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Kuronuma K, Miller RJH, Wei CC, Singh A, Lemley MH, Van Kriekinge SD, Kavanagh PB, Gransar H, Han D, Hayes SW, Thomson L, Dey D, Friedman JD, Berman DS, Slomka PJ. Downward myocardial creep during stress PET imaging is inversely associated with mortality. Eur J Nucl Med Mol Imaging 2024; 51:1622-1631. [PMID: 38253908 DOI: 10.1007/s00259-024-06611-2] [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: 10/17/2023] [Accepted: 01/08/2024] [Indexed: 01/24/2024]
Abstract
PURPOSE The myocardial creep is a phenomenon in which the heart moves from its original position during stress-dynamic PET myocardial perfusion imaging (MPI) that can confound myocardial blood flow measurements. Therefore, myocardial motion correction is important to obtain reliable myocardial flow quantification. However, the clinical importance of the magnitude of myocardial creep has not been explored. We aimed to explore the prognostic value of myocardial creep quantified by an automated motion correction algorithm beyond traditional PET-MPI imaging variables. METHODS Consecutive patients undergoing regadenoson rest-stress [82Rb]Cl PET-MPI were included. A newly developed 3D motion correction algorithm quantified myocardial creep, the maximum motion at stress during the first pass (60 s), in each direction. All-cause mortality (ACM) served as the primary endpoint. RESULTS A total of 4,276 patients (median age 71 years; 60% male) were analyzed, and 1,007 ACM events were documented during a 5-year median follow-up. Processing time for automatic motion correction was < 12 s per patient. Myocardial creep in the superior to inferior (downward) direction was greater than the other directions (median, 4.2 mm vs. 1.3-1.7 mm). Annual mortality rates adjusted for age and sex were reduced with a larger downward creep, with a 4.2-fold ratio between the first (0 mm motion) and 10th decile (11 mm motion) (mortality, 7.9% vs. 1.9%/year). Downward creep was associated with lower ACM after full adjustment for clinical and imaging parameters (adjusted hazard ratio, 0.93; 95%CI, 0.91-0.95; p < 0.001). Adding downward creep to the standard PET-MPI imaging model significantly improved ACM prediction (area under the receiver operating characteristics curve, 0.790 vs. 0.775; p < 0.001), but other directions did not (p > 0.5). CONCLUSIONS Downward myocardial creep during regadenoson stress carries additional information for the prediction of ACM beyond conventional flow and perfusion PET-MPI. This novel imaging biomarker is quantified automatically and rapidly from stress dynamic PET-MPI.
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Affiliation(s)
- Keiichiro Kuronuma
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA
- Department of Cardiology, Nihon University, Tokyo, Japan
| | - Robert J H Miller
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA
- Department of Cardiac Sciences, University of Calgary, Calgary, AB, Canada
| | - Chih-Chun Wei
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA
| | - Ananya Singh
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA
| | - Mark H Lemley
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA
| | - Serge D Van Kriekinge
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA
| | - Paul B Kavanagh
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA
| | - Heidi Gransar
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA
| | - Donghee Han
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA
| | - Sean W Hayes
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA
| | - Louise Thomson
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA
| | - Damini Dey
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA
| | - John D Friedman
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA
| | - Daniel S Berman
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA
| | - Piotr J Slomka
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA.
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Han D, Hyun MC, Miller RJH, Gransar H, Slomka PJ, Dey D, Hayes SW, Friedman JD, Thomson LEJ, Berman DS, Rozanski A. 10-year experience of utilizing a stress-first SPECT myocardial perfusion imaging. Int J Cardiol 2024; 401:131863. [PMID: 38365012 DOI: 10.1016/j.ijcard.2024.131863] [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: 08/09/2023] [Revised: 01/24/2024] [Accepted: 02/12/2024] [Indexed: 02/18/2024]
Abstract
BACKGROUND Despite its potential benefits, the utilization of stress-only protocol in clinical practice has been limited. We report utilizing stress-first single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). METHODS We assessed 12,472 patients who were referred for SPECT-MPI between 2013 and 2020. The temporal changes in frequency of stress-only imaging were assessed according to risk factors, mode of stress, prior coronary artery disease (CAD) history, left ventricular function, and symptom status. The clinical endpoint was all-cause mortality. RESULTS In our lab, stress/rest SPECT-MPI in place of rest/stress SPECT-MPI was first introduced in November 2011 and was performed more commonly than rest/stress imaging after 2013. Stress-only SPECT-MPI scanning has been performed in 30-34% of our SPECT-MPI studies since 2013 (i.e.. 31.7% in 2013 and 33.6% in 2020). During the study period, we routinely used two-position imaging (additional prone or upright imaging) to reduce attenuation and motion artifact and introduced SPECT/CT scanner in 2018. The rate of stress-only study remained consistent before and after implementing the SPECT/CT scanner. The frequency of stress-only imaging was 43% among patients without a history of prior CAD and 19% among those with a prior CAD history. Among patients undergoing treadmill exercise, the frequency of stress-only imaging was 48%, while 32% among patients undergoing pharmacologic stress test. In multivariate Cox analysis, there was no significant difference in mortality risk between stress-only and stress/rest protocols in patients with normal SPECT-MPI results (p = 0.271). CONCLUSION Implementation of a stress-first imaging protocol has consistently resulted in safe cancellation of 30% of rest SPECT-MPI studies.
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Affiliation(s)
- Donghee Han
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States of America.
| | - Mark C Hyun
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States of America
| | - Robert J H Miller
- Department of Cardiac Sciences, University of Calgary, Calgary, AB, Canada
| | - Heidi Gransar
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States of America
| | - Piotr J Slomka
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States of America
| | - Damini Dey
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States of America
| | - Sean W Hayes
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States of America
| | - John D Friedman
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States of America
| | - Louise E J Thomson
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States of America
| | - Daniel S Berman
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States of America
| | - Alan Rozanski
- The Division of Cardiology, Mount Sinai Morningside Hospital, Mount Sinai Heart, and the Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
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Han D, Park MY, Choi J, Shin H, Behrens R, Rhim S. Evaluation of force pain thresholds to ensure collision safety in worker-robot collaborative operations. Front Robot AI 2024; 11:1374999. [PMID: 38651053 PMCID: PMC11033501 DOI: 10.3389/frobt.2024.1374999] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 03/25/2024] [Indexed: 04/25/2024] Open
Abstract
With the growing demand for robots in the industrial field, robot-related technologies with various functions have been introduced. One notable development is the implementation of robots that operate in collaboration with human workers to share tasks, without the need of any physical barriers such as safety fences. The realization of such collaborative operations in practice necessitates the assurance of safety if humans and robots collide. Thus, it is important to establish criteria for such collision scenarios to ensure robot safety and prevent injuries. Collision safety must be ensured in both pinching (quasi-static contact) and impact (transient contact) situations. To this end, we measured the force pain thresholds associated with impacts and evaluated the biomechanical limitations. This measurements were obtained through clinical trials involving physical collisions between human subjects and a device designed for generating impacts, and the force pain thresholds associated with transient collisions between humans and robots were analyzed. Specifically, the force pain threshold was measured at two different locations on the bodies of 37 adults aged 19-32 years, using two impactors with different shapes. The force pain threshold was compared with the results of other relevant studies. The results can help identify biomechanical limitations in a precise and reliable manner to ensure the safety of robots in collaborative applications.
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Affiliation(s)
- D. Han
- Department of Mechanical Engineering, Kyung Hee University, Yongin-si, Republic of Korea
- Robotic Systems, Fraunhofer IFF, Magdeburg, Germany
| | - M. Y. Park
- Department of Industry-Academic Cooperation Foundation, Kyung Hee University, Yongin-si, Republic of Korea
| | - J. Choi
- Safetics, Seoul, Republic of Korea
| | - H. Shin
- Safetics, Seoul, Republic of Korea
| | - R. Behrens
- Robotic Systems, Fraunhofer IFF, Magdeburg, Germany
| | - S. Rhim
- Department of Mechanical Engineering, Kyung Hee University, Yongin-si, Republic of Korea
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Natanzon SS, Han D, Kuronuma K, Gransar H, Miller RJH, Slomka PJ, Dey D, Hayes SW, Friedman JD, Thomson LEJ, Berman DS, Rozanski A. Self-reported exercise activity influences the relationship between coronary computed tomography angiographic finding and mortality. J Cardiovasc Comput Tomogr 2024:S1934-5925(24)00070-4. [PMID: 38589269 DOI: 10.1016/j.jcct.2024.03.011] [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: 12/14/2023] [Revised: 03/04/2024] [Accepted: 03/22/2024] [Indexed: 04/10/2024]
Abstract
AIM Recent studies suggest that the application of exercise activity questionnaires, including the use of a single-item exercise question, can be additive to the prognostic efficacy of imaging findings. This study aims to evaluate the prognostic efficacy of exercise activity in patients undergoing coronary computed tomography angiography (CCTA). METHODS AND RESULTS We assessed 9772 patients who underwent CCTA at a single center between 2007 and 2020. Patients were divided into 4 groups of physical activity as no exercise (n = 1643, 17%), mild exercise (n = 3156, 32%), moderate exercise (n = 3542, 36%), and high exercise (n = 1431,15%), based on a single-item self-reported questionnaire. Coronary stenosis was categorized as no (0%), non-obstructive (1-49%), borderline (50-69%), and obstructive (≥70%). During a median follow-up of 4.64 (IQR 1.53-7.89) years, 490 (7.6%) died. There was a stepwise inverse relationship between exercise activity and mortality (p < 0.001). Compared with the high activity group, the no activity group had a 3-fold higher mortality risk (HR: 3.3, 95%CI (1.94-5.63), p < 0.001) after adjustment for age, clinical risk factors, symptoms, and statin use. For any level of CCTA stenosis, mortality rates were inversely associated with the degree of patients' exercise activity. The risk of all-cause mortality was similar among the patients with obstructive stenosis with high exercise versus those with no coronary stenosis but no exercise activity (p = 0.912). CONCLUSION Physical activity as assessed by a single-item self-reported questionnaire is a strong stepwise inverse predictor of mortality risk among patients undergoing CCTA.
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Affiliation(s)
- Sharon Shalom Natanzon
- Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Donghee Han
- Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Keiichiro Kuronuma
- Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Heidi Gransar
- Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Robert J H Miller
- Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Libin Cardiovascular Institute of Alberta, University of Calgary, Calgary, AB, Canada
| | - Piotr J Slomka
- Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Damini Dey
- Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Sean W Hayes
- Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - John D Friedman
- Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Louise E J Thomson
- Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Daniel S Berman
- Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Alan Rozanski
- Division of Cardiac Sciences, Mount Sinai Morningside Hospital, Mount Sinai Heart and the Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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Dang S, Han D, Duan H, Jiang Y, Aihemaiti A, Yu N, Yu Y, Duan X. The value of T2-weighted MRI contrast ratio combined with DWI in evaluating the pathological grade of solid lung adenocarcinoma. Clin Radiol 2024; 79:279-286. [PMID: 38216369 DOI: 10.1016/j.crad.2023.12.005] [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] [Received: 09/27/2023] [Revised: 11/30/2023] [Accepted: 12/09/2023] [Indexed: 01/14/2024]
Abstract
AIM To assess the predictive value of T2-weighted (T2W) magnetic resonance imaging (MRI) in combination with diffusion-weighted imaging (DWI) for determining the pathological grading of solid lung adenocarcinoma. MATERIALS AND METHODS The clinical and imaging data from 153 cases of solid lung adenocarcinoma (82 men, 71 women, mean age 63.2 years) confirmed at histopathology in The First Affiliated Hospital of Xi'an Jiaotong University from January 2017 to May 2022 were analysed retrospectively. Adenocarcinomas were classified into low-grade (G1 and G2) and high-grade (G3) groups following the 2020 pathological grading system proposed by the International Association for the Study of Lung Cancer. The T2-weighted contrast ratio (T2CR), calculated as the T2 signal intensity of the lung mass/nodule divided by the T2 signal intensity of the right rhomboid muscle was utilised. Two experienced radiologists reviewed the MRI images independently, measured the T2CR, and obtained apparent diffusion coefficient (ADC) values. The Mann-Whitney U-test was used to compare general characteristics (sex, age, maximum diameter), T2CR, and ADC values between the low-grade and high-grade groups. The non-parametric Kruskal-Wallis test determined differences in T2CR and ADC values among the five adenocarcinoma subtypes. Receiver characteristic curve (ROC) analysis, along with area under the curve (AUC) calculation, assessed the effectiveness of each parameter in distinguishing the pathological grade of lung adenocarcinoma. A Z-test was used to compare the AUC values. RESULTS Among the 153 patients with adenocarcinoma, 103 had low-grade adenocarcinoma, and 50 had high-grade adenocarcinoma. The agreement between T2CR and ADC observers was good (0.948 and 0.929, respectively). None of the parameters followed a normal distribution (p<0.05). The ADC value was lower in the high-grade adenocarcinoma group compared to the low-grade adenocarcinoma group (p=0.004), while the T2CR value was higher in the high-grade group (p=0.011). Statistically significant differences were observed in maximum diameter and gender between the two groups (p<0.001 and p=0.005, respectively), while no significant differences were noted in age (p=0.980). Among the five adenocarcinoma subtypes, only the lepidic and micropapillary subtypes displayed statistical differences in ADC values (p=0.047), with the remaining subtypes showing no statistical differences (p>0.05). The AUC values for distinguishing high-grade adenocarcinoma from low-grade adenocarcinoma were 0.645 for ADC and 0.627 for T2CR. Combining T2CR, ADC, sex, and maximum diameter resulted in an AUC of 0.778, sensitivity of 70%, and specificity of 75%. This combination significantly improved diagnostic efficiency compared to T2CR and ADC alone (p=0.008, z = 2.624; p=0.007, z = 2.679). CONCLUSION The MRI quantitative parameters are useful for distinguishing the pathological grades of solid lung adenocarcinoma, offering valuable insights for precise lung cancer treatment.
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Affiliation(s)
- S Dang
- The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shannxi 710061, China; Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang 712000, China
| | - D Han
- The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shannxi 710061, China; Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang 712000, China
| | - H Duan
- The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shannxi 710061, China; Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang 712000, China
| | - Y Jiang
- Shaanxi University of Chinese Medicine, Xianyang 712000, China
| | - A Aihemaiti
- Shaanxi University of Chinese Medicine, Xianyang 712000, China
| | - N Yu
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang 712000, China; Shaanxi University of Chinese Medicine, Xianyang 712000, China
| | - Y Yu
- The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shannxi 710061, China; Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang 712000, China; Shaanxi University of Chinese Medicine, Xianyang 712000, China
| | - X Duan
- The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shannxi 710061, China.
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Flores Tomasino G, Han D, Pimentel R, Paz W, Liang J, Cheng VY, Slomka P, Berman DS, Dey D. Reproducibility of artificial intelligence-enabled plaque measurements between systolic and diastolic phases from coronary computed tomography angiography. Eur Radiol 2024:10.1007/s00330-024-10688-6. [PMID: 38466392 DOI: 10.1007/s00330-024-10688-6] [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: 07/20/2023] [Revised: 02/07/2024] [Accepted: 02/13/2024] [Indexed: 03/13/2024]
Abstract
OBJECTIVES Current coronary CT angiography (CTA) guidelines suggest both end-systolic and mid-diastolic phases of the cardiac cycle can be used for CTA image acquisition. However, whether differences in the phase of the cardiac cycle influence coronary plaque measurements is not known. We aim to explore the potential impact of cardiac phases on quantitative plaque assessment. METHODS We enrolled 39 consecutive patients (23 male, age 66.2 ± 11.5 years) who underwent CTA with dual-source CT with visually evident coronary atherosclerosis and with good image quality. End-systolic and mid- to late-diastolic phase images were reconstructed from the same CTA scan. Quantitative plaque and stenosis were analyzed in both systolic and diastolic images using artificial intelligence (AI)-enabled plaque analysis software (Autoplaque). RESULTS Overall, 186 lesions from 39 patients were analyzed. There were excellent agreement and correlation between systolic and diastolic images for all plaque volume measurements (Lin's concordance coefficient ranging from 0.97 to 0.99; R ranging from 0.96 to 0.98). There were no substantial intrascan differences per patient between systolic and diastolic phases (p > 0.05 for all) for total (1017.1 ± 712.9 mm3 vs. 1014.7 ± 696.2 mm3), non-calcified (861.5 ± 553.7 mm3 vs. 856.5 ± 528.7 mm3), calcified (155.7 ± 229.3 mm3 vs. 158.2 ± 232.4 mm3), and low-density non-calcified plaque volume (151.4 ± 106.1 mm3 vs. 151.5 ± 101.5 mm3) and diameter stenosis (42.5 ± 18.4% vs 41.3 ± 15.1%). CONCLUSION Excellent agreement and no substantial differences were observed in AI-enabled quantitative plaque measurements on CTA in systolic and diastolic images. Following further validation, standardized plaque measurements can be performed from CTA in systolic or diastolic cardiac phase. CLINICAL RELEVANCE STATEMENT Quantitative plaque assessment using artificial intelligence-enabled plaque analysis software can provide standardized plaque quantification, regardless of cardiac phase. KEY POINTS • The impact of different cardiac phases on coronary plaque measurements is unknown. • Plaque analysis using artificial intelligence-enabled software on systolic and diastolic CT angiography images shows excellent agreement. • Quantitative coronary artery plaque assessment can be performed regardless of cardiac phase.
