H Robbins, Inga Soveri, Ingela Schnittger, Iqbal Karimullah, Ira M Dauber, Iram Rehman, Irena Peovska Mitevska, Irene Marthe Lang, Irina Subbotina, Irma Kalibataite-Rutkauskiene, Irni Yusnida, Isabel Estela Carvajal, Isabella C Palazzo, Isabelle Hogan, Isabelle Roy, Ishba Syed, Ishita Tejani, Ivan A Naryshkin, Ivana Jankovic, Iwona Niedzwiecka, J David Knight, Jacek Kusmierek, Jackie M White, Jackie Chow, Jacob Udell, Jacqueline E Tamis-Holland, Jacqueline Fannon, Jacquelyn A Quin, Jacquelyn Do, Jaekyeong Heo, Jakub Maksym, James E Davies, James H O'Keefe Jr, James J Jang, James Cha, James Harrison, James Hirsch, James Stafford, James Tatoulis, Jamie Rankin, Jan Henzel, Jan Orga, Jana Tancredi, Janaina Oliveira, Jane Burton, Jane Eckstein, Jane Marucci, Janet P Knight, Janet Blount, Janet Halliday, Janetta Kourzenkova, Janitha Raj, Jan-Malte Sinning, Jaqueline Pozzibon, Jaroslaw Drozdz, Jaroslaw Karwowski, Jason D Glover, Jason Loh Kwok, Jason T Call, Jason Linefsky, Jassira Gomes, Jati Anumpa, Javier J Garcia, Javier Courtis, Jay Meisner, K Jayakumar, Jayne Scales, Jean E Denaro, Jean Michel Juliard, Jean Ho, Jeanette K Stansborough, Jean-Michel Juliard, Jeanne Russo, Jeannette J M Schoep, Jeet Thambyrajah, Jeff Leimberger, Jeffery A Breall, Jeffrey A Kohn, Jeffrey C Milliken, Jeffrey Anderson, Jeffrey Blume, Jeffrey Kanters, Jeffrey Lorin, Jeffrey Moses, Jelena J Stepanovic, Jelena Celutkiene, Jelena Djokic, Jelena Stojkovic, Jenne M Jose, Jenne Manchery, Jennifer A Mull, Jennifer H Czerniak, Jennifer L Stanford, Jennifer Gillis, Jennifer Horst, Jennifer Isaacs, Jennifer Langdon, Jennifer Thomson, Jennifer Tomfohr, Jennifer White, Jen-Yuan Kuo, Jeremy Rautureau, Jerome Fleg, Jessica Berg, Jessica Rodriguez, Jessica Waldron, Jhina Patro, Jia Li, Jiajia Mao, Jiamin Liu, Jian'an Wang, Jianhua Li, Jianxin Zhang, Jie Qi, Jihyun Lyo, Jill Marcus, Jim Blankenship, Jing Zhang, Jingjing Liu, Jing-Yao Fan, Jiun-Yi Li, Jiwan Pradhan, Jiyan Chen, J M Rivera Caravaca, Jo Evans, Joan Garcia Picart, Joan Hecht, Joanna Jaroch, Joanna Zalewska, Joanne Kelly, Joanne Taaffe, João Reynaldo Abbud, João V Vitola, Joaquín V Peñafiel, Jocelyne Benatar, Jody Bindeman, Joe Sabik, Joel Klitch, Johann Christopher, Johannes Aspberg, John D Friedman, John F Beltrame, John F Heitner, John Joseph Graham, John R Davies, John Doan, John Kotter, John Kurian, John Mukai, John Pownall, Jolanta Sobolewska, Jon Kobashigawa, Jonathan L Goldberg, Jonathan W Bazeley, Jonathan Byrne, Jonathan Himmelfarb, Jonathan Leipsic, Jonean Thorsen, Jorge F Trejo Gutierrez, Jorge Escobedo, Jorik Timmer, José A Ortega-Ramírez, José Antonio Marin-Neto, Jose D Salas, Jose Enrique Castillo, Jose Francisco Saraiva, José J Cuenca-Castillo, Jose L Diez, José Luis Narro Villanueva, José Luiz da Vieira, José M Flores-Palacios, Jose Ramon Gonzalez, Jose Seijas Amigo, Jose Fragata, Josep Maria Padró, Josheph F X McGarvey Jr, Joseph Hannan, Joseph Sacco, Joseph Sweeny, Joseph Wiesel, Josephine D Abraham, Joshua P Loh, Joy Burkhardt, Joyce R White, Joyce Riestenberg-Smith, Judit Sebo, Judith L Meadows, Judith Wright, Judy Mae Foltz, Judy Hung, Judy Otis, Juergen Stumpf, Jui-Peng Tsai, Julia S Dionne, Julia de Aveiro Morata, Julie Bunke, Julie Morrow, Julio César Figal, Jun Fujita, Jun Jiang, Junhua Li, Junqing Yang, Juntima Euathrongchit, Jyotsna Garg, K Manjula Rani, K Preethi, Kaatje Goetschalckx, Kai Eggers, Kamalakar Surineni, Kanae Hirase, T R Kapilamoorthy, Karen Calfas, Karen Gratrix, Karen Hallett, Karen Hultberg, Karen Nugent, Karen Petrosyan, Karen Swan, Karolina Kryczka, Karolina Wojtczak-Soska, Karolina Wojtera, Karsten Lenk, Karthik Ramasamy, Katarzyna Łuczak, Katarzyna Malinowska, Kate Pointon, Kate Robb, Katherine Martin, Kathleen Claes, Kathryn Carruthers, Kathy E Siegel, Katia Drouin, Katie Fowler-Lehman, Kavita Rawat, Kay Rowe, Keiichi Fukuda, Keith A A Fox, Ken Mahaffey, Kendra Unterbrink, Kenneth Giedd, Kerrie Van Loo, Kerry Lee, Kerstin Bonin, Kevin R Bainey, Kevin T Harley, Kevin Anstrom, Kevin Chan, Kevin Croce, Kevin Landolfo, Kevin Marzo, Keyur Patel, Khaled Abdul-Nour, Khaled Alfakih, Khaled Dajani, Khaled Ziada, Khaula Baloch, Khrystyna Kushniriuk, Kian-Keong Poh, Kim F Ireland, Kim Holland, Kimberly Ann Byrne, Kimberly E Halverson, Kimberly Elmore, Kimberly Miller-Cox, Kiran Reddy, Kirsten J Quiles, Kirsty Abercrombie, Klaus