P3372Prediction of outcome in functional mitral regurgitation using the average pixel intensity method

Background Functional mitral regurgitation (FMR) is a frequent finding in patients with systolic heart failure. However, the echocardiographic grading of MR is challenging and different severity cut-offs are recommended by international guidelines. We developed and validated a novel echocardiographic parameter to grade MR, the average pixel intensity (API) method, based on pixel intensity analysis of the continuous wave Doppler signal. Purpose In this study, we assessed the long-term predictive value of the API method on clinical endpoints in FMR. Methods Transthoracic echocardiography was performed in consecutive heart failure patients with reduced EF (HF-REF) (n=221). MR was assessed using the API method, vena contracta width (VCW), effective regurgitant orifice area (PISA-EROA) and regurgitant volume (PISA-RV). The primary clinical events were major adverse cardiac events (MACE: cardiovascular mortality, mitral valve surgery, percutaneous mitral intervention or heart failure hospitalization). Results The API method was feasible in 97% of all FMR patients, which was significantly higher than parameters such as VCW, PISA-EROA and PISA-RV. 84 patients (37%) had one or more clinical events during the follow-up period (cardiovascular mortality (20%), mitral valve surgery (5%), percutaneous mitral intervention (5%), heart failure hospitalization (16%) or heart transplantation (2%)). Based on ROC curves, an API cut-off of 121 au was defined as “severe” MVP-MR with an overall better sensitivity and specificity than current guideline-recommended parameters. On multivariate analysis, MR graded with API was independently predictive for clinical events, whereas PISA-based methods were not independent. In addition, pulmonary pressures and NYHA class were powerful independent predictors of clinical outcome in FMR on multivariate analysis. Conclusions The API method better predicts clinical events and outcome in FMR compared to established grading methods. Therefore, the API method may be considered for grading FMR severity in clinical practice.