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

European Heart Journal

3 October 2022
Organised by: Logo
ESC Journals

Abstract

AbstractBackground

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<33% by PC-CMR (0.24±0.09 vs 0.49±0.22; p<0.001). AR etiology, jet eccentricity, ascending aorta diameters and ejection fraction (LVEF) also were not significantly different according to AR-RF group (Table 1).

CT-ROA significantly correlated with the RF>33% by PC-CMR (Pearson's correlation coefficient (R)=0,48, p<0.001) (Figure 1A).

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<0.001) (Figure 1B).

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.

Variables according to AR-RF 33% by CMR

Relationship between AR-RF and ROA-CT