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Affiliation(s)
- Guadalupe Flores Tomasino
- Departments of Imaging and Medicine, and the, Cedars-Sinai Medical Center , Smidt Heart Institute, Los Angeles, CA, USA
| | - Donghee Han
- Departments of Imaging and Medicine, and the, Cedars-Sinai Medical Center , Smidt Heart Institute, Los Angeles, CA, USA
| | - Raymond Pimentel
- Departments of Imaging and Medicine, and the, Cedars-Sinai Medical Center , Smidt Heart Institute, Los Angeles, CA, USA
| | - William Paz
- Departments of Imaging and Medicine, and the, Cedars-Sinai Medical Center , Smidt Heart Institute, Los Angeles, CA, USA
| | - Juni Liang
- Departments of Imaging and Medicine, and the, Cedars-Sinai Medical Center , Smidt Heart Institute, Los Angeles, CA, USA
| | | | - Piotr Slomka
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Daniel S Berman
- Departments of Imaging and Medicine, and the, Cedars-Sinai Medical Center , Smidt Heart Institute, Los Angeles, CA, USA
| | - Damini Dey
- Cedars-Sinai Medical Center, Biomedical Imaging Research Institute, 116 N Robertson Blvd, Los Angeles, CA, 90048, USA.
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Malhotra P, Han D, Chakravarty T, Thomson L, Dey D, Nakamura M, Patel D, Harutyunyan I, Tamarappoo B, Skaf S, Singh S, Rader F, Siegel R, Friedman J, Makkar R, Berman D. Increased CT angiography-derived extracellular volume fraction predicts less benefit in left ventricular remodeling and ejection fraction after transcatheter edge to edge repair for severe mitral regurgitation. J Cardiovasc Comput Tomogr 2024; 18:217-218. [PMID: 38302390 DOI: 10.1016/j.jcct.2024.01.002] [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] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 12/28/2023] [Accepted: 01/05/2024] [Indexed: 02/03/2024]
Affiliation(s)
- Pankaj Malhotra
- Mark Taper Imaging Center, Cedars Sinai Medical Center, Los Angeles, CA, USA; Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, CA, USA
| | - Donghee Han
- Mark Taper Imaging Center, Cedars Sinai Medical Center, Los Angeles, CA, USA
| | - Tarun Chakravarty
- Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, CA, USA
| | - Louise Thomson
- Mark Taper Imaging Center, Cedars Sinai Medical Center, Los Angeles, CA, USA
| | - Damini Dey
- Mark Taper Imaging Center, Cedars Sinai Medical Center, Los Angeles, CA, USA; Biomedical Imaging Research Institute, Cedars Sinai Medical Center, Los Angeles, CA, USA
| | - Mamoo Nakamura
- Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, CA, USA
| | - Dhairya Patel
- Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, CA, USA
| | | | - Balaji Tamarappoo
- Department of Cardiology, Indiana University Health, Indianapolis, IN, USA
| | - Sabah Skaf
- Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, CA, USA
| | - Siddharth Singh
- Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, CA, USA
| | - Florian Rader
- Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, CA, USA
| | - Robert Siegel
- Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, CA, USA
| | - John Friedman
- Mark Taper Imaging Center, Cedars Sinai Medical Center, Los Angeles, CA, USA
| | - Raj Makkar
- Mark Taper Imaging Center, Cedars Sinai Medical Center, Los Angeles, CA, USA; Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, CA, USA
| | - Daniel Berman
- Mark Taper Imaging Center, Cedars Sinai Medical Center, Los Angeles, CA, USA; Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, CA, USA.
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8
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Rozanski A, Miller RJH, Han D, Gransar H, Hayes SW, Friedman JD, Thomson LEJ, Berman DS. Comparative predictors of mortality among patients referred for stress single-photon emission computed tomography versus positron emission tomography myocardial perfusion imaging. J Nucl Cardiol 2024; 32:101811. [PMID: 38244976 DOI: 10.1016/j.nuclcard.2024.101811] [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] [Subscribe] [Scholar Register] [Indexed: 01/22/2024]
Abstract
BACKGROUND There is currently little information regarding the usage and comparative predictors of mortality among patients referred for single-photon emission computed tomography (SPECT) versus positron emission tomography (PET) myocardial perfusion imaging (MPI) within multimodality imaging laboratories. METHODS We compared the clinical characteristics and mortality outcomes among 15,718 patients referred for SPECT-MPI and 6202 patients referred for PET-MPI between 2008 and 2017. RESULTS Approximately two-thirds of MPI studies were performed using SPECT-MPI. The PET-MPI group was substantially older and included more patients with known coronary artery disease (CAD), hypertension, diabetes, and myocardial ischemia. The annualized mortality rate was also higher in the PET-MPI group, and this difference persisted after propensity matching 3615 SPECT-MPI and 3615 PET-MPI patients to have similar clinical profiles. Among the SPECT-MPI patients, the most potent predictor of mortality was exercise ability and performance, including consideration of patients' mode of stress testing and exercise duration. Among the PET-MPI patients, myocardial flow reserve (MFR) was the most potent predictor of mortality. CONCLUSIONS In our real-world setting, PET-MPI was more commonly employed among older patients with more cardiac risk factors than SPECT-MPI patients. The most potent predictors of mortality in our SPECT and PET-MPI groups were variables exclusive to each test: exercise ability/capacity for SPECT-MPI patients and MFR for PET-MPI patients.
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Affiliation(s)
- Alan Rozanski
- Division of Cardiology and Department of Medicine, Mount Sinai Morningside Hospital, Mount Sinai Heart and the Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Robert J H Miller
- Division of Cardiac Sciences, Libin Cardiovascular Institute of Alberta, University of Calgary, Canada
| | - Donghee Han
- The Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Heidi Gransar
- The Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Sean W Hayes
- The Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - John D Friedman
- The Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Louise E J Thomson
- The Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Daniel S Berman
- The Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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9
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Han D, Shanbhag A, Miller RJH, Kwok N, Waechter P, Builoff V, Newby DE, Dey D, Berman DS, Slomka P. Artificial intelligence-based automated left ventricular mass quantification from non-contrast cardiac CT scans: correlation with contrast CT and cardiac MRI. medRxiv 2024:2024.01.12.24301169. [PMID: 38260634 PMCID: PMC10802664 DOI: 10.1101/2024.01.12.24301169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Background Non-contrast CT scans are not used for evaluating left ventricle myocardial mass (LV mass), which is typically evaluated with contrast CT or cardiovascular magnetic resonance imaging (MRI). We assessed the feasibility of LV mass estimation from standard, ECG-gated, non-contrast CT using an artificial intelligence (AI) approach and compare it with coronary CT angiography (CTA) and cardiac MRI. Methods We enrolled consecutive patients who underwent coronary CTA, which included non-contrast CT calcium scanning and contrast CTA, and cardiac MRI. The median interval between coronary CTA and MRI was 22 days (IQR: 3-76). We utilized an nn-Unet AI model that automatically segmented non-contrast CT structures. AI measurement of LV mass was compared to contrast CTA and MRI. Results A total of 316 patients (Age: 57.1±16.7, 56% male) were included. The AI segmentation took on average 22 seconds per case. An excellent correlation was observed between AI and contrast CTA LV mass measures (r=0.84, p<0.001), with no significant differences (136.5±55.3 vs. 139.6±56.9 g, p=0.133). Bland-Altman analysis showed minimal bias of 2.9. When compared to MRI, measured LV mass was higher with AI (136.5±55.3 vs. 127.1±53.1 g, p<0.001). There was an excellent correlation between AI and MRI (r=0.85, p<0.001), with a small bias (-9.4). There were no statistical differences between the correlations of LV mass between contrast CTA and MRI, or AI and MRI. Conclusions The AI-based automated estimation of LV mass from non-contrast CT demonstrated excellent correlations and minimal biases when compared to contrast CTA and MRI.
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Affiliation(s)
- Donghee Han
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Aakash Shanbhag
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Signal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Robert JH Miller
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Cardiac Sciences, University of Calgary, Calgary AB, Canada
| | - Nicholas Kwok
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Parker Waechter
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Valerie Builoff
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - David E Newby
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom
| | - Damini Dey
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Daniel S Berman
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Piotr Slomka
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
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10
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Kuronuma K, Wei CC, Singh A, Lemley M, Hayes SW, Otaki Y, Hyun MC, Van Kriekinge SD, Kavanagh P, Huang C, Han D, Dey D, Berman DS, Slomka PJ. Automated Motion Correction for Myocardial Blood Flow Measurements and Diagnostic Performance of 82Rb PET Myocardial Perfusion Imaging. J Nucl Med 2024; 65:139-146. [PMID: 38050106 PMCID: PMC10755521 DOI: 10.2967/jnumed.123.266208] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 10/17/2023] [Indexed: 12/06/2023] Open
Abstract
Motion correction (MC) affects myocardial blood flow (MBF) measurements in 82Rb PET myocardial perfusion imaging (MPI); however, frame-by-frame manual MC of dynamic frames is time-consuming. This study aims to develop an automated MC algorithm for time-activity curves used in compartmental modeling and compare the predictive value of MBF with and without automated MC for significant coronary artery disease (CAD). Methods: In total, 565 patients who underwent PET-MPI were considered. Patients without angiographic findings were split into training (n = 112) and validation (n = 112) groups. The automated MC algorithm used simplex iterative optimization of a count-based cost function and was developed using the training group. MBF measurements with automated MC were compared with those with manual MC in the validation group. In a separate cohort, 341 patients who underwent PET-MPI and invasive coronary angiography were enrolled in the angiographic group. The predictive performance in patients with significant CAD (≥70% stenosis) was compared between MBF measurements with and without automated MC. Results: In the validation group (n = 112), MBF measurements with automated and manual MC showed strong correlations (r = 0.98 for stress MBF and r = 0.99 for rest MBF). The automatic MC took less time than the manual MC (<12 s vs. 10 min per case). In the angiographic group (n = 341), MBF measurements with automated MC decreased significantly compared with those without (stress MBF, 2.16 vs. 2.26 mL/g/min; rest MBF, 1.12 vs. 1.14 mL/g/min; MFR, 2.02 vs. 2.10; all P < 0.05). The area under the curve (AUC) for the detection of significant CAD by stress MBF with automated MC was higher than that without (AUC, 95% CI, 0.76 [0.71-0.80] vs. 0.73 [0.68-0.78]; P < 0.05). The addition of stress MBF with automated MC to the model with ischemic total perfusion deficit showed higher diagnostic performance for detection of significant CAD (AUC, 95% CI, 0.82 [0.77-0.86] vs. 0.78 [0.74-0.83]; P = 0.022), but the addition of stress MBF without MC to the model with ischemic total perfusion deficit did not reach significance (AUC, 95% CI, 0.81 [0.76-0.85] vs. 0.78 [0.74-0.83]; P = 0.067). Conclusion: Automated MC on 82Rb PET-MPI can be performed rapidly with excellent agreement with experienced operators. Stress MBF with automated MC showed significantly higher diagnostic performance than without MC.
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Affiliation(s)
- Keiichiro Kuronuma
- Division of Artificial Intelligence in Medicine, Imaging, and Biomedical Sciences, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California; and
- Department of Cardiology, Nihon University, Tokyo, Japan
| | - Chih-Chun Wei
- Division of Artificial Intelligence in Medicine, Imaging, and Biomedical Sciences, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California; and
| | - Ananya Singh
- Division of Artificial Intelligence in Medicine, Imaging, and Biomedical Sciences, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California; and
| | - Mark Lemley
- Division of Artificial Intelligence in Medicine, Imaging, and Biomedical Sciences, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California; and
| | - Sean W Hayes
- Division of Artificial Intelligence in Medicine, Imaging, and Biomedical Sciences, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California; and
| | - Yuka Otaki
- Division of Artificial Intelligence in Medicine, Imaging, and Biomedical Sciences, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California; and
| | - Mark C Hyun
- Division of Artificial Intelligence in Medicine, Imaging, and Biomedical Sciences, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California; and
| | - Serge D Van Kriekinge
- Division of Artificial Intelligence in Medicine, Imaging, and Biomedical Sciences, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California; and
| | - Paul Kavanagh
- Division of Artificial Intelligence in Medicine, Imaging, and Biomedical Sciences, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California; and
| | - Cathleen Huang
- Division of Artificial Intelligence in Medicine, Imaging, and Biomedical Sciences, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California; and
| | - Donghee Han
- Division of Artificial Intelligence in Medicine, Imaging, and Biomedical Sciences, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California; and
| | - Damini Dey
- Division of Artificial Intelligence in Medicine, Imaging, and Biomedical Sciences, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California; and
| | - Daniel S Berman
- Division of Artificial Intelligence in Medicine, Imaging, and Biomedical Sciences, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California; and
| | - Piotr J Slomka
- Division of Artificial Intelligence in Medicine, Imaging, and Biomedical Sciences, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California; and
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11
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Rozanski A, Han D, Miller RJH, Gransar H, Hayes SW, Friedman JD, Thomson L, Berman DS. Is typical angina still prognostically important? The influence of "treatment bias" upon prognostic assessments. J Nucl Cardiol 2024; 31:101778. [PMID: 38237364 DOI: 10.1016/j.nuclcard.2023.101778] [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] [Subscribe] [Scholar Register] [Indexed: 02/13/2024]
Abstract
BACKGROUND Since typical angina has become less frequent, it is unclear if this symptom still has prognostic significance. METHODS We evaluated 38,383 patients undergoing stress/rest SPECT myocardial perfusion imaging followed for a median of 10.9 years. After dividing patients by clinical symptoms, we evaluated the magnitude of myocardial ischemia and subsequent mortality among medically treated versus revascularized subgroups following testing. RESULTS Patients with typical angina had more frequent and greater ischemia than other symptom groups, but not higher mortality. Among typical angina patients, those who underwent early revascularization had substantially greater ischemia than the medically treated subgroup, including a far higher proportion with severe ischemia (44.9% vs 4.3%, P < 0.001) and transient ischemic dilation of the LV (31.3% vs 4.7%, P < 0.001). Nevertheless, the revascularized typical angina subgroup had a lower adjusted mortality risk than the medically treated subgroup (HR = 0.72, 95% CI: 0.57-0.92, P = 0.009) CONCLUSIONS: Typical angina is associated with substantially more ischemia than other clinical symptoms. However, the high referral of patients with typical angina patients with ischemia to early revascularization resulted in this group having a lower rather than higher mortality risk versus other symptom groups. These findings illustrate the need to account for "treatment bias" among prognostic studies.
