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Pan H, Shi Z, Wang S, Bai J, Zhang T. A predictive model of 30-day mortality in patients with acute type A aortic dissection. Eur J Radiol 2024; 175:111469. [PMID: 38636409 DOI: 10.1016/j.ejrad.2024.111469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Accepted: 04/14/2024] [Indexed: 04/20/2024]
Abstract
OBJECTIVE Acute type A aortic dissection (ATAAD) is a life-threatening cardiovascular disease that requires an effective predictive model to predict and assess a patient's risk of death. Our study aimed to construct a model for predicting the risk of 30-day death in patients with ATAAD and the prediction accuracy of the German Registry of Acute Aortic Dissection Type A (GERAADA) Score and the European System for Cardiac Operative Risk Evaluation (EuroSCORE II) was verified. MATERIALS AND METHODS Between June 2019 and June 2023, 109 patients with ATAAD underwent surgical treatment at our hospital (35 in the death group and 74 in the survival group). The differences in image parameters between the two groups were compared. Search for independent predictors and develop models that predict 30-day mortality in patients with ATAAD. GERAADA Score and EuroSCORE II were retrospectively calculated and indicated mortality was assessed using the receiver operating characteristic (ROC) curve. RESULTS Logistic regression analysis showed that ascending aortic length and pericardial effusion were independent predictors of death within 30 days in patients with ATAAD. We constructed four models, GERAADA Score (Model 1), EuroSCORE II (Model 2), Model 1, ascending aorta length, and pericardial effusion (Model 3), and Model 2, ascending aorta length, and pericardial effusion (Model 4). The area under the curve (AUC = 0.832) of Model 3 was significantly different from those of Models 1 (AUC = 0.683) and 2 (AUC = 0.599), respectively (p < 0.05, DeLong test). CONCLUSIONS Adding ascending aorta length and pericardial effusion to the GERAADA Score can improve the predictive power of 30-day mortality in patients with ATAAD.
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Affiliation(s)
- Hong Pan
- Department of Radiology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Zhenzhou Shi
- Department of Radiology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Shuting Wang
- Department of Radiology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Jinquan Bai
- Department of Radiology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Tong Zhang
- Department of Radiology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
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Makimoto H, Kohro T. Adopting artificial intelligence in cardiovascular medicine: a scoping review. Hypertens Res 2024; 47:685-699. [PMID: 37907600 DOI: 10.1038/s41440-023-01469-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 09/03/2023] [Accepted: 09/26/2023] [Indexed: 11/02/2023]
Abstract
Recent years have witnessed significant transformations in cardiovascular medicine, driven by the rapid evolution of artificial intelligence (AI). This scoping review was conducted to capture the breadth of AI applications within cardiovascular science. Employing a structured approach, we sourced relevant articles from PubMed, with an emphasis on journals encompassing general cardiology and digital medicine. We applied filters to highlight cardiovascular articles published in journals focusing on general internal medicine, cardiology and digital medicine, thereby identifying the prevailing trends in the field. Following a comprehensive full-text screening, a total of 140 studies were identified. Over the preceding 5 years, cardiovascular medicine's interplay with AI has seen an over tenfold augmentation. This expansive growth encompasses multiple cardiovascular subspecialties, including but not limited to, general cardiology, ischemic heart disease, heart failure, and arrhythmia. Deep learning emerged as the predominant methodology. The majority of AI endeavors in this domain have been channeled toward enhancing diagnostic and prognostic capabilities, utilizing resources such as hospital datasets, electrocardiograms, and echocardiography. A significant uptrend was observed in AI's application for omics data analysis. However, a clear gap persists in AI's full-scale integration into the clinical decision-making framework. AI, particularly deep learning, has demonstrated robust applications across cardiovascular subspecialties, indicating its transformative potential in this field. As we continue on this trajectory, ensuring the alignment of technological progress with medical ethics becomes crucial. The abundant digital health data today further accentuates the need for meticulous systematic reviews, tailoring them to each cardiovascular subspecialty.
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Affiliation(s)
- Hisaki Makimoto
- Data Science Center/Cardiovascular Center, Jichi Medical University, Shimotsuke, Japan.
