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Impact of machine learning–based coronary computed tomography angiography fractional flow reserve on treatment decisions and clinical outcomes in patients with suspected coronary artery disease
Objectives This study investigated the impact of machine learning (ML)–based fractional flow reserve derived from computed tomography (FFR CT ) compared to invasive coronary angiography (ICA) for therapeutic decision-making and patient outcome in patients with suspected coronary artery disease (CAD)...
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Published in: | European radiology 2020-11, Vol.30 (11), p.5841-5851 |
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container_title | European radiology |
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creator | Qiao, Hong Yan Tang, Chun Xiang Schoepf, U. Joseph Tesche, Christian Bayer, Richard R. Giovagnoli, Dante A Todd Hudson, H. Zhou, Chang Sheng Yan, Jing Lu, Meng Jie Zhou, Fan Lu, Guang Ming Jiang, Jian Wei Zhang, Long Jiang |
description | Objectives
This study investigated the impact of machine learning (ML)–based fractional flow reserve derived from computed tomography (FFR
CT
) compared to invasive coronary angiography (ICA) for therapeutic decision-making and patient outcome in patients with suspected coronary artery disease (CAD).
Methods
One thousand one hundred twenty-one consecutive patients with stable chest pain who underwent coronary computed tomography angiography (CCTA) followed ICA within 90 days between January 2007 and December 2016 were included in this retrospective study. Medical records were reviewed for the endpoint of major adverse cardiac events (MACEs). FFR
CT
values were calculated using an artificial intelligence (AI) ML platform. Disagreements between hemodynamic significant stenosis via FFR
CT
and severe stenosis on qualitative CCTA and ICA were also evaluated.
Results
After FFR
CT
results were revealed, a change in the proposed treatment regimen chosen based on ICA results was seen in 167 patients (14.9%). Over a median follow-up time of 26 months (4–48 months), FFR
CT
≤ 0.80 was associated with MACE (HR, 6.84 (95% CI, 3.57 to 13.11);
p <
0.001), with superior prognostic value compared to severe stenosis on ICA (HR, 1.84 (95% CI, 1.24 to 2.73),
p
= 0.002) and CCTA (HR, 1.47 (95% CI, 1.01 to 2.14,
p
= 0.045). Reserving ICA and revascularization for vessels with positive FFR
CT
could have reduced the rate of ICA by 54.5% and lead to 4.4% fewer percutaneous interventions.
Conclusions
This study indicated ML-based FFR
CT
had superior prognostic value when compared to severe anatomic stenosis on CCTA and adding FFR
CT
may direct therapeutic decision-making with the potential to improve efficiency of ICA.
Key Points
• ML-based FFR
CT
shows superior outcome prediction value when compared to severe anatomic stenosis on CCTA.
• FFR
CT
noninvasively informs therapeutic decision-making with potential to change diagnostic workflows and enhance efficiencies in patients with suspected CAD.
• Reserving ICA and revascularization for vessels with positive FFR
CT
may reduce the normalcy rate of ICA and improve its efficiency. |
doi_str_mv | 10.1007/s00330-020-06964-w |
