Deploying unsupervised clustering analysis to derive clinical phenotypes and risk factors associated with mortality risk in 2022 critically ill patients with COVID-19 in Spain.
Alejandro RodríguezManuel Ruiz-BotellaIgnacio Martín-LoechesMaría Jimenez HerreraJordi Solé-ViolanJosep GómezMaría BodíSandra TreflerElisabeth PapiolEmili DíazBorja SuberviolaMontserrat VallverduEric Mayor-VázquezAntonio Albaya MorenoAlfonso Canabal BerlangaMiguel SánchezMaría Del Valle OrtízJuan Carlos BallesterosLorena Martín IglesiasJudith Marín-CorralEsther López RamosVirginia Hidalgo ValverdeLoreto Vidaur Vidaur TelloSusana Sancho ChinestaFrancisco Javier Gonzáles de MolinaSandra Herrero GarcíaCarmen Carolina Sena PérezJuan Carlos Pozo LaderasRaquel Rodríguez GarcíaAngel EstellaRicard Ferrernull nullPublished in: Critical care (London, England) (2021)
The presented machine learning model identified three clinical phenotypes that significantly correlated with host-response patterns and ICU mortality. Different risk factors across the whole population and clinical phenotypes were observed which may limit the application of a "one-size-fits-all" model in practice.