151455 20251017144613.0 doi 10.1186/s13054-021-03487-8 sideral 143124 ART-2021-143124 eng Rodríguez, Alejandro 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 2021 Access copy available to the general public Unrestricted 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, 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. Results 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), fewer abnormal laboratory values and less development of complications, B (moderate) phenotype (623, 30.8%) had similar characteristics of A phenotype but were more likely to present shock. The C (severe) phenotype was the most common (857; 42.5%) and was characterized by the interplay of 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. Conclusion 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. info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es 19.344 2021 CRITICAL CARE MEDICINE 4 / 35 = 0.114 2021 Q1 T1 3.218 2021 Critical Care and Intensive Care Medicine 2021 Q1 14.2 2021 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Ruiz-Botella, Manuel Martín-Loeches, Ignacio Jimenez Herrera, María Solé-Violan, Jordi Gómez, Josep Bodí, María Trefler, Sandra Papiol, Elisabeth Díaz, Emili Suberviola, Borja Vallverdu, Montserrat Mayor-Vázquez, Eric Albaya Moreno, Antonio Canabal Berlanga, Alfonso Sánchez, Miguel del Valle Ortíz, María Ballesteros, Juan Carlos Martín Iglesias, Lorena Marín-Corral, Judith López Ramos, Esther Hidalgo Valverde, Virginia Vidaur Tello, Loreto Vidaur Sancho Chinesta, Susana Gonzáles de Molina, Francisco Javier Herrero García, Sandra Sena Pérez, Carmen Carolina Pozo Laderas, Juan Carlos Rodríguez García, Raquel Estella, Angel Ferrer, Ricard Loza, Ana Matallana Zapata, Diego Díaz Torres, Isabel Ibañez Cuadros, Sonia Recuerda Nuñez, María Carmona Pérez, Maria Luz Gómez Ramos, Jorge Villares Casas, Alba Cantón, María Luisa González Contreras, José Javier Pérez Chomón, Helena Alvarez Chicote, Nerissa Sousa González, Alberto De Alba Aparicio, María Moreno Cano, Sara Jorge García, Ruth Sánchez Montori, Laura Abanses Moreno, Paula Mayordomo García, Carlos Mallor Bonet, Tomás Omedas Bonafonte, Paula Franquesa Gonzalez, Enric Bueno Vidales, Nestor Ocabo Buil, Paula Serón Arbeloa, Carlos Universidad de Zaragoza (orcid)0000-0002-8068-5016 1007 610 Universidad de Zaragoza Dpto. Medicina, Psiqu. y Derm. Area Medicina 25 (2021), 63 [15 pp.] Crit. care Critical care 1364-8535 2285946 http://zaguan.unizar.es/record/151455/files/texto_completo.pdf Versión publicada 2407849 http://zaguan.unizar.es/record/151455/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:151455 articulos driver 2025-10-17-14:18:10 ARTICLE