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Coronary Artery Disease: HELP
Articles by Heidi Gransar
Based on 96 articles published since 2010
(Why 96 articles?)
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Between 2010 and 2020, Heidi Gransar wrote the following 96 articles about Coronary Artery Disease.
 
+ Citations + Abstracts
Pages: 1 · 2 · 3 · 4
1 Review Coronary artery calcium and primary prevention risk assessment: what is the evidence? An updated meta-analysis on patient and physician behavior. 2012

Whelton, Seamus P / Nasir, Khurram / Blaha, Michael J / Gransar, Heidi / Metkus, Thomas S / Coresh, Josef / Berman, Daniel S / Blumenthal, Roger S. ·Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21287, USA. swhelton@jhsph.edu ·Circ Cardiovasc Qual Outcomes · Pubmed #22811506.

ABSTRACT: -- No abstract --

2 Clinical Trial Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study. 2018

Dey, Damini / Gaur, Sara / Ovrehus, Kristian A / Slomka, Piotr J / Betancur, Julian / Goeller, Markus / Hell, Michaela M / Gransar, Heidi / Berman, Daniel S / Achenbach, Stephan / Botker, Hans Erik / Jensen, Jesper Moller / Lassen, Jens Flensted / Norgaard, Bjarne Linde. ·Biomedical Imaging Research Institute, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Taper building, A238, 8700 Beverly Blvd, Los Angeles, 90048, USA. Damini.Dey@cshs.org. · Department of Cardiology, Aarhus University Hospital, Aarhus, Denmark. · Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA. · Biomedical Imaging Research Institute, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Taper building, A238, 8700 Beverly Blvd, Los Angeles, 90048, USA. · Department of Cardiology, Friedrich-Alexander Universitat Erlangen-Nurnberg, Erlangen, Germany. ·Eur Radiol · Pubmed #29352380.

ABSTRACT: OBJECTIVES: We aimed to investigate if lesion-specific ischaemia by invasive fractional flow reserve (FFR) can be predicted by an integrated machine learning (ML) ischaemia risk score from quantitative plaque measures from coronary computed tomography angiography (CTA). METHODS: In a multicentre trial of 254 patients, CTA and invasive coronary angiography were performed, with FFR in 484 vessels. CTA data sets were analysed by semi-automated software to quantify stenosis and non-calcified (NCP), low-density NCP (LD-NCP, < 30 HU), calcified and total plaque volumes, contrast density difference (CDD, maximum difference in luminal attenuation per unit area) and plaque length. ML integration included automated feature selection and model building from quantitative CTA with a boosted ensemble algorithm, and tenfold stratified cross-validation. RESULTS: Eighty patients had ischaemia by FFR (FFR ≤ 0.80) in 100 vessels. Information gain for predicting ischaemia was highest for CDD (0.172), followed by LD-NCP (0.125), NCP (0.097), and total plaque volumes (0.092). ML exhibited higher area-under-the-curve (0.84) than individual CTA measures, including stenosis (0.76), LD-NCP volume (0.77), total plaque volume (0.74) and pre-test likelihood of coronary artery disease (CAD) (0.63); p < 0.006. CONCLUSIONS: Integrated ML ischaemia risk score improved the prediction of lesion-specific ischaemia by invasive FFR, over stenosis, plaque measures and pre-test likelihood of CAD. KEY POINTS: • Integrated ischaemia risk score improved prediction of ischaemia over quantitative plaque measures • Integrated ischaemia risk score showed higher prediction of ischaemia than standard approach • Contrast density difference had the highest information gain to identify lesion-specific ischaemia.

3 Clinical Trial Relationship Between Endothelial Wall Shear Stress and High-Risk Atherosclerotic Plaque Characteristics for Identification of Coronary Lesions That Cause Ischemia: A Direct Comparison With Fractional Flow Reserve. 2016

Han, Donghee / Starikov, Anna / Ó Hartaigh, Bríain / Gransar, Heidi / Kolli, Kranthi K / Lee, Ji Hyun / Rizvi, Asim / Baskaran, Lohendran / Schulman-Marcus, Joshua / Lin, Fay Y / Min, James K. ·Department of Radiology, Dalio Institute of Cardiovascular Imaging, NewYork-Presbyterian Hospital and the Weill Cornell Medicine, New York, NY. · Department of Imaging, Cedars Sinai Medical Center, Los Angeles, CA. · Department of Radiology, Dalio Institute of Cardiovascular Imaging, NewYork-Presbyterian Hospital and the Weill Cornell Medicine, New York, NY jkm2001@med.cornell.edu. ·J Am Heart Assoc · Pubmed #27993831.

ABSTRACT: BACKGROUND: Wall shear stress (WSS) is an established predictor of coronary atherosclerosis progression. Prior studies have reported that high WSS has been associated with high-risk atherosclerotic plaque characteristics (APCs). WSS and APCs are quantifiable by coronary computed tomography angiography, but the relationship of coronary lesion ischemia-evaluated by fractional flow reserve-to WSS and APCs has not been examined. METHODS AND RESULTS: WSS measures were obtained from 100 evaluable patients who underwent coronary computed tomography angiography and invasive coronary angiography with fractional flow reserve. Patients were categorized according to tertiles of mean WSS values defined as low, intermediate, and high. Coronary ischemia was defined as fractional flow reserve ≤0.80. Stenosis severity was determined by minimal luminal diameter. APCs were defined as positive remodeling, low attenuation plaque, and spotty calcification. The likelihood of having positive remodeling and low-attenuation plaque was greater in the high WSS group compared with the low WSS group after adjusting for minimal luminal diameter (odds ratio for positive remodeling: 2.54, 95% CI 1.12-5.77; odds ratio for low-attenuation plaque: 2.68, 95% CI 1.02-7.06; both P<0.05). No significant relationship was observed between WSS and fractional flow reserve when adjusting for either minimal luminal diameter or APCs. WSS displayed no incremental benefit above stenosis severity and APCs for detecting lesions that caused ischemia (area under the curve for stenosis and APCs: 0.87, 95% CI 0.81-0.93; area under the curve for stenosis, APCs, and WSS: 0.88, 95% CI 0.82-0.93; P=0.30 for difference). CONCLUSIONS: High WSS is associated with APCs independent of stenosis severity. WSS provided no added value beyond stenosis severity and APCs for detecting lesions with significant ischemia.

4 Clinical Trial Additive diagnostic value of atherosclerotic plaque characteristics to non-invasive FFR for identification of lesions causing ischaemia: results from a prospective international multicentre trial. 2016

Nakazato, Ryo / Park, Hyung-Bok / Gransar, Heidi / Leipsic, Jonathon A / Budoff, Matthew J / Mancini, G B John / Erglis, Andrejs / Berman, Daniel S / Min, James K. ·Cardiovascular Center, St. Luke's International Hospital, Tokyo, Japan. ·EuroIntervention · Pubmed #26348673.

ABSTRACT: AIMS: We evaluated the association between atherosclerotic plaque characteristics (APCs) by CT -including positive remodelling (PR), low attenuation plaque (LAP) and spotty calcification (SC)- and lesion ischaemia by fractional flow reserve (FFR). METHODS AND RESULTS: Two hundred and fifty-two patients (17 centres, five countries) underwent CT, FFR derived from CT (FFRCT) with invasive FFR performed for 407 coronary lesions. FFR ≤0.8 was indicative of lesion-specific ischaemia. CT diameter ≥50% stenosis was considered obstructive. APCs by CT were defined as: (1) PR, lesion diameter/reference diameter >1.10; (2) LAP, any voxel <30 HU; and (3) SC, nodular calcified plaque <3 mm. Odds ratios (OR) and area under the ROC curve (AUC) of APCs for lesion-specific ischaemia were analysed. PR, LAP and SC were associated with ischaemia, with a three to fivefold higher prevalence than in non-ischaemic lesions. Among individual APC, PR (OR 4.7, p<0.001), but not SC or LAP, was strongly associated with lesion-specific ischaemia and provided incremental prediction for lesion-specific ischaemia over CT stenosis plus FFRCT (AUC 0.87 vs. 0.83, p=0.002). CONCLUSIONS: APCs' features -especially PR- by CT improve identification and reclassification of coronary lesions which cause ischaemia over CT stenosis and FFRCT.

5 Article Machine Learning Framework to Identify Individuals at Risk of Rapid Progression of Coronary Atherosclerosis: From the PARADIGM Registry. 2020

Han, Donghee / Kolli, Kranthi K / Al'Aref, Subhi J / Baskaran, Lohendran / van Rosendael, Alexander R / Gransar, Heidi / Andreini, Daniele / Budoff, Matthew J / Cademartiri, Filippo / Chinnaiyan, Kavitha / Choi, Jung Hyun / Conte, Edoardo / Marques, Hugo / de Araújo Gonçalves, Pedro / Gottlieb, Ilan / Hadamitzky, Martin / Leipsic, Jonathon A / Maffei, Erica / Pontone, Gianluca / Raff, Gilbert L / Shin, Sangshoon / Kim, Yong-Jin / Lee, Byoung Kwon / Chun, Eun Ju / Sung, Ji Min / Lee, Sang-Eun / Virmani, Renu / Samady, Habib / Stone, Peter / Narula, Jagat / Berman, Daniel S / Bax, Jeroen J / Shaw, Leslee J / Lin, Fay Y / Min, James K / Chang, Hyuk-Jae. ·Division of Cardiology Severance Cardiovascular Hospital Yonsei University College of Medicine Yonsei University Health System Seoul South Korea. · Department of Radiology NewYork-Presbyterian Hospital and Weill Cornell Medicine New York NY. · Department of Imaging Cedars Sinai Medical Center Los Angeles CA. · Centro Cardiologico Monzino IRCCS Milan Italy. · Department of Medicine Los Angeles Biomedical Research Institute Torrance CA. · Cardiovascular Imaging Center SDN IRCCS Naples Italy. · Department of Cardiology William Beaumont Hospital Royal Oak MI. · Pusan National University Hospital Busan South Korea. · UNICA Unit of Cardiovascular Imaging Hospital da Luz Lisboa Portugal. · Department of Radiology Casa de Saude São Jose Rio de Janeiro Brazil. · Department of Radiology and Nuclear Medicine German Heart Center Munich Germany. · Department of Medicine and Radiology University of British Columbia Vancouver BC Canada. · Department of Radiology Area Vasta 1/ASUR Urbino Italy. · Ewha Womans University Seoul Hospital Seoul South Korea. · Seoul National University Hospital Seoul South Korea. · Gangnam Severance Hospital Yonsei University College of Medicine Seoul Korea. · Seoul National University Bundang Hospital Sungnam South Korea. · Department of Pathology CVPath Institute Gaithersburg MD. · Division of Cardiology Emory University School of Medicine Atlanta GA. · Cardiovascular Division Brigham and Women's Hospital Harvard Medical School Boston MA. · 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. · Department of Imaging and Medicine Cedars Sinai Medical Center Los Angeles CA. · Department of Cardiology Leiden University Medical Center Leiden the Netherlands. ·J Am Heart Assoc · Pubmed #32089046.

