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1,435 results on '"Chow, Benjamin"'

1. Multitask Deep Learning for Accurate Risk Stratification and Prediction of Next Steps for Coronary CT Angiography Patients

Author

Lu, Juan, Bennamoun, Mohammed, Stewart, Jonathon, Eshraghian, JasonK., Liu, Yanbin, Chow, Benjamin, Sanfilippo, Frank M., and Dwivedi, Girish

Subjects
Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing
Abstract

Diagnostic investigation has an important role in risk stratification and clinical decision making of patients with suspected and documented Coronary Artery Disease (CAD). However, the majority of existing tools are primarily focused on the selection of gatekeeper tests, whereas only a handful of systems contain information regarding the downstream testing or treatment. We propose a multi-task deep learning model to support risk stratification and down-stream test selection for patients undergoing Coronary Computed Tomography Angiography (CCTA). The analysis included 14,021 patients who underwent CCTA between 2006 and 2017. Our novel multitask deep learning framework extends the state-of-the art Perceiver model to deal with real-world CCTA report data. Our model achieved an Area Under the receiver operating characteristic Curve (AUC) of 0.76 in CAD risk stratification, and 0.72 AUC in predicting downstream tests. Our proposed deep learning model can accurately estimate the likelihood of CAD and provide recommended downstream tests based on prior CCTA data. In clinical practice, the utilization of such an approach could bring a paradigm shift in risk stratification and downstream management. Despite significant progress using deep learning models for tabular data, they do not outperform gradient boosting decision trees, and further research is required in this area. However, neural networks appear to benefit more readily from multi-task learning than tree-based models. This could offset the shortcomings of using single task learning approach when working with tabular data.

Published
2023

2. 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

3. 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

4. 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. 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2024
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5. Performance of multilabel machine learning models and risk stratification schemas for predicting stroke and bleeding risk in patients with non-valvular atrial fibrillation

Author

Lu, Juan, Hutchens, Rebecca, Hung, Joseph, Bennamoun, Mohammed, McQuillan, Brendan, Briffa, Tom, Sohel, Ferdous, Murray, Kevin, Stewart, Jonathon, Chow, Benjamin, Sanfilippo, Frank, and Dwivedi, Girish

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning
Abstract

Appropriate antithrombotic therapy for patients with atrial fibrillation (AF) requires assessment of ischemic stroke and bleeding risks. However, risk stratification schemas such as CHA2DS2-VASc and HAS-BLED have modest predictive capacity for patients with AF. Machine learning (ML) techniques may improve predictive performance and support decision-making for appropriate antithrombotic therapy. We compared the performance of multilabel ML models with the currently used risk scores for predicting outcomes in AF patients. Materials and Methods This was a retrospective cohort study of 9670 patients, mean age 76.9 years, 46% women, who were hospitalized with non-valvular AF, and had 1-year follow-up. The primary outcome was ischemic stroke and major bleeding admission. The secondary outcomes were all-cause death and event-free survival. The discriminant power of ML models was compared with clinical risk scores by the area under the curve (AUC). Risk stratification was assessed using the net reclassification index. Results Multilabel gradient boosting machine provided the best discriminant power for stroke, major bleeding, and death (AUC = 0.685, 0.709, and 0.765 respectively) compared to other ML models. It provided modest performance improvement for stroke compared to CHA2DS2-VASc (AUC = 0.652), but significantly improved major bleeding prediction compared to HAS-BLED (AUC = 0.522). It also had a much greater discriminant power for death compared with CHA2DS2-VASc (AUC = 0.606). Also, models identified additional risk features (such as hemoglobin level, renal function, etc.) for each outcome. Conclusions Multilabel ML models can outperform clinical risk stratification scores for predicting the risk of major bleeding and death in non-valvular AF patients.

Published
2022

6. 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

11. 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

12. The clinical adoption of cardiac computed tomography : following the Fryback model of efficacy

Author

Chow, Benjamin J. W.

Subjects
RC Internal medicine
Abstract

Coronary artery disease (CAD) is the leading cause of death globally. The accurate diagnosis of CAD is important to clinical decision making and patient care with the goal of reducing both morbidity and mortality. Cardiac computed tomography (CCT) is a new noninvasive imaging modality which has been recently adopted into clinical practice. Many contemporary guidelines have endorsed CCT as a first line test for patients with suspected CAD. Fryback and Thornbury proposed a six-tiered model outlining goals when investigating and establishing a new diagnostic tool into clinical care. These tiers include technical efficacy, diagnostic accuracy efficacy, diagnostic thinking efficacy, therapeutic efficacy, patient outcome efficacy and societal efficacy. This thesis summarizes the research that has contributed to CCT's global acceptance. The eight papers demonstrate how CCT satisfies the requirements of the Fryback model (accuracy, ability to risk stratify patients, guide therapeutic decision making and its impact on costeffectiveness). These peer-reviewed publications have transitioned CCT into clinical practice.

Published
2022

14. 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

16. Chest Pain Evaluation: Diagnostic Testing

Author

Chow, Benjamin J.W., Galiwango, Paul, Poulin, Anthony, Raggi, Paolo, Small, Gary, Juneau, Daniel, Kazmi, Mustapha, Ayach, Bilal, Beanlands, Rob S., Sanfilippo, Anthony J., Chow, Chi-Ming, Paterson, D. Ian, Chetrit, Michael, Jassal, Davinder S., Connelly, Kim, Larose, Eric, Bishop, Helen, Kass, Malek, Anderson, Todd J., Haddad, Haissam, Mancini, John, Doucet, Katie, Daigle, Jean-Sebastien, Ahmadi, Amir, Leipsic, Jonathan, Lim, Siok Ping, McRae, Andrew, and Chou, Annie Y.

