{"id":22661,"date":"2021-11-16T08:47:49","date_gmt":"2021-11-16T14:47:49","guid":{"rendered":"https:\/\/saluddigital.com\/?p=22661"},"modified":"2025-10-21T11:29:52","modified_gmt":"2025-10-21T17:29:52","slug":"estudio-sobre-la-prediccion-a-traves-de-inteligencia-artificial-de-la-supervivencia-renal-entre-receptores-de-trasplante-de-rinon","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/big-data\/estudio-sobre-la-prediccion-a-traves-de-inteligencia-artificial-de-la-supervivencia-renal-entre-receptores-de-trasplante-de-rinon\/","title":{"rendered":"Study on the prediction through Artificial Intelligence of renal survival among kidney transplant recipients"},"content":{"rendered":"
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The Lancet published the study: \u201cDynamic prediction of renal survival among deeply phenotyped kidney transplant recipients using artificial intelligence: an observational, international, multi-cohort study.\u201d <\/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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Renal allograft failure contributes to the number of people with end-stage renal disease. Approximately more than 7 million worldwide live with this condition, which is why a prediction model for renal allograft survival adapted to clinical practice is necessary, such as the one presented in the study published in The Lancet.<\/p>

The aim of the study was to develop a dynamic artificial intelligence approach to improve risk stratification for kidney transplant recipients. The method used was the generation of continuously refined survival predictions using clinical data updates.<\/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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As an observational study, the researchers used data from adult kidney transplant recipients at 18 specialized academic transplant centers in Europe, the United States, and South America. In addition, another cohort of patients from six randomized controlled trials was included.<\/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 four cohorts contained a total of 13,608 kidney recipients from 18 centers in seven countries, corresponding to 89,328 patient-years.<\/p>

The data collected were the following: clinical, histological, and immunological variables, and repeated measurements of estimated glomerular filtration rate (eGFR) and proteinuria (measured using the proteinuria\/creatininuria ratio). In this way it was possible to develop a dynamic prediction system based on clinical evaluations and repeated measurements with an Artificial Intelligence approach.<\/p>

"Due to its dynamic design, this model can be continuously updated and has value as a bedside tool that could refine clinicians' prognostic judgments in daily practice, thus improving precision medicine in the transplant setting," explains Dr. study.<\/p>

Check the results and the full study at the following link: https:\/\/www.thelancet.com\/journals\/landig\/article\/PIIS2589-7500(21)00209-0\/fulltext<\/a><\/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

THE LANCET<\/strong><\/p>

https:\/\/www.thelancet.com\/journals\/landig\/article\/PIIS2589-7500(21)00209-0\/fulltext<\/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":"

The Lancet public\u00f3 el estudio: \u201cPredicci\u00f3n din\u00e1mica de la supervivencia renal entre receptores de trasplante de ri\u00f1\u00f3n profundamente fenotipados utilizando inteligencia artificial: un estudio observacional, internacional, de m\u00faltiples cohortes\u201d. La insuficiencia del aloinjerto renal contribuye al n\u00famero de personas que padecen de enfermedad renal en etapa terminal. Aproximadamente m\u00e1s de 7 millones en todo el mundo viven con esta condici\u00f3n, por ello es necesario un modelo de predicci\u00f3n para la supervivencia del aloinjerto renal adaptado a la pr\u00e1ctica cl\u00ednica, como el que presenta el estudio publicado en The Lancet. El objetivo del estudio fue desarrollar un enfoque din\u00e1mico de inteligencia artificial para mejorar la estratificaci\u00f3n del riesgo para los receptores de trasplantes de ri\u00f1\u00f3n. El m\u00e9todo utilizado fue la generaci\u00f3n de predicciones de supervivencia continuamente refinadas utilizando actualizaciones de datos cl\u00ednicos. Al tratarse de un estudio observacional, los investigadores utilizaron datos de receptores adulto de trasplantes de ri\u00f1\u00f3n en 18 centro acad\u00e9micos especializados en trasplantes en Europa, Estados Unidos y Sudam\u00e9rica.  Adem\u00e1s, se incluy\u00f3 otra cohorte de pacientes de seis ensayos controlados aleatorios. Las cuatro cohortes conten\u00edan un total de 13,608 receptores de ri\u00f1\u00f3n de 18 centros en siete pa\u00edses, correspondientes a 89,328 pacientes-a\u00f1o. Los datos recolectados fueron los siguientes: variables cl\u00ednicas, histol\u00f3gicas, inmunol\u00f3gicas y mediciones repetidas de la tasa de filtraci\u00f3n glomerular estimada (eGFR) y proteinuria (medida usando la relaci\u00f3n proteinuria \/ creatininuria). De esta forma fue posible desarrollar un sistema de predicci\u00f3n din\u00e1mica basado en evaluaciones cl\u00ednicas y mediciones repetidas con un enfoque de Inteligencia Artificial. “Debido a su dise\u00f1o din\u00e1mico, este modelo se puede actualizar continuamente y tiene valor como una herramienta de cabecera que podr\u00eda refinar los juicios pron\u00f3sticos de los m\u00e9dicos en la pr\u00e1ctica diaria, mejorando as\u00ed la medicina de precisi\u00f3n en el entorno del trasplante”, explica el estudio. Consulta los resultados y el estudio completo en el siguiente enlace: https:\/\/www.thelancet.com\/journals\/landig\/article\/PIIS2589-7500(21)00209-0\/fulltext BIBLIOGRAF\u00cdA THE LANCET https:\/\/www.thelancet.com\/journals\/landig\/article\/PIIS2589-7500(21)00209-0\/fulltext<\/p>","protected":false},"author":1,"featured_media":22663,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[156,3393,160],"tags":[145],"class_list":["post-22661","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-big-data","category-inteligencia-artificial-y-ciencia","category-noticias","tag-noticias"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/22661","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=22661"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/22661\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/22663"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=22661"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=22661"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=22661"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}