ABSTRACT: Background Rapid coronary plaque progression (RPP) is associated with incident cardiovascular events. To date, no method exists for the identification of individuals at risk of RPP at a single point in time. This study integrated coronary computed tomography angiography-determined qualitative and quantitative plaque features within a machine learning (ML) framework to determine its performance for predicting RPP. Methods and Results Qualitative and quantitative coronary computed tomography angiography plaque characterization was performed in 1083 patients who underwent serial coronary computed tomography angiography from the PARADIGM (Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging) registry. RPP was defined as an annual progression of percentage atheroma volume ≥1.0%. We employed the following ML models: model 1, clinical variables; model 2, model 1 plus qualitative plaque features; model 3, model 2 plus quantitative plaque features. ML models were compared with the atherosclerotic cardiovascular disease risk score, Duke coronary artery disease score, and a logistic regression statistical model. 224 patients (21%) were identified as RPP. Feature selection in ML identifies that quantitative computed tomography variables were higher-ranking features, followed by qualitative computed tomography variables and clinical/laboratory variables. ML model 3 exhibited the highest discriminatory performance to identify individuals who would experience RPP when compared with atherosclerotic cardiovascular disease risk score, the other ML models, and the statistical model (area under the receiver operating characteristic curve in ML model 3, 0.83 [95% CI 0.78-0.89], versus atherosclerotic cardiovascular disease risk score, 0.60 [0.52-0.67]; Duke coronary artery disease score, 0.74 [0.68-0.79]; ML model 1, 0.62 [0.55-0.69]; ML model 2, 0.73 [0.67-0.80]; all

6 Article Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry. 2020

Al'Aref, Subhi J / Maliakal, Gabriel / Singh, Gurpreet / van Rosendael, Alexander R / Ma, Xiaoyue / Xu, Zhuoran / Alawamlh, Omar Al Hussein / Lee, Benjamin / Pandey, Mohit / Achenbach, Stephan / Al-Mallah, Mouaz H / Andreini, Daniele / Bax, Jeroen J / Berman, Daniel S / Budoff, Matthew J / Cademartiri, Filippo / Callister, Tracy Q / Chang, Hyuk-Jae / Chinnaiyan, Kavitha / Chow, Benjamin J W / Cury, Ricardo C / DeLago, Augustin / Feuchtner, Gudrun / Hadamitzky, Martin / Hausleiter, Joerg / Kaufmann, Philipp A / Kim, Yong-Jin / Leipsic, Jonathon A / Maffei, Erica / Marques, Hugo / Gonçalves, Pedro de Araújo / Pontone, Gianluca / Raff, Gilbert L / Rubinshtein, Ronen / Villines, Todd C / Gransar, Heidi / Lu, Yao / Jones, Erica C / Peña, Jessica M / Lin, Fay Y / Min, James K / Shaw, Leslee J. ·Dalio Institute of Cardiovascular Imaging, Weill Cornell Medicine and NewYork-Presbyterian Hospital, New York, NY, USA. · Department of Healthcare Policy and Research, New York-Presbyterian Hospital and the Weill Cornell Medical College, New York, NY, USA. · Department of Cardiology, Friedrich-Alexander-University Erlangen-Nuremburg, Germany. · Houston Methodist DeBakey Heart & Vascular Center, Houston Methodist Hospital, TX, USA. · Centro Cardiologico Monzino, IRCCS Milan, Italy. · Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands. · Department of Imaging and Medicine, Cedars Sinai Medical Center, Los Angeles, CA, USA. · Department of Medicine, Los Angeles Biomedical Research Institute, Torrance, CA, USA. · Cardiovascular Imaging Center, SDN IRCCS, Naples, Italy. · Tennessee Heart and Vascular Institute, Hendersonville, TN, USA. · Division of Cardiology, Severance Cardiovascular Hospital and Severance Biomedical Science Institute, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea. · Department of Cardiology, William Beaumont Hospital, Royal Oak, MI, USA. · Department of Medicine and Radiology, University of Ottawa, ON, Canada. · Department of Radiology, Miami Cardiac and Vascular Institute, Miami, FL, USA. · Capitol Cardiology Associates, Albany, NY, USA. · Department of Radiology, Medical University of Innsbruck, Innsbruck, Austria. · Department of Radiology and Nuclear Medicine, German Heart Center Munich, Munich, Germany. · Medizinische Klinik I der Ludwig-Maximilians-Universität München, Munich, Germany. · Department of Nuclear Medicine, University Hospital, Zurich, Switzerland and University of Zurich, Switzerland. · Seoul National University Hospital, Seoul, South Korea. · Department of Medicine and Radiology, University of British Columbia, Vancouver, BC, Canada. · Department of Radiology, Area Vasta 1/ASUR Marche, Urbino, Italy. · UNICA, Unit of Cardiovascular Imaging, Hospital da Luz, Lisboa, Portugal. · Department of Cardiology at the Lady Davis Carmel Medical Center, The Ruth and Bruce Rappaport School of Medicine, Technion-Israel Institute of Technology, Haifa, Israel. · Division of Cardiovascular Medicine, Department of Medicine, University of Virginia Health System, Charlottesville, VA, USA. ·Eur Heart J · Pubmed #31513271.

ABSTRACT: AIMS: Symptom-based pretest probability scores that estimate the likelihood of obstructive coronary artery disease (CAD) in stable chest pain have moderate accuracy. We sought to develop a machine learning (ML) model, utilizing clinical factors and the coronary artery calcium score (CACS), to predict the presence of obstructive CAD on coronary computed tomography angiography (CCTA). METHODS AND RESULTS: The study screened 35 281 participants enrolled in the CONFIRM registry, who underwent ≥64 detector row CCTA evaluation because of either suspected or previously established CAD. A boosted ensemble algorithm (XGBoost) was used, with data split into a training set (80%) on which 10-fold cross-validation was done and a test set (20%). Performance was assessed of the (1) ML model (using 25 clinical and demographic features), (2) ML + CACS, (3) CAD consortium clinical score, (4) CAD consortium clinical score + CACS, and (5) updated Diamond-Forrester (UDF) score. The study population comprised of 13 054 patients, of whom 2380 (18.2%) had obstructive CAD (≥50% stenosis). Machine learning with CACS produced the best performance [area under the curve (AUC) of 0.881] compared with ML alone (AUC of 0.773), CAD consortium clinical score (AUC of 0.734), and with CACS (AUC of 0.866) and UDF (AUC of 0.682), P < 0.05 for all comparisons. CACS, age, and gender were the highest ranking features. CONCLUSION: A ML model incorporating clinical features in addition to CACS can accurately estimate the pretest likelihood of obstructive CAD on CCTA. In clinical practice, the utilization of such an approach could improve risk stratification and help guide downstream management.

7 Article Risk Reclassification With Coronary Computed Tomography Angiography-Visualized Nonobstructive Coronary Artery Disease According to 2018 American College of Cardiology/American Heart Association Cholesterol Guidelines (from the Coronary Computed Tomography Angiography Evaluation for Clinical Outcomes : An International Multicenter Registry [CONFIRM]). 2019

Han, Donghee / Beecy, Ashley / Anchouche, Khalil / Gransar, Heidi / Dunham, Patricia C / Lee, Ji-Hyun / Achenbach, Stephan / Al-Mallah, Mouaz H / Andreini, Daniele / Berman, Daniel S / Bax, Jeroen J / Budoff, Matthew J / Cademartiri, Filippo / Callister, Tracy Q / Chang, Hyuk-Jae / Chinnaiyan, Kavitha / Chow, Benjamin J W / Cury, Ricardo C / DeLago, Augustin / Feuchtner, Gudrun / Hadamitzky, Martin / Hausleiter, Joerg / Kaufmann, Philipp A / Kim, Yong-Jin / Leipsic, Jonathon A / Maffei, Erica / Marques, Hugo / de Araújo Gonçalves, Pedro / Pontone, Gianluca / Raff, Gilbert L / Rubinshtein, Ronen / Villines, Todd C / Lu, Yao / Peña, Jessica M / Shaw, Leslee J / Min, James K / Lin, Fay Y. ·Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, New York; Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University Health System, Seoul, Republic of Korea; Department of Imaging, Cedars Sinai Medical Center, Los Angeles, California. · Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, New York. · Department of Imaging, Cedars Sinai Medical Center, Los Angeles, California. · Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, New York; Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University Health System, Seoul, Republic of Korea; Department of Cardiology, Myongji Hospital, Goyang-si, South Korea. · Department of Cardiology, Friedrich-Alexander-University Erlangen-Nuremberg, Germany. · Houston Methodist DeBakey Heart & Vascular Center, Houston Methodist Hospital, Houston, Texas. · Centro Cardiologico Monzino, IRCCS Milan, Italy. · Department of Imaging and Medicine, Cedars Sinai Medical Center, Los Angeles, California. · Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands. · Department of Medicine, Los Angeles Biomedical Research Institute, Torrance, California. · Cardiovascular Imaging Center, SDN IRCCS, Naples, Italy. · Tennessee Heart and Vascular Institute, Hendersonville, Tennessee. · Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University Health System, Seoul, Republic of Korea. · Department of Cardiology, William Beaumont Hospital, Royal Oak, Michigan. · Department of Medicine and Radiology, University of Ottawa, Ontario, Canada. · Department of Radiology, Miami Cardiac and Vascular Institute, Miami, Florida. · Capitol Cardiology Associates, Albany, New York. · Department of Radiology, Medical University of Innsbruck, Innsbruck, Austria. · Department of Radiology and Nuclear Medicine, German Heart Center Munich, Munich, Germany. · Medizinische Klinik I der Ludwig-Maximilians-Universität München, Munich, Germany. · Department of Nuclear Medicine, University Hospital, Zurich, Switzerland and University of Zurich, Switzerland. · Department of Internal Medicine, Seoul National University College of Medicine, Cardiovascular Center, Seoul National University Hospital, Seoul, South Korea. · Department of Medicine and Radiology, University of British Columbia, Vancouver, British Columbia, Canada. · Department of Radiology, Area Vasta 1/ASUR Marche, Urbino, Italy. · UNICA, Unit of Cardiovascular Imaging, Hospital da Luz, Lisboa, Portugal. · Department of Cardiology at the Lady Davis Carmel Medical Center, The Ruth and Bruce Rappaport School of Medicine, Technion-Israel Institute of Technology, Haifa, Israel. · Department of Medicine, University of Virginia Health System, Charlottesville, Virginia. · Department of Healthcare Policy and Research, New York-Presbyterian Hospital and the Weill Cornell Medical College, New York, New York. · Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, New York. Electronic address: fal9003@med.cornell.edu. ·Am J Cardiol · Pubmed #31547994.

ABSTRACT: The 2018 American College of Cardiology (ACC)/American Heart Association (AHA) cholesterol management guideline recommends risk enhancers in the borderline-risk and statin recommended/intermediate-risk groups. We determined the risk reclassification by the presence and severity of coronary computed tomography angiography (CCTA)-visualized coronary artery disease (CAD) according to statin eligibility groups. Of 35,281 individuals who underwent CCTA, 1,303 asymptomatic patients (age 59, 65% male) were identified. Patients were categorized as low risk, borderline risk, statin recommended/intermediate risk or statin recommended/high risk according to the guideline. CCTA-visualized CAD was categorized as no CAD, nonobstructive, or obstructive. Major adverse cardiovascular events (MACE) were defined as a composite outcome of all-cause mortality, nonfatal myocardial infarction, and late coronary revascularization (>90 days). We tested a reclassification wherein no CAD reclassifies downward, and the presence of any CAD reclassifies upward. During a median follow-up of 2.9 years, 93 MACE events (7.1%) were observed. Among the borderline-risk and statin-recommended/intermediate-risk groups eligible for risk enhancers, the presence or absence of any CCTA-visualized CAD led to a net increase of 2.3% of cases and 22.4% of controls correctly classified (net reclassification index [NRI] 0.27, 95% CI 0.13 to 0.41, p = 0.0002). The NRI was not significant among low- or statin-recommended/high-risk patients (all p >0.05). The presence or absence of CCTA-visualized CAD, including both obstructive and nonobstructive CAD, significantly improves reclassification in patients eligible for risk enhancers in 2018 ACC/AHA guidelines. Patients in low- and high-risk groups derive no significant improvement in risk reclassification from CCTA.

