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Schuuring MJ, Anwer S, Petersen SE, Moharem-Elgamal S, Muraru D. Social media for cardiac imagers: a review. Eur Heart J Cardiovasc Imaging 2024:jeae109. [PMID: 38650541 DOI: 10.1093/ehjci/jeae109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 04/15/2024] [Accepted: 04/16/2024] [Indexed: 04/25/2024] Open
Abstract
Cardiac imaging plays a pivotal role in the diagnosis and management of cardiovascular diseases. In the burgeoning landscape of digital technology and social media platforms, it becomes essential for cardiac imagers to know how to effectively increase the visibility and the impact of their activity. With the availability of social sites like X (formerly Twitter), Instagram and Facebook, cardiac imagers can now reach a wider audience and engage with peers, sharing their findings, insights, and discussions. The integration of persistent identifiers, such as Digital Object Identifiers (DOIs), facilitates traceability and citation of cardiac imaging publications across various digital platforms, further enhancing their discoverability. To maximize visibility, practical advice is provided, including the use of visually engaging infographics and videos, as well as the strategic implementation of relevant hashtags and keywords. These techniques can significantly improve the discoverability of cardiac imaging research through search engine optimization and social media algorithms. Tracking impact and engagement is crucial in the digital age, and this review discusses various metrics and tools to gauge the reach and influence of cardiac imaging publications. This includes traditional citation-based metrics and altmetrics. Moreover, this review underscores the importance of creating and updating professional profiles on social platforms and participating in relevant scientific communities online. The adoption of digital technology, social platforms, and a strategic approach to publication sharing can empower cardiac imaging professionals to enhance the visibility and impact of their research, ultimately advancing the field and improving patient care.
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Affiliation(s)
- Mark J Schuuring
- Amsterdam Cardiovascular Science, University of Amsterdam, Amsterdam, The Netherlands
- Department of Cardiology, Medisch Spectrum Twente, Enschede, the Netherlands
| | - Shehab Anwer
- Department of Cardiology, University Heart Center, Zurich, Switzerland
| | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London, EC1 M 6BQ, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, EC1A 7BE, London, UK
| | - Sarah Moharem-Elgamal
- Department of Cardiology, Liverpool Heart and Chest Hospital, Liverpool, United Kingdom
| | - Denisa Muraru
- Department of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
- Department of Cardiology, Istituto Auxologico Italiano, IRCCS, Milan, Italy
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Galli E, Soliman-Aboumarie H, Gargani L, Szymański P, Gimelli A, Petersen SE, Sade LE, Stankovic I, Donal E, Cosyns B, Agricola E, Dweck MR, Ajmone Marsan N, Delgado V, Muraru D. EACVI survey on radiation exposure in interventional echocardiography. Eur Heart J Cardiovasc Imaging 2024:jeae086. [PMID: 38635738 DOI: 10.1093/ehjci/jeae086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 03/15/2024] [Indexed: 04/20/2024] Open
Abstract
AIMS The European Association of Cardiovascular Imaging (EACVI) Scientific Initiatives Committee performed a global survey on radiation exposure in interventional echocardiography. The survey aimed to collect data on local practices for radioprotection in interventional echocardiography and to assess the awareness of echocardiography operators about radiation-related risks. METHODS AND RESULTS A total of 258 interventional echocardiographers from 52 different countries (48% European) responded to the survey. One hundred twenty-two (47%) participants were women. Two-thirds (76%) of interventional echocardiographers worked in tertiary care/university hospitals. Interventional echocardiography was the main clinical activity for 34% of the survey participants. The median time spent in the cath-lab for the echocardiographic monitoring of structural heart procedures was 10 (5-20) hours/month. Despite this, only 28% of interventional echocardiographers received periodic training and certification in radioprotection and 72% of them did not know their annual radiation dose. The main adopted personal protection devices were lead aprons and thyroid collars (95% and 92% of use, respectively). Dedicated architectural protective shielding was not available for 33% of interventional echocardiographers. Nearly two-thirds of responders thought that the radiation exposure of interventional echocardiographers was higher than that of interventional cardiologists and 72% claimed for an improvement in the radioprotection measures. CONCLUSION Radioprotection measures for interventional echocardiographers are widely variable across centres. Radioprotection devices are often underused by interventional echocardiographers, portending an increased radiation-related risk. International scientific societies working in the field should collaborate to endorse radioprotection training, promote reliable radiation dose assessment, and support the adoption of radioprotection shielding dedicated to interventional echocardiographers.
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Affiliation(s)
- E Galli
- University of Rennes, CHU Rennes, Inserm, LTSI-UMR 1099, F-35000 Rennes, France
| | - H Soliman-Aboumarie
- Department of Anesthetics and Critical Care, Harefield Hospital, Royal Brompton and Harefield Hospitals, Guy's and St. Thomas NHS Foundation Trust, London, UK
- School of Cardiovascular Sciences and Medicine, King's College, London, UK
| | - L Gargani
- Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa-Pisa, Italy
| | - P Szymański
- Centre for Postgraduate Medical Education, Warsaw, Poland
- Centre for Clinical Cardiology, National Institute of Medicine MSWiA, Warsaw, Poland
| | - A Gimelli
- Department of Imaging, Fondazione Toscana Gabriele Monasterio, Via Moruzzi 1, 56124 Pisa, Italy
| | - S E Petersen
- William Harvey Research Institute, Queen Mary University London, London, UK
| | - L E Sade
- University of Pittsburgh Medical Center, Heart and Vascular Institute, Pittsburgh, PA, USA
| | - I Stankovic
- Faculty of Medicine, Clinical Hospital Centre Zemun, University of Belgrade, Belgrade, Serbia
| | - E Donal
- University of Rennes, CHU Rennes, Inserm, LTSI-UMR 1099, F-35000 Rennes, France
| | - B Cosyns
- Cardiology Department, Centrum voor Hart en Vaatziekten (CHVZ), Universitair ziekenhuis Brussel, Brussels, Belgium
| | - E Agricola
- Cardiovascular Imaging Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - M R Dweck
- BHF Centre for Cardiovascular Science, University of Edinburgh, Chancellors Building, Little France Crescent, Edinburgh EH16 4SB, UK
| | - N Ajmone Marsan
- Department of Cardiology, Leiden University Medical Center, Albinusdreef 2, 2300 RC Leiden, The Netherlands
| | - V Delgado
- Department of Cardiovascular Imaging, Hospital Universitari Germans Trias i Pujol, Badalona, Barcelona, Spain
| | - D Muraru
- Department of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
- Department of Cardiology, Istituto Auxologico Italiano, IRCCS, San Luca Hospital, Milan, Italy
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Raisi-Estabragh Z, Szabo L, Schuermans A, Salih AM, Chin CWL, Vágó H, Altmann A, Ng FS, Garg P, Pavanello S, Marwick TH, Petersen SE. Noninvasive Techniques for Tracking Biological Aging of the Cardiovascular System: JACC Family Series. JACC Cardiovasc Imaging 2024:S1936-878X(24)00082-2. [PMID: 38597854 DOI: 10.1016/j.jcmg.2024.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 03/01/2024] [Accepted: 03/01/2024] [Indexed: 04/11/2024]
Abstract
Population aging is one of the most important demographic transformations of our time. Increasing the "health span"-the proportion of life spent in good health-is a global priority. Biological aging comprises molecular and cellular modifications over many years, which culminate in gradual physiological decline across multiple organ systems and predispose to age-related illnesses. Cardiovascular disease is a major cause of ill health and premature death in older people. The rate at which biological aging occurs varies across individuals of the same age and is influenced by a wide range of genetic and environmental exposures. The authors review the hallmarks of biological cardiovascular aging and their capture using imaging and other noninvasive techniques and examine how this information may be used to understand aging trajectories, with the aim of guiding individual- and population-level interventions to promote healthy aging.
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Affiliation(s)
- Zahra Raisi-Estabragh
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom; Barts Heart Centre, St. Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom.
| | - Liliana Szabo
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom; Barts Heart Centre, St. Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom; Semmelweis University, Heart and Vascular Center, Budapest, Hungary
| | - Art Schuermans
- Faculty of Medicine, KU Leuven, Leuven, Belgium; Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts, USA; Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ahmed M Salih
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom; Department of Population Health Sciences, University of Leicester, Leicester UK; Department of Computer Science, Faculty of Science, University of Zakho, Zakho, Kurdistan Region, Iraq
| | - Calvin W L Chin
- Department of Cardiology, National Heart Centre Singapore, Singapore, Singapore; Cardiovascular Academic Clinical Programme, Duke National University of Singapore Medical School, Singapore, Singapore
| | - Hajnalka Vágó
- Semmelweis University, Heart and Vascular Center, Budapest, Hungary
| | - Andre Altmann
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Fu Siong Ng
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Pankaj Garg
- University of East Anglia, Norwich Medical School, Norwich, United Kingdom; Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, United Kingdom
| | - Sofia Pavanello
- Occupational Medicine, Department of Cardio-Thoraco-Vascular Sciences and Public Health, University of Padua, Padua, Italy; Padua Hospital, Occupational Medicine Unit, Padua, Italy; University Center for Space Studies and Activities "Giuseppe Colombo" - CISAS, University of Padua, Padua, Italy
| | | | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom; Barts Heart Centre, St. Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom; Health Data Research UK, London, United Kingdom
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Aung N, Bartoli A, Rauseo E, Cortaredona S, Sanghvi MM, Fournel J, Ghattas B, Khanji MY, Petersen SE, Jacquier A. Left Ventricular Trabeculations at Cardiac MRI: Reference Ranges and Association with Cardiovascular Risk Factors in UK Biobank. Radiology 2024; 311:e232455. [PMID: 38563665 DOI: 10.1148/radiol.232455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Background The extent of left ventricular (LV) trabeculation and its relationship with cardiovascular (CV) risk factors is unclear. Purpose To apply automated segmentation to UK Biobank cardiac MRI scans to (a) assess the association between individual characteristics and CV risk factors and trabeculated LV mass (LVM) and (b) establish normal reference ranges in a selected group of healthy UK Biobank participants. Materials and Methods In this cross-sectional secondary analysis, prospectively collected data from the UK Biobank (2006 to 2010) were retrospectively analyzed. Automated segmentation of trabeculations was performed using a deep learning algorithm. After excluding individuals with known CV diseases, White adults without CV risk factors (reference group) and those with preexisting CV risk factors (hypertension, hyperlipidemia, diabetes mellitus, or smoking) (exposed group) were compared. Multivariable regression models, adjusted for potential confounders (age, sex, and height), were fitted to evaluate the associations between individual characteristics and CV risk factors and trabeculated LVM. Results Of 43 038 participants (mean age, 64 years ± 8 [SD]; 22 360 women), 28 672 individuals (mean age, 66 years ± 7; 14 918 men) were included in the exposed group, and 7384 individuals (mean age, 60 years ± 7; 4729 women) were included in the reference group. Higher body mass index (BMI) (β = 0.66 [95% CI: 0.63, 0.68]; P < .001), hypertension (β = 0.42 [95% CI: 0.36, 0.48]; P < .001), and higher physical activity level (β = 0.15 [95% CI: 0.12, 0.17]; P < .001) were associated with higher trabeculated LVM. In the reference group, the median trabeculated LVM was 6.3 g (IQR, 4.7-8.5 g) for men and 4.6 g (IQR, 3.4-6.0 g) for women. Median trabeculated LVM decreased with age for men from 6.5 g (IQR, 4.8-8.7 g) at age 45-50 years to 5.9 g (IQR, 4.3-7.8 g) at age 71-80 years (P = .03). Conclusion Higher trabeculated LVM was observed with hypertension, higher BMI, and higher physical activity level. Age- and sex-specific reference ranges of trabeculated LVM in a healthy middle-aged White population were established. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Kawel-Boehm in this issue.
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Affiliation(s)
- Nay Aung
- From the Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Department of Radiology, Hôpital de la Timone, AP-HM, 264 rue Saint-Pierre, 13385 Marseille CEDEX 05, France (A.B., A.J.); Center for Magnetic Resonance in Biology and Medicine, UMR CNRS 7339, Aix-Marseille University, Marseille, France (A.B., J.F., A.J.); Institut de Recherche pour le Developpement, VITROME, Aix-Marseille University, Marseille, France (S.C.); Aix-Marseille School of Economics, Aix-Marseille University, Marseille, France (B.G.); Newham University Hospital, Barts Health NHS Trust, London, England (M.Y.K.); Health Data Research UK, London, England (S.E.P.); and Alan Turing Institute, London, England (S.E.P.)
| | - Axel Bartoli
- From the Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Department of Radiology, Hôpital de la Timone, AP-HM, 264 rue Saint-Pierre, 13385 Marseille CEDEX 05, France (A.B., A.J.); Center for Magnetic Resonance in Biology and Medicine, UMR CNRS 7339, Aix-Marseille University, Marseille, France (A.B., J.F., A.J.); Institut de Recherche pour le Developpement, VITROME, Aix-Marseille University, Marseille, France (S.C.); Aix-Marseille School of Economics, Aix-Marseille University, Marseille, France (B.G.); Newham University Hospital, Barts Health NHS Trust, London, England (M.Y.K.); Health Data Research UK, London, England (S.E.P.); and Alan Turing Institute, London, England (S.E.P.)
| | - Elisa Rauseo
- From the Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Department of Radiology, Hôpital de la Timone, AP-HM, 264 rue Saint-Pierre, 13385 Marseille CEDEX 05, France (A.B., A.J.); Center for Magnetic Resonance in Biology and Medicine, UMR CNRS 7339, Aix-Marseille University, Marseille, France (A.B., J.F., A.J.); Institut de Recherche pour le Developpement, VITROME, Aix-Marseille University, Marseille, France (S.C.); Aix-Marseille School of Economics, Aix-Marseille University, Marseille, France (B.G.); Newham University Hospital, Barts Health NHS Trust, London, England (M.Y.K.); Health Data Research UK, London, England (S.E.P.); and Alan Turing Institute, London, England (S.E.P.)
| | - Sebastien Cortaredona
- From the Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Department of Radiology, Hôpital de la Timone, AP-HM, 264 rue Saint-Pierre, 13385 Marseille CEDEX 05, France (A.B., A.J.); Center for Magnetic Resonance in Biology and Medicine, UMR CNRS 7339, Aix-Marseille University, Marseille, France (A.B., J.F., A.J.); Institut de Recherche pour le Developpement, VITROME, Aix-Marseille University, Marseille, France (S.C.); Aix-Marseille School of Economics, Aix-Marseille University, Marseille, France (B.G.); Newham University Hospital, Barts Health NHS Trust, London, England (M.Y.K.); Health Data Research UK, London, England (S.E.P.); and Alan Turing Institute, London, England (S.E.P.)
| | - Mihir M Sanghvi
- From the Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Department of Radiology, Hôpital de la Timone, AP-HM, 264 rue Saint-Pierre, 13385 Marseille CEDEX 05, France (A.B., A.J.); Center for Magnetic Resonance in Biology and Medicine, UMR CNRS 7339, Aix-Marseille University, Marseille, France (A.B., J.F., A.J.); Institut de Recherche pour le Developpement, VITROME, Aix-Marseille University, Marseille, France (S.C.); Aix-Marseille School of Economics, Aix-Marseille University, Marseille, France (B.G.); Newham University Hospital, Barts Health NHS Trust, London, England (M.Y.K.); Health Data Research UK, London, England (S.E.P.); and Alan Turing Institute, London, England (S.E.P.)
| | - Joris Fournel
- From the Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Department of Radiology, Hôpital de la Timone, AP-HM, 264 rue Saint-Pierre, 13385 Marseille CEDEX 05, France (A.B., A.J.); Center for Magnetic Resonance in Biology and Medicine, UMR CNRS 7339, Aix-Marseille University, Marseille, France (A.B., J.F., A.J.); Institut de Recherche pour le Developpement, VITROME, Aix-Marseille University, Marseille, France (S.C.); Aix-Marseille School of Economics, Aix-Marseille University, Marseille, France (B.G.); Newham University Hospital, Barts Health NHS Trust, London, England (M.Y.K.); Health Data Research UK, London, England (S.E.P.); and Alan Turing Institute, London, England (S.E.P.)
| | - Badih Ghattas
- From the Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Department of Radiology, Hôpital de la Timone, AP-HM, 264 rue Saint-Pierre, 13385 Marseille CEDEX 05, France (A.B., A.J.); Center for Magnetic Resonance in Biology and Medicine, UMR CNRS 7339, Aix-Marseille University, Marseille, France (A.B., J.F., A.J.); Institut de Recherche pour le Developpement, VITROME, Aix-Marseille University, Marseille, France (S.C.); Aix-Marseille School of Economics, Aix-Marseille University, Marseille, France (B.G.); Newham University Hospital, Barts Health NHS Trust, London, England (M.Y.K.); Health Data Research UK, London, England (S.E.P.); and Alan Turing Institute, London, England (S.E.P.)
| | - Mohammed Y Khanji
- From the Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Department of Radiology, Hôpital de la Timone, AP-HM, 264 rue Saint-Pierre, 13385 Marseille CEDEX 05, France (A.B., A.J.); Center for Magnetic Resonance in Biology and Medicine, UMR CNRS 7339, Aix-Marseille University, Marseille, France (A.B., J.F., A.J.); Institut de Recherche pour le Developpement, VITROME, Aix-Marseille University, Marseille, France (S.C.); Aix-Marseille School of Economics, Aix-Marseille University, Marseille, France (B.G.); Newham University Hospital, Barts Health NHS Trust, London, England (M.Y.K.); Health Data Research UK, London, England (S.E.P.); and Alan Turing Institute, London, England (S.E.P.)
| | - Steffen E Petersen
- From the Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Department of Radiology, Hôpital de la Timone, AP-HM, 264 rue Saint-Pierre, 13385 Marseille CEDEX 05, France (A.B., A.J.); Center for Magnetic Resonance in Biology and Medicine, UMR CNRS 7339, Aix-Marseille University, Marseille, France (A.B., J.F., A.J.); Institut de Recherche pour le Developpement, VITROME, Aix-Marseille University, Marseille, France (S.C.); Aix-Marseille School of Economics, Aix-Marseille University, Marseille, France (B.G.); Newham University Hospital, Barts Health NHS Trust, London, England (M.Y.K.); Health Data Research UK, London, England (S.E.P.); and Alan Turing Institute, London, England (S.E.P.)
| | - Alexis Jacquier
- From the Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Department of Radiology, Hôpital de la Timone, AP-HM, 264 rue Saint-Pierre, 13385 Marseille CEDEX 05, France (A.B., A.J.); Center for Magnetic Resonance in Biology and Medicine, UMR CNRS 7339, Aix-Marseille University, Marseille, France (A.B., J.F., A.J.); Institut de Recherche pour le Developpement, VITROME, Aix-Marseille University, Marseille, France (S.C.); Aix-Marseille School of Economics, Aix-Marseille University, Marseille, France (B.G.); Newham University Hospital, Barts Health NHS Trust, London, England (M.Y.K.); Health Data Research UK, London, England (S.E.P.); and Alan Turing Institute, London, England (S.E.P.)
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Hesse K, Khanji MY, Aung N, Dabbagh GS, Petersen SE, Chahal CAA. Assessing heterogeneity on cardiovascular magnetic resonance imaging: a novel approach to diagnosis and risk stratification in cardiac diseases. Eur Heart J Cardiovasc Imaging 2024; 25:437-445. [PMID: 37982176 DOI: 10.1093/ehjci/jead285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 10/13/2023] [Accepted: 10/16/2023] [Indexed: 11/21/2023] Open
Abstract
Cardiac disease affects the heart non-uniformly. Examples include focal septal or apical hypertrophy with reduced strain in hypertrophic cardiomyopathy, replacement fibrosis with akinesia in an infarct-related coronary artery territory, and a pattern of scarring in dilated cardiomyopathy. The detail and versatility of cardiovascular magnetic resonance (CMR) imaging mean it contains a wealth of information imperceptible to the naked eye and not captured by standard global measures. CMR-derived heterogeneity biomarkers could facilitate early diagnosis, better risk stratification, and a more comprehensive prediction of treatment response. Small cohort and case-control studies demonstrate the feasibility of proof-of-concept structural and functional heterogeneity measures. Detailed radiomic analyses of different CMR sequences using open-source software delineate unique voxel patterns as hallmarks of histopathological changes. Meanwhile, measures of dispersion applied to emerging CMR strain sequences describe variable longitudinal, circumferential, and radial function across the myocardium. Two of the most promising heterogeneity measures are the mean absolute deviation of regional standard deviations on native T1 and T2 and the standard deviation of time to maximum regional radial wall motion, termed the tissue synchronization index in a 16-segment left ventricle model. Real-world limitations include the non-standardization of CMR imaging protocols across different centres and the testing of large numbers of radiomic features in small, inadequately powered patient samples. We, therefore, propose a three-step roadmap to benchmark novel heterogeneity biomarkers, including defining normal reference ranges, statistical modelling against diagnosis and outcomes in large epidemiological studies, and finally, comprehensive internal and external validations.
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Affiliation(s)
- Kerrick Hesse
- Cardiology Department, James Cook University Hospital, Marton Road, Middlesbrough TS4 3BW, UK
- Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London EC1M 6BQ, UK
| | - Mohammed Y Khanji
- Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London EC1M 6BQ, UK
- Newham University Hospital, Barts Health NHS Trust, Geln Road, Plaistow, London E13 8SL, UK
- Barts Heart Centre, Barts Health NHS Trust, St Bartholomew's Hospital, West Smithfield, London EC1A 7BE, UK
| | - Nay Aung
- Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, Barts Health NHS Trust, St Bartholomew's Hospital, West Smithfield, London EC1A 7BE, UK
| | - Ghaith Sharaf Dabbagh
- Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, USA
- Center for Inherited Cardiovascular Diseases, WellSpan Health, 30 Monument Road, York, PA 17403, USA
| | - Steffen E Petersen
- Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, Barts Health NHS Trust, St Bartholomew's Hospital, West Smithfield, London EC1A 7BE, UK
- Health Data Research UK, Gibbs Building, 215 Euston Road, London NW1 2BE, UK
- Alan Turing Institute, 96 Euston Road, London NW1 2DB, UK
| | - C Anwar A Chahal
- Barts Heart Centre, Barts Health NHS Trust, St Bartholomew's Hospital, West Smithfield, London EC1A 7BE, UK
- Center for Inherited Cardiovascular Diseases, WellSpan Health, 30 Monument Road, York, PA 17403, USA
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Str, SW Rochester, MN 55905, USA
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6
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Almeida AG, Grapsa J, Gimelli A, Bucciarelli-Ducci C, Gerber B, Ajmone-Marsan N, Bernard A, Donal E, Dweck MR, Haugaa KH, Hristova K, Maceira A, Mandoli GE, Mulvagh S, Morrone D, Plonska-Gosciniak E, Sade LE, Shivalkar B, Schulz-Menger J, Shaw L, Sitges M, von Kemp B, Pinto FJ, Edvardsen T, Petersen SE, Cosyns B. Cardiovascular multimodality imaging in women: a scientific statement of the European Association of Cardiovascular Imaging of the European Society of Cardiology. Eur Heart J Cardiovasc Imaging 2024; 25:e116-e136. [PMID: 38198766 DOI: 10.1093/ehjci/jeae013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 12/31/2023] [Indexed: 01/12/2024] Open
Abstract
Cardiovascular diseases (CVD) represent an important cause of mortality and morbidity in women. It is now recognized that there are sex differences regarding the prevalence and the clinical significance of the traditional cardiovascular (CV) risk factors as well as the pathology underlying a range of CVDs. Unfortunately, women have been under-represented in most CVD imaging studies and trials regarding diagnosis, prognosis, and therapeutics. There is therefore a clear need for further investigation of how CVD affects women along their life span. Multimodality CV imaging plays a key role in the diagnosis of CVD in women as well as in prognosis, decision-making, and monitoring of therapeutics and interventions. However, multimodality imaging in women requires specific consideration given the differences in CVD between the sexes. These differences relate to physiological changes that only women experience (e.g. pregnancy and menopause) as well as variation in the underlying pathophysiology of CVD and also differences in the prevalence of certain conditions such as connective tissue disorders, Takotsubo, and spontaneous coronary artery dissection, which are all more common in women. This scientific statement on CV multimodality in women, an initiative of the European Association of Cardiovascular Imaging of the European Society of Cardiology, reviews the role of multimodality CV imaging in the diagnosis, management, and risk stratification of CVD, as well as highlights important gaps in our knowledge that require further investigation.