Published
2023
Full Text
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24. When Does a Calcium Score Equate to Secondary Prevention?: Insights From the Multinational CONFIRM Registry

Author

Budoff, Matthew J., Kinninger, April, Gransar, Heidi, Achenbach, Stephan, Al-Mallah, Mouaz, Bax, Jeroen J., Berman, Daniel S., Cademartiri, Filippo, Callister, Tracy Q., Chang, Hyuk-Jae, Chow, Benjamin J.W., Cury, Ricardo C., Feuchtner, Gudrun, Hadamitzky, Martin, Hausleiter, Joerg, Kaufmann, Philipp A., Leipsic, Jonathon, Lin, Fay Y., Kim, Yong-Jin, Marques, Hugo, Pontone, Gianluca, Rubinshtein, Ronen, Shaw, Leslee J., Villines, Todd C., and Min, James K.

Published
2023
Full Text
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25. Age- and sex-related features of atherosclerosis from coronary computed tomography angiography in patients prior to acute coronary syndrome: results from the ICONIC study

Author

Conte, Edoardo, Dwivedi, Aeshita, Mushtaq, Saima, Pontone, Gianluca, Lin, Fay Y, Hollenberg, Emma J, Lee, Sang-Eun, Bax, Jeroen, Cademartiri, Filippo, Chinnaiyan, Kavitha, Chow, Benjamin JW, Cury, Ricardo C, Feuchtner, Gudrun, Hadamitzky, Martin, Kim, Yong-Jin, Baggiano, Andrea, Leipsic, Jonathon, Maffei, Erica, Marques, Hugo, Plank, Fabian, Raff, Gilbert L, van Rosendael, Alexander R, Villines, Todd C, Weirich, Harald G, Al’Aref, Subhi J, Baskaran, Lohendran, Cho, Iksung, Danad, Ibrahim, Han, Donghee, Heo, Ran, Lee, Ji Hyun, Stuijfzand, Wijnand J, Gransar, Heidi, Lu, Yao, Sung, Ji Min, Park, Hyung-Bok, Al-Mallah, Mouaz H, de Araújo Gonçalves, Pedro, Berman, Daniel S, Budoff, Matthew J, Samady, Habib, Shaw, Leslee J, Stone, Peter H, Virmani, Renu, Narula, Jagat, Min, James K, Chang, Hyuk-Jae, and Andreini, Daniele

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Heart Disease, Atherosclerosis, Biomedical Imaging, Aging, Clinical Research, Heart Disease - Coronary Heart Disease, Cardiovascular, Acute Coronary Syndrome, Aged, Computed Tomography Angiography, Coronary Angiography, Coronary Artery Disease, Female, Humans, Male, Plaque, Atherosclerotic, atherosclerosis, gender medicine, cardiac CT, high-risk plaque features, CCTA, Cardiovascular medicine and haematology
Abstract

AimsAlthough there is increasing evidence supporting coronary atherosclerosis evaluation by coronary computed tomography angiography (CCTA), no data are available on age and sex differences for quantitative plaque features. The aim of this study was to investigate sex and age differences in both qualitative and quantitative atherosclerotic features from CCTA prior to acute coronary syndrome (ACS).Methods and resultsWithin the ICONIC study, in which 234 patients with subsequent ACS were propensity matched 1:1 with 234 non-event controls, our current subanalysis included only the ACS cases. Both qualitative and quantitative advance plaque analysis by CCTA were performed by a core laboratory. In 129 cases, culprit lesions identified by invasive coronary angiography at the time of ACS were co-registered to baseline CCTA precursor lesions. The study population was then divided into subgroups according to sex and age (

Published
2021

28. A Boosted Ensemble Algorithm for Determination of Plaque Stability in High-Risk Patients on Coronary CTA

Author

Al'Aref, Subhi J, Singh, Gurpreet, Choi, Jeong W, Xu, Zhuoran, Maliakal, Gabriel, van Rosendael, Alexander R, Lee, Benjamin C, Fatima, Zahra, Andreini, Daniele, Bax, Jeroen J, Cademartiri, Filippo, Chinnaiyan, Kavitha, Chow, Benjamin JW, Conte, Edoardo, Cury, Ricardo C, Feuchtner, Gudruf, Hadamitzky, Martin, Kim, Yong-Jin, Lee, Sang-Eun, Leipsic, Jonathon A, Maffei, Erica, Marques, Hugo, Plank, Fabian, Pontone, Gianluca, Raff, Gilbert L, Villines, Todd C, Weirich, Harald G, Cho, Iksung, Danad, Ibrahim, Han, Donghee, Heo, Ran, Lee, Ji Hyun, Rizvi, Asim, Stuijfzand, Wijnand J, Gransar, Heidi, Lu, Yao, Sung, Ji Min, Park, Hyung-Bok, Berman, Daniel S, Budoff, Matthew J, Samady, Habib, Stone, Peter H, Virmani, Renu, Narula, Jagat, Chang, Hyuk-Jae, Lin, Fay Y, Baskaran, Lohendran, Shaw, Leslee J, and Min, James K

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Cardiovascular, Clinical Research, Heart Disease, Heart Disease - Coronary Heart Disease, Atherosclerosis, Detection, screening and diagnosis, 4.2 Evaluation of markers and technologies, Algorithms, Case-Control Studies, Computed Tomography Angiography, Coronary Angiography, Coronary Artery Disease, Coronary Stenosis, Humans, Plaque, Atherosclerotic, Predictive Value of Tests, Severity of Illness Index, acute coronary syndrome, coronary computed tomography angiography, diameter stenosis, machine learning, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Clinical sciences
Abstract