8 Article Upper reference limits of transient ischemic dilation ratio for different protocols on new-generation cadmium zinc telluride cameras: A report from REFINE SPECT registry. 2019

Hu, Lien-Hsin / Sharir, Tali / Miller, Robert J H / Einstein, Andrew J / Fish, Mathews B / Ruddy, Terrence D / Dorbala, Sharmila / Di Carli, Marcelo / Kaufmann, Philipp A / Sinusas, Albert J / Miller, Edward J / Bateman, Timothy M / Betancur, Julian / Germano, Guido / Liang, Joanna X / Commandeur, Frederic / Azadani, Peyman N / Gransar, Heidi / Otaki, Yuka / Tamarappoo, Balaji K / Dey, Damini / Berman, Daniel S / Slomka, Piotr J. ·Department of Imaging (Division of Nuclear Medicine), Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. A047N, Los Angeles, CA, 90048, USA. · Department of Nuclear Medicine, Taipei Veterans General Hospital, Taipei, Taiwan. · Department of Nuclear Cardiology, Assuta Medical Center, Tel Aviv, Israel. · Ben Gurion University of the Negev, Beer Sheba, Israel. · Division of Cardiology, Department of Medicine, and Department of Radiology, Columbia University Irving Medical Center and New York-Presbyterian Hospital, New York, NY, USA. · Oregon Heart and Vascular Institute, Sacred Heart Medical Center, Springfield, OR, USA. · Division of Cardiology, University of Ottawa Heart Institute, Ottawa, ON, Canada. · Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA. · Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Zurich, Switzerland. · Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA. · Cardiovascular Imaging Technologies LLC, Kansas City, MO, USA. · Department of Imaging (Division of Nuclear Medicine), Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. A047N, Los Angeles, CA, 90048, USA. slomkap@cshs.org. ·J Nucl Cardiol · Pubmed #31087268.

ABSTRACT: BACKGROUND: Upper reference limits for transient ischemic dilation (TID) have not been rigorously established for cadmium-zinc-telluride (CZT) camera systems. We aimed to derive TID limits for common myocardial perfusion imaging protocols utilizing a large, multicenter registry (REFINE SPECT). METHODS: One thousand six hundred and seventy-two patients with low likelihood of coronary artery disease with normal perfusion findings were identified. Images were processed with Quantitative Perfusion SPECT software (Cedars-Sinai Medical Center, Los Angeles, CA). Non-attenuation-corrected, camera-, radiotracer-, and stress protocol-specific TID limits in supine position were derived from 97.5th percentile and mean + 2 standard deviations (SD). Reference limits were compared for different solid-state cameras (D-SPECT vs. Discovery), radiotracers (technetium-99m-sestamibi vs. tetrofosmin), different types of stress (exercise vs. four different vasodilator-based protocols), and different vasodilator-based protocols. RESULTS: TID measurements did not follow Gaussian distribution in six out of eight subgroups. TID limits ranged from 1.18 to 1.52 (97.5th percentile) and 1.18 to 1.39 (mean + 2SD). No difference was noted between D-SPECT and Discovery cameras (P = 0.71) while differences between exercise and vasodilator-based protocols (adenosine, regadenoson, or regadenoson-walk) were noted (all P < 0.05). CONCLUSIONS: We used a multicenter registry to establish camera-, radiotracer-, and protocol-specific upper reference limits of TID for supine position on CZT camera systems. Reference limits did not differ between D-SPECT and Discovery camera.

9 Article Standardized volumetric plaque quantification and characterization from coronary CT angiography: a head-to-head comparison with invasive intravascular ultrasound. 2019

Matsumoto, Hidenari / Watanabe, Satoshi / Kyo, Eisho / Tsuji, Takafumi / Ando, Yosuke / Otaki, Yuka / Cadet, Sebastien / Gransar, Heidi / Berman, Daniel S / Slomka, Piotr / Tamarappoo, Balaji K / Dey, Damini. ·Department of Imaging and Medicine, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Los Angeles, CA, 90048, USA. matsumoto_hidenari@yahoo.co.jp. · Department of Cardiology, Kusatsu Heart Center, Kusatsu, Shiga, Japan. · Department of Imaging and Medicine, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Los Angeles, CA, 90048, USA. · The Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA. · Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA. ·Eur Radiol · Pubmed #31028446.

ABSTRACT: OBJECTIVES: We sought to evaluate the accuracy of standardized total plaque volume (TPV) measurement and low-density non-calcified plaque (LDNCP) assessment from coronary CT angiography (CTA) in comparison with intravascular ultrasound (IVUS). METHODS: We analyzed 118 plaques without extensive calcifications from 77 consecutive patients who underwent CTA prior to IVUS. CTA TPV was measured with semi-automated software comparing both scan-specific (automatically derived from scan) and fixed attenuation thresholds. From CTA, %LDNCP was calculated voxels below multiple LDNCP thresholds (30, 45, 60, 75, and 90 Hounsfield units [HU]) within the plaque. On IVUS, the lipid-rich component was identified by echo attenuation, and its size was measured using attenuation score (summed score ∕ analysis length) based on attenuation arc (1 = < 90°; 2 = 90-180°; 3 = 180-270°; 4 = 270-360°) every 1 mm. RESULTS: TPV was highly correlated between CTA using scan-specific thresholds and IVUS (r = 0.943, p < 0.001), with no significant difference (2.6 mm CONCLUSIONS: Standardized noninvasive plaque quantification from CTA using scan-specific thresholds correlates highly with IVUS. Use of a < 45-HU threshold for LDNCP quantification improves lipid-rich plaque assessment from CTA. KEY POINTS: • Standardized scan-specific threshold-based plaque quantification from coronary CT angiography provides an accurate total plaque volume measurement compared with intravascular ultrasound. • Attenuation histogram-based low-density non-calcified plaque quantification can improve lipid-rich plaque assessment from coronary CT angiography.

10 Article The Predictive Value of Coronary Artery Calcium Scoring for Major Adverse Cardiac Events According to Renal Function (from the Coronary Computed Tomography Angiography Evaluation for Clinical Outcomes: An International Multicenter [CONFIRM] Registry). 2019

Lee, Ji Hyun / Rizvi, Asim / Hartaigh, Bríain Ó / Han, Donghee / Park, Mahn Won / Roudsari, Hadi Mirhedayati / Stuijfzand, Wijnand J / Gransar, Heidi / Lu, Yao / Callister, Tracy Q / Berman, Daniel S / DeLago, Augustin / Hadamitzky, Martin / Hausleiter, Joerg / Al-Mallah, Mouaz H / Budoff, Matthew J / Kaufmann, Philipp A / Raff, Gilbert L / Chinnaiyan, Kavitha / Cademartiri, Filippo / Maffei, Erica / Villines, Todd C / Kim, Yong-Jin / Leipsic, Jonathon / Feuchtner, Gudrun / Pontone, Gianluca / Andreini, Daniele / Marques, Hugo / de Araújo Gonçalves, Pedro / Rubinshtein, Ronen / Achenbach, Stephan / Shaw, Leslee J / Chow, Benjamin J W / Cury, Ricardo C / Bax, Jeroen J / Chang, Hyuk-Jae / Jones, Erica C / Lin, Fay Y / Min, James K / Peña, Jessica M. ·Dalio Institute of Cardiovascular Imaging, Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, New York; Division of Cardiology, Department of Internal Medicine, Myongji Hospital, Hanyang University Medical Center, Goyang-si, South Korea; Division of Cardiology, Severance Cardiovascular Hospital and Severance Biomedical Science Institute, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea. · Dalio Institute of Cardiovascular Imaging, Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, New York; Department of Radiology, Mayo Clinic, Rochester, Minnesota. · Dalio Institute of Cardiovascular Imaging, Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, New York. · Dalio Institute of Cardiovascular Imaging, Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, New York; Division of Cardiology, Severance Cardiovascular Hospital and Severance Biomedical Science Institute, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea. · Department of Imaging, Cedars Sinai Medical Center, Los Angeles, California. · Department of Healthcare Policy and Research, New York-Presbyterian Hospital and the Weill Cornell Medical College, New York, New York. · Tennessee Heart and Vascular Institute, Hendersonville, Tennessee. · Department of Imaging and Medicine, Cedars Sinai Medical Center, Los Angeles, California. · Capitol Cardiology Associates, Albany, New York. · Department of Radiology and Nuclear Medicine, German Heart Center, Munich, Germany. · Medizinische Klinik I der Ludwig-Maximilians-Universität München, Munich, Germany. · Houston Methodist DeBakey Heart & Vascular Center, Houston Methodist Hospital, Houston, Texas. · Department of Medicine, Los Angeles Biomedical Research Institute, Torrance, California. · Department of Nuclear Medicine, University Hospital and University of Zurich, Zurich, Switzerland. · Department of Cardiology, William Beaumont Hospital, Royal Oak, Michigan. · Cardiovascular Imaging Center, Department of Radiology, SDN IRCCS, Naples, Italy. · Department of Radiology, Area Vasta 1/ASUR Marche, Urbino, Italy. · Department of Cardiology, Walter Reed National Military Medical Center, Bethesda, Maryland. · Seoul National University Hospital, Seoul, South Korea. · Department of Medicine and Radiology, University of British Columbia, Vancouver, British Columbia, Canada. · Department of Radiology, Medical University of Innsbruck, Innsbruck, Austria. · Centro Cardiologico Monzino, IRCCS Milan, Milan, Italy. · UNICA, Unit of Cardiovascular Imaging, Hospital da Luz, Lisboa, Portugal. · Department of Cardiology at the Lady Davis Carmel Medical Center, The Ruth and Bruce Rappaport School of Medicine, Technion-Israel Institute of Technology, Haifa, Israel. · Department of Cardiology, Friedrich-Alexander-University Erlangen-Nuremburg, Erlangen, Germany. · Department of Medicine and Radiology, University of Ottawa, Ottawa, Ontario, Canada. · Department of Radiology, Miami Cardiac and Vascular Institute, Miami, Florida, USA. · Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands. · Division of Cardiology, Severance Cardiovascular Hospital and Severance Biomedical Science Institute, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea. · Dalio Institute of Cardiovascular Imaging, Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, New York. Electronic address: jmp2003@med.cornell.edu. ·Am J Cardiol · Pubmed #30850210.

ABSTRACT: The prognostic performance of coronary artery calcium score (CACS) for predicting adverse outcomes in patients with decreased renal function remains unclear. We aimed to examine whether CACS improves risk stratification by demonstrating incremental value beyond a traditional risk score according to renal function status. 9,563 individuals without known coronary artery disease were enrolled. Estimated glomerular filtration rate (eGFR, ml/min/1.73 m

11 Article A cross-sectional survey of coronary plaque composition in individuals on non-statin lipid lowering drug therapies and undergoing coronary computed tomography angiography. 2019

