[EN]Disease progression in patients with mild-to-moderate aortic stenosis is heterogenous and requires periodic echocardiographic examinations to evaluate severity.
This study sought to explore the use of machine learning to optimize aortic stenosis echocardiographic surveillance automatically.
The study investigators trained, validated, and externally applied a machine learning model to predict whether a patient with mild-to-moderate aortic stenosis will develop severe valvular disease at 1, 2, or 3 years. Demographic and echocardiographic patient data to develop the model were obtained from a tertiary hospital consisting of 4,633 echocardiograms from 1,638 consecutive patients. The external cohort was obtained from an independent tertiary hospital, consisting of 4,531 echocardiograms from 1,533 patients. Echocardiographic surveillance timing results were compared with the European and American guidelines echocardiographic follow-up recommendations.
In internal validation, the model discriminated severe from nonsevere aortic stenosis development with an area under the receiver-operating characteristic curve (AUC-ROC) of 0.90, 0.92, and 0.92 for the 1-, 2-, or 3-year interval, respectively. In external application, the model showed an AUC-ROC of 0.85, 0.85, and 0.85, for the 1-, 2-, or 3-year interval. A simulated application of the model in the external validation cohort resulted in savings of 49% and 13% of unnecessary echocardiographic examinations per year compared with European and American guideline recommendations, respectively.
Machine learning provides real-time, automated, personalized timing of next echocardiographic follow-up examination for patients with mild-to-moderate aortic stenosis. Compared with European and American guidelines, the model reduces the number of patient examinations.Este estudio ha sido financiado por la Red Cardiovascular Española (CIBERCV) y los Proyectos de Desarrollo Tecnológico en Salud (DTS19/00098) y por un Proyecto de Investigación en Salud (PI21/00369); y ha sido financiado con recursos nacionales, públicos y competitivos del Instituto de Salud Carlos III (Ministerio de Ciencia e Innovación, España) financiados por el Fondo Europeo de Desarrollo Regional de la Unión Europea. Los Dres. Dorado-Díaz, Sampedro-Gómez, Sánchez-González, Vicente-Palacios y Sánchez son los inventores de una patente sobre el método (Sistema Experto de Seguimiento Ecocardiográfico de Estenosis Aórtica; Publicación Internacional N.º WO 2020/157212 A1) descrito en este artículo. Todos los demás autores han informado de que no tienen relaciones relevantes con el contenido de este artículo que revelar
