Repositori URV

Autor según el artículo: Rodríguez A; Ruiz-Botella M; Martín-Loeches I; Jimenez Herrera M; Solé-Violan J; Gómez J; Bodí M; Trefler S; Papiol E; Díaz E; Suberviola B; Vallverdu M; Mayor-Vázquez E; Albaya Moreno A; Canabal Berlanga A; Sánchez M; del Valle Ortíz M; Ballesteros JC; Martín Iglesias L; Marín-Corral J; López Ramos E; Hidalgo Valverde V; Vidaur Tello LV; Sancho Chinesta S; Gonzáles de Molina FJ; Herrero García S; Sena Pérez CC; Pozo Laderas JC; Rodríguez García R; Estella A; Ferrer R; Loza A; Zapata DM; Torres ID; Cuadros SI; Nuñez MR; Pérez MLC; Ramos JG; Casas AV; Cantón ML

Departamento: Infermeria

Autor/es de la URV: Bodi Saera, Maria Amparo / Gómez Alvarez, Josep / Jiménez Herrera, María Francisca / Rodríguez Oviedo, Alejandro Hugo / TREFLER CRESPO, SANDRA INES

Palabras clave: Validation Severe sars-cov-2 infection Risk factors Prognosis Phenotypes Machine learning

Resumen: © 2021, The Author(s). 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.

Áreas temáticas: Saúde coletiva Nutrição Medicina veterinaria Medicina iii Medicina ii Medicina i Interdisciplinar Farmacia Engenharias iv Enfermagem Educação física Critical care medicine Critical care and intensive care medicine Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciência de alimentos Biotecnología

Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/

Direcció de correo del autor: josep.gomez@urv.cat alejandrohugo.rodriguez@urv.cat maria.jimenez@urv.cat mariaamparo.bodi@urv.cat mariaamparo.bodi@urv.cat

Identificador del autor: 0000-0002-0573-7621 0000-0001-8828-5984 0000-0003-2599-3742 0000-0001-7652-8379 0000-0001-7652-8379

Fecha de alta del registro: 2024-07-27

Versión del articulo depositado: info:eu-repo/semantics/publishedVersion

Enlace a la fuente original: https://pubmed.ncbi.nlm.nih.gov/33588914/#affiliation-5

URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/

Referencia al articulo segun fuente origial: Critical Care. 25 (1): 63-

Referencia de l'ítem segons les normes APA: Rodríguez A; Ruiz-Botella M; Martín-Loeches I; Jimenez Herrera M; Solé-Violan J; Gómez J; Bodí M; Trefler S; Papiol E; Díaz E; Suberviola B; Vallverdu (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. Critical Care, 25(1), 63-. DOI: 10.1186/s13054-021-03487-8

DOI del artículo: 10.1186/s13054-021-03487-8

Entidad: Universitat Rovira i Virgili

Año de publicación de la revista: 2021

Tipo de publicación: Journal Publications