Artificial intelligence-analyzed computed tomography in patients undergoing transcatheter tricuspid valve repair
Baseline right ventricular (RV) function derived from 3-dimensional analyses has been demonstrated to be predictive in patients undergoing transcatheter tricuspid valve repair (TTVR). The complex nature of these cumbersome analyses makes patient selection based on established imaging methods challen...
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| Veröffentlicht in: | International journal of cardiology 2024-09, Vol.411, p.132233, Article 132233 |
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| container_title | International journal of cardiology |
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| creator | Kirchner, Johannes Gerçek, Muhammed Gesch, Johannes Omran, Hazem Friedrichs, Kai Rudolph, Felix Ivannikova, Maria Rossnagel, Tobias Piran, Misagh Pfister, Roman Blanke, Philipp Rudolph, Volker Rudolph, Tanja K. |
| description | Baseline right ventricular (RV) function derived from 3-dimensional analyses has been demonstrated to be predictive in patients undergoing transcatheter tricuspid valve repair (TTVR). The complex nature of these cumbersome analyses makes patient selection based on established imaging methods challenging. Artificial intelligence (AI)-driven computed tomography (CT) segmentation of the RV might serve as a fast and predictive tool for evaluating patients prior to TTVR.
Patients suffering from severe tricuspid regurgitation underwent full cycle cardiac CT. AI-driven analyses were compared to conventional CT analyses. Outcome measures were correlated with survival free of rehospitalization for heart-failure or death after TTVR as the primary endpoint.
Automated AI-based image CT-analysis from 100 patients (mean age 77 ± 8 years, 63% female) showed excellent correlation for chamber quantification compared to conventional, core-lab evaluated CT analysis (R 0.963–0.966; p |
| doi_str_mv | 10.1016/j.ijcard.2024.132233 |
| format | Article |
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Patients suffering from severe tricuspid regurgitation underwent full cycle cardiac CT. AI-driven analyses were compared to conventional CT analyses. Outcome measures were correlated with survival free of rehospitalization for heart-failure or death after TTVR as the primary endpoint.
Automated AI-based image CT-analysis from 100 patients (mean age 77 ± 8 years, 63% female) showed excellent correlation for chamber quantification compared to conventional, core-lab evaluated CT analysis (R 0.963–0.966; p < 0.001). At 1 year (mean follow-up 229 ± 134 days) the primary endpoint occurred significantly more frequently in patients with reduced RV ejection fraction (EF) <50% (36.6% vs. 13.7%; HR 2.864, CI 1.212–6.763; p = 0.016). Furthermore, patients with dysfunctional RVs defined as end-diastolic RV volume > 210 ml and RV EF <50% demonstrated worse outcome than patients with functional RVs (43.7% vs. 12.2%; HR 3.753, CI 1.621–8.693; p = 0.002).
Derived RVEF and dysfunctional RV were predictors for death and hospitalization after TTVR. AI-facilitated CT analysis serves as an inter- and intra-observer independent and time-effective tool which may thus aid in optimizing patient selection prior to TTVR in clinical routine and in trials.
•AI-based CT analysis is inter- and intra-observer-independent and can be a time-effective tool for daily clinical practice.•AI analyses demonstrated excellent agreement and correlation with manual CT data segmentation.•Reduced RVEF and dysfunctional RV were predictors for death and hospitalization after TTVR.</description><identifier>ISSN: 0167-5273</identifier><identifier>ISSN: 1874-1754</identifier><identifier>EISSN: 1874-1754</identifier><identifier>DOI: 10.1016/j.ijcard.2024.132233</identifier><identifier>PMID: 38848770</identifier><language>eng</language><publisher>Netherlands: Elsevier B.V</publisher><subject>Artificial intelligence ; Right ventricle ; Tricuspid regurgitation ; Valve repair</subject><ispartof>International journal of cardiology, 2024-09, Vol.411, p.132233, Article 132233</ispartof><rights>2024 The Authors</rights><rights>Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c311t-66628853a26f143a4c0e4b35435be19ebe60d6f6a6c7895925307529353720733</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0167527324008556$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38848770$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Kirchner, Johannes</creatorcontrib><creatorcontrib>Gerçek, Muhammed</creatorcontrib><creatorcontrib>Gesch, Johannes</creatorcontrib><creatorcontrib>Omran, Hazem</creatorcontrib><creatorcontrib>Friedrichs, Kai</creatorcontrib><creatorcontrib>Rudolph, Felix</creatorcontrib><creatorcontrib>Ivannikova, Maria</creatorcontrib><creatorcontrib>Rossnagel, Tobias</creatorcontrib><creatorcontrib>Piran, Misagh</creatorcontrib><creatorcontrib>Pfister, Roman</creatorcontrib><creatorcontrib>Blanke, Philipp</creatorcontrib><creatorcontrib>Rudolph, Volker</creatorcontrib><creatorcontrib>Rudolph, Tanja K.</creatorcontrib><title>Artificial intelligence-analyzed computed tomography in patients undergoing transcatheter tricuspid valve repair</title><title>International journal of cardiology</title><addtitle>Int J Cardiol</addtitle><description>Baseline right ventricular (RV) function derived from 3-dimensional analyses has been demonstrated to be predictive in patients undergoing transcatheter tricuspid valve repair (TTVR). The complex nature of these cumbersome analyses makes patient selection based on established imaging methods challenging. Artificial intelligence (AI)-driven computed tomography (CT) segmentation of the RV might serve as a fast and predictive tool for evaluating patients prior to TTVR.
