Multi-parametric approach to predict prosthetic valve size using CMR and clinical data: insights from SAVR
The purpose of this investigation was to characterize the CMR and clinical parameters that correlate to prosthetic valve size (PVS) determined at SAVR and develop a multi-parametric model to predict PVS. Sixty-two subjects were included. Linear/area measurements of the aortic annulus were performed...
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| Veröffentlicht in: | The International Journal of Cardiovascular Imaging 2021-07, Vol.37 (7), p.2269-2276 |
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| creator | Mordini, Federico E. Hynes, Conor F. Amdur, Richard L. Panting, Jeffrey Emerson, Dominic A. Morrissette, Jason Goheen-Thomas, Erin Greenberg, Michael D. Trachiotis, Gregory D. |
| description | The purpose of this investigation was to characterize the CMR and clinical parameters that correlate to prosthetic valve size (PVS) determined at SAVR and develop a multi-parametric model to predict PVS. Sixty-two subjects were included. Linear/area measurements of the aortic annulus were performed on cine CMR images in systole/diastole on long/short axis (SAX) views. Clinical parameters (age, habitus, valve lesion, valve morphology) were recorded. PVS determined intraoperatively was the reference value. Data were analyzed using Spearman correlation. A prediction model combining imaging and clinical parameters was generated. Imaging parameters had moderate to moderately strong correlation to PVS with the highest correlations from systolic SAX mean diameter (r = 0.73, p |
| doi_str_mv | 10.1007/s10554-021-02203-5 |
| format | Article |
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2
= 0.61). Model predicted mean PVS was 23.3 mm (SD 1.1); actual mean PVS was 23.3 mm (SD 1.3). The Spearman r of the model (0.80, 95% CI 0.683–0.874) was significantly higher than systolic SAX area (0.68, 95% CI 0.516–0.795). Clinical parameters like age and habitus impact PVS; valve lesion/morphology do not. A multi-parametric model demonstrated high accuracy in predicting PVS and was superior to a single imaging parameter. A multi-parametric approach to device sizing may have future application in TAVR.</description><identifier>ISSN: 1569-5794</identifier><identifier>EISSN: 1573-0743</identifier><identifier>EISSN: 1875-8312</identifier><identifier>DOI: 10.1007/s10554-021-02203-5</identifier><identifier>PMID: 33689099</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Age ; Aorta ; Cardiac Imaging ; Cardiology ; Diameters ; Diastole ; Habitus ; Imaging ; Lesions ; Mathematical models ; Medicine ; Medicine & Public Health ; Model accuracy ; Morphology ; Original Paper ; Parameters ; Parametric statistics ; Prediction models ; Prostheses ; Radiology ; Systole</subject><ispartof>The International Journal of Cardiovascular Imaging, 2021-07, Vol.37 (7), p.2269-2276</ispartof><rights>This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2021</rights><rights>This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-624b133c2fca8d384fb292eddc4eeae841ae5da5a818b5bcf3f109fe966d24843</citedby><cites>FETCH-LOGICAL-c375t-624b133c2fca8d384fb292eddc4eeae841ae5da5a818b5bcf3f109fe966d24843</cites><orcidid>0000-0003-4483-5678</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10554-021-02203-5$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10554-021-02203-5$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33689099$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Mordini, Federico E.</creatorcontrib><creatorcontrib>Hynes, Conor F.</creatorcontrib><creatorcontrib>Amdur, Richard L.</creatorcontrib><creatorcontrib>Panting, Jeffrey</creatorcontrib><creatorcontrib>Emerson, Dominic A.</creatorcontrib><creatorcontrib>Morrissette, Jason</creatorcontrib><creatorcontrib>Goheen-Thomas, Erin</creatorcontrib><creatorcontrib>Greenberg, Michael D.</creatorcontrib><creatorcontrib>Trachiotis, Gregory D.