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3. Sex differences in coronary atherosclerotic plaque activity using 18F-sodium fluoride positron emission tomography

Author

Kwiecinski, Jacek, Wang, Kang-Ling, Tzolos, Evangelos, Moss, Alastair, Daghem, Marwa, Adamson, Philip D., Dey, Damini, Molek-Dziadosz, Patrycja, Dawson, Dana, Arumugam, Parthiban, Sabharwal, Nikant, Greenwood, John P., Townend, John N., Calvert, Patrick A., Rudd, James HF., Berman, Daniel, Verjans, Johan W., Williams, Michelle C., Slomka, Piotr, Dweck, Marc R., and Newby, David E.

Published
2024
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5. Progression of non-obstructive coronary plaque: a practical CCTA-based risk score from the PARADIGM registry

Author

Pontone, Gianluca, Rossi, Alexia, Baggiano, Andrea, Andreini, Daniele, Conte, Edoardo, Fusini, Laura, Gebhard, Chaterine, Rabbat, Mark G., Guaricci, Andrea, Guglielmo, Marco, Muscogiuri, Giuseppe, Mushtaq, Saima, Al-Mallah, Mouaz H., Berman, Daniel S., Budoff, Matthew J., Cademartiri, Filippo, Chinnaiyan, Kavitha, Choi, Jung Hyun, Chun, Eun Ju, de Araújo Gonçalves, Pedro, Gottlieb, Ilan, Hadamitzky, Martin, Kim, Yong Jin, Lee, Byoung Kwon, Lee, Sang-Eun, Maffei, Erica, Marques, Hugo, Samady, Habib, Shin, Sanghoon, Sung, Ji Min, van Rosendael, Alexander, Virmani, Renu, Bax, Jeroen J., Leipsic, Jonathon A., Lin, Fay Y., Min, James K., Narula, Jagat, Shaw, Leslee J., and Chang, Hyuk-Jae

Published
2024
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6. Cardiovascular Testing in the United States during the COVID-19 Pandemic: Volume Recovery and Worldwide Comparison.

Author

Bullock-Palmer, Renee, Chen, Marcus, DiLorenzo, Michael, Doukky, Rami, Ferencik, Maros, Geske, Jeffrey, Hage, Fadi, Hendel, Robert, Koweek, Lynne, Murthy, Venkatesh, Narula, Jagat, Rodriguez Lozano, Patricia, Shah, Nishant, Shah, Amee, Soman, Prem, Thompson, Randall, Wolinsky, David, Cohen, Yosef, Malkovskiy, Eli, Randazzo, Michael, Lopez-Mattei, Juan, Parwani, Purvi, Shetty, Mrinali, Pascual, Thomas, Pynda, Yaroslav, Dondi, Maurizio, Paez, Diana, Einstein, Andrew, Hirschfeld, Cole, Dorbala, Sharmila, Shaw, Leslee, Villines, Todd, Choi, Andrew, Better, Nathan, Cerci, Rodrigo, Karthikeyan, Ganesan, Vitola, João, Williams, Michelle, Al-Mallah, Mouaz, Berman, Daniel, Bernheim, Adam, Biederman, Robert, Bravo, Paco, and Budoff, Matthew

Subjects
Angiography, COVID-19, CT, CT Angiography, Cardiac, Cardiac Testing, Cardiovascular Disease, Cardiovascular Imaging, Diagnostic Cardiovascular Procedure, Echocardiography, Epidemiology, MR Imaging, Radionuclide Studies, SPECT, SPECT/CT
Abstract

PURPOSE: To characterize the recovery of diagnostic cardiovascular procedure volumes in U.S. and non-U.S. facilities in the year following the initial COVID-19 outbreak. MATERIALS AND METHODS: The International Atomic Energy Agency (IAEA) coordinated a worldwide study called the IAEA Noninvasive Cardiology Protocols Study of COVID-19 2 (INCAPS COVID 2), collecting data from 669 facilities in 107 countries, including 93 facilities in 34 U.S. states, to determine the impact of the pandemic on diagnostic cardiovascular procedure volumes. Participants reported volumes for each diagnostic imaging modality used at their facility for March 2019 (baseline), April 2020, and April 2021. This secondary analysis of INCAPS COVID 2 evaluated differences in changes in procedure volume between U.S. and non-U.S. facilities and among U.S. regions. Factors associated with return to prepandemic volumes in the United States were also analyzed in a multivariable regression analysis. RESULTS: Reduction in procedure volumes in April 2020 compared with baseline was similar for U.S. and non-U.S. facilities (-66% vs -71%, P = .27). U.S. facilities reported greater return to baseline in April 2021 than did all non-U.S. facilities (4% vs -6%, P = .008), but there was no evidence of a difference when comparing U.S. facilities with non-U.S. high-income country (NUHIC) facilities (4% vs 0%, P = .18). U.S. regional differences in return to baseline were observed between the Midwest (11%), Northeast (9%), South (1%), and West (-7%, P = .03), but no studied factors were significant predictors of 2021 change from prepandemic baseline. CONCLUSION: The reductions in cardiac testing during the early pandemic have recovered within a year to prepandemic baselines in the United States and NUHICs, while procedure volumes remain depressed in lower-income countries.Keywords: SPECT, Cardiac, Epidemiology, Angiography, CT Angiography, CT, Echocardiography, SPECT/CT, MR Imaging, Radionuclide Studies, COVID-19, Cardiovascular Imaging, Diagnostic Cardiovascular Procedure, Cardiovascular Disease, Cardiac Testing Supplemental material is available for this article. © RSNA, 2023.

Published
2023

7. Sex and age-specific interactions of coronary atherosclerotic plaque onset and prognosis from coronary computed tomography.

Author

van Rosendael, Sophie, Bax, A, Lin, Fay, Achenbach, Stephan, Andreini, Daniele, Cademartiri, Filippo, Callister, Tracy, Chinnaiyan, Kavitha, Chow, Benjamin, Cury, Ricardo, DeLago, Augustin, Feuchtner, Gudrun, Hadamitzky, Martin, Hausleiter, Joerg, Kaufmann, Philipp, Kim, Yong-Jin, Leipsic, Jonathon, Maffei, Erica, Marques, Hugo, de Araújo Gonçalves, Pedro, Pontone, Gianluca, Raff, Gilbert, Rubinshtein, Ronen, Villines, Todd, Chang, Hyuk-Jae, Berman, Daniel, Min, James, Bax, Jeroen, Shaw, Leslee, van Rosendael, Alexander, and Budoff, Matthew

Subjects
coronary artery disease, coronary computed tomography angiography (CCTA), prognosis, sex differences, Humans, Male, Female, Child, Plaque, Atherosclerotic, Coronary Stenosis, Coronary Angiography, Coronary Artery Disease, Tomography, X-Ray Computed, Prognosis, Computed Tomography Angiography, Age Factors, Predictive Value of Tests
Abstract

AIMS: The totality of atherosclerotic plaque derived from coronary computed tomography angiography (CCTA) emerges as a comprehensive measure to assess the intensity of medical treatment that patients need. This study examines the differences in age onset and prognostic significance of atherosclerotic plaque burden between sexes. METHODS AND RESULTS: From a large multi-center CCTA registry the Leiden CCTA score was calculated in 24 950 individuals. A total of 11 678 women (58.5 ± 12.4 years) and 13 272 men (55.6 ± 12.5 years) were followed for 3.7 years for major adverse cardiovascular events (MACE) (death or myocardial infarction). The age where the median risk score was above zero was 12 years higher in women vs. men (64-68 years vs. 52-56 years, respectively, P < 0.001). The Leiden CCTA risk score was independently associated with MACE: score 6-20: HR 2.29 (1.69-3.10); score > 20: HR 6.71 (4.36-10.32) in women, and score 6-20: HR 1.64 (1.29-2.08); score > 20: HR 2.38 (1.73-3.29) in men. The risk was significantly higher for women within the highest score group (adjusted P-interaction = 0.003). In pre-menopausal women, the risk score was equally predictive and comparable with men. In post-menopausal women, the prognostic value was higher for women [score 6-20: HR 2.21 (1.57-3.11); score > 20: HR 6.11 (3.84-9.70) in women; score 6-20: HR 1.57 (1.19-2.09); score > 20: HR 2.25 (1.58-3.22) in men], with a significant interaction for the highest risk group (adjusted P-interaction = 0.004). CONCLUSION: Women developed coronary atherosclerosis approximately 12 years later than men. Post-menopausal women within the highest atherosclerotic burden group were at significantly higher risk for MACE than their male counterparts, which may have implications for the medical treatment intensity.

Published
2023

13. Prediction of Coronary Artery Calcium Using Deep Learning of Echocardiograms

Author

Yuan, Neal, Kwan, Alan C, Duffy, Grant, Theurer, John, Chen, Jonathan H, Nieman, Koen, Botting, Patrick, Dey, Damini, Berman, Daniel S, Cheng, Susan, and Ouyang, David

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Heart Disease - Coronary Heart Disease, Cardiovascular, Heart Disease, Prevention, Good Health and Well Being, Humans, Coronary Artery Disease, Calcium, Coronary Angiography, Coronary Vessels, Artificial Intelligence, Deep Learning, Risk Factors, Predictive Value of Tests, Echocardiography, Vascular Calcification, Coronary artery calcium, Echocardiogram, Deep learning, Machine learning, Convolutional neural network, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology
Abstract

BackgroundCoronary artery calcification (CAC), often assessed by computed tomography (CT), is a powerful marker of coronary artery disease that can guide preventive therapies. Computed tomographies, however, are not always accessible or serially obtainable. It remains unclear whether other widespread tests such as transthoracic echocardiograms (TTEs) can be used to predict CAC.MethodsUsing a data set of 2,881 TTE videos paired with coronary calcium CTs, we trained a video-based artificial intelligence convolutional neural network to predict CAC scores from parasternal long-axis views. We evaluated the model's ability to classify patients from a held-out sample as well as an external site sample into zero CAC and high CAC (CAC ≥ 400 Agatston units) groups by receiver operating characteristic and precision-recall curves. We also investigated whether such classifications prognosticated significant differences in 1-year mortality rates by the log-rank test of Kaplan-Meier curves.ResultsTransthoracic echocardiogram artificial intelligence models had high discriminatory abilities in predicting zero CAC (receiver operating characteristic area under the curve [AUC] = 0.81 [95% CI, 0.74-0.88], F1 score = 0.95) and high CAC (AUC = 0.74 [0.68-0.8], F1 score = 0.74). This performance was confirmed in an external test data set of 92 TTEs (AUC = 0.75 [0.65-0.85], F1 score = 0.77; and AUC = 0.85 [0.76-0.93], F1 score = 0.59, respectively). Risk stratification by TTE-predicted CAC performed similarly to CT CAC scores in prognosticating significant differences in 1-year survival in high-CAC patients (CT CAC ≥ 400 vs CT CAC < 400, P = .03; TTE-predicted CAC ≥ 400 vs TTE-predicted CAC < 400, P = .02).ConclusionsA video-based deep learning model successfully used TTE videos to predict zero CAC and high CAC with high accuracy. Transthoracic echocardiography-predicted CAC prognosticated differences in 1-year survival similar to CT CAC. Deep learning of TTEs holds promise for future adjunctive coronary artery disease risk stratification to guide preventive therapies.

Published
2023

14. Risk factors based vessel‐specific prediction for stages of coronary artery disease using Bayesian quantile regression machine learning method: Results from the PARADIGM registry

Author

Park, Hyung‐Bok, Lee, Jina, Hong, Yongtaek, Byungchang, So, Kim, Wonse, Lee, Byoung K, Lin, Fay Y, Hadamitzky, Martin, Kim, Yong‐Jin, Conte, Edoardo, Andreini, Daniele, Pontone, Gianluca, Budoff, Matthew J, Gottlieb, Ilan, Chun, Eun Ju, Cademartiri, Filippo, Maffei, Erica, Marques, Hugo, de A. Gonçalves, Pedro, Leipsic, Jonathon A, Shin, Sanghoon, Choi, Jung H, Virmani, Renu, Samady, Habib, Chinnaiyan, Kavitha, Stone, Peter H, Berman, Daniel S, Narula, Jagat, Shaw, Leslee J, Bax, Jeroen J, Min, James K, Kook, Woong, and Chang, Hyuk‐Jae

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Atherosclerosis, Heart Disease - Coronary Heart Disease, Prevention, Cardiovascular, Heart Disease, Humans, Angina Pectoris, Bayes Theorem, Coronary Angiography, Coronary Artery Disease, Coronary Vessels, Machine Learning, Registries, Risk Factors, cardiovascular risk factors, coronary artery disease, machine learning, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology
Abstract

Background and hypothesisThe recently introduced Bayesian quantile regression (BQR) machine-learning method enables comprehensive analyzing the relationship among complex clinical variables. We analyzed the relationship between multiple cardiovascular (CV) risk factors and different stages of coronary artery disease (CAD) using the BQR model in a vessel-specific manner.MethodsFrom the data of 1,463 patients obtained from the PARADIGM (NCT02803411) registry, we analyzed the lumen diameter stenosis (DS) of the three vessels: left anterior descending (LAD), left circumflex (LCx), and right coronary artery (RCA). Two models for predicting DS and DS changes were developed. Baseline CV risk factors, symptoms, and laboratory test results were used as the inputs. The conditional 10%, 25%, 50%, 75%, and 90% quantile functions of the maximum DS and DS change of the three vessels were estimated using the BQR model.ResultsThe 90th percentiles of the DS of the three vessels and their maximum DS change were 41%-50% and 5.6%-7.3%, respectively. Typical anginal symptoms were associated with the highest quantile (90%) of DS in the LAD; diabetes with higher quantiles (75% and 90%) of DS in the LCx; dyslipidemia with the highest quantile (90%) of DS in the RCA; and shortness of breath showed some association with the LCx and RCA. Interestingly, High-density lipoprotein cholesterol showed a dynamic association along DS change in the per-patient analysis.ConclusionsThis study demonstrates the clinical utility of the BQR model for evaluating the comprehensive relationship between risk factors and baseline-grade CAD and its progression.

