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Tian J, Li C, Qin Z, Zhang Y, Xu Q, Zheng Y, Meng X, Zhao P, Li K, Zhao S, Zhong S, Hou X, Peng X, Yang Y, Liu Y, Wu S, Wang Y, Xi X, Tian Y, Qu W, Sun N, Wang F, Wang Y, Xiong J, Ban X, Yonetsu T, Vergallo R, Zhang B, Yu B, Wang Z. Coronary artery calcification and cardiovascular outcome as assessed by intravascular OCT and artificial intelligence. BIOMEDICAL OPTICS EXPRESS 2024; 15:4438-4452. [PMID: 39347010 PMCID: PMC11427185 DOI: 10.1364/boe.524946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 06/16/2024] [Accepted: 06/16/2024] [Indexed: 10/01/2024]
Abstract
Coronary artery calcification (CAC) is a marker of atherosclerosis and is thought to be associated with worse clinical outcomes. However, evidence from large-scale high-resolution imaging data is lacking. We proposed a novel deep learning method that can automatically identify and quantify CAC in massive intravascular OCT data trained using efficiently generated sparse labels. 1,106,291 OCT images from 1,048 patients were collected and utilized to train and evaluate the method. The Dice similarity coefficient for CAC segmentation and the accuracy for CAC classification are 0.693 and 0.932, respectively, close to human-level performance. Applying the method to 1259 ST-segment elevated myocardial infarction patients imaged with OCT, we found that patients with a greater extent and more severe calcification in the culprit vessels were significantly more likely to have major adverse cardiovascular and cerebrovascular events (MACCE) (p < 0.05), while the CAC in non-culprit vessels did not differ significantly between MACCE and non-MACCE groups.
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Affiliation(s)
- Jinwei Tian
- Department of Cardiology, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Chao Li
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhifeng Qin
- Department of Cardiology, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yanwen Zhang
- Department of Cardiology, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Qinglu Xu
- Department of Cardiology, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yuqi Zheng
- Department of Cardiology, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Xiangyu Meng
- Department of Cardiology, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Peng Zhao
- Department of Cardiology, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Kaiwen Li
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Suhong Zhao
- Department of Cardiology, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Shan Zhong
- Department of Cardiology, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Xinyu Hou
- Department of Cardiology, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Xiang Peng
- Department of Cardiology, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yuxin Yang
- Department of Cardiology, First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Yu Liu
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Songzhi Wu
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Yidan Wang
- Department of Cardiology, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Xiangwen Xi
- Department of Cardiology, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yanan Tian
- Department of Cardiology, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Wenbo Qu
- Department of Cardiology, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Na Sun
- Department of Cardiology, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Fan Wang
- Department of Cardiology, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yan Wang
- Department of Cardiology, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Jie Xiong
- Department of Cardiology, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Xiaofang Ban
- Department of Cardiology, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Taishi Yonetsu
- Department of Cardiovascular Medicine, Tokyo Medical and Dental University, Tokyo, Japan
| | - Rocco Vergallo
- Department of Cardiovascular Medicine, Catholic University of the Sacred Heart, Rome, Italy
| | - Bo Zhang
- Department of Cardiology, First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Bo Yu
- Department of Cardiology, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Zhao Wang
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
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Perone F, Bernardi M, Redheuil A, Mafrica D, Conte E, Spadafora L, Ecarnot F, Tokgozoglu L, Santos-Gallego CG, Kaiser SE, Fogacci F, Sabouret A, Bhatt DL, Paneni F, Banach M, Santos R, Biondi Zoccai G, Ray KK, Sabouret P. Role of Cardiovascular Imaging in Risk Assessment: Recent Advances, Gaps in Evidence, and Future Directions. J Clin Med 2023; 12:5563. [PMID: 37685628 PMCID: PMC10487991 DOI: 10.3390/jcm12175563] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 08/14/2023] [Accepted: 08/23/2023] [Indexed: 09/10/2023] Open
Abstract
Optimal risk assessment for primary prevention remains highly challenging. Recent registries have highlighted major discrepancies between guidelines and daily practice. Although guidelines have improved over time and provide updated risk scores, they still fail to identify a significant proportion of at-risk individuals, who then miss out on effective prevention measures until their initial ischemic events. Cardiovascular imaging is progressively assuming an increasingly pivotal role, playing a crucial part in enhancing the meticulous categorization of individuals according to their risk profiles, thus enabling the customization of precise therapeutic strategies for patients with increased cardiovascular risks. For the most part, the current approach to patients with atherosclerotic cardiovascular disease (ASCVD) is homogeneous. However, data from registries (e.g., REACH, CORONOR) and randomized clinical trials (e.g., COMPASS, FOURIER, and ODYSSEY outcomes) highlight heterogeneity in the risks of recurrent ischemic events, which are especially higher in patients with poly-vascular disease and/or multivessel coronary disease. This indicates the need for a more individualized strategy and further research to improve definitions of individual residual risk, with a view of intensifying treatments in the subgroups with very high residual risk. In this narrative review, we discuss advances in cardiovascular imaging, its current place in the guidelines, the gaps in evidence, and perspectives for primary and secondary prevention to improve risk assessment and therapeutic strategies using cardiovascular imaging.
