Critical Care (Feb 2021)

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

  • 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

Journal volume & issue
Vol. 25, no. 1
pp. 1 – 15


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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, 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), 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.