Right Atrial Remodeling and Outcome in Patients with Secondary Tricuspid Regurgitation.

BACKGROUND: In patients with secondary tricuspid regurgitation (STR), right atrial remodeling (RAR) is a proven marker of disease progression. However, the prognostic value of RAR, assessed by indexed right atrial volume (RAVi) and reservoir strain (RAS), remains to be clarified. Accordingly, the aim of our study is to investigate the association with outcome of RAR in patients with STR. METHODS: We enrolled 397 patients (44% men, 72.7 ± 13 years old) with mild to severe STR. Complete two-dimensional and speckle-tracking echocardiography analysis of right atrial and right ventricular (RV) size and function were obtained in all patients. The primary end point was the composite of death from any cause and heart failure hospitalization. RESULTS: After a median follow-up of 15 months (interquartile range, 6-23), the end point was reached by 158 patients (39%). Patients with RAS <13% and RAVi >48 mL/m2 had significantly lower survival rates compared to patients with RAS ≥13% and RAVi ≤48 mL/m2 (log-rank P < .001). On multivariable analysis, RAS <13% (hazard ratio, 2.11; 95% CI, 1.43-3.11; P < .001) and RAVi > 48 mL/m2 (hazard ratio, 1.49; 95% CI, 1.01-2.18; P = .04) remained associated with the combined end point, even after adjusting for RV free-wall longitudinal strain, significant chronic kidney disease, and New York Heart Association class. Secondary tricuspid regurgitation excess mortality increased exponentially with values of 18.2% and 51.3 mL/m2 for RAS and RAVi, respectively. In nested models, the addition of RAS and RAVi provided incremental prognostic value over clinical, conventional echocardiographic parameters of RV size and function and RV free-wall longitudinal strain. CONCLUSIONS: In patients with STR, RAR was independently associated with mortality and heart failure hospitalization. Assessment of RAR could improve risk stratification of patients with STR, potentially identifying those who may benefit from optimization of medical therapy and a closer follow-up.

An accurate and time-efficient deep learning-based system for automated segmentation and reporting of cardiac magnetic resonance-detected ischemic scar.

BACKGROUND AND OBJECTIVES: Myocardial infarction scar (MIS) assessment by cardiac magnetic resonance provides prognostic information and guides patients' clinical management. However, MIS segmentation is time-consuming and not performed routinely. This study presents a deep-learning-based computational workflow for the segmentation of left ventricular (LV) MIS, for the first time performed on state-of-the-art dark-blood late gadolinium enhancement (DB-LGE) images, and the computation of MIS transmurality and extent. METHODS: DB-LGE short-axis images of consecutive patients with myocardial infarction were acquired at 1.5T in two centres between Jan 1, 2019, and June 1, 2021. Two convolutional neural network (CNN) models based on the U-Net architecture were trained to sequentially segment the LV and MIS, by processing an incoming series of DB-LGE images. A 5-fold cross-validation was performed to assess the performance of the models. Model outputs were compared respectively with manual (LV endo- and epicardial border) and semi-automated (MIS, 4-Standard Deviation technique) ground truth to assess the accuracy of the segmentation. An automated post-processing and reporting tool was developed, computing MIS extent (expressed as relative infarcted mass) and transmurality. RESULTS: The dataset included 1355 DB-LGE short-axis images from 144 patients (MIS in 942 images). High performance (> 0.85) as measured by the Intersection over Union metric was obtained for both the LV and MIS segmentations on the training sets. The performance for both LV and MIS segmentations was 0.83 on the test sets. Compared to the 4-Standard Deviation segmentation technique, our system was five times quicker (<1 min versus 7 ± 3 min), and required minimal user interaction. CONCLUSIONS: Our solution successfully addresses different issues related to automatic MIS segmentation, including accuracy, time-effectiveness, and the automatic generation of a clinical report.