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Affiliation(s)
- Francesco Perone
- Cardiac Rehabilitation Unit, Rehabilitation Clinic “Villa delle Magnolie”, Castel Morrone, 81020 Caserta, Italy;
| | - Marco Bernardi
- Department of Clinical, Internal Medicine, Anesthesiology and Cardiovascular Sciences, Sapienza University of Rome, 00185 Rome, Italy; (M.B.); (D.M.); (L.S.)
| | - Alban Redheuil
- Laboratoire d’Imagerie Biomédicale, Sorbonne University, INSERM 1146, CNRS 7371, 75005 Paris, France;
| | - Dario Mafrica
- Department of Clinical, Internal Medicine, Anesthesiology and Cardiovascular Sciences, Sapienza University of Rome, 00185 Rome, Italy; (M.B.); (D.M.); (L.S.)
| | - Edoardo Conte
- Cardiology Department, Galeazzi-Sant’Ambrogio Hospital IRCCS, 20100 Milan, Italy;
| | - Luigi Spadafora
- Department of Clinical, Internal Medicine, Anesthesiology and Cardiovascular Sciences, Sapienza University of Rome, 00185 Rome, Italy; (M.B.); (D.M.); (L.S.)
| | - Fiona Ecarnot
- Department of Cardiology, University Hospital Besancon, University of Franche-Comté, 25000 Besancon, France;
| | - Lale Tokgozoglu
- Department of Cardiology, Medical Faculty, Hacettepe University, 06230 Ankara, Turkey;
| | - Carlos G. Santos-Gallego
- Atherothrombosis Research Unit, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA;
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai Health System, New York, NY 10029, USA;
| | - Sergio Emanuel Kaiser
- Discipline of Clinical and Experimental Pathophysiology, Rio de Janeiro State University, Rio de Janeiro 23070-200, Brazil;
| | - Federica Fogacci
- Hypertension and Cardiovascular Risk Research Group, Medical and Surgical Sciences Department, Alma Mater Studiorum University of Bologna, 40126 Bologna, Italy;
| | | | - Deepak L. Bhatt
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai Health System, New York, NY 10029, USA;
| | - Francesco Paneni
- Department of Cardiology, University Heart Center, University Hospital Zurich, 8091 Zurich, Switzerland;
- Center for Translational and Experimental Cardiology (CTEC), University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland
| | - Maciej Banach
- Department of Preventive Cardiology and Lipidology, Medical University of Lodz (MUL), Rzgowska 281/289, 93-338 Lodz, Poland;
- Cardiovascular Research Centre, University of Zielona Gora, 65-417 Zielona Gora, Poland
| | - Raul Santos
- Heart Institute, University of Sao Paulo Medical School, São Paulo 05403-903, Brazil;
| | - Giuseppe Biondi Zoccai
- Department of Medical-Surgical Sciences and Biotechnologies, Sapienza University of Rome, 00185 Roma, Italy;
- Mediterranea Cardiocentro, 80122 Napoli, Italy
| | - Kausik K. Ray
- Imperial Centre for Cardiovascular Disease Prevention and Imperial Clinical Trials Unit, Department of Public Health and Primary Care, Imperial College London, London SW7 2BX, UK;
| | - Pierre Sabouret
- Heart Institute, Cardiology Department, Paris and National College of French Cardiologists, Pitié-Salpétrière Hospital, Sorbonne University, 75013 Paris, France
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Khan SS, Post WS, Guo X, Tan J, Zhu F, Bos D, Sedaghati-Khayat B, van Rooij J, Aday A, Allen NB, Bos MM, Uitterlinden AG, Budoff MJ, Lloyd-Jones DM, Mosley JD, Rotter JI, Greenland P, Kavousi M. Coronary Artery Calcium Score and Polygenic Risk Score for the Prediction of Coronary Heart Disease Events. JAMA 2023; 329:1768-1777. [PMID: 37219552 PMCID: PMC10208141 DOI: 10.1001/jama.2023.7575] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 04/19/2023] [Indexed: 05/24/2023]
Abstract
Importance Coronary artery calcium score and polygenic risk score have each separately been proposed as novel markers to identify risk of coronary heart disease (CHD), but no prior studies have directly compared these markers in the same cohorts. Objective To evaluate change in CHD risk prediction when a coronary artery calcium score, a polygenic risk score, or both are added to a traditional risk factor-based model. Design, Setting, and Participants Two observational population-based studies involving individuals aged 45 years through 79 years of European ancestry and free of clinical CHD at baseline: the Multi-Ethnic Study of Atherosclerosis (MESA) study involved 1991 participants at 6 US centers and the Rotterdam Study (RS) involved 1217 in Rotterdam, the Netherlands. Exposure Traditional risk factors were used to calculate CHD risk (eg, pooled cohort equations [PCEs]), computed tomography for the coronary artery calcium score, and genotyped samples for a validated polygenic risk score. Main Outcomes and Measures Model