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Affiliation(s)
- Alan Rozanski
- Department of Cardiology, Mount Sinai Morningside Hospital and Mount Sinai Heart, New York, NY, USA.
| | - Donghee Han
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Medicine, David Geffen School of Medicine, UCLA, Los Angeles, USA
| | - Robert J H Miller
- Department of Cardiac Sciences, University of Calgary, Calgary, CA, USA
| | - Heidi Gransar
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Medicine, David Geffen School of Medicine, UCLA, Los Angeles, USA
| | - Sean W Hayes
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Medicine, David Geffen School of Medicine, UCLA, Los Angeles, USA
| | - John D Friedman
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Medicine, David Geffen School of Medicine, UCLA, Los Angeles, USA
| | - Louise Thomson
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Medicine, David Geffen School of Medicine, UCLA, Los Angeles, USA
| | - Daniel S Berman
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Medicine, David Geffen School of Medicine, UCLA, Los Angeles, USA
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12
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Malhotra P, Han D, Chen B, Siegel R, Friedman J, Dey D, Makkar R, Berman DS, Tamarappoo B. Predictive Value of CTA-Derived Extracellular Volume for Pacemaker Implantation Post-TAVR in Low-Flow Low-Gradient Aortic Stenosis. JACC Cardiovasc Imaging 2024:S1936-878X(23)00537-5. [PMID: 38180414 DOI: 10.1016/j.jcmg.2023.11.012] [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: 10/16/2023] [Revised: 11/20/2023] [Accepted: 11/29/2023] [Indexed: 01/06/2024]
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13
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Miller RJH, Bednarski BP, Pieszko K, Kwiecinski J, Williams MC, Shanbhag A, Liang JX, Huang C, Sharir T, Hauser MT, Dorbala S, Di Carli MF, Fish MB, Ruddy TD, Bateman TM, Einstein AJ, Kaufmann PA, Miller EJ, Sinusas AJ, Acampa W, Han D, Dey D, Berman DS, Slomka PJ. Clinical phenotypes among patients with normal cardiac perfusion using unsupervised learning: a retrospective observational study. EBioMedicine 2024; 99:104930. [PMID: 38168587 PMCID: PMC10794922 DOI: 10.1016/j.ebiom.2023.104930] [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: 08/31/2023] [Revised: 11/24/2023] [Accepted: 12/05/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Myocardial perfusion imaging (MPI) is one of the most common cardiac scans and is used for diagnosis of coronary artery disease and assessment of cardiovascular risk. However, the large majority of MPI patients have normal results. We evaluated whether unsupervised machine learning could identify unique phenotypes among patients with normal scans and whether those phenotypes were associated with risk of death or myocardial infarction. METHODS Patients from a large international multicenter MPI registry (10 sites) with normal perfusion by expert visual interpretation were included in this cohort analysis. The training population included 9849 patients, and external testing population 12,528 patients. Unsupervised cluster analysis was performed, with separate training and external testing cohorts, to identify clusters, with four distinct phenotypes. We evaluated the clinical and imaging features of clusters and their associations with death or myocardial infarction. FINDINGS Patients in Clusters 1 and 2 almost exclusively underwent exercise stress, while patients in Clusters 3 and 4 mostly required pharmacologic stress. In external testing, the risk for Cluster 4 patients (20.2% of population, unadjusted hazard ratio [HR] 6.17, 95% confidence interval [CI] 4.64-8.20) was higher than the risk associated with pharmacologic stress (HR 3.03, 95% CI 2.53-3.63), or previous myocardial infarction (HR 1.82, 95% CI 1.40-2.36). INTERPRETATION Unsupervised learning identified four distinct phenotypes of patients with normal perfusion scans, with a significant proportion of patients at very high risk of myocardial infarction or death. Our results suggest a potential role for patient phenotyping to improve risk stratification of patients with normal imaging results. FUNDING This work was supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health [R35HL161195 to PS]. The REFINE SPECT database was supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health [R01HL089765 to PS]. MCW was supported by the British Heart Foundation [FS/ICRF/20/26002].
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Affiliation(s)
- Robert J H Miller
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Cardiac Sciences, University of Calgary and Libin Cardiovascular Institute, Calgary, AB, Canada
| | - Bryan P Bednarski
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Konrad Pieszko
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jacek Kwiecinski
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Interventional Cardiology and Angiology, Institute of Cardiology, Warsaw, Poland
| | - Michelle C Williams
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA; British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom
| | - Aakash Shanbhag
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Signal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Joanna X Liang
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Cathleen Huang
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Tali Sharir
- Department of Nuclear Cardiology, Assuta Medical Centers, Tel Aviv, Israel; Israel and Ben Gurion University of the Negev, Beer Sheba, Israel
| | - M Timothy Hauser
- Department of Nuclear Cardiology, Oklahoma Heart Hospital, Oklahoma City, OK, USA
| | - Sharmila Dorbala
- Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Marcelo F Di Carli
- Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Mathews B Fish
- Oregon Heart and Vascular Institute, Sacred Heart Medical Center, Springfield, OR, USA
| | - Terrence D Ruddy
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, ON, Canada
| | | | - Andrew J Einstein
- Division of Cardiology, Department of Medicine and Department of Radiology, Columbia University Irving Medical Center and New York-Presbyterian Hospital, New York, NY, USA
| | - Philipp A Kaufmann
- Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Zurich, Switzerland
| | - Edward J Miller
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Albert J Sinusas
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Wanda Acampa
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Donghee Han
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Damini Dey
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Daniel S Berman
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Piotr J Slomka
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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Miller RJH, Gransar H, Rozanski A, Dey D, Al‐Mallah M, Chow BJW, Kaufmann PA, Cademartiri F, Maffei E, Han D, Slomka PJ, Berman DS. Simplified Approach to Predicting Obstructive Coronary Disease With Integration of Coronary Calcium: Development and External Validation. J Am Heart Assoc 2023; 12:e031601. [PMID: 38108259 PMCID: PMC10863788 DOI: 10.1161/jaha.123.031601] [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: 07/31/2023] [Accepted: 11/13/2023] [Indexed: 12/19/2023]
Abstract
BACKGROUND The Diamond-Forrester model was used extensively to predict obstructive coronary artery disease (CAD) but overestimates probability in current populations. Coronary artery calcium (CAC) is a useful marker of CAD, which is not routinely integrated with other features. We derived simple likelihood tables, integrating CAC with age, sex, and cardiac chest pain to predict obstructive CAD. METHODS AND RESULTS The training population included patients from 3 multinational sites (n=2055), with 2 sites for external testing (n=3321). We determined associations between age, sex, cardiac chest pain, and CAC with the presence of obstructive CAD, defined as any stenosis ≥50% on coronary computed tomography angiography. Prediction performance was assessed using area under the receiver-operating characteristic curves (AUCs) and compared with the CAD Consortium models with and without CAC, which require detailed calculations, and the updated Diamond-Forrester model. In external testing, the proposed likelihood tables had higher AUC (0.875 [95% CI, 0.862-0.889]) than the CAD Consortium clinical+CAC score (AUC, 0.868 [95% CI, 0.855-0.881]; P=0.030) and the updated Diamond-Forrester model (AUC, 0.679 [95% CI, 0.658-0.699]; P<0.001). The calibration for the likelihood tables was better than the CAD Consortium model (Brier score, 0.116 versus 0.121; P=0.005). CONCLUSIONS We have developed and externally validated simple likelihood tables to integrate CAC with age, sex, and cardiac chest pain, demonstrating improved prediction performance compared with other risk models. Our tool affords physicians with the opportunity to rapidly and easily integrate a small number of important features to estimate a patient's likelihood of obstructive CAD as an aid to clinical management.
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Affiliation(s)
- Robert J. H. Miller
- Departments of Medicine (Division of Artificial Intelligence in Medicine)Imaging and Biomedical SciencesCedars‐Sinai Medical CenterLos AngelesCA
- Libin Cardiovascular Institute of AlbertaUniversity of CalgaryCalgaryAlbertaCanada
| | - Heidi Gransar
- Departments of Medicine (Division of Artificial Intelligence in Medicine)Imaging and Biomedical SciencesCedars‐Sinai Medical CenterLos AngelesCA
| | - Alan Rozanski
- Departments of Medicine (Division of Artificial Intelligence in Medicine)Imaging and Biomedical SciencesCedars‐Sinai Medical CenterLos AngelesCA
- Division of Cardiology and Department of MedicineMount Sinai Morningside HospitalMount Sinai Heart and the Icahn School of Medicine at Mount SinaiNew YorkNY
| | - Damini Dey
- Departments of Medicine (Division of Artificial Intelligence in Medicine)Imaging and Biomedical SciencesCedars‐Sinai Medical CenterLos AngelesCA
| | - Mouaz Al‐Mallah
- Houston Methodist DeBakey Heart and Vascular CenterHoustonTX
| | - Benjamin J. W. Chow
- Departments of Medicine (Cardiology and Nuclear Medicine) and RadiologyUniversity of Ottawa Heart InstituteOttawaOntarioCanada
| | - Philipp A. Kaufmann
- Department of Nuclear MedicineUniversity Hospital Zurich, University of ZurichZurichSwitzerland
| | | | - Erica Maffei
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) SYNLAB SDNNaplesItaly
| | - Donghee Han
- Departments of Medicine (Division of Artificial Intelligence in Medicine)Imaging and Biomedical SciencesCedars‐Sinai Medical CenterLos AngelesCA
| | - Piotr J. Slomka
- Departments of Medicine (Division of Artificial Intelligence in Medicine)Imaging and Biomedical SciencesCedars‐Sinai Medical CenterLos AngelesCA
| | - Daniel S. Berman
- Departments of Medicine (Division of Artificial Intelligence in Medicine)Imaging and Biomedical SciencesCedars‐Sinai Medical CenterLos AngelesCA
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15
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Rozanski A, Gransar H, Sakul S, Miller RJH, Han D, Hayes SW, Friedman JD, Thomson LEJ, Berman DS. Increasing frequency of dyspnea among patients referred for cardiac stress testing. J Nucl Cardiol 2023; 30:2303-2313. [PMID: 37861920 DOI: 10.1007/s12350-023-03375-4] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 08/09/2023] [Indexed: 10/21/2023]
Abstract
OBJECTIVE To assess the frequency, change in prevalence, and prognostic significance of dyspnea among contemporary patients referred for cardiac stress testing. PATIENTS AND METHODS We evaluated the prevalence of dyspnea and its relationship to all-cause mortality among 33,564 patients undergoing stress/rest SPECT-MPI between January 1, 2002 and December 31, 2017. Dyspnea was assessed as a single-item question. Patients were divided into three temporal groups. RESULTS The overall prevalence of dyspnea in our cohort was 30.2%. However, there was a stepwise increase in the temporal prevalence of dyspnea, which was present in 25.6% of patients studied between 2002 and 2006, 30.5% of patients studied between 2007 and 2011, and 38.7% of patients studied between 2012 and 2017. There was a temporal increase in the prevalence of dyspnea in each age, symptom, and risk factor subgroup. The adjusted hazard ratio for mortality was higher among patients with dyspnea vs those without dyspnea both among all patients, and within each chest pain subgroup. CONCLUSIONS Dyspnea has become increasingly prevalent among patients referred for cardiac stress testing and is now present among nearly two-fifths of contemporary cohorts referred for stress-rest SPECT-MPI. Prospective study is needed to standardize the assessment of dyspnea and evaluate the reasons for its increasing prevalence.
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Affiliation(s)
- Alan Rozanski
- Division of Cardiology and Department of Medicine, Mount Sinai Morningside Hospital, Mount Sinai Heart, and the Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Heidi Gransar
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Sakul Sakul
- Division of Cardiology and Department of Medicine, Mount Sinai Morningside Hospital, Mount Sinai Heart, and the Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Robert J H Miller
- Department of Cardiac Sciences, University of Calgary, Calgary, AB, Canada
| | - Donghee Han
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Sean W Hayes
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - John D Friedman
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Louise E J Thomson
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Daniel S Berman
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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Ahluwalia N, Roshankar G, Draycott L, Jimenez-Zepeda V, Fine N, Chan D, Han D, Miller RJH. Diagnostic accuracy of bone scintigraphy imaging for transthyretin cardiac amyloidosis: systematic review and meta-analysis. J Nucl Cardiol 2023; 30:2464-2476. [PMID: 37226006 DOI: 10.1007/s12350-023-03297-1] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 05/04/2023] [Indexed: 05/26/2023]
Abstract
BACKGROUND Bone scintigraphy imaging is frequently used to investigate patients with suspected transthyretin cardiac amyloidosis (ATTR-CM). However, the reported accuracy for interpretation approaches has changed over time. We performed a systematic review and meta-analysis to determine the diagnostic accuracy of visual planar grading, heart-to-contralateral (HCL) ratio, and quantitative analysis of SPECT imaging and evaluate reasons for shifts in reported accuracy. METHODS We performed a systematic review to identify studies of the diagnostic accuracy of bone scintigraphy for ATTR-CM from 1990 until February 2023 using PUBMED and EMBASE. Studies were reviewed separately by two authors for inclusion and for risk of bias assessment. Summary receiver operating characteristic curves and operating points were determined with hierarchical modeling. RESULTS Out of a total of 428 identified studies, 119 were reviewed in detail and 23 were included in the final analysis. The studies included a total of 3954 patients, with ATTR-CM diagnosed in 1337 (39.6%) patients and prevalence ranging from 21 to 73%. Visual planar grading and quantitative analysis had higher diagnostic accuracy (.99) than HCL ratio (.96). Quantitative analysis of SPECT imaging had the highest specificity (97%) followed by planar visual grade (96%) and HCL ratio (93%). ATTR-CM prevalence accounted for some of the observed between study heterogeneity. CONCLUSIONS Bone scintigraphy imaging is highly accurate for identifying patients with ATTR-CM, with between study heterogeneity in part explained by differences in disease prevalence. We identified small differences in specificity, which may have important clinical implications when applied to low-risk screening populations.
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Affiliation(s)
- Nanki Ahluwalia
- Department of Cardiac Sciences, University of Calgary, GAA08, 3230 Hospital Drive NW, Calgary, AB, T2N 2T9, Canada
| | - Golnaz Roshankar
- Department of Cardiac Sciences, University of Calgary, GAA08, 3230 Hospital Drive NW, Calgary, AB, T2N 2T9, Canada
| | - Logan Draycott
- Department of Cardiac Sciences, University of Calgary, GAA08, 3230 Hospital Drive NW, Calgary, AB, T2N 2T9, Canada
| | | | - Nowell Fine
- Department of Cardiac Sciences, University of Calgary, GAA08, 3230 Hospital Drive NW, Calgary, AB, T2N 2T9, Canada
| | - Denise Chan
- Department of Nuclear Medicine, University of Calgary, Calgary, AB, Canada
| | - Donghee Han
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Robert J H Miller
- Department of Cardiac Sciences, University of Calgary, GAA08, 3230 Hospital Drive NW, Calgary, AB, T2N 2T9, Canada.
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Rozanski A, Han D, Miller RJH, Gransar H, Slomka P, Hayes SW, Friedman JD, Thomson LEJ, Berman DS. Comparison of coronary artery calcium scores among patients referred for cardiac imaging tests. Prog Cardiovasc Dis 2023; 81:24-32. [PMID: 37858662 DOI: 10.1016/j.pcad.2023.10.005] [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: 10/15/2023] [Accepted: 10/15/2023] [Indexed: 10/21/2023]
Abstract
BACKGROUND While coronary artery calcium (CAC) can now be evaluated by multiple imaging modalities, there is presently scant study regarding how CAC scores may vary among populations of varying clinical risk. METHODS We evaluated the distribution of CAC scores among three patient groups: 18,941 referred for CAC scanning, 5101 referred for diagnostic coronary CT angiography (CCTA), and 3307 referred for diagnostic positron emission tomography (PET) myocardial perfusion imaging (MPI). We assessed the relationship between CAC score and myocardial ischemia, obstructive coronary artery disease (CAD), and all-cause mortality across imaging modalities. RESULTS Within each age group, the frequency of CAC abnormalities were relatively similar across testing modalities, despite an annualized mortality rate which varied from 0.5%/year among CAC patients to 3.8%/year among PET-MPI patients (p < 0.001). Among CCTA and PET-MPI patients, a zero CAC score was common, occurring in ~70% of patients <50 years, ~40% of patients 50-59 years, and ~ 25% of patients 60-69 years. Among CCTA patients, zero CAC was associated with a normal coronary angiogram with high frequency, ranging from 92.2% among patients <50 years to 87.9% among patients ≥70 years. Among PET-MPI patients, zero CAC was associated with a very low frequency of inducible ischemia across all age groups, ranging from 1.5% among patients <50 years to 0.9% among patients ≥70 years. CONCLUSIONS In our study, relatively similar CAC scores were noted among patients varying markedly in mortality risk. Clinically, zero CAC scores predicted both a low likelihood of obstructive CAD and inducible myocardial ischemia in all age groups and were observed with high frequency across diagnostic testing modalities.