Patients suffering from severe tricuspid regurgitation underwent full cycle cardiac CT. AI-driven analyses were compared to conventional CT analyses. Outcome measures were correlated with survival free of rehospitalization for heart-failure or death after TTVR as the primary endpoint.
Automated AI-based image CT-analysis from 100 patients (mean age 77 ± 8 years, 63% female) showed excellent correlation for chamber quantification compared to conventional, core-lab evaluated CT analysis (R 0.963–0.966; p < 0.001). At 1 year (mean follow-up 229 ± 134 days) the primary endpoint occurred significantly more frequently in patients with reduced RV ejection fraction (EF) <50% (36.6% vs. 13.7%; HR 2.864, CI 1.212–6.763; p = 0.016). Furthermore, patients with dysfunctional RVs defined as end-diastolic RV volume > 210 ml and RV EF <50% demonstrated worse outcome than patients with functional RVs (43.7% vs. 12.2%; HR 3.753, CI 1.621–8.693; p = 0.002).
Derived RVEF and dysfunctional RV were predictors for death and hospitalization after TTVR. AI-facilitated CT analysis serves as an inter- and intra-observer independent and time-effective tool which may thus aid in optimizing patient selection prior to TTVR in clinical routine and in trials.
•AI-based CT analysis is inter- and intra-observer-independent and can be a time-effective tool for daily clinical practice.•AI analyses demonstrated excellent agreement and correlation with manual CT data segmentation.•Reduced RVEF and dysfunctional RV were predictors for death and hospitalization after TTVR.</description><subject>Artificial intelligence</subject><subject>Right ventricle</subject><subject>Tricuspid regurgitation</subject><subject>Valve repair</subject><issn>0167-5273</issn><issn>1874-1754</issn><issn>1874-1754</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kE1rGzEQhkVpaZy0_6CUPfayrr6lvRRCSJqCIZf2LGTtrDNmvyppDc6vr8ymPfY0M_DMDO9DyCdGt4wy_fW4xWPwsd1yyuWWCc6FeEM2zBpZM6PkW7IpmKkVN-KKXKd0pJTKprHvyZWwVlpj6IbMtzFjhwF9X-GYoe_xAGOA2o--P79AW4VpmJdcmjwN0yH6-flcyGr2GWHMqVrGFuJhwvFQ5ejHFHx-hgyxTBiWNGNbnXx_girC7DF-IO863yf4-FpvyK-H-593j_Xu6fuPu9tdHQRjudZac2uV8Fx3TAovAwW5F0oKtQfWwB40bXWnvQ7GNqrhSlCjeCOUMJwaIW7Il_XuHKffC6TsBkyh5PMjTEtygmrVWCYbU1C5oiFOKUXo3Bxx8PHsGHUX1-7oVtfu4tqtrsva59cPy36A9t_SX7kF-LYCUHKeEKJLAS9yW4wQsmsn_P-HP_OPkxA</recordid><startdate>20240915</startdate><enddate>20240915</enddate><creator>Kirchner, Johannes</creator><creator>Gerçek, Muhammed</creator><creator>Gesch, Johannes</creator><creator>Omran, Hazem</creator><creator>Friedrichs, Kai</creator><creator>Rudolph, Felix</creator><creator>Ivannikova, Maria</creator><creator>Rossnagel, Tobias</creator><creator>Piran, Misagh</creator><creator>Pfister, Roman</creator><creator>Blanke, Philipp</creator><creator>Rudolph, Volker</creator><creator>Rudolph, Tanja K.</creator><general>Elsevier B.V</general><scope>6I.</scope><scope>AAFTH</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20240915</creationdate><title>Artificial intelligence-analyzed computed tomography in patients undergoing transcatheter tricuspid valve repair</title><author>Kirchner, Johannes ; Gerçek, Muhammed ; Gesch, Johannes ; Omran, Hazem ; Friedrichs, Kai ; Rudolph, Felix ; Ivannikova, Maria ; Rossnagel, Tobias ; Piran, Misagh ; Pfister, Roman ; Blanke, Philipp ; Rudolph, Volker ; Rudolph, Tanja K.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c311t-66628853a26f143a4c0e4b35435be19ebe60d6f6a6c7895925307529353720733</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Artificial intelligence</topic><topic>Right ventricle</topic><topic>Tricuspid regurgitation</topic><topic>Valve repair</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kirchner, Johannes</creatorcontrib><creatorcontrib>Gerçek, Muhammed</creatorcontrib><creatorcontrib>Gesch, Johannes</creatorcontrib><creatorcontrib>Omran, Hazem</creatorcontrib><creatorcontrib>Friedrichs, Kai</creatorcontrib><creatorcontrib>Rudolph, Felix</creatorcontrib><creatorcontrib>Ivannikova, Maria</creatorcontrib><creatorcontrib>Rossnagel, Tobias</creatorcontrib><creatorcontrib>Piran, Misagh</creatorcontrib><creatorcontrib>Pfister, Roman</creatorcontrib><creatorcontrib>Blanke, Philipp</creatorcontrib><creatorcontrib>Rudolph, Volker</creatorcontrib><creatorcontrib>Rudolph, Tanja K.