| | - Takahide Kohro
- Data Science Center/Cardiovascular Center, Jichi Medical University, Shimotsuke, Japan
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Raghavan A, Pirruccello JP, Ellinor PT, Lindsay ME. Using Genomics to Identify Novel Therapeutic Targets for Aortic Disease. Arterioscler Thromb Vasc Biol 2024; 44:334-351. [PMID: 38095107 PMCID: PMC10843699 DOI: 10.1161/atvbaha.123.318771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 11/21/2023] [Indexed: 01/04/2024]
Abstract
Aortic disease, including dissection, aneurysm, and rupture, carries significant morbidity and mortality and is a notable cause of sudden cardiac death. Much of our knowledge regarding the genetic basis of aortic disease has relied on the study of individuals with Mendelian aortopathies and, until recently, the genetic determinants of population-level variance in aortic phenotypes remained unclear. However, the application of machine learning methodologies to large imaging datasets has enabled researchers to rapidly define aortic traits and mine dozens of novel genetic associations for phenotypes such as aortic diameter and distensibility. In this review, we highlight the emerging potential of genomics for identifying causal genes and candidate drug targets for aortic disease. We describe how deep learning technologies have accelerated the pace of genetic discovery in this field. We then provide a blueprint for translating genetic associations to biological insights, reviewing techniques for locus and cell type prioritization, high-throughput functional screening, and disease modeling using cellular and animal models of aortic disease.
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Affiliation(s)
- Avanthi Raghavan
- Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute, Cambridge, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - James P. Pirruccello
- Division of Cardiology, University of California San Francisco, San Francisco, CA, USA
| | - Patrick T. Ellinor
- Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute, Cambridge, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Mark E. Lindsay
- Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute, Cambridge, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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4
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Zhang Y, Wang M, Zhang E, Wu Y. Artificial Intelligence in the Screening, Diagnosis, and Management of Aortic Stenosis. Rev Cardiovasc Med 2024; 25:31. [PMID: 39077660 PMCID: PMC11262349 DOI: 10.31083/j.rcm2501031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/30/2023] [Accepted: 09/13/2023] [Indexed: 07/31/2024] Open
Abstract
The integration of artificial intelligence (AI) into clinical management of aortic stenosis (AS) has redefined our approach to the assessment and management of this heterogenous valvular heart disease (VHD). While the large-scale early detection of valvular conditions is limited by socioeconomic constraints, AI offers a cost-effective alternative solution for screening by utilizing conventional tools, including electrocardiograms and community-level auscultations, thereby facilitating early detection, prevention, and treatment of AS. Furthermore, AI sheds light on the varied nature of AS, once considered a uniform condition, allowing for more nuanced, data-driven risk assessments and treatment plans. This presents an opportunity to re-evaluate the complexity of AS and to refine treatment using data-driven risk stratification beyond traditional guidelines. AI can be used to support treatment decisions including device selection, procedural techniques, and follow-up surveillance of transcatheter aortic valve replacement (TAVR) in a reproducible manner. While recognizing notable AI achievements, it is important to remember that AI applications in AS still require collaboration with human expertise due to potential limitations such as its susceptibility to bias, and the critical nature of healthcare. This synergy underpins our optimistic view of AI's promising role in the AS clinical pathway.
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Affiliation(s)
- Yuxuan Zhang
- Department of Cardiology, State Key Laboratory of Cardiovascular Disease,
Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of
Medical Sciences and Peking Union Medical College, 100037 Beijing, China
- Center for Structural Heart Diseases, State Key Laboratory of
Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular
Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College,
100037 Beijing, China
| | - Moyang Wang
- Department of Cardiology, State Key Laboratory of Cardiovascular Disease,
Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of
Medical Sciences and Peking Union Medical College, 100037 Beijing, China
- Center for Structural Heart Diseases, State Key Laboratory of
Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular
Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College,
100037 Beijing, China
| | - Erli Zhang
- Department of Cardiology, State Key Laboratory of Cardiovascular Disease,
Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of
Medical Sciences and Peking Union Medical College, 100037 Beijing, China
- Center for Structural Heart Diseases, State Key Laboratory of
Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular
Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College,
100037 Beijing, China
| | - Yongjian Wu
- Department of Cardiology, State Key Laboratory of Cardiovascular Disease,
Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of
Medical Sciences and Peking Union Medical College, 100037 Beijing, China
- Center for Structural Heart Diseases, State Key Laboratory of
Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular
Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College,
100037 Beijing, China
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5
