Deploying unsupervised clustering analysis to derive clinical phenotypes and risk factors associated with mortality risk in 2,022 critically ill patients with COVID-19 in Spain

• Published in: Critical Care 2021
• Main Authors: Rodríguez, Alejandro, Manuel Ruiz Botella, Matín-Loeches, Ignacio, María Jiménez Herrera, Solé-Violan, Jordi, Gómez, Josep, Bodí, María, Trefler, Sandra, Papiol, Elisabeth, Díaz, Emili, Suberviola, Borja, Vallverdú, Montserrat, Mayor-Vázquez, Eric, Moreno, Antonio Albaya, Alfonso Canabal Berlanga, Sánchez, Miguel, María del Valle Ortíz, Ballesteros, Juan Carlos, Lorena Martín Iglesias, Marín-Corral, Judith, Esther López Ramos, Virgina Hidalgo Valverde, Tello, Loreto Vidaur, Susana Sancho Chinesta, Francisco Javier González de Molina, Sandra Herrero García, Sena Perez, Carmen Carolina, Pozo Laderas, Juan Carlos, Raquel Rodríguez García, Angel Estella, Ferrer, Ricard
• Format: Text Resource
• Language: English
• Subjects: Coronaviruses; COVID-19; Demography; Generalized linear models; Genotype & phenotype; Intensive care; Machine learning; Mortality; Patients; Risk factors
• DOI: 10.21203/rs.3.rs-125422/v2

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 Intensive Care Units(ICU) 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 2,022 patients (mean age 64[IQR5-71] years, 1423(70.4%) male, median APACHE II score (13[IQR10-17]) and SOFA score (5[IQR3-7]) 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. 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.