Published
2023

15. Ischemia With Nonobstructive Coronary Arteries: Insights From the ISCHEMIA Trial.

Author

Min, James, Hague, Cameron, Berman, Daniel, Chaitman, Bernard, Picard, Michael, Hayes, Sean, Scherrer-Crosbie, Marielle, Kwong, Raymond, Lopes, Renato, Senior, Roxy, Dwivedi, Sudhanshu, Miller, Todd, Chow, Benjamin, de Silva, Ramesh, Stone, Gregg, Boden, William, Bangalore, Sripal, OBrien, Sean, Hochman, Judith, Maron, David, Reynolds, Harmony, Diaz, Ariel, Cyr, Derek, Shaw, Leslee, Mancini, G, Leipsic, Jonathon, and Budoff, Matthew

Subjects
coronary CT angiography, ischemia, ischemia with nonobstructive coronary arteries, stress testing, Female, Humans, Male, Atherosclerosis, Coronary Angiography, Coronary Artery Disease, Coronary Vessels, Ischemia, Myocardial Ischemia, Predictive Value of Tests
Abstract

BACKGROUND: Ischemia with nonobstructive coronary arteries (INOCA) is common clinically, particularly among women, but its prevalence among patients with at least moderate ischemia and the relationship between ischemia severity and non-obstructive atherosclerosis severity are unknown. OBJECTIVES: The authors investigated predictors of INOCA in enrolled, nonrandomized participants in ISCHEMIA (International Study of Comparative Health Effectiveness with Medical and Invasive Approaches), sex differences, and the relationship between ischemia and atherosclerosis in patients with INOCA. METHODS: Core laboratories independently reviewed screening noninvasive stress test results (nuclear imaging, echocardiography, magnetic resonance imaging or nonimaging exercise tolerance testing), and coronary computed tomography angiography (CCTA), blinded to results of the screening test. INOCA was defined as all stenoses

Published
2023

16. Atlas for reporting PET myocardial perfusion imaging and myocardial blood flow in clinical practice: an information statement from the American Society of Nuclear Cardiology

Author

Chareonthaitawee, Panithaya, Bateman, Timothy M., Beanlands, Rob S., Berman, Daniel S., Calnon, Dennis A., Di Carli, Marcelo F., Heller, Gary V., Murthy, Venkatesh L., Patel, Krishna K., Schindler, Thomas H., Taqueti, Viviany R., Wiefels, Christiane C., and Al-Mallah, Mouaz H.

Published
2023
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20. Factors Associated With Coronary Angiography Performed Within 6 Months of Randomization to the Conservative Strategy in the ISCHEMIA Trial

