RT Journal Article
T1 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.
A1 Rodríguez, Alejandro
A1 Ruiz-Botella, Manuel
A1 Martín-Loeches, Ignacio
A1 Jimenez Herrera, María
A1 Solé-Violan, Jordi
A1 Gómez, Josep
A1 Bodí, María
A1 Trefler, Sandra
A1 Papiol, Elisabeth
A1 Díaz, Emili
A1 Suberviola, Borja
A1 Vallverdu, Montserrat
A1 Mayor-Vázquez, Eric
A1 Albaya Moreno, Antonio
A1 Canabal Berlanga, Alfonso
A1 Sánchez, Miguel
A1 Del Valle Ortíz, María
A1 Ballesteros, Juan Carlos
A1 Martín Iglesias, Lorena
A1 Marín-Corral, Judith
A1 López Ramos, Esther
A1 Hidalgo Valverde, Virginia
A1 Vidaur Tello, Loreto Vidaur
A1 Sancho Chinesta, Susana
A1 Gonzáles de Molina, Francisco Javier
A1 Herrero García, Sandra
A1 Sena Pérez, Carmen Carolina
A1 Pozo Laderas, Juan Carlos
A1 Rodríguez García, Raquel
A1 Estella, Angel
A1 Ferrer, Ricard
A1 COVID-19 SEMICYUC Working Group, 
K1 Machine learning
K1 Phenotypes
K1 Prognosis
K1 Risk factors
K1 Severe SARS-CoV-2 infection
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, multicenter, observational study of critically ill patients with confirmed COVID-19 disease and acute respiratory failure admitted from 63 ICUs in Spain. The objective was to utilize an unsupervised clustering analysis to derive clinical COVID-19 phenotypes and to analyze patient's factors associated with mortality risk. Patient features including demographics and clinical data at ICU admission were analyzed. Generalized linear models were used to determine ICU morality risk factors. The prognostic models were validated and their performance was measured using accuracy test, sensitivity, specificity and ROC curves. The database included a total of 2022 patients (mean age 64 [IQR 5-71] years, 1423 (70.4%) male, median APACHE II score (13 [IQR 10-17]) and SOFA score (5 [IQR 3-7]) points. The ICU mortality rate was 32.6%. Of the 3 derived phenotypes, the A (mild) phenotype (537; 26.7%) included older age ( 65 years), high severity of illness and a higher likelihood of development shock. Crude ICU mortality was 20.3%, 25% and 45.4% for A, B and C phenotype respectively. The ICU mortality risk factors and model performance differed between whole population and phenotype classifications. 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.
YR 2021
FD 2021-02-15
LK http://hdl.handle.net/10668/17183
UL http://hdl.handle.net/10668/17183
LA en
DS RISalud
RD Apr 11, 2025