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4. Genetic architecture of cardiac dynamic flow volumes

Author

Gomes, Bruna, Singh, Aditya, O’Sullivan, Jack W., Schnurr, Theresia M., Goddard, Pagé C., Loong, Shaun, Amar, David, Hughes, J. Weston, Kostur, Mykhailo, Haddad, Francois, Salerno, Michael, Foo, Roger, Montgomery, Stephen B., Parikh, Victoria N., Meder, Benjamin, and Ashley, Euan A.

Published
2024
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9. Deep Learning-Derived Myocardial Strain

Author

Kwan, Alan C., Chang, Ernest W., Jain, Ishan, Theurer, John, Tang, Xiu, Francisco, Nadia, Haddad, Francois, Liang, David, Fábián, Alexandra, Ferencz, Andrea, Yuan, Neal, Merkely, Béla, Siegel, Robert, Cheng, Susan, Kovács, Attila, Tokodi, Márton, and Ouyang, David

Published
2024
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16. Identifying a stable and generalizable factor structure of major depressive disorder across three large longitudinal cohorts

Author

Schilsky, Richard L., Allen, Jennifer, Anderson, MaryAnn, Anstrom, Kevin, Araujo, Lucus, Arges, Kristine, Ardalan, Kaveh, Baldwin, Bridget, Balu, Suresh, Bashir, Mustafa R., Bhapkar, Manju, Bigelow, Robert, Black, Tanya, Blanco, Rosalia, Bloomfield, Gerald, Borkar, Durga, Bouk, Leah, Boulware, Ebony, Brugnoni, Nikki, Campbell, Erin, Campbell, Paul, Carin, Larry, Cassella, Tammy Jo, Cates, Tina, Montgomery, Ranee Chatterjee, Christian, Victoria, Choong, John, Cohen-Wolkowiez, Michael, Cook, Elizabeth, Cousins, Scott, Crawford, Ashley, Datta, Nisha, Daubert, Melissa, Davis, James, Dirkes, Jillian, Doan, Isabelle, Dockery, Marie, Douglas, Pamela S., Duckworth, Shelly, Dunham, Ashley, Dunn, Gary, Ebersohl, Ryan, Eckstrand, Julie, Fang, Vivienne, Flora, April, Ford, Emily, Foster, Lucia, Fraulo, Elizabeth, French, John, Ginsburg, Geoffrey S., Green, Cindy, Greene, Latoya, Guptill, Jeffrey, Hamel, Donna, Hamill, Jennifer, Harrington, Chris, Harrison, Rob, Hedges, Lauren, Heidenfelder, Brooke, Hernandez, Adrian F., Heydary, Cindy, Hicks, Tim, Hight, Lina, Hopkins, Deborah, Huang, Erich S., Huh, Grace, Hurst, Jillian, Inman, Kelly, Janas, Gemini, Jaffee, Glenn, Johnson, Janace, Keaton, Tiffanie, Khouri, Michel, King, Daniel, Korzekwinski, Jennifer, Koweek, Lynne H., Kuo, Anthony, Kwee, Lydia, Landis, Dawn, Lipsky, Rachele, Lopez, Desiree, Lowry, Carolyn, Marcom, Kelly, Marsolo, Keith, McAdams, Paige, McCall, Shannon, McGarrah, Robert, McGugan, John, Mee, Dani, Mervin-Blake, Sabrena, Mettu, Prithu, Meyer, Mathias, Meyers, Justin, Miller, Calire N., Moen, Rebecca, Muhlbaier, Lawrence H., Murphy, Michael, Neely, Ben, Newby, L. Kristin, Nicoldson, Jayne, Nguyen, Hoang, Nguyen, Maggie, O'Brien, Lori, Onal, Sumru, O'Quinn, Jeremey, Page, David, Pagidipati, Neha J., Parikh, Kishan, Palmer, Sarah R., Patrick-Lake, Bray, Pattison, Brenda, Pencina, Michael, Peterson, Eric D., Piccini, Jon, Poole, Terry, Povsic, Tom, Provencher, Alicia, Rabineau, Dawn, Rich, Annette, Rimmer, Susan, Schwartz, Fides, Serafin, Angela, Shah, Nishant, Shah, Svati, Shields, Kelly, Shipes, Steven, Shrader, Peter, Stiber, Jon, Sutton, Lynn, Swamy, Geeta, Thomas, Betsy, Torres, Sandra, Tucci, Debara, Twisdale, Anthony, Walker, Brooke, Whitney, Susan A., Williamson, Robin, Wilverding, Lauren, Wong, Charlene A., Wruck, Lisa, Young, Ellen, Perlmutter, Jane, Krug, Sarah, Bowman-Zatzkin, S. Whitney, Assimes, Themistocles, Bajaj, Vikram, Cheong, Maxwell, Das, Millie, Desai, Manisha, Fan, Alice C., Fleischmann, Dominik, Gambhir, Sanjiv S., Gold, Garry, Haddad, Francois, Hong, David, Langlotz, Curtis, Liao, Yaping J., Lu, Rong, Mahaffey, Kenneth W., Maron, David, McCue, Rebecca, Munshi, Rajan, Rodriguez, Fatima, Shashidhar, Sumana, Sledge, George, Spielman, Susie, Spitler, Ryan, Swope, Sue, Williams, Donna, Pepine, Carl J, Lantos, John D, Pignone, Michael, Heagerty, Patrick, Beskow, Laura, Bernard, Gordon, Abad, Kelley, Angi, Giulia, Califf, Robert M., Deang, Lawrence, Huynh, Joy, Liu, Manway, Mao, Cherry, Magdaleno, Michael, Marks, William J., Jr., Mega, Jessica, Miller, David, Ong, Nicole, Patel, Darshita, Ridaura, Vanessa, Shore, Scarlet, Short, Sarah, Tran, Michelle, Vu, Veronica, Wong, Celeste, Green, Robert C., Hernandez, John, Benge, Jolene, Negrete, Gislia, Sierra, Gelsey, Schaack, Terry, Tseng, Vincent W.S., Tharp, Jordan A., Reiter, Jacob E., Ferrer, Weston, Hong, David S., Doraiswamy, P. Murali, and Nickels, Stefanie