Matschke, Konrad Szymczyk, Koo Hui Chan, Kotiboinna Preethi, Kozhaya Sokhon, Krissada Meemuk, Kristian Thygesen, Kristin M Salmi, Kristin Newby, Kristina Wippler, Kristine Arges, Kristine Teoh, Krystal Etherington, Krystyna Łoboz-Grudzień, Krzysztof W Reczuch, Krzysztof Bury, Krzysztof Drzymalski, Krzysztof Kukuła, Kuo-Tzu Sung, Kurt Huber, Ladda Douangvila, Lance Sullenberger, Larissa Miranda Trama, Laszlone Matics, Laura Drew, Laura Flint, Laura Keinaite, Laura Sarti, Laurel Kolakaluri, Lawrence M Phillips, Lawrence Friedman, Lawrence Phillips, Lazar Velicki, Leah Howell, Leandro C Maranan, Leanne Cox, Ledjalem Daba, Lei Zhang, Lekshmi Dharmarajan, Leo Bockeria, Leonardo Pizzol Caetano, Leonardo Bridi, Leonid L Bershtein, Leszek Sokalski, Li Hai Yan, Li Li, Lia Nijmeijer, Lidia Sousa, Lihong Xu, Lihua Zhang, Lili Zhang, Lilia Schiavi, Lilian Mazza Barbosa, Lillian L Khor, Lina Felix-Stern, Linda L Hall, Linda M Hollenweger, Linda Arcand, Linda Davidson-Ray, Linda Schwarz, Lindsey N Sikora, Lingping Chi, Lino Patricio, Liping Zhang, Lisa Chaytor, Lisa Hatch, Lisa McCloy, Lisa Wong, Liselotte Persson, Lixin Jiang, Liz Low, Ljiljana Pupic, Loïc Bière, Lorenzo Monti, Lori Christensen, Lori Pritchard, Loriane Black, Lori-Ann Desimone, Lori-Ann Larmand, Lorraine McGregor, Louise Morby, Louise Thomson, Luc Harvey, Luciana de Pádua Baptista, Lucilla Garcia, Ludivine Eliahou, Ludmila Helmer, Luis F Smidt, Luis Bernanrdes, Luis Guzman, Luiz A Carvalho, Luyang Xiong, Lynette L Teo, Lynn M Neeson, Lynne Winstanley, M Barbara Srichai-Parsia, M Quintana Giner, M Sowjanya Reddy, M Valdés Chávarri, M Grazia Rossi, Maarten Simoons, Maayan Konigstein, Maciej Lesiak, Maciej Olsowka, Mafalda Selas, Magalie Corfias, Magdalena Madero Rovalo, Magdalena Łanocha, Magdalena Miller, Magdalena Misztal-Teodorczyk, Magdalena Rantinella, Magdy Abdelhamid, Magnolia Jimenez, Mahboob Alam, Mahevamma Mylarappa, Mahfouz El Shahawy, Mahmoud Mohamed, Mahmud Al-Bustami, Majo X Joseph, Malgorzata Frach, Małgorzta Celińska-Spodar, Malte Helm, Manas Chacko, Mandy Murphy, Manitha Vinod, Manjula Rani, Manu Dhawan, Manuela Mombelli, Marcel Weber, Marcello Galvani, Marcelo Jamus Rodrigues, Marcia F Dubin, Marcia F Werner Bayer, Marcin Szkopiak, Marco Antonio Monsalve, Marco Bizzaro Santos, Marco Magnoni, Marco Marini, Marco Sicuro, Marco Zenati, Marcos Valério Coimbra Resende, Marek Roik, Margalit Bentzvi, Margaret Gilsenan, Margaret Iraola, Margot C Quinn, Maria A Alfonso, Maria Antonieta Pereira Moraes, María Dolores Martínez-Ruíz, María Fernanda Canales, Maria Inês Caetano, Maria P Corral, Maria Pérez García, Maria Victoria Actis, Maria Aguirre, Maria Andreasson, Maria Aprile, Maria Colton, Maria Eugenia Martin, Maria Lasala, Maria Lorenzo, Maria Posada, Maria Shier, Maria Thottam, Mariana V Furtado, Mariana Yumi Okada, Marianna D A Dracoulakis, Marianne De Andrade, Mariano Rubio, Marie Essermark, Marielle Scherrer-Crosbie, Marija T Petrovic, Marija Zdravkovic, Marilyn Black, Marina Garcia, Mario J Garcia, Mariola Szulik, Marisa Orgera, Mark A de Belder, Mark Harbinson, Mark Hyun, Mark Peterson, Mark Xavier, Marlowe Mosley, Marta Capinha, Marta Marcinkiewicz-Siemion, Marta Swiderek, Martha Meyer, Martina Ceseri, Martina Tricoli, Marvin Kronenberg, Mary Williams, Mary Ann Champagne, Mary Colleen Rogge, Mary R Soltau, Mary Streif, Massimo Villella, Massoud Leesar, Matei Claudia, Mateusz Solecki, Matías Nicolás Mungo, Matthew Wall Jr, Matthew Budoff, Matthew Jezior, Matthew Luckie, Matthias Friedrich, Mauren P Haeffner, Maximilian Tscharre, Max-Paul Winter, Mayana Almeida, Mayil S Krishnam, Mayuri Patel, Meenakshi Mishra, Megan Manocchia, Meghana Kakade, Melanie J Munro, Melissa D Chaplin, Melissa LeFevre, Mervyn Andiapen, Michael A Gibson, Michael B Rubens, Michael C Turner, Michael D Shapiro, Michael W Lee, Michael Berlowitz, Michael Davidson, Michael Mack, Michael McDaniel, Michael Mumma, Michal Wlodarczyk, Michel G Khouri, Michel S Slama, Michele Rawlins, Michelle M Bonner, Michelle M Seib, Michelle Chang, Michelle Crowder, Michelle Dixon, Michelle Mayon, Michelle McEvoy, Michelle Yee, Miguel M Fernandes, Miguel Nobre Menezes, Miguel Souto Bayarri, Miguel Barrero, Mikhail T Torosoff, Milan R Dobric, Milan Dobric, Milica Nikola Dekleva, Milind Avdhoot Gadkari, Millie Gomez, Min Tun Kyaw, Miriam Brooks, Miroslav Stevo