Al'Aref, Subhi J / Su, Amanda / Gransar, Heidi / van Rosendael, Alexander R / Rizvi, Asim / Berman, Daniel S / Callister, Tracy Q / DeLago, Augustin / Hadamitzky, Martin / Hausleiter, Joerg / Al-Mallah, Mouaz H / Budoff, Matthew J / Kaufmann, Philipp A / Raff, Gilbert L / Chinnaiyan, Kavitha / Cademartiri, Filippo / Maffei, Erica / Villines, Todd C / Kim, Yong-Jin / Leipsic, Jonathon / Feuchtner, Gudrun / Pontone, Gianluca / Andreini, Daniele / Marques, Hugo / de Araújo Gonçalves, Pedro / Rubinshtein, Ronen / Achenbach, Stephan / Chang, Hyuk-Jae / Chow, Benjamin J W / Cury, Ricardo / Lu, Yao / Bax, Jeroen J / Jones, Erica C / Peña, Jessica M / Shaw, Leslee J / Min, James K / Lin, Fay Y. ·Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA. · Department of Imaging and Medicine, Cedars Sinai Medical Center, Los Angeles, CA, USA. · Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA; Department of Radiology, Mayo Clinic, Rochester, MN, USA. · Tennessee Heart and Vascular Institute, Hendersonville, TN, USA. · Capitol Cardiology Associates, Albany, NY, USA. · Department of Radiology and Nuclear Medicine, German Heart Center Munich, Munich, Germany. · Medizinische Klinik I der Ludwig-Maximilians-Universität München, Munich, Germany. · King Saud bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, King AbdulAziz Cardiac Center, Ministry of National Guard, Health Affairs, Riyadh, Saudi Arabia. · Department of Medicine, Los Angeles Biomedical Research Institute, Torrance, CA, USA. · Department of Nuclear Medicine, University Hospital, Zurich, Switzerland and University of Zurich, Switzerland. · Department of Cardiology, William Beaumont Hospital, Royal Oak, MI, USA. · Cardiovascular Imaging Center, SDN IRCCS, Naples, Italy. · Department of Radiology, Area Vasta 1/ASUR Marche, Urbino, Italy. · Cardiology Service, Walter Reed National Military Center, Bethesda, MD, USA. · Seoul National University Hospital, Seoul, South Korea. · Department of Medicine and Radiology, University of British Columbia, Vancouver, BC, Canada. · Department of Radiology, Medical University of Innsbruck, Innsbruck, Austria. · Centro Cardiologico Monzino, IRCCS Milan, Italy. · UNICA, Unit of Cardiovascular Imaging, Hospital da Luz, Lisboa, Portugal. · Department of Cardiology at the Lady Davis Carmel Medical Center, The Ruth and Bruce Rappaport School of Medicine, Technion-Israel Institute of Technology, Haifa, Israel. · Department of Cardiology, Friedrich-Alexander-University Erlangen-Nuremburg, Germany. · Division of Cardiology, Severance Cardiovascular Hospital and Severance Biomedical Science Institute, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea. · Department of Medicine and Radiology, University of Ottawa, ON, Canada. · Department of Radiology, Miami Cardiac and Vascular Institute, Miami, FL, USA. · Department of Healthcare Policy and Research, New York-Presbyterian Hospital and the Weill Cornell Medical College, New York, NY, USA. · Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands. · Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA. Electronic address: fal9003@med.cornell.edu. ·J Cardiovasc Comput Tomogr · Pubmed #30745132.

ABSTRACT: INTRODUCTION: Non-statin therapy (NST) is used as second-line treatment when statin monotherapy is inadequate or poorly tolerated. OBJECTIVE: To determine the association of NST with plaque composition, alone or in combination with statins, in patients undergoing coronary computed tomography angiography (coronary CTA). METHODS: From the multicenter CONFIRM registry, we analyzed individuals who underwent coronary CTA with known lipid-lowering therapy status and without prior coronary artery disease at baseline. We created a propensity score for being on NST, followed by stepwise multivariate linear regression, adjusting for the propensity score as well as risk factors, to determine the association between NST and the number of coronary artery segments with each plaque type (non-calcified (NCP), partially calcified (PCP) or calcified (CP)) and segment stenosis score (SSS). RESULTS: Of the 27,125 subjects in CONFIRM, 4,945 met the inclusion criteria; 371 (7.5%) took NST. At baseline, patients on NST had more prevalent risk factors and were more likely to be on concomitant cardiac medications. After multivariate and propensity score adjustment, NST was not associated with plaque composition: NCP (0.07 increase, 95% CI: -0.05, 0.20; p = 0.26), PCP (0.10 increase, 95% CI: -0.10, 0.31; p = 0.33), CP (0.18 increase, 95% CI: -0.10, 0.46; p = 0.21) or SSS (0.45 increase, 95% CI: -0.02,0.93; p = 0.06). The absence of an effect of NST on plaque type was not modified by statin use (p for interaction > 0.05 for all). CONCLUSION: In this cross-sectional study, non-statin therapy was not associated with differences in plaque composition as assessed by coronary CTA.

12 Article Superior Risk Stratification With Coronary Computed Tomography Angiography Using a Comprehensive Atherosclerotic Risk Score. 2019

van Rosendael, Alexander R / Shaw, Leslee J / Xie, Joe X / Dimitriu-Leen, Aukelien C / Smit, Jeff M / Scholte, Arthur J / van Werkhoven, Jacob M / Callister, Tracy Q / DeLago, Augustin / Berman, Daniel S / Hadamitzky, Martin / Hausleiter, Jeorg / Al-Mallah, Mouaz H / Budoff, Matthew J / Kaufmann, Philipp A / Raff, Gilbert / Chinnaiyan, Kavitha / Cademartiri, Filippo / Maffei, Erica / Villines, Todd C / Kim, Yong-Jin / Feuchtner, Gudrun / Lin, Fay Y / Jones, Erica C / Pontone, Gianluca / Andreini, Daniele / Marques, Hugo / Rubinshtein, Ronen / Achenbach, Stephan / Dunning, Allison / Gomez, Millie / Hindoyan, Niree / Gransar, Heidi / Leipsic, Jonathon / Narula, Jagat / Min, James K / Bax, Jeroen J. ·Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands; Dalio Institute of Cardiovascular Imaging, New York-Presbyterian Hospital and the Weill Cornell Medical College, New York, New York. · Division of Cardiology, Emory University School of Medicine, Atlanta, Georgia. · Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands. · Tennessee Heart and Vascular Institute, Hendersonville, Tennessee. · Capitol Cardiology Associates, Albany, New York. · Department of Imaging, Cedars Sinai Medical Center, Los Angeles, California. · Department of Radiology and Nuclear Medicine, German Heart Center Munich, Munich, Germany. · King Saud bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, King AbdulAziz Cardiac Center, Ministry of National Guard, Health Affairs, Riyadh, Saudi Arabia. · Department of Medicine, Harbor UCLA Medical Center, Los Angeles, California. · University Hospital, Zurich, Switzerland. · William Beaumont Hospital, Royal Oaks, Michigan. · Cardiovascular Imaging Center, IRCCS SDN, Naples, Italy. · Department of Radiology, Area Vasta 1/ASUR Marche, Urbino, Italy. · Department of Medicine, Walter Reed National Military Medical Center, Bethesda. · Seoul National University Hospital, Seoul, South Korea. · Department of Radiology, Medical University of Innsbruck, Innsbruck, Austria. · Dalio Institute of Cardiovascular Imaging, New York-Presbyterian Hospital and the Weill Cornell Medical College, New York, New York. · Department of Clinical Sciences and Community Health, University of Milan, Centro Cardiologico Monzino, IRCCS Milan, Italy. · UNICA, Unit of Cardiovascular Imaging, Hospital da Luz, Lisboa, Portugal. · Department of Cardiology at the Lady Davis Carmel Medical Center, The Ruth and Bruce Rappaport School of Medicine, Technion-Israel Institute of Technology, Haifa, Israel. · Department of Medicine, University of Erlangen, Erlangen, Germany. · Duke Clinical Research Institute, Durham, North Carolina. · Division of Cardiology, University of British Columbia, Vancouver, British Columbia, Canada. · Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York. · Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands. Electronic address: j.j.bax@lumc.nl. ·JACC Cardiovasc Imaging · Pubmed #30660516.

ABSTRACT: OBJECTIVES: This study was designed to assess the prognostic value of a new comprehensive coronary computed tomography angiography (CTA) score compared with the stenosis severity component of the Coronary Artery Disease-Reporting and Data System (CAD-RADS). BACKGROUND: Current risk assessment with coronary CTA is mainly focused on maximal stenosis severity. Integration of plaque extent, location, and composition in a comprehensive model may improve risk stratification. METHODS: A total of 2,134 patients with suspected but without known CAD were included. The predictive value of the comprehensive CTA score (ranging from 0 to 42 and divided into 3 groups: 0 to 5, 6 to 20, and >20) was compared with the CAD-RADS combined into 3 groups (0% to 30%, 30% to 70% and ≥70% stenosis). Its predictive performance was internally and externally validated (using the 5-year follow-up dataset of the CONFIRM [Coronary CT Angiography Evaluation for Clinical Outcomes: An International Multicenter Registry], n = 1,971). RESULTS: The mean age of patients was 55 ± 13 years, mean follow-up 3.6 ± 2.8 years, and 130 events (myocardial infarction or death) occurred. The new, comprehensive CTA score showed strong and independent predictive value using the Cox proportional hazard analysis. A model including clinical variables plus comprehensive CTA score showed better discrimination of events compared with a model consisting of clinical variables plus CAD-RADS (0.768 vs. 0.742, p = 0.001). Also, the comprehensive CTA score correctly reclassified a significant proportion of patients compared with the CAD-RADS (net reclassification improvement 12.4%, p < 0.001). Good predictive accuracy was reproduced in the external validation cohort. CONCLUSIONS: The new comprehensive CTA score provides better discrimination and reclassification of events compared with the CAD-RADS score based on stenosis severity only. The score retained similar prognostic accuracy when externally validated. Anatomic risk scores can be improved with the addition of extent, location, and compositional measures of atherosclerotic plaque. (Comprehensive CTA risk score calculator is available at: http://18.224.14.19/calcApp/).

13 Article Age- and gender-adjusted percentiles for number of calcified plaques in coronary artery calcium scanning. 2019

Wang, Frances / Rozanski, Alan / Dey, Damini / Arnson, Yoav / Gransar, Heidi / Friedman, John / Hayes, Sean W / Thomson, Louise E J / Tamarappoo, Balaji / Shaw, Leslee J / Min, James K / Rumberger, John A / Budoff, Matthew J / Miedema, Michael D / Blaha, Michael J / Berman, Daniel S. ·Department of Imaging, Cedars-Sinai Medical Center, Cedars-Sinai Heart Institute, Los Angeles, CA, USA. · Division of Cardiology, Mt Sinai St. Luke's Hospital, Mount Sinai Heart and the Icahn School of Medicine at Mount Sinai, New York, NY, USA. · Dalio Institute of Cardiovascular Imaging, Weill Cornell Medicine, New York, USA. · Princeton Longevity Center, Princeton, NJ, USA. · Los Angeles BioMedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA. · Minneapolis Heart Institute and Foundation, Minneapolis, MN, USA. · Prevention of Heart Disease, Division of Cardiology, Department of Medicine, Johns Hopkins Ciccarone Center, Baltimore, MD, USA. · Department of Imaging, Cedars-Sinai Medical Center, Cedars-Sinai Heart Institute, Los Angeles, CA, USA. Electronic address: bermand@cshs.org. ·J Cardiovasc Comput Tomogr · Pubmed #30598344.

ABSTRACT: BACKGROUND: Age- and gender-adjusted percentiles of coronary artery calcium (CAC) score are commonly reported to compare a patient's coronary atherosclerosis burden to that of others of the same age and gender. The number of calcified plaques (numCP) detected on CAC scanning, a measure of plaque diffusivity, is associated with increased cardiovascular risk and, in the intermediate CAC range, adds to the CAC score in predicting mortality. This study aims to develop adjusted percentiles for numCP to provide a better context for understanding CAC scan findings. METHODS AND RESULTS: Using nonparametric modeling techniques, the distribution of numCP was analyzed in 70,320 consecutive, asymptomatic patients without prior clinically-diagnosed cardiovascular disease who were part of the Coronary Artery Calcium Consortium and supplemented by additional patients referred for clinical CAC scanning in a single center between 1998 and 2016. Nomograms for age-adjusted numCP percentiles for each gender were generated using quantile regression. The prevalence and average number of calcified coronary plaque were found to be higher in men than women. Distribution of numCP in women was found to closely mirror that of men approximately a decade younger. NumCP increased consistently across age groups in both men and women for each quantile category. CONCLUSIONS: A nomogram for age and gender-adjusted percentiles for the numCP on CAC scans has been developed in a large population of asymptomatic patients studied across multiple centers. This numCP nomogram may provide an additional tool for refining physician recommendations regarding treatment and expressing to patients how their CAC findings relate to others of similar age and gender. The numCP percentiles may also provide a meaningful way to evaluate and report the rate of progression of CAC on serial studies.

