Impact of Coronary Computerized Tomography Angiography-Derived Plaque Quantification and Machine-Learning Computerized Tomography Fractional Flow Reserve on Adverse Cardiac Outcome
This study investigated the impact of coronary CT angiography (cCTA)-derived plaque markers and machine-learning-based CT-derived fractional flow reserve (CT-FFR) to identify adverse cardiac outcome. Data of 82 patients (60 ± 11 years, 62% men) who underwent cCTA and invasive coronary angiography (I...
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| Veröffentlicht in: | The American journal of cardiology 2019-11, Vol.124 (9), p.1340-1348 |
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| creator | von Knebel Doeberitz, Philipp L. De Cecco, Carlo N. Schoepf, U. Joseph Albrecht, Moritz H. van Assen, Marly De Santis, Domenico Gaskins, Jeffrey Martin, Simon Bauer, Maximilian J. Ebersberger, Ullrich Giovagnoli, Dante A. Varga-Szemes, Akos Bayer, Richard R. Schönberg, Stefan O. Tesche, Christian |
| description | This study investigated the impact of coronary CT angiography (cCTA)-derived plaque markers and machine-learning-based CT-derived fractional flow reserve (CT-FFR) to identify adverse cardiac outcome. Data of 82 patients (60 ± 11 years, 62% men) who underwent cCTA and invasive coronary angiography (ICA) were analyzed in this single-center retrospective, institutional review board-approved, HIPAA-compliant study. Follow-up was performed to record major adverse cardiac events (MACE). Plaque quantification of lesions responsible for MACE and control lesions was retrospectively performed semiautomatically from cCTA together with machine-learning based CT-FFR. The discriminatory value of plaque markers and CT-FFR to predict MACE was evaluated. After a median follow-up of 18.5 months (interquartile range 11.5 to 26.6 months), MACE was observed in 18 patients (21%). In a multivariate analysis the following markers were predictors of MACE (odds ratio [OR]): lesion length (OR 1.16, p = 0.018), low-attenuation plaque ( |
| doi_str_mv | 10.1016/j.amjcard.2019.07.061 |
| format | Article |
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Joseph ; Albrecht, Moritz H. ; van Assen, Marly ; De Santis, Domenico ; Gaskins, Jeffrey ; Martin, Simon ; Bauer, Maximilian J. ; Ebersberger, Ullrich ; Giovagnoli, Dante A. ; Varga-Szemes, Akos ; Bayer, Richard R. ; Schönberg, Stefan O. ; Tesche, Christian</creator><creatorcontrib>von Knebel Doeberitz, Philipp L. ; De Cecco, Carlo N. ; Schoepf, U. Joseph ; Albrecht, Moritz H. ; van Assen, Marly ; De Santis, Domenico ; Gaskins, Jeffrey ; Martin, Simon ; Bauer, Maximilian J. ; Ebersberger, Ullrich ; Giovagnoli, Dante A. ; Varga-Szemes, Akos ; Bayer, Richard R. ; Schönberg, Stefan O. ; Tesche, Christian</creatorcontrib><description>This study investigated the impact of coronary CT angiography (cCTA)-derived plaque markers and machine-learning-based CT-derived fractional flow reserve (CT-FFR) to identify adverse cardiac outcome. Data of 82 patients (60 ± 11 years, 62% men) who underwent cCTA and invasive coronary angiography (ICA) were analyzed in this single-center retrospective, institutional review board-approved, HIPAA-compliant study. Follow-up was performed to record major adverse cardiac events (MACE). Plaque quantification of lesions responsible for MACE and control lesions was retrospectively performed semiautomatically from cCTA together with machine-learning based CT-FFR. The discriminatory value of plaque markers and CT-FFR to predict MACE was evaluated. After a median follow-up of 18.5 months (interquartile range 11.5 to 26.6 months), MACE was observed in 18 patients (21%). In a multivariate analysis the following markers were predictors of MACE (odds ratio [OR]): lesion length (OR 1.16, p = 0.018), low-attenuation plaque (<30 HU) (OR 4.59, p = 0.003), Napkin ring sign (OR 2.71, p = 0.034), stenosis ≥50% (OR 3.83, p 0.042), and CT-FFR ≤0.80 (OR 7.78, p = 0.001). Receiver operating characteristics analysis including stenosis ≥50%, plaque markers and CT-FFR ≤0.80 (Area under the curve 0.94) showed incremental discriminatory power over stenosis ≥50% alone (Area under the curve 0.60, p <0.0001) for the prediction of MACE. cCTA-derived plaque markers and machine-learning CT-FFR demonstrate predictive value to identify MACE. In conclusion, combining plaque markers with machine-learning CT-FFR shows incremental discriminatory power over cCTA stenosis grading alone.</description><identifier>ISSN: 0002-9149</identifier><identifier>EISSN: 1879-1913</identifier><identifier>DOI: 10.1016/j.amjcard.2019.07.061</identifier><identifier>PMID: 31481177</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Angina pectoris ; Angiography ; Attenuation ; Body mass index ; Calcification ; Cardiovascular disease ; Computed tomography ; Computed Tomography Angiography - methods ; Coronary Angiography - methods ; Coronary Stenosis - diagnosis ; Coronary Stenosis - etiology ; Coronary Stenosis - mortality ; Coronary vessels ; Coronary Vessels - diagnostic imaging ; Coronary Vessels - physiopathology ; Electrocardiography ; Evaluation ; Female ; Follow-Up Studies ; Fractional Flow Reserve, Myocardial - physiology ; Heart ; Heart attacks ; Heart surgery ; Humans ; Learning algorithms ; Lesions ; Machine Learning ; Male ; Markers ; Medical imaging ; Medical records ; Middle Aged ; Multidetector Computed Tomography - methods ; Multivariate analysis ; Patients ; Plaque, Atherosclerotic - complications ; Plaque, Atherosclerotic - diagnosis ; Plaque, Atherosclerotic - mortality ; Predictions ; Prognosis ; Retrospective Studies ; ROC Curve ; Sensors ; Severity of Illness Index ; Software ; Stenosis ; Stents ; Survival Rate - trends ; United States - epidemiology</subject><ispartof>The American journal of cardiology, 2019-11, Vol.124 (9), p.1340-1348</ispartof><rights>2019 Elsevier Inc.</rights><rights>Copyright © 2019 Elsevier Inc. All rights reserved.</rights><rights>2019. Elsevier Inc.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c440t-a363628597595ff2a1c014e4c2433e05df7a9530b8eca96137321461d516de73</citedby><cites>FETCH-LOGICAL-c440t-a363628597595ff2a1c014e4c2433e05df7a9530b8eca96137321461d516de73</cites><orcidid>0000-0002-6164-5641 ; 0000-0003-3347-4520 ; 0000-0002-3584-4284</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0002914919308926$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31481177$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>von Knebel Doeberitz, Philipp L.</creatorcontrib><creatorcontrib>De Cecco, Carlo N.</creatorcontrib><creatorcontrib>Schoepf, U. Joseph</creatorcontrib><creatorcontrib>Albrecht, Moritz H.</creatorcontrib><creatorcontrib>van Assen, Marly</creatorcontrib><creatorcontrib>De Santis, Domenico</creatorcontrib><creatorcontrib>Gaskins, Jeffrey</creatorcontrib><creatorcontrib>Martin, Simon</creatorcontrib><creatorcontrib>Bauer, Maximilian J.</creatorcontrib><creatorcontrib>Ebersberger, Ullrich</creatorcontrib><creatorcontrib>Giovagnoli, Dante A.</creatorcontrib><creatorcontrib>Varga-Szemes, Akos</creatorcontrib><creatorcontrib>Bayer, Richard R.</creatorcontrib><creatorcontrib>Schönberg, Stefan O.