Published
2024
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19. Predicting post-operative right ventricular failure using video-based deep learning

Author

Shad, Rohan, Quach, Nicolas, Fong, Robyn, Kasinpila, Patpilai, Bowles, Cayley, Castro, Miguel, Guha, Ashrith, Suarez, Eddie, Jovinge, Stefan, Lee, Sangjin, Boeve, Theodore, Amsallem, Myriam, Tang, Xiu, Haddad, Francois, Shudo, Yasuhiro, Woo, Y. Joseph, Teuteberg, Jeffrey, Cunningham, John P., Langlotz, Curt P., and Hiesinger, William

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Non-invasive and cost effective in nature, the echocardiogram allows for a comprehensive assessment of the cardiac musculature and valves. Despite progressive improvements over the decades, the rich temporally resolved data in echocardiography videos remain underutilized. Human reads of echocardiograms reduce the complex patterns of cardiac wall motion, to a small list of measurements of heart function. Furthermore, all modern echocardiography artificial intelligence (AI) systems are similarly limited by design - automating measurements of the same reductionist metrics rather than utilizing the wealth of data embedded within each echo study. This underutilization is most evident in situations where clinical decision making is guided by subjective assessments of disease acuity, and tools that predict disease onset within clinically actionable timeframes are unavailable. Predicting the likelihood of developing post-operative right ventricular failure (RV failure) in the setting of mechanical circulatory support is one such clinical example. To address this, we developed a novel video AI system trained to predict post-operative right ventricular failure (RV failure), using the full spatiotemporal density of information from pre-operative echocardiography scans. We achieve an AUC of 0.729, specificity of 52% at 80% sensitivity and 46% sensitivity at 80% specificity. Furthermore, we show that our ML system significantly outperforms a team of human experts tasked with predicting RV failure on independent clinical evaluation. Finally, the methods we describe are generalizable to any cardiac clinical decision support application where treatment or patient selection is guided by qualitative echocardiography assessments., Comment: 12 pages, 3 figures