</creatorcontrib><title>Multi-parametric approach to predict prosthetic valve size using CMR and clinical data: insights from SAVR</title><title>The International Journal of Cardiovascular Imaging</title><addtitle>Int J Cardiovasc Imaging</addtitle><addtitle>Int J Cardiovasc Imaging</addtitle><description>The purpose of this investigation was to characterize the CMR and clinical parameters that correlate to prosthetic valve size (PVS) determined at SAVR and develop a multi-parametric model to predict PVS. Sixty-two subjects were included. Linear/area measurements of the aortic annulus were performed on cine CMR images in systole/diastole on long/short axis (SAX) views. Clinical parameters (age, habitus, valve lesion, valve morphology) were recorded. PVS determined intraoperatively was the reference value. Data were analyzed using Spearman correlation. A prediction model combining imaging and clinical parameters was generated. Imaging parameters had moderate to moderately strong correlation to PVS with the highest correlations from systolic SAX mean diameter (r = 0.73, p < 0.0001) and diastolic SAX area (r = 0.73, p < 0.0001). Age was negatively correlated to PVS (r = − 0.47, p = 0.0001). Weight was weakly correlated to PVS (r = 0.27, p = 0.032). AI and bicuspid valve were not predictors of PVS. A model combining clinical and imaging parameters had high accuracy in predicting PVS (R
2
= 0.61). Model predicted mean PVS was 23.3 mm (SD 1.1); actual mean PVS was 23.3 mm (SD 1.3). The Spearman r of the model (0.80, 95% CI 0.683–0.874) was significantly higher than systolic SAX area (0.68, 95% CI 0.516–0.795). Clinical parameters like age and habitus impact PVS; valve lesion/morphology do not. A multi-parametric model demonstrated high accuracy in predicting PVS and was superior to a single imaging parameter. A multi-parametric approach to device sizing may have future application in TAVR.</description><subject>Age</subject><subject>Aorta</subject><subject>Cardiac Imaging</subject><subject>Cardiology</subject><subject>Diameters</subject><subject>Diastole</subject><subject>Habitus</subject><subject>Imaging</subject><subject>Lesions</subject><subject>Mathematical models</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Model accuracy</subject><subject>Morphology</subject><subject>Original Paper</subject><subject>Parameters</subject><subject>Parametric statistics</subject><subject>Prediction models</subject><subject>Prostheses</subject><subject>Radiology</subject><subject>Systole</subject><issn>1569-5794</issn><issn>1573-0743</issn><issn>1875-8312</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><recordid>eNp9kUtrFEEQxxsxmBj9Ah6kwYuXMf2cmfYWFl-QIMTHtanprtntZV529wT009vrRgM5eCiqoH71r6L-hLzg7A1nrLlInGmtKiZ4CcFkpR-RM64bWbFGyceHujaVbow6JU9T2jPGBBPyCTmVsm4NM-aM7K_XIYdqgQgj5hgchWWJM7gdzTNdIvrgcslzyjvMpX0Lwy3SFH4hXVOYtnRzfUNh8tQNYQoOBuohw1saphS2u5xoH-eRfrn8fvOMnPQwJHx-l8_Jt_fvvm4-VlefP3zaXF5VTjY6V7VQHZfSid5B62Wr-k4Ygd47hQjYKg6oPWhoedvpzvWy58z0aOraC9UqeU5eH3XL1T9WTNmOITkcBphwXpMVyhgjDK_bgr56gO7nNU7lOiu0Fo02TB4ExZFy5Q0pYm-XGEaIPy1n9uCEPTphixP2jxNWl6GXd9JrN6L_N_L39QWQRyCV1rTFeL_7P7K_AaRolD4</recordid><startdate>20210701</startdate><enddate>20210701</enddate><creator>Mordini, Federico E.</creator><creator>Hynes, Conor F.</creator><creator>Amdur, Richard L.</creator><creator>Panting, Jeffrey</creator><creator>Emerson, Dominic A.</creator><creator>Morrissette, Jason</creator><creator>Goheen-Thomas, Erin</creator><creator>Greenberg, Michael D.</creator><creator>Trachiotis, Gregory D.</creator><general>Springer Netherlands</general><general>Springer Nature B.V</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FD</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>M7Z</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-4483-5678</orcidid></search><sort><creationdate>20210701</creationdate><title>Multi-parametric approach to predict prosthetic valve size using CMR and clinical data: insights from SAVR</title><author>Mordini, Federico E. ; Hynes, Conor F. ; Amdur, Richard L. ; Panting, Jeffrey ; Emerson, Dominic A. ; Morrissette, Jason ; Goheen-Thomas, Erin ; Greenberg, Michael D. ; Trachiotis, Gregory D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-624b133c2fca8d384fb292eddc4eeae841ae5da5a818b5bcf3f109fe966d24843</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Age</topic><topic>Aorta</topic><topic>Cardiac Imaging</topic><topic>Cardiology</topic><topic>Diameters</topic><topic>Diastole</topic><topic>Habitus</topic><topic>Imaging</topic><topic>Lesions</topic><topic>Mathematical models</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Model accuracy</topic><topic>Morphology</topic><topic>Original Paper</topic><topic>Parameters</topic><topic>Parametric statistics</topic><topic>Prediction models</topic><topic>Prostheses</topic><topic>Radiology</topic><topic>Systole</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mordini, Federico E.