1
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Palermi S, Vecchiato M, Saglietto A, Niederseer D, Oxborough D, Ortega-Martorell S, Olier I, Castelletti S, Baggish A, Maffessanti F, Biffi A, D'Andrea A, Zorzi A, Cavarretta E, D'Ascenzi F. Unlocking the potential of artificial intelligence in sports cardiology: does it have a role in evaluating athlete's heart? Eur J Prev Cardiol 2024; 31:470-482. [PMID: 38198776 DOI: 10.1093/eurjpc/zwae008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 01/01/2024] [Accepted: 01/03/2024] [Indexed: 01/12/2024]
Abstract
The integration of artificial intelligence (AI) technologies is evolving in different fields of cardiology and in particular in sports cardiology. Artificial intelligence offers significant opportunities to enhance risk assessment, diagnosis, treatment planning, and monitoring of athletes. This article explores the application of AI in various aspects of sports cardiology, including imaging techniques, genetic testing, and wearable devices. The use of machine learning and deep neural networks enables improved analysis and interpretation of complex datasets. However, ethical and legal dilemmas must be addressed, including informed consent, algorithmic fairness, data privacy, and intellectual property issues. The integration of AI technologies should complement the expertise of physicians, allowing for a balanced approach that optimizes patient care and outcomes. Ongoing research and collaborations are vital to harness the full potential of AI in sports cardiology and advance our management of cardiovascular health in athletes.
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Affiliation(s)
- Stefano Palermi
- Public Health Department, University of Naples Federico II, via Pansini 5, 80131 Naples, Italy
| | - Marco Vecchiato
- Sports and Exercise Medicine Division, Department of Medicine, University of Padova, 35128 Padova, Italy
| | - Andrea Saglietto
- Division of Cardiology, Cardiovascular and Thoracic Department, 'Citta della Salute e della Scienza' Hospital, 10129 Turin, Italy
- Department of Medical Sciences, University of Turin, 10129 Turin, Italy
| | - David Niederseer
- Department of Cardiology, University Heart Center Zurich, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland
| | - David Oxborough
- Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool, UK
| | - Sandra Ortega-Martorell
- Data Science Research Centre, Liverpool John Moores University, Liverpool, UK
- Liverpool Centre for Cardiovascular Science, Liverpool John Moores University, Liverpool, UK
| | - Ivan Olier
- Data Science Research Centre, Liverpool John Moores University, Liverpool, UK
- Liverpool Centre for Cardiovascular Science, Liverpool John Moores University, Liverpool, UK
| | - Silvia Castelletti
- Cardiology Department, Istituto Auxologico Italiano IRCCS, 20149 Milan, Italy
| | - Aaron Baggish
- Cardiovascular Performance Program, Massachusetts General Hospital, Boston, MA 02114, USA
| | | | - Alessandro Biffi
- Med-Ex, Medicine & Exercise, Medical Partner Scuderia Ferrari, 00187 Rome, Italy
| | - Antonello D'Andrea
- Department of Cardiology, Umberto I Hospital, 84014 Nocera Inferiore, Italy
| | - Alessandro Zorzi
- Department of Cardiac, Thoracic and Vascular Sciences and Public Health, University of Padova, 35128 Padova, Italy
| | - Elena Cavarretta
- Department of Medical-Surgical Sciences and Biotechnologies, Sapienza University of Rome, 04100 Latina, Italy
- Mediterranea Cardiocentro, 80122 Naples, Italy
| | - Flavio D'Ascenzi
- Department of Medical Biotechnologies, Division of Cardiology, University of Siena, 53100 Siena, Italy
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2
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Bernardino G, Sepúlveda-Martínez Á, Rodríguez-López M, Prat-González S, Pajuelo C, Perea RJ, Caralt MT, Crovetto F, González Ballester MA, Sitges M, Bijnens B, Crispi F. Association of central obesity with unique cardiac remodelling in young adults born small for gestational age. Eur Heart J Cardiovasc Imaging 2023:6986711. [PMID: 36644919 DOI: 10.1093/ehjci/jeac262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 12/05/2022] [Indexed: 01/17/2023] Open
Abstract
AIMS Being born small for gestational age (SGA, 10% of all births) is associated with increased risk of cardiovascular mortality in adulthood together with lower exercise tolerance, but mechanistic pathways are unclear. Central obesity is known to worsen cardiovascular outcomes, but it is uncertain how it affects the heart in adults born SGA. We aimed to assess whether central obesity makes young adults born SGA more susceptible to cardiac remodelling and dysfunction. METHODS AND RESULTS A perinatal cohort from a tertiary university hospital in Spain of young adults (30-40 years) randomly selected, 80 born SGA (birth weight below 10th centile) and 75 with normal birth weight (controls) was recruited. We studied the associations between SGA and central obesity (measured via the hip-to-waist ratio and used as a continuous variable) and cardiac regional structure and function, assessed by cardiac magnetic resonance using statistical shape analysis. Both SGA and waist-to-hip were highly associated to cardiac shape (F = 3.94, P < 0.001; F = 5.18, P < 0.001 respectively) with a statistically significant interaction (F = 2.29, P = 0.02). While controls tend to increase left ventricular end-diastolic volumes, mass and stroke volume with increasing waist-to-hip ratio, young adults born SGA showed a unique response with inability to increase cardiac dimensions or mass resulting in reduced stroke volume and exercise capacity. CONCLUSION SGA young adults show a unique cardiac adaptation to central obesity. These results support considering SGA as a risk factor that may benefit from preventive strategies to reduce cardiometabolic risk.