Martinovic, Mitchel B Lustre, Mohammad Tariq Vakani, Mohammad El-Hajjar, Mohammed Al-Amoodi, Mohammed Hussain, Mohammed Saleem, Moisés Blanco-Calvo, Moisés Jiménez-Santos, Mona Bhatia, Monica Rosca, Monika Laukyte, Montserrat Gracida Blanca, Montserrat Vila Perales, Mouaz H Al-Mallah, Moysés de Oliveira Filho, Mpiko Ntsekhe, Muhamed Saric, Mulei Chen, Myriam Brousseau, Myrthes Emy Takiuti, Nada Cemerlic-Adjic, Nadia Asif, Nadia Gakou, Nafisa Hussain, Nana O Katamadze, Nancy L Clapp, Nancy Aedy, Nandita Nataraj, Nanette K Wenger, Naomi Uchida, Nasrul Ismail, Natalia S Oliveira, Natalia de Carvalho Maffei, Natalie Spitzer, Natasha C Putnam, Naved Aslam, Neamat Mowafy, Neeraj Pandit, Neeraj Parakh, Nevena Garcevic, Ngaire Meadows, Nhi N Tran, Nicholas Danchin, Nicki Lakeman, Nicola Johnston, Nicolas W Shammas, Nicole Saint Vrestil, Nicole Deming, Nier Zhong, Niket Patel, Nikola N Boskovic, Nikolaos Karogiannis, Nikos Werner, Nina Johnston, Ning Zhang, Ning Zhou, Niree Hindoyan, Nirmal Kumar, Nitika Chadha, Nitish Naik, Nodira Aripova, Noloyiso Mtana, Nona A Eskelson, Noor Syamira Mokhtar, Noppon Taksaudom, Nor Asiah Basri, Nora Marchelletta, Norma Hogg, Nungshi Jungla, Nuno Ferreira, Oksana A Lubyanaya, Olga B Nikolaeva, Olga Cañavate, Olga Sobrino, Olga Walesiak, Olga Walter, Olga Zdończyk, Olivia J Lim, Olivia Anaya, Olivia Mancilla, Olivier Dubourg, Olugbenga Bello, Omar Almousalli, Omar Thompson, Oni Olurinde, Or Harel, Osama Raheem, Oscar Méndiz, Óscar Prada-Delgado, Oz Shapira, P Christian Schulze, Pachara Panpunuan, Pal Maurovich-Horvat, Pallav Garg, Paloma Moraga, Pam Singh, Pamela Julian, Pamela Ouyang, Pamela Sigel, Pamela Woodard, Panpan Zhou, Paola Emanuela Poggio, Paola Smanio, Paolo Calabro, Paramjit Jeetley, Pascal Goube, Patricia K Nguyen, Patricia Alarie, Patricia Arakelian, Patricia Arsenault, Patricia Blaise, Patricia Brito, Patricia Cowper, Patricia Endsley, Patricia Mieses, Patrick B Alexander, Patrick Donnelly, Patrick Wilmot, Patrycja Lebioda, Paul C Gordon, Paul Der Mesropian, Paul Galiwango, Paul Hauptman, Paul Kennedy, Paula Beardsley, Paula García-González, Paulo Cury Rezende, Paulo Ricardo Caramori, Pavel S Kozlov, Pedro Canas Silva, Pedro Gabriel Melo Barros E Silva, Pedro Píccaro de Oliveira, Pedro Carvalho, Pedro Modas, Pedro Rio, Peeyush Jain, Peiyu He, Peter A McCullough, Peter H Stone, Peter M Pollak, Peter Douglass, Peter Henriksen, Peter OKane, Peter Ong, Philip Jones, Philip Rogal, Philippe Généreux, Philippe Menasche, Philippe Rheault, Phoebe Goold, Pierre Gervais, Pierre Michaud, Pilar Calvillo, Ping Chai, Piotr Jakubowski, Piotr Pruszczyk, Piotr Slomka, Piyamitr Sritara, Poay-Huan Loh, Poonam Sonawane, Pouneh Samadi, Pragnesh P Parikh, Prakash Deedwania, Pranav M Patel, Praneeth Polamuri, Pratiksha Sharma, Precilia Vasquez, Preeti Kamath, Prince Thomas, Priyadarshani Arambam, Puja K Mehta, Purvez Grant, Pushpa Naik, Qi Zhong, Qian Zhao, Qiang Zhou, Qianqian Yuan, Qin Yu, Qingxian Li, Qiulan Xie, Qiutang Zeng, R J Vindhya, R James Gerlach, Rachel King, Rada Vučić, Radmila Lyubarova, Radoslaw Pracon, 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Jeffrey Kanters, Jeffrey Lorin, Jeffrey Moses, Jelena J Stepanovic, Jelena Celutkiene, Jelena Djokic, Jelena Stojkovic, Jenne M Jose, Jenne Manchery, Jennifer A Mull, Jennifer H Czerniak, Jennifer L Stanford, Jennifer Gillis, Jennifer Horst, Jennifer Isaacs, Jennifer Langdon, Jennifer Thomson, Jennifer Tomfohr, Jennifer White, Jen-Yuan Kuo, Jeremy Rautureau, Jerome Fleg, Jessica Berg, Jessica Rodriguez, Jessica Waldron, Jhina Patro, Jia Li, Jiajia Mao, Jiamin Liu, Jian'an Wang, Jianhua Li, Jianxin Zhang, Jie Qi, Jihyun Lyo, Jill Marcus, Jim Blankenship, Jing Zhang, Jingjing Liu, Jing-Yao Fan, Jiun-Yi Li, Jiwan Pradhan, Jiyan Chen, J M Rivera Caravaca, Jo Evans, Joan Garcia Picart, Joan Hecht, Joanna Jaroch, Joanna Zalewska, Joanne Kelly, Joanne Taaffe, João Reynaldo Abbud, João V Vitola, Joaquín V Peñafiel, Jocelyne Benatar, Jody Bindeman, Joe Sabik, Joel Klitch, Johann Christopher, Johannes Aspberg, John D Friedman, John F Beltrame, John F Heitner, John Joseph Graham, John R Davies, John Doan, John Kotter, John Kurian, John Mukai, John Pownall, Jolanta Sobolewska, Jon Kobashigawa, Jonathan L Goldberg, Jonathan W Bazeley, Jonathan Byrne, Jonathan Himmelfarb, Jonathan Leipsic, Jonean Thorsen, Jorge F Trejo