14 Article A Comparison of the Updated Diamond-Forrester, CAD Consortium, and CONFIRM History-Based Risk Scores for Predicting Obstructive Coronary Artery Disease in Patients With Stable Chest Pain: The SCOT-HEART Coronary CTA Cohort. 2019

Baskaran, Lohendran / Danad, Ibrahim / Gransar, Heidi / Ó Hartaigh, Bríain / Schulman-Marcus, Joshua / Lin, Fay Y / Peña, Jessica M / Hunter, Amanda / Newby, David E / Adamson, Philip D / Min, James K. ·Department of Radiology, New York-Presbyterian Hospital and the Weill Cornell Medical College, New York, New York; National Heart Centre, Singapore. · Department of Radiology, New York-Presbyterian Hospital and the Weill Cornell Medical College, New York, New York. · University of Edinburgh/BHF Centre for Cardiovascular Science, Edinburgh, United Kingdom. · Department of Radiology, New York-Presbyterian Hospital and the Weill Cornell Medical College, New York, New York. Electronic address: jkm2001@med.cornell.edu. ·JACC Cardiovasc Imaging · Pubmed #29680338.

ABSTRACT: OBJECTIVES: This study sought to compare the performance of history-based risk scores in predicting obstructive coronary artery disease (CAD) among patients with stable chest pain from the SCOT-HEART study. BACKGROUND: Risk scores for estimating pre-test probability of CAD are derived from referral-based populations with a high prevalence of disease. The generalizability of these scores to lower prevalence populations in the initial patient encounter for chest pain is uncertain. METHODS: We compared 3 scores among patients with suspected CAD in the coronary computed tomographic angiography (CTA) randomized arm of the SCOT-HEART study for the outcome of obstructive CAD by coronary CTA: the updated Diamond-Forrester score (UDF), CAD Consortium clinical score (CAD2), and CONFIRM risk score (CRS). We tested calibration with goodness-of-fit, discrimination with area under the receiver-operating curve (AUC), and reclassification with net reclassification improvement (NRI) to identify low-risk patients. RESULTS: In 1,738 patients (age 58 ± 10 years and 44.0% women), overall calibration was best for UDF, with underestimation by CRS and CAD2. Discrimination by AUC was highest for CAD2 at 0.79 (95% confidence interval [CI]: 0.77 to 0.81) than for UDF (0.77 [95% CI: 0.74 to 0.79]) or CRS (0.75 [95% CI: 0.73 to 0.77]) (p < 0.001 for both comparisons). Reclassification of low-risk patients at the 10% probability threshold was best for CAD2 (NRI 0.31, 95% CI: 0.27 to 0.35) followed by CRS (NRI 0.21, 95% CI: 0.17 to 0.25) compared with UDF (p < 0.001 for all comparisons), with a consistent trend at the 15% threshold. CONCLUSIONS: In this multicenter clinic-based cohort of patients with suspected CAD and uniform CAD evaluation by coronary CTA, CAD2 provided the best discrimination and classification, despite overestimation of obstructive CAD as evaluated by coronary CTA. CRS exhibited intermediate performance followed by UDF for discrimination and reclassification.

15 Article Usefulness of baseline statin therapy in non-obstructive coronary artery disease by coronary computed tomographic angiography: From the CONFIRM (COronary CT Angiography EvaluatioN For Clinical Outcomes: An InteRnational Multicenter) study. 2018

Cho, Yun-Kyeong / Nam, Chang-Wook / Koo, Bon-Kwon / Schulman-Marcus, Joshua / Hartaigh, Bríain Ó / Gransar, Heidi / Lu, Yao / Achenbach, Stephan / Al-Mallah, Mouaz / Andreini, Daniele / Bax, Jeroen J / Budoff, Matthew J / Cademartiri, Filippo / Callister, Tracy Q / Chang, Hyuk-Jae / Chinnaiyan, Kavitha / Chow, Benjamin J W / Cury, Ricardo C / Delago, Augustin / Feuchtner, Gudrun / Hadamitzky, Martin / Hausleiter, Jörg / Kaufmann, Philipp A / Kim, Yong-Jin / Leipsic, Jonathon / Maffei, Erica / Marques, Hugo / Pontone, Gianluca / Raff, Gilbert L / Rubinshtein, Ronen / Shaw, Leslee J / Villines, Todd C / Berman, Daniel S / Jones, Erica C / Peña, Jessica M / Lin, Fay Y / Min, James K. ·Department of Cardiology, Keimyung University Dongsan Medical Center, Daegu, Korea. · Department of Internal Medicine and Cardiovascular Center, Seoul National University College of Medicine, Seoul, Korea. · Department of Radiology, NewYork-Presbyterian Hospital and the Weill Cornell Medical College, New York, New York, United States of America. · Department of Imaging, Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States of America. · Department of Healthcare Policy and Research, New York-Presbyterian Hospital and the Weill Cornell Medical College, New York, New York, United States of America. · Department of Cardiology, Friedrich-Alexander-University Erlangen-Nuremburg, Germany. · King Saud bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, King AbdulAziz Cardiac Center, Ministry of National Guard, Health Affairs, Riyadh, Saudi Arabia. · Centro Cardiologico Monzino, IRCCS, Milan, Italy. · Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands. · Department of Medicine, Los Angeles Biomedical Research Institute, Torrance, California, United States of America. · Cardiovascular Imaging Center, SDN IRCCS, Naples, Italy. · Tennessee Heart and Vascular Institute, Hendersonville, Tennessee, United States of America. · Division of Cardiology, Severance Cardiovascular Hospital and Severance Biomedical Science Institute, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea. · Division of Cardiology, William Beaumont Hospital, Royal Oak, Michigan, United States of America. · Department of Medicine and Radiology, University of Ottawa, Ontario, Canada. · Department of Radiology, Miami Cardiac and Vascular Institute, Miami, Florida, United States of America. · Capitol Cardiology Associates, Albany, New York, United States of America. · Department of Radiology, Medical University of Innsbruck, Innsbruck, Austria. · Department of Radiology and Nuclear Medicine, German Heart Center Munich, Munich, Germany. · Medizinische Klinik I der Ludwig-Maximilians-Universität München, Munich, Germany. · Department of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland. · Department of Medicine and Radiology, University of British Columbia, Vancouver, British Columbia, Canada. · Department of Radiology, Area Vasta 1/ASUR Marche, Urbino, Italy. · UNICA, Unit of Cardiovascular Imaging, Hospital da Luz, Lisboa, Portugal. · Department of Cardiology at the Lady Davis Carmel Medical Center, The Ruth and Bruce Rappaport School of Medicine, Technion-Israel Institute of Technology, Haifa, Israel. · Cardiology Service, Walter Reed National Military Center, Bethesda, Maryland, United States of America. · Department of Imaging and Medicine, Cedars Sinai Medical Center, Los Angeles, California, United States of America. ·PLoS One · Pubmed #30540755.

ABSTRACT: BACKGROUND: The extent to which the presence and extent of subclinical atherosclerosis by coronary computed tomography angiography influences a potential mortality benefit of statin is unknown. We evaluated the relationship between statin therapy, mortality, and subclinical atherosclerosis. METHODS: In the CONFIRM study, patients with normal or non-obstructive plaque (<50% diameter stenosis) for whom data on baseline statin use was available were included. Coronary artery calcium (CAC) was quantified using the Agatston score. The extent of non-obstructive coronary atherosclerosis was quantified using the segment involvement score (SIS). 8,016 patients were followed for a median of 2.5 years with analysis of all-cause mortality and major adverse cardiac events (MACE) including all-cause mortality, myocardial infarction, unstable angina, target vessel revascularization, and coronary artery disease-related hospitalization. RESULTS: 1.2% of patients experienced all-cause mortality. Patients not on baseline statin therapy had a stepwise increased risk of all-cause mortality by CAC (relative to CAC = 0; CAC 1-99: hazard ratio [HR] 1.65, CAC 100-299: HR 2.19, and CAC≥300: HR 2.98) or SIS (relative to SIS = 0; SIS 1: HR 1.62, SIS 2-3: 2.48 and SIS≥4: 2.95). Conversely, in patients on baseline statin therapy, there was no significant increase in mortality risk with increasing CAC (p value for interaction = 0.049) or SIS (p value for interaction = 0.007). The incidence of MACE was 2.1%. Similar to the all-cause mortality, the risk of MACE was increased with CAC or SIS strata in patient not on baseline statin therapy. However, this relation was not observed in patient on baseline statin therapy. CONCLUSION: In individuals with non-obstructive coronary artery disease, increased risk of adverse events occurs with increasing CAC or SIS who are not on baseline statin therapy. Statin therapy is associated with a mitigation of risk of cardiac events in the presence of increasing atherosclerosis, with no particular threshold of disease burden.

16 Article Pericoronary Adipose Tissue Computed Tomography Attenuation and High-Risk Plaque Characteristics in Acute Coronary Syndrome Compared With Stable Coronary Artery Disease. 2018

Goeller, Markus / Achenbach, Stephan / Cadet, Sebastien / Kwan, Alan C / Commandeur, Frederic / Slomka, Piotr J / Gransar, Heidi / Albrecht, Moritz H / Tamarappoo, Balaji K / Berman, Daniel S / Marwan, Mohamed / Dey, Damini. ·Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California. · Faculty of Medicine, Department of Cardiology, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany. · Department of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, California. · Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany. ·JAMA Cardiol · Pubmed #30027285.

ABSTRACT: Importance: Pericoronary adipose tissue (PCAT) computed tomography (CT) attenuation measured from coronary CT angiography (CTA) may be a promising metric in identifying high-risk plaques. Objective: To determine whether high-risk plaque characteristics from coronary CTA are associated with PCAT CT attenuation in patients with a first acute coronary syndrome (ACS) and matched controls with stable coronary artery disease (CAD). Design, Setting, and Participants: This retrospective, single-center case-control study (data were acquired at the University of Erlangen from 2009-2010) analyzed the CTA data sets of 19 patients who presented with ACS and 16 controls with stable CAD who were matched based on sex, age, and risk factors. Study observers were blinded to patients' clinical data. Semiautomated software was used to quantify and characterize plaques. The CT attenuation (Hounsfield unit [HU]) of PCAT was automatically measured around all lesions. Main Outcomes and Measures: To investigate the association between high-risk plaque characteristics from CTA and PCAT CT attenuation as a novel surrogate measure of coronary inflammation. Results: A total of 35 patients (mean [SD] age, 59.5 [11.3] years; 30 men [86%] and 5 women [14%]) were included in the analysis. Low- and intermediate-attenuation noncalcified plaque (NCP) burden were increased in culprit lesions (n = 19) compared with both nonculprit lesions (n = 55) in patients with ACS (12.6% vs 3.6%; P < .001; 38.4% vs 19.4%; P < .001) and the control group's highest-grade stenosis lesions (n = 16) (12.6% vs 5.6%; P = .002; 38.4% vs 22.1%; P < .001). Pericoronary adipose tissue attenuation was increased around culprit lesions (n = 19) compared with nonculprit lesions (n = 55) in patients with ACS (-69.1 HU vs -74.8 HU; P = .01) and highest-grade stenosis lesions in control patients (n = 16) (-69.1 HU vs -76.4 HU; P = .01). Pericoronary adipose tissue CT attenuation of all lesions in patients with ACS (n = 74) correlated more strongly with intermediate-attenuation (r = 0.393; P = .001) over low-attenuation (r = 0.221; P = .06) and high-attenuation NCP burden (r = -0.103; P = .38). In a multivariable analysis, low- and intermediate-attenuation NCP burden and PCAT CT attenuation were independently associated with the presence of culprit lesions (P < .05). Conclusions and Relevance: Pericoronary CT attenuation was increased around culprit lesions compared with nonculprit lesions of patients with ACS and the lesions of matched controls. Combined quantitative high-risk plaque features and PCAT CT attenuation may allow for a more reliable identification of vulnerable plaques.