</creatorcontrib><creatorcontrib>Tesche, Christian</creatorcontrib><title>Impact of Coronary Computerized Tomography Angiography-Derived Plaque Quantification and Machine-Learning Computerized Tomography Fractional Flow Reserve on Adverse Cardiac Outcome</title><title>The American journal of cardiology</title><addtitle>Am J Cardiol</addtitle><description>This study investigated the impact of coronary CT angiography (cCTA)-derived plaque markers and machine-learning-based CT-derived fractional flow reserve (CT-FFR) to identify adverse cardiac outcome. Data of 82 patients (60 ± 11 years, 62% men) who underwent cCTA and invasive coronary angiography (ICA) were analyzed in this single-center retrospective, institutional review board-approved, HIPAA-compliant study. Follow-up was performed to record major adverse cardiac events (MACE). Plaque quantification of lesions responsible for MACE and control lesions was retrospectively performed semiautomatically from cCTA together with machine-learning based CT-FFR. The discriminatory value of plaque markers and CT-FFR to predict MACE was evaluated. After a median follow-up of 18.5 months (interquartile range 11.5 to 26.6 months), MACE was observed in 18 patients (21%). In a multivariate analysis the following markers were predictors of MACE (odds ratio [OR]): lesion length (OR 1.16, p = 0.018), low-attenuation plaque (<30 HU) (OR 4.59, p = 0.003), Napkin ring sign (OR 2.71, p = 0.034), stenosis ≥50% (OR 3.83, p 0.042), and CT-FFR ≤0.80 (OR 7.78, p = 0.001). Receiver operating characteristics analysis including stenosis ≥50%, plaque markers and CT-FFR ≤0.80 (Area under the curve 0.94) showed incremental discriminatory power over stenosis ≥50% alone (Area under the curve 0.60, p <0.0001) for the prediction of MACE. cCTA-derived plaque markers and machine-learning CT-FFR demonstrate predictive value to identify MACE. In conclusion, combining plaque markers with machine-learning CT-FFR shows incremental discriminatory power over cCTA stenosis grading alone.</description><subject>Angina pectoris</subject><subject>Angiography</subject><subject>Attenuation</subject><subject>Body mass index</subject><subject>Calcification</subject><subject>Cardiovascular disease</subject><subject>Computed tomography</subject><subject>Computed Tomography Angiography - methods</subject><subject>Coronary Angiography - methods</subject><subject>Coronary Stenosis - diagnosis</subject><subject>Coronary Stenosis - etiology</subject><subject>Coronary Stenosis - mortality</subject><subject>Coronary vessels</subject><subject>Coronary Vessels - diagnostic imaging</subject><subject>Coronary Vessels - physiopathology</subject><subject>Electrocardiography</subject><subject>Evaluation</subject><subject>Female</subject><subject>Follow-Up Studies</subject><subject>Fractional Flow Reserve, Myocardial - physiology</subject><subject>Heart</subject><subject>Heart attacks</subject><subject>Heart surgery</subject><subject>Humans</subject><subject>Learning algorithms</subject><subject>Lesions</subject><subject>Machine Learning</subject><subject>Male</subject><subject>Markers</subject><subject>Medical imaging</subject><subject>Medical records</subject><subject>Middle Aged</subject><subject>Multidetector Computed Tomography - methods</subject><subject>Multivariate analysis</subject><subject>Patients</subject><subject>Plaque, Atherosclerotic - complications</subject><subject>Plaque, Atherosclerotic - diagnosis</subject><subject>Plaque, Atherosclerotic - mortality</subject><subject>Predictions</subject><subject>Prognosis</subject><subject>Retrospective Studies</subject><subject>ROC Curve</subject><subject>Sensors</subject><subject>Severity of Illness Index</subject><subject>Software</subject><subject>Stenosis</subject><subject>Stents</subject><subject>Survival Rate - trends</subject><subject>United States - epidemiology</subject><issn>0002-9149</issn><issn>1879-1913</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>BENPR</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqFkcGO0zAQhiMEYsvCI4AsceGS4InjOD6hqkthpaIF1LvltSddR0kc7KRoeS4eEK9aOCAhTh5rvvntf_4sewm0AAr1267QQ2d0sEVJQRZUFLSGR9kKGiFzkMAeZytKaZlLqORF9izGLl0BeP00u2BQNQBCrLKf18OkzUx8SzY--FGH-1QM0zJjcD_Qkr0f_CHo6e6erMeDO9f5VWofU_tzr78tSL4sepxd64yenR-JHi35pM2dGzHfoQ6jGw__lN2G9IE0pXuy7f138hUjhiOSpLO2RwwRySb5dNqQm2U2fsDn2ZNW9xFfnM_LbL99v998zHc3H643611uqorOuWY1q8uGS8Elb9tSg6FQYWXKijGk3LZCS87obYNGyxqYYCVUNVgOtUXBLrM3J9kp-GQyzmpw0WDf6xH9ElVZNhWvOedlQl__hXZ-CclSohjlopYCeKL4iTLBxxiwVVNwQ1q5AqoeUlWdOqeqHlJVVKiUapp7dVZfbge0f6Z-x5iAdycA0zaODoOKxuFo0LqAZlbWu_888QsWXrh_</recordid><startdate>20191101</startdate><enddate>20191101</enddate><creator>von Knebel Doeberitz, Philipp L.