</creatorcontrib><creatorcontrib>Hynes, Conor F.</creatorcontrib><creatorcontrib>Amdur, Richard L.</creatorcontrib><creatorcontrib>Panting, Jeffrey</creatorcontrib><creatorcontrib>Emerson, Dominic A.</creatorcontrib><creatorcontrib>Morrissette, Jason</creatorcontrib><creatorcontrib>Goheen-Thomas, Erin</creatorcontrib><creatorcontrib>Greenberg, Michael D.</creatorcontrib><creatorcontrib>Trachiotis, Gregory D.</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biochemistry Abstracts 1</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><jtitle>The International Journal of Cardiovascular Imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mordini, Federico E.</au><au>Hynes, Conor F.</au><au>Amdur, Richard L.</au><au>Panting, Jeffrey</au><au>Emerson, Dominic A.</au><au>Morrissette, Jason</au><au>Goheen-Thomas, Erin</au><au>Greenberg, Michael D.</au><au>Trachiotis, Gregory D.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multi-parametric approach to predict prosthetic valve size using CMR and clinical data: insights from SAVR</atitle><jtitle>The International Journal of Cardiovascular Imaging</jtitle><stitle>Int J Cardiovasc Imaging</stitle><addtitle>Int J Cardiovasc Imaging</addtitle><date>2021-07-01</date><risdate>2021</risdate><volume>37</volume><issue>7</issue><spage>2269</spage><epage>2276</epage><pages>2269-2276</pages><issn>1569-5794</issn><eissn>1573-0743</eissn><eissn>1875-8312</eissn><abstract>The purpose of this investigation was to characterize the CMR and clinical parameters that correlate to prosthetic valve size (PVS) determined at SAVR and develop a multi-parametric model to predict PVS. Sixty-two subjects were included. Linear/area measurements of the aortic annulus were performed on cine CMR images in systole/diastole on long/short axis (SAX) views. Clinical parameters (age, habitus, valve lesion, valve morphology) were recorded. PVS determined intraoperatively was the reference value. Data were analyzed using Spearman correlation. A prediction model combining imaging and clinical parameters was generated. Imaging parameters had moderate to moderately strong correlation to PVS with the highest correlations from systolic SAX mean diameter (r = 0.73, p < 0.0001) and diastolic SAX area (r = 0.73, p < 0.0001). Age was negatively correlated to PVS (r = − 0.47, p = 0.0001). Weight was weakly correlated to PVS (r = 0.27, p = 0.032). AI and bicuspid valve were not predictors of PVS. A model combining clinical and imaging parameters had high accuracy in predicting PVS (R
2
= 0.61). Model predicted mean PVS was 23.3 mm (SD 1.1); actual mean PVS was 23.3 mm (SD 1.3). The Spearman r of the model (0.80, 95% CI 0.683–0.874) was significantly higher than systolic SAX area (0.68, 95% CI 0.516–0.795). Clinical parameters like age and habitus impact PVS; valve lesion/morphology do not. A multi-parametric model demonstrated high accuracy in predicting PVS and was superior to a single imaging parameter. A multi-parametric approach to device sizing may have future application in TAVR.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><pmid>33689099</pmid><doi>10.1007/s10554-021-02203-5</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0003-4483-5678</orcidid></addata></record> |
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| subjects | Age Aorta Cardiac Imaging Cardiology Diameters Diastole Habitus Imaging Lesions Mathematical models Medicine Medicine & Public Health Model accuracy Morphology Original Paper Parameters Parametric statistics Prediction models Prostheses Radiology Systole |
| title | Multi-parametric approach to predict prosthetic valve size using CMR and clinical data: insights from SAVR |
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