17 Article Rationale and design of the REgistry of Fast Myocardial Perfusion Imaging with NExt generation SPECT (REFINE SPECT). 2018

Slomka, Piotr J / Betancur, Julian / Liang, Joanna X / Otaki, Yuka / Hu, Lien-Hsin / Sharir, Tali / Dorbala, Sharmila / Di Carli, Marcelo / Fish, Mathews B / Ruddy, Terrence D / Bateman, Timothy M / Einstein, Andrew J / Kaufmann, Philipp A / Miller, Edward J / Sinusas, Albert J / Azadani, Peyman N / Gransar, Heidi / Tamarappoo, Balaji K / Dey, Damini / Berman, Daniel S / Germano, Guido. ·Department of Imaging (Division of Nuclear Medicine), Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. A047N, Los Angeles, CA, 90048, USA. Piotr.Slomka@cshs.org. · Department of Imaging (Division of Nuclear Medicine), Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. A047N, Los Angeles, CA, 90048, USA. · Department of Nuclear Medicine, Taipei Veterans General Hospital, Taipei, Taiwan. · Department of Nuclear Cardiology, Assuta Medical Centers, Tel Aviv, Israel. · Ben Gurion University of the Negev, Beer Sheba, Israel. · Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA. · Oregon Heart and Vascular Institute, Sacred Heart Medical Center, Springfield, OR, USA. · Division of Cardiology, University of Ottawa Heart Institute, Ottawa, ON, Canada. · Cardiovascular Imaging Technologies LLC, Kansas City, MO, USA. · Division of Cardiology, Department of Medicine, and Department of Radiology, Columbia University Irving Medical Center and New York-Presbyterian Hospital, New York, NY, USA. · Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Zurich, Switzerland. · Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA. ·J Nucl Cardiol · Pubmed #29923104.

ABSTRACT: BACKGROUND: We aim to establish a multicenter registry collecting clinical, imaging, and follow-up data for patients who undergo myocardial perfusion imaging (MPI) with the latest generation SPECT scanners. METHODS: REFINE SPECT (REgistry of Fast Myocardial Perfusion Imaging with NExt generation SPECT) uses a collaborative design with multicenter contribution of clinical data and images into a comprehensive clinical-imaging database. All images are processed by quantitative software. Over 290 individual imaging variables are automatically extracted from each image dataset and merged with clinical variables. In the prognostic cohort, patient follow-up is performed for major adverse cardiac events. In the diagnostic cohort (patients with correlating invasive angiography), angiography and revascularization results within 6 months are obtained. RESULTS: To date, collected prognostic data include scans from 20,418 patients in 5 centers (57% male, 64.0 ± 12.1 years) who underwent exercise (48%) or pharmacologic stress (52%). Diagnostic data include 2079 patients in 9 centers (67% male, 64.7 ± 11.2 years) who underwent exercise (39%) or pharmacologic stress (61%). CONCLUSION: The REFINE SPECT registry will provide a resource for collaborative projects related to the latest generation SPECT-MPI. It will aid in the development of new artificial intelligence tools for automated diagnosis and prediction of prognostic outcomes.

18 Article Coronary Atherosclerotic Precursors of Acute Coronary Syndromes. 2018

Chang, Hyuk-Jae / Lin, Fay Y / Lee, Sang-Eun / Andreini, Daniele / Bax, Jeroen / Cademartiri, Filippo / Chinnaiyan, Kavitha / Chow, Benjamin J W / Conte, Edoardo / Cury, Ricardo C / Feuchtner, Gudrun / Hadamitzky, Martin / Kim, Yong-Jin / Leipsic, Jonathon / Maffei, Erica / Marques, Hugo / Plank, Fabian / Pontone, Gianluca / Raff, Gilbert L / van Rosendael, Alexander R / Villines, Todd C / Weirich, Harald G / Al'Aref, Subhi J / Baskaran, Lohendran / Cho, Iksung / Danad, Ibrahim / Han, Donghee / Heo, Ran / Lee, Ji Hyun / Rivzi, Asim / Stuijfzand, Wijnand J / Gransar, Heidi / Lu, Yao / Sung, Ji Min / Park, Hyung-Bok / Berman, Daniel S / Budoff, Matthew J / Samady, Habib / Shaw, Leslee J / Stone, Peter H / Virmani, Renu / Narula, Jagat / Min, James K. ·Division of Cardiology, Severance Cardiovascular Hospital, Integrative Cardiovascular Imaging Research Center, Yonsei University College of Medicine, Seoul, South Korea. · Dalio Institute of Cardiovascular Imaging, Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, New York. · Department of Clinical Sciences and Community Health, University of Milan, Centro Cardiologico Monzino, IRCCS, Milan, Italy. · Department of Cardiology, Heart Lung Center, Leiden University Medical Center, Leiden, the Netherlands. · Cardiovascular Imaging Center, SDN IRCCS, Naples, Italy. · Department of Cardiology, William Beaumont Hospital, Royal Oaks, Michigan. · Department of Medicine and Radiology, University of Ottawa, Ottawa, Ontario, Canada. · Baptist Cardiac and Vascular Institute, Miami, Florida. · Department of Radiology, Medical University of Innsbruck, Innsbruck, Austria. · Department of Radiology and Nuclear Medicine, German Heart Center Munich, Munich, Germany. · Seoul National University College of Medicine, Seoul National University Hospital, Seoul, South Korea. · Department of Medicine and Radiology, University of British Columbia, Vancouver, British Columbia, Canada. · Department of Radiology, Area Vasta 1/ASUR Marche, Urbino, Italy. · UNICA, Unit of Cardiovascular Imaging, Hospital da Luz, Lisboa, Portugal. · Cardiology Service, Walter Reed National Military Center, Bethesda, Maryland. · Division of Cardiology, Severance Cardiovascular Hospital, Integrative Cardiovascular Imaging Research Center, Yonsei University College of Medicine, Seoul, South Korea; Dalio Institute of Cardiovascular Imaging, Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, New York; Chung-Ang University Hospital, Seoul, South Korea. · VU University Medical Center, Amsterdam, the Netherlands. · Division of Cardiology, Severance Cardiovascular Hospital, Integrative Cardiovascular Imaging Research Center, Yonsei University College of Medicine, Seoul, South Korea; Dalio Institute of Cardiovascular Imaging, Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, New York. · Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea. · Dalio Institute of Cardiovascular Imaging, Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, New York; Department of Radiology, Mayo Clinic, Rochester, Minnesota. · Department of Imaging and Medicine, Cedars Sinai Medical Center, Los Angeles, California. · Department of Medicine, Los Angeles Biomedical Research Institute, Torrance, California. · Division of Cardiology, Emory University School of Medicine, Atlanta, Georgia. · Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, Massachusetts. · CVPath Institute, Gaithersburg, Maryland. · 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, New York. · Dalio Institute of Cardiovascular Imaging, Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, New York. Electronic address: jkm2001@med.cornell.edu. ·J Am Coll Cardiol · Pubmed #29852975.

ABSTRACT: BACKGROUND: The association of atherosclerotic features with first acute coronary syndromes (ACS) has not accounted for plaque burden. OBJECTIVES: The purpose of this study was to identify atherosclerotic features associated with precursors of ACS. METHODS: We performed a nested case-control study within a cohort of 25,251 patients undergoing coronary computed tomographic angiography (CTA) with follow-up over 3.4 ± 2.1 years. Patients with ACS and nonevent patients with no prior coronary artery disease (CAD) were propensity matched 1:1 for risk factors and coronary CTA-evaluated obstructive (≥50%) CAD. Separate core laboratories performed blinded adjudication of ACS and culprit lesions and quantification of baseline coronary CTA for percent diameter stenosis (%DS), percent cross-sectional plaque burden (PB), plaque volumes (PVs) by composition (calcified, fibrous, fibrofatty, and necrotic core), and presence of high-risk plaques (HRPs). RESULTS: We identified 234 ACS and control pairs (age 62 years, 63% male). More than 65% of patients with ACS had nonobstructive CAD at baseline, and 52% had HRP. The %DS, cross-sectional PB, fibrofatty and necrotic core volume, and HRP increased the adjusted hazard ratio (HR) of ACS (1.010 per %DS, 95% confidence interval [CI]: 1.005 to 1.015; 1.008 per percent cross-sectional PB, 95% CI: 1.003 to 1.013; 1.002 per mm CONCLUSIONS: Although ACS increases with %DS, most precursors of ACS cases and culprit lesions are nonobstructive. Plaque evaluation, including HRP, PB, and plaque composition, identifies high-risk patients above and beyond stenosis severity and aggregate plaque burden.

19 Article Prognostic value of age adjusted segment involvement score as measured by coronary computed tomography: a potential marker of vascular age. 2018

Ayoub, Chadi / Kritharides, Leonard / Yam, Yeung / Chen, Li / Hossain, Alomgir / Achenbach, Stephan / Al-Mallah, Mouaz H / Andreini, Daniele / Berman, Daniel S / Budoff, Matthew J / Cademartiri, Filippo / Callister, Tracy Q / Chang, Hyuk-Jae / Chinnaiyan, Kavitha / Cury, Ricardo C / Delago, Augustin / Dunning, Allison / Feuchtner, Gudrun / Gomez, Millie / Gransar, Heidi / Hadamitzky, Martin / Hausleiter, Joerg / Hindoyan, Niree / Kaufmann, Philipp A / Kim, Yong-Jin / Leipsic, Jonathon / Maffei, Erica / Marques, Hugo / Pontone, Gianluca / Raff, Gilbert / Rubinshtein, Ronen / Shaw, Leslee J / Villines, Todd C / Min, James K / Chow, Benjamin J W. ·Department of Medicine (Cardiology), University of Ottawa Heart Institute, 40 Ruskin Street, Ottawa, ON, K1Y 4W7, Canada. · Department of Cardiovascular Medicine, Cleveland Clinic, Cleveland, OH, USA. · University of Sydney, Sydney, NSW, Australia. · Department of Cardiology, Concord Hospital, Sydney Local Health District, Sydney, NSW, Australia. · Department of Medicine, University of Erlangen, Erlangen, Germany. · King Abdullah International Medical Research Center, King AbdulAziz Cardiac Center, Riyadh, Saudi Arabia. · Department of Clinical Sciences and Community Health, University of Milan, Centro Cardiologico Monzino, IRCCS Milan, Milan, Italy. · Department of Imaging, Cedars Sinai Medical Center, Los Angeles, CA, USA. · Department of Medicine, Harbor UCLA Medical Center, Los Angeles, CA, USA. · Cardiovascular Imaging Unit, Giovanni XXIII Hospital, Monastier, Treviso, Italy. · Tennessee Heart and Vascular Institute, Hendersonville, TN, USA. · Division of Cardiology, Severance Cardiovascular Hospital and Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, South Korea. · William Beaumont Hospital, Royal Oaks, MI, USA. · Baptist Cardiac and Vascular Institute, Miami, FL, USA. · Capitol Cardiology Associates, Albany, NY, USA. · Duke Clinical Research Institute, Durham, NC, USA. · Department of Radiology, Medical University of Innsbruck, Innsbruck, Austria. · Dalio Institute of Cardiovascular Imaging, Weill Cornell Medical College and New York-Presbyterian Hospital, New York, NY, USA. · Division of Cardiology, Deutsches Herzzentrum Munchen, Munich, Germany. · University Hospital, Zurich, Switzerland. · Seoul National University Hospital, Seoul, South Korea. · Department of Medicine and Radiology, University of British Columbia, Vancouver, BC, Canada. · Department of Surgery, Curry Cabral Hospital, Lisbon, Portugal. · Department of Cardiology, Lady Davis Carmel Medical Center, Technion-Israel Institute of Technology, Haifa, Israel. · Division of Cardiology, Emory University School of Medicine, Atlanta, GA, USA. · Department of Medicine, Walter Reed Medical Center, Washington, DC, USA. · Department of Medicine (Cardiology), University of Ottawa Heart Institute, 40 Ruskin Street, Ottawa, ON, K1Y 4W7, Canada. bchow@ottawaheart.ca. · Department of Radiology, University of Ottawa, Ottawa, Canada. bchow@ottawaheart.ca. ·Heart Vessels · Pubmed #29797058.