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Affiliation(s)
- Ana G Almeida
- Heart and Vessels Department, University Hospital Santa Maria, CAML, CCUL, Faculty of Medicine of Lisbon University, Lisbon, Portugal
| | - Julia Grapsa
- Cardiology Department, Guys and St Thomas NHS Trust, London, UK
| | - Alessia Gimelli
- Imaging Department, Fondazione Toscana Gabriele Monasterio, Pisa, Italy
| | - Chiara Bucciarelli-Ducci
- Department of Cardiology, Royal Brompton and Harefield Hospitals, Guys' and St Thomas NHS Hospitals, London, UK
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Bernhard Gerber
- Service de Cardiologie, Département Cardiovasculaire, Cliniques Universitaires St. Luc, UCLouvain, Brussels, Belgium
- Division CARD, Institut de Recherche Expérimental et Clinique (IREC), UCLouvain, Brussels, Belgium
| | - Nina Ajmone-Marsan
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Anne Bernard
- EA4245 Transplantation, Immunologie, Inflammation, Université de Tours, Tours, France
- Service de Cardiologie, CHRU de Tours, Tours, France
| | - Erwan Donal
- CHU Rennes, Inserm, LTSI-UMR 1099, University of Rennes, Rennes, France
| | - Marc R Dweck
- Centre for Cardiovascular Science, Chancellors Building, Little France Crescent, Edinburgh, UK
| | - Kristina H Haugaa
- Department of Cardiology, Oslo University Hospital Rikshospitalet, Oslo, Norway
- ProCardio Center for Innovation, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Krassimira Hristova
- Center for Cardiovascular Diseases, Faculty of Medicine, Sofia University, Sofia, Bulgaria
| | - Alicia Maceira
- Ascires Biomedical Group, Valencia, Spain
- Department of Medicine, Health Sciences School, UCH-CEU University, Valencia, Spain
| | - Giulia Elena Mandoli
- Department of Medical Biotechnologies, Division of Cardiology, University of Siena, Siena, Italy
| | - Sharon Mulvagh
- Division of Cardiology, Dalhousie University, Halifax, NS, Canada
| | - Doralisa Morrone
- Division of Cardiology, Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, Pisa, Italy
| | | | - Leyla Elif Sade
- Cardiology Department, University of Baskent, Ankara, Turkey
- UPMC Heart and Vascular Institute, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Jeanette Schulz-Menger
- Charité ECRC Medical Faculty of the Humboldt University Berlin and Helios-Clinics, Berlin, Germany
- DZHK, Partner site Berlin, Berlin, Germany
| | - Leslee Shaw
- Department of Medicine (Cardiology), Icahn School of Medicine at Mount Sinai New York, NY, USA
| | - Marta Sitges
- Cardiovascular Institute, Hospital Clinic, University of Barcelona, Barcelona, Spain
- Institut Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- CIBERCV, Barcelona, Spain
| | - Berlinde von Kemp
- Cardiology, Centrum voor Hart en Vaatziekten (CHVZ), Universitair Ziejkenhuis Brussel (UZB), Vrij Universiteit Brussel (VUB), Brussels, Belgium
| | - Fausto J Pinto
- Heart and Vessels Department, University Hospital Santa Maria, CAML, CCUL, Faculty of Medicine of Lisbon University, Lisbon, Portugal
| | - Thor Edvardsen
- Department of Cardiology, Oslo University Hospital Rikshospitalet, Oslo, Norway
- ProCardio Center for Innovation, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, UK
| | - Bernard Cosyns
- Cardiology, Centrum voor Hart en Vaatziekten (CHVZ), Universitair Ziejkenhuis Brussel (UZB), Vrij Universiteit Brussel (VUB), Brussels, Belgium
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7
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Raisi-Estabragh Z, Szabo L, McCracken C, Bülow R, Aquaro GD, Andre F, Le TT, Suchá D, Condurache DG, Salih AM, Chadalavada S, Aung N, Lee AM, Harvey NC, Leiner T, Chin CWL, Friedrich MG, Barison A, Dörr M, Petersen SE. Cardiovascular Magnetic Resonance Reference Ranges From the Healthy Hearts Consortium. JACC Cardiovasc Imaging 2024:S1936-878X(24)00061-5. [PMID: 38613554 DOI: 10.1016/j.jcmg.2024.01.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 01/06/2024] [Accepted: 01/19/2024] [Indexed: 04/15/2024]
Abstract
BACKGROUND The absence of population-stratified cardiovascular magnetic resonance (CMR) reference ranges from large cohorts is a major shortcoming for clinical care. OBJECTIVES This paper provides age-, sex-, and ethnicity-specific CMR reference ranges for atrial and ventricular metrics from the Healthy Hearts Consortium, an international collaborative comprising 9,088 CMR studies from verified healthy individuals, covering the complete adult age spectrum across both sexes, and with the highest ethnic diversity reported to date. METHODS CMR studies were analyzed using certified software with batch processing capability (cvi42, version 5.14 prototype, Circle Cardiovascular Imaging) by 2 expert readers. Three segmentation methods (smooth, papillary, anatomic) were used to contour the endocardial and epicardial borders of the ventricles and atria from long- and short-axis cine series. Clinically established ventricular and atrial metrics were extracted and stratified by age, sex, and ethnicity. Variations by segmentation method, scanner vendor, and magnet strength were examined. Reference ranges are reported as 95% prediction intervals. RESULTS The sample included 4,452 (49.0%) men and 4,636 (51.0%) women with average age of 61.1 ± 12.9 years (range: 18-83 years). Among these, 7,424 (81.7%) were from White, 510 (5.6%) South Asian, 478 (5.3%) mixed/other, 341 (3.7%) Black, and 335 (3.7%) Chinese ethnicities. Images were acquired using 1.5-T (n = 8,779; 96.6%) and 3.0-T (n = 309; 3.4%) scanners from Siemens (n = 8,299; 91.3%), Philips (n = 498; 5.5%), and GE (n = 291, 3.2%). CONCLUSIONS This work represents a resource with healthy CMR-derived volumetric reference ranges ready for clinical implementation.
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Affiliation(s)
- Zahra Raisi-Estabragh
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, United Kingdom; Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, United Kingdom
| | - Liliana Szabo
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, United Kingdom; Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, United Kingdom; Semmelweis University, Heart and Vascular Center, Budapest, Hungary
| | - Celeste McCracken
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Robin Bülow
- Institute for Diagnostic Radiology and Neuroradiology, University Medicine, Ernst Moritz Arndt University Greifswald, Greifswald, Germany
| | - Giovanni Donato Aquaro
- Academic Radiology, Department of Surgical, Medical, and Molecular Pathology and of Critical Area, University of Pisa, Pisa, Italy
| | - Florian Andre
- Department of Cardiology, Angiology and Pneumology, University of Heidelberg, Heidelberg, Germany
| | - Thu-Thao Le
- National Heart Centre Singapore, Singapore; Cardiovascular Academic Clinical Programme, Duke-National University of Singapore Medical School, Singapore
| | - Dominika Suchá
- University Medical Centre Utrecht, Department of Radiology and Nuclear Medicine, Utrecht, the Netherlands
| | - Dorina-Gabriela Condurache
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, United Kingdom; Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, United Kingdom
| | - Ahmed M Salih
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, United Kingdom; Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, United Kingdom; Department of Computer Science, Faculty of Science, University of Zakho, Zakho, Kurdistan Region, Iraq
| | - Sucharitha Chadalavada
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, United Kingdom; Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, United Kingdom
| | - Nay Aung
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, United Kingdom; Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, United Kingdom
| | - Aaron Mark Lee
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, United Kingdom
| | - Nicholas C Harvey
- The Medical Research Council Lifecourse Epidemiology Centre, University of Southampton, Southampton, United Kingdom; NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, United Kingdom
| | - Tim Leiner
- University Medical Centre Utrecht, Department of Radiology and Nuclear Medicine, Utrecht, the Netherlands; Mayo Clinic, Department of Radiology, Rochester, Minnesota, USA
| | - Calvin W L Chin
- National Heart Centre Singapore, Singapore; Cardiovascular Academic Clinical Programme, Duke-National University of Singapore Medical School, Singapore
| | - Matthias G Friedrich
- Department of Cardiology, Angiology and Pneumology, University of Heidelberg, Heidelberg, Germany; Department of Medicine and Diagnostic Radiology, McGill University, Montreal, Quebec, Canada
| | - Andrea Barison
- Cardiology and Cardiovascular Medicine, Fondazione Toscana Gabriele Monasterio, Pisa, Italy
| | - Marcus Dörr
- Department of Internal Medicine B, Cardiology, Pneumology, Infectious Diseases, Intensive Care Medicine, University Medicine Greifswald, Greifswald, Germany; DZHK (German Centre for Cardiovascular Research), partner site Greifswald, Germany
| | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, United Kingdom; Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, United Kingdom; Health Data Research UK, London, United Kingdom; Alan Turing Institute, London, United Kingdom.
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8
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Remfry E, Ardissino M, McCracken C, Szabo L, Neubauer S, Harvey NC, Mamas MA, Robson J, Petersen SE, Raisi-Estabragh Z. Sex-based differences in risk factors for incident myocardial infarction and stroke in the UK Biobank. Eur Heart J Qual Care Clin Outcomes 2024; 10:132-142. [PMID: 37218687 PMCID: PMC10904726 DOI: 10.1093/ehjqcco/qcad029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 03/31/2023] [Accepted: 05/19/2023] [Indexed: 05/24/2023]
Abstract
AIM This study examined sex-based differences in associations of vascular risk factors with incident cardiovascular events in the UK Biobank. METHODS Baseline participant demographic, clinical, laboratory, anthropometric, and imaging characteristics were collected. Multivariable Cox regression was used to estimate independent associations of vascular risk factors with incident myocardial infarction (MI) and ischaemic stroke for men and women. Women-to-men ratios of hazard ratios (RHRs), and related 95% confidence intervals, represent the relative effect-size magnitude by sex. RESULTS Among the 363 313 participants (53.5% women), 8470 experienced MI (29.9% women) and 7705 experienced stroke (40.1% women) over 12.66 [11.93, 13.38] years of prospective follow-up. Men had greater risk factor burden and higher arterial stiffness index at baseline. Women had greater age-related decline in aortic distensibility. Older age [RHR: 1.02 (1.01-1.03)], greater deprivation [RHR: 1.02 (1.00-1.03)], hypertension [RHR: 1.14 (1.02-1.27)], and current smoking [RHR: 1.45 (1.27-1.66)] were associated with a greater excess risk of MI in women than men. Low-density lipoprotein cholesterol was associated with excess MI risk in men [RHR: 0.90 (0.84-0.95)] and apolipoprotein A (ApoA) was less protective for MI in women [RHR: 1.65 (1.01-2.71)]. Older age was associated with excess risk of stroke [RHR: 1.01 (1.00-1.02)] and ApoA was less protective for stroke in women [RHR: 2.55 (1.58-4.14)]. CONCLUSION Older age, hypertension, and smoking appeared stronger drivers of cardiovascular disease in women, whereas lipid metrics appeared stronger risk determinants for men. These findings highlight the importance of sex-specific preventive strategies and suggest priority targets for intervention in men and women.
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Affiliation(s)
- Elizabeth Remfry
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
| | - Maddalena Ardissino
- National Heart and Lung Institute, Imperial College London, Hammersmith Hospital, London SW3 6LY, UK
| | - Celeste McCracken
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Liliana Szabo
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
- Semmelweis University, Heart and Vascular Center, Hungary, Budapest 1122, Hungary
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield EC1A 7BE, UK
| | - Stefan Neubauer
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Nicholas C Harvey
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton SO16 6YD, UK
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton SO16 6YD, UK
| | - Mamas A Mamas
- Keele Cardiovascular Research Group, Keele University, Keele ST5 5BG, UK
- Institute of Population Health, University of Manchester, Manchester M13 9NT, UK
| | - John Robson
- Wolfson Institute of Population Health Sciences, Queen Mary University of London, London E1 4NS, UK
| | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield EC1A 7BE, UK
- Health Data Research UK, London NW1 2BE, UK
- Alan Turing Institute, London NW1 2DB, UK
| | - Zahra Raisi-Estabragh
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield EC1A 7BE, UK
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9
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Reinikainen J, Palosaari T, Canosa-Valls AJ, Schmidt CO, Wissa R, Chadalavada S, Codó L, Gelpí JL, Joseph B, van der Lugt A, Pacella E, Petersen SE, Pujadas ER, Szabo L, Zeller T, Niiranen T, Lekadir K, Kuulasmaa K. Cohort Profile: The Cardiovascular Research Data Catalogue. Int J Epidemiol 2024; 53:dyad175. [PMID: 38142238 DOI: 10.1093/ije/dyad175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 12/11/2023] [Indexed: 12/25/2023] Open
Affiliation(s)
- Jaakko Reinikainen
- Population Health Unit, Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Tarja Palosaari
- Population Health Unit, Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | | | - Carsten O Schmidt
- Functional Division Quality in the Health Sciences (QIHS), Department SHIP-KEF, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Rita Wissa
- Maelstrom Research, Research Institute of the McGill University Health Centre, Montreal, Canada
| | - Sucharitha Chadalavada
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, UK
| | - Laia Codó
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | - Josep Lluís Gelpí
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- Department of Biochemistry and Biomedicine, University of Barcelona, Barcelona, Spain
| | - Bijoy Joseph
- Data and Analytics Unit, Department of Knowledge Brokers, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Aad van der Lugt
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center Rotterdam, The Netherlands
| | - Elsa Pacella
- Scientific Affairs, Research Department, European Society of Cardiology, France
| | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, UK
- Health Data Research UK, London, UK
| | - Esmeralda Ruiz Pujadas
- Department of Mathematics and Computer Science, Artificial Intelligence in Medicine Lab (BCN-AIM), Barcelona, Spain
| | - Liliana Szabo
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, UK
- Semmelweis University, Heart and Vascular Centre, Budapest, Hungary
| | - Tanja Zeller
- University Center of Cardiovascular Science, University Heart and Vascular Center, Hamburg, Germany
- Department of Cardiology, University Heart and Vascular Center, Hamburg, Germany
- German Center of Cardiovascular Research, Partner site Hamburg/Lübeck/Kiel, Hamburg, Germany
| | - Teemu Niiranen
- Population Health Unit, Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Department of Internal Medicine, University of Turku and Turku University Hospital, Turku, Finland
| | - Karim Lekadir
- Department of Mathematics and Computer Science, Artificial Intelligence in Medicine Lab (BCN-AIM), Barcelona, Spain
| | - Kari Kuulasmaa
- Population Health Unit, Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
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10
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McCracken C, Raisi-Estabragh Z, Szabo L, Robson J, Raman B, Topiwala A, Roca-Fernández A, Husain M, Petersen SE, Neubauer S, Nichols TE. NHS Health Check attendance is associated with reduced multiorgan disease risk: a matched cohort study in the UK Biobank. BMC Med 2024; 22:1. [PMID: 38254067 PMCID: PMC10804500 DOI: 10.1186/s12916-023-03187-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 11/21/2023] [Indexed: 01/24/2024] Open
Abstract
BACKGROUND The NHS Health Check is a preventive programme in the UK designed to screen for cardiovascular risk and to aid in primary disease prevention. Despite its widespread implementation, the effectiveness of the NHS Health Check for longer-term disease prevention is unclear. In this study, we measured the rate of new diagnoses in UK Biobank participants who underwent the NHS Health Check compared with those who did not. METHODS Within the UK Biobank prospective study, 48,602 NHS Health Check recipients were identified from linked primary care records. These participants were then covariate-matched on an extensive range of socio-demographic, lifestyle, and medical factors with 48,602 participants without record of the check. Follow-up diagnoses were ascertained from health records over an average of 9 years (SD 2 years) including hypertension, diabetes, hypercholesterolaemia, stroke, dementia, myocardial infarction, atrial fibrillation, heart failure, fatty liver disease, alcoholic liver disease, liver cirrhosis, liver failure, acute kidney injury, chronic kidney disease (stage 3 +), cardiovascular mortality, and all-cause mortality. Time-varying survival modelling was used to compare adjusted outcome rates between the groups. RESULTS In the immediate 2 years after the NHS Health Check, higher diagnosis rates were observed for hypertension, high cholesterol, and chronic kidney disease among health check recipients compared to their matched counterparts. However, in the longer term, NHS Health Check recipients had significantly lower risk across all multiorgan disease outcomes and reduced rates of cardiovascular and all-cause mortality. CONCLUSIONS The NHS Health Check is linked to reduced incidence of disease across multiple organ systems, which may be attributed to risk modification through earlier detection and treatment of key risk factors such as hypertension and high cholesterol. This work adds important evidence to the growing body of research supporting the effectiveness of preventative interventions in reducing longer-term multimorbidity.
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Affiliation(s)
- Celeste McCracken
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK.
| | - Zahra Raisi-Estabragh
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, UK
| | - Liliana Szabo
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, UK
- Semmelweis University, Heart and Vascular Center, Budapest, Hungary
| | - John Robson
- Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Betty Raman
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Anya Topiwala
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK
| | | | - Masud Husain
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK
- Division of Clinical Neurology, John Radcliffe Hospital, Oxford University Hospitals Trust, Oxford, UK
- Department of Experimental Psychology, University of Oxford, Oxford, UK
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, UK
| | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, UK
- Health Data Research UK, London, UK
- Alan Turing Institute, London, UK
| | - Stefan Neubauer
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Thomas E Nichols
- Wolfson Institute of Population Health, Queen Mary University of London, London, UK
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, UK
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11
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Rasmussen LD, Albertsen LEB, Nissen L, Ejlersen JA, Isaksen C, Murphy T, Søndergaard HM, Kirk J, Brix L, Gormsen LC, Petersen SE, Bøttcher M, Winther S. Diagnostic performance of clinical likelihood models of obstructive coronary artery disease to predict myocardial perfusion defects. Eur Heart J Cardiovasc Imaging 2023; 25:39-47. [PMID: 37282714 DOI: 10.1093/ehjci/jead135] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 05/18/2023] [Accepted: 06/02/2023] [Indexed: 06/08/2023] Open
Abstract
AIMS Clinical likelihood (CL) models are designed based on a reference of coronary stenosis in patients with suspected obstructive coronary artery disease. However, a reference standard for myocardial perfusion defects (MPDs) could be more appropriate. We aimed to investigate the ability of the 2019 European Society of Cardiology pre-test probability (ESC-PTP), the risk-factor-weighted (RF-CL) model, and coronary artery calcium score-weighted (CACS-CL) model to diagnose MPDs. METHODS AND RESULTS Symptomatic stable de novo chest pain patients (n = 3374) underwent coronary computed tomography angiography and subsequent myocardial perfusion imaging by single-photon emission computed tomography, positron emission tomography, or cardiac magnetic resonance. For all modalities, MPD was defined as coronary computed tomography angiography with suspected stenosis and stress-perfusion abnormality in ≥2 segments. The ESC-PTP was calculated based on age, sex, and symptom typicality, and the RF-CL and CACS-CL additionally included a number of risk factors and CACS. In total, 219/3374 (6.5%) patients had an MPD. Both the RF-CL and the CACS-CL classified substantially more patients to low CL (<5%) of obstructive coronary artery disease compared with the ESC-PTP (32.5 and 54.1 vs. 12.0%, P < 0.001) with preserved low prevalences of MPD (<2% for all models). Compared with the ESC-PTP [area under the receiver-operating characteristic curve (AUC) 0.74 (0.71-0.78)], the discrimination of having an MPD was higher for the CACS-CL model [AUC 0.88 (0.86-0.91), P < 0.001], while it was similar for the RF-CL model [AUC 0.73 (0.70-0.76), P = 0.32]. CONCLUSION Compared with basic CL models, the RF-CL and CACS-CL models improve down classification of patients to a very low-risk group with a low prevalence of MPD.
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Affiliation(s)
- Laust Dupont Rasmussen
- Department of Cardiology, Gødstrup Hospital, Hospitalsparken 15, Herning DK-7400, Denmark
| | | | - Louise Nissen
- Department of Cardiology, Gødstrup Hospital, Hospitalsparken 15, Herning DK-7400, Denmark
| | | | - Christin Isaksen
- Department of Radiology, Diagnostic Centre, University Research Clinic for Innovative Patient Pathways, Silkeborg Regional Hospital, Silkeborg, Denmark
| | - Theodore Murphy
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, UK
| | | | - Jane Kirk
- Department of Cardiology, Regional Hospital Central Jutland, Silkeborg, Denmark
| | - Lau Brix
- Department of Radiology, Diagnostic Centre, University Research Clinic for Innovative Patient Pathways, Silkeborg Regional Hospital, Silkeborg, Denmark
- Department of Clinical Medicine, Comparative Medicine Lab, Aarhus University, Aarhus, Denmark
| | - Lars Christian Gormsen
- Department of Nuclear Medicine and PET Centre, Aarhus University Hospital, Aarhus, Denmark
| | - Steffen E Petersen
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, UK
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London, UK
| | - Morten Bøttcher
- Department of Cardiology, Gødstrup Hospital, Hospitalsparken 15, Herning DK-7400, Denmark
| | - Simon Winther
- Department of Cardiology, Gødstrup Hospital, Hospitalsparken 15, Herning DK-7400, Denmark
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Wong MYZ, Vargas JD, Naderi H, Sanghvi MM, Raisi-Estabragh Z, Suinesiaputra A, Bonazzola R, Attar R, Ravikumar N, Hann E, Neubauer S, Piechnik SK, Frangi AF, Petersen SE, Aung N. Concurrent Left Ventricular Myocardial Diffuse Fibrosis and Left Atrial Dysfunction Strongly Predict Incident Heart Failure. JACC Cardiovasc Imaging 2023:S1936-878X(23)00505-3. [PMID: 38180417 DOI: 10.1016/j.jcmg.2023.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 11/06/2023] [Accepted: 11/16/2023] [Indexed: 01/06/2024]
Affiliation(s)
- Mark Y Z Wong
- Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, NIHR Biomedical Research Centre at Barts, Queen Mary University of London, Charterhouse Square, London, United Kingdom
| | - Jose D Vargas
- Veterans Affairs Medical Center, Washington, DC, USA; Georgetown University, Washington, DC, USA
| | - Hafiz Naderi
- Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, NIHR Biomedical Research Centre at Barts, Queen Mary University of London, Charterhouse Square, London, United Kingdom; Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, United Kingdom
| | - Mihir M Sanghvi
- Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, NIHR Biomedical Research Centre at Barts, Queen Mary University of London, Charterhouse Square, London, United Kingdom; Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, United Kingdom
| | - Zahra Raisi-Estabragh
- Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, NIHR Biomedical Research Centre at Barts, Queen Mary University of London, Charterhouse Square, London, United Kingdom; Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, United Kingdom
| | | | | | - Rahman Attar
- School of Computing, University of Leeds, Leeds, United Kingdom
| | | | - Evan Hann
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Stefan Neubauer
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Stefan K Piechnik
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | | | - Steffen E Petersen
- Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, NIHR Biomedical Research Centre at Barts, Queen Mary University of London, Charterhouse Square, London, United Kingdom; Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, United Kingdom; Health Data Research UK, London, United Kingdom; Alan Turing Institute, London, United Kingdom
| | - Nay Aung
- Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, NIHR Biomedical Research Centre at Barts, Queen Mary University of London, Charterhouse Square, London, United Kingdom; Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, United Kingdom.