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Affiliation(s)
- Gabriel Bernardino
- CREATIS, UMR 5220, U1294, University Lyon, Université Claude Bernard Lyon 1, INSA-Lyon, CNRS, Inserm, 21 Av. Jean Capelle O, Villeurbanne 69621, France
| | - Álvaro Sepúlveda-Martínez
- BCNatal-Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), Institut Clínic de Ginecologia Obstetricia i Neonatologia, Centre for Biomedical Research on Rare Diseases (CIBER-ER), Universitat de Barcelona, 1 Sabino Arana, Barcelona 08028, Spain.,Fetal Medicine Unit, Department of Obstetrics and Gynecology, Hospital Clínico de la Universidad de Chile, 999 Dr. Carlos Lorca Tobar, Independencia, Región Metropolitana, Santiago de Chile 13108, Chile
| | - Mérida Rodríguez-López
- BCNatal-Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), Institut Clínic de Ginecologia Obstetricia i Neonatologia, Centre for Biomedical Research on Rare Diseases (CIBER-ER), Universitat de Barcelona, 1 Sabino Arana, Barcelona 08028, Spain.,Public Health and Epidemiology Department & Clinical Specialties Department, Pontificia Universidad Javeriana Seccional Cali, Cl. 18 #118-250, Barrio Pance, Cali, Valle del Cauca 760031, Colombia
| | - Susanna Prat-González
- Institut Clínic Cardiovascular, Hospital Clínic, Centre for Biomedical Research on CardioVascular Diseases (CIBERCV), Universitat de Barcelona, 170 Villarroel, Barcelona 08036, Spain.,Institut d'Investigacions Biomèdiques August Pi i Sunyer, 149 Roselló, Barcelona 08036, Spain
| | - Carolina Pajuelo
- Centre de Diagnòstic per la Imatge, Hospital Clínic, Universitat de Barcelona, 170 Villarroel, Barcelona 08036, Spain
| | - Rosario J Perea
- Institut d'Investigacions Biomèdiques August Pi i Sunyer, 149 Roselló, Barcelona 08036, Spain.,Centre de Diagnòstic per la Imatge, Hospital Clínic, Universitat de Barcelona, 170 Villarroel, Barcelona 08036, Spain
| | - Maria T Caralt
- Institut d'Investigacions Biomèdiques August Pi i Sunyer, 149 Roselló, Barcelona 08036, Spain
| | - Francesca Crovetto
- BCNatal-Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), Institut Clínic de Ginecologia Obstetricia i Neonatologia, Centre for Biomedical Research on Rare Diseases (CIBER-ER), Universitat de Barcelona, 1 Sabino Arana, Barcelona 08028, Spain.,Institut d'Investigacions Biomèdiques August Pi i Sunyer, 149 Roselló, Barcelona 08036, Spain.,Public Health and Epidemiology Department & Clinical Specialties Department, Pontificia Universidad Javeriana Seccional Cali, Cl. 18 #118-250, Barrio Pance, Cali, Valle del Cauca 760031, Colombia
| | - Miguel A González Ballester
- BCN Medtech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 122 Tànger, Barcelona 08018, Spain.,ICREA, 23 Passeig de Lluís Companys, Barcelona 08010, Spain
| | - Marta Sitges
- Institut Clínic Cardiovascular, Hospital Clínic, Centre for Biomedical Research on CardioVascular Diseases (CIBERCV), Universitat de Barcelona, 170 Villarroel, Barcelona 08036, Spain.,Institut d'Investigacions Biomèdiques August Pi i Sunyer, 149 Roselló, Barcelona 08036, Spain
| | - Bart Bijnens
- Institut d'Investigacions Biomèdiques August Pi i Sunyer, 149 Roselló, Barcelona 08036, Spain.,ICREA, 23 Passeig de Lluís Companys, Barcelona 08010, Spain
| | - Fàtima Crispi
- BCNatal-Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), Institut Clínic de Ginecologia Obstetricia i Neonatologia, Centre for Biomedical Research on Rare Diseases (CIBER-ER), Universitat de Barcelona, 1 Sabino Arana, Barcelona 08028, Spain.,Institut d'Investigacions Biomèdiques August Pi i Sunyer, 149 Roselló, Barcelona 08036, Spain
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3
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Asheghan MM, Javadikasgari H, Attary T, Rouhollahi A, Straughan R, Willi JN, Awal R, Sabe A, de la Cruz KI, Nezami FR. Predicting one-year left ventricular mass index regression following transcatheter aortic valve replacement in patients with severe aortic stenosis: A new era is coming. Front Cardiovasc Med 2023; 10:1130152. [PMID: 37082454 PMCID: PMC10111021 DOI: 10.3389/fcvm.2023.1130152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 03/16/2023] [Indexed: 04/22/2023] Open