Author

Pracoń, Radosław, Spertus, John A., Broderick, Samuel, Bangalore, Sripal, Rockhold, Frank W., Ruzyllo, Witold, Demchenko, Elena, Nageh, Thuraia, Grossman, Gabriel Blacher, Mavromatis, Kreton, Manjunath, Cholenahally N., Smanio, Paola E.P., Stone, Gregg W., Mancini, G.B. John, Boden, William E., Newman, Jonathan D., Reynolds, Harmony R., Hochman, Judith S., Maron, David J., Doan, John, Linefsky, Jason, Lee, Raven, Patel, Risha, Miller, Todd, Yang Cho, So, Milbrandt, Susan, Shelstad, Dawn, Banerjee, Subhash, Kamath, Preeti, Tejani, Ishita, Cobos, Stanley E., Quiles, Kirsten J., Dwyer, Raven R., Donnino, Robert M., Espinosa, Dalisa, Phillips, Lawrence M., Saric, Muhamed, Abdul-Nour, Khaled, Schley, Allison, Golden, Heather, Stone, Peter H., Osseni, Hermine, Wiyarand, Charlene, Douglass, Peter, Pomeroy, Hayley, Craft, Alexandra, Harvey, Bethany, Jang, James J., Anaya, Olivia, Yee, Gennie, Goold, Phoebe, Weitz, Steven, Giovannone, Steven, Pritchard, Lori, Arnold, Suzanne, Gans, Rosann, Henry O’Keefe, Jr, James, Kennedy, Paul, Shapiro, Michael D., Ganesan, Shobana, Schlichting, David, Naher, Aynun, El-Hajjar, Mohammad, Sidhu, Mandeep S., Fein, Steven A., Stewart, Wendy L., Torosoff, Mikhail T., Salmi, Kristin M., Lyubarova, Radmila, Mookherjee, Sulagna, Drzymalski, Krzysztof, McFalls, Edward O., Garcia, Santiago A., Bertog, Stefan C., Johnson, Debra K., Siddiqui, Rizwan A., Herrmann, Rebekah R., Ishani, Areef, Hansen, Ronnell A., Georges Khouri, Michel, Arges, Kristine, LeFevre, Melissa, Tomfohr, Jennifer, Goldberg, Jonathan L., Ann Byrne, Kimberly, Zappernick, Taissa, Goldweit, Richard, Canada, Sallie, Kakade, Meghana, Mieses, Patricia, Cobos, Stanley E., Dwyer, Raven R., Cohen, Ronny A., Espinosa, Dalisa, Mirrer, Brooks, Quiles, Kirsten J., Navarro, Victor, Rantinella, Magdalena, Rodriguez, Jessica, Mancilla, Olivia, Winchester, David E., Stinson, Susan, Kronenberg, Marvin, Weyand, Terry, Rogal, Philip, Crook, Sherron C., McFarren, Christopher, Heitner, John F., Ho, Jean, Khan, Saadat, Mohamed, Mahmoud, Dauber, Ira M., Soltau, Mary R., Rose, Delsa K., Wimmer, Rebecca J., Siegel, Kathy E., Derbyshire, Susan, Cannan, Charles, Dixon, Michelle, Leonard, Gerald, Sudarshan, Sriram, Heard, Ciarra, Gabriel, Viviana, Desire, Sukie, Mehta, Puja K., McDaniel, Michael, Rashid, Fauzia, Lerakis, Stamatios, Asier, Senait, Quyyumi, Arshed, Patel, Keyur, Wenger, Nanette K., Hedgepeth, Chester M., Gillis, Jennifer, Hurlburt, Heather, Manocchia, Megan, Rosen, Alan, Moore, Susan, Congdon, Elizabeth, Sahul, Zakir, Brandt, Gail, Marchelletta, Nora, Wippler, Kristina, Booth, David, Taul, Yvonne, Leung, Steve, Isaacs, Jennifer, Abdel-Latif, Ahmed, Bulkley, Viktoria, Reda, Hassan, Rodgers, Caroline, Ziada, Khaled, Setty, Sampoornima, Halverson, Kimberly E., Roraff, Christine, Thorsen, Jonean, Barua, Rajat S., Ojajuni, Amarachi, Olurinde, Oni, Surineni, Kamalakar, Hage, Fadi, Valaiyapathi, Badhma, Caldeira, Christiano, Davies, James E., Leesar, Massoud, Heo, Jaekyeong, Iskandrian, Amy, Al Solaiman, Firas, Singh, Satinder, Dajani, Khaled, Kartje, Carol M., El-Hajjar, Mohammad, Mesropian, Paul Der, Sacco, Joseph, Rawlins, Michele, McCandless, Brian, Thomson, Jennifer, Orgera, Marisa, Sidhu, Mandeep S., Colleen Rogge, Mary, Arif, Imran, Bunke, Julie, Kerr, Hanan, Unterbrink, Kendra, Fannon, Jacqueline, Burman, Cynthia, Trejo, Jorge F., Dubin, Marcia F., Fletcher, Gerald, Lane, Gary E., Neeson, Lynn M., Parikh, Pragnesh P., Pollak, Peter M., Shapiro, Brian P., Landolfo, Kevin, Gemignani, Anthony, Beaudry, Sarah, O’Rourke, Daniel, Meadows, Judith L., Tirado, Stephanie A., Halliday, Janet, Julian, Pamela, Call, Jason T., Lane, Stephanie M., Stanford, Jennifer L., Hannan, Joseph, Bojar, Robert, Arsenault, Patricia, Kumar, Deepti, Sigel, Pamela, Mukai, John, Martin, Edward T., Brooks, Miriam, Vorobiof, Gabriel, Douangvila, Ladda, Gevorgyan, Rubine, Moorman, Alec, Ranjbaran, Fatima, Smith, Bryn, Ohmart, Carly, Kinlay, Scott, Hamburger, Robert J., Rocco, Thomas P., Ly, Samantha, Bhatt, Deepak L., Quinn, Margot C., Croce, Kevin, Temiyasathit, Sara, Quin, Jacquelyn A, Do, Jacquelyn, Anumpa, Jati, Tobin, Desiree, Zenati, Marco, Faxon, David P, Rayos, Glenn, Langdon, Jennifer, Werner Bayer, Marcia, Seedhom, Ashraf, O’Malley, Amanda, Sullenberger, Lance, Orvis, Erin, Kumkumian, Gregory, Murphy, Mandy, Greenberg, Ann, Iraola, Margaret, Sedlis, Steven P., Maranan, Leandro C., Donnino, Robert M., Lorin, Jeffrey, Tamis-Holland, Jacqueline E., Malinay, Ammy, Kornberg, Robert, Leber, Robert, Saba, Souheil, Edillo, Candice P., Lee, Michael W., Small, Delano R., Nona, Wassim, Alexander, Patrick B., Rehman, Iram, Badami, Umesh, Ostrander, Ann, Wasmiller, Stephanie, Marzo, Kevin, Drewes, Wendy, Patel, Dipti, Robbins, Inga H., Levite, Howard A., White, Jackie M, Shetty, Sanjay, Hallam, Alison, Patel, Mayuri, Hamroff, Glenn S., Spooner, Benjamin J, Hollenweger, Linda M, Little, Raymond W., Little, Holly, Zimbelman, Brandi D., Little, Tiffany, Lui, Charles Y., Eskelson, Nona A, Smith, Brigham R., Vezina, Daniel P., Khor, Lillian L., Abraham, Josephine D., Bull, David A., McKellar, Stephen H., Booth, David, Taul, Yvonne, Kotter, John, Rodgers, Caroline, Abdel-Latif, Ahmed, Isaacs, Jennifer, Bulkley, Viktoria, Hu, Bob, Kaneshiro, Renee, Labovitz, Arthur J., Berlowitz, Michael, Kirby, Bonnie J., Rogal, Philip, Tran, Nhi N., McFarren, Christopher, Jahrsdorfer, Catherine, Matar, Fadi, Caldeira, Christiano, Rodriguez, Fatima, Yunis, Reem, Schnittger, Ingela, Patro, Jhina, Fearon, William F., Deedwania, Prakash, Vega, Antonia, Reddy, Kiran, Sweeny, Joseph, Bloise-Adames, Hugo, Jimenez, Santa, Saint Vrestil, Nicole, Bhandari, Reyna, Spizzieri, Christopher, Schade, Danielle, Yost, Roxanne, Hochberg, Claudia P, Beardsley, Paula, Fine, Denise, Salerno, William D., Tancredi, Jana, Arakelian, Patricia, Mathus, Susan, O’Neill, Deborah, Wyman, Ray, Burkhardt, Joy, Hosino, Suellen, Lubyanaya, Oksana A., Salas, Jose D., Zarka, Amer, Aguirre, Maria, Shah, Anil V., Dhawan, Manu, Parra, Diana, Tran, Tri, Haldis, Thomas, Weick, Catherine, Fowler-Lehman, Katie, Spitzer, Natalie, Riedberger, Casey, Weick, Catherine, Kohn, Jeffrey A., Cobos, Stanley E., Dwyer, Raven R., Espinosa, Dalisa, Quiles, Kirsten J., Girotra, Saket, Drum, Carrie, Miller-Cox, Kimberly, Ollinger, Amy, Almousalli, Omar, Capasso-Gulve, Elizabeth, Melanie Loehr, Alaine, Mosley, Marlowe, Krishnam, Mayil S., Heydari, Shirin, Milliken, Jeffrey C., Lundeen, Andrea M., Patel, Pranav M., Karanjah, Edgar, Seto, Arnold H., Marfori, Wanda C., Harley, Kevin T., Hernandez-Rangel, Eduardo, Gibson, Michael A., Singh, Pam, Allen, Byron J., Coram, Rita, Marie Webb, Anne, Fridell, Ellie, Wilson, Heidi, Thomas, Sabu, Kim, Angela, Schwartz, Ronald G, Wilmot, Patrick, Chen, Wei, El Shahawy, Mahfouz, Stevens, Ramona, Stafford, James, Black, Loriane, Abernethy, William B., Hull, Amber B., Lim, Olivia J., Tucker, Helen C., Putnam, Natasha C., Hall, Linda L., Cauthren, Tia, Tucker, Trish, Zurick, Andrew, Horton, Hollie, Orga, Jan, Meyer, Thomas M., White, Joyce R., Morford, Ronald G., Baumann, Cynthia, Rutkin, Bruce, Seeratan, Vidya, Bokhari, Sabahat, Jimenez, Magnolia, Sokol, Seth I., Schultz, Cidney, Meisner, Jay, Russo, Jeanne, Hamzeh, Ihab, Misra, Arunima, Huda, Zohra, Wall, Matthew, Boan, Araceli, Lenges De Rosen, Veronica, Alam, Mahboob, Turner, Michael C., Hinton, Christine R, Mulhearn, Thomas J., Good, Arnold P., Archer, Beth A., Dionne, Julia S., Allardyce, Cheryl A., Sikora, Lindsey N., Czerniak, Jennifer H., Mull, Jennifer A., Ferguson, Elizabeth, Laube, Frances, Shammas, Nicolas W., Shammas, Gail A, Christensen, Lori, Park, Holly, Chilton, Robert, Hecht, Joan, Nguyen, Patricia K., Vo, Davis, Hirsch, James, Jezior, Matthew, Bindeman, Jody, Salkind, Sara, Espinosa, Dalisa, Desimone, Lori-Ann, Gordon, Paul C., Felix-Stern, Lina, Crain, Thomas, Gomes, Jassira, Gordon, Catherine, Stenberg, Robert, Mann, Aimee, McCreary, Theresa, Pedalino, Ronald P., Cobos, Stanley E., Dwyer, Raven R., Espinosa, Dalisa, Quiles, Kirsten J., Wiesel, Joseph, Cobos, Stanley E., Dwyer, Raven R., Espinosa, Dalisa, Quiles, Kirsten J., Juang, George J., Gopaul, Candace, Hultberg, Karen, Huk, Tauqir, Hussain, Afshan, Al-Amoodi, Mohammed, Zambrano, Yesenia, Medina Rodriguez, Sarah, Milner, Trudie, Wohns, David, Mulder, Abbey, Van Oosterhout, Stacie, Lader, Ellis W., Meyer, Martha, Mumma, Michael, Clapp, Nancy L., Barrentine, Heather, Dharmarajan, Lekshmi, Jose, Jenne M., Cobos, Stanley E., Dwyer, Raven R., Espinosa, Dalisa, Quiles, Kirsten J., Manchery, Jenne, McGarvey Jr, Joseph F.X., McKinney, Vera, Schwarz, Linda, Downes, Thomas R., Kaczkowski, Scott M., Luckasen, Gary J., Jaskowiak, Adam J., Klitch, Joel, Cheong, Benjamin, Dees, Debra, Potluri, Srinivasa, Vasquez, Precilia, Mastouri, Ronald A., Breall, Jeffery A., Hannemann, Elise L., Revtyak, George E., Mae Foltz, Judy, Bazeley, Jonathan W., Li, Dayuan, DeRosa, Emily, Jorgenson, Beth, Riestenberg-Smith, Joyce, Giedd, Kenneth, Old, Wayne, Bariciano, Rebecca, Burt, Francis, Sokhon, Kozhaya, Waldron, Jessica, Mayon, Michelle, Gopal, Deepika, Valeti, Uma S., Ann Peichel, Gretchen, Kobashigawa, Jon, Starks, Brandy, Garcia, Lucilla, Thottam, Maria, Bhargava, Balram, Anand, Anjali, Chakanalil Govindan, Sajeev, Raj, Janitha, Gopalan Nair, Rajesh, Ravindran, Reshma, Rajalekshmi, VS, Nataraj, Nandita, Moorthy, Nagaraja, Nayak, Soundarya, Mylarappa, Mahevamma, Narayanappa, Suryaprakash, Pandit, Neeraj, Bajaj, Sheromani, Kumar Nath, Ranjit, Yadav, Vandana, Mishra, Girish, Dwivedi, S.K., Tewari, Roma, Narain, V.S., Mishra, Meenakshi, Chandra, Sharad, Patel, Shivali, Singh, Suman, Wander, Gurpreet S., Tandon, Rohit, Ralhan, Sarju, Kaur, Baljeet, Aslam, Naved, Gupta, Sonika, Goyal, Abhishek, Bhargava, Balram, Suvarna, Chandini, Karthikeyan, G., Ramakrishnan, S., Seth, Sandeep, Yadav, Rakesh, Singh, Sandeep, Roy, Ambuj, Parakh, Neeraj, Kumar Verma, Sunil, Narang, Rajiv, Mishra, Sundeep, Naik, Nitish, Sharma, Gautam, Kumar Choudhary, Shiv, Patel, Chetan, Gulati, Gurpreet, Sharma, Sanjeev, Bahl, V K, Mathew, Anoop, Mannekkattukudy Kurian, Binoy, Punnoose, Eapen, Avdhoot Gadkari, Milind, Rupesh Karwa, Sheetal, Gadage, Siddharth, Kolhe, Suvarna, Umesh Pillay, Tapan, Satheesh, Santhosh, Vindhya, R. J., Jain, Peeyush, Seth, Ashok, Singh Meharwal, Zile, Mathur, Atul, Verma, Atul, Kaul, Upendra, Bhatia, Mona, Sachdeva, Ankush, Indira Devi, Thounaojam, Jungla, Nungshi, Christopher, Johann, Manjula Rani, K., Menon, Rajeev, Sowjanya Reddy, M., Kumar, Nirmal, Preethi, K., Oomman, Abraham, sidh, Rinu R, Mao, Robert, Ramakrishnan, T., Solomon, Hilda, Francis, Rajesh, Naik, Sudhir, Vamshi, Priya P., Parveen Khan, Sajeeda, Christopher, Johann, Preethi, Kotiboinna, Kumar, Nirmal, Grant, Purvez, Hande, Shweta, Sonawane, Poonam, Kachru, Ranjan, Dubey, Abhishek, Rawat, Kavita, Kumar, Ajit, Ganapathi, Sanjay, K, Jayakumar, CP, Vineeth, Sivadasanpillai, Harikrishnan, Chacko, Manas, Sasidharan, Bijulal, Babu, Suresh, TR, Kapilamoorthy, Christopher, Johann, Reddy, Sowjanya, Polamuri, Praneeth, Rani, Manjula, Kaul, Upendra, Arambam, Priyadarshani, Singh, Bebek, Senior, Roxy, Fox, Keith AA, Young, Grace M., Carruthers, Kathryn, Senior, Roxy, Elghamaz, Ahmed, Gurunathan, Sothinathan, Karogiannis, Nikolaos, Young, Grace M., Shah, Benoy N, Kinsey, Christopher, Trimlett, Richard HJ, Kavalakkat, Raisa, Rubens, Michael B, Evans, Jo, Nicol, Edward D, Hassan, Ikraam, Mittal, Tarun K, Hampson, Reinette, Andreas Gamma, Reto, Williams, Sarah, Holland, Kim, Swan, Karen, de Belder, Mark A, Atkinson, Bev, Thambyrajah, Jeet, Kunhunny, Swapna, Davies, John R, Lindsay, Steven J., Atkinson, Craig, Kurian, John, Krannila, Carita, Jamil, Haqeel, Vinod, Manitha, Raheem, Osama, Hoye, Angela, Chaytor, Lisa, Cox, Leanne, Morrow, Julie, Rowe, Kay, Donnelly, Patrick, Kelly, Stephanie, Valecka, Bernardas, Regan, Susan, Turnbull, Dawn, Chauhan, Anoop, Fleming, Catherine, Ghosh, Arijit, Gratrix, Karen, Preston, Stephen, Barr, Craig, Cartwright, Anne, Alfakih, Khaled, Knighton, Abigail, Byrne, Jonathan, Martin, Katherine, Webb, Ian, Henriksen, Peter, Flint, Laura, Harrison, James, OKane, Peter, Lakeman, Nicki, Ljubez, Anja, de Silva, Ramesh, Conway, Dwayne S. G., Wright, Judith, Exley, Donna, Sirker, Alexander A, Andiapen, Mervyn, Richards, Amy J., Hoole, Stephen P, Wong, Lisa, Witherow, Fraser N., Munro, Melanie J., Johnston, Nicola, Harbinson, Mark, McEvoy, Michelle, Walsh, Simon, Brown, Caroline, Douglas, Hanna, Luckie, Matthew, Charles, Thabitha, Kolakaluri, Laurel, Phillips, Hannah, Sobolewska, Jolanta, Morby, Louise, Hallett, Karen, Corbett, Carolyn, Winstanley, Lynne, Jeetley, Paramjit, Smit, Angelique, Patel, Niket, Kotecha, Tushar, Travill, Christopher, Gent, Susan, Karimullah, Iqbal, Hussain, Nafisa, Al-Bustami, Mahmud, Braganza, Denise, Haines, Fiona, Taaffe, Joanne, Henderson, Robert, Burton, Jane, Pointon, Kate, Colton, Maria, Naik, Surendra, King, Rachel, Mathew, Thomas, Brown, Ammani, Docherty, Andrew, Berry, Colin, McCloy, Lisa, Collison, Damien, Robb, Kate, Roditi, Giles, Paterson, Craig, Crawford, Wenda, Kelly, Joanne, McGregor, Lorraine, Moriarty, Andrew J, Mackin, Anne, Glover, Jason D., Knight, Janet P, Pradhan, Jiwan, Mikhail, Ghada, Bose, Tuhina, Francis, Darrel P., Dzavik, Vladimir, Goodman, Shaun, Gosselin, Gilbert, Gosselin, Gilbert, Proietti, Anna, Brousseau, Myriam, Corfias, Magalie, Blaise, Patricia, Harvey, Luc, Diaz, Ariel, Rheault, Philippe, Barrero, Miguel, Gagné, Carl-Éric, Alarie, Patricia, Pépin-Dubois, Yanek, Arcand, Linda, Costa, Ricardo, Roy, Isabelle, Tung Sia, Ying, Montpetit, Estelle, Lemay, Catherine, Gisbert, Alejandro, Gervais, Pierre, Rheault, Alain, Drouin, Katia, Carl Phaneuf, Denis, Bergeron, Christine, Gosselin, Gilbert, Shelley, Christine, Masson, Christine, Garg, Pallav, Carr, Sandy, Bone, Catherine, Chow, Benjamin J.W., Moga, Ermina, Hessian, Renee C., Kourzenkova, Janetta, Beanlands, Rob S., Walter, Olga, Davies, Richard F., Bainey, Kevin R., Hogg, Norma, Welsh, Suzanne, Cheema, Asim N., Bagai, Akshay, Wald, Ron, Goodman, Shaun, Kushniriuk, Khrystyna, Joseph Graham, John, Hussain, Mohammed, Peterson, Mark, Bello, Olugbenga, Chow, Chi-Ming, Abramson, Beth, Nazir Cheema, Asim, Syed, Ishba, Hussain, Mohammed, Kushniriuk, Khrystyna, Cha, James, Otis, Judy, Otis, Rebecca, Howarth, Andrew G, Seib, Michelle M, Rivest, Sandra M, Sandonato, Rosa, Wong, Graham, Chow, Jackie, Starovoytov, Andrew, Uchida, Naomi, Meadows, Ngaire, Uxa, Amar, Asif, Nadia, Tavares, Suzana, Galiwango, Paul, Bozek, Bev, Kassam, Saleem, Shier, Maria, Mukherjee, Ashok, Larmand, Lori-Ann, Ricci, A. Joseph, Janmohamed, Amir, Hart, Brenda, Lam, Andy, Marucci, Jane, Tai, Sharon, Mehta, Shamir, Brons, Sonya, Beck, Chris, Wong, Glenda, Etherington, Krystal, Arumairajah, Thippeekaa, Udell, Jacob, Aprile, Maria, Karlsson, Sara, Webber, Susan, Généreux, Philippe, Mercure, Chantale, Hameed, Adnan, Aedy, Nancy, Daba, Ledjalem, Farquharson, Fran, Siddiqui, Anam, Carlos Carvalho, Antonio, Lopes, Renato D., Hueb, Whady, Emy Takiuti, Myrthes, Cury Rezende, Paulo, Eustáquio Ribeiro Silva, Expedito, Ciappina Hueb, Alexandre, Pizzol Caetano, Leonardo, Schaan de Quadros, Alexandre, Abdala Karam Kalil, Renato, Peixoto Deiro, Aline, Luiz da Costa Vieira, José, Manica Muller, Alice, Antonieta Pereira de Moraes, Maria, Píccaro de Oliveira, Pedro, Maria Ascoli, Bruna, Bridi, Leonardo, Zottis Poletti, Sílvia, Savaris, Simone, Vitola, João V, Cerci, Rodrigo J, Zier, Sandra S., Farias, Fabio R, Veiga Jr, Vilmar, Fernandes, Miguel M, Antonio Marin-Neto, José, Schmidt, André, de Oliveira Lima Filho, Moysés, Franca da Cunha, Diego, Mendes Oliveira, Ricardo, Reynaldo Abbud Chierice, João, Polanczyk, Carísi A., Rucatti, Guilherme G, Furtado, Mariana V., Igansi, Fernanda, Smidt, Luis F., Haeffner, Mauren P, Carlos Carvalho, Antonio, Almeida, Viviane, Pucci, Gustavo, Sanchez de Souza, Gabriela, Lyra, Flavio, Rabelo Alves Junior, Alvaro, Almeida, Mayana, dos Santos, Viviane, Dracoulakis, Marianna D. A., Oliveira, Natalia S, Lima, Rodolfo G. S. D, Figueiredo, Estevao, Edilena Paulino Azevedo, Bruna, Ricardo Caramori, Paulo, Bizzaro Santos, Marco, Germann, Amanda, Gomes, Vitor, Homem, Rosa, Magedanz, Ellen, Tumelero, Rogerio, Laimer, Rosane, Tognon, Alexandre, Dall’Orto, Frederico, Mesquita, Claudio T., Santos, Roberta P, Colafranseschi, Alexandre S., Oliveira, Amarino C., Carvalho, Luiz A., Palazzo, Isabella C., Sousa, Andre S., Eustáquio Ribeiro da Silva, Expedito, Gabriel Melo de Barros e Silva, Pedro, Yumi Okada, Mariana, de Pádua Silva Baptista, Luciana, Paula Batista, Ana, Jamus Rodrigues, Marcelo, Nogueira Rabaça, Aline, Valério Coimbra de Resende, Marcos, Francisco Saraiva, Jose, Miranda Trama, Larissa, Silva, Talita, Thais de Souza Ormundo, Camila, Vicente, Carla, Costantini, Costantino, Pinheiro, Caroline, Komar, Daniele, Szwed, Hanna, Demkow, Marcin, Kepka, Cezary, Teresinska, Anna, Walesiak, Olga, Kryczka, Karolina, Malinowska, Katarzyna, Henzel, Jan, Solecki, Mateusz, Kaczmarska, Edyta, Mazurek, Tomasz, Maksym, Jakub, Wojtera, Karolina, Fojt, Anna, Szczerba, Ewa, Drozdz, Jaroslaw, Czarniak, Bartosz, Frach, Malgorzata, Szymczyk, Konrad, Niedzwiecka, Iwona, Sobczak, Sebastian, Ciurus, Tomasz, Jakubowski, Piotr, Misztal-Teodorczyk, Magdalena, Teodorczyk, Dawid, Swiderek, Marta, Fratczak, Aleksandra, Wojtala, Ewelina, Szkopiak, Marcin, Lebioda, Patrycja, Wlodarczyk, Michal, Plachcinska, Anna, Kusmierek, Jacek, Miller, Magdalena, Marciniak, Halina, Wojtczak-Soska, Karolina, Łuczak, Katarzyna, Tarchalski, Tomasz, Cichocka-Radwan, Anna, Szwed, Hanna, Karwowski, Jaroslaw, Anna