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Douglas (2015)
Outcomes of anatomical versus functional testing for coronary artery diseaseN Engl J Med, 372
Ting He, Xing Liu, Nana Xu, Ying Li, Qiaoyu Wu, Meilin Liu, Hong Yuan (2017)
Diagnostic models of the pre-test probability of stable coronary artery disease: A systematic reviewClinics, 72
M. Budoff, G. Diamond, P. Raggi, Y. Arad, A. Guerci, T. Callister, D. Berman (2002)
Continuous Probabilistic Prediction of Angiographically Significant Coronary Artery Disease Using Electron Beam TomographyCirculation: Journal of the American Heart Association, 105
B. Mortazavi, Nicholas Downing, E. Bucholz, K. Dharmarajan, A. Manhapra, Shu-Xia Li, S. Negahban, H. Krumholz (2016)
Analysis of Machine Learning Techniques for Heart Failure ReadmissionsCirculation: Cardiovascular Quality and Outcomes, 9
L. Baskaran, I. Danad, H. Gransar, B. Hartaigh, Joshua Schulman-Marcus, F. Lin, J. Peña, Amanda Hunter, D. Newby, P. Adamson, J. Min (2019)
A Comparison of the Updated Diamond-Forrester, CAD Consortium, and CONFIRM History-Based Risk Scores for Predicting Obstructive Coronary Artery Disease in Patients With Stable Chest Pain: The SCOT-HEART Coronary CTA Cohort.JACC. Cardiovascular imaging
(2010)
Perioperative β-Blockers : Use With Caution Perioperative β Blockers in Patients Having Non-Cardiac Surgery : A Meta-Analysis
M. Patel, E. Peterson, David Dai, J. Brennan, R. Redberg, H. Anderson, R. Brindis, P. Douglas (2010)
Low diagnostic yield of elective coronary angiography.The New England journal of medicine, 362 10
F. Lin, L. Shaw, A. Dunning, T. LaBounty, Jin‐Ho Choi, J. Weinsaft, S. Koduru, Millie Gomez, A. Delago, T. Callister, D. Berman, J. Min (2011)
Mortality risk in symptomatic patients with nonobstructive coronary artery disease: a prospective 2-center study of 2,583 patients undergoing 64-detector row coronary computed tomographic angiography.Journal of the American College of Cardiology, 58 5
F. Meinel, U. Schoepf, Jacob Townsend, Brian Flowers, L. Geyer, U. Ebersberger, A. Krazinski, W. Kunz, K. Thierfelder, Deborah Baker, Ashan Khan, Valerian Fernandes, T. O’Brien (2018)
Diagnostic yield and accuracy of coronary CT angiography after abnormal nuclear myocardial perfusion imagingScientific Reports, 8
H. Hecht, P. Cronin, M. Blaha, M. Budoff, E. Kazerooni, J. Narula, D. Yankelevitz, S. Abbara (2017)
2016 SCCT/STR guidelines for coronary artery calcium scoring of noncontrast noncardiac chest CT scans: A report of the Society of Cardiovascular Computed Tomography and Society of Thoracic Radiology.Journal of thoracic imaging, 32 5
M. Budoff, D. Dowe, J. Jollis, M. Gitter, J. Sutherland, Edward Halamert, M. Scherer, R. Bellinger, Arthur Martin, R. Benton, A. Delago, J. Min (2008)
Diagnostic performance of 64-multidetector row coronary computed tomographic angiography for evaluation of coronary artery stenosis in individuals without known coronary artery disease: results from the prospective multicenter ACCURACY (Assessment by Coronary Computed Tomographic Angiography of IndiJournal of the American College of Cardiology, 52 21
Adriaan Coenen, Young-Hak Kim, M. Kruk, C. Tesche, J. Geer, A. Kurata, M. Lubbers, J. Daemen, L. Itu, S. Rapaka, Puneet Sharma, C. Schwemmer, A. Persson, J. Schoepf, C. Kȩpka, D. Yang, K. Nieman (2018)
Diagnostic Accuracy of a Machine-Learning Approach to Coronary Computed Tomographic Angiography–Based Fractional Flow Reserve: Result From the MACHINE ConsortiumCirculation: Cardiovascular Imaging, 11
G. Krestin (2012)
Prediction model to estimate presence of coronary artery disease: retrospective pooled analysis of existing cohortsBMJ : British Medical Journal, 344