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13
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van Duijvenboden S, Ramírez J, Orini M, Aung N, Petersen SE, Doherty A, Tinker A, Munroe PB, Lambiase PD. Prognostic Significance of Different Ventricular Ectopic Burdens During Submaximal Exercise in Asymptomatic UK Biobank Subjects. Circulation 2023; 148:1932-1944. [PMID: 37855144 PMCID: PMC10712993 DOI: 10.1161/circulationaha.123.064633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 09/22/2023] [Indexed: 10/20/2023]
Abstract
BACKGROUND The consequences of exercise-induced premature ventricular contractions (PVCs) in asymptomatic individuals remain unclear. This study aimed to assess the association between PVC burdens during submaximal exercise and major adverse cardiovascular events (MI/HF/LTVA: myocardial infarction [MI], heart failure [HF], and life-threatening ventricular arrhythmia [LTVA]), and all-cause mortality. Additional end points were MI, LTVA, HF, and cardiovascular mortality. METHODS A neural network was developed to count PVCs from ECGs recorded during exercise (6 minutes) and recovery (1 minute) in 48 315 asymptomatic participants from UK Biobank. Associations were estimated using multivariable Cox proportional hazard models. Explorative studies were conducted in subgroups with cardiovascular magnetic resonance imaging data (n=6290) and NT-proBNP (N-terminal Pro-B-type natriuretic peptide) levels (n=4607) to examine whether PVC burden was associated with subclinical cardiomyopathy. RESULTS Mean age was 56.8±8.2 years; 51.1% of the participants were female; and median follow-up was 12.6 years. Low PVC counts during exercise and recovery were both associated with MI/HF/LTVA risk, independently of clinical factors: adjusted hazard ratio (HR), 1.2 (1-5 exercise PVCs, P<0.001) and HR, 1.3 (1-5 recovery PVCs, P<0.001). Risks were higher with increasing PVC count: HR, 1.8 (>20 exercise PVCs, P<0.001) and HR, 1.6 (>5 recovery PVCs, P<0.001). A similar trend was observed for all-cause mortality, although associations were only significant for high PVC burdens: HRs, 1.6 (>20 exercise PVCs, P<0.001) and 1.5 (>5 recovery PVCs, P<0.001). Complex PVC rhythms were associated with higher risk compared with PVC count alone. PVCs were also associated with incident HF, LTVA, and cardiovascular mortality, but not MI. In the explorative studies, high PVC burden was associated with larger left ventricular volumes, lower ejection fraction, and higher levels of NT-proBNP compared with participants without PVCs. CONCLUSIONS In this cohort of middle-aged and older adults, PVC count during submaximal exercise and recovery were both associated with MI/HF/LTVA, all-cause mortality, HF, LTVAs, and cardiovascular mortality, independent of clinical and exercise test factors, indicating an incremental increase in risk as PVC count rises. Complex PVC rhythms were associated with higher risk compared with PVC count alone. Underlying mechanisms may include the presence of subclinical cardiomyopathy.
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Affiliation(s)
- Stefan van Duijvenboden
- Institute of Cardiovascular Science, University College London, United Kingdom (S.v.D., M.O., P.D.L.)
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, United Kingdom (S.v.D., J.R., N.A., S.E.P., A.T., P.B.M.)
- Nuffield Department of Population Health, University of Oxford, United Kingdom (S.v.D., A.D.)
| | - Julia Ramírez
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, United Kingdom (S.v.D., J.R., N.A., S.E.P., A.T., P.B.M.)
- Aragon Institute of Engineering Research, University of Zaragoza, Spain and Centro de Investigación Biomédica en Red – Bioingeniería, Biomateriales y Nanomedicina, Spain (J.R.)
| | - Michele Orini
- Institute of Cardiovascular Science, University College London, United Kingdom (S.v.D., M.O., P.D.L.)
- Barts Heart Centre, St Bartholomew’s Hospital, London, United Kingdom (M.O., N.A., S.E.P. P.D.L.)
| | - Nay Aung
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, United Kingdom (S.v.D., J.R., N.A., S.E.P., A.T., P.B.M.)
- Barts Heart Centre, St Bartholomew’s Hospital, London, United Kingdom (M.O., N.A., S.E.P. P.D.L.)
- NIHR Barts Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, United Kingdom (N.A., S.E.P., A.T., P.B.M.)
| | - Steffen E. Petersen
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, United Kingdom (S.v.D., J.R., N.A., S.E.P., A.T., P.B.M.)
- Barts Heart Centre, St Bartholomew’s Hospital, London, United Kingdom (M.O., N.A., S.E.P. P.D.L.)
- NIHR Barts Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, United Kingdom (N.A., S.E.P., A.T., P.B.M.)
| | - Aiden Doherty
- Nuffield Department of Population Health, University of Oxford, United Kingdom (S.v.D., A.D.)
| | - Andrew Tinker
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, United Kingdom (S.v.D., J.R., N.A., S.E.P., A.T., P.B.M.)
- NIHR Barts Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, United Kingdom (N.A., S.E.P., A.T., P.B.M.)
| | - Patricia B. Munroe
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, United Kingdom (S.v.D., J.R., N.A., S.E.P., A.T., P.B.M.)
- NIHR Barts Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, United Kingdom (N.A., S.E.P., A.T., P.B.M.)
| | - Pier D. Lambiase
- Institute of Cardiovascular Science, University College London, United Kingdom (S.v.D., M.O., P.D.L.)
- Barts Heart Centre, St Bartholomew’s Hospital, London, United Kingdom (M.O., N.A., S.E.P. P.D.L.)
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14
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Salih AM, Pujadas ER, Campello VM, McCracken C, Harvey NC, Neubauer S, Lekadir K, Nichols TE, Petersen SE, Raisi‐Estabragh Z. Image-Based Biological Heart Age Estimation Reveals Differential Aging Patterns Across Cardiac Chambers. J Magn Reson Imaging 2023; 58:1797-1812. [PMID: 36929232 PMCID: PMC10947470 DOI: 10.1002/jmri.28675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 02/27/2023] [Accepted: 02/28/2023] [Indexed: 03/18/2023] Open
Abstract
BACKGROUND Biological heart age estimation can provide insights into cardiac aging. However, existing studies do not consider differential aging across cardiac regions. PURPOSE To estimate biological age of the left ventricle (LV), right ventricle (RV), myocardium, left atrium, and right atrium using magnetic resonance imaging radiomics phenotypes and to investigate determinants of aging by cardiac region. STUDY TYPE Cross-sectional. POPULATION A total of 18,117 healthy UK Biobank participants including 8338 men (mean age = 64.2 ± 7.5) and 9779 women (mean age = 63.0 ± 7.4). FIELD STRENGTH/SEQUENCE A 1.5 T/balanced steady-state free precession. ASSESSMENT An automated algorithm was used to segment the five cardiac regions, from which radiomic features were extracted. Bayesian ridge regression was used to estimate biological age of each cardiac region with radiomics features as predictors and chronological age as the output. The "age gap" was the difference between biological and chronological age. Linear regression was used to calculate associations of age gap from each cardiac region with socioeconomic, lifestyle, body composition, blood pressure and arterial stiffness, blood biomarkers, mental well-being, multiorgan health, and sex hormone exposures (n = 49). STATISTICAL TEST Multiple testing correction with false discovery method (threshold = 5%). RESULTS The largest model error was with RV and the smallest with LV age (mean absolute error in men: 5.26 vs. 4.96 years). There were 172 statistically significant age gap associations. Greater visceral adiposity was the strongest correlate of larger age gaps, for example, myocardial age gap in women (Beta = 0.85, P = 1.69 × 10-26 ). Poor mental health associated with large age gaps, for example, "disinterested" episodes and myocardial age gap in men (Beta = 0.25, P = 0.001), as did a history of dental problems (eg LV in men Beta = 0.19, P = 0.02). Higher bone mineral density was the strongest associate of smaller age gaps, for example, myocardial age gap in men (Beta = -1.52, P = 7.44 × 10-6 ). DATA CONCLUSION This work demonstrates image-based heart age estimation as a novel method for understanding cardiac aging. EVIDENCE LEVEL 1. TECHNICAL EFFICACY Stage 1.
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Affiliation(s)
- Ahmed M. Salih
- William Harvey Research Institute, NIHR Barts Biomedical Research CentreQueen Mary University of LondonLondonUK
| | - Esmeralda Ruiz Pujadas
- Departament de Matemàtiques i InformàticaUniversitat de Barcelona, Artificial Intelligence in Medicine Lab (BCN‐AIM)BarcelonaSpain
| | - Víctor M. Campello
- Departament de Matemàtiques i InformàticaUniversitat de Barcelona, Artificial Intelligence in Medicine Lab (BCN‐AIM)BarcelonaSpain
| | - Celeste McCracken
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, National Institute for Health Research Oxford Biomedical Research CentreOxford University Hospitals NHS Foundation TrustOxfordUK
| | - Nicholas C. Harvey
- MRC Lifecourse Epidemiology CentreUniversity of SouthamptonSouthamptonUK
- NIHR Southampton Biomedical Research CentreUniversity of Southampton and University Hospital Southampton NHS Foundation TrustSouthamptonUK
| | - Stefan Neubauer
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, National Institute for Health Research Oxford Biomedical Research CentreOxford University Hospitals NHS Foundation TrustOxfordUK
| | - Karim Lekadir
- Departament de Matemàtiques i InformàticaUniversitat de Barcelona, Artificial Intelligence in Medicine Lab (BCN‐AIM)BarcelonaSpain
| | - Thomas E. Nichols
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population HealthUniversity of OxfordOxfordUK
| | - Steffen E. Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research CentreQueen Mary University of LondonLondonUK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West SmithfieldLondonUK
- Health Data Research UKLondonUK
- Alan Turing InstituteLondonUK
| | - Zahra Raisi‐Estabragh
- William Harvey Research Institute, NIHR Barts Biomedical Research CentreQueen Mary University of LondonLondonUK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West SmithfieldLondonUK
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15
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Brahier MS, Zou F, Abdulkareem M, Kochi S, Migliarese F, Thomaides A, Ma X, Wu C, Sandfort V, Bergquist PJ, Srichai MB, Piccini JP, Petersen SE, Vargas JD. Using machine learning to enhance prediction of atrial fibrillation recurrence after catheter ablation. J Arrhythm 2023; 39:868-875. [PMID: 38045451 PMCID: PMC10692862 DOI: 10.1002/joa3.12927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 08/22/2023] [Accepted: 09/03/2023] [Indexed: 12/05/2023] Open
Abstract
Background Traditional risk scores for recurrent atrial fibrillation (AF) following catheter ablation utilize readily available clinical and echocardiographic variables and yet have limited discriminatory capacity. Use of data from cardiac imaging and deep learning may help improve accuracy and prediction of recurrent AF after ablation. Methods We evaluated patients with symptomatic, drug-refractory AF undergoing catheter ablation. All patients underwent pre-ablation cardiac computed tomography (cCT). LAVi was computed using a deep-learning algorithm. In a two-step analysis, random survival forest (RSF) was used to generate prognostic models with variables of highest importance, followed by Cox proportional hazard regression analysis of the selected variables. Events of interest included early and late recurrence. Results Among 653 patients undergoing AF ablation, the most important factors associated with late recurrence by RSF analysis at 24 (+/-18) months follow-up included LAVi and early recurrence. In total, 5 covariates were identified as independent predictors of late recurrence: LAVi (HR per mL/m2 1.01 [1.01-1.02]; p < .001), early recurrence (HR 2.42 [1.90-3.09]; p < .001), statin use (HR 1.38 [1.09-1.75]; p = .007), beta-blocker use (HR 1.29 [1.01-1.65]; p = .043), and adjunctive cavotricuspid isthmus ablation [HR 0.74 (0.57-0.96); p = .02]. Survival analysis demonstrated that patients with both LAVi >66.7 mL/m2 and early recurrence had the highest risk of late recurrence risk compared with those with LAVi <66.7 mL/m2 and no early recurrence (HR 4.52 [3.36-6.08], p < .001). Conclusions Machine learning-derived, full volumetric LAVi from cCT is the most important pre-procedural risk factor for late AF recurrence following catheter ablation. The combination of increased LAVi and early recurrence confers more than a four-fold increased risk of late recurrence.
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Affiliation(s)
- Mark S. Brahier
- Georgetown University Medical CenterWashingtonDCUSA
- Duke University HospitalDurhamNorth CarolinaUSA
| | | | - Musa Abdulkareem
- Barts Heart CentreBarts Health National Health Service (NHS) TrustLondonUnited Kingdom
- National Institute for Health Research (NIHR) Barts Biomedical Research Centre, William Harvey Research InstituteQueen Mary University of LondonLondonUnited Kingdom
- Health Data Research UKLondonUnited Kingdom
| | | | | | | | - Xiaoyang Ma
- Georgetown University Medical CenterWashingtonDCUSA
| | - Colin Wu
- National Heart, Lung, and Blood InstituteBethesdaMarylandUSA
| | | | | | | | | | - Steffen E. Petersen
- Barts Heart CentreBarts Health National Health Service (NHS) TrustLondonUnited Kingdom
- National Institute for Health Research (NIHR) Barts Biomedical Research Centre, William Harvey Research InstituteQueen Mary University of LondonLondonUnited Kingdom
- Health Data Research UKLondonUnited Kingdom
- The Alan Turing InstituteLondonUnited Kingdom
| | - Jose D. Vargas
- Georgetown University Medical CenterWashingtonDCUSA
- Veterans Affairs Medical CenterWashingtonDCUSA
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16
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Westwood M, Almeida AG, Barbato E, Delgado V, Dellegrottaglie S, Fox KF, Gargani L, Huber K, Maurovich-Horvat P, Merino JL, Mindham R, Muraru D, Neubeck L, Nijveldt R, Papadakis M, Pontone G, Price S, Rosano GMC, Rossi A, Sade LE, Schulz-Menger J, Weidinger F, Achenbach S, Petersen SE. Competency-based cardiac imaging for patient-centred care. A statement of the European Society of Cardiology (ESC). With the contribution of the European Association of Cardiovascular Imaging (EACVI), and the support of the Association of Cardiovascular Nursing & Allied Professions (ACNAP), the Association for Acute CardioVascular Care (ACVC), the European Association of Preventive Cardiology (EAPC), the European Association of Percutaneous Cardiovascular Interventions (EAPCI), the European Heart Rhythm Association (EHRA), and the Heart Failure Association (HFA) of the ESC. Eur Heart J 2023; 44:4771-4780. [PMID: 37622660 PMCID: PMC10691193 DOI: 10.1093/eurheartj/ehad578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 07/31/2023] [Accepted: 08/21/2023] [Indexed: 08/26/2023] Open
Abstract
Imaging plays an integral role in all aspects of managing heart disease and cardiac imaging is a core competency of cardiologists. The adequate delivery of cardiac imaging services requires expertise in both imaging methodology-with specific adaptations to imaging of the heart-as well as intricate knowledge of heart disease. The European Society of Cardiology (ESC) and the European Association of Cardiovascular Imaging have developed and implemented a successful education and certification programme for all cardiac imaging modalities. This programme equips cardiologists to provide high quality competency-based cardiac imaging services ensuring they are adequately trained and competent in the entire process of cardiac imaging, from the clinical indication via selecting the best imaging test to answer the clinical question, to image acquisition, analysis, interpretation, storage, repository, and results dissemination. This statement emphasizes the need for competency-based cardiac imaging delivery which is key to optimal, effective and efficient, patient care.
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Affiliation(s)
- Mark Westwood
- William Harvey Research Institute, Queen Mary University of London,Charterhouse Square, London EC1M 6BQ, United Kingdom
- Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, United Kingdom
| | - Ana G Almeida
- Heart and Vessels Department, University Hospital Santa Maria, Faculty of Medicine of Lisbon University, Lisbon, Portugal
| | - Emanuele Barbato
- Department of Clinical and Molecular Medicine, Sapienza University of Rome, Rome, Italy
| | - Victoria Delgado
- Cardiovascular Imaging, Department of Cardiology, Hospital University Germans Trias i Pujol, Badalona, Spain
- Centre de Medicina Comparativa i Bioimatge (CMCIB), Badalona, Spain
| | | | - Kevin F Fox
- National Heart and Lung Institute, Imperial College, London, United Kingdom
| | - Luna Gargani
- Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, Pisa, Italy
| | - Kurt Huber
- 3rd Department of Internal Medicine, Cardiology and Intensive Care Medicine, Clinic Ottakring (Wilhelminenhospital), Vienna, Austria
- Medical School, Sigmund Freud University, Vienna, Austria
| | - Pál Maurovich-Horvat
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Jose L Merino
- Cardiology Department, La Paz University Hospital, Universidad Autonoma, IdiPaz, Madrid, Spain
| | | | - Denisa Muraru
- Department of Cardiology, Istituto Auxologico Italiano, IRCCS, Milan, Italy
- Department of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
| | - Lis Neubeck
- School of Health and Social Care, Edinburgh Napier University, Edinburgh, United Kingdom
| | - Robin Nijveldt
- Cardiology Department, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Michael Papadakis
- Cardiovascular Clinical Academic Group, St. George’s, University of London, London, United Kingdom
- St. George’s University Hospitals NHS Foundation Trust, London, United Kingdom
| | - Gianluca Pontone
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
| | - Susanna Price
- National Heart and Lung Institute, Imperial College, London, United Kingdom
- Cardiology and Critical Care, Royal Brompton & Harefield Hospitals, Part of GSTT NHS Foundation Trust, London, United Kingdom
| | | | - Alexia Rossi
- Department of Nuclear Medicine, University hospital Zurich, Zurich, Switzerland
| | - Leyla Elif Sade
- Cardiology Department, University of Pittsburgh Medical Center, Heart and Vascular Institute, Pittsburgh, PA, USA
| | - Jeanette Schulz-Menger
- Cardiology, WG CMR, Outpatient Research Department, Charite, University Medicine Berlin, Berlin, Germany
- Cardiology Department, Helios Clinics berlin-Buch, Berlin, Germany
| | - Franz Weidinger
- 2nd Department of Medicine with Cardiology and Intensive Care Medicine Vienna Healthcare Group Clinic Landstraße, Vienna, Austria
| | - Stephan Achenbach
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Department of Cardiology, University Hospital Erlangen, Erlangen, Germany
| | - Steffen E Petersen
- William Harvey Research Institute, Queen Mary University of London,Charterhouse Square, London EC1M 6BQ, United Kingdom
- Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, United Kingdom
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17
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Petersen SE, Muraru D, Westwood M, Dweck MR, Di Salvo G, Delgado V, Cosyns B. The year 2022 in the European Heart Journal-Cardiovascular Imaging: Part I. Eur Heart J Cardiovasc Imaging 2023; 24:1593-1604. [PMID: 37738411 DOI: 10.1093/ehjci/jead237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 09/13/2023] [Indexed: 09/24/2023] Open
Abstract
The European Heart Journal-Cardiovascular Imaging with its over 10 years existence is an established leading multi-modality cardiovascular imaging journal. Pertinent publications including original research, how-to papers, reviews, consensus documents, and in our journal from 2022 have been highlighted in two reports. Part I focuses on cardiomyopathies, heart failure, valvular heart disease, and congenital heart disease and related emerging techniques and technologies.
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Affiliation(s)
- Steffen E Petersen
- William Harvey Research Institute, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, Barts Health NHS Trust, London, UK
| | - Denisa Muraru
- Department of cardiology, Istituto Auxologico Italiano, IRCCS, Milan, Italy
- Department of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
| | - Mark Westwood
- William Harvey Research Institute, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, Barts Health NHS Trust, London, UK
| | - Marc R Dweck
- BHF Centre for Cardiovascular Science, University of Edinburgh, Little France Crescent, Edinburgh EH16 4SB, UK
| | - Giovanni Di Salvo
- Pediatric Cardiology and Congenital Heart Disease Unit, Department of Women's and Children's Health, University Hospital Padua, Padua, Italy
| | - Victoria Delgado
- Cardiovascular Imaging, Department of Cardiology, Hospital University Germans Trias i Pujol, Badalona, Spain
- Centre de Medicina Comparativa i Bioimatge (CMCIB), Badalona, Spain
| | - Bernard Cosyns
- Department of Cardiology, CHVZ (Centrum voor Hart en Vaatziekten), ICMI (In Vivo Cellular and Molecular Imaging) Laboratory, Universitair Ziekenhuis Brussel, 101 Laarbeeklaan, Brussels 1090, Belgium
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18
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Szabo L, Salih A, Pujadas ER, Bard A, McCracken C, Ardissino M, Antoniades C, Vago H, Maurovich-Horvat P, Merkely B, Neubauer S, Lekadir K, Petersen SE, Raisi-Estabragh Z. Radiomics of pericardial fat: a new frontier in heart failure discrimination and prediction. Eur Radiol 2023:10.1007/s00330-023-10311-0. [PMID: 37987834 DOI: 10.1007/s00330-023-10311-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 08/17/2023] [Accepted: 09/07/2023] [Indexed: 11/22/2023]
Abstract
OBJECTIVES To use pericardial adipose tissue (PAT) radiomics phenotyping to differentiate existing and predict future heart failure (HF) cases in the UK Biobank. METHODS PAT segmentations were derived from cardiovascular magnetic resonance (CMR) studies using an automated quality-controlled model to define the region-of-interest for radiomics analysis. Prevalent (present at time of imaging) and incident (first occurrence after imaging) HF were ascertained using health record linkage. We created balanced cohorts of non-HF individuals for comparison. PyRadiomics was utilised to extract 104 radiomics features, of which 28 were chosen after excluding highly correlated ones (0.8). These features, plus sex and age, served as predictors in binary classification models trained separately to detect (1) prevalent and (2) incident HF. We tested seven modeling methods using tenfold nested cross-validation and examined feature importance with explainability methods. RESULTS We studied 1204 participants in total, 297 participants with prevalent (60 ± 7 years, 21% female) and 305 with incident (61 ± 6 years, 32% female) HF, and an equal number of non-HF comparators. We achieved good discriminative performance for both prevalent (voting classifier; AUC: 0.76; F1 score: 0.70) and incident (light gradient boosting machine: AUC: 0.74; F1 score: 0.68) HF. Our radiomics models showed marginally better performance compared to PAT area alone. Increased PAT size (maximum 2D diameter in a given column or slice) and texture heterogeneity (sum entropy) were important features for prevalent and incident HF classification models. CONCLUSIONS The amount and character of PAT discriminate individuals with prevalent HF and predict incidence of future HF. CLINICAL RELEVANCE STATEMENT This study presents an innovative application of pericardial adipose tissue (PAT) radiomics phenotyping as a predictive tool for heart failure (HF), a major public health concern. By leveraging advanced machine learning methods, the research uncovers that the quantity and characteristics of PAT can be used to identify existing cases of HF and predict future occurrences. The enhanced performance of these radiomics models over PAT area alone supports the potential for better personalised care through earlier detection and prevention of HF. KEY POINTS •PAT radiomics applied to CMR was used for the first time to derive binary machine learning classifiers to develop models for discrimination of prevalence and prediction of incident heart failure. •Models using PAT area provided acceptable discrimination between cases of prevalent or incident heart failure and comparator groups. •An increased PAT volume (increased diameter using shape features) and greater texture heterogeneity captured by radiomics texture features (increased sum entropy) can be used as an additional classifier marker for heart failure.