Szulczyk, Grazyna, Witkowski, Adam, Kukuła, Krzysztof, Celińska-Spodar, Małgorzta, Zalewska, Joanna, Gajos, Grzegorz, Bury, Krzysztof, Pruszczyk, Piotr, Łabyk, Andrzej, Roik, Marek, Szramowska, Agnieszka, Zdończyk, Olga, Łoboz-Grudzień, Krystyna, Jaroch, Joanna, Sokalski, Leszek, Brzezińska, Barbara, Lesiak, Maciej, Łanocha, Magdalena, Reczuch, Krzysztof W., Kolodziej, Adam, Kalarus, Zbigniew, Swiatkowski, Andrzej, Szulik, Mariola, Musial, Wlodzimierz J., Marcinkiewicz-Siemion, Marta, Bockeria, Olga, Bockeria, Leo, Bockeria, Olga, Petrosyan, Karen, Kudzoeva, Zalina, Trifonova, Tatiana, Aripova, Nodira, Chernyavskiy, Alexander M., Naryshkin, Ivan A., Kretov, Evgeniy I., Kuleshova, Alena, Grazhdankin, Igor O., Malaev, Dastan, Bershtein, Leonid L., Sayganov, Sergey A., Subbotina, Irina, Kuzmina-Krutetskaya, Anastasia M., Gumerova, Victoria, Zbyshevskaya, Elizaveta V., Katamadze, Nana O., Nikolaeva, Olga B., Kozlov, Pavel S., Kozulin, Vikentiy Y., Lubinskaya, Ekaterina I., Luis Lopez-Sendon, Jose, Castro, Almudena, Lopez-Sendon, Jose, Fernández-Figares, Virginia, Castro, Almudena, Refoyo Salicio, Elena, Guzman, Gabriela, Galeote, Gabriel, Valbuena, Silvia, Peteiro, Jesús, Dolores Martínez-Ruíz, María, Pérez-Fernández, Ruth, Blanco-Calvo, Moisés, Cuenca-Castillo, José J, Alonso-Álvarez, Encarnación, Flores-Ríos, Xacobe, García-González, Paula, Prada-Delgado, Óscar, Barge-Caballero, Gonzalo, Ramon Gonzalez Juanatey, Jose, Seijas Amigo, Jose, Souto Bayarri, Miguel, Pubull Nuñez, Virginia, Ocaranza Sanchez, Raymundo, Cid Alvarez, Belen, Peña Gil, Carlos, Martinez Monzonis, Amparo, Sionis, Alessandro, Fernández Martínez, Ana, Vila Perales, Montserrat, Maria Padró, Josep, Serra Peñaranda, Antonio, García Picart, Joan, Ginel Iglesias, Antonino, Garcia-Moll Marimon, Xavier, Pons Lladó, Guillem, Carreras Costa, Francesc, Miro, Vicente, Igual, Begoña, Diez, Jose L, Calvillo, Pilar, Ortuño, F. Marin, Valdés Chávarri, M., Quintana Giner, M., Tello Montolliu, A., Romero Aniorte, A.I., Pinar Bermudez, E., Rivera Caravaca, JM., De La Morena, G., Gracida Blancas, Montserrat, Cañavate, Olga, Guerrero, Sonia, Riera, Silvia, Enrique Castillo Luena, Jose, Enrique Castillo Luena, Jose, Lasala, Maria, Fernandez-Aviles, Francisco, Lorenzo, Maria, Sobrino, Olga, Vazquez, Alexandra, Jiang, Lixin, Chen, Jiyan, Dong, Haojian, He, Peiyu, Xia, Chunli, Yang, Junqing, Zhong, Qi, Wu, Yongjian, Tian, Yanmeng, Li, Dongze, Ma, Yitong, Li, Xiaomei, Yang, Yining, Ma, Xiang, Yu, Zixiang, Zhao, Qian, Ji, Zheng, Li, Chunguang, Zhang, Lei, Zhao, Yu, Zhu, Bolin, Yang, Xinchun, Chen, Mulei, Chi, Hongjie, Wang, Yang, Zhang, Jing, Lin, Wenhua, Jing, Rui, Liu, Jingjing, Zeng, Hesong, Zhou, Qiang, Xu, Chang, Li, Zhuxi, Li, Junhua, Xiong, Luyang, Fu, Xin, Gao, Dan, Jiang, Dengke, Leng, Ran, Wang, Xutong, Yuan, Qianqian, Zhang, Lili, Yang, Bin, Bai, Ziliang, Li, Jianhua, Qi, Jie, Wang, Fei, Wang, Haitao, Yang, Bin, Yue, Zhou, Zhang, Zhulin, Wang, Songtao, Dong, Yumei, Mao, Jiajia, Zhang, Bin, Cheng, Gong, Li, Xiuhong, Yao, Xiaowei, Zhong, Nier, Zhou, Ning, Zhao, Yulan, Huang, Yaping, Zhou, Panpan, Fang, Xuehua, Su, Wei, Zeng, Qiutang, Kunwu, Yu, Peng, Yudong, Su, Xin, Su, Xi, Wang, Chen, Zhao, Yunhai, Li, Qingxian, Geng, Yaming, Wang, Yanfu, Nie, Shao-ping, Fan, Jing-yao, Feng, Si-ting, Wang, Xiao, Yan, Yan, Zhang, Hui-min, Yu, Qin, Chi, Lingping, Liu, Fang, Wang, Jian’an, Chen, Han, Jiang, Jun, Li, Huajun, Wang, Jian’an, Han, Yechen, Xu, Lihong, Zhang, Shuyang, Liu, Zhenyu, Liu, Zhenyu, Chen, Gang, Hu, Rongrong, Maggioni, Aldo P., Piero Perna, Gian, Pietrucci, Francesca, Marini, Marco, Gabrielli, Gabriele, Provasoli, Stefano, Di Donato, Anna, Verna, Edoardo, Monti, Lorenzo, Nardi, Barbara, Di Chiara, Antonio, Pezzetta, Francesca, Mortara, Andrea, Casali, Valentina, Galvani, Marcello, Attanasio, Chiara, Ottani, Filippo, Sicuro, Marco, Leone, Gianpiero, Pisano, Francesco, Bare, Cristina, Calabro, Paolo, Fimiani, Fabio, Formisano, Tiziana, Tarantini, Giuseppe, Barioli, Alberto, Cucchini, Umberto, Ramani, Federica, Luigi Andres, Anto, Racca, Emanuela, Rolfo, Fabrizio, Goletto, Cecilia, Briguori, Carlo, De Micco, Francesca, Amati, Roberto, Di Marco, Stefano, Vergoni, William, Tricoli, Martina, Russo, Aldo, Villella, Massimo, Fanelli, Raffaele, Douglas White, Harvey, Alsweiler, Caroline, Poh, Kian-Keong, Chai, Ping, Lau, Titus, Loh, Joshua P., Tay, Edgar L., Teoh, Kristine, Tan, Sik-Yin V, Teo, Lynette L., Sia, Winnie C, Ong, Ching-Ching, Leong, Audrey W, Wong, Raymond C., Loh, Poay-Huan, Kofidis, Theodoros, Xian Chan, Wan, Hui Chan, Koo, Foo, David, Hai Yan, Li, Loh Kwok Kong, Jason, Min Er, Ching, Haider Jafary, Fahim, Chua, Terrance, Ismail, Nasrul, Tun Kyaw, Min, Yip, Deborah, Doerr, Rolf, Doerr, Rolf, Stumpf, Juergen, Grahl, Dorit, Matschke, Klaus, Guenther, Franziska, Simonis, Gregor, Bonin, Kerstin, Kadalie, Clemens T., Sechtem, Udo, Wenzelburger, Ina, Ong, Peter, Gruensfelder, Susanne, Christian Schulze, P., Goebel, Bjoern, Lenk, Karsten, Nickenig, Georg, Sinning, Jan-Malte, Weber, Marcel, Werner, Nikos, Marthe Lang, Irene, Huber, Kurt, Schuchlenz, Herwig, Steinmaurer, Gudrun, Weikl, Stefan, Marthe Lang, Irene, Winter, Max-Paul, Andric, Tijana, Huber, Kurt, Tscharre, Maximilian, Jakl-Kotauschek, Gabriele, Wegmayr, Claudia, Jäger, Bernhard, Egger, Florian, Keltai, Matyas, Vertes, Andras, Sebo, Judit, Davidovits, Zoltan, Matics, Laszlone, Varga, Albert, Ágoston, Gergely, Fontos, Geza, Dekany, Gabor, Merkely, Bela, Bartykowszki, Andrea, Maurovich-Horvat, Pal, Kerecsen, Gabor, Jakal, Agnes, Hinic, Sasa, Djokic, Jelena, Zdravkovic, Marija, Mudrenovic, Vladan, Crnokrak, Bogdan, Beleslin, Branko D., Boskovic, Nikola N., Djordjevic-Dikic, Ana D., Petrovic, Marija T., Giga, Vojislav L., Dobric, Milan R., Stepanovic, Jelena J., Markovic, Zeljko Z., Mladenovic, Ana S., Cemerlic-Adjic, Nada, Velicki, Lazar, Kamenica, Sremska, Pupic, Ljiljana, Davidović, Goran, Simović, Stefan M., Vučić, Rada, Dekleva, Milica Nikola, Martinovic, Miroslav Stevo, Stevanovic, Gordana, Stankovic, Goran, Dobric, Milan, Apostolovic, Svetlana, Martinovic, Sonja Salinger, Stanojevic, Dragana, Escobedo, Jorge, Jesús-Pérez, Ramon de, Juarez, Benito, Baleón-Espinosa, Rubén, Campos-Santaolalla, Arturo S, Durán-Cortés, Elihú, Flores-Palacios, José M, García-Rincón, Andrés, Jiménez-Santos, Moisés, Peñafiel, Joaquín V, Ortega-Ramírez, José A, Valdespino-Estrada, Aquiles, Rosas, Erick Alexánderson, Canales Brassetti, María Fernanda, Vences Anaya, Diego Adrián, García, María Pérez, Carvajal Juarez, Isabel Estela, Rovalo, Magdalena Madero, Morales Rodríguez, Erick Donato, Selvanayagam, Joseph B., Rankin, Jamie, Murphy, Deirdre, Selvanayagam, Joseph B., Lee, Sau, Joseph, Majo X., Thomas, Prince, Thambar, Suku T., Chaplin, Melissa D, Boer, Stephanie C, Beltrame, John F., Stansborough, Jeanette K., Black, Marilyn, Hillis, Graham S., Bonner, Michelle M., Ireland, Kim F., Venn-Edmonds, Clare, Steg, Philippe-Gabriel, Abergel, Helene, Juliard, Jean-Michel, Thobois, Corine, Pasteur, C.H. Louis, Thuaire, Christophe, Tachot, Emilie, Dutoiu, Téodora, Laure, Christophe, Vassaliere, Christel, Steg, Philippe Gabriel, Abergel, Helene, Juliard, Jean-Michel, Fuentes, Axelle, Slama, Michel S., Eliahou, Ludivine, Cedex, Clamart, El Mahmoud, Rami, Dubourg, Olivier, Michaud, Pierre, Nicollet, Eric, Hadjih, Sarah, Cedex, Corbeil-Essonnes, Goube, Pascal, Brito, Patricia, Barone-Rochette, Gilles, Barone-Rochette, Gilles, Furber, Alain, Cornet, Charles, Bière, Loïc, Rautureau, Jeremy, Juceviciene, Agne, Kalibataite-Rutkauskiene, Irma, Keinaite, Laura, Laucevicius, Aleksandras, Laukyte, Monika, Celutkiene, Jelena, Mikolaitiene, Gelmina, Smigelskaite, Akvile, Tamasauskiene, Ilona, Urboniene, Agne, Kedhi, Elvin, Klinieken, Isala, Timmer, Jorik, Bouwhuis, Ilse, Hermanides, Rik, Nijmeijer, Lia, Kaplan, Eliza, Riezebos, Robert K., Samadi, Pouneh, Schoep Jeannette, J. M., Dongen, Elise van, Janzen, Elisabeth M., Niehe, Sander R., Suryapranata, Harry, Ahoud, Sandra, Vugt, Stijn van, Ramos, Ruben, Santa Marta, Hospital de, Cacela, Duarte, Santana, Ana, Fiarresga, Antonio, Sousa, Lidia, Marques, Hugo, Patricio, Lino, Selas, Mafalda, Bernanrdes, Luis, Silva, Filipa, Rio, Pedro, Freixo, Cláudia, Carvalho, Ramiro, Ferreira, Rui, Silva, Tiago, Rodrigues, Ines, Modas, Pedro, Portugal, Guilherme, Fragata, Jose, Pinto, Fausto J., Cabrita, Inês Zimbarra, Menezes, Miguel Nobre, Rocha, Andreia, Lopes, Guilhermina Cantinho, Figueiras, Francisca Patuleia, Almeida, Ana Gomes, Coelho, Andreia, CanVas Silva, Pedro, Capinha, Marta, Nobre, Angelo, Caetano, Maria Inês, Francisco, Ana Rita, Silva, Susana, Ferreira, Nuno, de Gaia, Vila Nova, Lopes, Ricardo L., Diaz, Rafael, Guzman, Luis, Tinnirello, Veronica, Figal, Julio César, Nicolás Mungo, Matías, Buenos Aires, Ciudad Autonoma de, Méndiz, Oscar, Cortés, Claudia, Favaloro, Roberto René, Alvarez, Carlos, Garcia, Marina, Blanca, Bahia, Courtis, Javier, Godoy, Valeria, Zeballos, Gabriela, Schiavi, Lilia, Actis, Maria Victoria, Rubio, Mariano, Scaro, Graciela, White, Harvey Douglas, Alsweiler, Caroline, Devlin, Gerard Patrick, Low, Liz, Fisher, Raewyn, Scales, Jayne, Abercrombie, Kirsty, Stewart, Ralph Alan Huston, Howell, Leah, White, Harvey Douglas, Patten, Cathrine, Benatar, Jocelyne, Kedev, Sasko, Mitevska, Irena Peovska, Kostovska, Elizabeta Srbinovska, Pejkov, Hristo, Held, Claes, Held, Claes, Eggers, Kai, Frostfelt, Gunnar, Björklund, Christina, Johnston, Nina, Andreasson, Maria, Olsowka, Maciej, Essermark, Marie, Åkerblom, Axel, Soveri, Inga, Aspberg, Johannes, Persson, Liselotte, Beyar, Rafael, Sharir, Tali, Nikolsky, Eugenia, Sharir, Tali, Harel, Or, Elian, Dan, Kerner, Arthur, Bentzvi, Margalit, Massalha, Samia, Helmer, Ludmila, Kohsaka, Shun, Fukuda, Keiichi, Ueda, Ikuko, Kohsaka, Shun, Fujita, Jun, Yasuda, Satoshi, Furukawa, Akemi, Hirase, Kanae, Nagai, Toshiyuki, Otsuka, Fumiyuki, Nishimura, Shigeyuki, Nakano, Shintaro, de Werf, Frans Van, Goetschalckx, Kaatje, Goetschalckx, Kaatje, Robesyn, Valerie, de Werf, Frans Van, Claes, Kathleen, White, Harvey Douglas, Alsweiler, Caroline, Hung, Chung-Lieh, Yang, Yi-Hsuan, Yun, Chun-Ho, Hou, Charles Jia-Yin, Kuo, Jen-Yuan, Yeh, Hung-I, Hung, Ta-Chuan, Li, Jiun-Yi, Chien, Chen-Yen, Tsai, Cheng-Ting, Liu, Chun-Chieh, Yu, Fa-Chang, Lin, Yueh-Hung, Lan, Wei-Ren, Yen, Chih-Hsuan, Tsai, Jui-Peng, Sung, Kuo-Tzu, Ntsekhe, Mpiko, Pandie, Shaheen, Philander (Nee Talliard), Constance, Viljoen, Charle A, Mtana, Noloyiso, De Andrade, Marianne, Maggioni, Aldo P., Moccetti, Tiziano, Anesini, Adriana, Rossi, M.Grazia, Maspoli, Simona, Mombelli, Manuela, Abdelhamid, Magdy, Talaat, Ahmed, Adel, Ahmed, Kamal, Ahmed, Mahrous, Hossam, Kaffas, Sameh El, Fishawy, Hussien El, Pop, Calin, Claudia, Matei, Popescu, Bogdan A., Ginghina, Carmen, Rosca, Monica, Deleanu, Dan, Beladan, Carmen C., Iliescu, Vlad A., Al-Mallah, Mouaz H., Zahrani, Sarah, Aljzeeri, Ahmed, Najm, Hani, Alghamdi, Ali, Mogrovejo Ramos, Walter Enrique, Monsalve Davila, Marco Antonio, White, Harvey Douglas, Alsweiler, Caroline, Kuanprasert, Srun, Mai, Chiang, Prommintikul, Arintaya, Nawarawong, Weerachai, Khwakhong, Supatchara, Woragidpoonpol, Surin, Chaiyasri, Anong, Tepsuwan, Thitipong, Mekara, Warangkana, Taksaudom, Noppon, Kulthawong, Supap, Rimsukcharoenchai, Chataroon, Amaritakomol, Anong, Euathrongchit, Juntima, Wannasopha, Yutthaphan, Yamwong, Sukit, Panpunuan, Pachara, Sritara, Piyamitr, Aramcharoen, Suthara, Meemuk, Krissada, White, Harvey Douglas, Alsweiler, Caroline, Khairuddin, Ahmad, Mokhtar, Noor Syamira, Hadi, Hafidz Abd, Basri, Nor Asiah, Yahaya, Shaiful Azmi, Yusnida, Irni, Hashim, Humayrah, Harrington, Robert, Williams, David, Alexander, Karen P., Berger, Jeffrey, Harrington, Robert, Mark, Daniel, O’Brien, Sean M., Rosenberg, Yves, Shaw, Leslee J., Ballantyne, Christie, Berman, Daniel, Beyar, Rafael, Bhargava, Balram, Buller, Chris, (Tony) Carvalho*, Antonio, Chaitman, Bernard R., Diaz, Rafael, Doerr, Rolf, Dzavik, Vladimir, Goodman, Shaun, Gosselin, Gilbert, Hachamovitch, Rory, Hamm, Christian, Held, Claes, Helm, Malte, Huber, Kurt, Jiang, Lixin, Keltai, Matyas, Kohsaka, Shun, Lang, Irene, Lopes, Renato, Lopez-Sendon, Jose, Maggioni, Aldo, Bairey Merz, C. Noel, Min, James, Peterson, Eric, Picard, Michael H., Selvanayagam, Joseph, Senior, Roxy, Sharir, Tali, Steg, Gabriel, Szwed, Hanna, de Werf, Frans Van, Weintraub, William, White, Harvey, Williams, David, Ballantyne, Christie, Calfas*, Karen, Chaitman, Bernard R., Champagne, Mary Ann, Davidson, Michael, Fleg, Jerome, McCullough, Peter A., Stone, Peter, Menasche, Philippe, Davidson*, Michael, Fremes, Stephen, Guyton, Robert, Mack, Michael, Mohr, Fred, Rao, Anupama, Sabik, Joe, Shapira, Oz, Taggart, David, Tatoulis, James, Williams, David, Blankenship, Jim, Brener, Sorin, Buller, Chris, Colombo, Antonio, Bruyne, Bernard de, Généreux, Philippe, Harrington, Robert, Kereiakes, Dean, Lefevre, Thierry, Moses, Jeffrey, Chaitman, Bernard R., Alexander, Karen P., Mahaffey, Ken, White, Harvey, Chaitman, Bernard R., Cruz-Flores, Salvador, Danchin, Nicholas, Feen, Eli, Garcia, Mario J., Hauptman, Paul, Laddu, Abhay A., Passamani, Eugene, Pina, Ileana L., Simoons, Maarten, Skali, Hicham, Thygesen, Kristian, Waters, David, Alexander, Karen P., Endsley, Patricia, Esposito, Gerard, Kanters, Jeffrey, Pownall, John, Stournaras, Dimitrios, Shaw, Leslee J., Berman, Daniel, Friedrich, Matthias, Hachamovitch, Rory, Kwong, Raymond, Min, James, Oliver, Dana, Picard, Michael H., Harrell, Frank, Blume, Jeffrey, Lee, Kerry, O’Brien, Sean M., Berger, Jeffrey, Held, Claes, Kullo, Iftikhar, McManus, Bruce, Newby, Kristin, Mark, Daniel, Cohen, David, Weintraub, William, Merz, C. Noel Bairey, Bugiardini, Raffaele, Celutkiene, Jelena, Escobedo, Jorge, Hoye, Angela, Lyubarova, Radmila, Mattina, Deirdre, Peteiro, Jesus, Alexander, Karen P., Berger, Jeffrey, Harrington, Robert, O’Brien, Sean M., Rosenberg, Yves, Mark, Daniel, Mark, Daniel, Shaw, Leslee J., Berman, Dan, Chaitman, Bernard R., Fleg, Jerome, Kwong, Raymond, Picard, Michael H., Senior, Roxy, Min, James, Leipsic, Jonathan, Ali, Ziad, Williams, David, Fleg, Jerome, Berger, Jeffrey, Chaitman, Bernard R., Alexander, Karen P., Alexander, Karen P., Fleg, Jerome, Mathew, Roy, O’Brien, Sean M., Sidhu, Mandeep, Friedman, Lawrence, Anderson, Jeffrey, Berg, Jessica, DeMets, David, Gibson, C. Michael, Lamas, Gervasio, Deming, Nicole, Himmelfarb, Jonathan, Ouyang, Pamela, Woodard, Pamela, Harrell, Frank, Nwosu, Samuel, Rosenberg, Yves, Fleg, Jerome, Kirby, Ruth, Jeffries, Neal, Berger, Jeffrey, Sidhu, Mandeep, Denaro*, Jean E., Mavromichalis, Stephanie, Chan, Kevin, Cobb, Gia, Contreras, Aira, Cukali, Diana, Ferket, Stephanie, Gabriel, Andre, Hansen, Antonietta, Roberts, Arline, Chang, Michelle, Islam, Sharder, Wayser, Graceanne, Yakubov, Solomon, Yee, Michelle, Callison, Caroline, Hogan, Isabelle, Qelaj, Albertina, Pirro, Charlotte, Loo, Kerrie Van, Wisniewski, Brianna, Gilsenan, Margaret, Lang, Bevin, Mohamed, Samaa, Esquenazi-Karonika, Shari, Mathews, Patenne, Naumova, Anna, Lyo, Jihyun, Setang, Vincent, Xavier, Mark, O’Brien, Sean M., Alexander, Karen P., Mark, Daniel B., Anstrom, Kevin, Baloch, Khaula, Blount, Janet, Cowper, Patricia, Davidson-Ray, Linda, Drew, Laura, Harding, Tina, Knight, J David, Liu, Diane Minshall, O’Neal, Betsy, Redick, Thomas, Jones, Philip, Nugent, Karen, Wang, Grace Jingyan, Shaw, Leslee J., Phillips, Lawrence, Goyal, Abhinav, Hetrick, Holly, Oliver, Dana, Berman, Daniel, Hayes, Sean W., Friedman, John D., Gerlach, R. James, Hyun, Mark, Miranda-Peats, Romalisa, Slomka, Piotr, Thomson, Louise, Kwong, Raymond Y., Friedrich, Matthias, Mongeon, Francois Pierre, Michael, Steven, Picard, Michael H., Hung, Judy, Scherrer-Crosbie, Marielle, Zeng, Xin, Chaitman, Bernard R., Eckstein, Jane, Guruge, Bandula, Streif, Mary, Ali, Ziad, Genereux, Philippe, Alfonso, Maria A., Corral, Maria P., Garcia, Javier J., Horst, Jennifer, Jankovic, Ivana, Konigstein, Maayan, Lustre, Mitchel B., Peralta, Yolayfi, Sanchez, Raquel, Min, James, Arsanjani, Reza, Budoff, Matthew, Elmore, Kimberly, Gomez, Millie, Hague, Cameron, Hindoyan, Niree, Leipsic, Jonathan, Nakanishi, Rine, Srichai-Parsia, M. Barbara, Yeoh, Eunice, Youn, Tricia, Maggioni, Aldo P., Bianchini, Francesca, Ceseri, Martina, Lorimer, Andrea, Magnoni, Marco, Orso, Francesco, Sarti, Laura, Tricoli, Martinia, Carvalho, Antonio, Lopes, Renato, Barbosa, Lilian Mazza, Duarte, Tauane Bello, Soares, Tamara Colaiácovo, Aveiro Morata, Julia de, Carvalho, Pedro, Carvalho Maffei, Natalia de, Egydio, Flávia, Kawakami, Anelise, Oliveira, Janaina, Piloto, Elissa Restelli, Pozzibon, Jaqueline, Goodman, Shaun, Camara, Diane, Mowafy, Neamat, Spindler, Caroline, Jiang, Lixin, Dai, Hao, Feng, Fang, Li, Jia, Li, Li, Liu, Jiamin, Xie, Qiulan, Zhang, Haibo, Zhang, Jianxin, Zhang, Lihua, Zhang, Liping, Zhang, Ning, Zhong, Hui, Diaz, Rafael, Escobar, Claudia, Martin, Maria Eugenia, Pascual, Andrea, Lopez-Sendon, José, Moraga, Paloma, Hernandez, Victoria, Castro, Almudena, Posada, Maria, Fernandez, Sara, Villanueva, José Luis Narro, Selgas, Rafael, Steg, Gabriel, Abergel, Helene, Juliard, Jean Michel, White, Harvey, Alsweiler, Caroline, de Werf, Frans Van, Claes, Kathleen, Goetschalckx, Kaatje, Luyten, Ann, Robesyn, Valerie, Selvanayagam, Joseph B., Murphy, Deirdre, Ahmed, Asker, Bhatt, Richa, Chadha, Nitika, Kumar, Vijay, Lubna, Sadath, Naik, Pushpa, Pandey, Shruti, Ramasamy, Karthik, Saleem, Mohammed, Sharma, Pratiksha, and Siddaram, Hemalata

