CXCL10 levels at hospital admission predict COVID-19 outcome: hierarchical assessment of 53 putative inflammatory biomarkers in an observational study.
Biomarkers
/ blood
C-Reactive Protein
/ metabolism
COVID-19
/ blood
Chemokine CXCL10
/ blood
Comorbidity
Coronary Artery Disease
/ blood
Creatine
/ blood
Diabetes Mellitus
/ blood
Female
Hospitalization
Humans
Hypertension
/ blood
Immunity, Humoral
Immunity, Innate
Inflammation
Intensive Care Units
L-Lactate Dehydrogenase
/ blood
Leukocyte Count
Lymphocytes
/ immunology
Male
Middle Aged
Neutrophils
/ immunology
Prognosis
Prospective Studies
Retrospective Studies
SARS-CoV-2
Severity of Illness Index
Survival Analysis
Biomarkers
COVID-19 severity predictors
CXCL10
Decision tree
Journal
Molecular medicine (Cambridge, Mass.)
ISSN: 1528-3658
Titre abrégé: Mol Med
Pays: England
ID NLM: 9501023
Informations de publication
Date de publication:
18 10 2021
18 10 2021
Historique:
received:
07
06
2021
accepted:
02
10
2021
entrez:
19
10
2021
pubmed:
20
10
2021
medline:
4
11
2021
Statut:
epublish
Résumé
Host inflammation contributes to determine whether SARS-CoV-2 infection causes mild or life-threatening disease. Tools are needed for early risk assessment. We studied in 111 COVID-19 patients prospectively followed at a single reference Hospital fifty-three potential biomarkers including alarmins, cytokines, adipocytokines and growth factors, humoral innate immune and neuroendocrine molecules and regulators of iron metabolism. Biomarkers at hospital admission together with age, degree of hypoxia, neutrophil to lymphocyte ratio (NLR), lactate dehydrogenase (LDH), C-reactive protein (CRP) and creatinine were analysed within a data-driven approach to classify patients with respect to survival and ICU outcomes. Classification and regression tree (CART) models were used to identify prognostic biomarkers. Among the fifty-three potential biomarkers, the classification tree analysis selected CXCL10 at hospital admission, in combination with NLR and time from onset, as the best predictor of ICU transfer (AUC [95% CI] = 0.8374 [0.6233-0.8435]), while it was selected alone to predict death (AUC [95% CI] = 0.7334 [0.7547-0.9201]). CXCL10 concentration abated in COVID-19 survivors after healing and discharge from the hospital. CXCL10 results from a data-driven analysis, that accounts for presence of confounding factors, as the most robust predictive biomarker of patient outcome in COVID-19.
Sections du résumé
BACKGROUND
Host inflammation contributes to determine whether SARS-CoV-2 infection causes mild or life-threatening disease. Tools are needed for early risk assessment.
METHODS
We studied in 111 COVID-19 patients prospectively followed at a single reference Hospital fifty-three potential biomarkers including alarmins, cytokines, adipocytokines and growth factors, humoral innate immune and neuroendocrine molecules and regulators of iron metabolism. Biomarkers at hospital admission together with age, degree of hypoxia, neutrophil to lymphocyte ratio (NLR), lactate dehydrogenase (LDH), C-reactive protein (CRP) and creatinine were analysed within a data-driven approach to classify patients with respect to survival and ICU outcomes. Classification and regression tree (CART) models were used to identify prognostic biomarkers.
RESULTS
Among the fifty-three potential biomarkers, the classification tree analysis selected CXCL10 at hospital admission, in combination with NLR and time from onset, as the best predictor of ICU transfer (AUC [95% CI] = 0.8374 [0.6233-0.8435]), while it was selected alone to predict death (AUC [95% CI] = 0.7334 [0.7547-0.9201]). CXCL10 concentration abated in COVID-19 survivors after healing and discharge from the hospital.
CONCLUSIONS
CXCL10 results from a data-driven analysis, that accounts for presence of confounding factors, as the most robust predictive biomarker of patient outcome in COVID-19.
Identifiants
pubmed: 34663207
doi: 10.1186/s10020-021-00390-4
pii: 10.1186/s10020-021-00390-4
pmc: PMC8521494
doi:
Substances chimiques
Biomarkers
0
CXCL10 protein, human
0
Chemokine CXCL10
0
C-Reactive Protein
9007-41-4
L-Lactate Dehydrogenase
EC 1.1.1.27
Creatine
MU72812GK0
Types de publication
Journal Article
Observational Study
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
129Subventions
Organisme : Ministero della Salute
ID : COVID-2020-12371617
Investigateurs
Nicola Farina
(N)
Luigi De Filippo
(L)
Marco Battista
(M)
Domenico Grosso
(D)
Francesca Gorgoni
(F)
Carlo Di Biase
(C)
Alessio Grazioli Moretti
(AG)
Lucio Granata
(L)
Filippo Bonaldi
(F)
Giulia Bettinelli
(G)
Elena Delmastro
(E)
Damiano Salvato
(D)
Giulia Magni
(G)
Monica Avino
(M)
Paolo Betti
(P)
Romina Bucci
(R)
Iulia Dumoa
(I)
Simona Bossolasco
(S)
Federica Morselli
(F)
Informations de copyright
© 2021. The Author(s).
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