TY  - JOUR
AU  - Rodríguez, Alejandro
AU  - Ruiz-Botella, Manuel
AU  - Martín-Loeches, Ignacio
AU  - Jimenez Herrera, María
AU  - Solé-Violan, Jordi
AU  - Gómez, Josep
AU  - Bodí, María
AU  - Trefler, Sandra
AU  - Papiol, Elisabeth
AU  - Díaz, Emili
AU  - Suberviola, Borja
AU  - Vallverdu, Montserrat
AU  - Mayor-Vázquez, Eric
AU  - Albaya Moreno, Antonio
AU  - Canabal Berlanga, Alfonso
AU  - Sánchez, Miguel
AU  - Del Valle Ortíz, María
AU  - Ballesteros, Juan Carlos
AU  - Martín Iglesias, Lorena
AU  - Marín-Corral, Judith
AU  - López Ramos, Esther
AU  - Hidalgo Valverde, Virginia
AU  - Vidaur Tello, Loreto Vidaur
AU  - Sancho Chinesta, Susana
AU  - Gonzáles de Molina, Francisco Javier
AU  - Herrero García, Sandra
AU  - Sena Pérez, Carmen Carolina
AU  - Pozo Laderas, Juan Carlos
AU  - Rodríguez García, Raquel
AU  - Estella, Angel
AU  - Ferrer, Ricard
AU  - COVID-19 SEMICYUC Working Group
PY  - 2021
DO  - 10.1186/s13054-021-03487-8
UR  - http://hdl.handle.net/10668/17183
T2  - Critical care (London, England)
AB  - 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. Prospective,...
LA  - en
KW  - Machine learning
KW  - Phenotypes
KW  - Prognosis
KW  - Risk factors
KW  - Severe SARS-CoV-2 infection
KW  - Aged
KW  - COVID-19
KW  - Cluster Analysis
KW  - Critical Illness
KW  - Female
KW  - Humans
KW  - Male
KW  - Middle Aged
KW  - Phenotype
KW  - Risk Assessment
KW  - Risk Factors
KW  - Spain
TI  - 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.
TY  - research article
VL  - 25
ER  -