Exposing and Overcoming Limitations of Clinical Laboratory Tests in COVID-19 by Adding Immunological Parameters; A Retrospective Cohort Analysis and Pilot Study
Sánchez-Montalvá, Adrián (Hospital Universitari Vall d'Hebron)
Álvarez-Sierra, Daniel (Hospital Universitari Vall d'Hebron)
Martínez Gallo, Mónica (Hospital Universitari Vall d'Hebron)
Perurena-Prieto, Janire (Hospital Universitari Vall d'Hebron)
Arrese-Muñoz, Iria (Hospital Universitari Vall d'Hebron)
Ruiz-Rodriguez, Juan Carlos (Hospital Universitari Vall d'Hebron)
Espinosa-Pereiro, Juan (Hospital Universitari Vall d'Hebron)
Bosch-Nicolau, Pau (Hospital Universitari Vall d'Hebron)
Martínez Gómez, Xavier (Hospital Universitari Vall d'Hebron)
Antón, Andrés (Hospital Universitari Vall d'Hebron)
Martínez-Valle, Ferran (Hospital Universitari Vall d'Hebron)
Riveiro Barciela, Mar (Hospital Universitari Vall d'Hebron)
Blanco-Grau, Albert (Hospital Universitari Vall d'Hebron)
Rodríguez Frías, Francisco (Hospital Universitari Vall d'Hebron)
Castellano-Escuder, Pol (Universitat de Barcelona)
Poyatos Cantón, E (Hospital Universitari de Bellvitge)
Bas, Jordi (Hospital Universitari de Bellvitge)
Martínez Cáceres, Eva María (Institut Germans Trias i Pujol. Hospital Universitari Germans Trias i Pujol)
Sánchez, Alex (Hospital Universitari Vall d'Hebron)
Zurera Egea, Coral (Institut Germans Trias i Pujol. Hospital Universitari Germans Trias i Pujol)
Teniente Serra, Aina (Institut Germans Trias i Pujol. Hospital Universitari Germans Trias i Pujol)
Hernández-González, Manuel (Hospital Universitari Vall d'Hebron)
Pujol-Borrell, Ricardo (Hospital Universitari Vall d'Hebron)
Universitat Autònoma de Barcelona

Fecha:	2022
Resumen:	Two years since the onset of the COVID-19 pandemic no predictive algorithm has been generally adopted for clinical management and in most algorithms the contribution of laboratory variables is limited. To measure the predictive performance of currently used clinical laboratory tests alone or combined with clinical variables and explore the predictive power of immunological tests adequate for clinical laboratories. Methods: Data from 2,600 COVID-19 patients of the first wave of the pandemic in the Barcelona area (exploratory cohort of 1,579, validation cohorts of 598 and 423 patients) including clinical parameters and laboratory tests were retrospectively collected. 28-day survival and maximal severity were the main outcomes considered in the multiparametric classical and machine learning statistical analysis. A pilot study was conducted in two subgroups (n=74 and n=41) measuring 17 cytokines and 27 lymphocyte phenotypes respectively. 1) Despite a strong association of clinical and laboratory variables with the outcomes in classical pairwise analysis, the contribution of laboratory tests to the combined prediction power was limited by redundancy. Laboratory variables reflected only two types of processes: inflammation and organ damage but none reflected the immune response, one major determinant of prognosis. 2) Eight of the thirty variables: age, comorbidity index, oxygen saturation to fraction of inspired oxygen ratio, neutrophil-lymphocyte ratio, C-reactive protein, aspartate aminotransferase/alanine aminotransferase ratio, fibrinogen, and glomerular filtration rate captured most of the combined statistical predictive power. 3) The interpretation of clinical and laboratory variables was moderately improved by grouping them in two categories i. e. , inflammation related biomarkers and organ damage related biomarkers; Age and organ damage-related biomarker tests were the best predictors of survival, and inflammatory-related ones were the best predictors of severity. 4) The pilot study identified immunological tests (CXCL10, IL-6, IL-1RA and CCL2), that performed better than most currently used laboratory tests. Laboratory tests for clinical management of COVID 19 patients are valuable but limited predictors due to redundancy; this limitation could be overcome by adding immunological tests with independent predictive power. Understanding the limitations of tests in use would improve their interpretation and simplify clinical management but a systematic search for better immunological biomarkers is urgent and feasible.
Ayudas:	Agencia Estatal de Investigación PID2019-104830RB-I00
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Acute phase reactants ; Chemokines ; Clinical laboratory tests ; CXCL10 ; Cytokines ; Flow cytometry ; Predictive risk-profile ; SARS-CoV-2 infection
Publicado en:	Frontiers in immunology, Vol. 13 (june 2022) , ISSN 1664-3224

DOI: 10.3389/fimmu.2022.902837
PMID: 35844497

19 p, 7.0 MB

El registro aparece en las colecciones:
Documentos de investigación > Documentos de los grupos de investigación de la UAB > Centros y grupos de investigación (producción científica) > Ciencias de la salud y biociencias > Institut d'Investigació en Ciencies de la Salut Germans Trias i Pujol (IGTP)
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