Abstract
Aortic stenosis (AS) is the most common valvular heart disease in the western world, particularly worrisome with an ever-aging population wherein postoperative outcome for aortic valve replacement is strongly related to the timing of surgery in the natural course of disease. Yet, guidelines for therapy planning overlook insightful, quantified measures from medical imaging to educate clinical decisions. Herein, we leverage statistical shape analysis (SSA) techniques combined with customized machine learning methods to extract latent information from segmented left ventricle (LV) shapes. This enabled us to predict left ventricular mass index (LVMI) regression a year after transcatheter aortic valve replacement (TAVR). LVMI regression is an expected phenomena in patients undergone aortic valve replacement reported to be tightly correlated with survival one and five year after the intervention. In brief, LV geometries were extracted from medical images of a cohort of AS patients using deep learning tools, and then analyzed to create a set of statistical shape models (SSMs). Then, the supervised shape features were extracted to feed a support vector regression (SVR) model to predict the LVMI regression. The average accuracy of the predictions was validated against clinical measurements calculating root mean square error and R 2 score which yielded the satisfactory values of 0.28 and 0.67, respectively, on test data. Our work reveals the promising capability of advanced mathematical and bioinformatics approaches such as SSA and machine learning to improve medical output prediction and treatment planning.
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Affiliation(s)
- Mohammad Mostafa Asheghan
- Division of Thoracic and Cardiac Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Hoda Javadikasgari
- Division of Thoracic and Cardiac Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Taraneh Attary
- Bio-Intelligence Unit, Sharif Brain Center, Electrical Engineering Department, Sharif University of Technology, Tehran, Iran
| | - Amir Rouhollahi
- Division of Thoracic and Cardiac Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Ross Straughan
- Division of Thoracic and Cardiac Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - James Noel Willi
- Division of Thoracic and Cardiac Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Rabina Awal
- Mechanical Engineering Department, University of Louisiana at Lafayette, Louisiana, LA, United States
| | - Ashraf Sabe
- Division of Thoracic and Cardiac Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Kim I. de la Cruz
- Division of Thoracic and Cardiac Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Farhad R. Nezami
- Division of Thoracic and Cardiac Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Correspondence: Farhad R. Nezami
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4
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Nguyen MB, Villemain O, Friedberg MK, Lovstakken L, Rusin CG, Mertens L. Artificial intelligence in the pediatric echocardiography laboratory: Automation, physiology, and outcomes. FRONTIERS IN RADIOLOGY 2022; 2:881777. [PMID: 37492680 PMCID: PMC10365116 DOI: 10.3389/fradi.2022.881777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 08/01/2022] [Indexed: 07/27/2023]
Abstract
Artificial intelligence (AI) is frequently used in non-medical fields to assist with automation and decision-making. The potential for AI in pediatric cardiology, especially in the echocardiography laboratory, is very high. There are multiple tasks AI is designed to do that could improve the quality, interpretation, and clinical application of echocardiographic data at the level of the sonographer, echocardiographer, and clinician. In this state-of-the-art review, we highlight the pertinent literature on machine learning in echocardiography and discuss its applications in the pediatric echocardiography lab with a focus on automation of the pediatric echocardiogram and the use of echo data to better understand physiology and outcomes in pediatric cardiology. We also discuss next steps in utilizing AI in pediatric echocardiography.