discrimination, calibration, and net reclassification improvement (at the recommended risk threshold of 7.5%) for prediction of incident CHD events were assessed. Results The median age was 61 years in MESA and 67 years in RS. Both log (coronary artery calcium+1) and polygenic risk score were significantly associated with 10-year risk of incident CHD (hazards ratio per SD, 2.60; 95% CI, 2.08-3.26 and 1.43; 95% CI, 1.20-1.71, respectively), in MESA. The C statistic for the coronary artery calcium score was 0.76 (95% CI, 0.71-0.79) and for the polygenic risk score, 0.69 (95% CI, 0.63-0.71). The change in the C statistic when each was added to the PCEs was 0.09 (95% CI, 0.06-0.13) for the coronary artery calcium score, 0.02 (95% CI, 0.00-0.04) for the polygenic risk score, and 0.10 (95% CI, 0.07-0.14) for both. Overall categorical net reclassification improvement was significant when the coronary artery calcium score (0.19; 95% CI, 0.06-0.28) but was not significant when the polygenic risk score (0.04; 95% CI, -0.05 to 0.10) was added to the PCEs. Calibration of the PCEs and models with coronary artery calcium and/or polygenic risk scores was adequate (all χ2<20). Subgroup analysis stratified by the median age demonstrated similar findings. Similar findings were observed for 10-year risk in RS and in longer-term follow-up in MESA (median, 16.0 years). Conclusions and Relevance In 2 cohorts of middle-aged to older adults from the US and the Netherlands, the coronary artery calcium score had better discrimination than the polygenic risk score for risk prediction of CHD. In addition, the coronary artery calcium score but not the polygenic risk score significantly improved risk discrimination and risk reclassification for CHD when added to traditional risk factors.
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Affiliation(s)
- Sadiya S. Khan
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Wendy S. Post
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, California
| | - Jingyi Tan
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, California
| | - Fang Zhu
- Department of Epidemiology Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Daniel Bos
- Department of Epidemiology Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Bahar Sedaghati-Khayat
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Jeroen van Rooij
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Aaron Aday
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Norrina B. Allen
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Maxime M. Bos
- Department of Epidemiology Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - André G. Uitterlinden
- Department of Epidemiology Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Matthew J. Budoff
- Department of Medicine, Lundquist Research Institute at Harbor-UCLA Medical Center, Torrance, California
| | - Donald M. Lloyd-Jones
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Jonathan D. Mosley
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, California
| | - Philip Greenland
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Senior Editor, JAMA
| | - Maryam Kavousi
- Department of Epidemiology Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
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4
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Tasdighi E, Blaha MJ. Coronary calcium scoring for guiding lipid-lowering therapy is cost-effective: time to remove barriers to its use. Med J Aust 2023; 218:214-215. [PMID: 36811155 PMCID: PMC10265314 DOI: 10.5694/mja2.51861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 01/25/2023] [Accepted: 01/25/2023] [Indexed: 02/24/2023]
Affiliation(s)
- Erfan Tasdighi
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Baltimore, MD, United States of America
| | - Michael J Blaha
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Baltimore, MD, United States of America
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Forrest IS, Petrazzini BO, Duffy Á, Park JK, Marquez-Luna C, Jordan DM, Rocheleau G, Cho JH, Rosenson RS, Narula J, Nadkarni GN, Do R. Machine learning-based marker for coronary artery disease: derivation and validation in two longitudinal cohorts. Lancet 2023; 401:215-225. [PMID: 36563696 PMCID: PMC10069625 DOI: 10.1016/s0140-6736(22)02079-7] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 10/05/2022] [Accepted: 10/18/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND Binary diagnosis of coronary artery disease does not preserve the complexity of disease or quantify its severity or its associated risk with death; hence, a quantitative marker of coronary artery disease is warranted. We