ObjectivesThis study sought to identify culprit lesion (CL) precursors among acute coronary syndrome (ACS) patients based on qualitative and quantitative computed tomography-based plaque characteristics.BackgroundCoronary computed tomography angiography (CTA) has been validated for patient-level prediction of ACS. However, the applicability of coronary CTA to CL assessment is not known.MethodsUtilizing the ICONIC (Incident COroNary Syndromes Identified by Computed Tomography) study, a nested case-control study of 468 patients with baseline coronary CTA, the study included ACS patients with invasive coronary angiography-adjudicated CLs that could be aligned to CL precursors on baseline coronary CTA. Separate blinded core laboratories adjudicated CLs and performed atherosclerotic plaque evaluation. Thereafter, the study used a boosted ensemble algorithm (XGBoost) to develop a predictive model of CLs. Data were randomly split into a training set (80%) and a test set (20%). The area under the receiver-operating characteristic curve of this model was compared with that of diameter stenosis (model 1), high-risk plaque features (model 2), and lesion-level features of CL precursors from the ICONIC study (model 3). Thereafter, the machine learning (ML) model was applied to 234 non-ACS patients with 864 lesions to determine model performance for CL exclusion.ResultsCL precursors were identified by both coronary angiography and baseline coronary CTA in 124 of 234 (53.0%) patients, with a total of 582 lesions (containing 124 CLs) included in the analysis. The ML model demonstrated significantly higher area under the receiver-operating characteristic curve for discriminating CL precursors (0.774; 95% confidence interval [CI]: 0.758 to 0.790) compared with model 1 (0.599; 95% CI: 0.599 to 0.599; p

Published
2020

29. Non-obstructive high-risk plaques increase the risk of future culprit lesions comparable to obstructive plaques without high-risk features: the ICONIC study

Author

Ferraro, Richard A, van Rosendael, Alexander R, Lu, Yao, Andreini, Daniele, Al-Mallah, Mouaz H, Cademartiri, Filippo, Chinnaiyan, Kavitha, Chow, Benjamin JW, Conte, Edoardo, Cury, Ricardo C, Feuchtner, Gudrun, de Araújo Gonçalves, Pedro, Hadamitzky, Martin, Kim, Yong-Jin, Leipsic, Jonathon, Maffei, Erica, Marques, Hugo, Plank, Fabian, Pontone, Gianluca, Raff, Gilbert L, Villines, Todd C, Lee, Sang-Eun, Al’Aref, Subhi J, Baskaran, Lohendran, Cho, Iksung, Danad, Ibrahim, Gransar, Heidi, Budoff, Matthew J, Samady, Habib, Stone, Peter H, Virmani, Renu, Narula, Jagat, Berman, Daniel S, Chang, Hyuk-Jae, Bax, Jeroen J, Min, James K, Shaw, Leslee J, and Lin, Fay Y

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Research, Heart Disease - Coronary Heart Disease, Heart Disease, Atherosclerosis, Cardiovascular, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, Acute Coronary Syndrome, Aged, Case-Control Studies, Coronary Angiography, Coronary Artery Disease, Coronary Vessels, Female, Humans, Male, Middle Aged, Plaque, Atherosclerotic, Predictive Value of Tests, Retrospective Studies, Risk Factors, Coronary computed tomographic angiography, coronary artery disease, myocardial infarction, Cardiovascular medicine and haematology
Abstract

AimsHigh-risk plaque (HRP) and non-obstructive coronary artery disease independently predict adverse events, but their importance to future culprit lesions has not been resolved. We sought to determine in patients prior to confirmed acute coronary syndrome (ACS) the association between lesion percent diameter stenosis (%DS), and the absolute number and prevalence of HRP. The secondary objective was to examine the relative importance of non-obstructive HRP in future culprit lesions.Methods and resultsWithin the ICONIC study, a nested case-control study of patients undergoing coronary computed tomographic angiography (coronary CT), we included ACS cases with culprit lesions confirmed by invasive coronary angiography and coregistered to baseline coronary CT. Quantitative CT was used to evaluate obstructive (≥50%) and non-obstructive (

Published
2020

31. Clinical risk factors and atherosclerotic plaque extent to define risk for major events in patients without obstructive coronary artery disease: the long-term coronary computed tomography angiography CONFIRM registry

Author

van Rosendael, Alexander R, Bax, A Maxim, Smit, Jeff M, van den Hoogen, Inge J, Ma, Xiaoyue, Al’Aref, Subhi, 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, Min, James K, and Bax, Jeroen J

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Heart Disease - Coronary Heart Disease, Aging, Patient Safety, Cardiovascular, Clinical Research, Atherosclerosis, Biomedical Imaging, Heart Disease, Prevention, Diabetes, 2.1 Biological and endogenous factors, Aetiology, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, Good Health and Well Being, Aged, Computed Tomography Angiography, Coronary Angiography, Coronary Artery Disease, Female, Humans, Male, Middle Aged, Plaque, Atherosclerotic, Predictive Value of Tests, Prognosis, Registries, Risk Factors, coronary computed tomography angiography, risk stratification, atherosclerosis, imaging, preventive cardiology, Cardiovascular medicine and haematology
Abstract