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Affiliation(s)
- Alan Rozanski
- Division of Cardiology, Mount Sinai Morningside Hospital, Mount Sinai Heart, the Icahn School of Medicine at Mount Sinai, New York, NY, United States of America.
| | - Donghee Han
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States of America
| | - Robert J H Miller
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States of America; Department of Cardiac Sciences, University of Calgary, Calgary, AB, Canada
| | - Heidi Gransar
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States of America
| | - Piotr Slomka
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States of America
| | - Sean W Hayes
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States of America
| | - John D Friedman
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States of America
| | - Louise E J Thomson
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States of America
| | - Daniel S Berman
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States of America
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Williams C, Han D, Takagi H, Fordyce CB, Sellers S, Blanke P, Lin FY, Shaw LJ, Lee SE, Andreini D, Al-Mallah MH, Budoff MJ, Cademartiri F, Chinnaiyan K, Choi JH, Conte E, Marques H, de Araújo Gonçalves P, Gottlieb I, Hadamitzky M, Maffei E, Pontone G, Shin S, Kim YJ, Lee BK, Chun EJ, Sung JM, Virmani R, Samady H, Stone PH, Berman DS, Narula J, Bax JJ, Leipsic JA, Chang HJ. Effects of renin-angiotensin-aldosterone-system inhibitors on coronary atherosclerotic plaques: The PARADIGM registry. Atherosclerosis 2023; 383:117301. [PMID: 37769454 DOI: 10.1016/j.atherosclerosis.2023.117301] [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: 02/24/2023] [Revised: 09/08/2023] [Accepted: 09/14/2023] [Indexed: 09/30/2023]
Abstract
BACKGROUND AND AIMS Inhibition of Renin-Angiotensin-Aldosterone-System (RAAS) has been hypothesized to improve endothelial function and reduce plaque inflammation, however, their impact on the progression of coronary atherosclerosis is unclear. We aim to study the effects of RAAS inhibitor on plaque progression and composition assessed by serial coronary CT angiography (CCTA). METHODS We performed a prospective, multinational study consisting of a registry of patients without history of CAD, who underwent serial CCTAs. Patients using RAAS inhibitors were propensity matched to RAAS inhibitor naïve patients based on clinical and CCTA characteristics at baseline. Atherosclerotic plaques in CCTAs were quantitatively analyzed for percent atheroma volume (PAV) according to plaque composition. Interactions between RAAS inhibitor use and baseline PAV on plaque progression were assessed in the unmatched cohort using a multivariate linear regression model. RESULTS Of 1248 patients from the registry, 299 RAAS inhibitor taking patients were matched to 299 RAAS inhibitor naïve patients. Over a mean interval of 3.9 years, there was no significant difference in annual progression of total PAV between RAAS inhibitor naïve vs taking patients (0.75 vs 0.79%/year, p = 0.66). With interaction testing in the unmatched cohort, however, RAAS inhibitor use was significantly associated with lower non-calcified plaque progression (Beta coefficient -0.100, adjusted p = 0.038) with higher levels of baseline PAV. CONCLUSIONS The use of RAAS inhibitors over a period of nearly 4 years did not significantly impact on total atherosclerotic plaque progression or various plaque components. However, interaction testing to assess the differential effect of RAAS inhibition based on baseline PAV suggested a significant decrease in progression of non-calcified plaque in patients with a higher burden of baseline atherosclerosis, which should be considered hypothesis generating.
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Affiliation(s)
- Curtis Williams
- Division of Cardiology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Donghee Han
- Department of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Hidenobu Takagi
- Department of Radiology and Centre for Heart Lung Innovation, St. Paul's Hospital and University of British Columbia, Vancouver, British Columbia, Canada; Dalio Institute of Cardiovascular Imaging, Department of Radiology, NewYork-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA
| | - Christopher B Fordyce
- Division of Cardiology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Stephanie Sellers
- Department of Radiology and Centre for Heart Lung Innovation, St. Paul's Hospital and University of British Columbia, Vancouver, British Columbia, Canada
| | - Philipp Blanke
- Department of Radiology and Centre for Heart Lung Innovation, St. Paul's Hospital and University of British Columbia, Vancouver, British Columbia, Canada
| | - Fay Y Lin
- Dalio Institute of Cardiovascular Imaging, Department of Radiology, NewYork-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA
| | - Leslee J Shaw
- Dalio Institute of Cardiovascular Imaging, Department of Radiology, NewYork-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA
| | - Sang-Eun Lee
- Division of Cardiology, Department of Internal Medicine, Ewha Womans University Seoul Hospital, Seoul, South Korea; Yonsei-Cedars-Sinai Integrative Cardiovascular Imaging Research Center, Yonsei University College of Medicine, Yonsei University Health System, South Korea
| | | | - Mouaz H Al-Mallah
- Houston Methodist DeBakey Heart & Vascular Center, Houston Methodist Hospital, Houston, TX, USA
| | - Matthew J Budoff
- Department of Medicine, Los Angeles Biomedical Research Institute, Torrance, CA, USA
| | | | | | | | | | - Hugo Marques
- UNICA, Unit of Cardiovascular Imaging, Hospital da Luz, Lisboa, Portugal
| | - Pedro de Araújo Gonçalves
- UNICA, Unit of Cardiovascular Imaging, Hospital da Luz, Lisboa, Portugal; Nova Medical School, Lisboa, Portugal
| | - Ilan Gottlieb
- Department of Radiology, Casa de Saude São Jose, Rio de Janeiro, Brazil
| | - Martin Hadamitzky
- Department of Radiology and Nuclear Medicine, German Heart Center Munich, Munich, Germany
| | - Erica Maffei
- Department of Radiology, Fondazione Monasterio/CNR, Pisa, Italy
| | | | - Sanghoon Shin
- Division of Cardiology, Department of Internal Medicine, Ewha Womans University Seoul Hospital, Seoul, South Korea
| | - Yong-Jin Kim
- Department of Internal Medicine, Seoul National University College of Medicine, Cardiovascular Center, Seoul National University Hospital, Seoul, South Korea
| | - Byoung Kwon Lee
- Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Eun Ju Chun
- Icahn School of Medicine at Mount Sinai, Mount Sinai Heart, Zena and Michael A. Wiener Cardiovascular Institute, and Marie-Josée and Henry R. Kravis Center for Cardiovascular Health, New York, NY, USA
| | - Ji Min Sung
- Yonsei-Cedars-Sinai Integrative Cardiovascular Imaging Research Center, Yonsei University College of Medicine, Yonsei University Health System, South Korea; Department of Cardiology, Heart Lung Center, Leiden University Medical Center, Leiden, the Netherlands
| | - Renu Virmani
- Department of Pathology, CVPath Institute, Gaithersburg, MD, USA
| | - Habib Samady
- Division of Cardiology, Georgia Heart Institute, Gainesville, USA
| | - Peter H Stone
- Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Daniel S Berman
- Department of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jagat Narula
- Icahn School of Medicine at Mount Sinai, Mount Sinai Heart, Zena and Michael A. Wiener Cardiovascular Institute, and Marie-Josée and Henry R. Kravis Center for Cardiovascular Health, New York, NY, USA
| | - Jeroen J Bax
- Department of Cardiology, Heart Lung Center, Leiden University Medical Center, Leiden, the Netherlands
| | - Jonathon A Leipsic
- Department of Radiology and Centre for Heart Lung Innovation, St. Paul's Hospital and University of British Columbia, Vancouver, British Columbia, Canada.
| | - Hyuk-Jae Chang
- Yonsei-Cedars-Sinai Integrative Cardiovascular Imaging Research Center, Yonsei University College of Medicine, Yonsei University Health System, South Korea; Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea
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Kuronuma K, Miller RJH, Van Kriekinge SD, Han D, Singh A, Gransar H, Dey D, Berman DS, Slomka PJ. Incremental prognostic value of stress phase entropy over standard PET myocardial perfusion imaging variables. Eur J Nucl Med Mol Imaging 2023; 50:3619-3629. [PMID: 37428217 PMCID: PMC10547643 DOI: 10.1007/s00259-023-06323-z] [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: 05/10/2023] [Accepted: 06/26/2023] [Indexed: 07/11/2023]
Abstract
PURPOSE Phase analysis can assess left ventricular dyssynchrony. The independent prognostic value of phase variables over positron emission tomography myocardial perfusion imaging (PET-MPI) variables including myocardial flow reserve (MFR) has not been studied. The aim of this study was to explore the prognostic value of phase variables for predicting mortality over standard PET-MPI variables. METHODS Consecutive patients who underwent pharmacological stress-rest 82Rb PET study were enrolled. All PET-MPI variables including phase variables (phase entropy, phase bandwidth, and phase standard deviation) were automatically obtained by QPET software (Cedars-Sinai, Los Angeles, CA). Cox proportional hazard analyses were used to assess associations with all-cause mortality (ACM). RESULTS In a total of 3963 patients (median age 71 years; 57% male), 923 patients (23%) died during a median follow-up of 5 years. Annualized mortality rates increased with stress phase entropy, with a 4.6-fold difference between the lowest and highest decile groups of entropy (2.6 vs. 12.0%/year). Abnormal stress phase entropy (optimal cutoff value, 43.8%) stratified ACM risk in patients with normal and impaired MFR (both p < 0.001). Among three phase variables, only stress phase entropy was significantly associated with ACM after the adjustment of standard clinical and PET-MPI variables including MFR and stress-rest change of phase variables, whether modeled as binary variables (adjusted hazard ratio, 1.44 for abnormal entropy [> 43.8%]; 95%CI, 1.18-1.75; p < 0.001) or continuous variables (adjusted hazard ratio, 1.05 per 5% increase; 95%CI, 1.01-1.10; p = 0.030). The addition of stress phase entropy to the standard PET-MPI variables significantly improved the discriminatory power for ACM prediction (p < 0.001), but the other phase variables did not (p > 0.1). CONCLUSION Stress phase entropy is independently and incrementally associated with ACM beyond standard PET-MPI variables including MFR. Phase entropy can be obtained automatically and included in clinical reporting of PET-MPI studies to improve patient risk prediction.
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Affiliation(s)
- Keiichiro Kuronuma
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd., Los Angeles, CA, 90048, USA
- Department of Cardiology, Nihon University, Tokyo, Japan
| | - Robert J H Miller
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd., Los Angeles, CA, 90048, USA
- Department of Cardiac Sciences, University of Calgary, Calgary, AB, Canada
| | - Serge D Van Kriekinge
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd., Los Angeles, CA, 90048, USA
| | - Donghee Han
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd., Los Angeles, CA, 90048, USA
| | - Ananya Singh
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd., Los Angeles, CA, 90048, USA
| | - Heidi Gransar
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd., Los Angeles, CA, 90048, USA
| | - Damini Dey
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd., Los Angeles, CA, 90048, USA
| | - Daniel S Berman
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd., Los Angeles, CA, 90048, USA
| | - Piotr J Slomka
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd., Los Angeles, CA, 90048, USA.
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20
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Han D, Lin A, Kuronuma K, Gransar H, Dey D, Friedman JD, Berman DS, Tamarappoo BK. Cardiac Computed Tomography for Quantification of Myocardial Extracellular Volume Fraction: A Systematic Review and Meta-Analysis. JACC Cardiovasc Imaging 2023; 16:1306-1317. [PMID: 37269267 DOI: 10.1016/j.jcmg.2023.03.021] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [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: 02/10/2023] [Revised: 03/30/2023] [Accepted: 03/30/2023] [Indexed: 06/05/2023]
Abstract
BACKGROUND Extracellular volume (ECV) is a quantitative measure of extracellular compartment expansion, and an increase in ECV is a marker of myocardial fibrosis. Although cardiac magnetic resonance (CMR) is considered the standard imaging tool for ECV quantification, cardiac computed tomography (CT) has also been used for ECV assessment. OBJECTIVES The aim of this meta-analysis was to evaluate the correlation and agreement in the quantification of myocardial ECV by CT and CMR. METHODS PubMed and Web of Science were searched for relevant publications reporting on the use of CT for ECV quantification compared with CMR as the reference standard. The authors employed a meta-analysis using the restricted maximum-likelihood estimator with a random-effects method to estimate summary correlation and mean difference. A subgroup analysis was performed to compare the correlation and mean differences between single-energy CT (SECT) and dual-energy CT (DECT) techniques for the ECV quantification. RESULTS Of 435 papers, 13 studies comprising 383 patients were identified. The mean age range was 57.3 to 82 years, and 65% of patients were male. Overall, there was an excellent correlation between CT-derived ECV and CMR-derived ECV (mean: 0.90 [95% CI: 0.86-0.95]). The pooled mean difference between CT and CMR was 0.96% (95% CI: 0.14%-1.78%). Seven studies reported correlation values using SECT, and 4 studies reported those using DECT. The pooled correlation from studies utilizing DECT for ECV quantification was significantly higher compared with those with SECT (mean: 0.94 [95% CI: 0.91-0.98] vs 0.87 [95% CI: 0.80-0.94], respectively; P = 0.01). There was no significant difference in pooled mean differences between SECT vs DECT (P = 0.85). CONCLUSIONS CT-derived ECV showed an excellent correlation and mean difference of <1% with CMR-derived ECV. However, the overall quality of the included studies was low, and larger, prospective studies are needed to examine the accuracy and diagnostic and prognostic utility of CT-derived ECV.
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Affiliation(s)
- Donghee Han
- Department of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Andrew Lin
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Keiichiro Kuronuma
- Department of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Heidi Gransar
- Department of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Damini Dey
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - John D Friedman
- Department of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Daniel S Berman
- Department of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA.
| | - Balaji K Tamarappoo
- Cardiovascular Institute, Indiana University School of Medicine, Indianapolis, Indiana, USA
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Sun LH, Shaniya N, Xu Q, Pan KJ, Bao YXM, Han D, Zhang J. [Expanding antiviral indications for chronic hepatitis B using the concept of chronic disease health management: act again!]. Zhonghua Gan Zang Bing Za Zhi 2023; 31:1002-1003. [PMID: 37872098 DOI: 10.3760/cma.j.cn501113-20220501-00233] [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] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Affiliation(s)
- L H Sun
- Center for Infection-Liver Diseases, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830000, China
| | - Niyazi Shaniya
- Center for Infection-Liver Diseases, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830000, China
| | - Q Xu
- Center for Infection-Liver Diseases, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830000, China
| | - K J Pan
- Center for Infection-Liver Diseases, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830000, China
| | - Y X M Bao
- Center for Infection-Liver Diseases, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830000, China
| | - D Han
- Center for Infection-Liver Diseases, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830000, China
| | - J Zhang
- Center for Infection-Liver Diseases, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830000, China
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22
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Ruan WY, Zhang YL, Zheng SG, Sun Y, Fan ZP, Song YL, Sun HC, Wang WM, Dai JW, Zhao ZJ, Zhang TT, Chen D, Pan YC, Jiang YG, Wang XD, Zheng LW, Zhu QL, He M, Xu BS, Jia ZL, Han D, Duan XH. [Expert consensus on the biobank development of oral genetic diseases and rare diseases and storage codes of related biological samples from craniofacial and oral region]. Zhonghua Kou Qiang Yi Xue Za Zhi 2023; 58:749-758. [PMID: 37550034 DOI: 10.3760/cma.j.cn112144-20230523-00210] [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] [Grants] [Subscribe] [Scholar Register] [Indexed: 08/09/2023]
Abstract
The biological samples of oral genetic diseases and rare diseases are extremely precious. Collecting and preserving these biological samples are helpful to elucidate the mechanisms and improve the level of diagnose and treatment of oral genetic diseases and rare diseases. The standardized construction of biobanks for oral genetic diseases and rare diseases is important for achieving these goals. At present, there is very little information on the construction of these biobanks, and the standards or suggestions for the classification and coding of biological samples from oral and maxillofacial sources, and this is not conducive to the standardization and information construction of biobanks for special oral diseases. This consensus summarizes the background, necessity, principles, and key points of constructing the biobank for oral genetic diseases and rare diseases. On the base of the group standard "Classification and Coding for Human Biomaterial" (GB/T 39768-2021) issued by the National Technical Committee for Standardization of Biological Samples, we suggest 76 new coding numbers for different of biological samples from oral and maxillofacial sources. We hope the consensus may promote the standardization, and smartization on the biobank construction as well as the overall research level of oral genetic diseases and rare diseases in China.