Gutierrez, Jorge Escobedo, Jorik Timmer, José A Ortega-Ramírez, José Antonio Marin-Neto, Jose D Salas, Jose Enrique Castillo, Jose Francisco Saraiva, José J Cuenca-Castillo, Jose L Diez, José Luis Narro Villanueva, José Luiz da Vieira, José M Flores-Palacios, Jose Ramon Gonzalez, Jose Seijas Amigo, Jose Fragata, Josep Maria Padró, Josheph F X McGarvey Jr, Joseph Hannan, Joseph Sacco, Joseph Sweeny, Joseph Wiesel, Josephine D Abraham, Joshua P Loh, Joy Burkhardt, Joyce R White, Joyce Riestenberg-Smith, Judit Sebo, Judith L Meadows, Judith Wright, Judy Mae Foltz, Judy Hung, Judy Otis, Juergen Stumpf, Jui-Peng Tsai, Julia S Dionne, Julia de Aveiro Morata, Julie Bunke, Julie Morrow, Julio César Figal, Jun Fujita, Jun Jiang, Junhua Li, Junqing Yang, Juntima Euathrongchit, Jyotsna Garg, K Manjula Rani, K Preethi, Kaatje Goetschalckx, Kai Eggers, Kamalakar Surineni, Kanae Hirase, T R Kapilamoorthy, Karen Calfas, Karen Gratrix, Karen Hallett, Karen Hultberg, Karen Nugent, Karen Petrosyan, Karen Swan, Karolina Kryczka, Karolina Wojtczak-Soska, Karolina Wojtera, Karsten Lenk, Karthik Ramasamy, Katarzyna Łuczak, Katarzyna Malinowska, Kate Pointon, Kate Robb, Katherine Martin, Kathleen Claes, Kathryn Carruthers, Kathy E Siegel, Katia Drouin, Katie Fowler-Lehman, Kavita Rawat, Kay Rowe, Keiichi Fukuda, Keith A A Fox, Ken Mahaffey, Kendra Unterbrink, Kenneth Giedd, Kerrie Van Loo, Kerry Lee, Kerstin Bonin, Kevin R Bainey, Kevin T Harley, Kevin Anstrom, Kevin Chan, Kevin Croce, Kevin Landolfo, Kevin Marzo, Keyur Patel, Khaled Abdul-Nour, Khaled Alfakih, Khaled Dajani, Khaled Ziada, Khaula Baloch, Khrystyna Kushniriuk, Kian-Keong Poh, Kim F Ireland, Kim Holland, Kimberly Ann Byrne, Kimberly E Halverson, Kimberly Elmore, Kimberly Miller-Cox, Kiran 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Pierre Michaud, Pilar Calvillo, Ping Chai, Piotr Jakubowski, Piotr Pruszczyk, Piotr Slomka, Piyamitr Sritara, Poay-Huan Loh, Poonam Sonawane, Pouneh Samadi, Pragnesh P Parikh, Prakash Deedwania, Pranav M Patel, Praneeth Polamuri, Pratiksha Sharma, Precilia Vasquez, Preeti Kamath, Prince Thomas, Priyadarshani Arambam, Puja K Mehta, Purvez Grant, Pushpa Naik, Qi Zhong, Qian Zhao, Qiang Zhou, Qianqian Yuan, Qin Yu, Qingxian Li, Qiulan Xie, Qiutang Zeng, R J Vindhya, R James Gerlach, Rachel King, Rada Vučić, Radmila Lyubarova, Radoslaw Pracon, Raewyn Fisher, Rafael Beyar, Rafael Diaz, Rafael Selgas, Raffaele Bugiardini, Raffaele Fanelli, Raisa Kavalakkat, V S Rajalekshmi, Rajat S Barua, Rajeev Menon, Rajesh Gopalan Nair, Rajesh Francis, Rajiv Narang, Rakesh Yadav, Ralph Alan Huston, T Ramakrishnan, Ramesh de Silva, Rami El Mahmoud, Ramiro Carvalho, Ramon de Jesús-Pérez, Ramona Stevens, Ran Leng, Ranjan Kachru, Ranjit Kumar Nath, Raquel Sanchez, Raven R Dwyer, Raven Lee, Ray Wyman, Raymond C Wong, Raymond W Little, Raymundo Ocaranza Sanchez, Rebecca J Wimmer, Rebecca Bariciano, Rebecca Otis, Rebekah R Herrmann, Reem Yunis, Reinette Hampson, Renato Abdala Karam, Renee C Hessian, Renee Kaneshiro, Reshma Ravindran, Reto Andreas Gamma, Reyna Bhandari, Reza Arsanjani, Ricardo L Lopes, Ricardo Mendes Oliveira, Ricardo Costa, Richa Bhatt, Richard F Davies, Richard H J Trimlett, Richard Goldweit, Rik Hermanides, Rine Nakanishi, Rinu R Sidh, Risha Patel, Rita Coram, Rizwan A Siddiqui, Rob S Beanlands, Robert J Hamburger, Robert K Riezebos, Robert M Donnino, Robert Bojar, Robert Chilton, Robert Guyton, Robert Henderson, Robert Kornberg, Robert Leber, Robert Mao, Robert Stenberg, Roberta P Santos, Roberto René Favaloro, Roberto Amati, Rodolfo G S D Lima, Rodrigo J Cerci, Rogerio Tumelero, Rohit Tandon, Roma Tewari, Romalisa Miranda-Peats, Ron Wald, Ronald A Mastouri, Ronald G Morford, Ronald G Schwartz, Ronald P Pedalino, Rongrong Hu, Ronnell A Hansen, Ronny A Cohen, Rory Hachamovitch, Rosa Homem, Rosa Sandonato, Rosane Laimer, Rosann Gans, Roxanne Yost, Roy Mathew, Rubén Baleón-Espinosa, Ruben Ramos, Rubine Gevorgyan, Rui Ferreira, Rui Jing, Ruth Pérez-Fernández, S K Dwivedi, S Ramakrishnan, Saadat Khan, Sabahat Bokhari, Sabu Thomas, Sadath Lubna, Sajeeda Parveen Khan, Sajeev Chakanalil Govindan, Saket Girotra, Saleem Kassam, Sallie Canada, Salvador Cruz-Flores, Samaa Mohamed, Samantha Ly, Sameh El Kaffas, Samia Massalha, Sampoornima Setty, Samuel Nwosu, Sandeep Seth, Sandeep Singh, Sander R Niehe, Sandra M Rivest, Sandra S