AimsIn patients without obstructive coronary artery disease (CAD), we examined the prognostic value of risk factors and atherosclerotic extent.Methods and resultsPatients from the long-term CONFIRM registry without prior CAD and without obstructive (≥50%) stenosis were included. Within the groups of normal coronary computed tomography angiography (CCTA) (N = 1849) and non-obstructive CAD (N = 1698), the prognostic value of traditional clinical risk factors and atherosclerotic extent (segment involvement score, SIS) was assessed with Cox models. Major adverse cardiac events (MACE) were defined as all-cause mortality, non-fatal myocardial infarction, or late revascularization. In total, 3547 patients were included (age 57.9 ± 12.1 years, 57.8% male), experiencing 460 MACE during 5.4 years of follow-up. Age, body mass index, hypertension, and diabetes were the clinical variables associated with increased MACE risk, but the magnitude of risk was higher for CCTA defined atherosclerotic extent; adjusted hazard ratio (HR) for SIS >5 was 3.4 (95% confidence interval [CI] 2.3-4.9) while HR for diabetes and hypertension were 1.7 (95% CI 1.3-2.2) and 1.4 (95% CI 1.1-1.7), respectively. Exclusion of revascularization as endpoint did not modify the results. In normal CCTA, presence of ≥1 traditional risk factors did not worsen prognosis (log-rank P = 0.248), while it did in non-obstructive CAD (log-rank P = 0.025). Adjusted for SIS, hypertension and diabetes predicted MACE risk in non-obstructive CAD, while diabetes did not increase risk in absence of CAD (P-interaction = 0.004).ConclusionAmong patients without obstructive CAD, the extent of CAD provides more prognostic information for MACE than traditional cardiovascular risk factors. An interaction was observed between risk factors and CAD burden, suggesting synergistic effects of both.

Published
2020

32. Coronary atherosclerosis scoring with semiquantitative CCTA risk scores for prediction of major adverse cardiac events: Propensity score-based analysis of diabetic and non-diabetic patients

Author

van den Hoogen, Inge J, van Rosendael, Alexander R, Lin, Fay Y, Lu, Yao, Dimitriu-Leen, Aukelien C, Smit, Jeff M, Scholte, Arthur JHA, 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, Jones, Erica C, Peña, Jessica M, Shaw, Leslee J, Min, James K, and Bax, Jeroen J

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Patient Safety, Prevention, Diabetes, Heart Disease, Cardiovascular, Heart Disease - Coronary Heart Disease, Clinical Research, Atherosclerosis, Biomedical Imaging, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, Aged, Case-Control Studies, Computed Tomography Angiography, Coronary Angiography, Coronary Artery Disease, Coronary Stenosis, Diabetes Mellitus, Disease Progression, Female, Humans, Male, Middle Aged, Multidetector Computed Tomography, Predictive Value of Tests, Prognosis, Propensity Score, Registries, Risk Assessment, Risk Factors, Severity of Illness Index, Computed tomography, Diabetes mellitus, Prognostic application, Risk stratification, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Clinical sciences, Applied computing
Abstract

AimsWe aimed to compare semiquantitative coronary computed tomography angiography (CCTA) risk scores - which score presence, extent, composition, stenosis and/or location of coronary artery disease (CAD) - and their prognostic value between patients with and without diabetes mellitus (DM). Risk scores derived from general chest-pain populations are often challenging to apply in DM patients, because of numerous confounders.MethodsOut of a combined cohort from the Leiden University Medical Center and the CONFIRM registry with 5-year follow-up data, we performed a secondary analysis in diabetic patients with suspected CAD who were clinically referred for CCTA. A total of 732 DM patients was 1:1 propensity-matched with 732 non-DM patients by age, sex and cardiovascular risk factors. A subset of 7 semiquantitative CCTA risk scores was compared between groups: 1) any stenosis ≥50%, 2) any stenosis ≥70%, 3) stenosis-severity component of the coronary artery disease-reporting and data system (CAD-RADS), 4) segment involvement score (SIS), 5) segment stenosis score (SSS), 6) CT-adapted Leaman score (CT-LeSc), and 7) Leiden CCTA risk score. Cox-regression analysis was performed to assess the association between the scores and the primary endpoint of all-cause death and non-fatal myocardial infarction. Also, area under the receiver-operating characteristics curves were compared to evaluate discriminatory ability.ResultsA total of 1,464 DM and non-DM patients (mean age 58 ± 12 years, 40% women) underwent CCTA and 155 (11%) events were documented after median follow-up of 5.1 years. In DM patients, the 7 semiquantitative CCTA risk scores were significantly more prevalent or higher as compared to non-DM patients (p ≤ 0.022). All scores were independently associated with the primary endpoint in both patients with and without DM (p ≤ 0.020), with non-significant interaction between the scores and diabetes (interaction p ≥ 0.109). Discriminatory ability of the Leiden CCTA risk score in DM patients was significantly better than any stenosis ≥50% and ≥70% (p = 0.003 and p = 0.007, respectively), but comparable to the CAD-RADS, SIS, SSS and CT-LeSc that also focus on the extent of CAD (p ≥ 0.265).ConclusionCoronary atherosclerosis scoring with semiquantitative CCTA risk scores incorporating the total extent of CAD discriminate major adverse cardiac events well, and might be useful for risk stratification of patients with DM beyond the binary evaluation of obstructive stenosis alone.