format | article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2407583640</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2407583640</sourcerecordid><originalsourceid>FETCH-LOGICAL-c347t-6c0a43e8e2c8ee7c5c9df61c84e658e77293e053c11a1e59710dbb2245ef74253</originalsourceid><addsrcrecordid>eNp9kU2O1DAQhS0EYpqBC7BAXrIJ-C9xskQjfkYaiQ2sLbdT6fEosYPLoTU77sCRuAknoaBnECsWVpXs7z09-TH2XIpXUgj7GoXQWjRC0emGzjTHB2wnjVaNFL15yHZi0H1jh8GcsSeIN0KIQRr7mJ1pZTpljNmxH5fL6kPleeKLD9cxAZ_BlxTT4ee373uPMPKQS06-3NKyrFulm5qXfCh-vb7lPh3i_T4VsorEznya85EXQChfgefEawFfF0iVjxAiEoQkJe85phhIkLdK9oA8Jr76GglFfoz1muOGK4T6bxBfKtAYIwIlfMoeTX5GeHY3z9nnd28_XXxorj6-v7x4c9UEbWxtuiC80dCDCj2ADW0YxqmToTfQtT1YqwYNotVBSi-hHawU436vlGlhska1-py9PPmuJX_ZAKtbIgaYZ58gb-iUEbbtdWcEoeqEhpIRC0xuLXGh6E4K97s8dyrPUXnuT3nuSKIXd_7bfoHxr-S-LQL0CUB6Sgco7iZvhb4b_2f7C1ZOrTA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2407583640</pqid></control><display><type>article</type><title>Impact of machine learning–based coronary computed tomography angiography fractional flow reserve on treatment decisions and clinical outcomes in patients with suspected coronary artery disease</title><source>Springer Nature</source><creator>Qiao, Hong Yan ; Tang, Chun Xiang ; Schoepf, U. Joseph ; Tesche, Christian ; Bayer, Richard R. ; Giovagnoli, Dante A ; Todd Hudson, H. ; Zhou, Chang Sheng ; Yan, Jing ; Lu, Meng Jie ; Zhou, Fan ; Lu, Guang Ming ; Jiang, Jian Wei ; Zhang, Long Jiang</creator><creatorcontrib>Qiao, Hong Yan ; Tang, Chun Xiang ; Schoepf, U. Joseph ; Tesche, Christian ; Bayer, Richard R. ; Giovagnoli, Dante A ; Todd Hudson, H. ; Zhou, Chang Sheng ; Yan, Jing ; Lu, Meng Jie ; Zhou, Fan ; Lu, Guang Ming ; Jiang, Jian Wei ; Zhang, Long Jiang</creatorcontrib><description>Objectives
This study investigated the impact of machine learning (ML)–based fractional flow reserve derived from computed tomography (FFR
CT
) compared to invasive coronary angiography (ICA) for therapeutic decision-making and patient outcome in patients with suspected coronary artery disease (CAD).
Methods
One thousand one hundred twenty-one consecutive patients with stable chest pain who underwent coronary computed tomography angiography (CCTA) followed ICA within 90 days between January 2007 and December 2016 were included in this retrospective study. Medical records were reviewed for the endpoint of major adverse cardiac events (MACEs). FFR
CT
values were calculated using an artificial intelligence (AI) ML platform. Disagreements between hemodynamic significant stenosis via FFR
CT
and severe stenosis on qualitative CCTA and ICA were also evaluated.
Results
After FFR
CT
results were revealed, a change in the proposed treatment regimen chosen based on ICA results was seen in 167 patients (14.9%). Over a median follow-up time of 26 months (4–48 months), FFR
CT
≤ 0.80 was associated with MACE (HR, 6.84 (95% CI, 3.57 to 13.11);
p <
0.001), with superior prognostic value compared to severe stenosis on ICA (HR, 1.84 (95% CI, 1.24 to 2.73),
p
= 0.002) and CCTA (HR, 1.47 (95% CI, 1.01 to 2.14,
p
= 0.045). Reserving ICA and revascularization for vessels with positive FFR
CT
could have reduced the rate of ICA by 54.5% and lead to 4.4% fewer percutaneous interventions.
Conclusions
This study indicated ML-based FFR
CT
had superior prognostic value when compared to severe anatomic stenosis on CCTA and adding FFR
CT
may direct therapeutic decision-making with the potential to improve efficiency of ICA.
Key Points
• ML-based FFR
CT
shows superior outcome prediction value when compared to severe anatomic stenosis on CCTA.
• FFR
CT
noninvasively informs therapeutic decision-making with potential to change diagnostic workflows and enhance efficiencies in patients with suspected CAD.