Zier, Sandra Ahoud, Sandy Carr, Sanjay Ganapathi, Sanjay Shetty, Sanjeev Sharma, Santa Jimenez, Santhosh Satheesh, Santiago A Garcia, Sara Fernandez, Sara Karlsson, Sara Salkind, Sara Temiyasathit, Sarah Medina Rodriguez, Sarah Beaudry, Sarah Hadjih, Sarah Williams, Sarah Zahrani, Sarju Ralhan, Sasa Hinic, Sasko Kedev, Satinder Singh, Satoshi Yasuda, Satvic Cholenahally Manjunath, Sau Lee, Scott M Kaczkowski, Scott Kinlay, Sean W Hayes, Sebastian Sobczak, Senait Asier, Sergey A Sayganov, Seth I Sokol, Shaheen Pandie, Shaiful Azmi Yahaya, Shamir Mehta, Shao-Ping Nie, Sharad Chandra, Sharder Islam, Sharon Tai, Sheetal Rupesh Karwa, Sheri Ussery, Sheromani Bajaj, Sherron C Crook, Shigeyuki Nishimura, Shintaro Nakano, Shirin Heydari, Shiv Kumar Choudhary, Shivali Patel, Shobana Ganesan, Shruti Pandey, Shuyang Zhang, Shweta Hande, Siddharth Gadage, Sik-Yin V Tan, Silvia Zottis Poletti, Silvia Riera, Silvia Valbuena, Simon Walsh, Simona Maspoli, Simone Savaris, Si-Ting Feng, So Yang Cho, Solomon Yakubov, Songlin Zhu, Songtao Wang, Sonia Guerrero, Sonika Gupta, Sonja Salinger Martinovic, Sonya Brons, Sorin Brener, Sothinathan Gurunathan, Souheil Saba, Soundarya Nayak, Sowjanya Reddy, Srinivasa Potluri, Sriram Sudarshan, Srun Kuanprasert, Stacie Van Oosterhout, Stamatios Lerakis, Stanley E Cobos, Stefan C Bertog, Stefan M Simović, Stefan Weikl, Stefano Di Marco, Stefano Provasoli, Stephanie A Tirado, Stephanie C Boer, Stephanie M Lane, Stephanie Ferket, Stephanie Kelly, Stephanie Wasmiller, Stephen H McKellar, Stephen P Hoole, Stephen Fremes, Stephen Preston, Steve Leung, Steven A Fein, Steven J Lindsay, Steven P Sedlis, Steven Giovannone, Steven Michael, Steven Weitz, Stijn van Vugt, Subhash Banerjee, Sudhir Naik, Suellen Hosino, Sukie Desire, Sukit Yamwong, Suku T Thambar, Sulagna Mookherjee, Suman Singh, Sundeep Mishra, Sunil Kumar Verma, Supap Kulthawong, Supatchara Khwakhong, Surendra Naik, Suresh Babu, Surin Woragidpoonpol, Suryaprakash Narayanappa, Susan Derbyshire, Susan Gent, Susan Mathus, Susan Milbrandt, Susan Moore, Susan Regan, Susan Stinson, Susan Webber, Susana Silva, Susanna Stevens, Susanne Gruensfelder, Suthara Aramcharoen, Suvarna Kolhe, Suzana Tavares, Suzanne Arnold, Suzanne Welsh, Svetlana Apostolovic, Swapna Kunhunny, Ta-Chuan Hung, Taissa Zappernick, Tali Sharir, Talita Silva, Tamara Colaiácovo Soares, Tapan Umesh Pillay, Tarun K Mittal, Tatiana Trifonova, Tauane Bello Duarte, Tauqir Huk, Téodora Dutoiu, Terrance Chua, Terry Weyand, Thabitha Charles, Theodoros Kofidis, Theresa McCreary, Thierry Lefevre, Thippeekaa Arumairajah, Thitipong Tepsuwan, Thomas J Mulhearn, Thomas M Meyer, Thomas P Rocco, Thomas R Downes, Thomas Crain, Thomas Haldis, Thomas Mathew, Thomas Redick, Thounaojam Indira Devi, Thuraia Nageh, Tia Cauthren, Tiago Silva, Tiffany Little, Tijana Andric, Tina Harding, Titus Lau, Tiziana Formisano, Tiziano Moccetti, Tomasz Ciurus, Tomasz Mazurek, Tomasz Tarchalski, Toshiyuki Nagai, Tri Tran, Tricia Youn, Trish Tucker, Trudie Milner, Tuhina Bose, Tushar Kotecha, Udo Sechtem, Uma S Valeti, Umberto Cucchini, Umesh Badami, Upendra Kaul, V K Bahl, V S Narain, Valentina Casali, Valeria Godoy, Valerie Robesyn, Vamshi P Priya, Vandana Yadav, Vera McKinney, Veronica De Lenges, Veronica Tinnirello, Vicente Miro, Victor Navarro, Victoria Gumerova, Victoria Hernandez, Vidya Seeratan, Vijay Kumar, Vikentiy Y Kozulin, Viktoria Bulkley, Vilmar Veiga Jr, Vincent Setang, C P Vineeth, Virginai Pubull Nuñez, Virginia Fernández-Figares, Vitor Gomes, Viviana Gabriel, Viviane Dos Santos, Viviane Almeida, Vlad A Iliescu, Vladan Mudrenovic, Vladimir Dzavik, Vojislav L Giga, Walter Enrique Mogrovejo, Wan Xian Chan, Wanda C Marfori, Wanda Parker, Warangkana Mekara, Wassim Nona, Wayne Old, Wayne Pennachi, Weerachai Nawarawong, Wei Chen, Wei Su, Weibing Xing, Wei-Ren Lan, Wenda Crawford, Wendy L Stewart, Wendy Drewes, Wenhua Lin, William B Abernethy, William D Salerno, William F Fearon, William Vergoni, William Weintraub, Winnie C Sia, Wlodzimierz J Musial, Xacobe Flores-Ríos, Xavier Garcia-Moll Marimon, Xi Su, Xiang Ma, Xiangqiong Gu, Xiao Wang, Xiaomei Li, Xiaowei Yao, Xin Fu, Xin Su, Xin Zeng, Xinchun Yang, Xiuhong Li, Xuehua Fang, Xutong Wang, Yaming Geng, Yan Yan, Yanek Pépin-Dubois, Yanfu Wang, Yang Wang, Yanmeng Tian, Yaping Huang, Yechen Han, Yesenia Zambrano, Yi-Hsuan Yang, Ying Tung Sia, Yining Yang, Yitong Ma, Yolayfi Peralta, Yongjian Wu, Yu Kunwu, Yu Zhao, Yudong Peng, Yueh-Hung Lin, Yulan Zhao, Yumei Dong, Yunhai Zhao, Yutthaphan Wannasopha, Yvonne Taul, Zakir Sahul, Zalina Kudzoeva, Zbigniew Kalarus, Zeljko Z Markovic, Zhen Huang, Zheng Ji, Zhenyu Liu, Zhou Yue, Zhulin Zhang, Zhuxi Li, Zile Singh Meharwal, Ziliang Bai, Zixiang Yu, Zohra Huda, Zoltan Davidovits