Published
2020

33. Increased long-term mortality in women with high left ventricular ejection fraction: data from the CONFIRM (COronary CT Angiography EvaluatioN For Clinical Outcomes: An InteRnational Multicenter) long-term registry

Author

Gebhard, Catherine, Maredziak, Monika, Messerli, Michael, Buechel, Ronny R, Lin, Fay, Gransar, Heidi, Achenbach, Stephan, Al-Mallah, Mouaz H, Andreini, Daniele, Bax, Jeroen J, Berman, Daniel S, Budoff, Matthew J, Cademartiri, Filippo, Callister, Tracy Q, Chang, Hyuk-Jae, Chinnaiyan, Kavitha, Chow, Benjamin JW, Cury, Ricardo C, DeLago, Augustin, Feuchtner, Gudrun, Hadamitzky, Martin, Hausleiter, Joerg, Kim, Yong-Jin, Leipsic, Jonathon, Maffei, Erica, Marques, Hugo, de Araújo Gonçalves, Pedro, Pontone, Gianluca, Raff, Gilbert L, Rubinshtein, Ronen, Shaw, Leslee J, Villines, Todd C, Lu, Yao, Jones, Erica C, Peña, Jessica M, Min, James K, and Kaufmann, Philipp A

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Aging, Cardiovascular, Heart Disease, Clinical Trials and Supportive Activities, Biomedical Imaging, Clinical Research, Atherosclerosis, Detection, screening and diagnosis, 4.2 Evaluation of markers and technologies, Good Health and Well Being, Aged, Computed Tomography Angiography, Coronary Angiography, Coronary Artery Disease, Female, Humans, Male, Middle Aged, Prognosis, Prospective Studies, Registries, Severity of Illness Index, Stroke Volume, Ventricular Function, Left, women, gender, coronary computed tomography angiography, left ventricular ejection fraction, cardiovascular, cardiovascular, Cardiovascular medicine and haematology
Abstract

AimsThere are significant sex-specific differences in left ventricular ejection fraction (LVEF), with a higher LVEF being observed in women. We sought to assess the clinical relevance of an increased LVEF in women and men.Methods and resultsA total of 4632 patients from the CONFIRM (COronary CT Angiography EvaluatioN For Clinical Outcomes: An InteRnational Multicenter) registry (44.8% women; mean age 58.7 ± 13.2 years in men and 59.5 ± 13.3 years in women, P = 0.05), in whom LVEF was measured by cardiac computed tomography, were categorized according to LVEF (low 65%). The prevalence of high LVEF was similar in both sexes (33.5% in women and 32.5% in men, P = 0.46). After 6 years of follow-up, no difference in mortality was observed in patients with high LVEF in the overall cohort (P = 0.41). When data were stratified by sex, women with high LVEF died more often from any cause as compared to women with normal LVEF (8.6% vs. 7.1%, log rank P = 0.032), while an opposite trend was observed in men (5.8% vs. 6.8% in normal LVEF, log rank P = 0.89). Accordingly, a first order interaction term of male sex and high LVEF was significant (hazard ratios 0.63, 95% confidence intervals 0.41-0.98, P = 0.043) in a Cox regression model of all-cause mortality adjusted for age, cardiovascular risk factors, and severity of coronary artery disease (CAD).ConclusionIncreased LVEF is highly prevalent in patients referred for evaluation of CAD and is associated with an increased risk of death in women, but not in men. Differentiating between normal and hyperdynamic left ventricles might improve risk stratification in women with CAD.Clinical trial registrationhttps://clinicaltrials.gov/ct2/show/NCT01443637.

Published
2020

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

Author

Al'Aref, Subhi J, Maliakal, Gabriel, Singh, Gurpreet, van Rosendael, Alexander R, Ma, Xiaoyue, Xu, Zhuoran, Alawamlh, Omar Al Hussein, Lee, Benjamin, Pandey, Mohit, Achenbach, Stephan, Al-Mallah, Mouaz H, Andreini, Daniele, Bax, Jeroen J, Berman, Daniel S, Budoff, Matthew J, Cademartiri, Filippo, Callister, Tracy Q, Chang, Hyuk-Jae, Chinnaiyan, Kavitha, Chow, Benjamin 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, Gonçalves, Pedro de Araújo, Pontone, Gianluca, Raff, Gilbert L, Rubinshtein, Ronen, Villines, Todd C, Gransar, Heidi, Lu, Yao, Jones, Erica C, Peña, Jessica M, Lin, Fay Y, Min, James K, and Shaw, Leslee J

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Biomedical Imaging, Clinical Research, Atherosclerosis, Cardiovascular, Heart Disease, Heart Disease - Coronary Heart Disease, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, Calcium, Computed Tomography Angiography, Coronary Angiography, Coronary Artery Disease, Coronary Vessels, Female, Humans, Machine Learning, Male, Middle Aged, Multidetector Computed Tomography, Predictive Value of Tests, Prospective Studies, ROC Curve, Registries, Coronary artery disease, Coronary artery calcium score, Machine learning, Coronary computed tomography angiography, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Clinical sciences
Abstract

AimsSymptom-based pretest probability scores that estimate the likelihood of obstructive coronary artery disease (CAD) in stable chest pain have moderate accuracy. We sought to develop a machine learning (ML) model, utilizing clinical factors and the coronary artery calcium score (CACS), to predict the presence of obstructive CAD on coronary computed tomography angiography (CCTA).Methods and resultsThe study screened 35 281 participants enrolled in the CONFIRM registry, who underwent ≥64 detector row CCTA evaluation because of either suspected or previously established CAD. A boosted ensemble algorithm (XGBoost) was used, with data split into a training set (80%) on which 10-fold cross-validation was done and a test set (20%). Performance was assessed of the (1) ML model (using 25 clinical and demographic features), (2) ML + CACS, (3) CAD consortium clinical score, (4) CAD consortium clinical score + CACS, and (5) updated Diamond-Forrester (UDF) score. The study population comprised of 13 054 patients, of whom 2380 (18.2%) had obstructive CAD (≥50% stenosis). Machine learning with CACS produced the best performance [area under the curve (AUC) of 0.881] compared with ML alone (AUC of 0.773), CAD consortium clinical score (AUC of 0.734), and with CACS (AUC of 0.866) and UDF (AUC of 0.682), P