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Affiliation(s)
- Liliana Szabo
- Semmelweis University, Heart and Vascular Center, Budapest, Hungary.
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK.
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK.
| | - Ahmed Salih
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - Esmeralda Ruiz Pujadas
- Departament de Matemàtiques I Informàtica, Universitat de Barcelona, Artificial Intelligence in Medicine Lab (BCN-AIM), Barcelona, Spain
| | - Andrew Bard
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - Celeste McCracken
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Maddalena Ardissino
- National Heart and Lung Institute, Imperial College London, London, W12 0HS, UK
- Royal Papworth Hospital, Papworth Rd, Trumpington, Cambridge, CB2 0AY, UK
| | - Charalambos Antoniades
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Hajnalka Vago
- Semmelweis University, Heart and Vascular Center, Budapest, Hungary
| | - Pal Maurovich-Horvat
- Semmelweis University, Medical Imaging Centre, Department of Radiology, Budapest, Hungary
| | - Bela Merkely
- Semmelweis University, Heart and Vascular Center, Budapest, Hungary
| | - Stefan Neubauer
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Karim Lekadir
- Departament de Matemàtiques I Informàtica, Universitat de Barcelona, Artificial Intelligence in Medicine Lab (BCN-AIM), Barcelona, Spain
| | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK
- Health Data Research UK, London, UK
- Alan Turing Institute, London, UK
| | - Zahra Raisi-Estabragh
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK
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Salih A, Ardissino M, Wagen AZ, Bard A, Szabo L, Ryten M, Petersen SE, Altmann A, Raisi‐Estabragh Z. Genome-Wide Association Study of Pericardial Fat Area in 28 161 UK Biobank Participants. J Am Heart Assoc 2023; 12:e030661. [PMID: 37889180 PMCID: PMC10727393 DOI: 10.1161/jaha.123.030661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 09/06/2023] [Indexed: 10/28/2023]
Abstract
BACKGROUND Pericardial adipose tissue (PAT) is the visceral adipose tissue compartment surrounding the heart. Experimental and observational research has suggested that greater PAT deposition might mediate cardiovascular disease, independent of general or subcutaneous adiposity. We characterize the genetic architecture of adiposity-adjusted PAT and identify causal associations between PAT and adverse cardiac magnetic resonance imaging measures of cardiac structure and function in 28 161 UK Biobank participants. METHODS AND RESULTS The PAT phenotype was extracted from cardiac magnetic resonance images using an automated image analysis tool previously developed and validated in this cohort. A genome-wide association study was performed with PAT area set as the phenotype, adjusting for age, sex, and other measures of obesity. Functional mapping and Bayesian colocalization were used to understand the biologic role of identified variants. Mendelian randomization analysis was used to examine potential causal links between genetically determined PAT and cardiac magnetic resonance-derived measures of left ventricular structure and function. We discovered 12 genome-wide significant variants, with 2 independent sentinel variants (rs6428792, P=4.20×10-9 and rs11992444, P=1.30×10-12) at 2 distinct genomic loci, that were mapped to 3 potentially causal genes: T-box transcription factor 15 (TBX15), tryptophanyl tRNA synthetase 2, mitochondrial (WARS2) and early B-cell factor-2 (EBF2) through functional annotation. Bayesian colocalization additionally suggested a role of RP4-712E4.1. Genetically predicted differences in adiposity-adjusted PAT were causally associated with adverse left ventricular remodeling. CONCLUSIONS This study provides insights into the genetic architecture determining differential PAT deposition, identifies causal links with left structural and functional parameters, and provides novel data about the pathophysiological importance of adiposity distribution.
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Affiliation(s)
- Ahmed Salih
- William Harvey Research Institute, National Institute for Health and Care Research (NIHR) Barts Biomedical Research CentreQueen Mary University of London, Charterhouse SquareLondonUnited Kingdom
| | - Maddalena Ardissino
- National Heart and Lung Institute, Imperial College LondonLondonUnited Kingdom
- Heart and Lung Research Institute, University of CambridgeCambridgeUnited Kingdom
| | - Aaron Z. Wagen
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child HealthUniversity College LondonLondonUnited Kingdom
- Department of Clinical and Movement NeurosciencesQueen Square Institute of NeurologyLondonUnited Kingdom
- Neurodegeneration Biology LaboratoryThe Francis Crick InstituteLondonUnited Kingdom
| | - Andrew Bard
- William Harvey Research Institute, National Institute for Health and Care Research (NIHR) Barts Biomedical Research CentreQueen Mary University of London, Charterhouse SquareLondonUnited Kingdom
| | - Liliana Szabo
- William Harvey Research Institute, National Institute for Health and Care Research (NIHR) Barts Biomedical Research CentreQueen Mary University of London, Charterhouse SquareLondonUnited Kingdom
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health National Health Service (NHS) Trust, West SmithfieldLondonUnited Kingdom
- Semmelweis University, Heart and Vascular CenterBudapestHungary
| | - Mina Ryten
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child HealthUniversity College LondonLondonUnited Kingdom
- NIHR Great Ormond Street Hospital Biomedical Research CentreUniversity College LondonLondonUnited Kingdom
| | - Steffen E. Petersen
- William Harvey Research Institute, National Institute for Health and Care Research (NIHR) Barts Biomedical Research CentreQueen Mary University of London, Charterhouse SquareLondonUnited Kingdom
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health National Health Service (NHS) Trust, West SmithfieldLondonUnited Kingdom
- Health Data Research UKLondonUnited Kingdom
- Alan Turing InstituteLondonUnited Kingdom
| | - André Altmann
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUnited Kingdom
| | - Zahra Raisi‐Estabragh
- William Harvey Research Institute, National Institute for Health and Care Research (NIHR) Barts Biomedical Research CentreQueen Mary University of London, Charterhouse SquareLondonUnited Kingdom
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health National Health Service (NHS) Trust, West SmithfieldLondonUnited Kingdom
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20
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Roca-Fernandez A, Banerjee R, Thomaides-Brears H, Telford A, Sanyal A, Neubauer S, Nichols TE, Raman B, McCracken C, Petersen SE, Ntusi NA, Cuthbertson DJ, Lai M, Dennis A, Banerjee A. Liver disease is a significant risk factor for cardiovascular outcomes - A UK Biobank study. J Hepatol 2023; 79:1085-1095. [PMID: 37348789 DOI: 10.1016/j.jhep.2023.05.046] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 05/25/2023] [Accepted: 05/31/2023] [Indexed: 06/24/2023]
Abstract
BACKGROUND & AIMS Chronic liver disease (CLD) is associated with increased cardiovascular disease (CVD) risk. We investigated whether early signs of liver disease (measured by iron-corrected T1-mapping [cT1]) were associated with an increased risk of major CVD events. METHODS Liver disease activity (cT1) and fat (proton density fat fraction [PDFF]) were measured using LiverMultiScan® between January 2016 and February 2020 in the UK Biobank imaging sub-study. Using multivariable Cox regression, we explored associations between liver cT1 (MRI) and primary CVD (coronary artery disease, atrial fibrillation [AF], embolism/vascular events, heart failure [HF] and stroke), and CVD hospitalisation and all-cause mortality. Liver blood biomarkers, general metabolism biomarkers, and demographics were also included. Subgroup analysis was conducted in those without metabolic syndrome (defined as at least three of: a large waist, high triglycerides, low high-density lipoprotein cholesterol, increased systolic blood pressure, or elevated haemoglobin A1c). RESULTS A total of 33,616 participants (mean age 65 years, mean BMI 26 kg/m2, mean haemoglobin A1c 35 mmol/mol) had complete MRI liver data with linked clinical outcomes (median time to major CVD event onset: 1.4 years [range: 0.002-5.1]; follow-up: 2.5 years [range: 1.1-5.2]). Liver disease activity (cT1), but not liver fat (PDFF), was associated with higher risk of any major CVD event (hazard ratio 1.14; 95% CI 1.03-1.26; p = 0.008), AF (1.30; 1.12-1.51; p <0.001); HF (1.30; 1.09-1.56; p= 0.004); CVD hospitalisation (1.27; 1.18-1.37; p <0.001) and all-cause mortality (1.19; 1.02-1.38; p = 0.026). FIB-4 index was associated with HF (1.06; 1.01-1.10; p = 0.007). Risk of CVD hospitalisation was independently associated with cT1 in individuals without metabolic syndrome (1.26; 1.13-1.4; p <0.001). CONCLUSION Liver disease activity, by cT1, was independently associated with a higher risk of incident CVD and all-cause mortality, independent of pre-existing metabolic syndrome, liver fibrosis or fat. IMPACT AND IMPLICATIONS Chronic liver disease (CLD) is associated with a twofold greater incidence of cardiovascular disease. Our work shows that early liver disease on iron-corrected T1 mapping was associated with a higher risk of major cardiovascular disease (14%), cardiovascular disease hospitalisation (27%) and all-cause mortality (19%). These findings highlight the prognostic relevance of a comprehensive evaluation of liver health in populations at risk of CVD and/or CLD, even in the absence of clinical manifestations or metabolic syndrome, when there is an opportunity to modify/address risk factors and prevent disease progression. As such, they are relevant to patients, carers, clinicians, and policymakers.
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Affiliation(s)
| | - Rajarshi Banerjee
- Perspectum Ltd., Oxford, United Kingdom; Oxford University Hospitals Foundation Trust, Oxford, United Kingdom
| | | | | | - Arun Sanyal
- Division of Gastroenterology, Hepatology and Nutrition, Virginia Commonwealth University School of Medicine, Richmond, VA, United States
| | - Stefan Neubauer
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, United Kingdom
| | - Thomas E Nichols
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Betty Raman
- Oxford University Hospitals Foundation Trust, Oxford, United Kingdom; Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, United Kingdom
| | - Celeste McCracken
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, United Kingdom
| | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London, United Kingdom; Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, UK; Health Data Research UK, London, UK; Alan Turing Institute, London, UK
| | - Ntobeko Ab Ntusi
- Division of Cardiology, Department of Medicine, University of Cape Town and Groote Schuur, J46, Old Main Building, Main Road, Observatory, Cape Town, 7925, South Africa
| | - Daniel J Cuthbertson
- Department of Cardiovascular and Metabolic Medicine, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK; Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | - Michele Lai
- Department of Medicine, Liver Centre, Beth Israel Deaconess Medical Centre, Harvard Medical School, 110 Francis Street, Suite 4A, Boston, USA
| | | | - Amitava Banerjee
- University College London Hospitals National Health Service Trust, London, United Kingdom; Institute of Health Informatics, University College London, London, United Kingdom; Barts Health National Health Service Trust, The Royal London Hospital, London, United Kingdom.
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21
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Mahmood A, Simon J, Cooper J, Murphy T, McCracken C, Quiroz J, Laranjo L, Aung N, Lee AM, Khanji MY, Neubauer S, Raisi-Estabragh Z, Maurovich-Horvat P, Petersen SE. Neuroticism personality traits are linked to adverse cardiovascular phenotypes in the UK Biobank. Eur Heart J Cardiovasc Imaging 2023; 24:1460-1467. [PMID: 37440761 PMCID: PMC10610755 DOI: 10.1093/ehjci/jead166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 06/26/2023] [Accepted: 06/27/2023] [Indexed: 07/15/2023] Open
Abstract
AIMS To evaluate the relationship between neuroticism personality traits and cardiovascular magnetic resonance (CMR) measures of cardiac morphology and function, considering potential differential associations in men and women. METHODS AND RESULTS The analysis includes 36 309 UK Biobank participants (average age = 63.9 ± 7.7 years; 47.8% men) with CMR available and neuroticism score assessed by the 12-item Eysenck Personality Questionnaire-Revised Short Form. CMR scans were performed on 1.5 Tesla scanners (MAGNETOM Aera, Siemens Healthcare, Erlangen, Germany) according to pre-defined protocols and analysed using automated pipelines. We considered measures of left ventricular (LV) and right ventricular (RV) structure and function, and indicators of arterial compliance. Multivariable linear regression was used to estimate association of neuroticism score with individual CMR metrics, with adjustment for age, sex, obesity, deprivation, smoking, diabetes, hypertension, hypercholesterolaemia, alcohol use, exercise, and education. Higher neuroticism scores were associated with smaller LV and RV end-diastolic volumes, lower LV mass, greater concentricity (higher LV mass to volume ratio), and higher native T1. Greater neuroticism was also linked to poorer LV and RV function (lower stroke volumes) and greater arterial stiffness. In sex-stratified analyses, the relationships between neuroticism and LV stroke volume, concentricity, and arterial stiffness were attenuated in women. In men, association (with exception of native T1) remained robust. CONCLUSION Greater tendency towards neuroticism personality traits is linked to smaller, poorer functioning ventricles with lower LV mass, higher myocardial fibrosis, and higher arterial stiffness. These relationships are independent of traditional vascular risk factors and are more robust in men than women.
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Affiliation(s)
- Adil Mahmood
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
| | - Judit Simon
- MTA-SE Cardiovascular Imaging Research Group, Department of Radiology, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Jackie Cooper
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
| | - Theodore Murphy
- Department of Cardiology and Cardiovascular Imaging, Beacon Hospital, Dublin, Ireland
| | - Celeste McCracken
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Juan Quiroz
- Centre for Big Data Research in Health (CBDRH), The University of New South Wales (UNSW), Sydney, Australia
| | - Liliana Laranjo
- Faculty of Medicine and Health, Westmead Applied Research Centre (WARC), University of Sydney, Australia
| | - Nay Aung
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, EC1A 7BE, London, UK
| | - Aaron Mark Lee
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
| | - Mohammed Y Khanji
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, EC1A 7BE, London, UK
| | - Stefan Neubauer
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Zahra Raisi-Estabragh
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, EC1A 7BE, London, UK
| | - Pal Maurovich-Horvat
- MTA-SE Cardiovascular Imaging Research Group, Department of Radiology, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, EC1A 7BE, London, UK
- Health Data Research UK, London, UK
- Alan Turing Institute, London, UK
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22
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Westwood M, Almeida AG, Barbato E, Delgado V, Dellegrottaglie S, Fox KF, Gargani L, Huber K, Maurovich-Horvat P, Merino JL, Mindham R, Muraru D, Neubeck L, Nijveldt R, Papadakis M, Pontone G, Price S, Rosano GMC, Rossi A, Sade LE, Schulz-Menger J, Weidinger F, Achenbach S, Petersen SE. Competency-based cardiac imaging for patient-centred care. A statement of the European Society of Cardiology (ESC). With the contribution of the European Association of Cardiovascular Imaging (EACVI), and the support of the Association of Cardiovascular Nursing & Allied Professions (ACNAP), the Association for Acute CardioVascular Care (ACVC), the European Association of Preventive Cardiology (EAPC), the European Association of Percutaneous Cardiovascular Interventions (EAPCI), the European Heart Rhythm Association (EHRA), and the Heart Failure Association (HFA) of the ESC. Eur Heart J Cardiovasc Imaging 2023; 24:1415-1424. [PMID: 37622662 PMCID: PMC10610731 DOI: 10.1093/ehjci/jead216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 07/31/2023] [Accepted: 08/21/2023] [Indexed: 08/26/2023] Open
Abstract
Imaging plays an integral role in all aspects of managing heart disease and cardiac imaging is a core competency of cardiologists. The adequate delivery of cardiac imaging services requires expertise in both imaging methodology-with specific adaptations to imaging of the heart-as well as intricate knowledge of heart disease. The European Society of Cardiology (ESC) and the European Association of Cardiovascular Imaging have developed and implemented a successful education and certification programme for all cardiac imaging modalities. This programme equips cardiologists to provide high quality competency-based cardiac imaging services ensuring they are adequately trained and competent in the entire process of cardiac imaging, from the clinical indication via selecting the best imaging test to answer the clinical question, to image acquisition, analysis, interpretation, storage, repository, and results dissemination. This statement emphasizes the need for competency-based cardiac imaging delivery which is key to optimal, effective and efficient, patient care.
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Affiliation(s)
- Mark Westwood
- William Harvey Research Institute, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, United Kingdom
- Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, United Kingdom
| | - Ana G Almeida
- Heart and Vessels Department, University Hospital Santa Maria, Faculty of Medicine of Lisbon University, Lisbon, Portugal
| | - Emanuele Barbato
- Department of Clinical and Molecular Medicine, Sapienza University of Rome, Rome, Italy
| | - Victoria Delgado
- Cardiovascular Imaging, Department of Cardiology, Hospital University Germans Trias i Pujol, Badalona, Spain
- Centre de Medicina Comparativa i Bioimatge (CMCIB), Badalona, Spain
| | | | - Kevin F Fox
- National Heart and Lung Institute, Imperial College, London, United Kingdom
| | - Luna Gargani
- Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, Pisa, Italy
| | - Kurt Huber
- 3rd Department of Internal Medicine, Cardiology and Intensive Care Medicine, Clinic Ottakring (Wilhelminenhospital), Vienna, Austria
- Medical School, Sigmund Freud University, Vienna, Austria
| | - Pál Maurovich-Horvat
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Jose L Merino
- Cardiology Department, La Paz University Hospital, Universidad Autonoma, IdiPaz, Madrid, Spain
| | | | - Denisa Muraru
- Department of Cardiology, Istituto Auxologico Italiano, IRCCS, Milan, Italy
- Department of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
| | - Lis Neubeck
- School of Health and Social Care, Edinburgh Napier University, Edinburgh, United Kingdom
| | - Robin Nijveldt
- Cardiology Department, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Michael Papadakis
- Cardiovascular Clinical Academic Group, St. George’s, University of London, London, United Kingdom
- St. George’s University Hospitals NHS Foundation Trust, London, United Kingdom
| | - Gianluca Pontone
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
| | - Susanna Price
- National Heart and Lung Institute, Imperial College, London, United Kingdom
- Cardiology and Critical Care, Royal Brompton & Harefield Hospitals, Part of GSTT NHS Foundation Trust, London, United Kingdom
| | | | - Alexia Rossi
- Department of Nuclear Medicine, University hospital Zurich, Zurich, Switzerland
| | - Leyla Elif Sade
- Cardiology Department, University of Pittsburgh Medical Center, Heart and Vascular Institute, Pittsburgh, PA, USA
| | - Jeanette Schulz-Menger
- Cardiology, WG CMR, Outpatient Research Department, Charite, University Medicine Berlin, Berlin, Germany
- Cardiology Department, Helios Clinics berlin-Buch, Berlin, Germany
| | - Franz Weidinger
- 2nd Department of Medicine with Cardiology and Intensive Care Medicine Vienna Healthcare Group Clinic Landstraße, Vienna, Austria
| | - Stephan Achenbach
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Department of Cardiology, University Hospital Erlangen, Erlangen, Germany
| | - Steffen E Petersen
- William Harvey Research Institute, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, United Kingdom
- Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, United Kingdom
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23
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Mariscal-Harana J, Asher C, Vergani V, Rizvi M, Keehn L, Kim RJ, Judd RM, Petersen SE, Razavi R, King AP, Ruijsink B, Puyol-Antón E. An artificial intelligence tool for automated analysis of large-scale unstructured clinical cine cardiac magnetic resonance databases. Eur Heart J Digit Health 2023; 4:370-383. [PMID: 37794871 PMCID: PMC10545512 DOI: 10.1093/ehjdh/ztad044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 06/05/2023] [Accepted: 07/12/2023] [Indexed: 10/06/2023]
Abstract
Aims Artificial intelligence (AI) techniques have been proposed for automating analysis of short-axis (SAX) cine cardiac magnetic resonance (CMR), but no CMR analysis tool exists to automatically analyse large (unstructured) clinical CMR datasets. We develop and validate a robust AI tool for start-to-end automatic quantification of cardiac function from SAX cine CMR in large clinical databases. Methods and results Our pipeline for processing and analysing CMR databases includes automated steps to identify the correct data, robust image pre-processing, an AI algorithm for biventricular segmentation of SAX CMR and estimation of functional biomarkers, and automated post-analysis quality control to detect and correct errors. The segmentation algorithm was trained on 2793 CMR scans from two NHS hospitals and validated on additional cases from this dataset (n = 414) and five external datasets (n = 6888), including scans of patients with a range of diseases acquired at 12 different centres using CMR scanners from all major vendors. Median absolute errors in cardiac biomarkers were within the range of inter-observer variability: <8.4 mL (left ventricle volume), <9.2 mL (right ventricle volume), <13.3 g (left ventricular mass), and <5.9% (ejection fraction) across all datasets. Stratification of cases according to phenotypes of cardiac disease and scanner vendors showed good performance across all groups. Conclusion We show that our proposed tool, which combines image pre-processing steps, a domain-generalizable AI algorithm trained on a large-scale multi-domain CMR dataset and quality control steps, allows robust analysis of (clinical or research) databases from multiple centres, vendors, and cardiac diseases. This enables translation of our tool for use in fully automated processing of large multi-centre databases.
