Novel TTE algorithms improved the identification of CMR-defined severe aortic regurgitation, achieving an AUC of 0.76 compared to 0.58 for current guidelines in the validation cohort.
Does a novel TTE-based algorithm improve the identification of CMR-defined severe aortic regurgitation compared to current guideline criteria?
A novel TTE-based algorithm utilizing regurgitant volume ≥45 mL and LVEDVi ≥93 mL/m2 improves the accuracy of identifying CMR-defined severe aortic regurgitation compared to current guideline criteria.
Absolute Event Rate: 0% vs 0%
Abstract Aims Cardiac magnetic resonance (CMR) compliments transthoracic echocardiography (TTE) for heart valve evaluation, however TTE remains more widely available. We sought to optimize transthoracic echocardiographic (TTE) quantification of significant aortic regurgitation (AR) by developing TTE-based algorithms to identify CMR-defined severe AR. Methods and Results Patients with ≥moderate-to-severe AR undergoing both TTE and CMR within 3-months were studied. A historical cohort 2006-2018 (n = 193) was used to derive TTE-based decision tree regression algorithms to best identify severe AR based on holodiastolic flow reversal (HDR) using CMR, then validated in a prospective AR cohort (n = 97) during 2019-2021. Mean AR regurgitant volumes, fractions and proportions with HDR by TTE/CMR were 48/31 mL, 41/25% and 43%/27% for the historical derivation cohort and 51/37 mL, 47/29% and 54%/41% for the prospective validation cohort. Decision-tree analyses found regurgitant volume≥45 mL and left ventricular end-diastolic volume index (LVEDVi) ≥ 93 mL/m2 by TTE to best identify CMR-derived severe AR. Areas under curves (95%CIs) of the novel algorithms (PISA and Doppler methods) compared with current guidelines criteria for detecting CMR-derived severe AR were 0.80 (0.71-0.88) and 0.74 (0.65-0.83) versus 0.72 (0.63-0.81) in the derivation cohort, and 0.76 (0.66-0.87) and 0.71 (0.61-0.82) versus 0.58 (0.46-0.70) in the validation cohort; and for predicting left ventricular remodeling where follow-up TTE wad available were 0.65 (0.58-0.73) and 0.62 (0.54-0.70) versus 0.53 (0.45-0.61) respectively. Conclusion Novel TTEs algorithm increased TTE accuracy of identifying significant AR defined by CMR especially in the prospective cohort, compared to the current guidelines criteria, and was able to modestly discriminate LV remodeling.
Wang et al. (Sun,) reported a other. Novel TTE algorithms improved the identification of CMR-defined severe aortic regurgitation, achieving an AUC of 0.76 compared to 0.58 for current guidelines in the validation cohort.
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