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Rastogi V, Guetter CR, Patel PB, Anjorin AC, Marcaccio CL, Yadavalli SD, Scali ST, Beck AW, Verhagen HJM, Schermerhorn ML. Clinical presentation, outcomes, and threshold for repair by sex in degenerative saccular vs fusiform aneurysms in the descending thoracic aorta. J Vasc Surg 2023; 78:1392-1401.e1. [PMID: 37652142 PMCID: PMC10841204 DOI: 10.1016/j.jvs.2023.06.104] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 06/22/2023] [Accepted: 06/25/2023] [Indexed: 09/02/2023]
Abstract
OBJECTIVE Saccular-shaped thoracic aortic aneurysms (TAAs) are often treated at smaller diameters compared with fusiform TAAs, despite a lack of strong clinical evidence to support this practice. The aim of this study was to examine differences in presentation, treatment, and outcomes between saccular TAAs and fusiform TAAs in the descending thoracic aorta. We also examined the need for sex-specific treatment thresholds for TAAs. METHODS All Vascular Quality Initiative (VQI) patients undergoing thoracic endovascular aneurysm repair (TEVAR) for degenerative TAAs in the descending thoracic aorta from 2012 through 2022 were reviewed. Patients were stratified by urgency: emergent/urgent vs elective repairs (ruptured/symptomatic). Demographics, comorbidities, anatomical/procedural characteristics, and outcomes for fusiform TAAs and saccular TAAs were compared. Cumulative distribution curves were used to plot the proportion of patients who underwent emergent/urgent repair according to sex-stratified aortic diameter. RESULTS Among 655 emergent/urgent TEVARs, 37% were performed for saccular TAAs, whereas among 1352 elective TEVARs, 35% had saccular TAA morphology. Compared with fusiform TAAs, saccular TAAs more frequently underwent emergent/urgent (ruptured/symptomatic) TEVAR below the repair threshold in both females (<50 mm: 38% vs 10%; relative risk, 3.39; 95% confidence interval [CI], 2.04-5.70; P < .001), and males (<55 mm: 47% vs 21%; relative risk, 2.26; 95% CI, 1.60-3.18; P < .001). Moreover, among patients with emergent/urgent fusiform TAAs, females presented at smaller diameters compared with males, whereas there was no difference in preoperative aneurysm diameter among patients with saccular TAAs. Regarding outcomes, emergent/urgent treated saccular TAAs had similar postoperative outcomes and 5-year mortality compared with fusiform TAAs. Nevertheless, in the elective cohort, patients with saccular TAAs had similar postoperative mortality compared with those with fusiform TAAs, but a lower rate of postoperative spinal cord ischemia (0.7% vs 3.2%; P = .010). Furthermore, patients with saccular TAAs had a higher rate of 5-year mortality compared with their fusiform counterparts (23% vs 17%; hazard ratio, 1.53; 95% CI, 1.12-2.10; P = .010). CONCLUSIONS Patients with saccular TAAs underwent emergent/urgent TEVAR at smaller diameters than those with fusiform TAAs, supporting current clinical practice guideline recommendations that saccular TAAs warrant treatment at smaller diameters. Furthermore, these data support a sex-specific treatment threshold for patients with fusiform TAAs, but not for those with saccular TAAs. Although there were no differences in outcomes following TEVAR between morphologies in the emergent/urgent cohort, patients with saccular TAAs who were treated electively were associated with higher 5-year mortality compared with those with fusiform TAAs.
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Affiliation(s)
- Vinamr Rastogi
- Department of Surgery, Division of Vascular and Endovascular Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA; Department of Vascular Surgery, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Camila R Guetter
- Department of Surgery, Division of Vascular and Endovascular Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Priya B Patel
- Department of Surgery, Division of Vascular and Endovascular Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA; Department of General Surgery, Rutgers Robert Wood Johnson University Hospital, New Brunswick, NJ
| | - Aderike C Anjorin
- Department of Surgery, Division of Vascular and Endovascular Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Christina L Marcaccio
- Department of Surgery, Division of Vascular and Endovascular Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Sai Divya Yadavalli
- Department of Surgery, Division of Vascular and Endovascular Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Salvatore T Scali
- Division of Vascular Surgery and Endovascular Therapy, University of Florida, Gainesville, FL
| | - Adam W Beck
- Division of Vascular Surgery and Endovascular Therapy, University of Alabama at Birmingham, Birmingham, AL
| | - Hence J M Verhagen
- Department of Vascular Surgery, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Marc L Schermerhorn
- Department of Surgery, Division of Vascular and Endovascular Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA.
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Lareyre F, Chaudhuri A, Nasr B, Raffort J. Machine Learning and Omics Analysis in Aortic Aneurysm. Angiology 2023:33197231206427. [PMID: 37817423 DOI: 10.1177/00033197231206427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/12/2023]
Abstract
Aortic aneurysm is a life-threatening condition and mechanisms underlying its formation and progression are still incompletely understood. Omics approach has brought new insights to identify a broad spectrum of biomarkers and better understand cellular and molecular pathways involved. Omics generate a large amount of data and several studies have highlighted that artificial intelligence (AI) and techniques such as machine learning (ML)/deep learning (DL) can be of use in analyzing such complex datasets. However, only a few studies have so far reported the use of ML/DL for omics analysis in aortic aneurysms. The aim of this study is to summarize recent advances on the use of ML/DL for omics analysis to decipher aortic aneurysm pathophysiology and develop patient-tailored risk prediction models. In the light of current knowledge, we discuss current limits and highlight future directions in the field.