Published
2024
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21. Aortic valve imaging using 18F-sodium fluoride: impact of triple motion correction

Author

Lassen, Martin Lyngby, Tzolos, Evangelos, Massera, Daniele, Cadet, Sebastien, Bing, Rong, Kwiecinski, Jacek, Dey, Damini, Berman, Daniel S, Dweck, Marc R, Newby, David E, and Slomka, Piotr J

Subjects
Medical and Biological Physics, Biomedical and Clinical Sciences, Clinical Sciences, Physical Sciences, Cardiovascular, Bioengineering, Heart Disease, Biomedical Imaging, Clinical Research, Detection, screening and diagnosis, 4.1 Discovery and preclinical testing of markers and technologies, 4.2 Evaluation of markers and technologies, Motion correction, PET/CT, Cardiac PET, F-18-sodium fluoride, Aortic valve imaging, 18F-sodium fluoride, Clinical sciences, Biomedical engineering, Medical and biological physics
Abstract

BackgroundCurrent 18F-NaF assessments of aortic valve microcalcification using 18F-NaF PET/CT are based on evaluations of end-diastolic or cardiac motion-corrected (ECG-MC) images, which are affected by both patient and respiratory motion. We aimed to test the impact of employing a triple motion correction technique (3 × MC), including cardiorespiratory and gross patient motion, on quantitative and qualitative measurements.Materials and methodsFourteen patients with aortic stenosis underwent two repeat 30-min PET aortic valve scans within (29 ± 24) days. We considered three different image reconstruction protocols; an end-diastolic reconstruction protocol (standard) utilizing 25% of the acquired data, an ECG-gated (four ECG gates) reconstruction (ECG-MC), and a triple motion-corrected (3 × MC) dataset which corrects for both cardiorespiratory and patient motion. All datasets were compared to aortic valve calcification scores (AVCS), using the Agatston method, obtained from CT scans using correlation plots. We report SUVmax values measured in the aortic valve and maximum target-to-background ratios (TBRmax) values after correcting for blood pool activity.ResultsCompared to standard and ECG-MC reconstructions, increases in both SUVmax and TBRmax were observed following 3 × MC (SUVmax: Standard = 2.8 ± 0.7, ECG-MC = 2.6 ± 0.6, and 3 × MC = 3.3 ± 0.9; TBRmax: Standard = 2.7 ± 0.7, ECG-MC = 2.5 ± 0.6, and 3 × MC = 3.3 ± 1.2, all p values ≤ 0.05). 3 × MC had improved correlations (R2 value) to the AVCS when compared to the standard methods (SUVmax: Standard = 0.10, ECG-MC = 0.10, and 3 × MC = 0.20; TBRmax: Standard = 0.20, ECG-MC = 0.28, and 3 × MC = 0.46).Conclusion3 × MC improves the correlation between the AVCS and SUVmax and TBRmax and should be considered in PET studies of aortic valves using 18F-NaF.

Published
2022

22. Discordance Between Coronary Artery Calcium Area and Density Predicts Long-Term Atherosclerotic Cardiovascular Disease Risk.

Author

Razavi, Alexander, van Assen, Marly, De Cecco, Carlo, Dardari, Zeina, Berman, Daniel, Miedema, Michael, Nasir, Khurram, Rozanski, Alan, Rumberger, John, Shaw, Leslee, Sperling, Laurence, Whelton, Seamus, Mortensen, Martin, Blaha, Michael, Dzaye, Omar, and Budoff, Matthew

Subjects
calcium density, cardiovascular diseases, coronary artery calcium, multidetector computed tomography, Female, Humans, Middle Aged, Male, Coronary Vessels, Vascular Calcification, Calcium, Coronary Artery Disease, Cardiovascular Diseases, Risk Assessment, Predictive Value of Tests, Atherosclerosis, Plaque, Atherosclerotic, Risk Factors
Abstract

BACKGROUND: Coronary artery calcium (CAC) is commonly quantified as the product of 2 generally correlated measures: plaque area and calcium density. OBJECTIVES: The authors sought to determine whether discordance between calcium area and density has long-term prognostic importance in atherosclerotic cardiovascular disease (ASCVD) risk. METHODS: The authors studied 10,373 primary prevention participants from the CAC Consortium with CAC >0. Based on their median values, calcium area and mean calcium density were divided into 4 mutually exclusive concordant/discordant groups. Cox proportional hazards regression assessed the association of calcium area/density groups with ASCVD mortality over a median of 11.7 years, adjusting for traditional risk factors and the Agatston CAC score. RESULTS: The mean age was 56.7 years, and 24% were female. The prevalence of plaque discordance was 19% (9% low calcium area/high calcium density, 10% high calcium area/low calcium density). Female sex (odds ratio [OR]: 1.48 [95% CI: 1.27-1.74]) and body mass index (OR: 0.81 [95% CI: 0.76-0.87], per 5 kg/m2 higher) were significantly associated with high calcium density discordance, whereas diabetes (OR: 2.23 [95% CI: 1.85-3.19]) was most strongly associated with discordantly low calcium density. Compared to those with low calcium area/low calcium density, individuals with low calcium area/high calcium density had a 71% lower risk of ASCVD death (HR: 0.29 [95% CI: 0.09-0.95]). CONCLUSIONS: For a given CAC score, high calcium density relative to plaque area confers lower long-term ASCVD risk, likely serving as an imaging marker of biological resilience for lesion vulnerability. Additional research is needed to define a robust definition of calcium area/density discordance for routine clinical risk prediction.