M. Ferencik, Ting Liu, T. Mayrhofer, S. Puchner, Michael Lu, P. Maurovich-Horvat, J. Pope, Q. Truong, J. Udelson, W. Peacock, C. White, P. Woodard, J. Fleg, J. Nagurney, J. Januzzi, U. Hoffmann (2015)
hs-Troponin I Followed by CT Angiography Improves Acute Coronary Syndrome Risk Stratification Accuracy and Work-Up in Acute Chest Pain Patients: Results From ROMICAT II Trial.JACC. Cardiovascular imaging, 8 11
Stephan Fihn, J. Gardin, Jonathan Abrams, Kathleen Berra, James Blankenship, Apostolos Dallas, P. Douglas, J. Foody, Thomas Gerber, A. Hinderliter, Spencer King, Paul Kligfield, H. Krumholz, Raymond Kwong, Michael Lim, Jane Linderbaum, Michael Mack, Mark Munger, Richard Prager, J. Sabik, Leslee Shaw, Joanna Sikkema, Craig Smith, Sidney Smith, J. Spertus, Sankey Williams (2012)
2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS Guideline for the diagnosis and management of patients with stable ischemic heart disease: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines, and the American College of Physicians, American AssJournal of the American College of Cardiology, 60 24
J. Leipsic, S. Abbara, S. Achenbach, R. Cury, J. Earls, G. Mancini, K. Nieman, G. Pontone, G. Raff (2014)
SCCT guidelines for the interpretation and reporting of coronary CT angiography: a report of the Society of Cardiovascular Computed Tomography Guidelines Committee.Journal of cardiovascular computed tomography, 8 5
Hecht (2017)
2016 SCCT/STR guidelines for coronary artery calcium scoring of noncontrast noncardiac chest CT scans: a report of the Society of Cardiovascular Computed Tomography and Society of Thoracic RadiologyJ Cardiovasc Comput Tomogr, 11
Scott Lundberg, Su-In Lee (2017)
A Unified Approach to Interpreting Model Predictions
Iksung Cho, H. Chang, B. Hartaigh, Sanghoon Shin, J. Sung, F. Lin, S. Achenbach, R. Heo, D. Berman, M. Budoff, T. Callister, M. Al-Mallah, Filippo Cademartiri, K. Chinnaiyan, B. Chow, A. Dunning, A. Delago, T. Villines, M. Hadamitzky, J. Hausleiter, J. Leipsic, L. Shaw, P. Kaufmann, R. Cury, G. Feuchtner, Yong‐Jin Kim, E. Maffei, G. Raff, G. Pontone, D. Andreini, J. Min (2015)
Incremental prognostic utility of coronary CT angiography for asymptomatic patients based upon extent and severity of coronary artery calcium: results from the COronary CT Angiography EvaluatioN For Clinical Outcomes InteRnational Multicenter (CONFIRM) study.European heart journal, 36 8
S. Abbara, P. Blanke, C. Maroules, M. Cheezum, A. Choi, B. Han, M. Marwan, C. Naoum, B. Nørgaard, R. Rubinshtein, P. Schoenhagen, T. Villines, J. Leipsic (2016)
SCCT guidelines for the performance and acquisition of coronary computed tomographic angiography: A report of the society of Cardiovascular Computed Tomography Guidelines Committee: Endorsed by the North American Society for Cardiovascular Imaging (NASCI).Journal of cardiovascular computed tomography, 10 6
Miller (2008)
Diagnostic performance of coronary angiography by 64-row CTN Engl J Med, 359
S. Al’Aref, K. Anchouche, Gurpreet Singh, P. Slomka, K. Kolli, Amit Kumar, Mohit Pandey, Gabriel Maliakal, A. Rosendael, Ashley Beecy, D. Berman, J. Leipsic, K. Nieman, D. Andreini, G. Pontone, U. Schoepf, L. Shaw, H. Chang, J. Narula, Jeroen Bax, Y. Guan, J. Min (2018)
Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging.European heart journal, 40 24
Jia Zhou, Yujie Liu, Lingyu Huang, Yahang Tan, Xingchen Li, Hong Zhang, Yanhe Ma, Ying Zhang (2017)
Validation and comparison of four models to calculate pretest probability of obstructive coronary artery disease in a Chinese population: A coronary computed tomographic angiography study.Journal of cardiovascular computed tomography, 11 4
K. Takamura, T. Kondo, S. Fujimoto, M. Hiki, R. Matsumori, Y. Kawaguchi, M. Amanuma, S. Takase, H. Daida (2016)