Published
2021
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20. Pulmonary Hypertension in the Context of Heart Failure With Preserved Ejection Fraction

Author

Inampudi, Chakradhari, Silverman, Daniel, Simon, Marc A, Leary, Peter J, Sharma, Kavita, Houston, Brian A, Vachiéry, Jean-Luc, Haddad, Francois, and Tedford, Ryan J

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Heart Disease, Clinical Research, Lung, Cardiovascular, Heart Failure, Humans, Hypertension, Pulmonary, Stroke Volume, Vascular Resistance, KEY WORDS, diastolic heart failure, heart failure with preserved ejection fraction, left heart disease, pulmonary hypertension, right ventricle, Clinical Sciences, Respiratory System, Cardiovascular medicine and haematology, Clinical sciences
Abstract

Heart failure with preserved ejection fraction (HFpEF) is the most common form of heart failure and frequently is associated with pulmonary hypertension (PH). HFpEF associated with PH may be difficult to distinguish from precapillary forms of PH, although this distinction is crucial because therapeutic pathways are divergent for the two conditions. A comprehensive and systematic approach using history, clinical examination, and noninvasive and invasive evaluation with and without provocative testing may be necessary for accurate diagnosis and phenotyping. After diagnosis, HFpEF associated with PH can be subdivided into isolated postcapillary pulmonary hypertension (IpcPH) and combined postcapillary and precapillary pulmonary hypertension (CpcPH) based on the presence or absence of elevated pulmonary vascular resistance. CpcPH portends a worse prognosis than IpcPH. Despite its association with reduced functional capacity and quality of life, heart failure hospitalizations, and higher mortality, therapeutic options focused on PH for HFpEF associated with PH remain limited. In this review, we aim to provide an updated overview on clinical definitions and hemodynamically characterized phenotypes of PH, pathophysiologic features, therapeutic strategies, and ongoing challenges in this patient population.

Published
2021

22. Clinical and Echocardiographic Diversity Associated With Physical Fitness in the Project Baseline Health Study: Implications for Heart Failure Staging

Author

CAUWENBERGHS, NICHOLAS, HADDAD, FRANCOIS, DAUBERT, MELISSA A., CHATTERJEE, RANEE, SALERNO, MICHAEL, MEGA, JESSICA L., HEIDENREICH, PAUL, HERNANDEZ, ADRIAN, AMSALLEM, MYRIAM, KOBAYASHI, YUKARI, MAHAFFEY, KENNETH W., SHAH, SVATI H., BLOOMFIELD, GERALD S., KUZNETSOVA, TATIANA, and DOUGLAS, PAMELA S.

Published
2023
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27. Evaluación multiparamétrica de la función ventricular derecha en la hipertensión arterial pulmonar asociada a cardiopatías congénitas

Author

Fournier, Emmanuelle, Selegny, Maëlle, Amsallem, Myriam, Haddad, Francois, Cohen, Sarah, Valdeolmillos, Estibaliz, Le Pavec, Jérôme, Humbert, Marc, Isorni, Marc-Antoine, Azarine, Arshid, Sitbon, Olivier, Jais, Xavier, Savale, Laurent, Montani, David, Fadel, Elie, Zoghbi, Joy, Belli, Emre, and Hascoët, Sebastien

Published
2023
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28. Cumulative Lifetime Burden of Cardiovascular Disease From Early Exposure to Air Pollution

