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

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

Abstract Background The identification of factors associated with Intensive Care Unit (ICU) mortality and derived clinical phenotypes in COVID-19 patients could help for a more tailored approach to clinical decision-making that improves prognostic outcomes. Methods Prospective, multicenter, observat...

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Bibliographic Details
Main Authors: Alejandro Rodríguez, Manuel Ruiz-Botella, Ignacio Martín-Loeches, María Jimenez Herrera, Jordi Solé-Violan, Josep Gómez, María Bodí, Sandra Trefler, Elisabeth Papiol, Emili Díaz, Borja Suberviola, Montserrat Vallverdu, Eric Mayor-Vázquez, Antonio Albaya Moreno, Alfonso Canabal Berlanga, Miguel Sánchez, María del Valle Ortíz, Juan Carlos Ballesteros, Lorena Martín Iglesias, Judith Marín-Corral, Esther López Ramos, Virginia Hidalgo Valverde, Loreto Vidaur Vidaur Tello, Susana Sancho Chinesta, Francisco Javier Gonzáles de Molina, Sandra Herrero García, Carmen Carolina Sena Pérez, Juan Carlos Pozo Laderas, Raquel Rodríguez García, Angel Estella, Ricard Ferrer, on behalf of COVID-19 SEMICYUC Working Group
Format: Article
Language:English
Published: BMC 2021-02-01
Series:Critical Care
Subjects:
Severe SARS-CoV-2 infection
Phenotypes
Risk factors
Prognosis
Machine learning
Online Access:https://doi.org/10.1186/s13054-021-03487-8

Internet

https://doi.org/10.1186/s13054-021-03487-8

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