Published
2020

35. Long-term prognostic utility of computed tomography coronary angiography in older populations.

Author

Gnanenthiran, Sonali R, Naoum, Christopher, Leipsic, Jonathon A, Achenbach, Stephan, Al-Mallah, Mouaz H, Andreini, Daniele, Bax, Jeroen J, Berman, Daniel S, Budoff, Matthew J, Cademartiri, Filippo, Callister, Tracy Q, Chang, Hyuk-Jae, Chinnaiyan, Kavitha, Chow, Benjamin JW, Cury, Ricardo C, DeLago, Augustin, Feuchtner, Gudrun, Hadamitzky, Martin, Hausleiter, Joerg, Kaufman, Philipp A, Kim, Yong-Jin, Maffei, Erica, Marques, Hugo, de Araújo Gonçalves, Pedro, Pontone, Gianluca, Raff, Gilbert L, Rubinshtein, Ronen, Shaw, Leslee J, Villines, Todd C, Gransar, Heidi, Lu, Yao, Jones, Erica C, Peña, Jessica M, Lin, Fay Y, Kritharides, Leonard, and Min, James K

Subjects
Prevention, Biomedical Imaging, Heart Disease - Coronary Heart Disease, Aging, Cardiovascular, Clinical Research, Atherosclerosis, Heart Disease, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, Good Health and Well Being, Age Factors, Aged, Aged, 80 and over, Computed Tomography Angiography, Coronary Angiography, Coronary Artery Disease, Female, Humans, Male, Middle Aged, Predictive Value of Tests, Prognosis, Prospective Studies, Registries, coronary computed tomography angiography, age, older populations, mortality, major adverse cardiovascular events, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology
Abstract

AimsThe long-term prognostic value of coronary computed tomography angiography (CCTA)-identified coronary artery disease (CAD) has not been evaluated in elderly patients (≥70 years). We compared the ability of coronary CCTA to predict 5-year mortality in older vs. younger populations.Methods and resultsFrom the prospective CONFIRM (COronary CT Angiography EvaluatioN For Clinical Outcomes: An InteRnational Multicenter) registry, we analysed CCTA results according to age 50%); (ii) segment involvement score (SIS): number of segments with plaque. Cox-proportional hazard models assessed the relationship between CCTA findings and time to mortality. At a mean 5.6 ± 1.1 year follow-up, CCTA-identified CAD predicted increased mortality compared with patients with a normal CCTA in both

Published
2019

36. Point of Care Clinical Risk Score to Improve the Negative Diagnostic Utility of an Agatston Score of Zero: Averting the Need for Coronary Computed Tomography Angiography.

Author

Alshahrani, Ali M, Mahmood, Hamza, Wells, George A, Hossain, Alomgir, Rybicki, Frank J, Achenbach, Stephan, Al-Mallah, Mouaz H, Andreini, Daniele, Bax, Jeroen J, Berman, Daniel S, Budoff, Matthew J, Cademartiri, Filippo, Callister, Tracy Q, Chang, Hyuk-Jae, Chinnaiyan, Kavitha, Cury, Ricardo C, DeLago, Augustin, Feuchtner, Gudrun, Hadamitzky, Martin, Hausleiter, Joerg, Kaufmann, Philipp A, Kim, Yong-Jin, Leipsic, Jonathon A, Maffei, Erica, Marques, Hugo, Pontone, Gianluca, Raff, Gilbert, Rubinshtein, Ronen, Shaw, Leslee J, Villines, Todd C, Lin, Fay Y, Min, James K, and Chow, Benjamin J

Subjects
Humans, Coronary Stenosis, Registries, Risk Assessment, Risk Factors, Retrospective Studies, Predictive Value of Tests, Algorithms, Middle Aged, Unnecessary Procedures, Point-of-Care Systems, Female, Male, Computed Tomography Angiography, chest pain, coronary artery disease, diabetes mellitus, humans, registries, Prevention, Cardiovascular, Atherosclerosis, Clinical Research, Heart Disease - Coronary Heart Disease, Biomedical Imaging, Heart Disease, 4.2 Evaluation of markers and technologies, Clinical Sciences, Cardiovascular System & Hematology
Abstract

BackgroundCoronary artery calcification is a marker of underlying atherosclerotic vascular disease. The absence of coronary artery calcification is associated with a low prevalence of obstructive coronary artery disease (CAD), but it cannot be ruled out completely. We sought to develop a clinical tool that can be added to Agatston score of zero to rule out obstructive CAD with high accuracy.MethodsWe developed a clinical score retrospectively from a cohort of 4903 consecutive patients with an Agatston score of zero. Patients with prior diagnosis of CAD, coronary percutaneous coronary intervention, or surgical revascularization were excluded. Obstructive CAD was defined as any epicardial vessel diameter narrowing of ≥50%. The score was validated using an external cohort of 4290 patients with an Agatston score of zero from a multinational registry.ResultsThe score consisted of 7 variables: age, sex, typical chest pain, dyslipidemia, hypertension, family history, and diabetes mellitus. The model was robust with an area under the curve of 0.70 (95% CI, 0.65-0.76) in the derivation cohort and 0.69 (95% CI, 0.65-0.72) in the validation cohort. Patients were divided into 3 risk groups based on the score: low (≤6), intermediate (7-13), and high (≥14). Patients who score ≤6 have a negative likelihood ratio of 0.42 for obstructive CAD, whereas those who score ≥14 have a positive likelihood ratio of >5.5 for obstructive CAD. The outcome was ruled out in >98% of patients with a score ≤6 in the validation cohort.ConclusionsWe developed a score that may be used to identify the likelihood of obstructive CAD in patients with an Agatston score of zero, which may be used to direct the need for additional testing. However, the results of this retrospective analysis are hypothesis generating and before clinical implementation should be validated in a trial with a prospectively collected data.