• Reserving ICA and revascularization for vessels with positive FFR
CT
may reduce the normalcy rate of ICA and improve its efficiency.</description><identifier>ISSN: 0938-7994</identifier><identifier>EISSN: 1432-1084</identifier><identifier>DOI: 10.1007/s00330-020-06964-w</identifier><identifier>PMID: 32462444</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Artificial Intelligence ; Computed Tomography ; Computed Tomography Angiography - methods ; Coronary Angiography - methods ; Coronary Artery Disease - diagnosis ; Coronary Artery Disease - physiopathology ; Coronary Artery Disease - therapy ; Decision Making ; Diagnostic Radiology ; Disease Management ; Female ; Fractional Flow Reserve, Myocardial - physiology ; Humans ; Imaging ; Internal Medicine ; Interventional Radiology ; Machine Learning ; Male ; Medicine ; Medicine & Public Health ; Middle Aged ; Neuroradiology ; Predictive Value of Tests ; Radiology ; Retrospective Studies ; Severity of Illness Index ; Ultrasound</subject><ispartof>European radiology, 2020-11, Vol.30 (11), p.5841-5851</ispartof><rights>European Society of Radiology 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c347t-6c0a43e8e2c8ee7c5c9df61c84e658e77293e053c11a1e59710dbb2245ef74253</citedby><cites>FETCH-LOGICAL-c347t-6c0a43e8e2c8ee7c5c9df61c84e658e77293e053c11a1e59710dbb2245ef74253</cites><orcidid>0000-0002-6664-7224</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32462444$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Qiao, Hong Yan</creatorcontrib><creatorcontrib>Tang, Chun Xiang</creatorcontrib><creatorcontrib>Schoepf, U. Joseph</creatorcontrib><creatorcontrib>Tesche, Christian</creatorcontrib><creatorcontrib>Bayer, Richard R.</creatorcontrib><creatorcontrib>Giovagnoli, Dante A</creatorcontrib><creatorcontrib>Todd Hudson, H.</creatorcontrib><creatorcontrib>Zhou, Chang Sheng</creatorcontrib><creatorcontrib>Yan, Jing</creatorcontrib><creatorcontrib>Lu, Meng Jie</creatorcontrib><creatorcontrib>Zhou, Fan</creatorcontrib><creatorcontrib>Lu, Guang Ming</creatorcontrib><creatorcontrib>Jiang, Jian Wei</creatorcontrib><creatorcontrib>Zhang, Long Jiang</creatorcontrib><title>Impact of machine learning–based coronary computed tomography angiography fractional flow reserve on treatment decisions and clinical outcomes in patients with suspected coronary artery disease</title><title>European radiology</title><addtitle>Eur Radiol</addtitle><addtitle>Eur Radiol</addtitle><description>Objectives
This study investigated the impact of machine learning (ML)–based fractional flow reserve derived from computed tomography (FFR
CT
) compared to invasive coronary angiography (ICA) for therapeutic decision-making and patient outcome in patients with suspected coronary artery disease (CAD).
Methods
One thousand one hundred twenty-one consecutive patients with stable chest pain who underwent coronary computed tomography angiography (CCTA) followed ICA within 90 days between January 2007 and December 2016 were included in this retrospective study. Medical records were reviewed for the endpoint of major adverse cardiac events (MACEs). FFR
CT
values were calculated using an artificial intelligence (AI) ML platform. Disagreements between hemodynamic significant stenosis via FFR
CT
and severe stenosis on qualitative CCTA and ICA were also evaluated.
Results
After FFR
CT
results were revealed, a change in the proposed treatment regimen chosen based on ICA results was seen in 167 patients (14.9%). Over a median follow-up time of 26 months (4–48 months), FFR
CT
≤ 0.80 was associated with MACE (HR, 6.84 (95% CI, 3.57 to 13.11);
p <
0.001), with superior prognostic value compared to severe stenosis on ICA (HR, 1.84 (95% CI, 1.24 to 2.73),
p
= 0.002) and CCTA (HR, 1.47 (95% CI, 1.01 to 2.14,
p
= 0.045). Reserving ICA and revascularization for vessels with positive FFR
CT
could have reduced the rate of ICA by 54.5% and lead to 4.4% fewer percutaneous interventions.
Conclusions
This study indicated ML-based FFR
CT
had superior prognostic value when compared to severe anatomic stenosis on CCTA and adding FFR
CT
may direct therapeutic decision-making with the potential to improve efficiency of ICA.
Key Points
• ML-based FFR
CT
shows superior outcome prediction value when compared to severe anatomic stenosis on CCTA.
• FFR
CT
noninvasively informs therapeutic decision-making with potential to change diagnostic workflows and enhance efficiencies in patients with suspected CAD.
