128 Clinical impact of changes in mitral regurgitation severity after optimization of medical therapy in heart failure: insights from BIOSTAT-CHF

Aims Few data are available regarding changes in mitral regurgitation (MR) severity with guideline-directed medical therapy (GDMT) in heart failure (HF). We evaluated the evolution and impact of MR after GDMT in the BIOlogy Study to TAilored Treatment in Chronic Heart Failure (BIOSTAT-CHF). Methods and results A retrospective post hoc analysis was performed on HF patients from BIOSTAT-CHF with available data on MR status at baseline and at 9-month follow-up after GRMT optimization. The primary endpoint was a composite of all-cause death or HF hospitalization. Among 1022 patients with data at both time-points, 462 (45.2%) had moderate-severe MR at baseline and 360 (35.2%) had it at 9-month follow-up. Regression of moderate–severe MR from baseline to 9 months occurred in 192/462 patients (41.6%) and worsening from baseline to moderate–severe MR at 9 months occurred in 90/560 patients (16.1%). The presence of moderate-severe MR at 9 months, independent from baseline severity, was associated with an increased risk of the primary endpoint [unadjusted hazard ratio (HR), 2.03; 95% confidence interval (CI): 1.57–2.63; P < 0.001], also after adjusting for the BIOSTAT-CHF risk-prediction model (adjusted HR: 1.85; 95% CI: 1.43–2.39; P < 0.001). Younger age, LVEF ≥50% and treatment with higher ACEi/ARB doses were associated with a lower likelihood of moderate–severe MR at 9 months, whereas older age was the only predictor of worsening MR. Conclusions Among patients with HF undergoing GDMT optimization, ACEi/ARB up-titration and HFpEF were associated with MR improvement, and the presence of moderate–severe MR after GRMT was associated with worse outcome.

Automatic Assessment of Mitral Regurgitation Severity Using the Mask R-CNN Algorithm with Color Doppler Echocardiography Images

Accurate assessment of mitral regurgitation (MR) severity is critical in clinical diagnosis and treatment. No single echocardiographic method has been recommended for MR quantification thus far. We sought to define the feasibility and accuracy of the mask regions with a convolutional neural network (Mask R-CNN) algorithm in the automatic qualitative evaluation of MR using color Doppler echocardiography images. The authors collected 1132 cases of MR from hospital A and 295 cases of MR from hospital B and divided them into the following four types according to the 2017 American Society of Echocardiography (ASE) guidelines: grade I (mild), grade II (moderate), grade III (moderate), and grade IV (severe). Both grade II and grade III are moderate. After image marking with the LabelMe software, a method using the Mask R-CNN algorithm based on deep learning (DL) was used to evaluate MR severity. We used the data from hospital A to build the artificial intelligence (AI) model and conduct internal verification, and we used the data from hospital B for external verification. According to severity, the accuracy of classification was 0.90, 0.89, and 0.91 for mild, moderate, and severe MR, respectively. The Macro F1 and Micro F1 coefficients were 0.91 and 0.92, respectively. According to grading, the accuracy of classification was 0.90, 0.87, 0.81, and 0.91 for grade I, grade II, grade III, and grade IV, respectively. The Macro F1 and Micro F1 coefficients were 0.89 and 0.89, respectively. Automatic assessment of MR severity is feasible with the Mask R-CNN algorithm and color Doppler electrocardiography images collected in accordance with the 2017 ASE guidelines, and the model demonstrates reasonable performance and provides reliable qualitative results for MR severity.