{"id":16581,"date":"2021-02-17T12:26:20","date_gmt":"2021-02-17T11:26:20","guid":{"rendered":"https:\/\/idibell.cat\/?p=16581"},"modified":"2021-02-17T14:20:50","modified_gmt":"2021-02-17T13:20:50","slug":"la-intelligencia-artificial-ajuda-a-predir-les-recaigudes-dels-pacients-recuperats-dun-infart-o-angina-de-pit","status":"publish","type":"post","link":"https:\/\/idibell.cat\/2021\/02\/la-intelligencia-artificial-ajuda-a-predir-les-recaigudes-dels-pacients-recuperats-dun-infart-o-angina-de-pit\/","title":{"rendered":"La intel\u00b7lig\u00e8ncia artificial ajuda a predir les recaigudes dels pacients recuperats d\u2019un infart o angina de pit"},"content":{"rendered":"
Noves eines d’intel\u00b7lig\u00e8ncia artificial<\/strong> permetran predir les possibilitats que, una persona que hagi patit un infart de miocardi o angina de pit, mori o pateixi un nou episodi greu en l’any seg\u00fcent despr\u00e9s de l’alta. Aquestes s\u00f3n les conclusions d’un estudi multic\u00e8ntric internacional que s’acaba de publicar a la revista The Lancet<\/a><\/strong><\/em>.<\/p>\n A l’estudi, liderat per especialistes de la Universitat de Tor\u00ed<\/strong> (It\u00e0lia), hi han participat tamb\u00e9 investigadors de l’IDIBELL<\/strong><\/a> i l’Hospital Universitari de Bellvitge<\/strong><\/a>.<\/p>\n Els pacients que s’han recuperat d’una s\u00edndrome coron\u00e0ria aguda tenen un major risc de tornar a patir un nou infart, hemorr\u00e0gies, o altres complicacions. Per prevenir-ho, sovint reben ter\u00e0pia antiplaquet\u00e0ria dual, que redueix el risc d’isqu\u00e8mia miocard\u00edaca i augmenta el d’hemorr\u00e0gia. Aix\u00f2 fa sigui molt important saber quin risc concret t\u00e9 cada pacient, per aix\u00ed poder individualitzar els tractaments<\/strong>.<\/p>\n Els autors de l’estudi van crear i testar inicialment quatre nous models d’estratificaci\u00f3 de riscos a trav\u00e9s del processament de dades amb sistemes d’aprenentatge autom\u00e0tic. Van introduir en aquests models un total de 25 variables cl\u00edniques, terap\u00e8utiques, angiogr\u00e0fiques i procedimentals extretes de 19.826 pacients<\/strong> ingressats entre el 2003 i el 2016 a diversos centres d’arreu del m\u00f3n \u2013entre els quals l’Hospital Universitari de Bellvitge\u2013, juntament amb les dades de l’evoluci\u00f3 posterior dels pacients.<\/p>\n El model m\u00e9s efectiu, anomenat PRAISE<\/strong>, va ser aplicat despr\u00e9s a un altre conjunt de 3.444 pacients i es va comparar amb les eines de determinaci\u00f3 de riscos utilitzades fins ara. Els resultats finals han constatat que la nova eina basada en la intel\u00b7lig\u00e8ncia artificial prediu molt millor la mortalitat, el risc d’hemorr\u00e0gia i el risc de patir un nou infart.<\/p>\n El Dr. Albert Ariza<\/strong>, un dels autors de l’estudi, destaca que “l’avantatge dels m\u00e8todes d’aprenentatge autom\u00e0tic \u00e9s que apliquen algoritmes a conjunts extensos i variats de dades, captant relacions entre aquestes dades que sistemes m\u00e9s simples no poden captar<\/em>“. Ariza, que \u00e9s coordinador de la Unitat de Cures Intensives Cardiol\u00f2giques del Servei de Cardiologia de l’Hospital de Bellvitge i investigador de l’IDIBELL, subratlla que es tracta d’eines “que s’estan comen\u00e7ant a utilitzar de forma progressiva i ja han demostrat la seva utilitat en algun altre \u00e0mbit cl\u00ednic; esperem que aviat estiguin a l’abast de tots els cardi\u00f2legs per ajudar-nos a la presa de decisions a partir de l’avaluaci\u00f3 de riscos personalitzada<\/em>“.<\/p>\n Ara, l’algoritme haur\u00e0 de ser validat en futurs estudis on els pacients rebin el tractament que recomani PRAISE, especialment pel que fa a la durada de la ter\u00e0pia antiplaquet\u00e0ria dual.<\/p>\n","protected":false},"excerpt":{"rendered":" L\u2019algoritme, basat en dades de prop de 20.000 pacients, permetr\u00e0 en un futur decidir la millor opci\u00f3 terap\u00e8utica per a cada persona per evitar nous episodis d\u2019insufici\u00e8ncia card\u00edaca<\/p>\n","protected":false},"author":8,"featured_media":16589,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"default","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"default","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center 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