Improved Interobserver Variability and Accuracy of Echocardiographic Visual Left Ventricular Ejection Fraction Assessment through a Self-Directed Learning Program Using Cardiac Magnetic Resonance Images

Abstract

Although not recommended in isolation, visual estimation of echocardiographic ejection fraction (EF) is widely applied to confirm quantitative EF. However, interobserver variability for EF estimation has been reported to be as high as 14%. The aim of this study was to determine whether self-directed education could improve the accuracy and interobserver variability of visual estimation of EF and whether a multireader estimate improves measurement precision. Thirty-one participants provided single-point EF estimates for 30 echocardiograms with a spectrum of EFs, image quality, and clinical contexts in patients undergoing cardiac magnetic resonance (CMR) within 48 hours. Participants received their own case-by-case variance from CMR EF, and the 10 cases with the largest reader variability were discussed along with corresponding CMR images. Self-directed learning was undertaken by side-by-side review of echocardiographic and CMR images. Two months later, 20 new cases were shown to the same 31 participants, using the same methodology. The baseline interobserver variability of ±0.120 improved to ±0.097 after the intervention. EF misclassification (defined as ±0.05 of CMR EF) was reduced from 56% to 47% (P < .001), and the intervention also resulted in a decrease in the absolute difference between CMR and echocardiography for all cases and all readers (from 0.07 ± 0.01 to 0.06 ± 0.01, P = .0001). This improvement was most prominent for the readers with lower baseline accuracy. A combined physician-sonographer EF estimate improved the precision of EF determination by 25% compared with individual reads. In readers with varying levels of experience, a simple, mostly self-directed intervention modestly decreased interobserver variability and improved the accuracy of EF measurements. Combined physician-sonographer EF reporting improved the precision of EF estimates.