Author

Kim, Juyong Brian, Prunicki, Mary, Haddad, Francois, Dant, Christopher, Sampath, Vanitha, Patel, Rushali, Smith, Eric, Akdis, Cezmi, Balmes, John, Snyder, Michael P, Wu, Joseph C, and Nadeau, Kari C

Subjects
Aging, Climate-Related Exposures and Conditions, Prevention, Cardiovascular, Heart Disease, Aetiology, 2.3 Psychological, social and economic factors, 2.2 Factors relating to the physical environment, Good Health and Well Being, Sustainable Cities and Communities, Adolescent, Adolescent Development, Adult, Age Factors, Aged, Aged, 80 and over, Air Pollutants, Air Pollution, Cardiovascular Diseases, Child, Child Development, Child, Preschool, Comorbidity, Environmental Exposure, Female, Global Health, Humans, Infant, Infant, Newborn, Male, Middle Aged, Risk Assessment, Risk Factors, Time Factors, Young Adult, air pollutants, environmental, cardiovascular abnormalities, cardiovascular disease, epithelial barrier, air pollutants, environmental, Cardiorespiratory Medicine and Haematology
Abstract

The disease burden associated with air pollution continues to grow. The World Health Organization (WHO) estimates ≈7 million people worldwide die yearly from exposure to polluted air, half of which-3.3 million-are attributable to cardiovascular disease (CVD), greater than from major modifiable CVD risks including smoking, hypertension, hyperlipidemia, and diabetes mellitus. This serious and growing health threat is attributed to increasing urbanization of the world's populations with consequent exposure to polluted air. Especially vulnerable are the elderly, patients with pre-existing CVD, and children. The cumulative lifetime burden in children is particularly of concern because their rapidly developing cardiopulmonary systems are more susceptible to damage and they spend more time outdoors and therefore inhale more pollutants. World Health Organization estimates that 93% of the world's children aged

Published
2020

31. Identifying consistent echocardiographic thresholds for risk stratification in pulmonary arterial hypertension

Author

Celestin, Bettia E., primary, Bagherzadeh, Shadi P., additional, Ichimura, Kenzo, additional, Santana, Everton J., additional, Sanchez, Pablo Amador, additional, Tobore, Tobore, additional, Hemnes, Anna R., additional, Noordegraaf, Anton Vonk, additional, Salerno, Michael, additional, Zamanian, Roham T., additional, Sweatt, Andrew J., additional, and Haddad, Francois, additional

Published
2024
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32. Association of left ventricular diastolic function with coronary artery calcium score: A Project Baseline Health Study

Author

Haddad, Francois, Cauwenberghs, Nicholas, Daubert, Melissa A., Kobayashi, Yukari, Bloomfield, Gerald S., Fleischman, Dominik, Koweek, Lynne, Maron, David J., Rodriguez, Fatima, Liao, Yaping Joyce, Moneghetti, Kegan, Amsallem, Myriam, Mega, Jessica, Hernandez, Adrian, Califf, Robert, Mahaffey, Kenneth W., Shah, Svati H., Kuznetsova, Tatiana, and Douglas, Pamela S.

Published
2022
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33. A longitudinal big data approach for precision health.

Author

Schüssler-Fiorenza Rose, Sophia Miryam, Contrepois, Kévin, Moneghetti, Kegan J, Zhou, Wenyu, Mishra, Tejaswini, Mataraso, Samson, Dagan-Rosenfeld, Orit, Ganz, Ariel B, Dunn, Jessilyn, Hornburg, Daniel, Rego, Shannon, Perelman, Dalia, Ahadi, Sara, Sailani, M Reza, Zhou, Yanjiao, Leopold, Shana R, Chen, Jieming, Ashland, Melanie, Christle, Jeffrey W, Avina, Monika, Limcaoco, Patricia, Ruiz, Camilo, Tan, Marilyn, Butte, Atul J, Weinstock, George M, Slavich, George M, Sodergren, Erica, McLaughlin, Tracey L, Haddad, Francois, and Snyder, Michael P