Subjects: Male, Cardiac Catheterization, Computed Tomography Angiography, medicine.medical_treatment, Myocardial Ischemia, Coronary Disease, Coronary Artery Disease, Kaplan-Meier Estimate, 030204 cardiovascular system & hematology, Coronary Angiography, ISCHEMIA Research Group, law.invention, Angina, Coronary artery disease, 0302 clinical medicine, Randomized controlled trial, law, Cardiovascular Disease, Myocardial Revascularization, 030212 general & internal medicine, Coronary Artery Bypass, 11 Medical and Health Sciences, Cardiac catheterization, General Medicine, Middle Aged, humanities, Cardiovascular Diseases, Cardiology, Female, Human, medicine.medical_specialty, Ischemia, Article, 03 medical and health sciences, Geriatric cardiology, Percutaneous Coronary Intervention, General & Internal Medicine, Internal medicine, medicine, Humans, Angina, Unstable, Aged, business.industry, Coronary Artery Bypa, Percutaneous coronary intervention, Bayes Theorem, medicine.disease, Heart failure, Quality of Life, business
Abstract: BACKGROUND: Among patients with stable coronary disease and moderate or severe ischemia, whether clinical outcomes are better in those who receive an invasive intervention plus medical therapy than in those who receive medical therapy alone is uncertain. METHODS: We randomly assigned 5179 patients with moderate or severe ischemia to an initial invasive strategy (angiography and revascularization when feasible) and medical therapy or to an initial conservative strategy of medical therapy alone and angiography if medical therapy failed. The primary outcome was a composite of death from cardiovascular causes, myocardial infarction, or hospitalization for unstable angina, heart failure, or resuscitated cardiac arrest. A key secondary outcome was death from cardiovascular causes or myocardial infarction. RESULTS: Over a median of 3.2 years, 318 primary outcome events occurred in the invasive-strategy group and 352 occurred in the conservative-strategy group. At 6 months, the cumulative event rate was 5.3% in the invasive-strategy group and 3.4% in the conservative-strategy group (difference, 1.9 percentage points; 95% confidence interval [CI], 0.8 to 3.0); at 5 years, the cumulative event rate was 16.4% and 18.2%, respectively (difference, -1.8 percentage points; 95% CI, -4.7 to 1.0). Results were similar with respect to the key secondary outcome. The incidence of the primary outcome was sensitive to the definition of myocardial infarction; a secondary analysis yielded more procedural myocardial infarctions of uncertain clinical importance. There were 145 deaths in the invasive-strategy group and 144 deaths in the conservative-strategy group (hazard ratio, 1.05; 95% CI, 0.83 to 1.32). CONCLUSIONS: Among patients with stable coronary disease and moderate or severe ischemia, we did not find evidence that an initial invasive strategy, as compared with an initial conservative strategy, reduced the risk of ischemic cardiovascular events or death from any cause over a median of 3.2 years. The trial findings were sensitive to the definition of myocardial infarction that was used. (Funded by the National Heart, Lung, and Blood Institute and others; ISCHEMIA ClinicalTrials.gov number, NCT01471522.).
Published: 2020
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44. Developing an Echocardiography-Based, Automatic Deep Learning Framework for the Differentiation of Increased Left Ventricular Wall Thickness Etiologies

Author: Imon Banerjee, James Li, Chieh-Ju Chao, Jiwoong Jason Jeong, Amith Seri R, Timothy Barry, Hana Neuman, Megan Campany, Merna Abdou, Michael O’Shea, Sean Smith, Bishoy Abraham, Seyedeh Maryam Hosseini, Yuxiang Wang, Steven Lester, Said Alsidawi, susan Wilansky, Eric Steidley, Julie Rosenthal, Chadi Ayoub, Christopher Appleton, Win-Kuang Shen, Martha Grogan, Garvan Kane, Jae Oh, Bhavik N. Patel, and Reza Arsanjani
Abstract: 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.