Published
2022

23. CAD-RADS™ 2.0 – 2022 Coronary Artery Disease – Reporting and Data System An Expert Consensus Document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Cardiology (ACC), the American College of Radiology (ACR) and the North America Society of Cardiovascular Imaging (NASCI)

Author

Cury, Ricardo C, Leipsic, Jonathon, Abbara, Suhny, Achenbach, Stephan, Berman, Daniel, Bittencourt, Marcio, Budoff, Matthew, Chinnaiyan, Kavitha, Choi, Andrew D, Ghoshhajra, Brian, Jacobs, Jill, Koweek, Lynne, Lesser, John, Maroules, Christopher, Rubin, Geoffrey D, Rybicki, Frank J, Shaw, Leslee J, Williams, Michelle C, Williamson, Eric, White, Charles S, Villines, Todd C, and Blankstein, Ron

Subjects
Biomedical and Clinical Sciences, Engineering, Biomedical Engineering, Heart Disease, Heart Disease - Coronary Heart Disease, Biomedical Imaging, Cardiovascular, Atherosclerosis, Detection, screening and diagnosis, 4.2 Evaluation of markers and technologies, Coronary Artery Disease, Coronary CTA, CAD-RADS, Reporting and Data System, Stenosis Severity, Report Standardization Termi-nology, Plaque Burden, Ischemia, Report Standardization Terminology
Abstract

Coronary Artery Disease Reporting and Data System (CAD-RADS) was created to standardize reporting system for patients undergoing coronary CT angiography (CCTA) and to guide possible next steps in patient management. The goal of this updated 2022 CAD-RADS 2.0 is to improve the initial reporting system for CCTA by considering new technical developments in Cardiac CT, including data from recent clinical trials and new clinical guidelines. The updated CAD-RADS classification will follow an established framework of stenosis, plaque burden, and modifiers, which will include assessment of lesion-specific ischemia using CT fractional-flow-reserve (CT-FFR) or myocardial CT perfusion (CTP), when performed. Similar to the method used in the original CAD-RADS version, the determinant for stenosis severity classification will be the most severe coronary artery luminal stenosis on a per-patient basis, ranging from CAD-RADS 0 (zero) for absence of any plaque or stenosis to CAD-RADS 5 indicating the presence of at least one totally occluded coronary artery. Given the increasing data supporting the prognostic relevance of coronary plaque burden, this document will provide various methods to estimate and report total plaque burden. The addition of P1 to P4 descriptors are used to denote increasing categories of plaque burden. The main goal of CAD-RADS, which should always be interpreted together with the impression found in the report, remains to facilitate communication of test results with referring physicians along with suggestions for subsequent patient management. In addition, CAD-RADS will continue to provide a framework of standardization that may benefit education, research, peer-review, artificial intelligence development, clinical trial design, population health and quality assurance with the ultimate goal of improving patient care. Keywords: Coronary Artery Disease, Coronary CTA, CAD-RADS, Reporting and Data System, Stenosis Severity, Report Standardization Terminology, Plaque Burden, Ischemia Supplemental material is available for this article. This article is published synchronously in Radiology: Cardiothoracic Imaging, Journal of Cardiovascular Computed Tomography, JACC: Cardiovascular Imaging, Journal of the American College of Radiology, and International Journal for Cardiovascular Imaging. © 2022 Society of Cardiovascular Computed Tomography. Published by RSNA with permission.

Published
2022

24. Prognostic significance of plaque location in non-obstructive coronary artery disease: from the CONFIRM registry.

Author

Han, Donghee, Chen, Billy, Gransar, Heidi, Achenbach, Stephan, Al-Mallah, Mouaz H, Budoff, Matthew J, Cademartiri, Filippo, Maffei, Erica, Callister, Tracy Q, Chinnaiyan, Kavitha, Chow, Benjamin JW, DeLago, Augustin, Hadamitzky, Martin, Hausleiter, Joerg, Kaufmann, Philipp A, Villines, Todd C, Kim, Yong Jin, Leipsic, Jonathon, Feuchtner, Gudrun, Cury, Ricardo C, Pontone, Gianluca, Andreini, Daniele, Marques, Hugo, Rubinshtein, Ronen, Chang, Hyuk Jae, Lin, Fay Y, Shaw, Leslee J, Min, James K, and Berman, Daniel S

Subjects
Heart Disease - Coronary Heart Disease, Prevention, Clinical Research, Heart Disease, Atherosclerosis, Biomedical Imaging, Cardiovascular, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, Computed Tomography Angiography, Coronary Angiography, Coronary Artery Disease, Humans, Plaque, Atherosclerotic, Predictive Value of Tests, Prognosis, Registries, Risk Assessment, Risk Factors, coronary artery disease, non-obstructive, plaque location, prognosis, computed tomography, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology
Abstract

AimObstructive coronary artery disease (CAD) in proximal coronary segments is associated with a poor prognosis. However, the relative importance of plaque location regarding the risk for major adverse cardiovascular events (MACE) in patients with non-obstructive CAD has not been well defined.Methods and resultsFrom the Coronary CT Angiography Evaluation for Clinical Outcomes: An International Multicenter (CONFIRM) registry, 4644 patients without obstructive CAD were included in this study. The degree of stenosis was classified as 0 (no) and 1-49% (non-obstructive). Proximal involvement was defined as any plaque present in the left main or the proximal segment of the left anterior descending artery, left circumflex artery, and right coronary artery. Extensive CAD was defined as segment involvement score of >4. During a median follow-up of 5.2 years (interquartile range 4.1-6.0), 340 (7.3%) MACE occurred. Within the non-obstructive CAD group (n = 2065), proximal involvement was observed in 1767 (85.6%) cases. When compared to non-obstructive CAD patients without proximal involvement, those with proximal involvement had an increased MACE risk (log-rank P = 0.033). Multivariate Cox analysis showed when compared to patients with no CAD, proximal non-obstructive CAD was associated with increased MACE risk [hazard ratio (HR) 1.90, 95% confidence interval (CI) 1.47-2.45, P < 0.001] after adjusting for extensive CAD and conventional cardiovascular risk factors; however, non-proximal non-obstructive CAD did not increase MACE risk (HR 1.26, 95% CI 0.79-2.01, P = 0.339).ConclusionsIndependent of plaque extent, proximal coronary involvement was associated with increased MACE risk in patients with non-obstructive CAD. The plaque location information by coronary computed tomography angiography may provide additional risk prediction over CAD extent in patients with non-obstructive CAD.

Published
2022

25. Longitudinal quantitative assessment of coronary atherosclerosis related to normal systolic blood pressure maintenance in the absence of established cardiovascular disease.

Author

Won, Ki-Bum, Park, Hyung-Bok, Heo, Ran, Lee, Byoung Kwon, Lin, Fay Y, Hadamitzky, Martin, Kim, Yong-Jin, Sung, Ji Min, Conte, Edoardo, Andreini, Daniele, Pontone, Gianluca, Budoff, Matthew J, Gottlieb, Ilan, Chun, Eun Ju, Cademartiri, Filippo, Maffei, Erica, Marques, Hugo, Gonçalves, Pedro de Araújo, Leipsic, Jonathon A, Lee, Sang-Eun, Shin, Sanghoon, Choi, Jung Hyun, Virmani, Renu, Samady, Habib, Chinnaiyan, Kavitha, Berman, Daniel S, Narula, Jagat, Bax, Jeroen J, Min, James K, and Chang, Hyuk-Jae

Subjects
Humans, Cardiovascular Diseases, Disease Progression, Polyvinyl Chloride, Coronary Angiography, Risk Factors, Blood Pressure, Female, Male, Coronary Artery Disease, Plaque, Atherosclerotic, Computed Tomography Angiography, atherosclerosis, coronary artery disease, coronary computed tomography angiography, systolic blood pressure, Aging, Clinical Research, Cardiovascular, Heart Disease, Atherosclerosis, Heart Disease - Coronary Heart Disease, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology
Abstract

BackgroundAtherosclerosis-related adverse events are commonly observed even in conditions with low cardiovascular (CV) risk. Longitudinal data regarding the association of normal systolic blood pressure maintenance (SBPmaintain ) with coronary plaque volume changes (PVC) has been limited in adults without traditional CV disease.HypothesisNormal SBPmaintain is important to attenuate coronary atherosclerosis progression in adults without baseline CV disease.MethodsWe analyzed 95 adults (56.7 ± 8.5 years; 40.0% men) without baseline CV disease who underwent serial coronary computed tomographic angiography with mean 3.5 years of follow-up. All participants were divided into two groups of normal SBPmaintain (follow-up SBP

Published
2022

26. Coronary Artery Calcium for Risk Stratification of Sudden Cardiac Death The Coronary Artery Calcium Consortium

Author

Razavi, Alexander C, Uddin, SM Iftekhar, Dardari, Zeina A, Berman, Daniel S, Budoff, Matthew J, Miedema, Michael D, Osei, Albert D, Obisesan, Olufunmilayo H, Nasir, Khurram, Rozanski, Alan, Rumberger, John A, Shaw, Leslee J, Sperling, Laurence S, Whelton, Seamus P, Mortensen, Martin Bødtker, Blaha, Michael J, and Dzaye, Omar

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Clinical Research, Cardiovascular, Heart Disease, Patient Safety, Atherosclerosis, Prevention, Heart Disease - Coronary Heart Disease, Good Health and Well Being, Calcium, Coronary Artery Disease, Death, Sudden, Cardiac, Female, Humans, Male, Middle Aged, Predictive Value of Tests, Risk Assessment, Risk Factors, Vascular Calcification, cardiovascular diseases, coronary artery calcium, multidetector computed tomography, sudden cardiac death, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Clinical sciences
Abstract

BackgroundCoronary artery calcium (CAC) is a marker of plaque burden. Whether CAC improves risk stratification for incident sudden cardiac death (SCD) beyond atherosclerotic cardiovascular disease (ASCVD) risk factors is unknown.ObjectivesSCD is a common initial manifestation of coronary heart disease (CHD); however, SCD risk prediction remains elusive.MethodsThe authors studied 66,636 primary prevention patients from the CAC Consortium. Multivariable competing risks regression and C-statistics were used to assess the association between CAC and SCD, adjusting for demographics and traditional risk factors.ResultsThe mean age was 54.4 years, 33% were women, 11% were of non-White ethnicity, and 55% had CAC >0. A total of 211 SCD events (0.3%) were observed during a median follow-up of 10.6 years, 91% occurring among those with baseline CAC >0. Compared with CAC = 0, there was a stepwise higher risk (P trend 1,000 (SHR: 4.9; 95% CI: 2.6-9.9). CAC provided incremental improvements in the C-statistic for the prediction of SCD among individuals with a 10-year risk 20% (ΔC-statistic = +0.01; P = 0.54).ConclusionsHigher CAC burden strongly associates with incident SCD beyond traditional risk factors, particularly among primary prevention patients with low-intermediate risk. SCD risk stratification can be useful in the early stages of CHD through the measurement of CAC, identifying patients most likely to benefit from further downstream testing.

Published
2022

27. Relationship between epicardial adipose tissue and coronary atherosclerosis by CCTA in young adults (18–45)

Author

Filtz, Annalisa, Lorenzatti, Daniel, Scotti, Andrea, Piña, Pamela, Fernandez-Hazim, Carol, Huang, Dou, Ippolito, Paul, Skendelas, John P, Kuno, Toshiki, Rodriguez, Carlos J, Schenone, Aldo L, Latib, Azeem, Lavie, Carl J, Shaw, Leslee J., Blankstein, Ron, Shapiro, Michael D, Garcia, Mario J, Berman, Daniel S, Dey, Damini, Virani, Salim S, and Slipczuk, Leandro

Published
2024
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30. Left Main Coronary Artery Calcium and Diabetes Confer Very-High-Risk Equivalence in Coronary Artery Calcium >1,000

Author

Razavi, Alexander C., Shaw, Leslee J., Berman, Daniel S., Budoff, Matthew J., Wong, Nathan D., Vaccarino, Viola, van Assen, Marly, De Cecco, Carlo N., Quyyumi, Arshed A., Mehta, Anurag, Muntner, Paul, Miedema, Michael D., Rozanski, Alan, Rumberger, John A., Nasir, Khurram, Blumenthal, Roger S., Sperling, Laurence S., Mortensen, Martin Bødtker, Whelton, Seamus P., Blaha, Michael J., and Dzaye, Omar

Published
2024
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33. Relationship between impaired myocardial blood flow by positron emission tomography and low-attenuation plaque burden and pericoronary adipose tissue attenuation from coronary computed tomography: From the prospective PACIFIC trial

Author

Kuronuma, Keiichiro, van Diemen, Pepijn A., Han, Donghee, Lin, Andrew, Grodecki, Kajetan, Kwiecinski, Jacek, Motwani, Manish, McElhinney, Priscilla, Tomasino, Guadalupe Flores, Park, Caroline, Kwan, Alan, Tzolos, Evangelos, Klein, Eyal, Shou, Benjamin, Tamarappoo, Balaji, Cadet, Sebastien, Danad, Ibrahim, Driessen, Roel S., Berman, Daniel S., Slomka, Piotr J., Dey, Damini, and Knaapen, Paul

Published
2023
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34. Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging

Author

Williams, Michelle C., Bednarski, Bryan P., Pieszko, Konrad, Miller, Robert J. H., Kwiecinski, Jacek, Shanbhag, Aakash, Liang, Joanna X., Huang, Cathleen, Sharir, Tali, Dorbala, Sharmila, Di Carli, Marcelo F., Einstein, Andrew J., Sinusas, Albert J., Miller, Edward J., Bateman, Timothy M., Fish, Mathews B., Ruddy, Terrence D., Acampa, Wanda, Hauser, M. Timothy, Kaufmann, Philipp A., Dey, Damini, Berman, Daniel S., and Slomka, Piotr J.

