Capability of CT aortic regurgitant orifice to predict aortic regurgitation severity with cardiovascular magnetic resonance as reference

Autor: M D Martin L Descalzo, M D David Vilades Medel, M D Abdier Vizcarra Tellez, M D Juan Fernandez Martinez, M D Sandra Pujadas Olano, M D Abdel Hakim Moustafa, M D Lidia Bos Real, M D Jose Alberto Hidalgo Perez, M D Ruben Leta Petracca
Rok vydání: 2022
Předmět:
Zdroj: European Heart Journal. 43
ISSN: 1522-9645
0195-668X
DOI: 10.1093/eurheartj/ehac544.239
Popis: Background Regurgitant orifice area (ROA) measurements by cardiac CT have not been extensively compared with a quantitative classification of aortic regurgitation (AR) severity based on regurgitant fraction (RF) by phase-contrast cardiovascular magnetic resonance (PC-CMR) [1–3]. Purpose To evaluate ROA diagnostic accuracy by 256-slice CT for predicting severe AR with PC-CMR as reference. Material and methods We consecutively enrolled 57 patients with AR prior to surgery assessed by echocardiography, PC-CRM, and CT. Mean age 67.3±15.3 (84% males). Cardiac CT data sets were reconstructed at 75–78% of the R-R interval to measure ROA. According to previous data (4), a cut-off value of 33% by PC-CMR defined severe AR. Receiver operating curve (ROC) were calculated to detect the best CT-ROA cut-off to predict severe AR by CMR. Results There were no differences between both groups of AR patients in terms of age, gender, or anthropometric measures. CT-ROA was significantly smaller in patients with RF CT-ROA significantly correlated with the RF>33% by PC-CMR (Pearson's correlation coefficient (R)=0,48, p In the ROC curve analysis to predict significant RF, a CT-ROA cut-off of 0.27 cm2 correctly classified 93% of patients with sensitivity of 97.6% (CI95%:87,7–99,6); specificity of 73.33% (CI95%:48–89); PPV 95,3% (CI95%: 84,5–98,7); NPV 91,7% (CI95%: 64,6% a 98,5%) and area under the curve (AUC) of 0.93 (CI95%: 0.85–1.00; p Conclusion CT-RAO could be useful to distinguish AR severity determined by PC-CMR with a cut-off value of 0.27 cm2 as best predictor. Funding Acknowledgement Type of funding sources: None.
Databáze: OpenAIRE
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