CXCL10 levels at hospital admission predict COVID-19 outcome : hierarchical assessment of 53 putative inflammatory biomarkers in an observational study
© 2021. The Author(s)..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:27 |
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| Enthalten in: |
Molecular medicine (Cambridge, Mass.) - 27(2021), 1 vom: 18. Okt., Seite 129 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Lorè, Nicola I [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 03.11.2021 Date Revised 03.11.2021 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1186/s10020-021-00390-4 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM332035158 |
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| 100 | 1 | |a Lorè, Nicola I |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a CXCL10 levels at hospital admission predict COVID-19 outcome |b hierarchical assessment of 53 putative inflammatory biomarkers in an observational study |
| 264 | 1 | |c 2021 | |
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| 500 | |a Date Completed 03.11.2021 | ||
| 500 | |a Date Revised 03.11.2021 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2021. The Author(s). | ||
| 520 | |a BACKGROUND: Host inflammation contributes to determine whether SARS-CoV-2 infection causes mild or life-threatening disease. Tools are needed for early risk assessment | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Observational Study | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Biomarkers | |
| 650 | 4 | |a COVID-19 severity predictors | |
| 650 | 4 | |a CXCL10 | |
| 650 | 4 | |a Decision tree | |
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| 700 | 1 | |a De Lorenzo, Rebecca |e verfasserin |4 aut | |
| 700 | 1 | |a Rancoita, Paola M V |e verfasserin |4 aut | |
| 700 | 1 | |a Cugnata, Federica |e verfasserin |4 aut | |
| 700 | 1 | |a Agresti, Alessandra |e verfasserin |4 aut | |
| 700 | 1 | |a Benedetti, Francesco |e verfasserin |4 aut | |
| 700 | 1 | |a Bianchi, Marco E |e verfasserin |4 aut | |
| 700 | 1 | |a Bonini, Chiara |e verfasserin |4 aut | |
| 700 | 1 | |a Capobianco, Annalisa |e verfasserin |4 aut | |
| 700 | 1 | |a Conte, Caterina |e verfasserin |4 aut | |
| 700 | 1 | |a Corti, Angelo |e verfasserin |4 aut | |
| 700 | 1 | |a Furlan, Roberto |e verfasserin |4 aut | |
| 700 | 1 | |a Mantegani, Paola |e verfasserin |4 aut | |
| 700 | 1 | |a Maugeri, Norma |e verfasserin |4 aut | |
| 700 | 1 | |a Sciorati, Clara |e verfasserin |4 aut | |
| 700 | 1 | |a Saliu, Fabio |e verfasserin |4 aut | |
| 700 | 1 | |a Silvestri, Laura |e verfasserin |4 aut | |
| 700 | 1 | |a Tresoldi, Cristina |e verfasserin |4 aut | |
| 700 | 0 | |a Bio Angels for COVID-BioB Study Group |e verfasserin |4 aut | |
| 700 | 1 | |a Ciceri, Fabio |e verfasserin |4 aut | |
| 700 | 1 | |a Rovere-Querini, Patrizia |e verfasserin |4 aut | |
| 700 | 1 | |a Di Serio, Clelia |e verfasserin |4 aut | |
| 700 | 1 | |a Cirillo, Daniela M |e verfasserin |4 aut | |
| 700 | 1 | |a Manfredi, Angelo A |e verfasserin |4 aut | |
| 700 | 1 | |a Farina, Nicola |e investigator |4 oth | |
| 700 | 1 | |a De Filippo, Luigi |e investigator |4 oth | |
| 700 | 1 | |a Battista, Marco |e investigator |4 oth | |
| 700 | 1 | |a Grosso, Domenico |e investigator |4 oth | |
| 700 | 1 | |a Gorgoni, Francesca |e investigator |4 oth | |
| 700 | 1 | |a Di Biase, Carlo |e investigator |4 oth | |
| 700 | 1 | |a Moretti, Alessio Grazioli |e investigator |4 oth | |
| 700 | 1 | |a Granata, Lucio |e investigator |4 oth | |
| 700 | 1 | |a Bonaldi, Filippo |e investigator |4 oth | |
| 700 | 1 | |a Bettinelli, Giulia |e investigator |4 oth | |
| 700 | 1 | |a Delmastro, Elena |e investigator |4 oth | |
| 700 | 1 | |a Salvato, Damiano |e investigator |4 oth | |
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