Abstract 9821: Fully Automated Measurements of Right Ventricular Strain in Children: Validation Using Conventional Manual Technique

Abstract

Introduction: Fully automated strain measurements have been developed for the left ventricle. However, they have not been studied in the right ventricle (RV) of adults or children. Hypothesis: Automated strain measurements of RV will be comparable with manual strain measurements. Methods: We prospectively enrolled 94 patients, who underwent clinically indicated echocardiograms (mean age: 13.0±4.4 years). Strain measurements included RV free wall longitudinal strain (RVFWLS) and RV “global” longitudinal strain (RVGLS), both measured from the RV-focused apical 4-chamber view. Strain parameters were measured using three methods: 1) Fully automated (AutoStrain, Phillips Medical Systems), 2) Semiautomated (manual correction after automated measurements), 3) Manual measurements (2D-CPA, Tomtec). We also recorded the time required for measurements in all patients. Automated measurements relied on an anatomical intelligence program that was trained by adult datasets. Results: Fully automated measurements were feasible in all cases. However, 40% of patients required manual correction after automated measurements, mostly at the apex and RV lateral annulus at endsystole. The results are depicted in Table 1. Fully automated RVFWS showed moderate to good correlation with manual measurements (r=0.71, p<0.001). Semiautomated measurements showed a stronger correlation (r=0.78, p<0.001) and less bias than fully automated measurements ( Figure 1 ). The time needed for fully and semi-automated analyses were 9±2.6 and 68±25 seconds, respectively (p<0.001). Conclusions: Automated strain may provide rapid and clinically feasible means to assess RV function in children. Presently, a significant number of patients need manual correction, especially at endsystole. We speculate that accuracy of AutoStrain may improve in the future with inclusion of pediatric training datasets, as the anatomical intelligence program currently relies on adult datasets.