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Affiliation(s)
- W Y Ruan
- Clinic of Oral Rare Diseases and Genetic Diseases & Department of Oral Biology, School of Stomatology, The Fourth Military Medical University, State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Center for Oral Diseases, Shaanxi Key Laboratory of Stomatology, Xi'an 710032, China
| | - Y L Zhang
- Clinic of Oral Rare Diseases and Genetic Diseases & Department of Oral Biology, School of Stomatology, The Fourth Military Medical University, State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Center for Oral Diseases, Shaanxi Key Laboratory of Stomatology, Xi'an 710032, China
| | - S G Zheng
- Department of Preventive Dentistry, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology, Beijing 100081, China
| | - Y Sun
- Department of Oral Implantology, Stomatological Hospital and Dental School of Tongji University, Shanghai Engineering Research Center of Tooth Restoration and Regeneration, Shanghai 200072, China
| | - Z P Fan
- Capital Medical University School of Stomatology & Beijing Key Laboratory of Tooth Regeneration and Function Reconstruction, Beijing 100050, China
| | - Y L Song
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - H C Sun
- Department of Oral Pathology, Hospital of Stomatology, Jilin University, Changchun 130021, China
| | - W M Wang
- Department of Oral Mucosal Diseases, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing 210008, China
| | - J W Dai
- Department of Oral and Craniomaxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine & College of Stomatology, Shanghai Jiao Tong University & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai 200011, China
| | - Z J Zhao
- The First Outpatient Department, School and Hospital of Stomatology, China Medical University, Liaoning Provincial Key Laboratory of Oral Diseases, Shenyang 110002, China
| | - T T Zhang
- Department of Oral and Maxillofacial Surgery, Stomatological Hospital, Tianjin Medical University, Tianjin 300070, China
| | - D Chen
- Department of Polyclinics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Y C Pan
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University & Jiangsu Province Key Laboratory of Oral Diseases & Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing 210029, China
| | - Y G Jiang
- Department of Cariology & Endodontics, College of Stomatology, Xi'an Jiaotong University, Xi'an 710004, China
| | - X D Wang
- Department of Oral and Craniomaxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine & College of Stomatology, Shanghai Jiao Tong University & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai 200011, China
| | - L W Zheng
- Deparment of Pediatric Dentistry, West China Hospital of Stomatology, Sichuan University & State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Chengdu 610041, China
| | - Q L Zhu
- Department of Operative Dentistry and Endodontics, School of Stomatology, The Fourth Military Medical University, State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Center for Oral Diseases, Shaanxi Key Laboratory of Stomatology, Xi'an 710032, China
| | - M He
- Deparment of Pediatric Dentistry, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - B S Xu
- Department of Oral and Maxillofacial Surgery, Institute of Stomatological Research, Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University & Guangdong Provincial Key Laboratory of Stomatology, Guangzhou 510080, China
| | - Z L Jia
- Deparment of Cleft Lip and Palate Surgery, West China Hospital of Stomatology, Sichuan University & State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Chengdu 610041, China
| | - D Han
- Department of Prosthodontics, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology, Beijing 100081, China
| | - X H Duan
- Clinic of Oral Rare Diseases and Genetic Diseases & Department of Oral Biology, School of Stomatology, The Fourth Military Medical University, State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Center for Oral Diseases, Shaanxi Key Laboratory of Stomatology, Xi'an 710032, China
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23
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Ding JN, Liu HC, Yu M, Liu Y, Han D. [Measurement and analysis of the crown conical degree of maxillary incisors in patients with congenital tooth agenesis caused by different gene mutations]. Zhonghua Kou Qiang Yi Xue Za Zhi 2023; 58:821-828. [PMID: 37550043 DOI: 10.3760/cma.j.cn112144-20230328-00119] [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] [Grants] [Subscribe] [Scholar Register] [Indexed: 08/09/2023]
Abstract
Objective: To measure the crown conical degree of the remaining maxillary incisors in patients with congenital tooth agenesis, and to analyze the influence of different gene mutations on the crown conical degree of patients. Methods: Whole exome sequencing was performed on 85 patients with congenital tooth agenesis (50 males, 35 females, median age 19 years old) who visited the Department of Prosthodontics, Peking University School and Hospital of Stomatology from January 2019 to January 2023. The pathogenic gene was identified. The width of the crowns of the maxillary central and lateral incisors at the incisal 1/3 and gingival 1/3 were measured on the panoramic radiograph, and the ratio was defined as the crown conical degree. The smaller the ratio is, the more likely is the crown to be peg shaped teeth. The control group was matched by age and gender with 85 other patients with intact maxillary permanent incisors who were treated in the Department of Prosthodontics, Peking University School and Hospital of Stomatology from January 2019 to January 2023. The conical degree of the tooth agenesis group was compared with the control group by t-test, and the differences in the crown conical degree in different gene mutation groups were compared using one-way analysis of variance, and the multiple comparisons among gene groups were carried out using the LSD method. Results: Among the 85 tooth agenesis patients, the numbers of patients in each gene mutation group were 20 in ectodysplasin A (EDA) group, 8 in ectodysplasin A receptor (EDAR) group, 15 in wingless-type MMTV integration site family, member 10A (WNT10A) group, 16 in paired box 9 (PAX9) group, 10 in Msh homeobox 1 (MSX1) group, 10 in low-density lipoprotein receptor related protein 6 (LRP6) group, and 6 in bone morphogenetic protein4 (BMP4) group. The number of missing teeth were 1-27, median number 15 among the tooth agenesis patients. There was no significant difference in the conical degree between the left and right homonymous teeth in the congenital tooth agenesis group and the control group (P>0.05). The crown conical degree of maxillary central incisor and lateral incisor in the congenital missing teeth group (0.95±0.24, 0.90±0.22) was significantly smaller than that in the control group (1.12±0.09, 1.13±0.09) (t=-8.50, P<0.001; t=-11.47, P<0.001). In WNT10A mutants, the conical degree of lateral incisors (0.89±0.18) was less than that of central incisors (1.07±0.15)(t=3.68, P<0.001). The conical degree of central incisors and lateral incisors (0.70±0.23, 0.57±0.15) of EDA mutants was significantly lower than that in patients with other gene mutations (P>0.05). Conclusions: Compared with the normal control group, the remaining maxillary central and lateral incisors of the seven gene mutation groups of patients with congenital tooth agenesis all had different degrees of conical crown. Among them, the crown conical degree of maxillary central and lateral incisors of the EDA mutation was the most severe, and the WNT10A mutation affected the maxillary lateral incisors more specifically.
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Affiliation(s)
- J N Ding
- Department of Prosthodontics, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology, Beijing 100081, China
| | - H C Liu
- Department of Prosthodontics, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology, Beijing 100081, China
| | - M Yu
- Department of Prosthodontics, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology, Beijing 100081, China
| | - Y Liu
- Department of Prosthodontics, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology, Beijing 100081, China
| | - D Han
- Department of Prosthodontics, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology, Beijing 100081, China
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24
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Kuronuma K, van Diemen PA, Han D, Lin A, Grodecki K, Kwiecinski J, Motwani M, McElhinney P, Tomasino GF, Park C, Kwan A, Tzolos E, Klein E, Shou B, Tamarappoo B, Cadet S, Danad I, Driessen RS, Berman DS, Slomka PJ, Dey D, Knaapen P. Relationship between impaired myocardial blood flow by positron emission tomography and low-attenuation plaque burden and pericoronary adipose tissue attenuation from coronary computed tomography: From the prospective PACIFIC trial. J Nucl Cardiol 2023; 30:1558-1569. [PMID: 36645580 DOI: 10.1007/s12350-022-03194-z] [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: 07/31/2022] [Accepted: 12/02/2022] [Indexed: 01/17/2023]
Abstract
BACKGROUND Positron emission tomography (PET) is the clinical gold standard for quantifying myocardial blood flow (MBF). Pericoronary adipose tissue (PCAT) attenuation may detect vascular inflammation indirectly. We examined the relationship between MBF by PET and plaque burden and PCAT on coronary CT angiography (CCTA). METHODS This post hoc analysis of the PACIFIC trial included 208 patients with suspected coronary artery disease (CAD) who underwent [15O]H2O PET and CCTA. Low-attenuation plaque (LAP, < 30HU), non-calcified plaque (NCP), and PCAT attenuation were measured by CCTA. RESULTS In 582 vessels, 211 (36.3%) had impaired per-vessel hyperemic MBF (≤ 2.30 mL/min/g). In multivariable analysis, LAP burden was independently and consistently associated with impaired hyperemic MBF (P = 0.016); over NCP burden (P = 0.997). Addition of LAP burden improved predictive performance for impaired hyperemic MBF from a model with CAD severity and calcified plaque burden (P < 0.001). There was no correlation between PCAT attenuation and hyperemic MBF (r = - 0.11), and PCAT attenuation was not associated with impaired hyperemic MBF in univariable or multivariable analysis of all vessels (P > 0.1). CONCLUSION In patients with stable CAD, LAP burden was independently associated with impaired hyperemic MBF and a stronger predictor of impaired hyperemic MBF than NCP burden. There was no association between PCAT attenuation and hyperemic MBF.
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Affiliation(s)
- Keiichiro Kuronuma
- Department of Imaging and Medicine and the Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Cardiology, Nihon University, Tokyo, Japan
| | | | - Donghee Han
- Department of Imaging and Medicine and the Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Andrew Lin
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 116 N Robertson Boulevard, Los Angeles, CA, 90048, USA
| | - Kajetan Grodecki
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 116 N Robertson Boulevard, Los Angeles, CA, 90048, USA
| | - Jacek Kwiecinski
- Department of Interventional Cardiology and Angiology, Institute of Cardiology, Warsaw, Poland
| | - Manish Motwani
- Manchester Heart Centre, Manchester University NHS Foundation Trust, Manchester, UK
| | - Priscilla McElhinney
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 116 N Robertson Boulevard, Los Angeles, CA, 90048, USA
| | - Guadalupe Flores Tomasino
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 116 N Robertson Boulevard, Los Angeles, CA, 90048, USA
| | - Caroline Park
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 116 N Robertson Boulevard, Los Angeles, CA, 90048, USA
| | - Alan Kwan
- Department of Imaging and Medicine and the Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Evangelos Tzolos
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Eyal Klein
- Department of Imaging and Medicine and the Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Benjamin Shou
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 116 N Robertson Boulevard, Los Angeles, CA, 90048, USA
| | - Balaji Tamarappoo
- Department of Imaging and Medicine and the Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Sebastien Cadet
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 116 N Robertson Boulevard, Los Angeles, CA, 90048, USA
| | - Ibrahim Danad
- Department of Cardiology, Amsterdam UMC, VUmc, Amsterdam, The Netherlands
| | - Roel S Driessen
- Department of Cardiology, Amsterdam UMC, VUmc, Amsterdam, The Netherlands
| | - Daniel S Berman
- Department of Imaging and Medicine and the Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Piotr J Slomka
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Damini Dey
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 116 N Robertson Boulevard, Los Angeles, CA, 90048, USA.
| | - Paul Knaapen
- Department of Cardiology, Amsterdam UMC, VUmc, Amsterdam, The Netherlands
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25
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Rozanski A, Han D, Miller RJH, Gransar H, Slomka PJ, Hayes SW, Friedman JD, Thomson LEJ, Berman DS. Decline in typical angina among patients referred for cardiac stress testing. J Nucl Cardiol 2023; 30:1309-1320. [PMID: 37415006 DOI: 10.1007/s12350-023-03305-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [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: 12/16/2022] [Accepted: 04/12/2023] [Indexed: 07/08/2023]
Abstract
OBJECTIVE To evaluate temporal trends in the prevalence of typical angina and its clinical correlates among patients referred for stress/rest SPECT myocardial perfusion imaging (MPI). PATIENTS AND METHODS We evaluated the prevalence of chest pain symptoms and their relationship to inducible myocardial ischemia among 61,717 patients undergoing stress/rest SPECT-MPI between January 2, 1991 and December 31, 2017. We also assessed the relationship between chest pain symptom and angiographic findings among 6,579 patients undergoing coronary CT angiography between 2011 and 2017. RESULTS The prevalence of typical angina among SPECT-MPI patients declined from 16.2% between 1991 and 1997 to 3.1% between 2011 and 2017, while the prevalence of dyspnea without any chest pain increased from 5.9 to 14.5% over the same period. The frequency of inducible myocardial ischemia declined over time within all symptom groups, but its frequency among current patients (2011-2017) with typical angina was approximately three-fold higher versus other symptom groups (28.4% versus 8.6%, p < 0.001). Overall, patients with typical angina had a higher prevalence of obstructive CAD on CCTA than those with other clinical symptoms, but 33.3% of typical angina patients had no coronary stenoses, 31.1% had 1-49% stenoses, and 35.4% had ≥ 50% stenoses. CONCLUSIONS The prevalence of typical angina has declined to a very low level among contemporary patients referred for noninvasive cardiac tests. The angiographic findings among current typical angina patients are now quite heterogeneous, with one-third of such patients having normal coronary angiograms. However, typical angina remains associated with a substantially higher frequency of inducible myocardial ischemia compared to patients with other cardiac symptoms.
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Affiliation(s)
- Alan Rozanski
- Division of Cardiology and Department of Medicine, Mount Sinai Morningside Hospital, Mount Sinai Heart, and the Icahn School of Medicine at Mount Sinai, 1111 Amsterdam Avenue, New York, NY, 10025, USA.
| | - Donghee Han
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Robert J H Miller
- Department of Cardiac Sciences, University of Calgary, Calgary, AB, Canada
| | - Heidi Gransar
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Piotr J Slomka
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Sean W Hayes
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - John D Friedman
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Louise E J Thomson
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Daniel S Berman
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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26
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Lee H, Ahn HJ, Park HE, Han D, Chang HJ, Chun EJ, Han HW, Sung J, Jung HO, Choi SY. The effect of non-optimal lipids on the progression of coronary artery calcification in statin-naïve young adults: results from KOICA registry. Front Cardiovasc Med 2023; 10:1173289. [PMID: 37534276 PMCID: PMC10392939 DOI: 10.3389/fcvm.2023.1173289] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 06/29/2023] [Indexed: 08/04/2023] Open
Abstract
Background Despite the importance of attaining optimal lipid levels from a young age to secure long-term cardiovascular health, the detailed impact of non-optimal lipid levels in young adults on coronary artery calcification (CAC) is not fully explored. We sought to investigate the risk of CAC progression as per lipid profiles and to demonstrate lipid optimality in young adults. Methods From the KOrea Initiative on Coronary Artery calcification (KOICA) registry that was established in six large volume healthcare centers in Korea, 2,940 statin-naïve participants aged 20-45 years who underwent serial coronary calcium scans for routine health check-ups between 2002 and 2017 were included. The study outcome was CAC progression, which was assessed by the square root method. The risk of CAC progression was analyzed according to the lipid optimality and each lipid parameter. Results In this retrospective cohort (mean age, 41.3 years; men 82.4%), 477 participants (16.2%) had an optimal lipid profile, defined as triglycerides <150 mg/dl, LDL cholesterol <100 mg/dl, and HDL cholesterol >60 mg/dl. During follow-up (median, 39.7 months), CAC progression was observed in 434 participants (14.8%), and more frequent in the non-optimal lipid group (16.5% vs. 5.7%; p < 0.001). Non-optimal lipids independently increased the risk of CAC progression [adjusted hazard ratio (aHR), 1.97; p = 0.025], in a dose-dependent manner. Even in relatively low-risk participants with an initial calcium score of zero (aHR, 2.13; p = 0.014), in their 20 s or 30 s (aHR 2.15; p = 0.041), and without other risk factors (aHR 1.45; p = 0.038), similar results were demonstrable. High triglycerides had the greatest impact on CAC progression in this young adult population. Conclusion Non-optimal lipid levels were significantly associated with the risk of CAC progression in young adults, even at low-risk. Screening and intervention for non-optimal lipid levels, particularly triglycerides, from an early age might be of clinical value.