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Affiliation(s)
- Fabien Lareyre
- Department of Vascular Surgery, Hospital of Antibes Juan-les-Pins, Nice, France
- Inserm U1065, C3M, Université Côte d'Azur, Nice, France
| | - Arindam Chaudhuri
- Bedfordshire-Milton Keynes Vascular Centre, Bedfordshire Hospitals NHS Foundation Trust, Bedford, UK
| | - Bahaa Nasr
- Department of Vascular and Endovascular Surgery, Brest University Hospital, Brest, France
- INSERM UMR 1101, LaTIM, Brest, France
| | - Juliette Raffort
- Inserm U1065, C3M, Université Côte d'Azur, Nice, France
- Clinical Chemistry Laboratory, University Hospital of Nice, Nice, France
- 3IA Institute, Université Côte d'Azur, Nice, France
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7
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Kany S, Rämö JT, Hou C, Jurgens SJ, Nauffal V, Cunningham J, Lau ES, Butte AJ, Ho JE, Olgin JE, Elmariah S, Lindsay ME, Ellinor PT, Pirruccello JP. Assessment of valvular function in over 47,000 people using deep learning-based flow measurements. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.29.23289299. [PMID: 37205587 PMCID: PMC10187336 DOI: 10.1101/2023.04.29.23289299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Valvular heart disease is associated with a high global burden of disease. Even mild aortic stenosis confers increased morbidity and mortality, prompting interest in understanding normal variation in valvular function at scale. We developed a deep learning model to study velocity-encoded magnetic resonance imaging in 47,223 UK Biobank participants. We calculated eight traits, including peak velocity, mean gradient, aortic valve area, forward stroke volume, mitral and aortic regurgitant volume, greatest average velocity, and ascending aortic diameter. We then computed sex-stratified reference ranges for these phenotypes in up to 31,909 healthy individuals. In healthy individuals, we found an annual decrement of 0.03cm 2 in the aortic valve area. Participants with mitral valve prolapse had a 1 standard deviation [SD] higher mitral regurgitant volume (P=9.6 × 10 -12 ), and those with aortic stenosis had a 4.5 SD-higher mean gradient (P=1.5 × 10 -431 ), validating the derived phenotypes' associations with clinical disease. Greater levels of ApoB, triglycerides, and Lp(a) assayed nearly 10 years prior to imaging were associated with higher gradients across the aortic valve. Metabolomic profiles revealed that increased glycoprotein acetyls were also associated with an increased aortic valve mean gradient (0.92 SD, P=2.1 x 10 -22 ). Finally, velocity-derived phenotypes were risk markers for aortic and mitral valve surgery even at thresholds below what is considered relevant disease currently. Using machine learning to quantify the rich phenotypic data of the UK Biobank, we report the largest assessment of valvular function and cardiovascular disease in the general population.
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Belmont JW. Genetic Epidemiology Highlights the Role of Aortic Strain and Distensibility in Cardiovascular Disease. J Am Coll Cardiol 2023; 81:1336-1338. [PMID: 37019579 DOI: 10.1016/j.jacc.2023.02.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 02/07/2023] [Accepted: 02/16/2023] [Indexed: 04/07/2023]
Affiliation(s)
- John W Belmont
- Departments of Molecular and Human Genetics and Pediatrics, Baylor College of Medicine, Houston, Texas, USA; and the Genetics and Genomics Services Inc, Houston, Texas, USA.
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Tonegawa-Kuji R, Kanaoka K, Iwanaga Y. Current status of real-world big data research in the cardiovascular field in Japan. J Cardiol 2023; 81:307-315. [PMID: 36126909 DOI: 10.1016/j.jjcc.2022.09.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 09/02/2022] [Indexed: 02/01/2023]
Abstract
Real-world data (RWD) are observational data obtained by collecting, structuring, and accumulating patient information among the medical big data. RWD are derived from a variety of patient medical care and health information outside of conventional research data, and include electronic health records, claims data, registry data of disease, drug and device, health check-up data, and more recently, patient information data from wearable devices. They are currently being utilized in various forms for optimal medical care and real-world evidence (RWE) is constructed through a process of hypothesis generation and verification based on the RWD research. Together with classic clinical research and pragmatic trials, RWE shapes the learning healthcare system and contributes to the improvement of medical care. In the cardiovascular medical care of the current super-aged society, the need for a variety of RWE and the research is increasing, since the guidelines established over time and the medical care based on it cannot necessarily be the best in accordance with the current medical situation. In this review, we focus on the RWD and RWE studies in the cardiovascular medical field and outlines their current status in Japan. Furthermore, we discuss the potential for extending the studies and issues related to the use of medical big data and RWD.
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Affiliation(s)
- Reina Tonegawa-Kuji
- Department of Medical and Health Information Management, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Koshiro Kanaoka
- Department of Medical and Health Information Management, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Yoshitaka Iwanaga
- Department of Medical and Health Information Management, National Cerebral and Cardiovascular Center, Suita, Japan.