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Affiliation(s)
- Minh B. Nguyen
- Division of Cardiology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
- Department of Pediatric Cardiology, Baylor College of Medicine, Houston, TX, United States
| | - Olivier Villemain
- Division of Cardiology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
| | - Mark K. Friedberg
- Division of Cardiology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
| | - Lasse Lovstakken
- Centre for Innovative Ultrasound Solutions and Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Craig G. Rusin
- Department of Pediatric Cardiology, Baylor College of Medicine, Houston, TX, United States
| | - Luc Mertens
- Division of Cardiology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
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5
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Benkarim O, Paquola C, Park BY, Kebets V, Hong SJ, Vos de Wael R, Zhang S, Yeo BTT, Eickenberg M, Ge T, Poline JB, Bernhardt BC, Bzdok D. Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging. PLoS Biol 2022; 20:e3001627. [PMID: 35486643 PMCID: PMC9094526 DOI: 10.1371/journal.pbio.3001627] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 05/11/2022] [Accepted: 04/11/2022] [Indexed: 12/18/2022] Open
Abstract
Brain imaging research enjoys increasing adoption of supervised machine learning for single-participant disease classification. Yet, the success of these algorithms likely depends on population diversity, including demographic differences and other factors that may be outside of primary scientific interest. Here, we capitalize on propensity scores as a composite confound index to quantify diversity due to major sources of population variation. We delineate the impact of population heterogeneity on the predictive accuracy and pattern stability in 2 separate clinical cohorts: the Autism Brain Imaging Data Exchange (ABIDE, n = 297) and the Healthy Brain Network (HBN, n = 551). Across various analysis scenarios, our results uncover the extent to which cross-validated prediction performances are interlocked with diversity. The instability of extracted brain patterns attributable to diversity is located preferentially in regions part of the default mode network. Collectively, our findings highlight the limitations of prevailing deconfounding practices in mitigating the full consequences of population diversity.
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Affiliation(s)
- Oualid Benkarim
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Canada
| | - Casey Paquola
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Canada
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Jülich, Germany
| | - Bo-yong Park
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Canada
- Department of Data Science, Inha University, Incheon, South Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Sungkyunkwan University, Suwon, South Korea
| | - Valeria Kebets
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Canada
| | - Seok-Jun Hong
- Center for Neuroscience Imaging Research, Institute for Basic Science, Sungkyunkwan University, Suwon, South Korea
- Center for the Developing Brain, Child Mind Institute, New York, New York, United States of America
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
| | - Reinder Vos de Wael
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Canada
| | - Shaoshi Zhang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | - B. T. Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | | | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Jean-Baptiste Poline
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Canada
| | - Boris C. Bernhardt
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Canada
| | - Danilo Bzdok
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Canada
- Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, Canada
- School of Computer Science, McGill University, Montreal, Canada
- Mila—Quebec Artificial Intelligence Institute, Montreal, Canada
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6
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Crispi F, Rodríguez-López M, Bernardino G, Sepúlveda-Martínez Á, Prat-González S, Pajuelo C, Perea RJ, Caralt MT, Casu G, Vellvé K, Crovetto F, Burgos F, De Craene M, Butakoff C, González Ballester MÁ, Blanco I, Sitges M, Bijnens B, Gratacós E. Exercise Capacity in Young Adults Born Small for Gestational Age. JAMA Cardiol 2021; 6:1308-1316. [PMID: 34287644 DOI: 10.1001/jamacardio.2021.2537] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Importance Being born small for gestational age (SGA), approximately 10% of all births, is associated with increased risk of cardiovascular mortality in adulthood, but mechanistic pathways are unclear. Cardiac remodeling and dysfunction occur in fetuses SGA and children born SGA, but it is uncertain whether and how these changes persist into adulthood. Objective To evaluate baseline cardiac function and structure and exercise capacity in young adults born SGA. Design, Setting, and Participants This cohort study conducted from January 2015 to January 2018 assessed a perinatal cohort born at a tertiary university hospital in Spain between 1975 and 1995. Participants included 158 randomly selected young adults aged 20 to 40 years born SGA (birth weight below the 10th centile) or with intrauterine growth within standard reference ranges (controls). Participants provided their medical history, filled out questionnaires regarding smoking and physical activity habits, and underwent incremental cardiopulmonary exercise stress testing, cardiac magnetic resonance imaging, and a