External validation of unsupervised COVID-19 clinical phenotypes and their prognostic impact
Creators: name: Giacobbe, Daniele Roberto
name: Di Maria, Emilio
name: Tagliafico, Alberto Stefano
name: Bavastro, Martina
name: Trombetta, Carlo Simone
name: Marelli, Cristina
name: Di Meco, Gabriele
name: Cattardico, Greta
name: Mora, Sara
name: Signori, Alessio
name: Vena, Antonio
name: Mikulska, Malgorzata
name: Dentone, Chiara
name: Bruzzone, Bianca
name: Bignotti, Bianca
name: Orsi, Andrea
name: Robba, Chiara
name: Ball, Lorenzo
name: Brunetti, Iole
name: Battaglini, Denise
name: Di Biagio, Antonio
name: Sormani, Maria Pia
name: Pelosi, Paolo
name: Giacomini, Mauro
name: Icardi, Giancarlo
name: Bassetti, Matteo
Publisher: name: Taylor & Francis
Year: 2023
Subjects: subject: Medicine
subject: Genetics
subject: FOS: Biological sciences
subject: Biological Sciences not elsewhere classified
subject: Cancer
subject: Science Policy
subject: Infectious Diseases
subject: FOS: Health sciences
subject: Computational Biology

description: Hospitalized patients with coronavirus disease 2019 (COVID-19) can be classified into different clinical phenotypes based on their demographic, clinical, radiology, and laboratory features. We aimed to validate in an external cohort of hospitalized COVID-19 patients the prognostic value of a previously described phenotyping system (FEN-COVID-19) and to assess the reproducibility of phenotypes development as a secondary analysis. Patients were classified in phenotypes A, B or C according to the severity of oxygenation impairment, inflammatory response, hemodynamic and laboratory tests according to the FEN-COVID-19 method. Overall, 992 patients were included in the study, and 181 (18%), 757 (76%) and 54 (6%) of them were assigned to the FEN-COVID-19 phenotypes A, B, and C, respectively. An association with mortality was observed for phenotype C vs. A (hazard ratio [HR] 3.10, 95% confidence interval [CI] 1.81–5.30, <i>p</i> &lt; 0.001) and for phenotype C vs. B (HR 2.20, 95% CI 1.50–3.23, <i>p</i> &lt; 0.001). A non-statistically significant trend towards higher mortality was also observed for phenotype B vs. A (HR 1.41; 95% CI 0.92–2.15, <i>p</i> = 0.115). By means of cluster analysis, three different phenotypes were also identified in our cohort, with an overall similar gradient in terms of prognostic impact to that observed when patients were assigned to FEN-COVID-19 phenotypes. The prognostic impact of FEN-COVID-19 phenotypes was confirmed in our external cohort, although with less difference in mortality between phenotypes A and B than in the original study. Hospitalized patients with COVID-19 can be classified into different clinical phenotypes based on their demographic, clinical, radiology, and laboratory featuresIn this study, we externally confirmed the prognostic impact of clinical phenotypes previously identified by Gutierrez-Gutierrez and colleagues in a Spanish cohort of hospitalized patients with COVID-19, and the usefulness of their simplified probabilistic model for phenotypes assignmentThis could indirectly support the validity of both phenotype’s development and their extrapolation to other hospitals and countries for management decisions during other possible future viral pandemics Hospitalized patients with COVID-19 can be classified into different clinical phenotypes based on their demographic, clinical, radiology, and laboratory features In this study, we externally confirmed the prognostic impact of clinical phenotypes previously identified by Gutierrez-Gutierrez and colleagues in a Spanish cohort of hospitalized patients with COVID-19, and the usefulness of their simplified probabilistic model for phenotypes assignment This could indirectly support the validity of both phenotype’s development and their extrapolation to other hospitals and countries for management decisions during other possible future viral pandemics