• Reserving ICA and revascularization for vessels with positive FFR
CT
may reduce the normalcy rate of ICA and improve its efficiency.</description><subject>Artificial Intelligence</subject><subject>Computed Tomography</subject><subject>Computed Tomography Angiography - methods</subject><subject>Coronary Angiography - methods</subject><subject>Coronary Artery Disease - diagnosis</subject><subject>Coronary Artery Disease - physiopathology</subject><subject>Coronary Artery Disease - therapy</subject><subject>Decision Making</subject><subject>Diagnostic Radiology</subject><subject>Disease Management</subject><subject>Female</subject><subject>Fractional Flow Reserve, Myocardial - physiology</subject><subject>Humans</subject><subject>Imaging</subject><subject>Internal Medicine</subject><subject>Interventional Radiology</subject><subject>Machine Learning</subject><subject>Male</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Middle Aged</subject><subject>Neuroradiology</subject><subject>Predictive Value of Tests</subject><subject>Radiology</subject><subject>Retrospective Studies</subject><subject>Severity of Illness Index</subject><subject>Ultrasound</subject><issn>0938-7994</issn><issn>1432-1084</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kU2O1DAQhS0EYpqBC7BAXrIJ-C9xskQjfkYaiQ2sLbdT6fEosYPLoTU77sCRuAknoaBnECsWVpXs7z09-TH2XIpXUgj7GoXQWjRC0emGzjTHB2wnjVaNFL15yHZi0H1jh8GcsSeIN0KIQRr7mJ1pZTpljNmxH5fL6kPleeKLD9cxAZ_BlxTT4ee373uPMPKQS06-3NKyrFulm5qXfCh-vb7lPh3i_T4VsorEznya85EXQChfgefEawFfF0iVjxAiEoQkJe85phhIkLdK9oA8Jr76GglFfoz1muOGK4T6bxBfKtAYIwIlfMoeTX5GeHY3z9nnd28_XXxorj6-v7x4c9UEbWxtuiC80dCDCj2ADW0YxqmToTfQtT1YqwYNotVBSi-hHawU436vlGlhska1-py9PPmuJX_ZAKtbIgaYZ58gb-iUEbbtdWcEoeqEhpIRC0xuLXGh6E4K97s8dyrPUXnuT3nuSKIXd_7bfoHxr-S-LQL0CUB6Sgco7iZvhb4b_2f7C1ZOrTA</recordid><startdate>20201101</startdate><enddate>20201101</enddate><creator>Qiao, Hong Yan</creator><creator>Tang, Chun Xiang</creator><creator>Schoepf, U. Joseph</creator><creator>Tesche, Christian</creator><creator>Bayer, Richard R.</creator><creator>Giovagnoli, Dante A</creator><creator>Todd Hudson, H.</creator><creator>Zhou, Chang Sheng</creator><creator>Yan, Jing</creator><creator>Lu, Meng Jie</creator><creator>Zhou, Fan</creator><creator>Lu, Guang Ming</creator><creator>Jiang, Jian Wei</creator><creator>Zhang, Long Jiang</creator><general>Springer Berlin Heidelberg</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-6664-7224</orcidid></search><sort><creationdate>20201101</creationdate><title>Impact of machine learning–based coronary computed tomography angiography fractional flow reserve on treatment decisions and clinical outcomes in patients with suspected coronary artery disease</title><author>Qiao, Hong Yan ; Tang, Chun Xiang ; Schoepf, U. Joseph ; Tesche, Christian ; Bayer, Richard R. ; Giovagnoli, Dante A ; Todd Hudson, H. ; Zhou, Chang Sheng ; Yan, Jing ; Lu, Meng Jie ; Zhou, Fan ; Lu, Guang Ming ; Jiang, Jian Wei ; Zhang, Long Jiang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c347t-6c0a43e8e2c8ee7c5c9df61c84e658e77293e053c11a1e59710dbb2245ef74253</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Artificial Intelligence</topic><topic>Computed Tomography</topic><topic>Computed Tomography Angiography - methods</topic><topic>Coronary Angiography - methods</topic><topic>Coronary Artery Disease - diagnosis</topic><topic>Coronary Artery Disease - physiopathology</topic><topic>Coronary Artery Disease - therapy</topic><topic>Decision Making</topic><topic>Diagnostic Radiology</topic><topic>Disease Management</topic><topic>Female</topic><topic>Fractional Flow Reserve, Myocardial - physiology</topic><topic>Humans</topic><topic>Imaging</topic><topic>Internal Medicine</topic><topic>Interventional Radiology</topic><topic>Machine Learning</topic><topic>Male</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Middle Aged</topic><topic>Neuroradiology</topic><topic>Predictive Value of Tests</topic><topic>Radiology</topic><topic>Retrospective Studies</topic><topic>Severity of Illness Index</topic><topic>Ultrasound</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Qiao, Hong Yan</creatorcontrib><creatorcontrib>Tang, Chun Xiang</creatorcontrib><creatorcontrib>Schoepf, U. Joseph</creatorcontrib><creatorcontrib>Tesche, Christian</creatorcontrib><creatorcontrib>Bayer, Richard R.</creatorcontrib><creatorcontrib>Giovagnoli, Dante A</creatorcontrib><creatorcontrib>Todd Hudson, H.