Published: 2022
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45. A New Insight Into Nonbacterial Thrombotic Endocarditis: A Systematic Review of Cases

Author: Nithin R, Venepally, Reza, Arsanjani, Pradyumna, Agasthi, Panwen, Wang, Banveet K, Khetarpal, Timothy, Barry, Chieh-Ju, Chao, Ayman R, Fath, and Farouk, Mookadam
Subjects: Adult, Endocarditis, Non-Infective, Thromboembolism, Myocardial Infarction, Humans, Mitral Valve Insufficiency, Female, Middle Aged, Antiphospholipid Syndrome, Pulmonary Embolism
Abstract: Nonbacterial thrombotic endocarditis is characterized by the presence of organized thrombi on cardiac valves, often associated with hypercoagulable states. There is a paucity of data regarding the predictors of mortality in patients with nonbacterial thrombotic endocarditis. Our primary aim was to identify predictors of in-hospital mortality in patients with nonbacterial thrombotic endocarditis.A systematic literature review of all published cases and case series was performed until May 2018 according to Preferred Reporting Items for Systematic Review and Meta-analyses statement guidelines. We applied random forest machine learning model to identify predictors of in-patient mortality in patients with nonbacterial thrombotic endocarditis.Our search generated a total of 163 patients (mean age, 46 ± 17 years; women, 69%) with newly diagnosed nonbacterial thrombotic endocarditis. The in-hospital mortality rate in the study cohort was 30%. Among the patients who died in the hospital, initial presentation of pulmonary embolism (12.2 vs. 2.6%), splenic (38.7 vs. 10.5%), and renal (40.8 vs. 9.6%) infarcts were higher compared to patients alive at the time of discharge. Higher rates of malignancy (71.4 vs. 39.4%, P = .0003) and lower rates of antiphospholipid syndrome (8.1 vs. 48.2%, P = .0001) were noted in deceased patients. Random forest machine learning analysis showed that older age, presence of antiphospholipid syndrome, splenic infarct, renal infarct, peripheral thromboembolism, pulmonary embolism, myocardial infarction, and mitral valve regurgitation were significantly associated with increased risk of in-hospital mortality.Patients admitted with nonbacterial thrombotic endocarditis have a high rate of in-hospital mortality. Factors including older age, presence of antiphospholipid syndrome, splenic/renal infarct, lower limb thromboembolism, pulmonary embolism, myocardial infarction, and mitral valve regurgitation were significantly associated with increased risk of in-hospital mortality in patients with nonbacterial thrombotic endocarditis.
Published: 2022

46. Long-Term Clinical Outcomes of Underdosed Direct Oral Anticoagulants in Patients with Atrial Fibrillation and Atrial Flutter

Author: Pradyumna Agasthi, Nirat Beohar, David R. Holmes, Mohamed Allam, Pattara Rattanawong, Sai Harika Pujari, Komandoor Srivathsan, Dan Sorajja, Win Kuang Shen, William K. Freeman, Ramila A. Mehta, Hicham El Masry, Farouk Mookadam, Eric H. Yang, Hasan Ashraf, Xiaoxi Yao, Anusha Shanbhag, Reza Arsanjani, Siva K. Mulpuru, and Peter A. Noseworthy
Subjects: Adult, Male, medicine.medical_specialty, Time Factors, Statistics, Nonparametric, Body Mass Index, Time, chemistry.chemical_compound, Edoxaban, Internal medicine, Atrial Fibrillation, medicine, Humans, Myocardial infarction, Stroke, Aged, Retrospective Studies, Analysis of Variance, Rivaroxaban, business.industry, Atrial fibrillation, General Medicine, Middle Aged, medicine.disease, Thrombosis, Atrial Flutter, chemistry, Cardiology, Female, Apixaban, business, Atrial flutter, Factor Xa Inhibitors, Follow-Up Studies, medicine.drug
Abstract: Background Although direct oral anticoagulants (DOACs) have been shown to be effective at reducing the risk of stroke in patients with atrial fibrillation/flutter (AF), they are sometimes underdosed off-label to mitigate their associated higher bleeding risk. We sought to evaluate frequency and clinical outcomes of inappropriate underdosing of DOACS in patients with AF. Methods We conducted a study of subjects with AF who had a clinical indication for stroke prophylaxis (with a congestive heart failure, hypertension, age ≥75 years, diabetes mellitus, stroke or transient ischemic attack, vascular disease, age 65 to 47 years, sex category [CHA2DS2-VASc] of 2 or greater) and were prescribed 1 of the 4 clinically approved DOACs (apixaban, rivaroxaban, dabigatran, or edoxaban). We compared all-cause mortality, composite of stroke and systemic embolism, composite of myocardial infarction (MI), acute coronary syndromes (ACS), and coronary revascularization, and major bleeding between patients appropriately dosed and inappropriately underdosed. Results A total of 8125 patients met inclusion criteria, with a mean follow up of 2.2 ± 2 years. Of those, 1724 patients (21.2%) were inappropriately dosed. After adjusting for baseline variables, there was no difference in all-cause mortality, risk of stroke or systemic embolism, International Society on Thrombosis and Haemostasis (ISTH) major bleeding, or composite of myocardial infarction, acute coronary syndromes, or coronary revascularization between patients appropriately dosed and inappropriately underdosed. In subgroup analysis, only apixaban demonstrated an increased incidence all-cause mortality (hazard ratio [HR] 1.24, 95% confidence interval [CI] 1.03-1.49) with inappropriate underdosing. There was no difference in the remaining clinical outcomes noted on subgroup analysis. Conclusion Underdosing of DOACs did not minimize risk of bleeding, systemic embolization or all-cause mortality in patients with AF. Inappropriate underdosing with apixaban in particular was associated with increased all-cause mortality.
