CXCL10 levels at hospital admission predict COVID-19 outcome: hierarchical assessment of 53 putative inflammatory biomarkers in an observational study

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Abstract

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.

Graphic abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s10020-021-00390-4.

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Most cited references 42

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Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

Fei Zhou, Ting Yu, Ronghui Du … (2020)

Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p<0·0001), and d-dimer greater than 1 μg/mL (18·42, 2·64–128·55; p=0·0033) on admission. Median duration of viral shedding was 20·0 days (IQR 17·0–24·0) in survivors, but SARS-CoV-2 was detectable until death in non-survivors. The longest observed duration of viral shedding in survivors was 37 days. Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

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Immunological memory to SARS-CoV-2 assessed for up to 8 months after infection

Jennifer M. Dan, Jose Mateus, Yu Kato … (2021)

Understanding immune memory to SARS-CoV-2 is critical for improving diagnostics and vaccines, and for assessing the likely future course of the COVID-19 pandemic. We analyzed multiple compartments of circulating immune memory to SARS-CoV-2 in 254 samples from 188 COVID-19 cases, including 43 samples at ≥ 6 months post-infection. IgG to the Spike protein was relatively stable over 6+ months. Spike-specific memory B cells were more abundant at 6 months than at 1 month post symptom onset. SARS-CoV-2-specific CD4+ T cells and CD8+ T cells declined with a half-life of 3-5 months. By studying antibody, memory B cell, CD4+ T cell, and CD8+ T cell memory to SARS-CoV-2 in an integrated manner, we observed that each component of SARS-CoV-2 immune memory exhibited distinct kinetics.

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An inflammatory cytokine signature predicts COVID-19 severity and survival

Diane Marie Del Valle, Seunghee Kim-Schulze, Hsin-Hui Huang … (2020)

Several studies have revealed that the hyper-inflammatory response induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a major cause of disease severity and death. However, predictive biomarkers of pathogenic inflammation to help guide targetable immune pathways are critically lacking. We implemented a rapid multiplex cytokine assay to measure serum interleukin (IL)-6, IL-8, tumor necrosis factor (TNF)-α and IL-1β in hospitalized patients with coronavirus disease 2019 (COVID-19) upon admission to the Mount Sinai Health System in New York. Patients (n = 1,484) were followed up to 41 d after admission (median, 8 d), and clinical information, laboratory test results and patient outcomes were collected. We found that high serum IL-6, IL-8 and TNF-α levels at the time of hospitalization were strong and independent predictors of patient survival (P < 0.0001, P = 0.0205 and P = 0.0140, respectively). Notably, when adjusting for disease severity, common laboratory inflammation markers, hypoxia and other vitals, demographics, and a range of comorbidities, IL-6 and TNF-α serum levels remained independent and significant predictors of disease severity and death. These findings were validated in a second cohort of patients (n = 231). We propose that serum IL-6 and TNF-α levels should be considered in the management and treatment of patients with COVID-19 to stratify prospective clinical trials, guide resource allocation and inform therapeutic options.

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Author and article information

Contributors

Nicola I. Lorè:

ORCID: http://orcid.org/0000-0001-9159-1632

lore.nicolaivan@hsr.it

Journal

Journal ID (nlm-ta): Mol Med

Journal ID (iso-abbrev): Mol Med

Title: Molecular Medicine

Publisher: BioMed Central (London )

ISSN (Print): 1076-1551

ISSN (Electronic): 1528-3658

Publication date (Electronic): 18 October 2021

Publication date PMC-release: 18 October 2021

Publication date Collection: 2021

Volume: 27

Electronic Location Identifier: 129

Affiliations

[1 ]GRID grid.18887.3e, ISNI 0000000417581884, Division of Immunology, Transplantation and Infectious Diseases, , IRCCS San Raffaele Scientific Institute, ; Via Olgettina 60, 20132 Milano, Italy

[2 ]GRID grid.15496.3f, ISNI 0000 0001 0439 0892, Vita-Salute San Raffaele University, ; Milano, Italy

[3 ]GRID grid.18887.3e, ISNI 0000000417581884, Emerging Bacterial Pathogens Unit, , IRCCS San Raffaele Scientific Institute, ; Milano, Italy

[4 ]GRID grid.15496.3f, ISNI 0000 0001 0439 0892, University Centre for Statistics in the Biomedical Sciences (CUSSB), Vita-Salute San Raffaele University, ; Milan, Italy

[5 ]GRID grid.18887.3e, ISNI 0000000417581884, Division of Genetics and Cell Biology, , IRCCS San Raffaele Scientific Institute, ; Milano, Italy

[6 ]GRID grid.18887.3e, ISNI 0000000417581884, Division of Neuroscience, , IRCCS San Raffaele Scientific Institute, ; Milano, Italy

[7 ]GRID grid.18887.3e, ISNI 0000000417581884, Division of Experimental Oncology, , IRCCS San Raffaele Scientific Institute, ; Milano, Italy

[8 ]GRID grid.18887.3e, ISNI 0000000417581884, Hematology and Bone Marrow Transplant, , IRCCS San Raffaele Scientific Institute, ; Milano, Italy

[9 ]Faculty of Biomedical Sciences, Swiss University, Lugano, Switzerland

Author information

Nicola I. Lorè http://orcid.org/0000-0001-9159-1632

Article

Publisher ID: 390

DOI: 10.1186/s10020-021-00390-4

PMC ID: 8521494

PubMed ID: 34663207

SO-VID: 276bdac8-5891-46a6-98a7-01c8f4051923

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 7 June 2021

Date accepted : 2 October 2021

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100003196, Ministero della Salute;

Award ID: COVID-2020-12371617

Award Recipient : Angelo A. Manfredi

Funded by: EHA

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Keywords: covid-19 severity predictors,biomarkers,decision tree,cxcl10

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Keywords: covid-19 severity predictors, biomarkers, decision tree, cxcl10

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CXCL10 levels at hospital admission predict COVID-19 outcome: hierarchical assessment of 53 putative inflammatory biomarkers in an observational study

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Abstract

Background

Methods

Results

Conclusions

Graphic abstract

Supplementary Information

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Novel Coronavirus Disease COVID-19

Most cited references 42

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

Immunological memory to SARS-CoV-2 assessed for up to 8 months after infection

An inflammatory cytokine signature predicts COVID-19 severity and survival

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Cited by 29

Most referenced authors 1,581