evaluated a quantitative marker of coronary artery disease derived from probabilities of a machine learning model. METHODS In this cohort study, we developed and validated a coronary artery disease-predictive machine learning model using 95 935 electronic health records and assessed its probabilities as in-silico scores for coronary artery disease (ISCAD; range 0 [lowest probability] to 1 [highest probability]) in participants in two longitudinal biobank cohorts. We measured the association of ISCAD with clinical outcomes-namely, coronary artery stenosis, obstructive coronary artery disease, multivessel coronary artery disease, all-cause death, and coronary artery disease sequelae. FINDINGS Among 95 935 participants, 35 749 were from the BioMe Biobank (median age 61 years [IQR 18]; 14 599 [41%] were male and 21 150 [59%] were female; 5130 [14%] were with diagnosed coronary artery disease) and 60 186 were from the UK Biobank (median age 62 [15] years; 25 031 [42%] male and 35 155 [58%] female; 8128 [14%] with diagnosed coronary artery disease). The model predicted coronary artery disease with an area under the receiver operating characteristic curve of 0·95 (95% CI 0·94-0·95; sensitivity of 0·94 [0·94-0·95] and specificity of 0·82 [0·81-0·83]) and 0·93 (0·92-0·93; sensitivity of 0·90 [0·89-0·90] and specificity of 0·88 [0·87-0·88]) in the BioMe validation and holdout sets, respectively, and 0·91 (0·91-0·91; sensitivity of 0·84 [0·83-0·84] and specificity of 0·83 [0·82-0·83]) in the UK Biobank external test set. ISCAD captured coronary artery disease risk from known risk factors, pooled cohort equations, and polygenic risk scores. Coronary artery stenosis increased quantitatively with ascending ISCAD quartiles (increase per quartile of 12 percentage points), including risk of obstructive coronary artery disease, multivessel coronary artery disease, and stenosis of major coronary arteries. Hazard ratios (HRs) and prevalence of all-cause death increased stepwise over ISCAD deciles (decile 1: HR 1·0 [95% CI 1·0-1·0], 0·2% prevalence; decile 6: 11 [3·9-31], 3·1% prevalence; and decile 10: 56 [20-158], 11% prevalence). A similar trend was observed for recurrent myocardial infarction. 12 (46%) undiagnosed individuals with high ISCAD (≥0·9) had clinical evidence of coronary artery disease according to the 2014 American College of Cardiology/American Heart Association Task Force guidelines. INTERPRETATION Electronic health record-based machine learning was used to generate an in-silico marker for coronary artery disease that can non-invasively quantify atherosclerosis and risk of death on a continuous spectrum, and identify underdiagnosed individuals. FUNDING National Institutes of Health.
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Affiliation(s)
- Iain S Forrest
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York, NY, USA; The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ben O Petrazzini
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Áine Duffy
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joshua K Park
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Carla Marquez-Luna
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Daniel M Jordan
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ghislain Rocheleau
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Judy H Cho
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Robert S Rosenson
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Metabolism and Lipids Unit, Zena and Michael A Wiener Cardiovascular Institute, Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Jagat Narula
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Girish N Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ron Do
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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Error in the First Paragraph. JAMA Cardiol 2021; 7:233. [PMID: 34878497 DOI: 10.1001/jamacardio.2021.5225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
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7
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Blaha MJ, Dzaye O. Subthreshold coronary artery calcium - Redefining the coronary artery calcium score of zero? J Cardiovasc Comput Tomogr 2021; 16:155-157. [PMID: 34862148 DOI: 10.1016/j.jcct.2021.11.008] [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: 11/10/2021] [Accepted: 11/12/2021] [Indexed: 11/28/2022]
Affiliation(s)
- Michael J Blaha
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, USA.
| | - Omar Dzaye
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, USA
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