Fully automated CMR derived stroke volume correlates with right heart catheter measurements in patients with suspected pulmonary hypertension

Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): Welcome Trust (UK), NIHR (UK) Introduction Cardiac magnetic resonance (CMR) assessment plays a significant role in the diagnosis, prognosis and monitoring of patients with pulmonary hypertension (PH). We developed a deep learning model to automatically generate biventricular contours and validated its result in a prospective cohort of patients with suspected PH who underwent right heart catheterization (RHC). Methods A deep learning CMR contouring model was developed in a retrospective multi-vendor (Siemens and General Electric), multi-pathology cohort of patients, predominantly with heart failure, lung disease and PH (n = 400, ASPIRE registry). Biventricular segmentations were made on all CMR studies across cardiac phases. A prospective validation cohort of 102 suspected PH patients was recruited and they had RHC within 24 hours of the CMR. To test the accuracy of the automatic segmentation, the RHC-thermodilution and CMR-derived measures of stroke volume (SV) were compared for manual and automated measurements. Results The mean and standard deviation for the derived SV was 59 ml ± 21 measured by RHC and 75 ml ± 25 for automated and 79 ml ± 26 for manual CMR measurements. Automatic and manual CMR measurement correlated strongly with RHC derived SV; 0.73, 95% CI [0.62, 0.81] and 0.78, 95% CI [0.69, 0.85], respectively (figure 1). The agreement between automatic and manual SV was high; interclass correlation coefficient (ICC) = 0.88, 95% CI [0.83, 0.92] and Bland-Altman plots showed a narrow spread of mean differences between manual and automatic measurements (figure 2). Conclusion In a prospective cohort, fully automatic CMR assessments corresponded accurately to invasive hemodynamics performed within 24 hours of a CMR study.