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Chou E, Pirruccello JP, Ellinor PT, Lindsay ME. Genetics and mechanisms of thoracic aortic disease. Nat Rev Cardiol 2023; 20:168-180. [PMID: 36131050 DOI: 10.1038/s41569-022-00763-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/03/2022] [Indexed: 11/09/2022]
Abstract
Aortic disease has many forms including aortic aneurysm and dissection, aortic coarctation or abnormalities in aortic function, such as loss of aortic distensibility. Genetic analysis in humans is one of the most important experimental approaches in uncovering disease mechanisms, but the relative infrequency of thoracic aortic disease compared with other cardiovascular conditions such as coronary artery disease has hindered large-scale identification of genetic associations. In the past decade, advances in machine learning technology coupled with large imaging datasets from biobank repositories have facilitated a rapid expansion in our capacity to measure and genotype aortic traits, resulting in the identification of dozens of genetic associations. In this Review, we describe the history of technological advances in genetic discovery and explain how newer technologies such as deep learning can rapidly define aortic traits at scale. Furthermore, we integrate novel genetic observations provided by these advances into our current biological understanding of thoracic aortic disease and describe how these new findings can contribute to strategies to prevent and treat aortic disease.
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Affiliation(s)
- Elizabeth Chou
- Division of Vascular and Endovascular Surgery, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute, Cambridge, MA, USA
| | - James P Pirruccello
- Harvard Medical School, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute, Cambridge, MA, USA
- Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA
| | - Patrick T Ellinor
- Harvard Medical School, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute, Cambridge, MA, USA
- Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA
| | - Mark E Lindsay
- Harvard Medical School, Boston, MA, USA.
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.
- Cardiovascular Disease Initiative, Broad Institute, Cambridge, MA, USA.
- Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA.
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11
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Yu R, Jin M, Wang Y, Cai X, Zhang K, Shi J, Zhou Z, Fan F, Pan J, Zhou Q, Tang X, Wang D. A machine learning approach for predicting descending thoracic aortic diameter. Front Cardiovasc Med 2023; 10:1097116. [PMID: 36860275 PMCID: PMC9969122 DOI: 10.3389/fcvm.2023.1097116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Accepted: 01/27/2023] [Indexed: 02/15/2023] Open
Abstract
Background To establish models for predicting descending thoracic aortic diameters and provide evidence for selecting the size of the stent graft for TBAD patients. Methods A total of 200 candidates without severe deformation of aorta were included. CTA information was collected and 3D reconstructed. In the reconstructed CTA, a total of 12 cross-sections of peripheral vessels were made perpendicular to the axis of flow of the aorta. Parameters of the cross sections and basic clinical characteristics were used for prediction. The data was randomly split into the training set and the test set in an 8:2 ratio. To fully describe diameters of descending thoracic aorta, three predicted points were set based quadrisection, and a total of 12 models at three predicted points were established using four algorithms included linear regression (LR), support vector machine (SVM), Extra-Tree regression (ETR) and random forest regression (RFR). The performance of models was evaluated by mean square error (MSE) of the prediction value, and the ranking of feature importance was given by Shapley value. After modeling, prognosis of five TEVAR cases and stent oversizing were compared. Results We identified a series of parameters which affect the diameter of descending thoracic aorta, including age, hypertension, the area of proximal edge of superior mesenteric artery, etc. Among four predictive models, all the MSEs of SVM models at three different predicted position were less than 2 mm2, with approximately 90% predicted diameters error less than 2 mm in the test sets. In patients with dSINE, stent oversizing was about 3 mm, while only 1 mm in patients without complications. Conclusion The predictive models established by machine learning revealed the relationship between basic characteristics and diameters of different segment of descending aorta, which help to provide evidence for selecting the matching distal size of the stent for TBAD patients, thereby reducing the incidence of TEVAR complications.