ABSTRACT: Extent of coronary atherosclerotic disease (CAD) burden on coronary computed tomography angiography (CCTA) as measured by segment involvement score (SIS) has a prognostic value. We sought to investigate the incremental prognostic value of 'age adjusted SIS' (aSIS), which may be a marker of premature atherosclerosis and vascular age. Consecutive patients were prospectively enrolled into the CONFIRM (Coronary CT Angiography EvaluatioN For Clinical Outcomes: An InteRnational Multicentre) multinational observational study. Patients were followed for the outcome of all-cause death. aSIS was calculated on CCTA for each patient, and its incremental prognostic value was evaluated. A total of 22,211 patients [mean age 58.5 ± 12.7 years, 55.8% male) with a median follow-up of 27.3 months (IQR 17.8, 35.4)] were identified. After adjustment for clinical factors and presence of obstructive CAD, higher aSIS was associated with increased death on multivariable analysis, with hazard ratio (HR) 2.40 (1.83-3.16, p < 0.001), C-statistic 0.723 (0.700-0.756), net reclassification improvement (NRI) 0.36 (0.26-0.47, p < 0.001), and relative integrated discrimination improvement (IDI) 0.33 (p = 0.009). aSIS had HR 3.48 (2.33-5.18, p < 0.001) for mortality in those without obstructive CAD, compared to HR 1.79 (1.25-2.58, p = 0.02) in those with obstructive CAD. In conclusion, aSIS has an incremental prognostic value to traditional risk factors and obstructive CAD, and may enhance CCTA risk stratification.

20 Article Improvement in LDL is associated with decrease in non-calcified plaque volume on coronary CTA as measured by automated quantitative software. 2018

Tamarappoo, Balaji / Otaki, Yuka / Doris, Mhairi / Arnson, Yoav / Gransar, Heidi / Hayes, Sean / Friedman, John / Thomson, Louise / Wang, Frances / Rozanski, Alan / Slomka, Piotr / Dey, Damini / Berman, Daniel. ·Department of Imaging, Cedars Sinai Medical Center, Los Angeles, CA, USA; Department of Medicine and Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, CA, USA; Biomedical Imaging Research Institute, Cedars Sinai Medical Center, Los Angeles, CA, USA. Electronic address: balaji.tamarappoo@cshs.org. · Department of Imaging, Cedars Sinai Medical Center, Los Angeles, CA, USA. · Department of Imaging, Cedars Sinai Medical Center, Los Angeles, CA, USA; Biomedical Imaging Research Institute, Cedars Sinai Medical Center, Los Angeles, CA, USA. · Department of Imaging, Cedars Sinai Medical Center, Los Angeles, CA, USA; Department of Medicine and Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, CA, USA; Biomedical Imaging Research Institute, Cedars Sinai Medical Center, Los Angeles, CA, USA. ·J Cardiovasc Comput Tomogr · Pubmed #29793847.

ABSTRACT: BACKGROUND: Computed tomography coronary angiography (CTA) can be used for assessment of plaque characteristics; however, quantitative assessment of changes in plaque composition in response to LDL lowering has not been performed with CTA. We sought to assess the association between LDL reduction and changes in plaque composition with quantitative CTA. METHODS: Quantification of total, calcified, non-calcified and low-density non-calcified plaque volumes (TPV, CPV, NCPV and LD-NCPV) was performed using semi-automated software in 234 vessels from 116 consecutive patients (89 men, 60 ± 10 years) with baseline LDL>70 mg/dl. Significant reduction in LDL was defined as a decrease by >10% of baseline LDL. Changes (Δ) in plaque volumes between the second and baseline study were compared between patients with LDL reduction (n = 63) and those with no decrease in LDL (n = 53). RESULTS: Median LDL at baseline was 98 mg/dl [interquartile range (IQR) 83-119 mg/dl] and median ΔLDL was -14 mg/dl (IQR -38 to 3 mg/dl). Mean interval between sequential CTA was 3.5 ± 1.6 years. TPV, NCPV, and LD-NCPV decreased in patients with a reduction in LDL compared to baseline; whereas, patients without reduction in LDL experienced an increase in TPV, NCPV and LD-NCPV. After adjusting for age, statin use, diabetes, baseline LDL and baseline TPV, reduction in LDL was associated with a decrease in TPV (P = 0.005), NCPV (P = 0.002) and LD-NCPV (P = 0.011) compared to patients without a reduction in LDL. CONCLUSION: Reduction in LDL was associated with beneficial changes in the amount and composition of noncalcified plaque as measured using semi-automated quantitative software by CTA.

21 Article Non-invasive fractional flow reserve in vessels without severe obstructive stenosis is associated with coronary plaque burden. 2018

Doris, Mhairi K / Otaki, Yuka / Arnson, Yoav / Tamarappoo, Balaji / Goeller, Markus / Gransar, Heidi / Wang, Frances / Hayes, Sean / Friedman, John / Thomson, Louise / Slomka, Piotr / Dey, Damini / Berman, Daniel. ·Department of Imaging and Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Centre for Cardiovascular Science, University of Edinburgh, Scotland, UK. Electronic address: Mhairi.Doris@ed.ac.uk. · Department of Imaging and Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA. Electronic address: Yuka.Otaki@cshs.org. · Department of Imaging and Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA. Electronic address: Yoav.Arnson@cshs.org. · Department of Imaging and Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA. Electronic address: Balaji.Tamarappoo@cshs.org. · Department of Imaging and Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA. Electronic address: markus.goeller@cshs.org. · Department of Imaging and Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA. Electronic address: Heidi.Gransar@cshs.org. · Department of Imaging and Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA. Electronic address: Frances.Wang@cshs.org. · Department of Imaging and Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA. Electronic address: Sean.Hayes@cshs.org. · Department of Imaging and Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA. Electronic address: John.Friedman@cshs.org. · Department of Imaging and Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA. Electronic address: Louise.Thomson@cshs.org. · Department of Imaging and Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA. Electronic address: Piotr.Slomka@cshs.org. · Department of Imaging and Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA. Electronic address: Damini.Dey@cshs.org. · Department of Imaging and Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA. Electronic address: Daniel.Berman@cshs.org. ·J Cardiovasc Comput Tomogr · Pubmed #29784622.

ABSTRACT: AIMS: Non-invasive fractional flow reserve derived from coronary CT angiography (FFR METHODS: FFR RESULTS: Vessels with abnormal V-FFR CONCLUSION: Abnormal V-FFR

22 Article Maximization of the usage of coronary CTA derived plaque information using a machine learning based algorithm to improve risk stratification; insights from the CONFIRM registry. 2018

van Rosendael, Alexander R / Maliakal, Gabriel / Kolli, Kranthi K / Beecy, Ashley / Al'Aref, Subhi J / Dwivedi, Aeshita / Singh, Gurpreet / Panday, Mohit / Kumar, Amit / Ma, Xiaoyue / Achenbach, Stephan / Al-Mallah, Mouaz H / Andreini, Daniele / Bax, Jeroen J / Berman, Daniel S / Budoff, Matthew J / Cademartiri, Filippo / Callister, Tracy Q / Chang, Hyuk-Jae / Chinnaiyan, Kavitha / Chow, Benjamin J W / Cury, Ricardo C / DeLago, Augustin / Feuchtner, Gudrun / Hadamitzky, Martin / Hausleiter, Joerg / Kaufmann, Philipp A / Kim, Yong-Jin / Leipsic, Jonathon A / Maffei, Erica / Marques, Hugo / Pontone, Gianluca / Raff, Gilbert L / Rubinshtein, Ronen / Shaw, Leslee J / Villines, Todd C / Gransar, Heidi / Lu, Yao / Jones, Erica C / Peña, Jessica M / Lin, Fay Y / Min, James K. ·Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA. · Department of Cardiology, Friedrich-Alexander-University Erlangen-Nuremburg, Germany. · King Saud bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, King AbdulAziz Cardiac Center, Ministry of National Guard, Health Affairs, Riyadh, Saudi Arabia. · Centro Cardiologico Monzino, IRCCS, Milan, Italy. · Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands. · Department of Imaging and Medicine, Cedars Sinai Medical Center, Los Angeles, CA, USA. · Department of Medicine, Los Angeles Biomedical Research Institute, Torrance CA, USA. · Cardiovascular Imaging Center, SDN IRCCS, Naples, Italy. · Tennessee Heart and Vascular Institute, Hendersonville, TN, USA. · Division of Cardiology, Severance Cardiovascular Hospital and Severance Biomedical Science Institute, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea. · Department of Cardiology, William Beaumont Hospital, Royal Oak, MI, USA. · Department of Medicine and Radiology, University of Ottawa, ON, Canada. · Department of Radiology, Miami Cardiac and Vascular Institute, Miami, FL, USA. · Capitol Cardiology Associates, Albany, NY, USA. · Department of Radiology, Medical University of Innsbruck, Innsbruck, Austria. · Department of Radiology and Nuclear Medicine, German Heart Center Munich, Munich, Germany. · Medizinische Klinik I der Ludwig-Maximilians-UniversitätMünchen, Munich, Germany. · Department of Nuclear Medicine, University Hospital, Zurich, Switzerland, University of Zurich, Switzerland. · Seoul National University Hospital, Seoul, South Korea. · Department of Medicine and Radiology, University of British Columbia, Vancouver, BC, CA, USA. · Department of Radiology, Area Vasta 1/ASUR Marche, Urbino, Italy. · UNICA, Unit of Cardiovascular Imaging, Hospital da Luz, Lisboa, Portugal. · Department of Cardiology at the Lady Davis Carmel Medical Center, The Ruth and Bruce Rappaport School of Medicine, Technion-Israel Institute of Technology, Haifa, Israel. · Division of Cardiology, Emory University School of Medicine, Atlanta, GA, USA. · Cardiology Service, Walter Reed National Military Center, Bethesda, MD, USA. · Department of Imaging, Cedars Sinai Medical Center, Los Angeles, CA, USA. · Department of Healthcare Policy and Research, New York-Presbyterian Hospital and the Weill Cornell Medical College, New York, NY, USA. · Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA. Electronic address: jkm2001@med.cornell.edu. ·J Cardiovasc Comput Tomogr · Pubmed #29753765.