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Affiliation(s)
- Heesun Lee
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
- Division of Cardiology, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Republic of Korea
| | - Hyo-Jeong Ahn
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
- Division of Cardiology, Cardiovascular Center, Seoul National University Hospital, Seoul, Republic of Korea
| | - Hyo Eun Park
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
- Division of Cardiology, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Republic of Korea
| | - Donghee Han
- Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Hyuk-Jae Chang
- Division of Cardiology, Yonsei Cardiovascular Center, Yonsei University Health System, Seoul, Republic of Korea
| | - Eun Ju Chun
- Division of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Hae-Won Han
- Department of Internal Medicine, Gangnam Heartscan Clinic, Seoul, Republic of Korea
| | - Jidong Sung
- Division of Cardiology, Heart Stroke and Vascular Institute, Samsung Medical Center, Seoul, Republic of Korea
| | - Hae Ok Jung
- Division of Cardiology, Department of Internal Medicine, College of Medicine, Seoul St. Mary’s Hospital, The Catholic University of Korea, Seoul, Republic of Korea
| | - Su-Yeon Choi
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
- Division of Cardiology, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Republic of Korea
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27
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Miller RJH, Mamas MA, Tamarappoo B, Rozanski A, Han D, Gransar H, Slomka PJ, Dey D, Berman DS. Extensive coronary artery calcification is associated with all-cause mortality patients with a history of cancer. J Cardiovasc Comput Tomogr 2023; 17:284-285. [PMID: 37059633 DOI: 10.1016/j.jcct.2023.04.001] [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] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/16/2023] [Accepted: 04/01/2023] [Indexed: 04/16/2023]
Affiliation(s)
- Robert J H Miller
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, United States; Libin Cardiovascular Institute of Alberta and University of Calgary, Calgary, AB, Canada
| | - Mamas A Mamas
- Keele Cardiovascular Research Group, Keele University, Keele, UK
| | - Balaji Tamarappoo
- Department of Cardiology, Indiana University, Indianapolis, IN, United States
| | - Alan Rozanski
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, United States; Division of Cardiology and Department of Medicine, Mount Sinai Morningside Hospital, Mount Sinai Heart and the Icahn School of Medicine at Mount Sinai, Division of Cardiac Sciences, New York, NY, United States
| | - Donghee Han
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Heidi Gransar
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Piotr J Slomka
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Damini Dey
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Daniel S Berman
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
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28
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Xie XJ, Chen JY, Jiang J, Duan H, Wu Y, Zhang XW, Yang SJ, Zhao W, Shen SS, Wu L, He B, Ding YY, Luo H, Liu SY, Han D. [Development and validation of prognostic nomogram for malignant pleural mesothelioma]. Zhonghua Zhong Liu Za Zhi 2023; 45:415-423. [PMID: 37188627 DOI: 10.3760/cma.j.cn12152-20211124-00871] [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] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Objective: To development the prognostic nomogram for malignant pleural mesothelioma (MPM). Methods: Two hundred and ten patients pathologically confirmed as MPM were enrolled in this retrospective study from 2007 to 2020 in the People's Hospital of Chuxiong Yi Autonomous Prefecture, the First and Third Affiliated Hospital of Kunming Medical University, and divided into training (n=112) and test (n=98) sets according to the admission time. The observation factors included demography, symptoms, history, clinical score and stage, blood cell and biochemistry, tumor markers, pathology and treatment. The Cox proportional risk model was used to analyze the prognostic factors of 112 patients in the training set. According to the results of multivariate Cox regression analysis, the prognostic prediction nomogram was established. C-Index and calibration curve were used to evaluate the model's discrimination and consistency in raining and test sets, respectively. Patients were stratified according to the median risk score of nomogram in the training set. Log rank test was performed to compare the survival differences between the high and low risk groups in the two sets. Results: The median overall survival (OS) of 210 MPM patients was 384 days (IQR=472 days), and the 6-month, 1-year, 2-year, and 3-year survival rates were 75.7%, 52.6%, 19.7%, and 13.0%, respectively. Cox multivariate regression analysis showed that residence (HR=2.127, 95% CI: 1.154-3.920), serum albumin (HR=1.583, 95% CI: 1.017-2.464), clinical stage (stage Ⅳ: HR=3.073, 95% CI: 1.366-6.910) and the chemotherapy (HR=0.476, 95% CI: 0.292-0.777) were independent prognostic factors for MPM patients. The C-index of the nomogram established based on the results of Cox multivariate regression analysis in the training and test sets were 0.662 and 0.613, respectively. Calibration curves for both the training and test sets showed moderate consistency between the predicted and actual survival probabilities of MPM patients at 6 months, 1 year, and 2 years. The low-risk group had better outcomes than the high-risk group in both training (P=0.001) and test (P=0.003) sets. Conclusion: The survival prediction nomogram established based on routine clinical indicators of MPM patients provides a reliable tool for prognostic prediction and risk stratification.
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Affiliation(s)
- X J Xie
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming 650032, China
| | - J Y Chen
- Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Kunming 650106, China
| | - J Jiang
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming 650032, China
| | - H Duan
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming 650032, China
| | - Y Wu
- Department of Radiology, Chuxiong People's Hospital, Chuxiong 675099, China
| | - X W Zhang
- Department of Radiology, Chuxiong People's Hospital, Chuxiong 675099, China
| | - S J Yang
- Department of Thoracic Surgery, Chuxiong People's Hospital, Chuxiong 675099, China
| | - W Zhao
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming 650032, China
| | - S S Shen
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming 650032, China
| | - L Wu
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming 650032, China
| | - B He
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming 650032, China
| | - Y Y Ding
- Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Kunming 650106, China
| | - H Luo
- Deputy President's Office, Chuxiong People's Hospital, Chuxiong 675099, China
| | - S Y Liu
- GE Healthcare (China), Beijing 100176, China
| | - D Han
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming 650032, China
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29
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Lee M, Shechter A, Han D, Nguyen LC, Kim MS, Berman DS, Rader F, Siegel RJ. Left ventricular morphologic progression in apical hypertrophic cardiomyopathy. Int J Cardiol 2023; 381:62-69. [PMID: 37028709 DOI: 10.1016/j.ijcard.2023.04.006] [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: 12/27/2022] [Revised: 03/27/2023] [Accepted: 04/03/2023] [Indexed: 04/09/2023]
Abstract
BACKGROUND Left ventricular (LV) morphologic progression in apical hypertrophic cardiomyopathy (AHC) has not been well studied. We evaluated serial echocardiographic changes in LV morphology. METHODS Serial echocardiograms in AHC patients were assessed. LV morphology was categorized according to the presence of an apical pouch or aneurysm, and LV hypertrophic severity and extent; relative, pure, and apical-mid type defined as mild (<15 mm thickness) apical hypertrophy, significant (≥15 mm) apical hypertrophy, and both apical and midventricular hypertrophy, respectively. Adverse clinical events and late gadolinium enhancement (LGE) extent on cardiac magnetic resonance were evaluated for each morphologic type. RESULTS In 41 patients, 165 echocardiograms (maximal interval: 4.2 [IQR, 2.3-11.8] years) were evaluated. Morphologic changes were observed in 19 (46%) patients. Eleven (27%) patients displayed the progression of LV hypertrophy toward pure or apical-mid type. Five (12%) and 6 (15%) patients developed new pouches and aneurysms. Patients with progression tended to be younger (50 ± 15.6 vs 59 ± 14.4 years, P = 0.058) and had a longer period of follow-up (12 [5-14] vs 3 [2-4] years, P < 0.001). During a follow-up of 7.6 (IQR 3.0-12.1) years, 21 (51%) experienced clinical events. The relative, pure, and apical-mid types showed different LGE extents (2%, 6%, and 19%, P = 0.004). Patients with severe hypertrophic and apical involvement showed higher clinical event rates. CONCLUSIONS About half of AHC patients had a progression of LV morphology to more hypertrophic involvement and/or an apical pouch or aneurysm formation. Advanced AHC morphologic types were associated with higher event rates and scar burdens.
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Affiliation(s)
- Mirae Lee
- Division of Cardiology, Department of Internal Medicine, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, Republic of Korea; Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Alon Shechter
- Department of Cardiology, Rabin Medical Center, Faculty of Medicine, Tel Aviv University, Israel; Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Donghee Han
- Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, USA
| | - Long-Co Nguyen
- Department of Internal Medicine, Cedars Sinai Medical Center, Los Angeles, CA, USA
| | - Min Sun Kim
- Division of Cardiology, Department of Internal Medicine, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, Republic of Korea
| | - Daniel S Berman
- Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, USA
| | - Florian Rader
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Robert J Siegel
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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Liu H, Han D, Mao Y, Vonder M, Heuvelmans M, Yi J, Ye Z, De Koning H, Oudkerk M. 108P Optimization of automatic emphysema detection in lung cancer screening dataset. J Thorac Oncol 2023. [DOI: 10.1016/s1556-0864(23)00363-5] [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] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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Mao Y, Lancaster H, Jiang B, Han D, Vonder M, Dorrius M, Yu D, Yi J, de Bock G, Oudkerk M. 107P Artificial intelligence-based volumetric classification of pulmonary nodules in Chinese baseline lung cancer screening population (NELCIN-B3). J Thorac Oncol 2023. [DOI: 10.1016/s1556-0864(23)00362-3] [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] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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Jiang B, Han D, Heuvelmans M, van der Aalst C, De Koning H, Oudkerk M. 110P Volumetric tumor volume doubling time in lung cancer: A systematic review and meta-analysis. J Thorac Oncol 2023. [DOI: 10.1016/s1556-0864(23)00365-9] [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] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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Shanbhag AD, Miller RJH, Pieszko K, Lemley M, Kavanagh P, Feher A, Miller EJ, Sinusas AJ, Kaufmann PA, Han D, Huang C, Liang JX, Berman DS, Dey D, Slomka PJ. Deep Learning-Based Attenuation Correction Improves Diagnostic Accuracy of Cardiac SPECT. J Nucl Med 2023; 64:472-478. [PMID: 36137759 PMCID: PMC10071806 DOI: 10.2967/jnumed.122.264429] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [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: 06/21/2022] [Revised: 09/16/2022] [Accepted: 09/16/2022] [Indexed: 11/16/2022] Open
Abstract
To improve diagnostic accuracy, myocardial perfusion imaging (MPI) SPECT studies can use CT-based attenuation correction (AC). However, CT-based AC is not available for most SPECT systems in clinical use, increases radiation exposure, and is impacted by misregistration. We developed and externally validated a deep-learning model to generate simulated AC images directly from non-AC (NC) SPECT, without the need for CT. Methods: SPECT myocardial perfusion imaging was performed using 99mTc-sestamibi or 99mTc-tetrofosmin on contemporary scanners with solid-state detectors. We developed a conditional generative adversarial neural network that applies a deep learning model (DeepAC) to generate simulated AC SPECT images. The model was trained with short-axis NC and AC images performed at 1 site (n = 4,886) and was tested on patients from 2 separate external sites (n = 604). We assessed the diagnostic accuracy of the stress total perfusion deficit (TPD) obtained from NC, AC, and DeepAC images for obstructive coronary artery disease (CAD) with area under the receiver-operating-characteristic curve. We also quantified the direct count change among AC, NC, and DeepAC images on a per-voxel basis. Results: DeepAC could be obtained in less than 1 s from NC images; area under the receiver-operating-characteristic curve for obstructive CAD was higher for DeepAC TPD (0.79; 95% CI, 0.72-0.85) than for NC TPD (0.70; 95% CI, 0.63-0.78; P < 0.001) and similar to AC TPD (0.81; 95% CI, 0.75-0.87; P = 0.196). The normalcy rate in the low-likelihood-of-coronary-disease population was higher for DeepAC TPD (70.4%) and AC TPD (75.0%) than for NC TPD (54.6%, P < 0.001 for both). The positive count change (increase in counts) was significantly higher for AC versus NC (median, 9.4; interquartile range, 6.0-14.2; P < 0.001) than for AC versus DeepAC (median, 2.4; interquartile range, 1.3-4.2). Conclusion: In an independent external dataset, DeepAC provided improved diagnostic accuracy for obstructive CAD, as compared with NC images, and this accuracy was similar to that of actual AC. DeepAC simplifies the task of artifact identification for physicians, avoids misregistration artifacts, and can be performed rapidly without the need for CT hardware and additional acquisitions.
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Affiliation(s)
- Aakash D Shanbhag
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Robert J H Miller
- Department of Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada
| | - Konrad Pieszko
- Department of Interventional Cardiology and Cardiac Surgery, University of Zielona Góra, Zielona Góra, Poland
| | - Mark Lemley
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Paul Kavanagh
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Attila Feher
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut; and
| | - Edward J Miller
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut; and
| | - Albert J Sinusas
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut; and
| | - Philipp A Kaufmann
- Cardiac Imaging, Department of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland
| | - Donghee Han
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Cathleen Huang
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Joanna X Liang
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Daniel S Berman
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Damini Dey
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Piotr J Slomka
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California;
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Han D, van Diemen P, Kuronuma K, Lin A, Motwani M, McElhinney P, Tomasino GF, Park C, Kwan A, Tzolos E, Klein E, Grodecki K, Shou B, Tamarappoo B, Cadet S, Danad I, Driessen RS, Berman DS, Slomka PJ, Dey D, Knaapen P. Sex differences in computed tomography angiography-derived coronary plaque burden in relation to invasive fractional flow reserve. J Cardiovasc Comput Tomogr 2023; 17:112-119. [PMID: 36670043 PMCID: PMC10148895 DOI: 10.1016/j.jcct.2022.12.002] [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: 05/13/2022] [Revised: 12/12/2022] [Accepted: 12/13/2022] [Indexed: 01/20/2023]
Abstract
BACKGROUND Distinct sex-related differences exist in coronary artery plaque burden and distribution. We aimed to explore sex differences in quantitative plaque burden by coronary CT angiography (CCTA) in relation to ischemia by invasive fractional flow reserve (FFR). METHODS This post-hoc analysis of the PACIFIC trial included 581 vessels in 203 patients (mean age 58.1 ± 8.7 years, 63.5% male) who underwent CCTA and per-vessel invasive FFR. Quantitative assessment of total, calcified, non-calcified, and low-density non-calcified plaque burden were performed using semiautomated software. Significant ischemia was defined as invasive FFR ≤0.8. RESULTS The per-vessel frequency of ischemia was higher in men than women (33.5% vs. 7.5%, p < 0.001). Women had a smaller burden of all plaque subtypes (all p < 0.01). There was no sex difference on total, calcified, or non-calcified plaque burdens in vessels with ischemia; only low-density non-calcified plaque burden was significantly lower in women (beta: -0.183, p = 0.035). The burdens of all plaque subtypes were independently associated with ischemia in both men and women (For total plaque burden (5% increase): Men, OR: 1.15, 95%CI: 1.06-1.24, p = 0.001; Women, OR: 1.96, 95%CI: 1.11-3.46, p = 0.02). No significant interaction existed between sex and total plaque burden for predicting ischemia (interaction p = 0.108). The addition of quantitative plaque burdens to stenosis severity and adverse plaque characteristics improved the discrimination of ischemia in both men and women. CONCLUSIONS In symptomatic patients with suspected CAD, women have a lower CCTA-derived burden of all plaque subtypes compared to men. Quantitative plaque burden provides independent and incremental predictive value for ischemia, irrespective of sex.