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Affiliation(s)
- Ronghuang Yu
- Medical School, Department of Cardio-Thoracic Surgery, Affiliated Drum Tower Hospital, Nanjing University, Nanjing, China
| | - Min Jin
- Medical School, Department of Cardio-Thoracic Surgery, Affiliated Drum Tower Hospital, Nanjing University, Nanjing, China,Nanjing Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, China
| | - Yaohui Wang
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Xiujuan Cai
- Nanjing Drum Tower Hospital, Clinical College of Nanjing University of Chinese Medicine, Nanjing, China
| | - Keyin Zhang
- Medical School, Department of Cardio-Thoracic Surgery, Affiliated Drum Tower Hospital, Nanjing University, Nanjing, China
| | - Jian Shi
- Medical School, Department of Cardio-Thoracic Surgery, Affiliated Drum Tower Hospital, Nanjing University, Nanjing, China
| | - Zeyi Zhou
- Medical School, Department of Cardio-Thoracic Surgery, Affiliated Drum Tower Hospital, Nanjing University, Nanjing, China
| | - Fudong Fan
- Medical School, Department of Cardio-Thoracic Surgery, Affiliated Drum Tower Hospital, Nanjing University, Nanjing, China
| | - Jun Pan
- Medical School, Department of Cardio-Thoracic Surgery, Affiliated Drum Tower Hospital, Nanjing University, Nanjing, China
| | - Qing Zhou
- Medical School, Department of Cardio-Thoracic Surgery, Affiliated Drum Tower Hospital, Nanjing University, Nanjing, China
| | - Xinlong Tang
- Medical School, Department of Cardio-Thoracic Surgery, Affiliated Drum Tower Hospital, Nanjing University, Nanjing, China,Nanjing Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, China,*Correspondence: Xinlong Tang,
| | - Dongjin Wang
- Medical School, Department of Cardio-Thoracic Surgery, Affiliated Drum Tower Hospital, Nanjing University, Nanjing, China,Nanjing Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, China,Nanjing Drum Tower Hospital, Clinical College of Nanjing University of Chinese Medicine, Nanjing, China,Dongjin Wang,
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12
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Higginson C, Willner N, Petruescu L, Beauchesne L, Coutinho T, Boodhwani M, Chan KL, Burwash IG, Messika-Zeitoun D. Prevalence and Phenotypic Characterization of Patients with Bicuspid Aortic Valve and Large Aortic Annular Diameter. J Am Soc Echocardiogr 2022; 36:436-437. [PMID: 36574931 DOI: 10.1016/j.echo.2022.12.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 12/22/2022] [Accepted: 12/22/2022] [Indexed: 12/26/2022]
Affiliation(s)
- Casey Higginson
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Nadav Willner
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Laura Petruescu
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Luc Beauchesne
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Thais Coutinho
- Division of Cardiology and Division of Cardiac Prevention and Rehabilitation, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Munir Boodhwani
- Division of Cardiac Surgery, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Kwan L Chan
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Ian G Burwash
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - David Messika-Zeitoun
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
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13
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Pirruccello JP, Lin H, Khurshid S, Nekoui M, Weng LC, Ramachandran VS, Isselbacher EM, Benjamin EJ, Lubitz SA, Lindsay ME, Ellinor PT. Development of a Prediction Model for Ascending Aortic Diameter Among Asymptomatic Individuals. JAMA 2022; 328:1935-1944. [PMID: 36378208 PMCID: PMC9667326 DOI: 10.1001/jama.2022.19701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
IMPORTANCE Ascending thoracic aortic disease is an important cause of sudden death in the US, yet most aortic aneurysms are identified incidentally. OBJECTIVE To develop and validate a clinical score to estimate ascending aortic diameter. DESIGN, SETTING, AND PARTICIPANTS Using an ongoing magnetic resonance imaging substudy of the UK Biobank cohort study, which had enrolled participants from 2006 through 2010, score derivation was performed in 30 018 participants and internal validation in an additional 6681. External validation was performed in 1367 participants from the Framingham Heart Study (FHS) offspring cohort who had undergone computed tomography from 2002 through 2005, and in 50 768 individuals who had undergone transthoracic echocardiography in the Community Care Cohort Project, a retrospective hospital-based cohort of longitudinal primary care patients in the Mass General Brigham (MGB) network between 2001-2018. EXPOSURES Demographic and clinical variables (11 covariates that would not independently prompt thoracic imaging). MAIN OUTCOMES AND MEASURES Ascending aortic diameter was modeled with hierarchical group least absolute shrinkage and selection operator (LASSO) regression. Correlation between estimated and measured diameter and performance for identifying diameter 4.0 cm or greater were assessed. RESULTS The 30 018-participant training cohort (52% women), were a median age of 65.1 years (IQR, 58.6-70.6 years). The mean (SD) ascending aortic diameter was 3.04 (0.31) cm for women and 3.32 (0.34) cm for men. A score to estimate ascending aortic diameter explained 28.2% of the variance in aortic diameter in the UK Biobank validation cohort (95% CI, 26.4%-30.0%), 30.8% in the FHS cohort (95% CI, 26.8%-34.9%), and 32.6% in the MGB cohort (95% CI, 31.9%-33.2%). For detecting individuals with an ascending aortic diameter of 4 cm or greater, the score had an area under the receiver operator characteristic curve of 0.770 (95% CI, 0.737-0.803) in the UK Biobank, 0.813 (95% CI, 0.772-0.854) in the FHS, and 0.766 (95% CI, 0.757-0.774) in the MGB cohorts, although the model significantly overestimated or underestimated aortic diameter in external validation. Using a fixed-score threshold of 3.537, 9.7 people in UK Biobank, 1.8 in the FHS, and 4.6 in the MGB cohorts would need imaging to confirm 1 individual with an ascending aortic diameter of 4 cm or greater. The sensitivity at that threshold was 8.9% in the UK Biobank, 11.3% in the FHS, and 18.8% in the MGB cohorts, with specificities of 98.1%, 99.2%, and 96.2%, respectively. CONCLUSIONS AND RELEVANCE A prediction model based on common clinically available data was derived and validated to predict ascending aortic diameter. Further research is needed to optimize the prediction model and to determine whether its use is associated with improved outcomes.