</creator><creator>De Cecco, Carlo N.</creator><creator>Schoepf, U. 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Joseph ; Albrecht, Moritz H. ; van Assen, Marly ; De Santis, Domenico ; Gaskins, Jeffrey ; Martin, Simon ; Bauer, Maximilian J. ; Ebersberger, Ullrich ; Giovagnoli, Dante A. ; Varga-Szemes, Akos ; Bayer, Richard R. ; Schönberg, Stefan O. ; Tesche, Christian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c440t-a363628597595ff2a1c014e4c2433e05df7a9530b8eca96137321461d516de73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Angina pectoris</topic><topic>Angiography</topic><topic>Attenuation</topic><topic>Body mass index</topic><topic>Calcification</topic><topic>Cardiovascular disease</topic><topic>Computed tomography</topic><topic>Computed Tomography Angiography - methods</topic><topic>Coronary Angiography - methods</topic><topic>Coronary Stenosis - diagnosis</topic><topic>Coronary Stenosis - etiology</topic><topic>Coronary Stenosis - mortality</topic><topic>Coronary vessels</topic><topic>Coronary Vessels - diagnostic imaging</topic><topic>Coronary Vessels - physiopathology</topic><topic>Electrocardiography</topic><topic>Evaluation</topic><topic>Female</topic><topic>Follow-Up Studies</topic><topic>Fractional Flow Reserve, Myocardial - physiology</topic><topic>Heart</topic><topic>Heart attacks</topic><topic>Heart surgery</topic><topic>Humans</topic><topic>Learning algorithms</topic><topic>Lesions</topic><topic>Machine Learning</topic><topic>Male</topic><topic>Markers</topic><topic>Medical imaging</topic><topic>Medical records</topic><topic>Middle Aged</topic><topic>Multidetector Computed Tomography - methods</topic><topic>Multivariate analysis</topic><topic>Patients</topic><topic>Plaque, Atherosclerotic - complications</topic><topic>Plaque, Atherosclerotic - diagnosis</topic><topic>Plaque, Atherosclerotic - mortality</topic><topic>Predictions</topic><topic>Prognosis</topic><topic>Retrospective Studies</topic><topic>ROC Curve</topic><topic>Sensors</topic><topic>Severity of Illness Index</topic><topic>Software</topic><topic>Stenosis</topic><topic>Stents</topic><topic>Survival Rate - trends</topic><topic>United States - epidemiology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>von Knebel Doeberitz, Philipp L.</creatorcontrib><creatorcontrib>De Cecco, Carlo N.</creatorcontrib><creatorcontrib>Schoepf, U. 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Joseph</au><au>Albrecht, Moritz H.</au><au>van Assen, Marly</au><au>De Santis, Domenico</au><au>Gaskins, Jeffrey</au><au>Martin, Simon</au><au>Bauer, Maximilian J.</au><au>Ebersberger, Ullrich</au><au>Giovagnoli, Dante A.</au><au>Varga-Szemes, Akos</au><au>Bayer, Richard R.</au><au>Schönberg, Stefan O.</au><au>Tesche, Christian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Impact of Coronary Computerized Tomography Angiography-Derived Plaque Quantification and Machine-Learning Computerized Tomography Fractional Flow Reserve on Adverse Cardiac Outcome</atitle><jtitle>The American journal of cardiology</jtitle><addtitle>Am J Cardiol</addtitle><date>2019-11-01</date><risdate>2019</risdate><volume>124</volume><issue>9</issue><spage>1340</spage><epage>1348</epage><pages>1340-1348</pages><issn>0002-9149</issn><eissn>1879-1913</eissn><abstract>This study investigated the impact of coronary CT angiography (cCTA)-derived plaque markers and machine-learning-based CT-derived fractional flow reserve (CT-FFR) to identify adverse cardiac outcome. Data of 82 patients (60 ± 11 years, 62% men) who underwent cCTA and invasive coronary angiography (ICA) were analyzed in this single-center