Publications by authors named "A Coisne"
Utility of an Echocardiographic Machine Learning Model to Predict Outcomes in ICCU Patients.
J Am Soc Echocardiogr
December 2024
Article Synopsis
- The study focused on the use of a machine learning model using initial transthoracic echocardiography (TTE) to predict in-hospital major adverse events (MAEs) in patients admitted to intensive cardiac care units (ICCU).
- A total of 1,499 patients were evaluated, and the model showed significant accuracy, highlighting five key TTE parameters that contributed to its predictions.
- The machine learning model outperformed traditional scoring methods, indicating it could serve as a better tool for risk stratification in heart patients.
Despite the challenges associated with periprocedural imaging, transcatheter tricuspid valve interventions have shown important impact on outcomes. A comprehensive understanding of the anatomy of the right heart and surrounding structures is crucial. One way to optimize these interventions is by identifying the optimal fluoroscopic viewing angles along the S-curve of the tricuspid valve.
View Article and Find Full Text PDFBackground: Severe tricuspid regurgitation (TR) is an adverse prognostic factor. The presence of potential racial/ethnic disparities in patient characteristics and outcomes remain unexplored. This study aimed to investigate the impact of race/ethnicity on the clinical course of severe TR.
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