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Affiliation(s)
- James P. Pirruccello
- Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts
- Cardiovascular Research Center, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Harvard Medical School, Boston, Massachusetts
- Division of Cardiology, University of California San Francisco
| | - Honghuang Lin
- Framingham Heart Study, Boston University, Framingham, Massachusetts
- University of Massachusetts Medical School, Worcester
- National Heart, Lung, and Blood Institute, Framingham, Massachusetts
| | - Shaan Khurshid
- Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts
- Cardiovascular Research Center, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Mahan Nekoui
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Harvard Medical School, Boston, Massachusetts
- Department of Medicine, Massachusetts General Hospital, Boston
| | - Lu-Chen Weng
- Cardiovascular Research Center, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Vasan S. Ramachandran
- Framingham Heart Study, Boston University, Framingham, Massachusetts
- National Heart, Lung, and Blood Institute, Framingham, Massachusetts
- Department of Medicine, Cardiology and Preventive Medicine Sections, Boston Medical Center, Boston University School of Medicine, Boston, Massachusetts
- Epidemiology Department, Boston University School of Public Health, Boston, Massachusetts
| | - Eric M. Isselbacher
- Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
- Thoracic Aortic Center, Massachusetts General Hospital, Boston
| | - Emelia J. Benjamin
- Framingham Heart Study, Boston University, Framingham, Massachusetts
- National Heart, Lung, and Blood Institute, Framingham, Massachusetts
- Department of Medicine, Cardiology and Preventive Medicine Sections, Boston Medical Center, Boston University School of Medicine, Boston, Massachusetts
- Epidemiology Department, Boston University School of Public Health, Boston, Massachusetts
| | - Steven A. Lubitz
- Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts
- Cardiovascular Research Center, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Harvard Medical School, Boston, Massachusetts
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston
- Novartis Institutes for Biomedical Research, Cambridge, Massachusetts
| | - Mark E. Lindsay
- Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts
- Cardiovascular Research Center, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Harvard Medical School, Boston, Massachusetts
- Thoracic Aortic Center, Massachusetts General Hospital, Boston
| | - Patrick T. Ellinor
- Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts
- Cardiovascular Research Center, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Harvard Medical School, Boston, Massachusetts
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston
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14
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Chou EL, Chaffin M, Simonson B, Pirruccello JP, Akkad AD, Nekoui M, Cardenas CLL, Bedi KC, Nash C, Juric D, Stone JR, Isselbacher EM, Margulies KB, Klattenhoff C, Ellinor PT, Lindsay ME. Aortic Cellular Diversity and Quantitative Genome-Wide Association Study Trait Prioritization Through Single-Nuclear RNA Sequencing of the Aneurysmal Human Aorta. Arterioscler Thromb Vasc Biol 2022; 42:1355-1374. [PMID: 36172868 PMCID: PMC9613617 DOI: 10.1161/atvbaha.122.317953] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 09/16/2022] [Indexed: 12/30/2022]
Abstract
BACKGROUND Mural cells in ascending aortic aneurysms undergo phenotypic changes that promote extracellular matrix destruction and structural weakening. To explore this biology, we analyzed the transcriptional features of thoracic aortic tissue. METHODS Single-nuclear RNA sequencing was performed on 13 samples from human donors, 6 with thoracic aortic aneurysm, and 7 without aneurysm. Individual transcriptomes were then clustered based on transcriptional profiles. Clusters were used for between-disease differential gene expression analyses, subcluster analysis, and analyzed for intersection with genetic aortic trait data. RESULTS We sequenced 71 689 nuclei from human thoracic aortas and identified 14 clusters, aligning with 11 cell types, predominantly vascular smooth muscle cells (VSMCs) consistent with aortic histology. With unbiased methodology, we found 7 vascular smooth muscle cell and 6 fibroblast subclusters. Differentially expressed genes analysis revealed a vascular smooth muscle cell group accounting for the majority of differential gene expression. Fibroblast populations in aneurysm exhibit distinct behavior with almost complete disappearance of quiescent fibroblasts. Differentially expressed genes were used to prioritize genes at aortic diameter and distensibility genome-wide association study loci highlighting the genes JUN, LTBP4 (latent transforming growth factor beta-binding protein 1), and IL34 (interleukin 34) in fibroblasts, ENTPD1, PDLIM5 (PDZ and LIM domain 5), ACTN4 (alpha-actinin-4), and GLRX in vascular smooth muscle cells, as well as LRP1 in macrophage populations. CONCLUSIONS Using nuclear RNA sequencing, we describe the cellular diversity of healthy and aneurysmal human ascending aorta. Sporadic aortic aneurysm is characterized by differential gene expression within known cellular classes rather than by the appearance of novel cellular forms. Single-nuclear RNA sequencing of aortic tissue can be used to prioritize genes at aortic trait loci.