</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>International journal of cardiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kirchner, Johannes</au><au>Gerçek, Muhammed</au><au>Gesch, Johannes</au><au>Omran, Hazem</au><au>Friedrichs, Kai</au><au>Rudolph, Felix</au><au>Ivannikova, Maria</au><au>Rossnagel, Tobias</au><au>Piran, Misagh</au><au>Pfister, Roman</au><au>Blanke, Philipp</au><au>Rudolph, Volker</au><au>Rudolph, Tanja K.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Artificial intelligence-analyzed computed tomography in patients undergoing transcatheter tricuspid valve repair</atitle><jtitle>International journal of cardiology</jtitle><addtitle>Int J Cardiol</addtitle><date>2024-09-15</date><risdate>2024</risdate><volume>411</volume><spage>132233</spage><pages>132233-</pages><artnum>132233</artnum><issn>0167-5273</issn><issn>1874-1754</issn><eissn>1874-1754</eissn><abstract>Baseline right ventricular (RV) function derived from 3-dimensional analyses has been demonstrated to be predictive in patients undergoing transcatheter tricuspid valve repair (TTVR). The complex nature of these cumbersome analyses makes patient selection based on established imaging methods challenging. Artificial intelligence (AI)-driven computed tomography (CT) segmentation of the RV might serve as a fast and predictive tool for evaluating patients prior to TTVR.
Patients suffering from severe tricuspid regurgitation underwent full cycle cardiac CT. AI-driven analyses were compared to conventional CT analyses. Outcome measures were correlated with survival free of rehospitalization for heart-failure or death after TTVR as the primary endpoint.
Automated AI-based image CT-analysis from 100 patients (mean age 77 ± 8 years, 63% female) showed excellent correlation for chamber quantification compared to conventional, core-lab evaluated CT analysis (R 0.963–0.966; p < 0.001). At 1 year (mean follow-up 229 ± 134 days) the primary endpoint occurred significantly more frequently in patients with reduced RV ejection fraction (EF) <50% (36.6% vs. 13.7%; HR 2.864, CI 1.212–6.763; p = 0.016). Furthermore, patients with dysfunctional RVs defined as end-diastolic RV volume > 210 ml and RV EF <50% demonstrated worse outcome than patients with functional RVs (43.7% vs. 12.2%; HR 3.753, CI 1.621–8.693; p = 0.002).
Derived RVEF and dysfunctional RV were predictors for death and hospitalization after TTVR. AI-facilitated CT analysis serves as an inter- and intra-observer independent and time-effective tool which may thus aid in optimizing patient selection prior to TTVR in clinical routine and in trials.
•AI-based CT analysis is inter- and intra-observer-independent and can be a time-effective tool for daily clinical practice.•AI analyses demonstrated excellent agreement and correlation with manual CT data segmentation.•Reduced RVEF and dysfunctional RV were predictors for death and hospitalization after TTVR.</abstract><cop>Netherlands</cop><pub>Elsevier B.V</pub><pmid>38848770</pmid><doi>10.1016/j.ijcard.2024.132233</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Artificial intelligence Right ventricle Tricuspid regurgitation Valve repair |
| title | Artificial intelligence-analyzed computed tomography in patients undergoing transcatheter tricuspid valve repair |
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