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Affiliation(s)
- Donghee Han
- Department of Imaging and Medicine and the Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
| | - Pepijn van Diemen
- Department of Cardiology, VU University Medical Center, Amsterdam, the Netherlands
| | - Keiichiro Kuronuma
- Department of Imaging and Medicine and the Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Andrew Lin
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Manish Motwani
- Manchester Heart Centre, Manchester University NHS Foundation Trust, Manchester, United Kingdom
| | - Priscilla McElhinney
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | | | - Caroline Park
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Alan Kwan
- Department of Imaging and Medicine and the Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Evangelos Tzolos
- Department of Imaging and Medicine and the Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, United Kingdom
| | - Eyal Klein
- Department of Imaging and Medicine and the Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Kajetan Grodecki
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Benjamin Shou
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Balaji Tamarappoo
- Department of Imaging and Medicine and the Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Cardiovascular Institute, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Sebastien Cadet
- Department of Imaging and Medicine and the Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Ibrahim Danad
- Department of Cardiology, VU University Medical Center, Amsterdam, the Netherlands
| | - Roel S Driessen
- Department of Cardiology, VU University Medical Center, Amsterdam, the Netherlands
| | - Daniel S Berman
- Department of Imaging and Medicine and the Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Piotr J Slomka
- Artificial Interlligence in Medicine Program, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Damini Dey
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Paul Knaapen
- Department of Cardiology, VU University Medical Center, Amsterdam, the Netherlands
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Shah M, Han D, Tabak SW, Makkar RR, Tompkins R. TRANSCATHETER AORTIC VALVE REPLACEMENT IN PREVIOUSLY REPAIRED TETRALOGY OF FALLOT COMPLICATED BY PARAVALVULAR LEAK FROM OVERRIDING AORTA. J Am Coll Cardiol 2023. [DOI: 10.1016/s0735-1097(23)02894-2] [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] [Indexed: 03/06/2023]
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Lin F, Gianni U, SATO YU, Tantawy S, Lu Y, Wijeratne R, Han D, Chang HJ, Earls JP, Min JK, Narula J, Narula N, Finn AV, Shaw LJ, Virmani R. HISTOLOGIC ARTERIAL LANDMARKS FOR CCTA VISUALIZED ATHEROSCLEROTIC PLAQUE: THE DISCOVER-VP STUDY. J Am Coll Cardiol 2023. [DOI: 10.1016/s0735-1097(23)01817-x] [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] [Indexed: 03/06/2023]
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Malhotra P, Gheyath B, Han D, Dey D, Hayes SW, Friedman JD, Thomson L, Kwan A, Berman DS. ACCURACY OF CORONARY CT ANGIOGRAPHY IN PATIENTS WITH CORONARY STENTS: COMPARISON WITH QUANTITATIVE CORONARY ANGIOGRAPHY. J Am Coll Cardiol 2023. [DOI: 10.1016/s0735-1097(23)01949-6] [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] [Indexed: 03/06/2023]
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Kuronuma K, Miller RJH, Wei CC, Singh A, Lemley M, Van Kriekinge SD, Kavanagh P, Gransar H, Han D, Hayes SW, Thomson L, Dey D, Friedman JD, Berman DS, Slomka P. DOWNWARD MYOCARDIAL CREEP AUTOMATICALLY QUANTIFIED DURING STRESS POSITRON EMISSION TOMOGRAPHY MYOCARDIAL PERFUSION IMAGING IS INVERSELY ASSOCIATED WITH MORTALITY. J Am Coll Cardiol 2023. [DOI: 10.1016/s0735-1097(23)01948-4] [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] [Indexed: 03/06/2023]
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Natanzon S, Han D, Dey D, Rozanski A, Chakravarty T, Nakamura M, Makkar RR, Berman DS. PULMONARY ARTERY DILATION AND CLINICAL OUTCOMES FOLLOWING TRANSCATHETER EDGE-EDGE MITRAL VALVE REPAIR. J Am Coll Cardiol 2023. [DOI: 10.1016/s0735-1097(23)01951-4] [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] [Indexed: 03/06/2023]
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Lin B, Han D, Gransar H, Rozanski A, Miller R, Dey D, Hayes SW, Friedman JD, Thomson L, Berman DS. CORONARY ARTERY CALCIUM SCORE IS MORE PREDICTIVE FOR RISK OF MORTALITY THAN SEGMENT INVOLVEMENT SCORE BY CORONARY ARTERY DISEASE REPORTING AND DATA SYSTEM 2.0 CLASSIFICATION CATEGORIES FOR PLAQUE BURDEN. J Am Coll Cardiol 2023. [DOI: 10.1016/s0735-1097(23)01809-0] [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] [Indexed: 03/06/2023]
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Malhotra P, Han D, Singh A, Miller RJH, Gransar H, Hayes SW, Friedman JD, Thomson L, Rozanski A, Slomka P, Berman DS. DIFFERENCES IN MYOCARDIAL FLOW RESERVE AND PROGNOSIS BETWEEN PATIENTS WITH AND WITHOUT DIABETES. J Am Coll Cardiol 2023. [DOI: 10.1016/s0735-1097(23)01944-7] [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] [Indexed: 03/07/2023]
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Miller RJH, Rozanski A, Slomka PJ, Han D, Gransar H, Hayes SW, Friedman JD, Thomson LEJ, Berman DS. Development and validation of ischemia risk scores. J Nucl Cardiol 2023; 30:324-334. [PMID: 35484468 DOI: 10.1007/s12350-022-02976-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 03/27/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND The likelihood of ischemia on myocardial perfusion imaging is central to physician decisions regarding test selection, but dedicated risk scores are lacking. We derived and validated two novel ischemia risk scores to support physician decision making. METHODS Risk scores were derived using 15,186 patients and validated with 2,995 patients from a different center. Logistic regression was used to assess associations with ischemia to derive point-based and calculated ischemia scores. Predictive performance for ischemia was assessed using area under the receiver operating characteristic curve (AUC) and compared with the CAD consortium basic and clinical models. RESULTS During derivation, the calculated ischemia risk score (0.801) had higher AUC compared to the point-based score (0.786, p < 0.001). During validation, the calculated ischemia score (0.716, 95% CI 0.684- 0.748) had higher AUC compared to the point-based ischemia score (0.699, 95% CI 0.666- 0.732, p = 0.016) and the clinical CAD model (AUC 0.667, 95% CI 0.633- 0.701, p = 0.002). Calibration for both ischemia scores was good in both populations (Brier score < 0.100). CONCLUSIONS We developed two novel risk scores for predicting probability of ischemia on MPI which demonstrated high accuracy during model derivation and in external testing. These scores could support physician decisions regarding diagnostic testing strategies.
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Affiliation(s)
- Robert J H Miller
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Libin Cardiovascular Institute of Alberta, University of Calgary, Calgary, AB, Canada
| | - Alan Rozanski
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Division of Cardiology and Department of Medicine, Mount Sinai Morningside Hospital, Mount Sinai Heart and the Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Division of Cardiac Sciences, Mount Sinai Morningside Hospital, Mount Sinai Heart and the Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Piotr J Slomka
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Donghee Han
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Heidi Gransar
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Sean W Hayes
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - John D Friedman
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Louise E J Thomson
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Daniel S Berman
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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Singh A, Miller RJH, Otaki Y, Kavanagh P, Hauser MT, Tzolos E, Kwiecinski J, Van Kriekinge S, Wei CC, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Liang JX, Huang C, Han D, Dey D, Berman DS, Slomka PJ. Direct Risk Assessment From Myocardial Perfusion Imaging Using Explainable Deep Learning. JACC Cardiovasc Imaging 2023; 16:209-220. [PMID: 36274041 PMCID: PMC10980287 DOI: 10.1016/j.jcmg.2022.07.017] [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: 03/30/2022] [Revised: 06/21/2022] [Accepted: 07/21/2022] [Indexed: 01/19/2023]
Abstract
BACKGROUND Myocardial perfusion imaging (MPI) is frequently used to provide risk stratification, but methods to improve the accuracy of these predictions are needed. OBJECTIVES The authors developed an explainable deep learning (DL) model (HARD MACE [major adverse cardiac events]-DL) for the prediction of death or nonfatal myocardial infarction (MI) and validated its performance in large internal and external testing groups. METHODS Patients undergoing single-photon emission computed tomography MPI were included, with 20,401 patients in the training and internal testing group (5 sites) and 9,019 in the external testing group (2 different sites). HARD MACE-DL uses myocardial perfusion, motion, thickening, and phase polar maps combined with age, sex, and cardiac volumes. The primary outcome was all-cause mortality or nonfatal MI. Prognostic accuracy was evaluated using area under the receiver-operating characteristic curve (AUC). RESULTS During internal testing, patients with normal perfusion and elevated HARD MACE-DL risk were at higher risk than patients with abnormal perfusion and low HARD MACE-DL risk (annualized event rate, 2.9% vs 1.2%; P < 0.001). Patients in the highest quartile of HARD MACE-DL score had an annual rate of death or MI (4.8%) 10-fold higher than patients in the lowest quartile (0.48% per year). In external testing, the AUC for HARD MACE-DL (0.73; 95% CI: 0.71-0.75) was higher than a logistic regression model (AUC: 0.70), stress total perfusion deficit (TPD) (AUC: 0.65), and ischemic TPD (AUC: 0.63; all P < 0.01). Calibration, a measure of how well predicted risk matches actual risk, was excellent in both groups (Brier score, 0.079 for internal and 0.070 for external). CONCLUSIONS The DL model predicts death or MI directly from MPI, by estimating patient-level risk with good calibration and improved accuracy compared with traditional quantitative approaches. The model incorporates mechanisms to explain to the physician which image regions contribute to the adverse event prediction.
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Affiliation(s)
- Ananya Singh
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Robert J H Miller
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Cardiac Sciences, University of Calgary and Libin Cardiovascular Institute, Calgary, Alberta, Canada
| | - Yuka Otaki
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Paul Kavanagh
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Michael T Hauser
- Department of Nuclear Cardiology, Oklahoma Heart Hospital, Oklahoma City, Oklahoma, USA
| | - Evangelos Tzolos
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA; BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom
| | - Jacek Kwiecinski
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Interventional Cardiology and Angiology, Institute of Cardiology, Warsaw, Poland
| | - Serge Van Kriekinge
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Chih-Chun Wei
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Tali Sharir
- Department of Nuclear Cardiology, Assuta Medical Center, Tel Aviv, Israel; Department of Nuclear Cardiology, Ben Gurion University of the Negev, Beer Sheba, Israel
| | - Andrew J Einstein
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and New York-Presbyterian Hospital, New York, New York, USA; Department of Radiology, Columbia University Irving Medical Center and New York-Presbyterian Hospital, New York, New York, USA
| | - Mathews B Fish
- Oregon Heart and Vascular Institute, Sacred Heart Medical Center, Springfield, Oregon, USA
| | - Terrence D Ruddy
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Philipp A Kaufmann
- Division of Cardiac Imaging, Department of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland
| | - Albert J Sinusas
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Edward J Miller
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut, USA
| | | | - Sharmila Dorbala
- Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Marcelo Di Carli
- Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Joanna X Liang
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Cathleen Huang
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Donghee Han
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Damini Dey
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Daniel S Berman
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Piotr J Slomka
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA.
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Han D, So J. Energy-Efficient Resource Allocation Based on Deep Q-Network in V2V Communications. Sensors (Basel) 2023; 23:1295. [PMID: 36772334 PMCID: PMC9921350 DOI: 10.3390/s23031295] [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] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 01/18/2023] [Accepted: 01/19/2023] [Indexed: 06/18/2023]
Abstract
Recently, with the development of autonomous driving technology, vehicle-to-everything (V2X) communication technology that provides a wireless connection between vehicles, pedestrians, and roadside base stations has gained significant attention. Vehicle-to-vehicle (V2V) communication should provide low-latency and highly reliable services through direct communication between vehicles, improving safety. In particular, as the number of vehicles increases, efficient radio resource management becomes more important. In this paper, we propose a deep reinforcement learning (DRL)-based decentralized resource allocation scheme in the V2X communication network in which the radio resources are shared between the V2V and vehicle-to-infrastructure (V2I) networks. Here, a deep Q-network (DQN) is utilized to find the resource blocks and transmit power of vehicles in the V2V network to maximize the sum rate of the V2I and V2V links while reducing the power consumption and latency of V2V links. The DQN also uses the channel state information, the signal-to-interference-plus-noise ratio (SINR) of V2I and V2V links, and the latency constraints of vehicles to find the optimal resource allocation scheme. The proposed DQN-based resource allocation scheme ensures energy-efficient transmissions that satisfy the latency constraints for V2V links while reducing the interference of the V2V network to the V2I network. We evaluate the performance of the proposed scheme in terms of the sum rate of the V2X network, the average power consumption of V2V links, and the average outage probability of V2V links using a case study in Manhattan with nine blocks of 3GPP TR 36.885. The simulation results show that the proposed scheme greatly reduces the transmit power of V2V links when compared to the conventional reinforcement learning-based resource allocation scheme without sacrificing the sum rate of the V2X network or the outage probability of V2V links.
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Affiliation(s)
| | - Jaewoo So
- Correspondence: ; Tel.: +82-2-705-8464
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Baskaran L, Lee JK, Ko MSM, Al’Aref SJ, Neo YP, Ho JS, Huang W, Yoon YE, Han D, Nakanishi R, Tan SY, Al-Mallah M, Budoff MJ, Shaw LJ. Comparing the pooled cohort equations and coronary artery calcium scores in a symptomatic mixed Asian cohort. Front Cardiovasc Med 2023; 10:1059839. [PMID: 36733301 PMCID: PMC9887040 DOI: 10.3389/fcvm.2023.1059839] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Accepted: 01/02/2023] [Indexed: 01/18/2023] Open
Abstract
Background The value of pooled cohort equations (PCE) as a predictor of major adverse cardiovascular events (MACE) is poorly established among symptomatic patients. Coronary artery calcium (CAC) assessment further improves risk prediction, but non-Western studies are lacking. This study aims to compare PCE and CAC scores within a symptomatic mixed Asian cohort, and to evaluate the incremental value of CAC in predicting MACE, as well as in subgroups based on statin use. Methods Consecutive patients with stable chest pain who underwent cardiac computed tomography were recruited. Logistic regression was performed to determine the association between risk factors and MACE. Cohort and statin-use subgroup comparisons were done for PCE against Agatston score in predicting MACE. Results Of 501 patients included, mean (SD) age was 53.7 (10.8) years, mean follow-up period was 4.64 (0.66) years, 43.5% were female, 48.3% used statins, and 50.0% had no CAC. MI occurred in 8 subjects while 9 subjects underwent revascularization. In the general cohort, age, presence of CAC, and ln(Volume) (OR = 1.05, 7.95, and 1.44, respectively) as well as age and PCE score for the CAC = 0 subgroup (OR = 1.16 and 2.24, respectively), were significantly associated with MACE. None of the risk factors were significantly associated with MACE in the CAC > 0 subgroup. Overall, the PCE, Agatston, and their combination obtained an area under the receiver operating characteristic curve (AUC) of 0.501, 0.662, and 0.661, respectively. Separately, the AUC of PCE, Agatston, and their combination for statin non-users were 0.679, 0.753, and 0.734, while that for statin-users were 0.585, 0.615, and 0.631, respectively. Only the performance of PCE alone was statistically significant (p = 0.025) when compared between statin-users (0.507) and non-users (0.783). Conclusion In a symptomatic mixed Asian cohort, age, presence of CAC, and ln(Volume) were independently associated with MACE for the overall subgroup, age and PCE score for the CAC = 0 subgroup, and no risk factor for the CAC > 0 subgroup. Whilst the PCE performance deteriorated in statin versus non-statin users, the Agatston score performed consistently in both groups.