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Affiliation(s)
- Elizabeth L. Chou
- Division of Vascular and Endovascular Surgery,
Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Research Center, Massachusetts General
Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute,
Cambridge, Massachusetts, USA
| | - Mark Chaffin
- Cardiovascular Disease Initiative, Broad Institute,
Cambridge, Massachusetts, USA
- Precision Cardiology Laboratory, The Broad Institute,
Cambridge, MA, USA 02142
| | - Bridget Simonson
- Cardiovascular Disease Initiative, Broad Institute,
Cambridge, Massachusetts, USA
- Precision Cardiology Laboratory, The Broad Institute,
Cambridge, MA, USA 02142
| | - James P. Pirruccello
- Cardiology Division, Massachusetts General Hospital,
Boston, Massachusetts, USA
- Cardiovascular Research Center, Massachusetts General
Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute,
Cambridge, Massachusetts, USA
- Precision Cardiology Laboratory, The Broad Institute,
Cambridge, MA, USA 02142
- Demoulas Center for Cardiac Arrhythmias, Massachusetts
General Hospital, Boston, Massachusetts, USA
| | - Amer-Denis Akkad
- Precision Cardiology Laboratory, Bayer US LLC, Cambridge,
MA, USA 02142
| | - Mahan Nekoui
- Cardiovascular Disease Initiative, Broad Institute,
Cambridge, Massachusetts, USA
- Demoulas Center for Cardiac Arrhythmias, Massachusetts
General Hospital, Boston, Massachusetts, USA
| | - Christian Lacks Lino Cardenas
- Cardiology Division, Massachusetts General Hospital,
Boston, Massachusetts, USA
- Cardiovascular Research Center, Massachusetts General
Hospital, Boston, Massachusetts, USA
| | - Kenneth C. Bedi
- Perelman School of Medicine, University of Pennsylvania,
Philadelphia, PA, USA 19104
| | - Craig Nash
- Cardiovascular Disease Initiative, Broad Institute,
Cambridge, Massachusetts, USA
- Precision Cardiology Laboratory, The Broad Institute,
Cambridge, MA, USA 02142
| | - Dejan Juric
- Cancer Center, Massachusetts General Hospital, Boston,
Massachusetts, USA
| | - James R. Stone
- Department of Pathology, Massachusetts General
Hospital, Boston, Massachusetts, USA
| | - Eric M. Isselbacher
- Cardiology Division, Massachusetts General Hospital,
Boston, Massachusetts, USA
- Cardiovascular Research Center, Massachusetts General
Hospital, Boston, Massachusetts, USA
- Thoracic Aortic Center, Massachusetts General Hospital,
Boston, Massachusetts, USA
| | - Kenneth B. Margulies
- Perelman School of Medicine, University of Pennsylvania,
Philadelphia, PA, USA 19104
| | - Carla Klattenhoff
- Precision Cardiology Laboratory, Bayer US LLC, Cambridge,
MA, USA 02142
| | - Patrick T. Ellinor
- Cardiology Division, Massachusetts General Hospital,
Boston, Massachusetts, USA
- Cardiovascular Research Center, Massachusetts General
Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute,
Cambridge, Massachusetts, USA
- Precision Cardiology Laboratory, The Broad Institute,
Cambridge, MA, USA 02142
- Demoulas Center for Cardiac Arrhythmias, Massachusetts
General Hospital, Boston, Massachusetts, USA
| | - Mark E. Lindsay
- Cardiology Division, Massachusetts General Hospital,
Boston, Massachusetts, USA
- Cardiovascular Research Center, Massachusetts General
Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute,
Cambridge, Massachusetts, USA
- Thoracic Aortic Center, Massachusetts General Hospital,
Boston, Massachusetts, USA
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15
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Priest JR. Leveraging Machine Learning for Translational Genetics of Cardiovascular Imaging. J Am Coll Cardiol 2022; 80:498-499. [PMID: 35902172 DOI: 10.1016/j.jacc.2022.05.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 05/24/2022] [Indexed: 10/16/2022]
Affiliation(s)
- James R Priest
- Division of Pediatric Cardiology, Stanford University School of Medicine, Stanford, California, USA; Tenaya Therapeutics, San Francisco, California, USA.
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