Published
2023
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35. Longitudinal Quantitative Assessment of Coronary Atherosclerotic Plaque Burden Related to Serum Hemoglobin Levels

Author

Won, Ki-Bum, Lee, Byoung Kwon, Heo, Ran, Park, Hyung-Bok, Lin, Fay Y, Hadamitzky, Martin, Kim, Yong-Jin, Sung, Ji Min, Conte, Edoardo, Andreini, Daniele, Pontone, Gianluca, Budoff, Matthew J, Gottlieb, Ilan, Chun, Eun Ju, Cademartiri, Filippo, Maffei, Erica, Marques, Hugo, de Araújo Gonçalves, Pedro, Leipsic, Jonathon A, Lee, Sang-Eun, Shin, Sanghoon, Choi, Jung Hyun, Virmani, Renu, Samady, Habib, Chinnaiyan, Kavitha, Berman, Daniel S, Narula, Jagat, Bax, Jeroen J, Min, James K, and Chang, Hyuk-Jae

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Heart Disease, Clinical Research, Cardiovascular, Hematology, Atherosclerosis, Heart Disease - Coronary Heart Disease, Evaluation of treatments and therapeutic interventions, 6.1 Pharmaceuticals, CCTA, coronary computed tomographic angiography, CV, cardiovascular, PVC, plaque volume changes, atherosclerosis, coronary computed tomography angiography, hemoglobin, Δ hemoglobin, hemoglobin level changes
Abstract

BackgroundDespite a potential role of hemoglobin in atherosclerosis, data on coronary plaque volume changes (PVC) related to serum hemoglobin levels are limited.ObjectivesThe authors sought to evaluate coronary atherosclerotic plaque burden changes related to serum hemoglobin levels using serial coronary computed tomographic angiography (CCTA).MethodsA total of 830 subjects (age 61 ± 10 years, 51.9% male) who underwent serial CCTA were analyzed. The median interscan period was 3.2 (IQR: 2.5-4.4) years. Quantitative assessment of coronary plaques was performed at both scans. All participants were stratified into 4 groups based on the quartile of baseline hemoglobin levels. Annualized total PVC (mm3/year) was defined as total PVC divided by the interscan period.ResultsBaseline total plaque volume (mm3) was not different among all groups (group I [lowest]: 34.1 [IQR: 0.0-127.4] vs group II: 28.8 [IQR: 0.0-123.0] vs group III: 49.9 [IQR: 5.6-135.0] vs group IV [highest]: 34.3 [IQR: 0.0-130.7]; P = 0.235). During follow-up, serum hemoglobin level changes (Δ hemoglobin; per 1 g/dL) was related to annualized total PVC (β = -0.114) in overall participants (P < 0.05). After adjusting for age, sex, traditional risk factors, baseline hemoglobin and creatinine levels, baseline total plaque volume, and the use of aspirin, beta-blocker, angiotensin-converting enzyme inhibitor or angiotensin receptor blocker, and statin, Δ hemoglobin significantly affected annualized total PVC in only the composite of groups I and II (β = -2.401; P = 0.004).ConclusionsSerial CCTA findings suggest that Δ hemoglobin has an independent effect on coronary atherosclerosis. This effect might be influenced by baseline hemoglobin levels. (Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging [PARADIGM]; NCT02803411).

Published
2022

36. Handling missing values in machine learning to predict patient-specific risk of adverse cardiac events: Insights from REFINE SPECT registry

Author

Rios, Richard, Miller, Robert JH, Manral, Nipun, Sharir, Tali, Einstein, Andrew J, Fish, Mathews B, Ruddy, Terrence D, Kaufmann, Philipp A, Sinusas, Albert J, Miller, Edward J, Bateman, Timothy M, Dorbala, Sharmila, Di Carli, Marcelo, Van Kriekinge, Serge D, Kavanagh, Paul B, Parekh, Tejas, Liang, Joanna X, Dey, Damini, Berman, Daniel S, and Slomka, Piotr J

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Health Services and Systems, Health Sciences, Information and Computing Sciences, Applied Computing, Patient Safety, Cardiovascular, Heart Disease, Good Health and Well Being, Humans, Machine Learning, Myocardial Perfusion Imaging, Registries, Tomography, Emission-Computed, Single-Photon, Machine learning, Clinical implementation, Missing values, Prognosis, Myocardial perfusion imaging, Engineering, Medical and Health Sciences, Biomedical Engineering, Bioinformatics and computational biology, Health services and systems, Applied computing
Abstract

BackgroundMachine learning (ML) models can improve prediction of major adverse cardiovascular events (MACE), but in clinical practice some values may be missing. We evaluated the influence of missing values in ML models for patient-specific prediction of MACE risk.MethodsWe included 20,179 patients from the multicenter REFINE SPECT registry with MACE follow-up data. We evaluated seven methods for handling missing values: 1) removal of variables with missing values (ML-Remove), 2) imputation with median and unique category for continuous and categorical variables, respectively (ML-Traditional), 3) unique category for missing variables (ML-Unique), 4) cluster-based imputation (ML-Cluster), 5) regression-based imputation (ML-Regression), 6) missRanger imputation (ML-MR), and 7) multiple imputation (ML-MICE). We trained ML models with full data and simulated missing values in testing patients. Prediction performance was evaluated using area under the receiver-operating characteristic curve (AUC) and compared with a model without missing values (ML-All), expert visual diagnosis and total perfusion deficit (TPD).ResultsDuring mean follow-up of 4.7 ± 1.5 years, 3,541 patients experienced at least one MACE (3.7% annualized risk). ML-All (reference model-no missing values) had AUC 0.799 for MACE risk prediction. All seven models with missing values had lower AUC (ML-Remove: 0.778, ML-MICE: 0.774, ML-Cluster: 0.771, ML-Traditional: 0.771, ML-Regression: 0.770, ML-MR: 0.766, and ML-Unique: 0.766; p

Published
2022

37. Bypass Grafting and Native Coronary Artery Disease Activity

Author

Kwiecinski, Jacek, Tzolos, Evangelos, Fletcher, Alexander J, Nash, Jennifer, Meah, Mohammed N, Cadet, Sebastien, Adamson, Philip D, Grodecki, Kajetan, Joshi, Nikhil, Williams, Michelle C, van Beek, Edwin JR, Lai, Chi, Tavares, Adriana AS, MacAskill, Mark G, Dey, Damini, Baker, Andrew H, Leipsic, Jonathon, Berman, Daniel S, Sellers, Stephanie L, Newby, David E, Dweck, Marc R, and Slomka, Piotr J

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Heart Disease - Coronary Heart Disease, Atherosclerosis, Cardiovascular, Heart Disease, Clinical Research, Aged, Calcinosis, Calcium, Coronary Angiography, Coronary Artery Disease, Disease Progression, Female, Humans, Male, Middle Aged, Plaque, Atherosclerotic, Predictive Value of Tests, Sodium Fluoride, F-18-NaF, CABG, coronary artery bypass graft, coronary artery disease, PET/CT, (18)F-NaF, Cardiorespiratory Medicine and Haematology, Clinical Sciences, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Clinical sciences
Abstract

ObjectivesThe aim of this study was to describe the potential of 18F-sodium fluoride (18F-NaF) positron emission tomography (PET) to identify graft vasculopathy and to investigate the influence of coronary artery bypass graft (CABG) surgery on native coronary artery disease activity and progression.BackgroundAs well as developing graft vasculopathy, CABGs have been proposed to accelerate native coronary atherosclerosis.MethodsPatients with established coronary artery disease underwent baseline 18F-NaF PET, coronary artery calcium scoring, coronary computed tomographic angiography, and 1-year repeat coronary artery calcium scoring. Whole-vessel coronary microcalcification activity (CMA) on 18F-NaF PET and change in calcium scores were quantified in patients with and without CABG surgery.ResultsAmong 293 participants (mean age 65 ± 9 years, 84% men), 48 (16%) underwent CABG surgery 2.7 years [IQR: 1.4-10.4 years] previously. Although all arterial and the majority (120 of 128 [94%]) of vein grafts showed no 18F-NaF uptake, 8 saphenous vein grafts in 7 subjects had detectable CMA. Bypassed native coronary arteries had 3 times higher CMA values (2.1 [IQR: 0.4-7.5] vs 0.6 [IQR: 0-2.7]; P < 0.001) and greater progression of 1-year calcium scores (118 Agatston unit [IQR: 48-194 Agatston unit] vs 69 [IQR: 21-142 Agatston unit]; P = 0.01) compared with patients who had not undergone CABG, an effect confined largely to native coronary plaques proximal to the graft anastomosis. In sensitivity analysis, bypassed native coronary arteries had higher CMA (2.0 [IQR: 0.4-7.5] vs 0.8 [IQR: 0.3-3.2]; P < 0.001) and faster disease progression (24% [IQR: 16%-43%] vs 8% [IQR: 0%-24%]; P = 0.002) than matched patients (n = 48) with comparable burdens of coronary artery disease and cardiovascular comorbidities in the absence of bypass grafting.ConclusionsNative coronary arteries that have been bypassed demonstrate increased disease activity and more rapid disease progression than nonbypassed arteries, an observation that appears independent of baseline atherosclerotic plaque burden. Microcalcification activity is not a dominant feature of graft vasculopathy.

Published
2022

38. Distribution of Coronary Artery Calcium by Age, Sex, and Race Among Patients 30-45 Years Old

Author

Javaid, Aamir, Dardari, Zeina A, Mitchell, Joshua D, Whelton, Seamus P, Dzaye, Omar, Lima, Joao AC, Lloyd-Jones, Donald M, Budoff, Matthew, Nasir, Khurram, Berman, Daniel S, Rumberger, John, Miedema, Michael D, Villines, Todd C, and Blaha, Michael J

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Heart Disease - Coronary Heart Disease, Prevention, Aging, Heart Disease, Cardiovascular, Adult, Atherosclerosis, Calcium, Cardiovascular Diseases, Coronary Artery Disease, Coronary Vessels, Female, Humans, Male, Middle Aged, Risk Assessment, Risk Factors, Vascular Calcification, Young Adult, cardiovascular diseases, cardiovascular risk, coronary artery calcium, coronary artery disease, multidetector computed tomography, percentiles, premature atherosclerosis, primary prevention, young adults, Cardiorespiratory Medicine and Haematology, Public Health and Health Services, Cardiovascular System & Hematology, Cardiovascular medicine and haematology
Abstract

BackgroundCoronary artery calcium (CAC) is a measure of atherosclerotic burden and is well-validated for risk stratification in middle- to older-aged adults. Few studies have investigated CAC in younger adults, and there is no calculator for determining age-, sex-, and race-based percentiles among individuals aged 0 and develop age-sex-race percentiles for U.S. adults aged 30-45 years.MethodsWe harmonized 3 datasets-CARDIA (Coronary Artery Risk Development in Young Adults), the CAC Consortium, and the Walter Reed Cohort-to study CAC in 19,725 asymptomatic Black and White individuals aged 30-45 years without known atherosclerotic cardiovascular disease. After weighting each cohort equally, the probability of CAC >0 and age-sex-race percentiles of CAC distributions were estimated using nonparametric techniques.ResultsThe prevalence of CAC >0 was 26% among White males, 16% among Black males, 10% among White females, and 7% among Black females. CAC >0 automatically placed all females at >90th percentile. CAC >0 placed White males at the 90th percentile at age 34 years compared with Black males at age 37 years. An interactive webpage allows one to enter an age, sex, race, and CAC score to obtain the corresponding estimated percentile.ConclusionsIn a large cohort of U.S. adults aged 30-45 years without symptomatic atherosclerotic cardiovascular disease, the probability of CAC >0 varied by age, sex, and race. Estimated percentiles may help interpretation of CAC scores among young adults relative to their age-sex-race matched peers and can henceforth be included in CAC score reporting.

Published
2022

39. Aspirin and Statin Therapy for Nonobstructive Coronary Artery Disease: Five-year Outcomes from the CONFIRM Registry

Author

Indraratna, Praveen, Naoum, Christopher, Ben Zekry, Sagit, Gransar, Heidi, Blanke, Philipp, Sellers, Stephanie, Achenbach, Stephan, Al-Mallah, Mouaz H, Andreini, Daniele, Berman, Daniel S, Budoff, Matthew J, Cademartiri, Filippo, Callister, Tracy Q, Chang, Hyuk-Jae, Chinnaiyan, Kavitha, Chow, Benjamin JW, Cury, Ricardo C, DeLago, Augustin, Feuchtner, Gudrun, Hadamitzky, Martin, Hausleiter, Joerg, Kaufmann, Philipp A, Kim, Yong-Jin, Maffei, Erica, Marques, Hugo, Gonçalves, Pedro de Araújo, Pontone, Gianluca, Raff, Gilbert L, Rubinshtein, Ronen, Villines, Todd C, Lin, Fay Y, Shaw, Leslee J, Narula, Jagat, Bax, Jeroen J, and Leipsic, Jonathon A

Subjects
Epidemiology, Biomedical and Clinical Sciences, Health Sciences, Clinical Research, Cardiovascular, Heart Disease - Coronary Heart Disease, Heart Disease, Clinical Trials and Supportive Activities, Atherosclerosis, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, Good Health and Well Being, Aspirin, CT Angiography, Coronary Artery Disease, Nonobstructive Coronary Artery Disease, Statin
Abstract

PurposeIn this cohort study, 5-year data from the Coronary CT Angiography Evaluation for Clinical Outcomes: An International Multicenter Registry (ie, CONFIRM) were examined to identify associations of baseline aspirin and statin use with mortality, major adverse cardiovascular events (MACE), and myocardial infarction (MI) in individuals without substantial (≥50%) stenosis.Materials and methodsIn this prospective cohort study, all participants in the registry underwent coronary CT angiography and were classified as having no detectable coronary plaque or having nonobstructive coronary artery disease (CAD) (1%-49% stenosis). Participants with obstructive (≥50%) stenosis were excluded from analysis. The study commenced in June 2003 and was completed in March 2016. All unadjusted and risk-adjusted analyses utilized the Cox proportional hazard model with hospital sites modeled using shared frailty.ResultsA total of 6386 participants with no detectable plaque or with nonobstructive CAD were included (mean age, 56.0 years ± 13.3 [SD], 52% men). The mean follow-up period was 5.66 years ± 1.10. Nonobstructive CAD (n = 2815, 44% of all participants included in the study) was associated with a greater risk of all-cause mortality (10.6% [298 of 2815] vs 4.8% [170 of 3571], P < .001) compared to those without CAD (n = 3571, 56%). Baseline aspirin and statin use was documented for 1415 and 1429 participants, respectively, with nonobstructive CAD, and for 1560 and 1565 participants without detectable plaque, respectively. In individuals with nonobstructive CAD, baseline aspirin use was not associated with a reduction in MACE (10.9% [102 of 936] vs 14.7% [52 of 355], P = .06), all-cause mortality (9.6% [95 of 991] vs 10.9% [46 of 424], P = .468), or MI (4.4% [41 of 936] vs 6.2% [22 of 355], P = .18). On multivariate risk-adjusted analysis, baseline statin use was associated with a lower rate of MACE (hazard ratio, 0.59; 95% CI: 0.40, 0.87; P = .007). Neither therapy improved clinical outcomes for participants with no detectable plaque.ConclusionIn participants with nonobstructive CAD, baseline use of statins, but not of aspirin, was associated with improved clinical outcomes. Neither therapy was associated with benefit in participants without plaque.Keywords: Aspirin, Statin, Coronary Artery Disease, CT Angiography, Nonobstructive Coronary Artery DiseaseClinical trial registration no. NCT01443637 Supplemental material is available for this article. © RSNA, 2022See also the commentary by Canan and Navar in this issue.

Published
2022

41. Association of cardiometabolic and vascular atherosclerosis phenotypes on non-contrast chest CT with incident heart failure in patients with severe hypercholesterolemia

Author

Piña, Pamela, Lorenzatti, Daniel, Castagna, Francesco, Miles, Jeremy, Kuno, Toshiki, Scotti, Andrea, Arce, Javier, Feinberg, Ari, Huang, Dou, Gilman, Jake, Leiderman, Ephraim, Daich, Jonathan, Ippolito, Paul, Gongora, Carlos A., Schenone, Aldo L., Zhang, Lili, Rodriguez, Carlos J., Blaha, Michael J., Dey, Damini, Berman, Daniel S., Virani, Salim S., Levsky, Jeffrey M., Garcia, Mario J., and Slipczuk, Leandro

Published
2024
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44. Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging

Author

Pieszko, Konrad, Shanbhag, Aakash D., Singh, Ananya, Hauser, M. Timothy, Miller, Robert J. H., Liang, Joanna X., Motwani, Manish, Kwieciński, Jacek, Sharir, Tali, Einstein, Andrew J., Fish, Mathews B., Ruddy, Terrence D., Kaufmann, Philipp A., Sinusas, Albert J., Miller, Edward J., Bateman, Timothy M., Dorbala, Sharmila, Di Carli, Marcelo, Berman, Daniel S., Dey, Damini, and Slomka, Piotr J.