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Affiliation(s)
- Jorge Mariscal-Harana
- School of Biomedical Engineering & Imaging Sciences Rayne Institute, 4th Floor, Lambeth Wing St. Thomas' Hospital Westminster Bridge Road London SE1 7EH
| | - Clint Asher
- School of Biomedical Engineering & Imaging Sciences Rayne Institute, 4th Floor, Lambeth Wing St. Thomas' Hospital Westminster Bridge Road London SE1 7EH
- Department of Adult and Paediatric Cardiology, Guy’s and St Thomas’ NHS Foundation Trust, Westminster Bridge Road, London SE1 7EH, London, UK
| | - Vittoria Vergani
- School of Biomedical Engineering & Imaging Sciences Rayne Institute, 4th Floor, Lambeth Wing St. Thomas' Hospital Westminster Bridge Road London SE1 7EH
| | - Maleeha Rizvi
- School of Biomedical Engineering & Imaging Sciences Rayne Institute, 4th Floor, Lambeth Wing St. Thomas' Hospital Westminster Bridge Road London SE1 7EH
- Department of Adult and Paediatric Cardiology, Guy’s and St Thomas’ NHS Foundation Trust, Westminster Bridge Road, London SE1 7EH, London, UK
| | - Louise Keehn
- Department of Clinical Pharmacology, King’s College London British Heart Foundation Centre, St Thomas’ Hospital, London, Westminster Bridge Road, London SE1 7EH, UK
| | - Raymond J Kim
- Division of Cardiology, Department of Medicine, Duke University, 40 Duke Medicine Circle, Durham, NC 27710, USA
| | - Robert M Judd
- Division of Cardiology, Department of Medicine, Duke University, 40 Duke Medicine Circle, Durham, NC 27710, USA
| | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, W Smithfield, London EC1A 7BE, UK
- Health Data Research UK, Gibbs Building, 215 Euston Rd., London NW1 2BE, UK
- Alan Turing Institute, 96 Euston Rd., London NW1 2DB, UK
| | - Reza Razavi
- School of Biomedical Engineering & Imaging Sciences Rayne Institute, 4th Floor, Lambeth Wing St. Thomas' Hospital Westminster Bridge Road London SE1 7EH
- Department of Adult and Paediatric Cardiology, Guy’s and St Thomas’ NHS Foundation Trust, Westminster Bridge Road, London SE1 7EH, London, UK
| | - Andrew P King
- School of Biomedical Engineering & Imaging Sciences Rayne Institute, 4th Floor, Lambeth Wing St. Thomas' Hospital Westminster Bridge Road London SE1 7EH
| | - Bram Ruijsink
- School of Biomedical Engineering & Imaging Sciences Rayne Institute, 4th Floor, Lambeth Wing St. Thomas' Hospital Westminster Bridge Road London SE1 7EH
- Department of Adult and Paediatric Cardiology, Guy’s and St Thomas’ NHS Foundation Trust, Westminster Bridge Road, London SE1 7EH, London, UK
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, 3584 CX Utrecht, the Netherlands
| | - Esther Puyol-Antón
- School of Biomedical Engineering & Imaging Sciences Rayne Institute, 4th Floor, Lambeth Wing St. Thomas' Hospital Westminster Bridge Road London SE1 7EH
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Elghazaly H, McCracken C, Szabo L, Malcolmson J, Manisty CH, Davies AH, Piechnik SK, Harvey NC, Neubauer S, Mohiddin SA, Petersen SE, Raisi-Estabragh Z. Characterizing the hypertensive cardiovascular phenotype in the UK Biobank. Eur Heart J Cardiovasc Imaging 2023; 24:1352-1360. [PMID: 37309807 PMCID: PMC10531143 DOI: 10.1093/ehjci/jead123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 05/22/2023] [Indexed: 06/14/2023] Open
Abstract
AIMS To describe hypertension-related cardiovascular magnetic resonance (CMR) phenotypes in the UK Biobank considering variations across patient populations. METHODS AND RESULTS We studied 39 095 (51.5% women, mean age: 63.9 ± 7.7 years, 38.6% hypertensive) participants with CMR data available. Hypertension status was ascertained through health record linkage. Associations between hypertension and CMR metrics were estimated using multivariable linear regression adjusting for major vascular risk factors. Stratified analyses were performed by sex, ethnicity, time since hypertension diagnosis, and blood pressure (BP) control. Results are standardized beta coefficients, 95% confidence intervals, and P-values corrected for multiple testing. Hypertension was associated with concentric left ventricular (LV) hypertrophy (increased LV mass, wall thickness, concentricity index), poorer LV function (lower global function index, worse global longitudinal strain), larger left atrial (LA) volumes, lower LA ejection fraction, and lower aortic distensibility. Hypertension was linked to significantly lower myocardial native T1 and increased LV ejection fraction. Women had greater hypertension-related reduction in aortic compliance than men. The degree of hypertension-related LV hypertrophy was greatest in Black ethnicities. Increasing time since diagnosis of hypertension was linked to adverse remodelling. Hypertension-related remodelling was substantially attenuated in hypertensives with good BP control. CONCLUSION Hypertension was associated with concentric LV hypertrophy, reduced LV function, dilated poorer functioning LA, and reduced aortic compliance. Whilst the overall pattern of remodelling was consistent across populations, women had greater hypertension-related reduction in aortic compliance and Black ethnicities showed the greatest LV mass increase. Importantly, adverse cardiovascular remodelling was markedly attenuated in hypertensives with good BP control.
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Affiliation(s)
- Hussein Elghazaly
- Department of Surgery and Cancer, Imperial College London and Imperial College NHS Trust, South Kensington, SW7 2BX London, UK
| | - Celeste McCracken
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Liliana Szabo
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
- Semmelweis University, Heart and Vascular Center, BudapestHungary
| | - James Malcolmson
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
| | - Charlotte H Manisty
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
- Institute of Cardiovascular Science, University College London, London, UK
| | - Alun H Davies
- Department of Surgery and Cancer, Imperial College London and Imperial College NHS Trust, South Kensington, SW7 2BX London, UK
| | - Stefan K Piechnik
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Nicholas C Harvey
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton SO16 6YD, UK
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton SO16 6YD, UK
| | - Stefan Neubauer
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Saidi A Mohiddin
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
| | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
- Health Data Research UK, London, UK
- Alan Turing Institute, London, UK
| | - Zahra Raisi-Estabragh
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
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25
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Rauseo E, Abdulkareem M, Khan A, Cooper J, Lee AM, Aung N, Slabaugh GG, Petersen SE. Phenotyping left ventricular systolic dysfunction in asymptomatic individuals for improved risk stratification. Eur Heart J Cardiovasc Imaging 2023; 24:1363-1373. [PMID: 37699069 PMCID: PMC10531121 DOI: 10.1093/ehjci/jead218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 08/23/2023] [Indexed: 09/14/2023] Open
Abstract
AIMS Left ventricular systolic dysfunction (LSVD) is a heterogeneous condition with several factors influencing prognosis. Better phenotyping of asymptomatic individuals can inform preventative strategies. This study aims to explore the clinical phenotypes of LVSD in initially asymptomatic subjects and their association with clinical outcomes and cardiovascular abnormalities through multi-dimensional data clustering. METHODS AND RESULTS Clustering analysis was performed on 60 clinically available variables from 1563 UK Biobank participants without pre-existing heart failure (HF) and with left ventricular ejection fraction (LVEF) < 50% on cardiovascular magnetic resonance (CMR) assessment. Risks of developing HF, other cardiovascular events, death, and a composite of major adverse cardiovascular events (MACE) associated with clusters were investigated. Cardiovascular imaging characteristics, not included in the clustering analysis, were also evaluated. Three distinct clusters were identified, differing considerably in lifestyle habits, cardiovascular risk factors, electrocardiographic parameters, and cardiometabolic profiles. A stepwise increase in risk profile was observed from Cluster 1 to Cluster 3, independent of traditional risk factors and LVEF. Compared with Cluster 1, the lowest risk subset, the risk of MACE ranged from 1.42 [95% confidence interval (CI): 1.03-1.96; P < 0.05] for Cluster 2 to 1.72 (95% CI: 1.36-2.35; P < 0.001) for Cluster 3. Cluster 3, the highest risk profile, had features of adverse cardiovascular imaging with the greatest LV re-modelling, myocardial dysfunction, and decrease in arterial compliance. CONCLUSIONS Clustering of clinical variables identified three distinct risk profiles and clinical trajectories of LVSD amongst initially asymptomatic subjects. Improved characterization may facilitate tailored interventions based on the LVSD sub-type and improve clinical outcomes.
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Affiliation(s)
- Elisa Rauseo
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
| | - Musa Abdulkareem
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
- Health Data Research UK, 215 Euston Rd, London NW1 2BE, UK
| | - Abbas Khan
- School of Electronic Engineering and Computer Science, Queen Mary University of London, UK
- Digital Environment Research Institute, Queen Mary University of London, UK
| | - Jackie Cooper
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London EC1M 6BQ, UK
| | - Aaron M Lee
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London EC1M 6BQ, UK
| | - Nay Aung
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
| | - Gregory G Slabaugh
- School of Electronic Engineering and Computer Science, Queen Mary University of London, UK
- Digital Environment Research Institute, Queen Mary University of London, UK
- Alan Turing Institute, British Library, 96 Euston Rd, London NW1 2DB, UK
| | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
- Health Data Research UK, 215 Euston Rd, London NW1 2BE, UK
- Alan Turing Institute, British Library, 96 Euston Rd, London NW1 2DB, UK
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26
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Raisi-Estabragh Z, Martin-Isla C, Nissen L, Szabo L, Campello VM, Escalera S, Winther S, Bøttcher M, Lekadir K, Petersen SE. Radiomics analysis enhances the diagnostic performance of CMR stress perfusion: a proof-of-concept study using the Dan-NICAD dataset. Front Cardiovasc Med 2023; 10:1141026. [PMID: 37781298 PMCID: PMC10541220 DOI: 10.3389/fcvm.2023.1141026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 08/29/2023] [Indexed: 10/03/2023] Open
Abstract
Objectives To assess the feasibility of extracting radiomics signal intensity based features from the myocardium using cardiovascular magnetic resonance (CMR) imaging stress perfusion sequences. Furthermore, to compare the diagnostic performance of radiomics models against standard-of-care qualitative visual assessment of stress perfusion images, with the ground truth stenosis label being defined by invasive Fractional Flow Reserve (FFR) and quantitative coronary angiography. Methods We used the Dan-NICAD 1 dataset, a multi-centre study with coronary computed tomography angiography, 1,5 T CMR stress perfusion, and invasive FFR available for a subset of 148 patients with suspected coronary artery disease. Image segmentation was performed by two independent readers. We used the Pyradiomics platform to extract radiomics first-order (n = 14) and texture (n = 75) features from the LV myocardium (basal, mid, apical) in rest and stress perfusion images. Results Overall, 92 patients (mean age 62 years, 56 men) were included in the study, 39 with positive FFR. We double-cross validated the model and, in each inner fold, we trained and validated a per territory model. The conventional analysis results reported sensitivity of 41% and specificity of 84%. Our final radiomics model demonstrated an improvement on these results with an average sensitivity of 53% and specificity of 86%. Conclusion In this proof-of-concept study from the Dan-NICAD dataset, we demonstrate the feasibility of radiomics analysis applied to CMR perfusion images with a suggestion of superior diagnostic performance of radiomics models over conventional visual analysis of perfusion images in picking up perfusion defects defined by invasive coronary angiography.
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Affiliation(s)
- Zahra Raisi-Estabragh
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, London, United Kingdom
| | - Carlos Martin-Isla
- Dept. de Matematiques I Informatica, University of Barcelona, Barcelona, Spain
| | - Louise Nissen
- Department of Cardiology, Regionshospital Gødstrup, Herning, Denmark
| | - Liliana Szabo
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, London, United Kingdom
- Heart and Vascular Center, Semmelweis University, Budapest, Hungary
| | - Victor M. Campello
- Dept. de Matematiques I Informatica, University of Barcelona, Barcelona, Spain
| | - Sergio Escalera
- Departament de Matemàtiques & Informàtica, Universitat de Barcelona, Barcelona, Spain
- Computer Vision Center, Univeritat Autònoma de Barcelona, Barcelona, Spain
| | - Simon Winther
- Department of Cardiology, Regionshospital Gødstrup, Herning, Denmark
| | - Morten Bøttcher
- Department of Cardiology, Regionshospital Gødstrup, Herning, Denmark
| | - Karim Lekadir
- Dept. de Matematiques I Informatica, University of Barcelona, Barcelona, Spain
| | - Steffen E. Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, London, United Kingdom
- Health Data Research UK, London, United Kingdom
- Alan Turing Institute, London, United Kingdom
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27
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Asatryan B, Shah RA, Sharaf Dabbagh G, Landstrom AP, Darbar D, Khanji MY, Lopes LR, van Duijvenboden S, Muser D, Lee AM, Haggerty CM, Arora P, Semsarian C, Reichlin T, Somers VK, Owens AT, Petersen SE, Deo R, Munroe PB, Aung N, Chahal CAA. Predicted Deleterious Variants in Cardiomyopathy Genes Prognosticate Mortality and Composite Outcomes in UK Biobank. JACC Heart Fail 2023:S2213-1779(23)00492-4. [PMID: 37715771 DOI: 10.1016/j.jchf.2023.07.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 07/14/2023] [Accepted: 07/19/2023] [Indexed: 09/18/2023]
Abstract
BACKGROUND Inherited cardiomyopathies present with broad variation of phenotype. Data are limited regarding genetic screening strategies and outcomes associated with predicted deleterious variants in cardiomyopathy-associated genes in the general population. OBJECTIVES The authors aimed to determine the risk of mortality and composite cardiomyopathy-related outcomes associated with predicted deleterious variants in cardiomyopathy-associated genes in the UK Biobank. METHODS Using whole exome sequencing data, variants in dilated, hypertrophic, and arrhythmogenic right ventricular cardiomyopathy-associated genes with at least moderate evidence of disease causality according to ClinGen Expert Panel curations were annotated using REVEL (≥0.65) and ANNOVAR (predicted loss-of-function) considering gene-disease mechanisms. Genotype-positive and genotype-negative groups were compared using time-to-event analyses for the primary (all-cause mortality) and secondary outcomes (diagnosis of cardiomyopathy; composite outcome of diagnosis of cardiomyopathy, heart failure, arrhythmia, stroke, and death). RESULTS Among 200,619 participants (age at recruitment 56.46 ± 8.1 years), 5,292 (2.64%) were found to host ≥1 predicted deleterious variants in cardiomyopathy-associated genes (CMP-G+). After adjusting for age and sex, CMP-G+ individuals had higher risk for all-cause mortality (HR: 1.13 [95% CI: 1.01-1.25]; P = 0.027), increased risk for being diagnosed with cardiomyopathy later in life (HR: 5.75 [95% CI: 4.58-7.23]; P < 0.0001), and elevated risk for composite outcome (HR: 1.29 [95% CI: 1.20-1.39]; P < 0.0001) than CMP-G- individuals. The higher risk for being diagnosed with cardiomyopathy and composite outcomes in the genotype-positive subjects remained consistent across all cardiomyopathy subgroups. CONCLUSIONS Adults with predicted deleterious variants in cardiomyopathy-associated genes exhibited a slightly higher risk of mortality and a significantly increased risk of developing cardiomyopathy, and cardiomyopathy-related composite outcomes, in comparison with genotype-negative controls.
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Affiliation(s)
- Babken Asatryan
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland; Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Ravi A Shah
- Northwick Park Hospital, London North West University Healthcare NHS Trust, London, UK
| | - Ghaith Sharaf Dabbagh
- Center for Inherited Cardiovascular Diseases, WellSpan Health, Lancaster, Pennsylvania; University of Michigan, Division of Cardiovascular Medicine, Ann Arbor, Michigan
| | - Andrew P Landstrom
- Departments of Pediatrics, Division of Cardiology, and Cell Biology, Duke University School of Medicine, Durham, North Carolina
| | | | - Mohammed Y Khanji
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, West Smithfield, UK; NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, UK; Newham University Hospital, Barts Health NHS Trust, London, UK
| | - Luis R Lopes
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, West Smithfield, UK; Centre for Heart Muscle Disease, Institute of Cardiovascular Science, University College London, London, UK
| | - Stefan van Duijvenboden
- NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Daniele Muser
- Cardiac Electrophysiology, Cardiovascular Division, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania; Dipartimento Cardiotoracico, U.O.C. di Cardiologia, Presidio Ospedaliero Universitario "Santa Maria Della Misericordia," Udine, Italy
| | - Aaron Mark Lee
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, West Smithfield, UK; NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Christopher M Haggerty
- Department of Translational Data Science and Informatics, Geisinger, Danville, Pennsylvania
| | - Pankaj Arora
- Division of Cardiovascular Disease, University of Alabama at Birmingham, Alabama
| | - Christopher Semsarian
- Agnes Ginges Centre for Molecular Cardiology at Centenary Institute, The University of Sydney, Sydney, New South Wales, Australia; Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia; Department of Cardiology, Royal Prince Alfred Hospital, Sydney, New South Wales, Australia
| | - Tobias Reichlin
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Virend K Somers
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | - Anjali T Owens
- Center for Inherited Cardiovascular Disease, Cardiovascular Division, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Steffen E Petersen
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, West Smithfield, UK; NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Rajat Deo
- Center for Inherited Cardiovascular Disease, Cardiovascular Division, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Patricia B Munroe
- NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Nay Aung
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, West Smithfield, UK; NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - C Anwar A Chahal
- Center for Inherited Cardiovascular Diseases, WellSpan Health, Lancaster, Pennsylvania; Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, West Smithfield, UK; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
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28
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Woodward G, Bajre M, Bhattacharyya S, Breen M, Chiocchia V, Dawes H, Dehbi HM, Descamps T, Frangou E, Fazakarley CA, Harris V, Hawkes W, Hewer O, Johnson CL, Krasner S, Laidlaw L, Lau J, Marwick T, Petersen SE, Piotrowska H, Ridgeway G, Ripley DP, Sanderson E, Savage N, Sarwar R, Tetlow L, Thompson B, Thulborn S, Williamson V, Woodward W, Upton R, Leeson P. PROTEUS Study: A Prospective Randomized Controlled Trial Evaluating the Use of Artificial Intelligence in Stress Echocardiography. Am Heart J 2023; 263:123-132. [PMID: 37192698 DOI: 10.1016/j.ahj.2023.05.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 05/03/2023] [Accepted: 05/04/2023] [Indexed: 05/18/2023]
Abstract
BACKGROUND Stress echocardiography (SE) is one of the most commonly used diagnostic imaging tests for coronary artery disease (CAD) but requires clinicians to visually assess scans to identify patients who may benefit from invasive investigation and treatment. EchoGo Pro provides an automated interpretation of SE based on artificial intelligence (AI) image analysis. In reader studies, use of EchoGo Pro when making clinical decisions improves diagnostic accuracy and confidence. Prospective evaluation in real world practice is now important to understand the impact of EchoGo Pro on the patient pathway and outcome. METHODS PROTEUS is a randomized, multicenter, 2-armed, noninferiority study aiming to recruit 2,500 participants from National Health Service (NHS) hospitals in the UK referred to SE clinics for investigation of suspected CAD. All participants will undergo a stress echocardiogram protocol as per local hospital policy. Participants will be randomized 1:1 to a control group, representing current practice, or an intervention group, in which clinicians will receive an AI image analysis report (EchoGo Pro, Ultromics Ltd, Oxford, UK) to use during image interpretation, indicating the likelihood of severe CAD. The primary outcome will be appropriateness of clinician decision to refer for coronary angiography. Secondary outcomes will assess other health impacts including appropriate use of other clinical management approaches, impact on variability in decision making, patient and clinician qualitative experience and a health economic analysis. DISCUSSION This will be the first study to assess the impact of introducing an AI medical diagnostic aid into the standard care pathway of patients with suspected CAD being investigated with SE. TRIAL REGISTRATION Clinicaltrials.gov registration number NCT05028179, registered on 31 August 2021; ISRCTN: ISRCTN15113915; IRAS ref: 293515; REC ref: 21/NW/0199.
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Affiliation(s)
| | - Mamta Bajre
- Oxford Academic Health Science Network, Magdalen Centre-Whitehead Building, Oxford, UK
| | - Sanjeev Bhattacharyya
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, UK
| | | | - Virginia Chiocchia
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Helen Dawes
- Oxford Clinical Allied Technology and Trial Services Unit, Oxford Brookes University, Oxford, UK
| | - Hakim-Moulay Dehbi
- UCL Comprehensive Clinical Trials Unit, University College London, London, UK
| | | | | | | | - Victoria Harris
- Department of Primary Health Care Sciences, University of Oxford, Oxford, UK
| | | | | | - Casey L Johnson
- Cardiovascular Clinical Research Facility, RDM Division of Cardiovascular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Samuel Krasner
- Cardiovascular Clinical Research Facility, RDM Division of Cardiovascular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Lynn Laidlaw
- British Society of Echocardiography. (BSE) Wavelength PPI group member, NIHR and BMJ lay reviewer
| | | | - Tom Marwick
- Baker Heart and Diabetes Institute, Melbourne Victoria, Australia
| | - Steffen E Petersen
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, UK; William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London, UK
| | | | - Ged Ridgeway
- Oxford Brain Diagnostics Ltd, Oxford Centre for Innovation, Oxford, UK
| | - David P Ripley
- School of Medicine, Faculty of Health Sciences and Wellbeing, University of Sunderland, London, UK
| | | | - Natalie Savage
- Cardiovascular Clinical Research Facility, RDM Division of Cardiovascular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | | | | | | | | | | | - William Woodward
- Cardiovascular Clinical Research Facility, RDM Division of Cardiovascular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | | | - Paul Leeson
- Cardiovascular Clinical Research Facility, RDM Division of Cardiovascular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK.
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29
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Aung N, Wang Q, van Duijvenboden S, Burns R, Stoma S, Raisi-Estabragh Z, Ahmet S, Allara E, Wood A, Di Angelantonio E, Danesh J, Munroe PB, Young A, Harvey NC, Codd V, Nelson CP, Petersen SE, Samani NJ. Association of Longer Leukocyte Telomere Length With Cardiac Size, Function, and Heart Failure. JAMA Cardiol 2023; 8:808-815. [PMID: 37494011 PMCID: PMC10372756 DOI: 10.1001/jamacardio.2023.2167] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 05/31/2023] [Indexed: 07/27/2023]
Abstract
Importance Longer leukocyte telomere length (LTL) is associated with a lower risk of adverse cardiovascular outcomes. The extent to which variation in LTL is associated with intermediary cardiovascular phenotypes is unclear. Objective To evaluate the associations between LTL and a diverse set of cardiovascular imaging phenotypes. Design, Setting, and Participants This is a population-based cross-sectional study of UK Biobank participants recruited from 2006 to 2010. LTL was measured using a quantitative polymerase chain reaction method. Cardiovascular measurements were derived from cardiovascular magnetic resonance using machine learning. The median (IQR) duration of follow-up was 12.0 (11.3-12.7) years. The associations of LTL with imaging measurements and incident heart failure (HF) were evaluated by multivariable regression models. Genetic associations between LTL and significantly associated traits were investigated by mendelian randomization. Data were analyzed from January to May 2023. Exposure LTL. Main Outcomes and Measures Cardiovascular imaging traits and HF. Results Of 40 459 included participants, 19 529 (48.3%) were men, and the mean (SD) age was 55.1 (7.6) years. Longer LTL was independently associated with a pattern of positive cardiac remodeling (higher left ventricular mass, larger global ventricular size and volume, and higher ventricular and atrial stroke volumes) and a lower risk of incident HF (LTL fourth quartile vs first quartile: hazard ratio, 0.86; 95% CI, 0.81-0.91; P = 1.8 × 10-6). Mendelian randomization analysis suggested a potential causal association between LTL and left ventricular mass, global ventricular volume, and left ventricular stroke volume. Conclusions and Relevance In this cross-sectional study, longer LTL was associated with a larger heart with better cardiac function in middle age, which could potentially explain the observed lower risk of incident HF.
