Abstract 4360886: Update to Non-invasive, Automated Approach to Estimate Septal Curvature as a Surrogate of Mean Pulmonary Arterial Pressure for Pediatric Pulmonary Hypertension Patients

Background: Pediatric pulmonary hypertension can be diagnosed by echocardiography and right heart catheterization, but cardiac MRI-based septal curvature (SC) measurement can also be used as a surrogate of mean pulmonary arterial pressure (mPAP), which is an invasive measurement to follow-up patients. We developed an automated approach to measure SC, demonstrating its superiority over a manual measurement. However, its performance relative to other septal wall measurements and clinical markers are unclear. Hypothesis: Automated SC is better correlated with mPAP, less observer dependent, and better associated with adverse outcomes than interventricular septal angle (IVS) and right ventricular ejection fraction (RVEF). Aims: To compare the automated SC, IVS, and RVEF in terms of observer variability, correlation to mPAP, and correlation with adverse outcomes. Methods: Patients with pulmonary hypertension who had both catheterization and cardiac MRI were retrospectively included. Automated SC and IVS were measured using a mid-slice of short-axis stack imaging for both ventricles using cvi42, a custom MATLAB tool and Fuji PACs (Fig.1). RVEF was collected from the MRI scan report. Adverse outcomes were death, transplant, and/or indication for transplant of heart and/or lung and were collected from electronic health record. Pearson correlation was used for correlation between the metrics and mPAP. A receiver-operating characteristic (ROC) curve was used to investigate the association between the metrics and outcomes. Intraclass correlation coefficient (ICC) was used for interobserver variability analysis. P<0.05 was considered statistically significant. Results: 25 patients (17.0 [12.0 – 18.0] years; 13 with adverse outcomes) were included. Automated SC had a better correlation with mPAP (R=-0.82, p<0.001) than IVS (R=0.66, p<0.001) and RVEF (R=-0.49, p=0.01) (Fig.2). The capability to differentiate adverse outcomes was significant and better for RVEF (area under the curve of 0.82, p=0.007) while it was not significant for automated SC (0.72, p=0.06) and IVS (0.63, p=0.28) (Fig.3). Interobserver analysis found comparable ICCs (0.98, 95%CI, 0.97 – 0.99 for automated SC; 0.97, 95%CI 0.94 – 0.98 for IVS). ICC was not estimated for RVEF due to retrospective nature of the data collection. Conclusion: The automated SC better correlated with mPAP, with comparable observer dependency to IVS but was not able to better differentiate adverse outcomes than RVEF.