Incremental predictive value for obstructive coronary artery disease by combination of Duke Clinical Score and Agatston score.European heart journal cardiovascular Imaging, 17 5
F. Wu, Ming-Ting Wu (2015)
2014 SCCT guidelines for the interpretation and reporting of coronary CT angiography: a report of the Society of Cardiovascular Computed Tomography Guidelines Committee.Journal of cardiovascular computed tomography, 9 2
T. Genders, E. Steyerberg, H. Alkadhi, S. Leschka, L. Desbiolles, K. Nieman, T. Galema, W. Meijboom, N. Mollet, P. Feyter, Filippo Cademartiri, E. Maffei, M. Dewey, E. Zimmermann, M. Laule, F. Pugliese, R. Barbagallo, V. Sinitsyn, J. Bogaert, K. Goetschalckx, U. Schoepf, G. Rowe, J. Schuijf, Jeroen Bax, F. Graaf, J. Knuuti, S. Kajander, C. Mieghem, M. Meijs, M. Cramer, D. Gopalan, G. Feuchtner, G. Friedrich, G. Krestin, M. Hunink (2011)
A clinical prediction rule for the diagnosis of coronary artery disease: validation, updating, and extension.European heart journal, 32 11
A. Rosendael, Gabriel Maliakal, K. Kolli, Ashley Beecy, S. Al’Aref, A. Dwivedi, Gurpreet Singh, M. Panday, Amit Kumar, Xiaoyue Ma, S. Achenbach, M. Al-Mallah, D. Andreini, Jeroen Bax, D. Berman, M. Budoff, Filippo Cademartiri, T. Callister, H. Chang, K. Chinnaiyan, B. Chow, R. Cury, A. Delago, G. Feuchtner, M. Hadamitzky, J. Hausleiter, P. Kaufmann, Yong‐Jin Kim, J. Leipsic, E. Maffei, H. Marques, G. Pontone, G. Raff, R. Rubinshtein, L. Shaw, T. Villines, H. Gransar, Yao Lu, E. Jones, J. Peña, F. Lin, J. Min (2018)
Maximization of the usage of coronary CTA derived plaque information using a machine learning based algorithm to improve risk stratification; insights from the CONFIRM registry.Journal of cardiovascular computed tomography, 12 3
Matthew Kalscheur, R. Kipp, M. Tattersall, Chaoqun Mei, K. Buhr, D. DeMets, M. Field, L. Eckhardt, C. Page (2018)
Machine Learning Algorithm Predicts Cardiac Resynchronization Therapy Outcomes: Lessons From the COMPANION TrialCirculation: Arrhythmia and Electrophysiology, 11
M. Motwani, D. Dey, D. Berman, G. Germano, S. Achenbach, M. Al-Mallah, D. Andreini, M. Budoff, Filippo Cademartiri, T. Callister, H. Chang, K. Chinnaiyan, B. Chow, R. Cury, A. Delago, Millie Gomez, H. Gransar, M. Hadamitzky, J. Hausleiter, Niree Hindoyan, G. Feuchtner, P. Kaufmann, Yong‐Jin Kim, J. Leipsic, F. Lin, E. Maffei, H. Marques, G. Pontone, G. Raff, R. Rubinshtein, L. Shaw, J. Stehli, T. Villines, A. Dunning, J. Min, P. Slomka (2016)
Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysisEuropean Heart Journal, 38
G. Montalescot, U. Sechtem, S. Achenbach, F. Andreotti, C. Arden, A. Budaj, R. Bugiardini, F. Crea, T. Cuisset, C. Mario, J. Ferreira, B. Gersh, A. Gitt, J. Hulot, N. Marx, L. Opie, M. Pfisterer, E. Prescott, F. Ruschitzka, M. Sabaté, R. Senior, D. Taggart, E. Wall, C. Vrints, J. Zamorano, H. Baumgartner, Jeroen Bax, H. Bueno, V. Dean, C. Deaton, Ç. Erol, R. Fagard, R. Ferrari, D. Hasdai, A. Hoes, P. Kirchhof, J. Knuuti, P. Kolh, P. Lancellotti, A. Linhart, P. Nihoyannopoulos, M. Piepoli, P. Ponikowski, P. Sirnes, J. Tamargo, M. Tendera, A. Torbicki, W. Wijns, S. Windecker, M. Valgimigli, M. Claeys, N. Donner‐Banzhoff, Herbert Frank, C. Funck-Brentano, O. Gaemperli, J. González-Juanatey, M. Hamilos, S. Husted, S. James, K. Kervinen, S. Kristensen, A. Maggioni, A. Pries, F. Romeo, L. Rydén, M. Simoons, P. Steg, A. Timmis, A. Yıldırır (2013)
2013 ESC guidelines on the management of stable coronary artery disease: the Task Force on the management of stable coronary artery disease of the European Society of Cardiology.European heart journal, 34 38
S. Malhotra, Prem Soman (2015)
A selection of recent, original research papersJournal of Nuclear Cardiology, 22