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Affiliation(s)
- Nay Aung
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
- National Institute for Health and Care Research Barts Cardiovascular Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London, United Kingdom
| | - Qingning Wang
- Department of Cardiovascular Sciences, University of Leicester and NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
- National Institute for Health and Care Research Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Stefan van Duijvenboden
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
- National Institute for Health and Care Research Barts Cardiovascular Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Richard Burns
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Svetlana Stoma
- Department of Cardiovascular Sciences, University of Leicester and NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Zahra Raisi-Estabragh
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
- National Institute for Health and Care Research Barts Cardiovascular Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London, United Kingdom
| | - Selda Ahmet
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London, United Kingdom
| | - Elias Allara
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, United Kingdom
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, United Kingdom
| | - Angela Wood
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, United Kingdom
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, United Kingdom
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, United Kingdom
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, United Kingdom
- Cambridge Centre of Artificial Intelligence in Medicine, Cambridge, United Kingdom
| | - Emanuele Di Angelantonio
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, United Kingdom
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, United Kingdom
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, United Kingdom
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, United Kingdom
- Health Data Science Centre, Human Technopole, Milan, Italy
| | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, United Kingdom
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, United Kingdom
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, United Kingdom
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, United Kingdom
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom
| | - Patricia B. Munroe
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
- National Institute for Health and Care Research Barts Cardiovascular Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
| | - Alistair Young
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Nicholas C. Harvey
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, United Kingdom
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, United Kingdom
| | - Veryan Codd
- Department of Cardiovascular Sciences, University of Leicester and NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Christopher P. Nelson
- Department of Cardiovascular Sciences, University of Leicester and NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Steffen E. Petersen
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
- National Institute for Health and Care Research Barts Cardiovascular Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London, United Kingdom
| | - Nilesh J. Samani
- Department of Cardiovascular Sciences, University of Leicester and NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
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30
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Simon J, Fung K, Raisi-Estabragh Z, Aung N, Khanji MY, Zsarnóczay E, Merkely B, Munroe PB, Harvey NC, Piechnik SK, Neubauer S, Leeson P, Petersen SE, Maurovich-Horvat P. Association between subclinical atherosclerosis and cardiac structure and function-results from the UK Biobank Study. Eur Heart J Imaging Methods Pract 2023; 1:qyad010. [PMID: 37822973 PMCID: PMC10563379 DOI: 10.1093/ehjimp/qyad010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 07/03/2023] [Indexed: 10/13/2023]
Abstract
Aims Heart failure (HF) is a major health problem and early diagnosis is important. Atherosclerosis is the main cause of HF and carotid intima-media thickness (IMT) is a recognized early measure of atherosclerosis. This study aimed to investigate whether increased carotid IMT is associated with changes in cardiac structure and function in middle-aged participants of the UK Biobank Study without overt cardiovascular disease. Methods and results Participants of the UK Biobank who underwent CMR and carotid ultrasound examinations were included in this study. Patients with heart failure, angina, atrial fibrillation, and history of myocardial infarction or stroke were excluded. We used multivariable linear regression models adjusted for age, sex, physical activity, body mass index, body surface area, hypertension, diabetes, smoking, ethnicity, socioeconomic status, alcohol intake, and laboratory parameters. In total, 4301 individuals (61.6 ± 7.5 years, 45.9% male) were included. Multivariable linear regression analyses showed that increasing quartiles of IMT was associated with increased left and right ventricular (LV and RV) and left atrial volumes and greater LV mass. Moreover, increased IMT was related to lower LV end-systolic circumferential strain, torsion, and both left and right atrial ejection fractions (all P < 0.05). Conclusion Increased IMT showed an independent association over traditional risk factors with enlargement of all four cardiac chambers, decreased function in both atria, greater LV mass, and subclinical LV dysfunction. There may be additional risk stratification that can be derived from the IMT to identify those most likely to have early cardiac structural/functional changes.
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Affiliation(s)
- Judit Simon
- MTA-SE Cardiovascular Imaging Research Group, Medical Imaging Centre, Semmelweis University, Üllői út 78, H-1082 Budapest, Hungary
- Heart and Vascular Center, Semmelweis University, Budapest, Hungary, Városmajor u 68, H-1122 Budapest, Hungary
| | - Kenneth Fung
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London EC1M 6BQ, United Kingdom
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, United Kingdom
| | - Zahra Raisi-Estabragh
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London EC1M 6BQ, United Kingdom
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, United Kingdom
| | - Nay Aung
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London EC1M 6BQ, United Kingdom
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, United Kingdom
| | - Mohammed Y Khanji
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London EC1M 6BQ, United Kingdom
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, United Kingdom
- Barts Health NHS Trust, Newham University Hospital, Glen Road, Plaistow, London E1 1BB, United Kingdom
| | - Emese Zsarnóczay
- MTA-SE Cardiovascular Imaging Research Group, Medical Imaging Centre, Semmelweis University, Üllői út 78, H-1082 Budapest, Hungary
- Heart and Vascular Center, Semmelweis University, Budapest, Hungary, Városmajor u 68, H-1122 Budapest, Hungary
| | - Béla Merkely
- Heart and Vascular Center, Semmelweis University, Budapest, Hungary, Városmajor u 68, H-1122 Budapest, Hungary
| | - Patricia B Munroe
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London EC1M 6BQ, United Kingdom
| | - Nicholas C Harvey
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton SO16 6YD, United Kingdom
| | - Stefan K Piechnik
- National Institute for Health Research, Oxford Biomedical Research Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, United Kingdom
| | - Stefan Neubauer
- National Institute for Health Research, Oxford Biomedical Research Centre, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, United Kingdom
| | - Paul Leeson
- Oxford Cardiovascular Clinical Research Facility, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Level 1, John Radcliffe Hospital, Oxford OX3 9DU, United Kingdom
| | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London EC1M 6BQ, United Kingdom
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, United Kingdom
| | - Pál Maurovich-Horvat
- MTA-SE Cardiovascular Imaging Research Group, Medical Imaging Centre, Semmelweis University, Üllői út 78, H-1082 Budapest, Hungary
- Heart and Vascular Center, Semmelweis University, Budapest, Hungary, Városmajor u 68, H-1122 Budapest, Hungary
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Coy-Canguçu A, Antunes-Correa LM, Mazzali M, Abrão P, Ronco F, Teixeira CM, Viana KP, Cordeiro G, Longato M, Coelho OR, Matos-Souza JR, Nadruz W, Sposito AC, Petersen SE, Jerosch-Herold M, Coelho-Filho OR. Prognostic role of renal replacement therapy among hospitalized patients with heart failure in the Brazilian national public health system. Front Cardiovasc Med 2023; 10:1226481. [PMID: 37680567 PMCID: PMC10482263 DOI: 10.3389/fcvm.2023.1226481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Accepted: 08/02/2023] [Indexed: 09/09/2023] Open
Abstract
Introduction Data on patients hospitalized with acute heart failure in Brazil scarce. Methods We performed a cross-sectional, retrospective, records-based study using data retrieved from a large public database of heart failure admissions to any hospital from the Brazilian National Public Health System (SUS) (SUS Hospital Information System [SIHSUS] registry) to determine the in-hospital all-cause mortality rate, in-hospital renal replacement therapy rate and its association with outcome. Results In total, 910,128 hospitalizations due to heart failure were identified in the SIHSUS registry between April 2017 and August 2021, of which 106,383 (11.7%) resulted in in-hospital death. Renal replacement therapy (required by 8,179 non-survivors [7.7%] and 11,496 survivors [1.4%, p < 0.001]) was associated with a 56% increase in the risk of death in the univariate regression model (HR 1.56, 95% CI 1.52 -1.59), a more than threefold increase of the duration of hospitalization, and a 45% or greater increase of cost per day. All forms of renal replacement therapy remained independently associated with in-hospital mortality in multivariable analysis (intermittent hemodialysis: HR 1.64, 95% CI 1.60 -1.69; continuous hemodialysis: HR 1.52, 95% CI 1.42 -1.63; peritoneal dialysis: HR 1.47, 95% CI 1.20 -1.88). Discussion The in-hospital mortality rate of 11.7% observed among patients with acute heart failure admitted to Brazilian public hospitals was alarmingly high, exceeding that of patients admitted to North American and European institutions. This is the first report to quantify the rate of renal replacement therapy in patients hospitalized with acute heart failure in Brazil.
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Affiliation(s)
- Andréa Coy-Canguçu
- Catholic Pontifical University of Campinas Medical School, Campinas, Brazil
- Department of Medicine, State University of Campinas School of Medical Sciences, Campinas, Brazil
| | - Lígia M. Antunes-Correa
- Department of Medicine, State University of Campinas School of Medical Sciences, Campinas, Brazil
| | - Marilda Mazzali
- Department of Medicine, State University of Campinas School of Medical Sciences, Campinas, Brazil
| | | | | | | | | | | | | | - Otávio Rizzi Coelho
- Department of Medicine, State University of Campinas School of Medical Sciences, Campinas, Brazil
| | - José Roberto Matos-Souza
- Department of Medicine, State University of Campinas School of Medical Sciences, Campinas, Brazil
| | - Wilson Nadruz
- Department of Medicine, State University of Campinas School of Medical Sciences, Campinas, Brazil
| | - Andrei C. Sposito
- Department of Medicine, State University of Campinas School of Medical Sciences, Campinas, Brazil
| | - Steffen E. Petersen
- William Harvey Research Institute NIHR Barts Biomedical Research Centre, Queen Mary University London, London, United Kingdom
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, London, United Kingdom
| | - Michael Jerosch-Herold
- Non-Invasive Cardiovascular Imaging Program, Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, United States
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Althunayyan AM, Alborikan S, Badiani S, Wong K, Uppal R, Patel N, Petersen SE, Lloyd G, Bhattacharyya S. Determinants of post-operative left ventricular dysfunction in degenerative mitral regurgitation. Eur Heart J Cardiovasc Imaging 2023; 24:1252-1257. [PMID: 37140153 DOI: 10.1093/ehjci/jead093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 02/02/2023] [Accepted: 04/02/2023] [Indexed: 05/05/2023] Open
Abstract
AIMS Chronic degenerative mitral regurgitation leads to volume overload causing left ventricular (LV) enlargement and eventually LV impairment. Current guidelines determining thresholds for intervention are based on LV diameters and ejection fraction (LVEF). There are sparse data examining the value of LV volumes and newer markers of LV performance on outcomes of surgery in mitral valve prolapse. The aim of this study is to identify the best marker of LV impairment after mitral valve surgery. METHODS AND RESULTS Prospective, observational study of patients with mitral valve prolapse undergoing mitral valve surgery. Pre-operative LV diameters, volumes, LVEF, global longitudinal strain (GLS), and myocardial work measured. Post-operative LV impairment defined as LVEF < 50% at 1 year post-surgery. Eighty-seven patients included. Thirteen percent developed post-operative LV impairment. Patients with post-operative LV dysfunction showed significantly larger indexed LV end-systolic diameters, indexed LV end-systolic volumes (LVESVi), lower LVEF, and more abnormal GLS than patients without post-operative LV dysfunction. In multivariate analysis, LVESVi [odds ratio 1.11 (95% CI 1.01-1.23), P = 0.039] and GLS [odds ratio 1.46 (95% CI 1.00-2.14), P = 0.054] were the only independent predictors of post-operative LV dysfunction. The optimal cut-off of 36.3 mL/m2 for LVESVi had a sensitivity of 82% and specificity of 78% for detection of post-operative LV impairment. CONCLUSION Post-operative LV impairment is common. Indexed LV volumes (36.3 mL/m2) provided the best marker of post-operative LV impairment.
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Affiliation(s)
- Aeshah M Althunayyan
- Heart Valve Clinic & Echocardiography Laboratory, Barts Heart Centre, St Bartholomew's Hospital, West Smithfield, EC1A 7BE London, UK
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London EC1M 6BQ, UK
- Department of Cardiac Technology, College of Applied Medical Sciences, Imam Abdulrahman Bin Faisal University, Dammam, Kingdom of Saudi Arabia
| | - Sahar Alborikan
- Heart Valve Clinic & Echocardiography Laboratory, Barts Heart Centre, St Bartholomew's Hospital, West Smithfield, EC1A 7BE London, UK
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London EC1M 6BQ, UK
| | - Sveeta Badiani
- Heart Valve Clinic & Echocardiography Laboratory, Barts Heart Centre, St Bartholomew's Hospital, West Smithfield, EC1A 7BE London, UK
| | - Kit Wong
- Cardiothoracic Surgery, St Bartholomew's Hospital, London, UK
| | - Rakesh Uppal
- Cardiothoracic Surgery, St Bartholomew's Hospital, London, UK
| | - Nikhil Patel
- Eastbourne District General Hospital, Eastbourne, UK
| | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, EC1A 7BE London, UK
- Health Data Research UK, Gibbs Building, 215 Euston Road, NW1 2BE London, UK
- Alan Turing Institute, 96 Euston Road, NW1 2DB London, UK
| | - Guy Lloyd
- Heart Valve Clinic & Echocardiography Laboratory, Barts Heart Centre, St Bartholomew's Hospital, West Smithfield, EC1A 7BE London, UK
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London EC1M 6BQ, UK
- Institute of Cardiovascular Sciences, UCL, 62 Huntley Street, WC1E 6DD London, UK
| | - Sanjeev Bhattacharyya
- Heart Valve Clinic & Echocardiography Laboratory, Barts Heart Centre, St Bartholomew's Hospital, West Smithfield, EC1A 7BE London, UK
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London EC1M 6BQ, UK
- Institute of Cardiovascular Sciences, UCL, 62 Huntley Street, WC1E 6DD London, UK
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Naderi H, Ramírez J, van Duijvenboden S, Pujadas ER, Aung N, Wang L, Anwar Ahmed Chahal C, Lekadir K, Petersen SE, Munroe PB. Predicting left ventricular hypertrophy from the 12-lead electrocardiogram in the UK Biobank imaging study using machine learning. Eur Heart J Digit Health 2023; 4:316-324. [PMID: 37538142 PMCID: PMC10393938 DOI: 10.1093/ehjdh/ztad037] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 05/05/2023] [Accepted: 05/31/2023] [Indexed: 08/05/2023]
Abstract
Aims Left ventricular hypertrophy (LVH) is an established, independent predictor of cardiovascular disease. Indices derived from the electrocardiogram (ECG) have been used to infer the presence of LVH with limited sensitivity. This study aimed to classify LVH defined by cardiovascular magnetic resonance (CMR) imaging using the 12-lead ECG for cost-effective patient stratification. Methods and results We extracted ECG biomarkers with a known physiological association with LVH from the 12-lead ECG of 37 534 participants in the UK Biobank imaging study. Classification models integrating ECG biomarkers and clinical variables were built using logistic regression, support vector machine (SVM) and random forest (RF). The dataset was split into 80% training and 20% test sets for performance evaluation. Ten-fold cross validation was applied with further validation testing performed by separating data based on UK Biobank imaging centres. QRS amplitude and blood pressure (P < 0.001) were the features most strongly associated with LVH. Classification with logistic regression had an accuracy of 81% [sensitivity 70%, specificity 81%, Area under the receiver operator curve (AUC) 0.86], SVM 81% accuracy (sensitivity 72%, specificity 81%, AUC 0.85) and RF 72% accuracy (sensitivity 74%, specificity 72%, AUC 0.83). ECG biomarkers enhanced model performance of all classifiers, compared to using clinical variables alone. Validation testing by UK Biobank imaging centres demonstrated robustness of our models. Conclusion A combination of ECG biomarkers and clinical variables were able to predict LVH defined by CMR. Our findings provide support for the ECG as an inexpensive screening tool to risk stratify patients with LVH as a prelude to advanced imaging.
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Affiliation(s)
- Hafiz Naderi
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK
| | - Julia Ramírez
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
- Aragon Institute of Engineering Research, University of Zaragoza, Zaragoza, Spain
| | - Stefan van Duijvenboden
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
- Big Data Institute, La Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | | | - Nay Aung
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
- National Institute of Health and Care Research Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK
| | - Lin Wang
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
| | - Choudhary Anwar Ahmed Chahal
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK
- Cardiac Electrophysiology Section, Division of Cardiovascular Diseases, University of Pennsylvania, Philadelphia, PA, USA
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA
| | - Karim Lekadir
- Faculty of Mathematics and Computer Science, University of Barcelona, Barcelona, Spain
| | - Steffen E Petersen
- Corresponding authors. Tel: +44 (0) 207882 7188. (S.E.P.); Tel: +44 (0) 207882 3586. (P.B.M.)
| | - Patricia B Munroe
- Corresponding authors. Tel: +44 (0) 207882 7188. (S.E.P.); Tel: +44 (0) 207882 3586. (P.B.M.)
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Rasooly D, Peloso GM, Pereira AC, Dashti H, Giambartolomei C, Wheeler E, Aung N, Ferolito BR, Pietzner M, Farber-Eger EH, Wells QS, Kosik NM, Gaziano L, Posner DC, Bento AP, Hui Q, Liu C, Aragam K, Wang Z, Charest B, Huffman JE, Wilson PWF, Phillips LS, Whittaker J, Munroe PB, Petersen SE, Cho K, Leach AR, Magariños MP, Gaziano JM, Langenberg C, Sun YV, Joseph J, Casas JP. Genome-wide association analysis and Mendelian randomization proteomics identify drug targets for heart failure. Nat Commun 2023; 14:3826. [PMID: 37429843 PMCID: PMC10333277 DOI: 10.1038/s41467-023-39253-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 06/05/2023] [Indexed: 07/12/2023] Open
Abstract
We conduct a large-scale meta-analysis of heart failure genome-wide association studies (GWAS) consisting of over 90,000 heart failure cases and more than 1 million control individuals of European ancestry to uncover novel genetic determinants for heart failure. Using the GWAS results and blood protein quantitative loci, we perform Mendelian randomization and colocalization analyses on human proteins to provide putative causal evidence for the role of druggable proteins in the genesis of heart failure. We identify 39 genome-wide significant heart failure risk variants, of which 18 are previously unreported. Using a combination of Mendelian randomization proteomics and genetic cis-only colocalization analyses, we identify 10 additional putatively causal genes for heart failure. Findings from GWAS and Mendelian randomization-proteomics identify seven (CAMK2D, PRKD1, PRKD3, MAPK3, TNFSF12, APOC3 and NAE1) proteins as potential targets for interventions to be used in primary prevention of heart failure.
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Affiliation(s)
- Danielle Rasooly
- Division of Aging, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St., Boston, MA, 02130, USA.
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 150. S. Huntington Ave, Boston, MA, 02130, USA.
| | - Gina M Peloso
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 150. S. Huntington Ave, Boston, MA, 02130, USA
- Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Ave Crosstown Centre, Boston, MA, 02118, USA
| | - Alexandre C Pereira
- Laboratory of Genetics and Molecular Cardiology, Heart Institute, University of São Paulo, Av Dr Eneas de Carvalho Aguiar 54, São Paulo, 5403000, Brazil
- Genetics Department, Harvard Medical School, Harvard University, 77 Avenue Louis Pasteur, Boston, MA, 02115, USA
| | - Hesam Dashti
- Division of Aging, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St., Boston, MA, 02130, USA
- Broad Institute of MIT and Harvard, 415 Main St., Cambridge, MA, 02142, USA
| | - Claudia Giambartolomei
- Health Data Science Centre, Human Technopole, V.le Rita Levi-Montalcini, 1, Milan, 20157, Italy
- Central RNA Lab, Non-coding RNAs and RNA-based Therapeutics, Istituto Italiano di Tecnologia, Via Morego 30, 16163, Genova, Italy
| | - Eleanor Wheeler
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Addenbrookes Hospital, IMS, Box 285, Cambridge, CB2 0QQ, UK
| | - Nay Aung
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, UK
| | - Brian R Ferolito
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 150. S. Huntington Ave, Boston, MA, 02130, USA
| | - Maik Pietzner
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Addenbrookes Hospital, IMS, Box 285, Cambridge, CB2 0QQ, UK
- Computational Medicine, Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Kapelle Ufer 2, Berlin, 10117, Germany
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Eric H Farber-Eger
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Quinn Stanton Wells
- Vanderbilt University Med. Ctr., Departments of Medicine (Cardiology), Biomedical Informatics, and Pharmacology, Nashville, TN, USA
| | - Nicole M Kosik
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 150. S. Huntington Ave, Boston, MA, 02130, USA
| | - Liam Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 150. S. Huntington Ave, Boston, MA, 02130, USA
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Worts Causeway, Cambridge, CB1 8RN, UK
| | - Daniel C Posner
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 150. S. Huntington Ave, Boston, MA, 02130, USA
| | - A Patrícia Bento
- Department of Chemical Biology, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, UK
| | - Qin Hui
- Department of Epidemiology, Emory University Rollins School of Public Health, 1518 Clifton Rd NE, Atlanta, GA, 30322, USA
- Atlanta VA Health Care System, 1670 Clairmont Road, Decatur, GA, 30033, USA
| | - Chang Liu
- Department of Epidemiology, Emory University Rollins School of Public Health, 1518 Clifton Rd NE, Atlanta, GA, 30322, USA
| | - Krishna Aragam
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 150. S. Huntington Ave, Boston, MA, 02130, USA
- Broad Institute of MIT and Harvard, 415 Main St., Cambridge, MA, 02142, USA
- Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Zeyuan Wang
- Department of Epidemiology, Emory University Rollins School of Public Health, 1518 Clifton Rd NE, Atlanta, GA, 30322, USA
| | - Brian Charest
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 150. S. Huntington Ave, Boston, MA, 02130, USA
| | - Jennifer E Huffman
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 150. S. Huntington Ave, Boston, MA, 02130, USA
| | - Peter W F Wilson
- Atlanta VA Health Care System, 1670 Clairmont Road, Decatur, GA, 30033, USA
- Division of Cardiology, Department of Medicine, Emory University School of Medicine, 1639 Pierce Dr NE, Atlanta, GA, 30322, USA
| | - Lawrence S Phillips
- Atlanta VA Health Care System, 1670 Clairmont Road, Decatur, GA, 30033, USA
- Division of Endocrinology, Emory University, 101 Woodruff Circle, WMRB 1027, Atlanta, GA, 30322, USA
| | - John Whittaker
- MRC Biostatistics Unit, University of Cambridge, Cambridge, CB2 0SR, United Kingdom
| | - Patricia B Munroe
- William Harvey Research Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
- National Institute for Health Research, Barts Biomedical Research Centre, Queen Mary University of London, London, UK
| | - Steffen E Petersen
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, UK
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 68Q, UK
| | - Kelly Cho
- Division of Aging, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St., Boston, MA, 02130, USA
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 150. S. Huntington Ave, Boston, MA, 02130, USA
| | - Andrew R Leach
- Department of Chemical Biology, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, UK
| | - María Paula Magariños
- Department of Chemical Biology, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, UK
| | - John Michael Gaziano
- Division of Aging, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St., Boston, MA, 02130, USA
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 150. S. Huntington Ave, Boston, MA, 02130, USA
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Addenbrookes Hospital, IMS, Box 285, Cambridge, CB2 0QQ, UK
- Computational Medicine, Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Kapelle Ufer 2, Berlin, 10117, Germany
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Yan V Sun
- Department of Epidemiology, Emory University Rollins School of Public Health, 1518 Clifton Rd NE, Atlanta, GA, 30322, USA
- Atlanta VA Health Care System, 1670 Clairmont Road, Decatur, GA, 30033, USA
- Department of Biomedical Informatics, Emory University School of Medicine, 1639 Pierce Dr NE, Atlanta, GA, 30332, USA
| | - Jacob Joseph
- Cardiology Section, VA Providence Healthcare System, 830 Chalkstone Avenue, Providence, RI, 02908, USA.