Published
2019

37. Coronary Flow Reserve, Inflammation, and Myocardial Strain: The CIRT-CFR Trial

Author

Taqueti, Viviany R., Shah, Amil M., Everett, Brendan M., Pradhan, Aruna D., Piazza, Gregory, Bibbo, Courtney, Hainer, Jon, Morgan, Victoria, Carolina do A. H. de Souza, Ana, Skali, Hicham, Blankstein, Ron, Dorbala, Sharmila, Goldhaber, Samuel Z., Le May, Michel R., Chow, Benjamin J.W., deKemp, Robert A., Hage, Fadi G., Beanlands, Rob S., Libby, Peter, Glynn, Robert J., Solomon, Scott D., Ridker, Paul M., and Di Carli, Marcelo F.

Published
2023
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38. Prognostic Significance of Nonobstructive Left Main Coronary Artery Disease in Patients With and Without Diabetes: Long-Term Outcomes From the CONFIRM Registry

Author

Lee, Juhwan, Shaikh, Kashif, Nakanishi, Rine, Gransar, Heidi, Achenbach, Stephan, Al-Mallah, Mouaz H., Andreini, Daniele, Bax, Jeroen J., Berman, Daniel S., Cademartiri, Filippo, Callister, Tracy Q., Chang, Hyuk-Jae, Chinnaiyan, Kavitha, Chow, Benjamin J.W., Cury, Ricardo C., DeLago, Augustin, Feuchtner, Gudrun, Hadamitzky, Martin, Hausleiter, Joerg, Kaufmann, Philipp A., Kim, Yong-Jin, Leipsic, Jonathon A., Maffei, Erica, Marques, Hugo, de Araújo Gonçalves, Pedro, Pontone, Gianluca, Rubinshtein, Ronen, Villines, Todd C., Lu, Yao, Peña, Jessica M., Lin, Fay Y., Min, James K., Shaw, Leslee J., and Budoff, Matthew J.

Published
2023
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40. Ischemia With Nonobstructive Coronary Arteries: Insights From the ISCHEMIA Trial

Author

Reynolds, Harmony R., Diaz, Ariel, Cyr, Derek D., Shaw, Leslee J., Mancini, G.B. John, Leipsic, Jonathon, Budoff, Matthew J., Min, James K., Hague, Cameron J., Berman, Daniel S., Chaitman, Bernard R., Picard, Michael H., Hayes, Sean W., Scherrer-Crosbie, Marielle, Kwong, Raymond Y., Lopes, Renato D., Senior, Roxy, Dwivedi, Sudhanshu K., Miller, Todd D., Chow, Benjamin J.W., de Silva, Ramesh, Stone, Gregg W., Boden, William E., Bangalore, Sripal, O’Brien, Sean M., Hochman, Judith S., and Maron, David J.

Published
2023
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41. Extent of subclinical atherosclerosis on coronary computed tomography and impact of statins in patients with diabetes without known coronary artery disease: Results from CONFIRM registry

Author

Shaikh, Kashif, Ahmed, Arslan, Gransar, Heidi, Lee, JuHwan, Leipsic, Jonathon, Nakanishi, Rine, Alla, Venkata, Bax, Jeroen J., Chow, Benjamin J.W., Berman, Daniel S., Maffei, Erica, Lin, Fay Y., Ahmad, Aiza, DeLago, Augustin, Pontone, Gianluca, Feuchtner, Gudrun, Marques, Hugo, Min, James K., Hausleiter, Joerg, Hadamitzky, Martin, Kaufmann, Philipp A., de Araújo Gonçalves, Pedro, Cury, Ricardo C., Kim, Yong-Jin, Chang, Hyuk-Jae, Rubinshtein, Ronen, Villines, Todd C., Lu, Yao, Shaw, Leslee J., Acenbach, Stephen, Al.Mallah, Mouaz H., Andreini, Daniele, Cademartiri, Filippo, Callister, Tracy Q., and Budoff, Matthew J.

Published
2022
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45. Age related compositional plaque burden by CT in patients with future ACS

Author

van Rosendael, Alexander R., van den Hoogen, Inge J., Lin, Fay Y., Gianni, Umberto, Lu, Yao, Andreini, Daniele, Al-Mallah, Mouaz H., Cademartiri, Filippo, Chinnaiyan, Kavitha, Chow, Benjamin J.W., Conte, Edoardo, Cury, Ricardo C., Feuchtner, Gudrun, de Araújo Gonçalves, Pedro, Hadamitzky, Martin, Kim, Yong-Jin, Leipsic, Jonathon A., Maffei, Erica, Marques, Hugo, Plank, Fabian, Pontone, Gianluca, Raff, Gilbert L., Villines, Todd C., Lee, Sang-Eun, Al’Aref, Subhi J., Baskaran, Lohendran, Cho, Iksung, Danad, Ibrahim, Gransar, Heidi, Budoff, Matthew J., Samady, Habib, Virmani, Renu, Min, James K., Narula, Jagat, Berman, Daniel S., Chang, Hyuk-Jae, Shaw, Leslee J., and Bax, Jeroen J.