Genders (2012)
Prediction model to estimate presence of coronary artery disease: retrospective pooled analysis of existing cohortsBMJ, 344
Tianqi Chen, Carlos Guestrin (2016)
XGBoost: A Scalable Tree Boosting SystemProceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
S. Investigators (2015)
CT coronary angiography in patients with suspected angina due to coronary heart disease (SCOT-HEART): an open-label, parallel-group, multicentre trialThe Lancet, 385
U. Sechtem, A. Budaj, J. Ferreira, B. Gersh (2013)
ESC guidelines on the management of stable coronary artery disease — addenda The Task Force on the management of stable coronary artery disease of the European Society of Cardiology
R. Haberl, A. Becker, A. Leber, A. Knez, C. Becker, Christine Lang, R. Brüning, M. Reiser, G. Steinbeck (2001)
Correlation of coronary calcification and angiographically documented stenoses in patients with suspected coronary artery disease: results of 1,764 patients.Journal of the American College of Cardiology, 37 2
K. Chinnaiyan, P. Peyser, T. Goraya, K. Ananthasubramaniam, Michael Gallagher, Ann Depetris, J. Boura, E. Kazerooni, C. Poopat, M. Al-Mallah, S. Saba, Smita Patel, S. Girard, Thomas Song, D. Share, G. Raff (2012)
Impact of a continuous quality improvement initiative on appropriate use of coronary computed tomography angiography. Results from a multicenter, statewide registry, the Advanced Cardiovascular Imaging Consortium.Journal of the American College of Cardiology, 60 13
H. Isma'eel, Mustapha Serhan, G. Sakr, Nader Lamaa, Torkom Garabedian, I. Elhajj, H. Skouri, A. Abchee (2016)
Diamond-Forrester and Morise risk models perform poorly in predicting obstructive coronary disease in Middle Eastern Cohort.International journal of cardiology, 203
M. Patel, David Dai, Adrian Hernandez, P. Douglas, J. Messenger, K. Garratt, T. Maddox, E. Peterson, M. Roe (2014)
Prevalence and predictors of nonobstructive coronary artery disease identified with coronary angiography in contemporary clinical practice.American heart journal, 167 6
L. Abbate (2013)
Impact of a Continuous Quality Improvement Initiative on Appropriate Use of Coronary Computed Tomography AngiographyJournal of Emergency Medicine, 44
M. Bittencourt, E. Hulten, Tamar Polonsky, U. Hoffmann, K. Nasir, S. Abbara, M. Carli, R. Blankstein (2016)
European Society of Cardiology–Recommended Coronary Artery Disease Consortium Pretest Probability Scores More Accurately Predict Obstructive Coronary Disease and Cardiovascular Events Than the Diamond and Forrester Score: The Partners RegistryCirculation, 134
J. Min, A. Dunning, F. Lin, S. Achenbach, M. Al-Mallah, D. Berman, M. Budoff, Filippo Cademartiri, T. Callister, H. Chang, V. Cheng, K. Chinnaiyan, B. Chow, A. Delago, M. Hadamitzky, J. Hausleiter, R. Karlsberg, P. Kaufmann, E. Maffei, K. Nasir, M. Pencina, G. Raff, L. Shaw, T. Villines (2011)
Rationale and design of the CONFIRM (COronary CT Angiography EvaluatioN For Clinical Outcomes: An InteRnational Multicenter) Registry.Journal of cardiovascular computed tomography, 5 2
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 < 0.05 for all comparisons. CACS, age, and gender were the highest ranking features. ConclusionA ML model incorporating clinical features in addition to CACS can accurately estimate the pretest likelihood of obstructive CAD on CCTA. In clinical practice, the utilization of such an approach could improve risk stratification and help guide downstream management.
European Heart Journal – Oxford University Press
Published: Sep 12, 2019
Keywords: Coronary artery disease; Coronary artery calcium score; Machine learning; Coronary computed tomography angiography
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