- Department of Medicine, Warren Alpert Medical School of Brown University, 222 Richmond Street, Providence, RI, 02903, USA.
| | - Juan P Casas
- Division of Aging, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St., Boston, MA, 02130, USA
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 150. S. Huntington Ave, Boston, MA, 02130, USA
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Khanji MY, Karim S, Cooper J, Chahal A, Aung N, Somers VK, Neubauer S, Petersen SE. Impact of Sleep Duration and Chronotype on Cardiac Structure and Function: The UK Biobank Study. Curr Probl Cardiol 2023; 48:101688. [PMID: 36906161 DOI: 10.1016/j.cpcardiol.2023.101688] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 03/02/2023] [Indexed: 03/11/2023]
Abstract
Sleep duration and chronotype have been associated with increased morbidity and mortality. We assessed for associations between sleep duration and chronotype on cardiac structure and function. UK Biobank participants with CMR data and without known cardiovascular disease were included. Self-reported sleep duration was categorized as short (<7 h/d), normal (7-9 h/d) and long (>9 h/d). Self-reported chronotype was categories as "definitely morning" or "definitely evening." Analysis included 3903 middle-aged adults: 929 short, 2924 normal and 50 long sleepers; with 966 definitely-morning and 355 definitely-evening chronotypes. Long sleep was independently associated with lower left ventricular (LV) mass (-4.8%, P = 0.035), left atrial maximum volume (-8.1%, P = 0.041) and right ventricular (RV) end-diastolic volume (-4.8%, P = 0.038) compared to those with normal sleep duration. Evening chronotype was independently associated with lower LV end-diastolic volume (-2.4%, P = 0.021), RV end-diastolic volume (-3.6%, P = 0.0006), RV end systolic volume (-5.1%, P = 0.0009), RV stroke volume (RVSV -2.7%, P = 0.033), right atrial maximal volume (-4.3%, P = 0.011) and emptying fraction (+1.3%, P = 0.047) compared to morning chronotype. Sex interactions existed for sleep duration and chronotype and age interaction for chronotype even after considering potential confounders. In conclusion, longer sleep duration was independently associated with smaller LV mass, left atrial volume and RV volume. Evening chronotype was independently associated with smaller LV and RV and reduced RV function compared to morning chronotype. Sex interactions exist with cardiac remodeling most evident in males with long sleep duration and evening chronotype. Recommendations for sleep chronotype and duration may need to be individualized based on sex.
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Affiliation(s)
- Mohammed Y Khanji
- William Harvey Research Institute, NIHR Barts Biomedical Centre, Queen Mary University London, Charterhouse Square, London, UK; Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, UK; Newham University Hospital, Barts Health NHS Trust, London, UK.
| | - Shahid Karim
- William Harvey Research Institute, NIHR Barts Biomedical Centre, Queen Mary University London, Charterhouse Square, London, UK; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Jackie Cooper
- William Harvey Research Institute, NIHR Barts Biomedical Centre, Queen Mary University London, Charterhouse Square, London, UK
| | - Anwar Chahal
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, UK; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN; Division of Cardiology, Hospital of the University of Pennsylvania, Philadelphia, PA
| | - Nay Aung
- William Harvey Research Institute, NIHR Barts Biomedical Centre, Queen Mary University London, Charterhouse Square, London, UK; Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, UK
| | - Virend K Somers
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Stefan Neubauer
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK
| | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Centre, Queen Mary University London, Charterhouse Square, London, UK; Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, UK
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Rauseo E, Salih A, Raisi-Estabragh Z, Aung N, Khanderia N, Slabaugh GG, Marshall CR, Neubauer S, Radeva P, Galazzo IB, Menegaz G, Petersen SE. Ischemic Heart Disease and Vascular Risk Factors Are Associated With Accelerated Brain Aging. JACC Cardiovasc Imaging 2023; 16:905-915. [PMID: 37407123 DOI: 10.1016/j.jcmg.2023.01.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 12/06/2022] [Accepted: 01/05/2023] [Indexed: 07/07/2023]
Abstract
BACKGROUND Ischemic heart disease (IHD) has been linked with poor brain outcomes. The brain magnetic resonance imaging-derived difference between predicted brain age and actual chronological age (brain-age delta in years, positive for accelerated brain aging) may serve as an effective means of communicating brain health to patients to promote healthier lifestyles. OBJECTIVES The authors investigated the impact of prevalent IHD on brain aging, potential underlying mechanisms, and its relationship with dementia risk, vascular risk factors, cardiovascular structure, and function. METHODS Brain age was estimated in subjects with prevalent IHD (n = 1,341) using a Bayesian ridge regression model with 25 structural (volumetric) brain magnetic resonance imaging features and built using UK Biobank participants with no prevalent IHD (n = 35,237). RESULTS Prevalent IHD was linked to significantly accelerated brain aging (P < 0.001) that was not fully mediated by microvascular injury. Brain aging (positive brain-age delta) was associated with increased risk of dementia (OR: 1.13 [95% CI: 1.04-1.22]; P = 0.002), vascular risk factors (such as diabetes), and high adiposity. In the absence of IHD, brain aging was also associated with cardiovascular structural and functional changes typically observed in aging hearts. However, such alterations were not linked with risk of dementia. CONCLUSIONS Prevalent IHD and coexisting vascular risk factors are associated with accelerated brain aging and risk of dementia. Positive brain-age delta representing accelerated brain aging may serve as an effective communication tool to show the impact of modifiable risk factors and disease supporting preventative strategies.
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Affiliation(s)
- Elisa Rauseo
- William Harvey Research Institute, National Institute for Health Research (NIHR) Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London, United Kingdom; Barts Heart Centre, St Bartholomew's Hospital, Barts Health National Health Service (NHS) Trust, West Smithfield, London, United Kingdom
| | - Ahmed Salih
- William Harvey Research Institute, National Institute for Health Research (NIHR) Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London, United Kingdom; Barts Heart Centre, St Bartholomew's Hospital, Barts Health National Health Service (NHS) Trust, West Smithfield, London, United Kingdom; Department of Computer Science, University of Verona, Verona, Italy
| | - Zahra Raisi-Estabragh
- William Harvey Research Institute, National Institute for Health Research (NIHR) Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London, United Kingdom; Barts Heart Centre, St Bartholomew's Hospital, Barts Health National Health Service (NHS) Trust, West Smithfield, London, United Kingdom
| | - Nay Aung
- William Harvey Research Institute, National Institute for Health Research (NIHR) Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London, United Kingdom; Barts Heart Centre, St Bartholomew's Hospital, Barts Health National Health Service (NHS) Trust, West Smithfield, London, United Kingdom
| | - Neha Khanderia
- Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Gregory G Slabaugh
- School of Electronic Engineering and Computer Science, Queen Mary University of London, United Kingdom; Alan Turing Institute, London, United Kingdom; Digital Environment Research Institute, Queen Mary University of London, London, United Kingdom
| | - Charles R Marshall
- Preventive Neurology Unit, Wolfson Institute of Population Health, Charterhouse Square, London, United Kingdom; Neurology Department, Royal London Hospital, Barts Health NHS Trust, London, United Kingdom
| | - Stefan Neubauer
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford NIHR Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Petia Radeva
- Department of Mathematics and Computer Science, University of Barcelona, Barcelona, Spain
| | | | - Gloria Menegaz
- Department of Computer Science, University of Verona, Verona, Italy.
| | - Steffen E Petersen
- William Harvey Research Institute, National Institute for Health Research (NIHR) Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London, United Kingdom; Barts Heart Centre, St Bartholomew's Hospital, Barts Health National Health Service (NHS) Trust, West Smithfield, London, United Kingdom; Alan Turing Institute, London, United Kingdom; Health Data Research UK, London, United Kingdom.
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Szabo L, McCracken C, Cooper J, Rider OJ, Vago H, Merkely B, Harvey NC, Neubauer S, Petersen SE, Raisi-Estabragh Z. The role of obesity-related cardiovascular remodelling in mediating incident cardiovascular outcomes: a population-based observational study. Eur Heart J Cardiovasc Imaging 2023; 24:921-929. [PMID: 36660920 PMCID: PMC10284050 DOI: 10.1093/ehjci/jeac270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 12/01/2022] [Indexed: 01/21/2023] Open
Abstract
AIMS We examined associations of obesity with incident cardiovascular outcomes and cardiovascular magnetic resonance (CMR) phenotypes, integrating information from body mass index (BMI) and waist-to-hip ratio (WHR). Then, we used multiple mediation to define the role of obesity-related cardiac remodelling in driving obesity-outcome associations, independent of cardiometabolic diseases. METHODS AND RESULTS In 491 606 UK Biobank participants, using Cox proportional hazard models, greater obesity (higher WHR, higher BMI) was linked to significantly greater risk of incident ischaemic heart disease, atrial fibrillation (AF), heart failure (HF), all-cause mortality, and cardiovascular disease (CVD) mortality. In combined stratification by BMI and WHR thresholds, elevated WHR was associated with greater risk of adverse outcomes at any BMI level. Individuals with overweight BMI but normal WHR had weaker disease associations. In the subset of participants with CMR (n = 31 107), using linear regression, greater obesity was associated with higher left ventricular (LV) mass, greater LV concentricity, poorer LV systolic function, lower myocardial native T1, larger left atrial (LA) volumes, poorer LA function, and lower aortic distensibility. Of note, higher BMI was linked to higher, whilst greater WHR was linked to lower LV end-diastolic volume (LVEDV). In Cox models, greater LVEDV and LV mass (LVM) were linked to increased risk of CVD, most importantly HF and an increased LA maximal volume was the key predictive measure of new-onset AF. In multiple mediation analyses, hypertension and adverse LV remodelling (higher LVM, greater concentricity) were major independent mediators of the obesity-outcome associations. Atrial remodelling and native T1 were additional mediators in the associations of obesity with AF and HF, respectively. CONCLUSIONS We demonstrate associations of obesity with adverse cardiovascular phenotypes and their significant independent role in mediating obesity-outcome relationships. In addition, our findings support the integrated use of BMI and WHR to evaluate obesity-related cardiovascular risk.
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Affiliation(s)
- Liliana Szabo
- NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
- Heart and Vascular Center, Semmelweis University, 1122, Budapest, Varosmajor utca 68, Hungary
| | - Celeste McCracken
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Jackie Cooper
- NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
| | - Oliver J Rider
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Hajnalka Vago
- Heart and Vascular Center, Semmelweis University, 1122, Budapest, Varosmajor utca 68, Hungary
| | - Bela Merkely
- Heart and Vascular Center, Semmelweis University, 1122, Budapest, Varosmajor utca 68, Hungary
| | - Nicholas C Harvey
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton General Hospital, Tremona Road, Southampton SO16 6YD, UK
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Tremona Road, Southampton SO16 6YD, UK
| | - Stefan Neubauer
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Steffen E Petersen
- NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
- Health Data Research UK, Gibbs Building, 215 Euston Rd, London NW1 2BE, UK
- Alan Turing Institute, British Library, 96 Euston Rd, London NW1 2DB, UK
| | - Zahra Raisi-Estabragh
- NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
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Althunayyan A, Alborikan S, Badiani S, Wong K, Uppal R, Patel N, Petersen SE, Lloyd G, Bhattacharyya S. Clinical and Prognostic Implications of Cardiopulmonary Exercise Stress Echocardiography in Asymptomatic Degenerative Mitral Regurgitation. Am J Cardiol 2023; 201:8-15. [PMID: 37348153 DOI: 10.1016/j.amjcard.2023.05.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 04/18/2023] [Accepted: 05/21/2023] [Indexed: 06/24/2023]
Abstract
The current guidelines recommend intervention in severe degenerative mitral regurgitation (MR) in symptomatic patients or asymptomatic patients with left ventricular dilatation or dysfunction. The insidious onset of symptoms may mean that patients do not report their symptoms. The role of systematic exercise testing for symptoms in MR is not clearly defined. A total of 97 patients with moderate to severe asymptomatic MR underwent exercise echocardiography combined with cardiopulmonary exercise testing. The predictors of exercise-induced dyspnea, symptom-free survival, and mitral valve intervention were identified. A total of 18 patients (19%) developed limiting dyspnea on exercise. Spontaneous symptom-free survival at 24 months was significantly higher in those without exercise-induced symptoms than those with exercise-induced symptoms, p <0.0001. The only independent predictors of spontaneous symptoms at 2 years were effective regurgitant orifice area (odds ratio 27.45, 95% confidence interval [CI] 1.43 to 528.40, p = 0.03) and exercise-induced symptoms (odds ratio 11.56, 95% CI 1.71 to 78.09, p = 0.01). The only independent predictor of surgery was indexed left ventricular systolic volumes (odds ratio 1.17, 95% CI 1.04 to 1.30, p = 0.006). Where only the patients who underwent surgery due to symptoms were included, the only independent predictor was exercise-induced symptoms (odds ratio 13.94, 95% CI 1.39 to 140.27, p = 0.025). In conclusion, in patients with primary asymptomatic degenerative MR, 1/5 develop revealed symptoms during exercise. This predicts a subsequent development of spontaneous symptoms and mitral valve intervention due to symptoms.
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Affiliation(s)
- Aeshah Althunayyan
- Heart Valve Clinic & Echocardiography Laboratory, Barts Heart Centre; William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, London, United Kingdom; Department of Cardiac Technology, College of Applied Medical Sciences, Imam Abdulrahman Bin Faisal University, Dammam, Kingdom of Saudi Arabia
| | - Sahar Alborikan
- Heart Valve Clinic & Echocardiography Laboratory, Barts Heart Centre; William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, London, United Kingdom
| | - Sveeta Badiani
- Heart Valve Clinic & Echocardiography Laboratory, Barts Heart Centre
| | | | | | - Nikhil Patel
- Eastbourne District General Hospital, Eastbourne, United Kingdom
| | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, London, United Kingdom; Barts Heart Centre, Barts Health NHS Trust, St Bartholomew's Hospital, London, United Kingdom; Health Data Research UK, London, United Kingdom; Alan Turing Institute, London, United Kingdom
| | - Guy Lloyd
- Heart Valve Clinic & Echocardiography Laboratory, Barts Heart Centre; William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, London, United Kingdom; Institute of Cardiovascular Sciences, University College London, London, United Kingdom
| | - Sanjeev Bhattacharyya
- Heart Valve Clinic & Echocardiography Laboratory, Barts Heart Centre; William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, London, United Kingdom; Institute of Cardiovascular Sciences, University College London, London, United Kingdom.
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Raisi-Estabragh Z, Cooper J, McCracken C, Crosbie EJ, Walter FM, Manisty CH, Robson J, Mamas MA, Harvey NC, Neubauer S, Petersen SE. Incident cardiovascular events and imaging phenotypes in UK Biobank participants with past cancer. Heart 2023; 109:1007-1015. [PMID: 37072241 PMCID: PMC10314020 DOI: 10.1136/heartjnl-2022-321888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 12/28/2022] [Indexed: 04/20/2023] Open
Abstract
OBJECTIVES To evaluate incident cardiovascular outcomes and imaging phenotypes in UK Biobank participants with previous cancer. METHODS Cancer and cardiovascular disease (CVD) diagnoses were ascertained using health record linkage. Participants with cancer history (breast, lung, prostate, colorectal, uterus, haematological) were propensity matched on vascular risk factors to non-cancer controls. Competing risk regression was used to calculate subdistribution HRs (SHRs) for associations of cancer history with incident CVD (ischaemic heart disease (IHD), non-ischaemic cardiomyopathy (NICM), heart failure (HF), atrial fibrillation/flutter, stroke, pericarditis, venous thromboembolism (VTE)) and mortality outcomes (any CVD, IHD, HF/NICM, stroke, hypertensive disease) over 11.8±1.7 years of prospective follow-up. Linear regression was used to assess associations of cancer history with left ventricular (LV) and left atrial metrics. RESULTS We studied 18 714 participants (67% women, age: 62 (IQR: 57-66) years, 97% white ethnicities) with cancer history, including 1354 individuals with cardiovascular magnetic resonance. Participants with cancer had high burden of vascular risk factors and prevalent CVDs. Haematological cancer was associated with increased risk of all incident CVDs considered (SHRs: 1.92-3.56), larger chamber volumes, lower ejection fractions, and poorer LV strain. Breast cancer was associated with increased risk of selected CVDs (NICM, HF, pericarditis and VTE; SHRs: 1.34-2.03), HF/NICM death, hypertensive disease death, lower LV ejection fraction, and lower LV global function index. Lung cancer was associated with increased risk of pericarditis, HF, and CVD death. Prostate cancer was linked to increased VTE risk. CONCLUSIONS Cancer history is linked to increased risk of incident CVDs and adverse cardiac remodelling independent of shared vascular risk factors.
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Affiliation(s)
- Zahra Raisi-Estabragh
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, UK
| | - Jackie Cooper
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, UK
| | - Celeste McCracken
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Emma J Crosbie
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- Department of Obstetrics and Gynaecology, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Fiona M Walter
- Wolfson Institute of Population Health, Queen Mary University of London, London, UK
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Charlotte H Manisty
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, UK
- Institute of Cardiovascular Science, University College London, London, UK
| | - John Robson
- Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Mamas A Mamas
- Institute of Population Health, Manchester University, manchester, UK
- Keele Cardiovascular Research Group, Keele University, Keele, UK
| | - Nicholas C Harvey
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Stefan Neubauer
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, UK
- Health Data Research UK, London, UK
- Alan Turing Institute, London, UK
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Leiner T, Lekadir K, Petersen SE, Young AA. Editorial: Current and future role of artificial intelligence in cardiac imaging, volume II. Front Cardiovasc Med 2023; 10:1220836. [PMID: 37363092 PMCID: PMC10285516 DOI: 10.3389/fcvm.2023.1220836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 06/02/2023] [Indexed: 06/28/2023] Open
Affiliation(s)
- Tim Leiner
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Karim Lekadir
- Departament de Matemàtiques and Informàtica, Universitat de Barcelona, Artificial Intelligence in Medicine Lab (BCN-AIM), Barcelona, Spain
| | - Steffen E. Petersen
- Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom
- NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Alistair A. Young
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
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Ricci F, Khanji MY, Bisaccia G, Cipriani A, Di Cesare A, Ceriello L, Mantini C, Zimarino M, Fedorowski A, Gallina S, Petersen SE, Bucciarelli-Ducci C. Diagnostic and Prognostic Value of Stress Cardiovascular Magnetic Resonance Imaging in Patients With Known or Suspected Coronary Artery Disease: A Systematic Review and Meta-analysis. JAMA Cardiol 2023:2805949. [PMID: 37285143 DOI: 10.1001/jamacardio.2023.1290] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Importance The clinical utility of stress cardiovascular magnetic resonance imaging (CMR) in stable chest pain is still debated, and the low-risk period for adverse cardiovascular (CV) events after a negative test result is unknown. Objective To provide contemporary quantitative data synthesis of the diagnostic accuracy and prognostic value of stress CMR in stable chest pain. Data Sources PubMed and Embase databases, the Cochrane Database of Systematic Reviews, PROSPERO, and the ClinicalTrials.gov registry were searched for potentially relevant articles from January 1, 2000, through December 31, 2021. Study Selection Selected studies evaluated CMR and reported estimates of diagnostic accuracy and/or raw data of adverse CV events for participants with either positive or negative stress CMR results. Prespecified combinations of keywords related to the diagnostic accuracy and prognostic value of stress CMR were used. A total of 3144 records were evaluated for title and abstract; of those, 235 articles were included in the full-text assessment of eligibility. After exclusions, 64 studies (74 470 total patients) published from October 29, 2002, through October 19, 2021, were included. Data Extraction and Synthesis This systematic review and meta-analysis adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Main Outcomes and Measures Diagnostic odds ratios (DORs), sensitivity, specificity, area under the receiver operating characteristic curve (AUROC), odds ratio (OR), and annualized event rate (AER) for all-cause death, CV death, and major adverse cardiovascular events (MACEs) defined as the composite of myocardial infarction and CV death. Results A total of 33 diagnostic studies pooling 7814 individuals and 31 prognostic studies pooling 67 080 individuals (mean [SD] follow-up, 3.5 [2.1] years; range, 0.9-8.8 years; 381 357 person-years) were identified. Stress CMR yielded a DOR of 26.4 (95% CI, 10.6-65.9), a sensitivity of 81% (95% CI, 68%-89%), a specificity of 86% (95% CI, 75%-93%), and an AUROC of 0.84 (95% CI, 0.77-0.89) for the detection of functionally obstructive coronary artery disease. In the subgroup analysis, stress CMR yielded higher diagnostic accuracy in the setting of suspected coronary artery disease (DOR, 53.4; 95% CI, 27.7-103.0) or when using 3-T imaging (DOR, 33.2; 95% CI, 19.9-55.4). The presence of stress-inducible ischemia was associated with higher all-cause mortality (OR, 1.97; 95% CI, 1.69-2.31), CV mortality (OR, 6.40; 95% CI, 4.48-9.14), and MACEs (OR, 5.33; 95% CI, 4.04-7.04). The presence of late gadolinium enhancement (LGE) was associated with higher all-cause mortality (OR, 2.22; 95% CI, 1.99-2.47), CV mortality (OR, 6.03; 95% CI, 2.76-13.13), and increased risk of MACEs (OR, 5.42; 95% CI, 3.42-8.60). After a negative test result, pooled AERs for CV death were less than 1.0%. Conclusion and Relevance In this study, stress CMR yielded high diagnostic accuracy and delivered robust prognostication, particularly when 3-T scanners were used. While inducible myocardial ischemia and LGE were associated with higher mortality and risk of MACEs, normal stress CMR results were associated with a lower risk of MACEs for at least 3.5 years.