Published
2022
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46. A cross-sectional survey of coronary plaque composition in individuals on non-statin lipid lowering drug therapies and undergoing coronary computed tomography angiography

Author

Al'Aref, Subhi J, Su, Amanda, Gransar, Heidi, van Rosendael, Alexander R, Rizvi, Asim, Berman, Daniel S, Callister, Tracy Q, DeLago, Augustin, Hadamitzky, Martin, Hausleiter, Joerg, Al-Mallah, Mouaz H, Budoff, Matthew J, Kaufmann, Philipp A, Raff, Gilbert L, Chinnaiyan, Kavitha, Cademartiri, Filippo, Maffei, Erica, Villines, Todd C, Kim, Yong-Jin, Leipsic, Jonathon, Feuchtner, Gudrun, Pontone, Gianluca, Andreini, Daniele, Marques, Hugo, de Araújo Gonçalves, Pedro, Rubinshtein, Ronen, Achenbach, Stephan, Chang, Hyuk-Jae, Chow, Benjamin JW, Cury, Ricardo, Lu, Yao, Bax, Jeroen J, Jones, Erica C, Peña, Jessica M, Shaw, Leslee J, Min, James K, and Lin, Fay Y

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Heart Disease - Coronary Heart Disease, Atherosclerosis, Cardiovascular, Heart Disease, Clinical Research, Clinical Trials and Supportive Activities, 6.1 Pharmaceuticals, Aetiology, 2.1 Biological and endogenous factors, Evaluation of treatments and therapeutic interventions, Aged, Asia, Biomarkers, Computed Tomography Angiography, Coronary Angiography, Coronary Artery Disease, Coronary Stenosis, Coronary Vessels, Cross-Sectional Studies, Drug Therapy, Combination, Dyslipidemias, Europe, Female, Humans, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Hypolipidemic Agents, Lipids, Male, Middle Aged, North America, Plaque, Atherosclerotic, Predictive Value of Tests, Prevalence, Registries, Risk Factors, Coronary computed tomography angiography, Coronary plaque composition, Non-statin therapy, Ezetimibe, Fibrate, Niacin, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Clinical sciences, Applied computing
Abstract

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

Published
2019

47. Applying Modern Virtual and Augmented Reality Technologies to Medical Images and Models

Author

Sutherland, Justin, Belec, Jason, Sheikh, Adnan, Chepelev, Leonid, Althobaity, Waleed, Chow, Benjamin JW, Mitsouras, Dimitrios, Christensen, Andy, Rybicki, Frank J, and La Russa, Daniel J

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Patient Safety, Networking and Information Technology R&D (NITRD), Augmented Reality, Diagnostic Imaging, Humans, Models, Biological, Printing, Three-Dimensional, Virtual Reality, Virtual reality, Augmented reality, Radiology, Visualization, Nuclear Medicine & Medical Imaging, Clinical sciences
Abstract

Recent technological innovations have created new opportunities for the increased adoption of virtual reality (VR) and augmented reality (AR) applications in medicine. While medical applications of VR have historically seen greater adoption from patient-as-user applications, the new era of VR/AR technology has created the conditions for wider adoption of clinician-as-user applications. Historically, adoption to clinical use has been limited in part by the ability of the technology to achieve a sufficient quality of experience. This article reviews the definitions of virtual and augmented reality and briefly covers the history of their development. Currently available options for consumer-level virtual and augmented reality systems are presented, along with a discussion of technical considerations for their adoption in the clinical environment. Finally, a brief review of the literature of medical VR/AR applications is presented prior to introducing a comprehensive conceptual framework for the viewing and manipulation of medical images in virtual and augmented reality. Using this framework, we outline considerations for placing these methods directly into a radiology-based workflow and show how it can be applied to a variety of clinical scenarios.

Published
2019

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

Author

Ayoub, Chadi, Kritharides, Leonard, Yam, Yeung, Chen, Li, Hossain, Alomgir, Achenbach, Stephan, Al-Mallah, Mouaz H, Andreini, Daniele, Berman, Daniel S, Budoff, Matthew J, Cademartiri, Filippo, Callister, Tracy Q, Chang, Hyuk-Jae, Chinnaiyan, Kavitha, Cury, Ricardo C, Delago, Augustin, Dunning, Allison, Feuchtner, Gudrun, Gomez, Millie, Gransar, Heidi, Hadamitzky, Martin, Hausleiter, Joerg, Hindoyan, Niree, Kaufmann, Philipp A, Kim, Yong-Jin, Leipsic, Jonathon, Maffei, Erica, Marques, Hugo, Pontone, Gianluca, Raff, Gilbert, Rubinshtein, Ronen, Shaw, Leslee J, Villines, Todd C, Min, James K, and Chow, Benjamin JW

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Heart Disease, Clinical Research, Biomedical Imaging, Cardiovascular, Atherosclerosis, Aging, Heart Disease - Coronary Heart Disease, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, Good Health and Well Being, Age Factors, Aged, Computed Tomography Angiography, Coronary Angiography, Coronary Artery Disease, Coronary Vessels, Female, Follow-Up Studies, Humans, Male, Middle Aged, Plaque, Atherosclerotic, Predictive Value of Tests, Prognosis, Prospective Studies, Registries, Risk Factors, Time Factors, Coronary, Computed tomography, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology
Abstract

Extent of coronary atherosclerotic disease (CAD) burden on coronary computed tomography angiography (CCTA) as measured by segment involvement score (SIS) has a prognostic value. We sought to investigate the incremental prognostic value of 'age adjusted SIS' (aSIS), which may be a marker of premature atherosclerosis and vascular age. Consecutive patients were prospectively enrolled into the CONFIRM (Coronary CT Angiography EvaluatioN For Clinical Outcomes: An InteRnational Multicentre) multinational observational study. Patients were followed for the outcome of all-cause death. aSIS was calculated on CCTA for each patient, and its incremental prognostic value was evaluated. A total of 22,211 patients [mean age 58.5 ± 12.7 years, 55.8% male) with a median follow-up of 27.3 months (IQR 17.8, 35.4)] were identified. After adjustment for clinical factors and presence of obstructive CAD, higher aSIS was associated with increased death on multivariable analysis, with hazard ratio (HR) 2.40 (1.83-3.16, p

Published
2018

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