{"id":23220,"date":"2021-12-22T08:58:13","date_gmt":"2021-12-22T14:58:13","guid":{"rendered":"https:\/\/saluddigital.com\/?p=23220"},"modified":"2025-10-21T11:19:38","modified_gmt":"2025-10-21T17:19:38","slug":"aprendizaje-automatico-para-la-prediccion-de-mortalidad-de-enfermedades-de-arterias-coronarias","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/big-data\/aprendizaje-automatico-para-la-prediccion-de-mortalidad-de-enfermedades-de-arterias-coronarias\/","title":{"rendered":"Machine learning for coronary artery disease mortality prediction"},"content":{"rendered":"
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A recent study was able to predict the probability of death in patients with coronary artery disease, thanks to the use of Artificial Intelligence (AI).<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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The study was presented in December 2021, at the EuroEcho 2021 congress, an event of the European Society of Cardiology (ESC). The study showed a more accurate forecast of patients with coronary artery disease dying within 10 years.<\/p>

The study included information from images of the heart measured by cardiovascular magnetic resonance imaging (CMR) of stress, unlike methods where they are only based on clinical data. "This is the first study to show that machine learning with clinical parameters plus stress CMR can very accurately predict the risk of death," explained study author Theo Pezel, MD, of Johns Hopkins Hospital.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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The findings showed that patients with chest pain, dyspnea, or risk factors for cardiovascular disease should undergo a stress CMR exam to calculate their score. This will allow professionals to provide more intense follow-up and advice on exercise, diet and other habits for people who require it.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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The study included information from 31,752 patients referred for stress CMR between 2008 and 2018, in Paris, France. The average age was 64 years and the 66% were men. Some symptoms included chest pain, shortness of breath with exercise, or risk of cardiovascular disease, but no symptoms. High risks were determined as hypertension, diabetes, dyslipidemia, and smoking. The patients were followed up for six years and the deaths were obtained from the French national death registry. In that period, 2,679 patients died, that is, 8.4%.<\/p>

\u201cMachine learning was carried out in two steps. First, it was used to select which of the clinical and CMR parameters could predict death and which could not. Second, machine learning was used to build an algorithm based on the important parameters identified in step one, assigning a different emphasis to each to create the best prediction. Patients were then given a score of 0 (low risk) to 10 (high risk) for the probability of death within 10 years.<\/p>

Finally, Dr. Pezel explained that stress CMR is a technique that, in addition to not using radiation, allowed imaging information to be combined with clinical data in the algorithm, and that "it could be a useful tool to help prevent cardiovascular disease and cardiac death." sudden in patients with symptoms or cardiovascular risk factors\u201d.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/i><\/span>\n\t\t\t\t\t\t\t\t<\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t BIBLIOGRAPHY<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t

HEALTH IT ANALYTICS<\/strong><\/p>

https:\/\/healthitanalytics.com\/news\/artificial-intelligence-predicts-coronary-artery-disease-mortality<\/a><\/p>

EUREKALERT<\/strong><\/p>

https:\/\/www.eurekalert.org\/news-releases\/937287<\/a><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"

Estudio reciente fue capaz de pronosticar la probabilidad de muerte en pacientes con enfermedad arterial coronaria, gracias al uso de Inteligencia Artificial (IA). El estudio fue presentado en diciembre de 2021, en el congreso EuroEcho 2021, un evento de la Sociedad Europea de Cardiolog\u00eda (ESC). El estudio mostr\u00f3 un pron\u00f3stico m\u00e1s preciso de que pacientes que padecen enfermedad arterial coronaria mueran en un plazo de diez a\u00f1os. El estudio incluy\u00f3 informaci\u00f3n de im\u00e1genes del coraz\u00f3n medidas por resonancia magn\u00e9tica cardiovascular (RMC) de estr\u00e9s, a diferencia de m\u00e9todos donde solo se basan en datos cl\u00ednicos. \u201cEste es el primer estudio que muestra que el aprendizaje autom\u00e1tico con par\u00e1metros cl\u00ednicos m\u00e1s la RMC de estr\u00e9s puede predecir con mucha precisi\u00f3n el riesgo de muerte\u201d, explic\u00f3 Dr. Theo Pezel, del Hospital Johns Hopkins y autor del estudio. Los hallazgos mostraron que pacientes con dolor tor\u00e1cico, disnea o factores de riesgo de enfermedad cardiovascular deben someterse a un examen de RMC de estr\u00e9s para calcular su puntuaci\u00f3n. Esto permitir\u00e1 que los profesionales brinden un seguimiento m\u00e1s intenso y consejos sobre ejercicios, dieta y otros h\u00e1bitos para las personas que lo requieren. El estudio incluy\u00f3 informaci\u00f3n de 31,752 pacientes remitidos para RMC de estr\u00e9s entre 2008 y 2018, en Par\u00eds, Francia. La edad promedio fue de 64 a\u00f1os y el 66% eran hombres. Algunos s\u00edntomas inclu\u00edan dolor en el pecho, dificultad para respirar al ejercitarse, o riesgo de enfermedad cardiovascular, pero sin s\u00edntomas. Los riesgos altos se determinaron como hipertensi\u00f3n, diabetes, dislipidemia y tabaquismo. Los pacientes fueron seguidos durante seis a\u00f1os y las muertes se obtuvieron del registro nacional de defunci\u00f3n de Francia, en dicho periodo fallecieron 2,679 pacientes, es decir el 8.4%. \u201cEl aprendizaje autom\u00e1tico se llev\u00f3 a cabo en dos pasos. En primer lugar, se utiliz\u00f3 para seleccionar cu\u00e1les de los par\u00e1metros cl\u00ednicos y de RMC pod\u00edan predecir la muerte y cu\u00e1les no. En segundo lugar, se utiliz\u00f3 el aprendizaje autom\u00e1tico para construir un algoritmo basado en los par\u00e1metros importantes identificados en el paso uno, asignando un \u00e9nfasis diferente a cada uno para crear la mejor predicci\u00f3n. Luego, a los pacientes se les dio una puntuaci\u00f3n de 0 (riesgo bajo) a 10 (riesgo alto) para la probabilidad de muerte dentro de los 10 a\u00f1os\u201d, explica el comunicado que muestra los hallazgos. Finalmente, el Dr. Pezel explic\u00f3 que la RMC de estr\u00e9s es una t\u00e9cnica que adem\u00e1s de no usar radiaci\u00f3n permiti\u00f3 combinar informaci\u00f3n de im\u00e1genes con datos cl\u00ednicos en el algoritmo, y que \u201cpodr\u00eda ser una herramienta \u00fatil para ayudar a prevenir enfermedades cardiovasculares y muerte card\u00edaca s\u00fabita en pacientes con s\u00edntomas o factores de riesgo cardiovasculares\u201d. BIBLIOGRAF\u00cdA HEALTH IT ANALYTICS https:\/\/healthitanalytics.com\/news\/artificial-intelligence-predicts-coronary-artery-disease-mortality EUREKALERT https:\/\/www.eurekalert.org\/news-releases\/937287<\/p>","protected":false},"author":1,"featured_media":23228,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3399,156,3393,160,1418],"tags":[145],"class_list":["post-23220","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-analitica","category-big-data","category-inteligencia-artificial-y-ciencia","category-noticias","category-resena-de-publicaciones-cientificas","tag-noticias"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/23220","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/comments?post=23220"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/23220\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/23228"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=23220"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=23220"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=23220"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}