Published: 2021
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47. Artificial Intelligence Trumps TAVI2-SCORE and CoreValve Score in Predicting 1-Year Mortality Post-Transcatheter Aortic Valve Replacement

Author: John P. Sweeney, Nirat Beohar, Mohamed Allam, Banveet K. Khetarpal, Matthew R. Buras, Sai Harika Pujari, David R. Holmes, Hasan Ashraf, Nithin R. Venepally, Pradyumna Agasthi, Kevin L. Greason, Farouk Mookadam, Floyd David Fortuin, Marlene Girardo, Andrew S. Tseng, Robert J. Siegel, Reza Arsanjani, and Mackram F. Eleid
Subjects: medicine.medical_specialty, Creatinine, medicine.diagnostic_test, Receiver operating characteristic, business.industry, medicine.medical_treatment, Retrospective cohort study, General Medicine, 030204 cardiovascular system & hematology, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, Blood pressure, Valve replacement, chemistry, Internal medicine, Cardiology, Medicine, 030212 general & internal medicine, Cardiac skeleton, Cardiology and Cardiovascular Medicine, business, Body mass index, Electrocardiography
Abstract: Background/purpose Machine learning has been used to predict procedural risk in patients undergoing various medical interventions and procedures. One-year mortality in patients after Transcatheter Aortic Valve Replacement (TAVR) has a wide range (from 8.5 to 24% in various studies). We sought to apply machine learning to determine predictors of one year mortality in patients undergoing TAVR. Methods/materials A retrospective study of 1055 patients who underwent TAVR (Jan 2014–June 2017) with one-year follow up was completed. Baseline demographics, clinical, electrocardiography (ECG), Computed Tomography (CT) and echocardiography data were abstracted. Variables with near zero variance or ≥50% missing data were excluded. The Gradient Boosting Machine learning (GBM) prediction model included 163 variables and was optimized using 5-fold cross-validation repeated 10-times. The receiver operator characteristic (ROC) for the GBM model was calculated to predict one-year mortality post TAVR, and then compared to the TAVI2-SCORE and CoreValve score. Results Among 1055 TAVR patients (mean age 80.9 ± 7.9 years, 42% female), 14.02% died at one year. 78% had balloon expandable valves placed. Based on GBM, the ten most predictive variables for one-year survival were cardiac power index, hemoglobin, systolic blood pressure, INR, diastolic blood pressure, body mass index, valve calcium score, serum creatinine, aortic annulus area, and albumin. The area under ROC to predict survival for the GBM model vs TAVI2-SCORE and CoreValve Score was 0.72 (95% CI 0.68–0.78) vs 0.56 (95%CI 0.51–0.62) and 0.53 (95% CI 0.47–0.59) respectively with p Conclusion The GBM model outperforms TAVI2-SCORE and CoreValve Score in predicting mortality one-year post TAVR.
Published: 2021
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48. Assessment of Image Quality for Selective Intracoronary Contrast-Injected CT Angiography in a Hybrid Angio-CT System: A Feasibility Study in Swine

Author: Sunghee Jung, Reza Arsanjani, Hackjoon Shim, Byunghwan Jeon, Byoung Kwon Lee, Seongmin Ha, Yeonggul Jang, Youngtaek Hong, Hyung Bok Park, Se-Il Park, Hyuk Jae Chang, and Sanghoon Shin
Subjects: Cardiac & Cardiovascular Systems, Intraclass correlation, Image quality, Computed Tomography Angiography, Swine, medicine.medical_treatment, Lumen (anatomy), Contrast Media, 030204 cardiovascular system & hematology, Coronary Angiography, Radiation Dosage, 03 medical and health sciences, 0302 clinical medicine, Image noise, Image Processing, Computer-Assisted, Medicine, Animals, cardiovascular diseases, Computed tomography angiography, medicine.diagnostic_test, business.industry, image quality enhancement, percutaneous coronary intervention, Percutaneous coronary intervention, General Medicine, Image Enhancement, Coronary Vessels, Contrast medium, 030220 oncology & carcinogenesis, Angiography, Feasibility Studies, Original Article, Female, business, Nuclear medicine
Abstract: PURPOSE To compare image quality in selective intracoronary contrast-injected computed tomography angiography (Selective-CTA) with that in conventional intravenous contrast-injected CTA (IV-CTA). MATERIALS AND METHODS Six pigs (35 to 40 kg) underwent both IV-CTA using an intravenous injection (60 mL) and Selective-CTA using an intracoronary injection (20 mL) through a guide-wire during/after percutaneous coronary intervention. Images of the common coronary artery were acquired. Scans were performed using a combined machine comprising an invasive coronary angiography suite and a 320-channel multi-slice CT scanner. Quantitative image quality parameters of CT attenuation, image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), mean lumen diameter (MLD), and mean lumen area (MLA) were measured and compared. Qualitative analysis was performed using intraclass correlation coefficient (ICC), which was calculated for analysis of interobserver agreement. RESULTS Quantitative image quality, determined by assessing the uniformity of CT attenuation (399.06 vs. 330.21, p
Published: 2021

49. National Cardiovascular Data Registry Model Predicts Long-Term Mortality in Patients Undergoing Percutaneous Coronary Interventions

Author: Panwen Wang, Chieh-Ju Chao, Pradyumna Agasthi, Reza Arsanjani, and Eric H. Yang
Subjects: medicine.medical_specialty, Percutaneous, Receiver operating characteristic, business.industry, Mortality rate, medicine.medical_treatment, Psychological intervention, Percutaneous coronary intervention, Logistic regression, Internal medicine, Conventional PCI, medicine, Pharmacology (medical), In patient, Cardiology and Cardiovascular Medicine, business
Abstract: National Cardiovascular Data Registry (NCDR)-based logistic regression model is available for clinicians to predict in-hospital all-cause mortality after a percutaneous coronary intervention (PCI). However, this model has never been used to predict long-term all-cause mortality after PCI. Therefore, we sought to test the ability of the NCDR model to predict the short- and long-term risk of all-cause mortality in patients undergoing PCI. All patients undergoing PCI in the Mayo Clinic Health System were enrolled in the Mayo Clinic CathPCI registry. Patient-level demographic, clinical, and angiographic data from January 2006 to December 2017 were extracted from the registry. Patients who underwent coronary artery bypass graft surgery (CABG) were excluded. The area under the receiver operator characteristic curve (AUC) was calculated to assess the ability of the NCDR model to predict outcomes of interest (6-month, 1-year, 2-year, and 5-year all-cause mortality) after PCI. A total of 17,356 unique patients were included for the final analysis after excluding 165 patients who underwent CABG surgery. The mean age was 66.9 ± 12.5 years, and 71% were men. The 6-month, 1-year, 2-year, and 5-year all-cause mortality rates were 4.2% (n = 737), 5.8% (n = 1,005), 8.06% (n = 1,399), and 14.2% (n = 2,472), respectively. The AUCs of the NCDR model to predict 6-month, 1-year, 2-year, and 5-year all-cause mortality were 0.84 (95% CI: 0.82–0.86), 0.82 (95% CI: 0.80–0.84), 0.80 (95% CI: 0.79–0.81), and 0.78 (95% CI: 0.77–0.79), respectively. The NCDR model was able to accurately predict both short- and long-term all-cause mortality after PCI.
Published: 2021
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50. Light on valvular bumps

Author: David Majdalany, Reza Arsanjani, and Tasneem Z. Naqvi
Subjects: medicine.medical_specialty, business.industry, Ultrasound, Medicine, Radiology, Nuclear Medicine and imaging, Radiology, business
Published: 2021
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