</creatorcontrib><creatorcontrib>Zhou, Chang Sheng</creatorcontrib><creatorcontrib>Yan, Jing</creatorcontrib><creatorcontrib>Lu, Meng Jie</creatorcontrib><creatorcontrib>Zhou, Fan</creatorcontrib><creatorcontrib>Lu, Guang Ming</creatorcontrib><creatorcontrib>Jiang, Jian Wei</creatorcontrib><creatorcontrib>Zhang, Long Jiang</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>European radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Qiao, Hong Yan</au><au>Tang, Chun Xiang</au><au>Schoepf, U. Joseph</au><au>Tesche, Christian</au><au>Bayer, Richard R.</au><au>Giovagnoli, Dante A</au><au>Todd Hudson, H.</au><au>Zhou, Chang Sheng</au><au>Yan, Jing</au><au>Lu, Meng Jie</au><au>Zhou, Fan</au><au>Lu, Guang Ming</au><au>Jiang, Jian Wei</au><au>Zhang, Long Jiang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Impact of machine learning–based coronary computed tomography angiography fractional flow reserve on treatment decisions and clinical outcomes in patients with suspected coronary artery disease</atitle><jtitle>European radiology</jtitle><stitle>Eur Radiol</stitle><addtitle>Eur Radiol</addtitle><date>2020-11-01</date><risdate>2020</risdate><volume>30</volume><issue>11</issue><spage>5841</spage><epage>5851</epage><pages>5841-5851</pages><issn>0938-7994</issn><eissn>1432-1084</eissn><abstract>Objectives
This study investigated the impact of machine learning (ML)–based fractional flow reserve derived from computed tomography (FFR
CT
) compared to invasive coronary angiography (ICA) for therapeutic decision-making and patient outcome in patients with suspected coronary artery disease (CAD).
Methods
One thousand one hundred twenty-one consecutive patients with stable chest pain who underwent coronary computed tomography angiography (CCTA) followed ICA within 90 days between January 2007 and December 2016 were included in this retrospective study. Medical records were reviewed for the endpoint of major adverse cardiac events (MACEs). FFR
CT
values were calculated using an artificial intelligence (AI) ML platform. Disagreements between hemodynamic significant stenosis via FFR
CT
and severe stenosis on qualitative CCTA and ICA were also evaluated.
Results
After FFR
CT
results were revealed, a change in the proposed treatment regimen chosen based on ICA results was seen in 167 patients (14.9%). Over a median follow-up time of 26 months (4–48 months), FFR
CT
≤ 0.80 was associated with MACE (HR, 6.84 (95% CI, 3.57 to 13.11);
p <
0.001), with superior prognostic value compared to severe stenosis on ICA (HR, 1.84 (95% CI, 1.24 to 2.73),
p
= 0.002) and CCTA (HR, 1.47 (95% CI, 1.01 to 2.14,
p
= 0.045). Reserving ICA and revascularization for vessels with positive FFR
CT
could have reduced the rate of ICA by 54.5% and lead to 4.4% fewer percutaneous interventions.
Conclusions
This study indicated ML-based FFR
CT
had superior prognostic value when compared to severe anatomic stenosis on CCTA and adding FFR
CT
may direct therapeutic decision-making with the potential to improve efficiency of ICA.
Key Points
• ML-based FFR
CT
shows superior outcome prediction value when compared to severe anatomic stenosis on CCTA.
• FFR
CT
noninvasively informs therapeutic decision-making with potential to change diagnostic workflows and enhance efficiencies in patients with suspected CAD.
• Reserving ICA and revascularization for vessels with positive FFR
CT
may reduce the normalcy rate of ICA and improve its efficiency.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>32462444</pmid><doi>10.1007/s00330-020-06964-w</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-6664-7224</orcidid></addata></record> |
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language | eng |
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source | Springer Nature |
subjects | Artificial Intelligence Computed Tomography Computed Tomography Angiography - methods Coronary Angiography - methods Coronary Artery Disease - diagnosis Coronary Artery Disease - physiopathology Coronary Artery Disease - therapy Decision Making Diagnostic Radiology Disease Management Female Fractional Flow Reserve, Myocardial - physiology Humans Imaging Internal Medicine Interventional Radiology Machine Learning Male Medicine Medicine & Public Health Middle Aged Neuroradiology Predictive Value of Tests Radiology Retrospective Studies Severity of Illness Index Ultrasound |
title | Impact of machine learning–based coronary computed tomography angiography fractional flow reserve on treatment decisions and clinical outcomes in patients with suspected coronary artery disease |
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