ABSTRACT: INTRODUCTION: Machine learning (ML) is a field in computer science that demonstrated to effectively integrate clinical and imaging data for the creation of prognostic scores. The current study investigated whether a ML score, incorporating only the 16 segment coronary tree information derived from coronary computed tomography angiography (CCTA), provides enhanced risk stratification compared with current CCTA based risk scores. METHODS: From the multi-center CONFIRM registry, patients were included with complete CCTA risk score information and ≥3 year follow-up for myocardial infarction and death (primary endpoint). Patients with prior coronary artery disease were excluded. Conventional CCTA risk scores (conventional CCTA approach, segment involvement score, duke prognostic index, segment stenosis score, and the Leaman risk score) and a score created using ML were compared for the area under the receiver operating characteristic curve (AUC). Only 16 segment based coronary stenosis (0%, 1-24%, 25-49%, 50-69%, 70-99% and 100%) and composition (calcified, mixed and non-calcified plaque) were provided to the ML model. A boosted ensemble algorithm (extreme gradient boosting; XGBoost) was used and the entire data was randomly split into a training set (80%) and testing set (20%). First, tuned hyperparameters were used to generate a trained model from the training data set (80% of data). Second, the performance of this trained model was independently tested on the unseen test set (20% of data). RESULTS: In total, 8844 patients (mean age 58.0 ± 11.5 years, 57.7% male) were included. During a mean follow-up time of 4.6 ± 1.5 years, 609 events occurred (6.9%). No CAD was observed in 48.7% (3.5% event), non-obstructive CAD in 31.8% (6.8% event), and obstructive CAD in 19.5% (15.6% event). Discrimination of events as expressed by AUC was significantly better for the ML based approach (0.771) vs the other scores (ranging from 0.685 to 0.701), P < 0.001. Net reclassification improvement analysis showed that the improved risk stratification was the result of down-classification of risk among patients that did not experience events (non-events). CONCLUSION: A risk score created by a ML based algorithm, that utilizes standard 16 coronary segment stenosis and composition information derived from detailed CCTA reading, has greater prognostic accuracy than current CCTA integrated risk scores. These findings indicate that a ML based algorithm can improve the integration of CCTA derived plaque information to improve risk stratification.

23 Article Diagnostic Performance of a Novel Coronary CT Angiography Algorithm: Prospective Multicenter Validation of an Intracycle CT Motion Correction Algorithm for Diagnostic Accuracy. 2018

Andreini, Daniele / Lin, Fay Y / Rizvi, Asim / Cho, Iksung / Heo, Ran / Pontone, Gianluca / Bartorelli, Antonio L / Mushtaq, Saima / Villines, Todd C / Carrascosa, Patricia / Choi, Byoung Wook / Bloom, Stephen / Wei, Han / Xing, Yan / Gebow, Dan / Gransar, Heidi / Chang, Hyuk-Jae / Leipsic, Jonathon / Min, James K. ·1 Centro Cardiologico Monzino, IRCCS, Milan, Italy. · 2 Dalio Institute of Cardiovascular Imaging, Weill Cornell Medicine and New York-Presbyterian Hospital, 413 E 69th St, Ste 108, New York, NY 10021. · 3 Chung-Ang University Hospital, Seoul, South Korea. · 4 Severance Cardiovascular Hospital, Yonsei University Health System, Seoul, South Korea. · 5 Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea. · 6 Department of Medicine, Walter Reed National Military Medical Center, Bethesda, MD. · 7 Department of Computed Tomography, Diagnóstico Maipú, Buenos Aires, Argentina. · 8 Midwest Heart and Vascular Specialists, Overland Park, KS. · 9 Beijing Military Hospital, Beijing, China. · 10 First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China. · 11 MDDX, San Francisco, CA. · 12 Department of Imaging, Cedars Sinai Medical Center, Los Angeles, CA. · 13 Department of Medicine and Radiology, University of British Columbia, Vancouver, BC, Canada. ·AJR Am J Roentgenol · Pubmed #29667891.

ABSTRACT: OBJECTIVE: Motion artifact can reduce the diagnostic accuracy of coronary CT angiography (CCTA) for coronary artery disease (CAD). The purpose of this study was to compare the diagnostic performance of an algorithm dedicated to correcting coronary motion artifact with the performance of standard reconstruction methods in a prospective international multicenter study. SUBJECTS AND METHODS: Patients referred for clinically indicated invasive coronary angiography (ICA) for suspected CAD prospectively underwent an investigational CCTA examination free from heart rate-lowering medications before they underwent ICA. Blinded core laboratory interpretations of motion-corrected and standard reconstructions for obstructive CAD (≥ 50% stenosis) were compared with ICA findings. Segments unevaluable owing to artifact were considered obstructive. The primary endpoint was per-subject diagnostic accuracy of the intracycle motion correction algorithm for obstructive CAD found at ICA. RESULTS: Among 230 patients who underwent CCTA with the motion correction algorithm and standard reconstruction, 92 (40.0%) had obstructive CAD on the basis of ICA findings. At a mean heart rate of 68.0 ± 11.7 beats/min, the motion correction algorithm reduced the number of nondiagnostic scans compared with standard reconstruction (20.4% vs 34.8%; p < 0.001). Diagnostic accuracy for obstructive CAD with the motion correction algorithm (62%; 95% CI, 56-68%) was not significantly different from that of standard reconstruction on a per-subject basis (59%; 95% CI, 53-66%; p = 0.28) but was superior on a per-vessel basis: 77% (95% CI, 74-80%) versus 72% (95% CI, 69-75%) (p = 0.02). The motion correction algorithm was superior in subgroups of patients with severely obstructive (≥ 70%) stenosis, heart rate ≥ 70 beats/min, and vessels in the atrioventricular groove. CONCLUSION: The motion correction algorithm studied reduces artifacts and improves diagnostic performance for obstructive CAD on a per-vessel basis and in selected subgroups on a per-subject basis.

24 Article Influence of symptom typicality for predicting MACE in patients without obstructive coronary artery disease: From the CONFIRM Registry (Coronary Computed Tomography Angiography Evaluation for Clinical Outcomes: An International Multicenter Registry). 2018

Lee, Ji Hyun / Han, Donghee / Hartaigh, Bríain Ó / Gransar, Heidi / Lu, Yao / Rizvi, Asim / Park, Mahn Won / Roudsari, Hadi Mirhedayati / Stuijfzand, Wijnand J / Berman, Daniel S / Callister, Tracy Q / DeLago, Augustin / Hadamitzky, Martin / Hausleiter, Joerg / Al-Mallah, Mouaz H / Budoff, Matthew J / Kaufmann, Philipp A / Raff, Gilbert / Chinnaiyan, Kavitha / Cademartiri, Filippo / Maffei, Erica / Villines, Todd C / Kim, Yong-Jin / Leipsic, Jonathon / Feuchtner, Gudrun / Pontone, Gianluca / Andreini, Daniele / Marques, Hugo / Rubinshtein, Ronen / Achenbach, Stephan / Shaw, Leslee J / Chang, Hyuk-Jae / Bax, Jeroen / Chow, Benjamin / Cury, Ricardo C / Gomez, Millie / Jones, Erica C / Lin, Fay Y / Min, James K / Peña, Jessica M. ·Dalio Institute of Cardiovascular Imaging, Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, New York. · Division of Cardiology, Severance Cardiovascular Hospital and Severance Biomedical Science Institute, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea. · Division of Cardiology, Department of Internal Medicine, Myongji Hospital, Goyang-si, South Korea. · Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California. · Tennessee Heart and Vascular Institute, Hendersonville, Tennessee. · Capitol Cardiology Associates, Albany, New York. · Department of Radiology and Nuclear Medicine, German Heart Center Munich, Munich, Germany. · Medizinische Klinik I der Ludwig-Maximilians-Universität München, Munich, Germany. · King Saud bin Abdulaziz University of Health Sciences, King Abdullah International Medical Research Center, King Abdulaziz Cardiac Center, Ministry of National Guard, Health Affairs, Riyadh, Saudi Arabia. · Department of Medicine, Harbor-UCLA Medical Center, Los Angeles, California. · Department of Nuclear Medicine, Cardiac Imaging, University Hospital, Zurich, University of Zurich, Switzerland. · William Beaumont Hospital, Royal, Michigan. · Cardiovascular Imaging Center, Department of Radiology, SDN IRCCS Naples, Italy. · Department of Radiology, Area Vasta 1/ASUR Marche, Urbino, Italy. · Department of Medicine, Walter Reed Medical Center, Washington, D.C. · Cardiovascular Center and Internal Medicine, Seoul National University Hospital, Seoul, South Korea. · Department of Medicine and Radiology, University of British Columbia, Vancouver, Canada. · Department of Radiology, Medical University of Innsbruck, Innsbruck, Austria. · Department of Clinical Sciences and Community Health, University of Milan, Centro Cardiologico Monzino, IRCCS, Milan, Italy. · UNICA, Cardiac CT and MRI Unit, Hospital da Luz, Lisbon, Portugal. · Department of Cardiology at the Lady Davis Carmel Medical Center, The Ruth and Bruce Rappaport School of Medicine, Technion-Israel Institute of Technology, Haifa, Israel. · Department of Medicine, University of Erlangen, Erlangen, Germany. · Division of Cardiology, Emory University School of Medicine, Atlanta, Georgia. · Department of Cardiology, Leiden University Medical Center, HARTS, Leiden, The Netherlands. · Department of Medicine and Radiology, University of Ottawa, Ontario, Canada. · Baptist Cardiac and Vascular Institute, Miami, Florida. ·Clin Cardiol · Pubmed #29521447.

ABSTRACT: Our objective was to assess the prognostic value of symptom typicality in patients without obstructive coronary artery disease (CAD), determined by coronary computed tomographic angiography (CCTA). We identified 4215 patients without prior history of CAD and without obstructive CAD (<50% CCTA stenosis). CAD severity was categorized as nonobstructive (1%-49%) and none (0%). Based upon the Diamond-Forrester criteria for angina pectoris, symptom typicality was classified as asymptomatic, nonanginal, atypical, and typical. Multivariable Cox proportional hazards models were used to assess the risk of major adverse cardiac events (MACE), comprising all-cause mortality, myocardial infarction, unstable angina, and late revascularization, according to symptom typicality. Mean patient age was 57.0 ±12.0 years (54.9% male). During a median follow-up of 5.3 years (interquartile range, 4.6-5.9 years), MACE were reported in 312 (7.4%) patients. Among patients with nonobstructive CAD, there was an association between symptom typicality and MACE (P for interaction = 0.05), driven by increased risk of MACE among those with typical angina and nonobstructive CAD (hazard ratio: 1.62, 95% confidence interval: 1.06-2.48, P = 0.03). No consistent relationship was found between symptom typicality and MACE among patients without any CAD (hazard ratio: 0.73, 95% confidence interval: 0.34-1.57, P = 0.08). In the CONFIRM registry, patients who presented with concomitant typical angina and nonobstructive CAD had a higher rate of MACE than did asymptomatic patients with nonobstructive CAD. However, the presence of typical angina did not appear to portend worse prognosis in patients with no CAD.

25 Article Association between epicardial fat volume and fractional flow reserve: An analysis of the determination of fractional flow reserve (DeFACTO) study. 2018

Beecy, Ashley / Hartaigh, Bríain Ó / Schulman-Marcus, Joshua / Anchouche, Khalil / Gransar, Heidi / Al'Aref, Subhi / Elmore, Kimberly / Lin, Fay Y / Min, James K / Peña, Jessica M. ·Dalio Institute of Cardiovascular Imaging, NewYork Presbyterian Hospital and Weill Cornell Medicine, NewYork-Presbyterian Hospital, New York, NY, USA. · Division of Cardiology, Albany Medical Center, Albany, NY, USA. · Department of Imaging, Cedars Sinai Medical Center, Los Angeles, CA, USA. · Dalio Institute of Cardiovascular Imaging, NewYork Presbyterian Hospital and Weill Cornell Medicine, NewYork-Presbyterian Hospital, New York, NY, USA. Electronic address: jmp2003@med.cornell.edu. ·Clin Imaging · Pubmed #29414521.

ABSTRACT: BACKGROUND: This study examines the relationship between epicardial fat volume (EFV) and lesion-specific ischemia by fractional flow reserve (FFR). METHODS: In a study of 173 patients (63.0 ± 8.3 years) undergoing FFR, EFV was determined using cardiac computed tomography. Relationships between EFV and FFR were assessed using multivariable linear and logistic regression. RESULTS: Using multivariable linear and logistic regression, no association between EFV and FFR was observed (β [SE] = -0.001 [0.003], P = 0.6, OR [95% CI]: 1.02 [0.94-1.11], P = 0.64, respectively). CONCLUSION: In patients with suspected or known coronary artery disease undergoing invasive angiography, EFV was not associated with FFR.

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