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Affiliation(s)
- Lohendran Baskaran
- Department of Cardiology, National Heart Centre Singapore, Singapore, Singapore,Duke-NUS Medical School, National University of Singapore, Singapore, Singapore,*Correspondence: Lohendran Baskaran,
| | - Jing Kai Lee
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Michelle Shi Min Ko
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Subhi J. Al’Aref
- Division of Cardiology, Department of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Yu Pei Neo
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Jien Sze Ho
- Department of Cardiology, National Heart Centre Singapore, Singapore, Singapore,Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Weiting Huang
- Department of Cardiology, National Heart Centre Singapore, Singapore, Singapore,Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | | | - Donghee Han
- Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Rine Nakanishi
- Department of Cardiovascular Medicine, Toho University Graduate School of Medicine, Tokyo, Japan
| | - Swee Yaw Tan
- Department of Cardiology, National Heart Centre Singapore, Singapore, Singapore,Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Mouaz Al-Mallah
- Houston Methodist DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, TX, United States
| | - Matthew J. Budoff
- Department of Medicine, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, United States
| | - Leslee J. Shaw
- Icahn School of Medicine at Mount Sinai, Blavatnik Family Women’s Health Research Institute, New York, NY, United States
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Lin FY, Goebel BP, Lee BC, Lu Y, Baskaran L, Yoon YE, Maliakal GT, Gianni U, Bax AM, Sengupta PP, Slomka PJ, Dey DS, Rozanski A, Han D, Berman DS, Budoff MJ, Miedema MD, Nasir K, Rumberger J, Whelton SP, Blaha MJ, Shaw LJ. Mortality impact of low CAC density predominantly occurs in early atherosclerosis: explainable ML in the CAC consortium. J Cardiovasc Comput Tomogr 2023; 17:28-33. [PMID: 36376147 DOI: 10.1016/j.jcct.2022.10.001] [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] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 09/15/2022] [Accepted: 10/28/2022] [Indexed: 11/13/2022]
Abstract
BACKGROUND Machine learning (ML) models of risk prediction with coronary artery calcium (CAC) and CAC characteristics exhibit high performance, but are not inherently interpretable. OBJECTIVES To determine the direction and magnitude of impact of CAC characteristics on 10-year all-cause mortality (ACM) with explainable ML. METHODS We analyzed asymptomatic subjects in the CAC consortium. We trained ML models on 80% and tested on 20% of the data with XGBoost, using clinical characteristics + CAC (ML 1) and additional CAC characteristics of CAC density and number of calcified vessels (ML 2). We applied SHAP, an explainable ML tool, to explore the relationship of CAC and CAC characteristics with 10-year all-cause and CV mortality. RESULTS 2376 deaths occurred among 63,215 patients [68% male, median age 54 (IQR 47-61), CAC 3 (IQR 0-94.3)]. ML2 was similar to ML1 to predict all-cause mortality (Area Under the Curve (AUC) 0.819 vs 0.821, p = 0.23), but superior for CV mortality (0.847 vs 0.845, p = 0.03). Low CAC density increased mortality impact, particularly ≤0.75. Very low CAC density ≤0.75 was present in only 4.3% of the patients with measurable density, and 75% occurred in CAC1-100. The number of diseased vessels did not increase mortality overall when simultaneously accounting for CAC and CAC density. CONCLUSION CAC density contributes to mortality risk primarily when it is very low ≤0.75, which is primarily observed in CAC 1-100. CAC and CAC density are more important for mortality prediction than the number of diseased vessels, and improve prediction of CV but not all-cause mortality. Explainable ML techniques are useful to describe granular relationships in otherwise opaque prediction models.
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Affiliation(s)
- Fay Y Lin
- Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA; Department of Population Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Benjamin P Goebel
- Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA
| | - Benjamin C Lee
- Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA
| | - Yao Lu
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Lohendran Baskaran
- Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA; Department of Cardiology, National Heart Centre Singapore, Singapore
| | - Yeonyee E Yoon
- Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA; Department of Internal Medicine, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Sungnam, South Korea
| | - Gabriel Thomas Maliakal
- Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA; Department of Computer Science, Michigan State University, East Lansing, MI, USA
| | - Umberto Gianni
- Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA
| | - A Maxim Bax
- Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA
| | - Partho P Sengupta
- Division of Cardiology, Rutgers Robert Wood Medical School and University Hospital, New Brunswick, NJ, USA
| | - Piotr J Slomka
- Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Damini S Dey
- Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Alan Rozanski
- Department of Cardiology, Mount Sinai St. Luke's Hospital, New York, NY, USA
| | - Donghee Han
- Biomedical Imaging Research Institute, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Daniel S Berman
- Biomedical Imaging Research Institute, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Matthew J Budoff
- Department of Medicine, Lundquist Institute at Harbor UCLA Medical Center, Torrance, CA, USA
| | - Michael D Miedema
- Cardiovascular Prevention, Minneapolis Heart Institute Foundation, Minneapolis Heart Institute, Minneapolis, MN, USA
| | - Khurram Nasir
- Division of Cardiovascular Prevention and Wellness, Department of Cardiology, Houston Methodist Hospital, Houston, TX, USA
| | - John Rumberger
- Princeton Longevity Center, Princeton Forrestal Village, Princeton, NJ, USA
| | - Seamus P Whelton
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Baltimore, MD, USA
| | - Michael J Blaha
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Baltimore, MD, USA
| | - Leslee J Shaw
- Department of Population Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Kuronuma K, Han D, Miller RJH, Rozanski A, Gransar H, Dey D, Hayes SW, Friedman JD, Thomson L, Slomka PJ, Berman DS. Long-term Survival Benefit From Revascularization Compared With Medical Therapy in Patients With or Without Diabetes Undergoing Myocardial Perfusion Single Photon Emission Computed Tomography. Diabetes Care 2022; 45:3016-3023. [PMID: 36001757 DOI: 10.2337/dc22-0454] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [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: 03/04/2022] [Accepted: 07/25/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To explore the long-term association of survival benefit from early revascularization with the magnitude of ischemia in patients with diabetes compared with those without diabetes using a large observational cohort of patients undergoing single photon emission computed tomography myocardial perfusion imaging (SPECT-MPI). RESEARCH DESIGN AND METHODS Of 41,982 patients who underwent stress and rest SPECT-MPI from 1998 to 2017, 8,328 (19.8%) had diabetes. A propensity score was used to match 8,046 patients with diabetes to 8,046 patients without diabetes. Early revascularization was defined as occurring within 90 days after SPECT-MPI. The percentage of myocardial ischemia was assessed from the magnitude of reversible myocardial perfusion defect on SPECT-MPI. RESULTS Over a median 10.3-year follow-up, the annualized mortality rate was higher for the patients with diabetes compared with those without diabetes (4.7 vs. 3.6%; P < 0.001). There were significant interactions between early revascularization and percent myocardial ischemia in patients with and without diabetes (all interaction P values <0.05). After adjusting for confounding variables, survival benefit from early revascularization was observed in patients with diabetes above a threshold of >8.6% ischemia and in patients without diabetes above a threshold of >12.1%. Patients with diabetes receiving insulin had a higher mortality rate (6.2 vs. 4.1%; P < 0.001), but there was no interaction between revascularization and insulin use (interaction P value = 0.405). CONCLUSIONS Patients with diabetes, especially those on insulin treatment, had higher mortality rate compared with patients without diabetes. Early revascularization was associated with a mortality benefit at a lower ischemic threshold in patients with diabetes compared with those without diabetes.
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Affiliation(s)
- Keiichiro Kuronuma
- Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA.,Department of Cardiology, Nihon University, Tokyo, Japan
| | - Donghee Han
- Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Robert J H Miller
- Department of Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada
| | - Alan Rozanski
- Department of Cardiology, Mount Sinai Morningside Hospital and Mount Sinai Heart, New York, NY
| | - Heidi Gransar
- Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Damini Dey
- Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Sean W Hayes
- Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA
| | - John D Friedman
- Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Louise Thomson
- Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Piotr J Slomka
- Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Daniel S Berman
- Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA
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Rozanski A, Miller RJH, Han D, Gransar H, Slomka P, Dey D, Hayes SB, Friedman J, Thomson LB, Berman DS. The prevalence and predictors of inducible myocardial ischemia among patients referred for radionuclide stress testing. J Nucl Cardiol 2022; 29:2839-2849. [PMID: 34608604 DOI: 10.1007/s12350-021-02797-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 07/30/2021] [Indexed: 01/18/2023]
Abstract
BACKGROUND The frequency of inducible myocardial ischemia has declined in contemporary stress test cohorts, suggesting a need to re-evaluate its optimal use. To-date, however, a comprehensive analysis of the most potent predictors of myocardial ischemia among cardiac stress test patients has not been conducted. METHODS We assessed 27,615 patients referred for stress-rest SPECT myocardial perfusion imaging between January 1, 2004 and December 31, 2017. Chi-square analysis was used to ascertain the most potent predictors of ischemia. RESULTS Among our cohort, CAD status (presence/absence of known CAD), rest left ventricular ejection fraction (LVEF), and typical angina were the most potent predictors of ischemia. The frequency of ischemia was only 6.6% among patients with an LVEF > 55% but 38.1% for patients with LVEF < 45% (P < 0.001). The frequency of myocardial ischemia was fourfold higher among patients with known CAD vs no known CAD (28.0% vs 6.5%, P < 0.001) and approximately threefold higher among patients with typical angina vs patients with atypical symptoms (P < 0.001). CONCLUSIONS The frequency of myocardial ischemia varies markedly according to the common clinical parameters and is particularly high among patients with known CAD, low LVEF, and typical angina. These observations may be used to develop more cost-effective strategies for referring patients for cardiac stress testing.
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Affiliation(s)
- Alan Rozanski
- Division of Cardiology and Department of Medicine, Mount Sinai Morningside Hospital, Mount Sinai Heart and the Icahn School of Medicine at Mount Sinai, 1111 Amsterdam Avenue, New York, NY, 10025, USA.
| | - Robert J H Miller
- Division of Cardiac Sciences, Libin Cardiovascular Institute of Alberta, University of Calgary, Calgary, Canada
| | - Donghee Han
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Heidi Gransar
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Piotr Slomka
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Damini Dey
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Sean B Hayes
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - John Friedman
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Louise B Thomson
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Daniel S Berman
- Departments of Imaging and Medicine and Burns and Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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Han D, Rozanski A, Gransar H, Tzolos E, Miller RJH, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Liang JX, Hu LH, Dey D, Berman DS, Slomka PJ. Comparison of diabetes to other prognostic predictors among patients referred for cardiac stress testing: A contemporary analysis from the REFINE SPECT Registry. J Nucl Cardiol 2022; 29:3003-3014. [PMID: 34757571 PMCID: PMC9085969 DOI: 10.1007/s12350-021-02810-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [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: 05/17/2021] [Accepted: 08/12/2021] [Indexed: 01/22/2023]
Abstract
BACKGROUND Diabetes mellitus (DM) is increasingly prevalent among contemporary populations referred for cardiac stress testing, but its potency as a predictor for major adverse cardiovascular events (MACE) vs other clinical variables is not well delineated. METHODS AND RESULTS From 19,658 patients who underwent SPECT-MPI, we identified 3122 patients with DM without known coronary artery disease (CAD) (DM+/CAD-) and 3564 without DM with known CAD (DM-/CAD+). Propensity score matching was used to control for the differences in characteristics between DM+/CAD- and DM-/CAD+ groups. There was comparable MACE in the matched DM+/CAD- and DM-/CAD+ groups (HR 1.15, 95% CI 0.97-1.37). By Chi-square analysis, type of stress (exercise or pharmacologic), total perfusion deficit (TPD), and left ventricular function were the most potent predictors of MACE, followed by CAD and DM status. The combined consideration of mode of stress, TPD, and DM provided synergistic stratification, an 8.87-fold (HR 8.87, 95% CI 7.27-10.82) increase in MACE among pharmacologically stressed patients with DM and TPD > 10% (vs non-ischemic, exercised stressed patients without DM). CONCLUSIONS Propensity-matched patients with DM and no known CAD have similar MACE risk compared to patients with known CAD and no DM. DM is synergistic with mode of stress testing and TPD in predicting the risk of cardiac stress test patients.
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Affiliation(s)
- Donghee Han
- Departments of Medicine (Division of Artificial Intelligence), Imaging, and Biomedical Sciences, Cedars-Sinai, Los Angeles, CA, USA
| | - Alan Rozanski
- Division of Cardiology, Mount Sinai St. Luke's Hospital, New York, NY, USA
| | - Heidi Gransar
- Departments of Medicine (Division of Artificial Intelligence), Imaging, and Biomedical Sciences, Cedars-Sinai, Los Angeles, CA, USA
| | - Evangelos Tzolos
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Robert J H Miller
- Department of Cardiac Sciences, University of Calgary, Calgary, AB, Canada
| | - Tali Sharir
- Department of Nuclear Cardiology, Assuta Medical Centers, Tel Aviv, Israel
- Ben Gurion University of the Negev, Beersheba, Israel
| | - Andrew J Einstein
- Division of Cardiology, Department of Medicine, and Department of Radiology, Columbia University Irving Medical Center and New York-Presbyterian Hospital, New York, NY, USA
| | - Mathews B Fish
- Oregon Heart and Vascular Institute, Sacred Heart Medical Center, Springfield, OR, USA
| | - Terrence D Ruddy
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, ON, Canada
| | - Philipp A Kaufmann
- Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Zurich, Switzerland
| | - Albert J Sinusas
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Edward J Miller
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | | | - Sharmila Dorbala
- Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Marcelo Di Carli
- Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Joanna X Liang
- Departments of Medicine (Division of Artificial Intelligence), Imaging, and Biomedical Sciences, Cedars-Sinai, Los Angeles, CA, USA
| | - Lien-Hsin Hu
- Department of Nuclear Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Damini Dey
- Departments of Medicine (Division of Artificial Intelligence), Imaging, and Biomedical Sciences, Cedars-Sinai, Los Angeles, CA, USA
| | - Daniel S Berman
- Departments of Medicine (Division of Artificial Intelligence), Imaging, and Biomedical Sciences, Cedars-Sinai, Los Angeles, CA, USA
| | - Piotr J Slomka
- Departments of Medicine (Division of Artificial Intelligence), Imaging, and Biomedical Sciences, Cedars-Sinai, Los Angeles, CA, USA.
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50
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Han D, Rozanski A, Miller RJH, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Liang JX, Dey D, Berman DS, Slomka PJ. Prevalence and predictors of automatically quantified myocardial ischemia within a multicenter international registry. J Nucl Cardiol 2022; 29:3221-3232. [PMID: 35174442 PMCID: PMC9378748 DOI: 10.1007/s12350-021-02829-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [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: 07/17/2021] [Accepted: 09/13/2021] [Indexed: 01/22/2023]
Abstract
BACKGROUND The utility of cardiac stress testing depends on the prevalence of myocardial ischemia within candidate populations. However, a comprehensive assessment of the factors influencing frequency of myocardial ischemia within contemporary populations referred for stress testing has not been performed. METHODS We assessed 19,690 patients undergoing nuclear stress testing from a multicenter registry. The chi-square test was used to assess the relative importance of features for predicting myocardial ischemia. RESULTS In the overall cohort, LVEF, male gender, and rest total perfusion deficit (TPD) were the top three predictors of ischemia, followed by CAD status, age, typical angina, and CAD risk factors. Myocardial ischemia was observed in 13.6 % of patients with LVEF > 55 %, in 26.2 % of patients with LVEF 45 %-54 %, and in 48.3% among patients with LVEF < 45 % (P < 0.001). A similar pattern was noted for rest TPD (P < 0.001). Men had a threefold higher frequency of ischemia versus women (25.8 % vs. 8.4%, P < 0.001). Although the relative ranking of ischemia predictors varied among centers, LVEF and/or rest TPD were among the two most potent predictors of myocardial ischemia within each center. CONCLUSION The prevalence of myocardial ischemia varied markedly according to clinical and imaging characteristics. LVEF and rest TPD are robust predictors of myocardial ischemia.
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Affiliation(s)
- Donghee Han
- Department of Imaging (Division of Nuclear Medicine), Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Alan Rozanski
- Department of Cardiology, Mount Sinai Morningside Hospital, Mount Sinai Heart, and the Icahn School of Medicine at Mount Sinai, 1111 Amsterdam Avenue, New York, NY, 10025, USA.
| | - Robert J H Miller
- Department of Cardiac Sciences, University of Calgary, Calgary, AB, Canada
| | - Tali Sharir
- Department of Nuclear Cardiology, Assuta Medical Centers, Tel Aviv, Israel
- Ben Gurion University of the Negev, Beer Sheba, Israel
| | - Andrew J Einstein
- Division of Cardiology, Departments of Medicine and Radiology, Columbia University Irving Medical Center and New York-Presbyterian Hospital, New York, NY, USA
| | - Mathews B Fish
- Oregon Heart and Vascular Institute, Sacred Heart Medical Center, Springfield, OR, USA
| | - Terrence D Ruddy
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, ON, Canada
| | - Philipp A Kaufmann
- Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Zurich, Switzerland
| | - Albert J Sinusas
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Edward J Miller
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | | | - Sharmila Dorbala
- Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Brigham and Women's Hospital, Boston, MA, USA
| | - Marcelo Di Carli
- Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Brigham and Women's Hospital, Boston, MA, USA
| | - Joanna X Liang
- Department of Imaging (Division of Nuclear Medicine), Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Damini Dey
- Department of Imaging (Division of Nuclear Medicine), Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Daniel S Berman
- Department of Imaging (Division of Nuclear Medicine), Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Piotr J Slomka
- Department of Imaging (Division of Nuclear Medicine), Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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