retrospective, institutional review board-approved, HIPAA-compliant study. Follow-up was performed to record major adverse cardiac events (MACE). Plaque quantification of lesions responsible for MACE and control lesions was retrospectively performed semiautomatically from cCTA together with machine-learning based CT-FFR. The discriminatory value of plaque markers and CT-FFR to predict MACE was evaluated. After a median follow-up of 18.5 months (interquartile range 11.5 to 26.6 months), MACE was observed in 18 patients (21%). In a multivariate analysis the following markers were predictors of MACE (odds ratio [OR]): lesion length (OR 1.16, p = 0.018), low-attenuation plaque (<30 HU) (OR 4.59, p = 0.003), Napkin ring sign (OR 2.71, p = 0.034), stenosis ≥50% (OR 3.83, p 0.042), and CT-FFR ≤0.80 (OR 7.78, p = 0.001). Receiver operating characteristics analysis including stenosis ≥50%, plaque markers and CT-FFR ≤0.80 (Area under the curve 0.94) showed incremental discriminatory power over stenosis ≥50% alone (Area under the curve 0.60, p <0.0001) for the prediction of MACE. cCTA-derived plaque markers and machine-learning CT-FFR demonstrate predictive value to identify MACE. In conclusion, combining plaque markers with machine-learning CT-FFR shows incremental discriminatory power over cCTA stenosis grading alone.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>31481177</pmid><doi>10.1016/j.amjcard.2019.07.061</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-6164-5641</orcidid><orcidid>https://orcid.org/0000-0003-3347-4520</orcidid><orcidid>https://orcid.org/0000-0002-3584-4284</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0002-9149 |
| ispartof | The American journal of cardiology, 2019-11, Vol.124 (9), p.1340-1348 |
| issn | 0002-9149 1879-1913 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_2284565552 |
| source | MEDLINE; Elsevier ScienceDirect Journals Complete |
| subjects | Angina pectoris Angiography Attenuation Body mass index Calcification Cardiovascular disease Computed tomography Computed Tomography Angiography - methods Coronary Angiography - methods Coronary Stenosis - diagnosis Coronary Stenosis - etiology Coronary Stenosis - mortality Coronary vessels Coronary Vessels - diagnostic imaging Coronary Vessels - physiopathology Electrocardiography Evaluation Female Follow-Up Studies Fractional Flow Reserve, Myocardial - physiology Heart Heart attacks Heart surgery Humans Learning algorithms Lesions Machine Learning Male Markers Medical imaging Medical records Middle Aged Multidetector Computed Tomography - methods Multivariate analysis Patients Plaque, Atherosclerotic - complications Plaque, Atherosclerotic - diagnosis Plaque, Atherosclerotic - mortality Predictions Prognosis Retrospective Studies ROC Curve Sensors Severity of Illness Index Software Stenosis Stents Survival Rate - trends United States - epidemiology |
| title | Impact of Coronary Computerized Tomography Angiography-Derived Plaque Quantification and Machine-Learning Computerized Tomography Fractional Flow Reserve on Adverse Cardiac Outcome |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-12T13%3A17%3A50IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Impact%20of%20Coronary%20Computerized%20Tomography%20Angiography-Derived%20Plaque%20Quantification%20and%20Machine-Learning%20Computerized%20Tomography%20Fractional%20Flow%20Reserve%20on%20Adverse%20Cardiac%20Outcome&rft.jtitle=The%20American%20journal%20of%20cardiology&rft.au=von%20Knebel%20Doeberitz,%20Philipp%20L.&rft.date=2019-11-01&rft.volume=124&rft.issue=9&rft.spage=1340&rft.epage=1348&rft.pages=1340-1348&rft.issn=0002-9149&rft.eissn=1879-1913&rft_id=info:doi/10.1016/j.amjcard.2019.07.061&rft_dat=%3Cproquest_cross%3E2284565552%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2305769715&rft_id=info:pmid/31481177&rft_els_id=S0002914919308926&rfr_iscdi=true |