Artificial intelligence-analyzed computed tomography in patients undergoing transcatheter tricuspid valve repair

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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Bibliographic Details
Published in:International journal of cardiology Vol. 411; p. 132233
Main Authors: 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.
Format: Journal Article
Language:English
Published: Netherlands Elsevier B.V 15-09-2024
Subjects:
Artificial intelligence
Right ventricle
Tricuspid regurgitation
Valve repair
EF
RVEF
RV
TTE
Right ventricle
ROC
AI
LVEF
HR
TAPSE
Tricuspid regurgitation
cMRI
RA
CT
RCA
SDD
ASE
RVEDV
Valve repair
TTVR
Artificial intelligence
TR
LoA
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Summary: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.
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ISSN:0167-5273
1874-1754
1874-1754
DOI:10.1016/j.ijcard.2024.132233