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Affiliation(s)
- Fabrizio Ricci
- Department of Neuroscience, Imaging and Clinical Sciences, Gabriele d'Annunzio University of Chieti-Pescara, Chieti, Italy
- Department of Clinical Sciences, Lund University, Malmö, Sweden
- William Harvey Research Institute, Barts Biomedical Research Centre, National Institute for Health and Care Research, Queen Mary University London, Charterhouse Square, London, United Kingdom
| | - Mohammed Y Khanji
- William Harvey Research Institute, Barts Biomedical Research Centre, National Institute for Health and Care Research, Queen Mary University London, Charterhouse Square, London, United Kingdom
- Newham University Hospital, Barts Health NHS Trust, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, United Kingdom
| | - Giandomenico Bisaccia
- Department of Neuroscience, Imaging and Clinical Sciences, Gabriele d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Alberto Cipriani
- Department of Cardiac, Thoracic and Vascular Sciences and Public Health, University of Padova, Padova, Italy
| | - Annamaria Di Cesare
- Cardiology Unit, Rimini Hospital, Local Health Authority of Romagna, Rimini, Italy
| | - Laura Ceriello
- Department of Neuroscience, Imaging and Clinical Sciences, Gabriele d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Cesare Mantini
- Department of Neuroscience, Imaging and Clinical Sciences, Gabriele d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Marco Zimarino
- Department of Neuroscience, Imaging and Clinical Sciences, Gabriele d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Artur Fedorowski
- Department of Clinical Sciences, Lund University, Malmö, Sweden
- Department of Cardiology, Karolinska University Hospital, Stockholm, Sweden
- Department of Medicine, Karolinska Institute, Stockholm, Sweden
| | - Sabina Gallina
- Department of Neuroscience, Imaging and Clinical Sciences, Gabriele d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Steffen E Petersen
- Newham University Hospital, Barts Health NHS Trust, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, United Kingdom
- The Alan Turing Institute, London, United Kingdom
- Health Data Research UK, London, United Kingdom
| | - Chiara Bucciarelli-Ducci
- Royal Brompton and Harefield Hospitals, Guys and St Thomas NHS Trust London, London, United Kingdom
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, Kings College London, London, United Kingdom
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Petersen SE, Jensen B, Aung N, Friedrich MG, McMahon CJ, Mohiddin SA, Pignatelli RH, Ricci F, Anderson RH, Bluemke DA. Reply: Discourage LVNC or Revise the Criteria of LVNC? JACC Cardiovasc Imaging 2023; 16:869. [PMID: 37286274 DOI: 10.1016/j.jcmg.2023.03.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 03/23/2023] [Indexed: 06/09/2023]
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Ng M, Guo F, Biswas L, Petersen SE, Piechnik SK, Neubauer S, Wright G. Estimating Uncertainty in Neural Networks for Cardiac MRI Segmentation: A Benchmark Study. IEEE Trans Biomed Eng 2023; 70:1955-1966. [PMID: 37015623 DOI: 10.1109/tbme.2022.3232730] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
OBJECTIVE Convolutional neural networks (CNNs) have demonstrated promise in automated cardiac magnetic resonance image segmentation. However, when using CNNs in a large real-world dataset, it is important to quantify segmentation uncertainty and identify segmentations which could be problematic. In this work, we performed a systematic study of Bayesian and non-Bayesian methods for estimating uncertainty in segmentation neural networks. METHODS We evaluated Bayes by Backprop, Monte Carlo Dropout, Deep Ensembles, and Stochastic Segmentation Networks in terms of segmentation accuracy, probability calibration, uncertainty on out-of-distribution images, and segmentation quality control. RESULTS We observed that Deep Ensembles outperformed the other methods except for images with heavy noise and blurring distortions. We showed that Bayes by Backprop is more robust to noise distortions while Stochastic Segmentation Networks are more resistant to blurring distortions. For segmentation quality control, we showed that segmentation uncertainty is correlated with segmentation accuracy for all the methods. With the incorporation of uncertainty estimates, we were able to reduce the percentage of poor segmentation to 5% by flagging 31-48% of the most uncertain segmentations for manual review, substantially lower than random review without using neural network uncertainty (reviewing 75-78% of all images). CONCLUSION This work provides a comprehensive evaluation of uncertainty estimation methods and showed that Deep Ensembles outperformed other methods in most cases. SIGNIFICANCE Neural network uncertainty measures can help identify potentially inaccurate segmentations and alert users for manual review.
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Salih A, Nichols T, Szabo L, Petersen SE, Raisi-Estabragh Z. Conceptual Overview of Biological Age Estimation. Aging Dis 2023; 14:583-588. [PMID: 37191413 PMCID: PMC10187689 DOI: 10.14336/ad.2022.1107] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Accepted: 11/07/2022] [Indexed: 05/17/2023] Open
Abstract
Chronological age is an imperfect measure of the aging process, which is affected by a wide range of genetic and environmental exposures. Biological age estimates may be derived using mathematical modelling with biomarkers set as predictors and chronological age as the output. The difference between biological and chronological age is denoted the "age gap" and considered a complementary indicator of aging. The utility of the "age gap" metric is assessed through examination of its associations with exposures of interest and the demonstration of additional information provided by this metric over chronological age alone. This paper reviews the key concepts of biological age estimation, the age gap metric, and approaches to assessment of model performance in this context. We further discuss specific challenges for the field, in particular the limited generalisability of effect sizes across studies owing to dependency of the age gap metric on pre-processing and model building methods. The discussion will be centred on brain age estimation, but the concepts are transferable to all biological age estimation.
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Affiliation(s)
- Ahmed Salih
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK.
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK.
| | - Thomas Nichols
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
| | - Liliana Szabo
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK.
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK.
| | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK.
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK.
- Health Data Research UK, London, UK.
- Alan Turing Institute, London, UK.
| | - Zahra Raisi-Estabragh
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK.
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK.
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Pujadas ER, Raisi-Estabragh Z, Szabo L, McCracken C, Morcillo CI, Campello VM, Martín-Isla C, Atehortua AM, Vago H, Merkely B, Maurovich-Horvat P, Harvey NC, Neubauer S, Petersen SE, Lekadir K. Prediction of incident cardiovascular events using machine learning and CMR radiomics. Eur Radiol 2023; 33:3488-3500. [PMID: 36512045 PMCID: PMC10121487 DOI: 10.1007/s00330-022-09323-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 09/28/2022] [Accepted: 11/28/2022] [Indexed: 12/15/2022]
Abstract
OBJECTIVES Evaluation of the feasibility of using cardiovascular magnetic resonance (CMR) radiomics in the prediction of incident atrial fibrillation (AF), heart failure (HF), myocardial infarction (MI), and stroke using machine learning techniques. METHODS We identified participants from the UK Biobank who experienced incident AF, HF, MI, or stroke during the continuous longitudinal follow-up. The CMR indices and the vascular risk factors (VRFs) as well as the CMR images were obtained for each participant. Three-segmented regions of interest (ROIs) were computed: right ventricle cavity, left ventricle (LV) cavity, and LV myocardium in end-systole and end-diastole phases. Radiomics features were extracted from the 3D volumes of the ROIs. Seven integrative models were built for each incident cardiovascular disease (CVD) as an outcome. Each model was built with VRF, CMR indices, and radiomics features and a combination of them. Support vector machine was used for classification. To assess the model performance, the accuracy, sensitivity, specificity, and AUC were reported. RESULTS AF prediction model using the VRF+CMR+Rad model (accuracy: 0.71, AUC 0.76) obtained the best result. However, the AUC was similar to the VRF+Rad model. HF showed the most significant improvement with the inclusion of CMR metrics (VRF+CMR+Rad: 0.79, AUC 0.84). Moreover, adding only the radiomics features to the VRF reached an almost similarly good performance (VRF+Rad: accuracy 0.77, AUC 0.83). Prediction models looking into incident MI and stroke reached slightly smaller improvement. CONCLUSIONS Radiomics features may provide incremental predictive value over VRF and CMR indices in the prediction of incident CVDs. KEY POINTS • Prediction of incident atrial fibrillation, heart failure, stroke, and myocardial infarction using machine learning techniques. • CMR radiomics, vascular risk factors, and standard CMR indices will be considered in the machine learning models. • The experiments show that radiomics features can provide incremental predictive value over VRF and CMR indices in the prediction of incident cardiovascular diseases.
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Affiliation(s)
- Esmeralda Ruiz Pujadas
- Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain.
| | - Zahra Raisi-Estabragh
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK
| | - Liliana Szabo
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK
- Semmelweis University Heart and Vascular Center, Budapest, Hungary
| | - Celeste McCracken
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Cristian Izquierdo Morcillo
- Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Víctor M Campello
- Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Carlos Martín-Isla
- Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Angelica M Atehortua
- Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Hajnalka Vago
- Semmelweis University Heart and Vascular Center, Budapest, Hungary
| | - Bela Merkely
- Semmelweis University Heart and Vascular Center, Budapest, Hungary
| | | | - Nicholas C Harvey
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Stefan Neubauer
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK
- Health Data Research UK, London, UK
- Alan Turing Institute, London, UK
| | - Karim Lekadir
- Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain.
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Chadalavada S, Reinikainen J, Andersson J, Di Castelnuovo A, Iacoviello L, Jousilahti P, Kårhus LL, Linneberg A, Söderberg S, Tunstall-Pedoe H, Lekadir K, Aung N, Jensen MT, Kuulasmaa K, Niiranen TJ, Petersen SE. Diabetes and heart failure associations in women and men: Results from the MORGAM consortium. Front Cardiovasc Med 2023; 10:1136764. [PMID: 37180793 PMCID: PMC10167048 DOI: 10.3389/fcvm.2023.1136764] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 03/20/2023] [Indexed: 05/16/2023] Open
Abstract
Background Diabetes and its cardiovascular complications are a growing concern worldwide. Recently, some studies have demonstrated that relative risk of heart failure (HF) is higher in women with type 1 diabetes (T1DM) than in men. This study aims to validate these findings in cohorts representing five countries across Europe. Methods This study includes 88,559 (51.8% women) participants, 3,281 (46.3% women) of whom had diabetes at baseline. Survival analysis was performed with the outcomes of interest being death and HF with a follow-up time of 12 years. Sub-group analysis according to sex and type of diabetes was also performed for the HF outcome. Results 6,460 deaths were recorded, of which 567 were amongst those with diabetes. Additionally, HF was diagnosed in 2,772 individuals (446 with diabetes). A multivariable Cox proportional hazard analysis showed that there was an increased risk of death and HF (hazard ratio (HR) of 1.73 [1.58-1.89] and 2.12 [1.91-2.36], respectively) when comparing those with diabetes and those without. The HR for HF was 6.72 [2.75-16.41] for women with T1DM vs. 5.80 [2.72-12.37] for men with T1DM, but the interaction term for sex differences was insignificant (p for interaction 0.45). There was no significant difference in the relative risk of HF between men and women when both types of diabetes were combined (HR 2.22 [1.93-2.54] vs. 1.99 [1.67-2.38] respectively, p for interaction 0.80). Conclusion Diabetes is associated with increased risks of death and heart failure, and there was no difference in relative risk according to sex.
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Affiliation(s)
- Sucharitha Chadalavada
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, United Kingdom
| | - Jaakko Reinikainen
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare (THL), Helsinki, Finland
| | - Jonas Andersson
- Department of Public Health and Clinical Medicine, Skellefteå Research Unit, Umeå University, Skellefteå, Sweden
| | | | - Licia Iacoviello
- Department of Epidemiology and Prevention, IRCCS Neuromed, Pozzilli, Italy
- Research Center in Epidemiology and Preventive Medicine-EPIMED, Department of Medicine and Surgery, University of Insubria, Varese, Italy
| | - Pekka Jousilahti
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare (THL), Helsinki, Finland
| | - Line Lund Kårhus
- Center for Clinical Research and Prevention, Copenhagen University Hospital-Bispebjerg and Frederiksberg, Copenhagen, Denmark
| | - Allan Linneberg
- Center for Clinical Research and Prevention, Copenhagen University Hospital-Bispebjerg and Frederiksberg, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Stefan Söderberg
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Hugh Tunstall-Pedoe
- Cardiovascular Epidemiology Unit, Institute of Cardiovascular Research, University of Dundee, Dundee, United Kingdom
| | - Karim Lekadir
- Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques and Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Nay Aung
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, United Kingdom
| | - Magnus T Jensen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, United Kingdom
- Steno Diabetes Center Copenhagen, Borgmester Ib Juuls Vej 83, Herlev, Denmark
| | - Kari Kuulasmaa
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare (THL), Helsinki, Finland
| | - Teemu J Niiranen
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare (THL), Helsinki, Finland
- Department of Internal Medicine, University of Turku and Turku University Hospital, Turku, Finland
| | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, United Kingdom
- Health Data Research UK, London, United Kingdom
- National Institute for Health and Care Research, London, United Kingdom
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Martin-Isla C, Campello VM, Izquierdo C, Kushibar K, Sendra-Balcells C, Gkontra P, Sojoudi A, Fulton MJ, Arega TW, Punithakumar K, Li L, Sun X, Khalil YA, Liu D, Jabbar S, Queiros S, Galati F, Mazher M, Gao Z, Beetz M, Tautz L, Galazis C, Varela M, Hullebrand M, Grau V, Zhuang X, Puig D, Zuluaga MA, Mohy-Ud-Din H, Metaxas D, Breeuwer M, Geest RJVD, Noga M, Bricq S, Rentschler ME, Guala A, Petersen SE, Escalera S, Palomares JFR, Lekadir K. Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&ms Challenge. IEEE J Biomed Health Inform 2023; PP. [PMID: 37067963 DOI: 10.1109/jbhi.2023.3267857] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
In recent years, several deep learning models have been proposed to accurately quantify and diagnose cardiac pathologies. These automated tools heavily rely on the accurate segmentation of cardiac structures in MRI images. However, segmentation of the right ventricle is challenging due to its highly complex shape and ill-defined borders. Hence, there is a need for new methods to handle such structure's geometrical and textural complexities, notably in the presence of pathologies such as Dilated Right Ventricle, Tricuspid Regurgitation, Arrhythmogenesis, Tetralogy of Fallot, and Inter-atrial Communication. The last MICCAI challenge on right ventricle segmentation was held in 2012 and included only 48 cases from a single clinical center. As part of the 12th Workshop on Statistical Atlases and Computational Models of the Heart (STACOM 2021), the M&Ms-2 challenge was organized to promote the interest of the research community around right ventricle segmentation in multi-disease, multi-view, and multi-center cardiac MRI. Three hundred sixty CMR cases, including short-axis and long-axis 4-chamber views, were collected from three Spanish hospitals using nine different scanners from three different vendors, and included a diverse set of right and left ventricle pathologies. The solutions provided by the participants show that nnU-Net achieved the best results overall. However, multi-view approaches were able to capture additional information, highlighting the need to integrate multiple cardiac diseases, views, scanners, and acquisition protocols to produce reliable automatic cardiac segmentation algorithms.
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Raisi-Estabragh Z, McCracken C, Hann E, Condurache DG, Harvey NC, Munroe PB, Ferreira VM, Neubauer S, Piechnik SK, Petersen SE. Incident Clinical and Mortality Associations of Myocardial Native T1 in the UK Biobank. JACC Cardiovasc Imaging 2023; 16:450-460. [PMID: 36648036 PMCID: PMC10102720 DOI: 10.1016/j.jcmg.2022.06.011] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 04/19/2022] [Accepted: 06/17/2022] [Indexed: 01/18/2023]
Abstract
BACKGROUND Cardiac magnetic resonance native T1-mapping provides noninvasive, quantitative, and contrast-free myocardial characterization. However, its predictive value in population cohorts has not been studied. OBJECTIVES The associations of native T1 with incident events were evaluated in 42,308 UK Biobank participants over 3.17 ± 1.53 years of prospective follow-up. METHODS Native T1-mapping was performed in 1 midventricular short-axis slice using the Shortened Modified Look-Locker Inversion recovery technique (WIP780B) in 1.5-T scanners (Siemens Healthcare). Global myocardial T1 was calculated using an automated tool. Associations of T1 with: 1) prevalent risk factors (eg, diabetes, hypertension, and high cholesterol); 2) prevalent and incident diseases (eg, any cardiovascular disease [CVD], any brain disease, valvular heart disease, heart failure, nonischemic cardiomyopathies, cardiac arrhythmias, atrial fibrillation [AF], myocardial infarction, ischemic heart disease [IHD], and stroke); and 3) mortality (eg, all-cause, CVD, and IHD) were examined. Results are reported as odds ratios (ORs) or HRs per SD increment of T1 value with 95% CIs and corrected P values, from logistic and Cox proportional hazards regression models. RESULTS Higher myocardial T1 was associated with greater odds of a range of prevalent conditions (eg, any CVD, brain disease, heart failure, nonischemic cardiomyopathies, AF, stroke, and diabetes). The strongest relationships were with heart failure (OR: 1.41 [95% CI: 1.26-1.57]; P = 1.60 × 10-9) and nonischemic cardiomyopathies (OR: 1.40 [95% CI: 1.16-1.66]; P = 2.42 × 10-4). Native T1 was positively associated with incident AF (HR: 1.25 [95% CI: 1.10-1.43]; P = 9.19 × 10-4), incident heart failure (HR: 1.47 [95% CI: 1.31-1.65]; P = 4.79 × 10-11), all-cause mortality (HR: 1.24 [95% CI: 1.12-1.36]; P = 1.51 × 10-5), CVD mortality (HR: 1.40 [95% CI: 1.14-1.73]; P = 0.0014), and IHD mortality (HR: 1.36 [95% CI: 1.03-1.80]; P = 0.0310). CONCLUSIONS This large population study demonstrates the utility of myocardial native T1-mapping for disease discrimination and outcome prediction.
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Affiliation(s)
- Zahra Raisi-Estabragh
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London, United Kingdom; Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, United Kingdom
| | - Celeste McCracken
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Evan Hann
- Oxford Centre for Clinical Magnetic Resonance Research (OCMR), Division of Cardiovascular Medicine, British Heart Foundation Centre of Research Excellence, Oxford NIHR Biomedical Research Centre, University of Oxford, United Kingdom
| | | | - Nicholas C Harvey
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, United Kingdom; NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, United Kingdom
| | - Patricia B Munroe
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London, United Kingdom
| | - Vanessa M Ferreira
- Oxford Centre for Clinical Magnetic Resonance Research (OCMR), Division of Cardiovascular Medicine, British Heart Foundation Centre of Research Excellence, Oxford NIHR Biomedical Research Centre, University of Oxford, United Kingdom
| | - Stefan Neubauer
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Stefan K Piechnik
- National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London, United Kingdom; Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, United Kingdom; Health Data Research UK, London, United Kingdom; Alan Turing Institute, London, United Kingdom.
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Salih A, Boscolo Galazzo I, Gkontra P, Lee AM, Lekadir K, Raisi-Estabragh Z, Petersen SE. Explainable Artificial Intelligence and Cardiac Imaging: Toward More Interpretable Models. Circ Cardiovasc Imaging 2023; 16:e014519. [PMID: 37042240 DOI: 10.1161/circimaging.122.014519] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/13/2023]
Abstract
Artificial intelligence applications have shown success in different medical and health care domains, and cardiac imaging is no exception. However, some machine learning models, especially deep learning, are considered black box as they do not provide an explanation or rationale for model outcomes. Complexity and vagueness in these models necessitate a transition to explainable artificial intelligence (XAI) methods to ensure that model results are both transparent and understandable to end users. In cardiac imaging studies, there are a limited number of papers that use XAI methodologies. This article provides a comprehensive literature review of state-of-the-art works using XAI methods for cardiac imaging. Moreover, it provides simple and comprehensive guidelines on XAI. Finally, open issues and directions for XAI in cardiac imaging are discussed.
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Affiliation(s)
- Ahmed Salih
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, United Kingdom (A.S., A.M.L., Z.R.-E., S.E.P.)
| | | | - Polyxeni Gkontra
- Department of de Matemàtiques i Informàtica, University of Barcelona, Spain (P.G., K.L.)
| | - Aaron Mark Lee
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, United Kingdom (A.S., A.M.L., Z.R.-E., S.E.P.)
| | - Karim Lekadir
- Department of de Matemàtiques i Informàtica, University of Barcelona, Spain (P.G., K.L.)
| | - Zahra Raisi-Estabragh
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, United Kingdom (A.S., A.M.L., Z.R.-E., S.E.P.)
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom (Z.R.-E., S.E.P.)
| | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, United Kingdom (A.S., A.M.L., Z.R.-E., S.E.P.)
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom (Z.R.-E., S.E.P.)
- Health Data Research UK, London (S.E.P.)
- Alan Turing Institute, London, United Kingdom (S.E.P.)
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50
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Clark J, Ionescu A, Chahal CAA, Bhattacharyya S, Lloyd G, Galanti K, Gallina S, Chong JH, Petersen SE, Ricci F, Khanji MY. Interchangeability in Left Ventricular Ejection Fraction Measured by Echocardiography and cardiovascular Magnetic Resonance: Not a Perfect Match in the Real World. Curr Probl Cardiol 2023; 48:101721. [PMID: 37001574 DOI: 10.1016/j.cpcardiol.2023.101721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Accepted: 03/21/2023] [Indexed: 05/12/2023]
Abstract
Comparisons of transthoracic echocardiography (TTE) and cardiovascular magnetic resonance (CMR) derived left ventricular ejection fraction (LVEF) have been reported in core-lab settings but are limited in the real-world setting. We retrospectively identified outpatients from 4 hospital sites who had clinically indicated quantitative assessment of LVEFTTE and LVEFCMR and evaluated their concordance. In 767 patients (mean age 47.6 years; 67.9% males) the median inter-modality interval was 35 days. There was significant positive correlation between the 2 modalities (r = 0.75; P < 0.001). Median LVEF was 54% (IQR 47%, 60%) for TTE and 59% (IQR 51%, 64%) for CMR, (P < 0.001). Normal LVEFTTE was confirmed by CMR in 90.6% of cases. Of patients with severely impaired LVEFTTE, 42.3% were upwardly reclassified by CMR as less severely impaired. The overall proportion of patients that had their LVEF category confirmed by both imaging modalities was 64.4%; Cohen's Kappa 0.41, indicating fair-to-moderate agreement. Overall, CMR upwardly reclassified 28% of patients using the British Society of Echocardiography LVEF grading, 18.6% using the European Society of Cardiology heart failure classification, and 29.6% using specific reference ranges for each modality. In a multi-site "real-worldˮ clinical setting, there was significant discrepancy between LVEFTTE and LVEFCMR measurement. Only 64.4% had their LVEF category confirmed by both imaging modalities. LVEFTTE was generally lower than LVEFCMR. LVEFCMR upwardly reclassified almost half of patients with severe LV dysfunction by LVEFTTE. Clinicians should consider the inter-modality variation before making therapeutic recommendations, particularly as clinical trial LVEF thresholds have historically been guided by echocardiography.
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Affiliation(s)
- Joseph Clark
- Newham University Hospital, Barts Health NHS Trust, London, UK
| | - Adrian Ionescu
- Morriston Cardiac Centre, Morriston Swansea, Swansea, UK
| | - C Anwar A Chahal
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, West Smithfield, UK; Center for Inherited Cardiovascular Diseases, WellSpan Health, Lancaster, PA; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN; Cardiac Electrophysiology, Cardiovascular Division, Hospital of the University of Pennsylvania, Philadelphia, PA
| | - Sanjeev Bhattacharyya
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, West Smithfield, UK; NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, UK
| | - Guy Lloyd
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, West Smithfield, UK; NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, UK
| | - Kristian Galanti
- Department of Neuroscience, Imaging and Clinical Sciences, "G.d'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - Sabina Gallina
- Department of Neuroscience, Imaging and Clinical Sciences, "G.d'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - Jun Hua Chong
- National Heart Centre Singapore, Singapore; Cardiovascular Sciences Academic Clinical Programme, Duke-National University of Singapore Medical School, Singapore
| | - Steffen E Petersen
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, West Smithfield, UK; NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, UK
| | - Fabrizio Ricci
- Department of Neuroscience, Imaging and Clinical Sciences, "G.d'Annunzio" University of Chieti-Pescara, Chieti, Italy; Department of Clinical Sciences, Lund University, Malmö, Sweden; Fondazione Villaserena per la Ricerca, Cittá Sant'Angelo, Italy
| | - Mohammed Y Khanji
- Newham University Hospital, Barts Health NHS Trust, London, UK; Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, West Smithfield, UK; NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, UK.
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