Published
2023
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45. RNAi-mediated rheostat for dynamic control of AAV-delivered transgenes

Author

Subramanian, Megha, McIninch, James, Zlatev, Ivan, Schlegel, Mark K., Kaittanis, Charalambos, Nguyen, Tuyen, Agarwal, Saket, Racie, Timothy, Alvarado, Martha Arbaiza, Wassarman, Kelly, Collins, Thomas S., Chickering, Tyler, Brown, Christopher R., Schmidt, Karyn, Castoreno, Adam B., Shulga-Morskaya, Svetlana, Stamenova, Elena, Buckowing, Kira, Berman, Daniel, Barry, Joseph D., Bisbe, Anna, Maier, Martin A., Fitzgerald, Kevin, and Jadhav, Vasant

Published
2023
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46. Mean Versus Peak Coronary Calcium Density on Non-Contrast CT Calcium Scoring and ASCVD Risk Prediction

Author

Dzaye, Omar, Razavi, Alexander C, Dardari, Zeina A, Berman, Daniel S, Budoff, Matthew J, Miedema, Michael D, Obisesan, Olufunmilayo H, Boakye, Ellen, Nasir, Khurram, Rozanski, Alan, Rumberger, John A, Shaw, Leslee J, Mortensen, Martin Bødtker, Whelton, Seamus P, and Blaha, Michael J

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Heart Disease - Coronary Heart Disease, Prevention, Atherosclerosis, Heart Disease, Cardiovascular, Good Health and Well Being, Adult, Calcium, Coronary Disease, Female, Humans, Male, Plaque, Atherosclerotic, Predictive Value of Tests, Tomography, X-Ray Computed, Vascular Calcification, Young Adult, atherosclerotic plaque, calcium density, cardiovascular disease, coronary artery calcium, multidetector computed tomography, primary prevention, risk, vascular calcification, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Clinical sciences
Abstract

ObjectivesThis study sought to assess the relationship between mean vs peak calcified plaque density and their impact on calculating coronary artery calcium (CAC) scores and to compare the corresponding differential prediction of atherosclerotic cardiovascular disease (ASCVD) and coronary heart disease (CHD) mortality.BackgroundThe Agatston CAC score is quantified per lesion as the product of plaque area and a 4-level categorical peak calcium density factor. However, mean calcium density may more accurately measure the heterogenous mixture of lipid-rich, fibrous, and calcified plaque reflective of ASCVD risk.MethodsWe included 10,373 individuals from the CAC Consortium who had CAC >0 and per-vessel measurements of peak calcium density factor and mean calcium density. Area under the curve and continuous net reclassification improvement analyses were performed for CHD and ASCVD mortality to compare the predictive abilities of mean calcium density vs peak calcium density factor when calculating the Agatston CAC score.ResultsParticipants were on average 53.4 years of age, 24.4% were women, and the median CAC score was 68 Agatston units. The average values for mean calcium density and peak calcium density factor were 210 ± 50 HU and 3.1 ± 0.5, respectively. Individuals younger than 50 years of age and/or those with a total plaque area 100.ConclusionMean and peak calcium density may differentially describe plaque composition early in the atherosclerotic process. Mean calcium density performs better than peak calcium density factor when combined with plaque area for ASCVD mortality prediction among persons with Agatston CAC 1-99.

Published
2022

47. Observer repeatability and interscan reproducibility of 18F-sodium fluoride coronary microcalcification activity

Author

Tzolos, Evangelos, Kwiecinski, Jacek, Lassen, Martin Lyngby, Cadet, Sebastien, Adamson, Philip D, Moss, Alastair J, Joshi, Nikhil, Williams, Michelle C, van Beek, Edwin JR, Dey, Damini, Berman, Daniel S, Dweck, Marc R, Newby, David E, and Slomka, Piotr J

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Heart Disease, Biomedical Imaging, Atherosclerosis, Cardiovascular, Clinical Research, Heart Disease - Coronary Heart Disease, Detection, screening and diagnosis, 4.2 Evaluation of markers and technologies, Calcinosis, Fluorine Radioisotopes, Humans, Observer Variation, Positron Emission Tomography Computed Tomography, Reproducibility of Results, Sodium, Sodium Fluoride, Motion correction, PET, CT, cardiac PET, F-18-NaF, vulnerable plaque, coronary microcalcification activity, 18F-NaF, PET/CT, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology
Abstract

BackgroundWe aimed to establish the observer repeatability and interscan reproducibility of coronary 18F-sodium-fluoride positron emission tomography (PET) uptake using a novel semi-automated approach, coronary microcalcification activity (CMA).MethodsPatients with multivessel coronary artery disease underwent repeated hybrid PET and computed tomography angiography (CTA) imaging (PET/CTA). CMA was defined as the integrated standardized uptake values (SUV) in the entire coronary tree exceeding 2 standard deviations above the background SUV. Coefficients of repeatability between the same observer (intraobserver repeatability), between 2 observers (interobserver repeatability) and coefficient of reproducibility between 2 scans (interscan reproducibility), were determined at vessel and patient level.ResultsIn 19 patients, CMA was assessed twice in 43 coronary vessels on two PET/CT scans performed 12 ± 5 days apart. There was excellent intraclass correlation for intraobserver and interobserver repeatability as well as interscan reproducibility (all ≥ 0.991). There was 100% intraobserver, interobserver and interscan agreement for the presence (CMA > 0) or absence (CMA = 0) of coronary18F-NaF uptake. Mean CMA was 3.12 ± 0.62 with coefficients of repeatability of ≤ 10% for all measures: intraobserver 0.24 and 0.22, interobserver 0.30 and 0.29 and interscan 0.33 and 0.32 at a per-vessel and per-patient level, respectively.ConclusionsCMA is a repeatable and reproducible global measure of coronary atherosclerotic activity.

Published
2022

48. Associations between dyspnoea, coronary atherosclerosis, and cardiovascular outcomes: results from the long-term follow-up CONFIRM registry.

Author

van Rosendael, Alexander R, Bax, A Maxim, van den Hoogen, Inge J, Smit, Jeff M, Al'Aref, Subhi J, Achenbach, Stephan, Al-Mallah, Mouaz H, Andreini, Daniele, Berman, Daniel S, Budoff, Matthew J, Cademartiri, Filippo, Callister, Tracy Q, Chang, Hyuk-Jae, Chinnaiyan, Kavitha, Chow, Benjamin JW, 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, Gransar, Heidi, Lu, Yao, Peña, Jessica M, Lin, Fay Y, Shaw, Leslee J, Narula, Jagat, Min, James K, and Bax, Jeroen J

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Atherosclerosis, Cardiovascular, Clinical Research, Biomedical Imaging, Heart Disease, Heart Disease - Coronary Heart Disease, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, Aged, Coronary Angiography, Coronary Artery Disease, Dyspnea, Follow-Up Studies, Humans, Male, Middle Aged, Predictive Value of Tests, Prognosis, Prospective Studies, Registries, Risk Factors, coronary artery disease, coronary computed tomography angiography, dyspnoea, prognosis, Cardiovascular medicine and haematology
Abstract

AimsThe relationship between dyspnoea, coronary artery disease (CAD), and major cardiovascular events (MACE) is poorly understood. This study evaluated (i) the association of dyspnoea with the severity of anatomical CAD by coronary computed tomography angiography (CCTA) and (ii) to which extent CAD explains MACE in patients with dyspnoea.Methods and resultsFrom the international COronary CT Angiography EvaluatioN for Clinical Outcomes: An InteRnational Multicenter (CONFIRM) registry, 4425 patients (750 with dyspnoea) with suspected but without known CAD were included and prospectively followed for ≥5 years. First, the association of dyspnoea with CAD severity was assessed using logistic regression analysis. Second, the prognostic value of dyspnoea for MACE (myocardial infarction and death), and specifically, the interaction between dyspnoea and CAD severity was investigated using Cox proportional-hazard analysis. Mean patient age was 60.3 ± 11.9 years, 63% of patients were male and 592 MACE events occurred during a median follow-up duration of 5.4 (IQR 5.1-6.0) years. On uni- and multivariable analysis (adjusting for age, sex, body mass index, chest pain typicality, and risk factors), dyspnoea was associated with two- and three-vessel/left main (LM) obstructive CAD. The presence of dyspnoea increased the risk for MACE [hazard ratio (HR) 1.57, 95% confidence interval (CI): 1.29-1.90], which was modified after adjusting for clinical predictors and CAD severity (HR 1.26, 95% CI: 1.02-1.55). Conversely, when stratified by CAD severity, dyspnoea did not provide incremental prognostic value in one-, two-, or three-vessel/LM obstructive CAD, but dyspnoea did provide incremental prognostic value in non-obstructive CAD.ConclusionIn patients with suspected CAD, dyspnoea was independently associated with severe obstructive CAD on CCTA. The severity of obstructive CAD explained the elevated MACE rates in patients presenting with dyspnoea, but in patients with non-obstructive CAD, dyspnoea portended additional risk.

Published
2022

49. Cardiovascular and hepatic disease associations by magnetic resonance imaging: A retrospective cohort study

Author

Kwan, Alan C, Sun, Nancy, Driver, Matthew, Botting, Patrick, Navarrette, Jesse, Ouyang, David, Hussain, Shehnaz K, Noureddin, Mazen, Li, Debiao, Ebinger, Joseph E, Berman, Daniel S, and Cheng, Susan

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Clinical Research, Biomedical Imaging, Cardiovascular, Digestive Diseases, Liver Disease, Heart Disease, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, cardiac MRI, hepatic fibrosis, liver disease, NAFLD, FIB-4, Cardiovascular medicine and haematology
Abstract

BackgroundHepatic disease is linked to cardiovascular events but the independent association between hepatic and cardiovascular disease remains unclear, given shared risk factors.MethodsThis was a retrospective study of consecutive patients with a clinical cardiac MRI (CMR) and a serological marker of hepatic fibrosis, the FIB-4 score, within one year of clinical imaging. We assessed the relations between FIB-4 scores grouped based on prior literature: low (< 1.3), moderate (1.3-3.25), and high (>3.25), and abnormalities detected by comprehensive CMR grouped into 4 domains: cardiac structure (end diastolic volumes, atrial dimensions, wall thickness); cardiac function (ejection fractions, wall motion abnormalities, cardiac output); vascular structure (ascending aortic and pulmonary arterial sizes); and cardiac composition (late gadolinium enhancement, T1 and T2 times). We used Poisson regression to examine the association between the conventionally defined FIB-4 category (low 3.25) and any CMR abnormality while adjusting for demographics and traditional cardiovascular risk factors.ResultsOf the 1668 patients studied (mean age: 55.971 ± 7.28, 901 [54%] male), 85.9% had ≥1 cardiac abnormality with increasing prevalence seen within the low (82.0%) to moderate (88.8%) to high (92.3%) FIB-4 categories. Multivariable analyses demonstrated the presence of any cardiac abnormality was significantly associated with having a high-range FIB-4 (prevalence ratio 1.07, 95% CI: 1.01-1.13); notably, the presence of functional cardiac abnormalities were associated with being in the high FIB-4 range (1.41, 1.21-1.65) and any vascular abnormalities with being in the moderate FIB-4 range (1.22, 1.01-1.47).ConclusionsElevated FIB-4 was associated with cardiac functional and vascular abnormalities even after adjustment for shared risk factors in a cohort of patients with clinically referred CMR. These CMR findings indicate that cardiovascular abnormalities exist in the presence of subclinical hepatic fibrosis, irrespective of shared risk factors, underscoring the need for further studies of the heart-liver axis.

Published
2022

50. Glycemic control is independently associated with rapid progression of coronary atherosclerosis in the absence of a baseline coronary plaque burden: a retrospective case–control study from the PARADIGM registry

Author

Won, Ki-Bum, Lee, Byoung Kwon, Lin, Fay Y, Hadamitzky, Martin, Kim, Yong-Jin, Sung, Ji Min, Conte, Edoardo, Andreini, Daniele, Pontone, Gianluca, Budoff, Matthew J, Gottlieb, Ilan, Chun, Eun Ju, Cademartiri, Filippo, Maffei, Erica, Marques, Hugo, de Araújo Gonçalves, Pedro, Leipsic, Jonathon A, Lee, Sang-Eun, Shin, Sanghoon, Choi, Jung Hyun, Virmani, Renu, Samady, Habib, Chinnaiyan, Kavitha, Berman, Daniel S, Narula, Jagat, Shaw, Leslee J, Bax, Jeroen J, Min, James K, and Chang, Hyuk-Jae

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Aging, Heart Disease - Coronary Heart Disease, Atherosclerosis, Cardiovascular, Heart Disease, Biomedical Imaging, Clinical Research, Prevention, Humans, Male, Middle Aged, Aged, Female, Plaque, Atherosclerotic, Coronary Artery Disease, Retrospective Studies, Coronary Angiography, Case-Control Studies, Glycemic Control, Glycated Hemoglobin, Prospective Studies, Disease Progression, Computed Tomography Angiography, Coronary Vessels, Registries, Predictive Value of Tests, Hemoglobin A1c, Coronary artery disease, Progression, Coronary computed tomography angiography, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology
Abstract

The baseline coronary plaque burden is the most important factor for rapid plaque progression (RPP) in the coronary artery. However, data on the independent predictors of RPP in the absence of a baseline coronary plaque burden are limited. Thus, this study aimed to investigate the predictors for RPP in patients without coronary plaques on baseline coronary computed tomography angiography (CCTA) images. A total of 402 patients (mean age: 57.6 ± 10.0 years, 49.3% men) without coronary plaques at baseline who underwent serial coronary CCTA were identified from the Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging (PARADIGM) registry and included in this retrospective study. RPP was defined as an annual change of ≥ 1.0%/year in the percentage atheroma volume (PAV). During a median inter-scan period of 3.6 years (interquartile range: 2.7-5.0 years), newly developed coronary plaques and RPP were observed in 35.6% and 4.2% of the patients, respectively. The baseline traditional risk factors, i.e., advanced age (≥ 60 years), male sex, hypertension, diabetes mellitus, hyperlipidemia, obesity, and current smoking status, were not significantly associated with the risk of RPP. Multivariate linear regression analysis showed that the serum hemoglobin A1c level (per 1% increase) measured at follow-up CCTA was independently associated with the annual change in the PAV (β: 0.098, 95% confidence interval [CI]: 0.048-0.149; P

Published
2022

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