physical examination, with blood pressure, glucose level, and lipid profile data collected. Exposure Being born SGA. Main Outcomes and Measures Cardiac structure and function assessed by cardiac magnetic resonance imaging, including biventricular end-diastolic shape analysis. Exercise capacity assessed by incremental exercise stress testing. Results This cohort study included 81 adults born SGA (median age at study, 34.4 years [IQR, 30.8-36.7 years]; 43 women [53%]) and 77 control participants (median age at study, 33.7 years [interquartile range (IQR), 31.0-37.1 years]; 33 women [43%]). All participants were of White race/ethnicity and underwent imaging, whereas 127 participants (80% of the cohort; 66 control participants and 61 adults born SGA) completed the exercise test. Cardiac shape analysis showed minor changes at rest in right ventricular geometry (DeLong test z, 2.2098; P = .02) with preserved cardiac function in individuals born SGA. However, compared with controls, adults born SGA had lower exercise capacity, with decreased maximal workload (mean [SD], 180 [62] W vs 214 [60] W; P = .006) and oxygen consumption (median, 26.0 mL/min/kg [IQR, 21.5-33.5 mL/min/kg vs 29.5 mL/min/kg [IQR, 24.0-36.0 mL/min/kg]; P = .02). Exercise capacity was significantly correlated with left ventricular mass (ρ = 0.7934; P < .001). Conclusions and Relevance This cohort of young adults born SGA had markedly reduced exercise capacity. These results support further research to clarify the causes of impaired exercise capacity and the potential association with increased cardiovascular mortality among adults born SGA.
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Affiliation(s)
- Fàtima Crispi
- Fetal Medicine Research Center, BCNatal-Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), Institut Clínic de Ginecologia Obstetricia i Neonatologia, Universitat de Barcelona, Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain.,Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
| | - Mérida Rodríguez-López
- Fetal Medicine Research Center, BCNatal-Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), Institut Clínic de Ginecologia Obstetricia i Neonatologia, Universitat de Barcelona, Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain.,Pontificia Universidad Javeriana seccional Cali, Cali, Colombia
| | - Gabriel Bernardino
- BCN Medtech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Álvaro Sepúlveda-Martínez
- Fetal Medicine Research Center, BCNatal-Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), Institut Clínic de Ginecologia Obstetricia i Neonatologia, Universitat de Barcelona, Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain.,Fetal Medicine Unit, Department of Obstetrics and Gynecology, Hospital Clínico de la Universidad de Chile, Santiago de Chile, Chile
| | - Susanna Prat-González
- Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain.,Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Centre for Biomedical Research on CardioVascular Diseases (CIBERCV), Barcelona, Spain
| | - Carolina Pajuelo
- Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Centre for Biomedical Research on CardioVascular Diseases (CIBERCV), Barcelona, Spain
| | - Rosario J Perea
- Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain.,Centre de Diagnòstic per la Imatge, Hospital Clínic, Universitat de Barcelona, Barcelona, Spain
| | - Maria T Caralt
- Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain.,Centre de Diagnòstic per la Imatge, Hospital Clínic, Universitat de Barcelona, Barcelona, Spain
| | - Giulia Casu
- Fetal Medicine Research Center, BCNatal-Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), Institut Clínic de Ginecologia Obstetricia i Neonatologia, Universitat de Barcelona, Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
| | - Kilian Vellvé
- Fetal Medicine Research Center, BCNatal-Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), Institut Clínic de Ginecologia Obstetricia i Neonatologia, Universitat de Barcelona, Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
| | - Francesca Crovetto
- Fetal Medicine Research Center, BCNatal-Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), Institut Clínic de Ginecologia Obstetricia i Neonatologia, Universitat de Barcelona, Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
| | - Felip Burgos
- Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain.,Respiratory Medicine Department, Hospital Clínic, Universitat de Barcelona, Centre for Biomedical Research on Respiratory Diseases (CIBERES), Barcelona, Spain
| | | | | | - Miguel Á González Ballester
- BCN Medtech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.,Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain
| | - Isabel Blanco
- Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain.,Respiratory Medicine Department, Hospital Clínic, Universitat de Barcelona, Centre for Biomedical Research on Respiratory Diseases (CIBERES), Barcelona, Spain
| | - Marta Sitges
- Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain.,Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Centre for Biomedical Research on CardioVascular Diseases (CIBERCV), Barcelona, Spain
| | - Bart Bijnens
- Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain.,Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain
| | - Eduard Gratacós
- Fetal Medicine Research Center, BCNatal-Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), Institut Clínic de Ginecologia Obstetricia i Neonatologia, Universitat de Barcelona, Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain.,Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
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