Subjects
Humans, Cardiovascular Diseases, Diabetes Mellitus, Type 2, Insulin Resistance, Risk Factors, Cohort Studies, Longitudinal Studies, Models, Biological, Adult, Aged, Middle Aged, Female, Male, Metabolome, Transcriptome, Exome, Gastrointestinal Microbiome, Precision Medicine, Big Data, Diabetes Mellitus, Type 2, Models, Biological, Immunology, Medical and Health Sciences
Abstract

Precision health relies on the ability to assess disease risk at an individual level, detect early preclinical conditions and initiate preventive strategies. Recent technological advances in omics and wearable monitoring enable deep molecular and physiological profiling and may provide important tools for precision health. We explored the ability of deep longitudinal profiling to make health-related discoveries, identify clinically relevant molecular pathways and affect behavior in a prospective longitudinal cohort (n = 109) enriched for risk of type 2 diabetes mellitus. The cohort underwent integrative personalized omics profiling from samples collected quarterly for up to 8 years (median, 2.8 years) using clinical measures and emerging technologies including genome, immunome, transcriptome, proteome, metabolome, microbiome and wearable monitoring. We discovered more than 67 clinically actionable health discoveries and identified multiple molecular pathways associated with metabolic, cardiovascular and oncologic pathophysiology. We developed prediction models for insulin resistance by using omics measurements, illustrating their potential to replace burdensome tests. Finally, study participation led the majority of participants to implement diet and exercise changes. Altogether, we conclude that deep longitudinal profiling can lead to actionable health discoveries and provide relevant information for precision health.

Published
2019

41. Factors associated with lower quarter performance-based balance and strength tests: a cross-sectional analysis from the project baseline health study.

Author

Taylor, Kenneth A., Carroll, Megan K., Short, Sarah A., Celestin, Bettia E., Gilbertson, Adam, Olivier, Christoph B., Haddad, Francois, and Cauwenberghs, Nicholas

Subjects
MUSCLE strength, BIOMARKERS, C-reactive protein, CARDIOVASCULAR diseases, HEALTH behavior
Abstract

Objectives: Physical performance tests are predictive of mortality and may screen for certain health conditions (e.g., sarcopenia); however, their diagnostic and/or prognostic value has primarily been studied in age-limited or disease-specific cohorts. Our objective was to identify the most salient characteristics associated with three lower quarter balance and strength tests in a cohort of community-dwelling adults. Methods: We applied a stacked elastic net approach on detailed data on sociodemographic, health and health-related behaviors, and biomarker data from the first visit of the Project Baseline Health Study (N = 2,502) to determine which variables were most associated with three physical performance measures: single-legged balance test (SLBT), sitting-rising test (SRT), and 30-second chair-stand test (30CST). Analyses were stratified by age (<65 and =65). Results: Female sex, Black or African American race, lower educational attainment, and health conditions such as non-alcoholic fatty liver disease and cardiovascular conditions (e.g., hypertension) were consistently associated with worse performance across all three tests. Several other health conditions were associated with either better or worse test performance, depending on age group and test. C-reactive protein was the only laboratory value associated with performance across age and test groups with some consistency. Conclusions: Our results highlighted previously identified and several novel salient factors associated with performance on the SLBT, SRT, and 30CST. These tests could represent affordable, noninvasive biomarkers of prevalent and/or future disease in adult individuals; future research should validate these findings. [ABSTRACT FROM AUTHOR]

Published
2024
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42. Novel Domain Knowledge-Encoding Algorithm Enables Label-Efficient Deep Learning for Cardiac CT Segmentation to Guide Atrial Fibrillation Treatment in a Pilot Dataset.

Author

Ganesan, Prasanth, Feng, Ruibin, Deb, Brototo, Tjong, Fleur V. Y., Rogers, Albert J., Ruipérez-Campillo, Samuel, Somani, Sulaiman, Clopton, Paul, Baykaner, Tina, Rodrigo, Miguel, Zou, James, Haddad, Francois, Zaharia, Matei, and Narayan, Sanjiv M.

Subjects
ARTIFICIAL neural networks, ARRHYTHMIA, ATRIAL fibrillation, DEEP learning, LEFT heart atrium
Abstract

Background: Segmenting computed tomography (CT) is crucial in various clinical applications, such as tailoring personalized cardiac ablation for managing cardiac arrhythmias. Automating segmentation through machine learning (ML) is hindered by the necessity for large, labeled training data, which can be challenging to obtain. This article proposes a novel approach for automated, robust labeling using domain knowledge to achieve high-performance segmentation by ML from a small training set. The approach, the domain knowledge-encoding (DOKEN) algorithm, reduces the reliance on large training datasets by encoding cardiac geometry while automatically labeling the training set. The method was validated in a hold-out dataset of CT results from an atrial fibrillation (AF) ablation study. Methods: The DOKEN algorithm parses left atrial (LA) structures, extracts "anatomical knowledge" by leveraging digital LA models (available publicly), and then applies this knowledge to achieve high ML segmentation performance with a small number of training samples. The DOKEN-labeled training set was used to train a nnU-Net deep neural network (DNN) model for segmenting cardiac CT in N = 20 patients. Subsequently, the method was tested in a hold-out set with N = 100 patients (five times larger than training set) who underwent AF ablation. Results: The DOKEN algorithm integrated with the nn-Unet model achieved high segmentation performance with few training samples, with a training to test ratio of 1:5. The Dice score of the DOKEN-enhanced model was 96.7% (IQR: 95.3% to 97.7%), with a median error in surface distance of boundaries of 1.51 mm (IQR: 0.72 to 3.12) and a mean centroid–boundary distance of 1.16 mm (95% CI: −4.57 to 6.89), similar to expert results (r = 0.99; p < 0.001). In digital hearts, the novel DOKEN approach segmented the LA structures with a mean difference for the centroid–boundary distances of −0.27 mm (95% CI: −3.87 to 3.33; r = 0.99; p < 0.0001). Conclusions: The proposed novel domain knowledge-encoding algorithm was able to perform the segmentation of six substructures of the LA, reducing the need for large training data sets. The combination of domain knowledge encoding and a machine learning approach could reduce the dependence of ML on large training datasets and could potentially be applied to AF ablation procedures and extended in the future to other imaging, 3D printing, and data science applications. [ABSTRACT FROM AUTHOR]

Published
2024
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44. Assessment of Right Ventricular Function in the Research Setting: Knowledge Gaps and Pathways Forward. An Official American Thoracic Society Research Statement

Author

Lahm, Tim, Douglas, Ivor S, Archer, Stephen L, Bogaard, Harm J, Chesler, Naomi C, Haddad, Francois, Hemnes, Anna R, Kawut, Steven M, Kline, Jeffrey A, Kolb, Todd M, Mathai, Stephen C, Mercier, Olaf, Michelakis, Evangelos D, Naeije, Robert, Tuder, Rubin M, Ventetuolo, Corey E, Vieillard-Baron, Antoine, Voelkel, Norbert F, Vonk-Noordegraaf, Anton, and Hassoun, Paul M

Subjects
Cardiovascular, Heart Disease, Lung, Rare Diseases, Animals, Humans, Research, Societies, Medical, United States, Ventricular Dysfunction, Right, Ventricular Function, Right, right ventricle, pulmonary hypertension, pulmonary embolism, acute respiratory distress syndrome, pulmonary circulation, American Thoracic Society Assembly on Pulmonary Circulation, Medical and Health Sciences, Respiratory System
Abstract

BackgroundRight ventricular (RV) adaptation to acute and chronic pulmonary hypertensive syndromes is a significant determinant of short- and long-term outcomes. Although remarkable progress has been made in the understanding of RV function and failure since the meeting of the NIH Working Group on Cellular and Molecular Mechanisms of Right Heart Failure in 2005, significant gaps remain at many levels in the understanding of cellular and molecular mechanisms of RV responses to pressure and volume overload, in the validation of diagnostic modalities, and in the development of evidence-based therapies.MethodsA multidisciplinary working group of 20 international experts from the American Thoracic Society Assemblies on Pulmonary Circulation and Critical Care, as well as external content experts, reviewed the literature, identified important knowledge gaps, and provided recommendations.ResultsThis document reviews the knowledge in the field of RV failure, identifies and prioritizes the most pertinent research gaps, and provides a prioritized pathway for addressing these preclinical and clinical questions. The group identified knowledge gaps and research opportunities in three major topic areas: 1) optimizing the methodology to assess RV function in acute and chronic conditions in preclinical models, human studies, and clinical trials; 2) analyzing advanced RV hemodynamic parameters at rest and in response to exercise; and 3) deciphering the underlying molecular and pathogenic mechanisms of RV function and failure in diverse pulmonary hypertension syndromes.ConclusionsThis statement provides a roadmap to further advance the state of knowledge, with the ultimate goal of developing RV-targeted therapies for patients with RV failure of any etiology.

Published
2018

45. Author Correction: Circulating Biomarkers to Identify Responders in Cardiac Cell therapy.

Author

Jokerst, Jesse V, Cauwenberghs, Nicholas, Kuznetsova, Tatiana, Haddad, Francois, Sweeney, Timothy, Hou, Jiayi, Rosenberg-Hasson, Yael, Zhao, Eric, Schutt, Robert, Bolli, Roberto, Traverse, Jay H, Pepine, Carl J, Henry, Timothy D, Schulman, Ivonne H, Moyé, Lem, Taylor, Doris A, and Yang, Phillip C

Abstract

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

Published
2018

47. Temporal changes in soluble angiotensin-converting enzyme 2 associated with metabolic health, body composition, and proteome dynamics during a weight loss diet intervention: a randomized trial with implications for the COVID-19 pandemic

Author

Cauwenberghs, Nicholas, Prunicki, Mary, Sabovčik, František, Perelman, Dalia, Contrepois, Kévin, Li, Xiao, Snyder, Michael P, Nadeau, Kari C, Kuznetsova, Tatiana, Haddad, Francois, and Gardner, Christopher D

Published
2021
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49. CTA pulmonary artery enlargement in patients with severe aortic stenosis: Prognostic impact after TAVR

Author

Turner, Valery L., Jubran, Ayman, Kim, Juyong Brian, Maret, Eva, Moneghetti, Kegan J., Haddad, Francois, Amsallem, Myriam, Codari, Marina, Hinostroza, Virginia, Mastrodicasa, Domenico, Sailer, Anna M., Kobayashi, Yukari, Nishi, Takeshi, Yeung, Alan C., Watkins, Amelia C., Lee, Anson M., Miller, D. Craig, Fischbein, Michael P., Fearon, William F., Willemink, Martin J., and Fleischmann, Dominik

Published
2021
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50. Abstract 14174: Abnormal Exercise Electrocardiography With Normal Stress Echocardiography is Associated With Increased Subclinical Coronary Atherosclerosis: Insights From the Project Baseline Health Study

Author

Duran, Jessica M, Nguyen, Maggie, Haddad, Francois, Cauwenberghs, Nicholas, Kuznetsova, Tatiana, Bloomfield, Gerald S, Mahaffey, Kenneth, Hernandez, Adrian F, Mega, Jessica L, Short, Sarah, Douglas, Pamela S, Shah, Svati H, and Daubert, Melissa A

Published
2022
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