The Role of Immunological and Clinical Biomarkers to Predict Clinical COVID-19 Severity and Response to Therapy—A Prospective Longitudinal Study

Abstract

Background
The association of pro-inflammatory markers such as interleukin-6 (IL-6) and other biomarkers with severe coronavirus disease 2019 (COVID-19) is of increasing interest, however their kinetics, response to current COVID-related treatments, association with disease severity and comparison with other disease states associated with potential cytokine storm (CS) such as Staphylococcus aureus bacteraemia (SAB) are ill-defined.

Methods
A cohort of 55 hospitalized SARS-CoV-2 positive patients was prospectively recruited – blood sampling was performed at baseline, post-treatment and hospital discharge. Serum IL-6, C-reactive protein (CRP) and other laboratory investigations were compared between treatment groups and across timepoints. Acute serum IL-6 and CRP levels were then compared to those with suspected COVID-19 (SCOVID) and age and sex matched patients with SAB and patients hospitalized for any non-infectious condition (NIC).

Results
IL-6 was elevated at admission in the SARS-CoV-2 cohort but at lower levels compared to matched SAB patients. Median (IQR) IL-6 at admission was 73.89 pg/mL (30.9, 126.39) in SARS-CoV-2 compared to 92.76 pg/mL (21.75, 246.55) in SAB (p=0.017); 12.50 pg/mL (3.06, 35.77) in patients with NIC; and 95.51 pg/mL (52.17, 756.67) in SCOVID. Median IL-6 and CRP levels decreased between admission and discharge timepoints. This reduction was amplified in patients treated with remdesivir and/or dexamethasone. CRP and bedside vital signs were the strongest predictors of COVID-19 severity.

Conclusions
Knowledge of the kinetics of IL-6 did not offer enhanced predictive value for disease severity in COVID-19 over common investigations such as CRP and vital signs.

Full text

The Role of Immunological and
Clinical Biomarkers to Predict
Clinical COVID-19 Severity and
Response to Therapy—A Prospective
Longitudinal Study
Ana Copaescu
1,2,3
*
†
, Fiona James
1†
,Effie Mouhtouris
1
, Sara Vogrin
4
, Olivia C. Smibert
5
,
Claire L. Gordon
5,6
, George Drewett
5
, Natasha E. Holmes
1,5,7‡
and Jason A. Trubiano
1,5,8,9‡
1
Centre for Antibiotic Allergy and Research, Department of Infectious Diseases, Austin Health, Heidelberg, VIC, Australia,
2
Clinical Immunology and Allergy, Department of Medicine, McGill University Health Center, Montre
´al, QC, Canada,
3
The Research Institute of the McGill University Health Centre, McGill University, Montreal, QC, Canada,
4
Department of
Medicine, St Vincent’s Hospital, University of Melbourne, Fitzroy, VIC, Australia,
5
Department of Medicine (Austin Health),
The University of Melbourne, Heidelberg, VIC, Australia,
6
Department of Microbiology and Immunology, The University of
Melbourne, Parkville, VIC, Australia,
7
Department of Critical Care, Melbourne Medical School, The University of Melbourne,
Parkville, VIC, Australia,
8
Department of Oncology, Sir Peter MacCallum Cancer Centre, The University of Melbourne, Parkville,
VIC, Australia,
9
The National Centre for Infections inCancer, Peter MacCallum Cancer Centre, Parkville, VIC, Australia
Background: The association of pro-inflammatory markers such as interleukin-6 (IL-6)
and other biomarkers with severe coronavirus disease 2019 (COVID-19) is of increasing
interest, however their kinetics, response to current COVID-related treatments,
association with disease severity and comparison with other disease states associated
with potential cytokine storm (CS) such as Staphylococcus aureus bacteraemia (SAB)
are ill-defined.
Methods: A cohort of 55 hospitalized SARS-CoV-2 positive patients was prospectively
recruited –blood sampling was performed at baseline, post-treatment and hospital
discharge. Serum IL-6, C-reactive protein (CRP) and other laboratory investigations
were compared between treatment groups and across timepoints. Acute serum IL-6
and CRP levels were then compared to those with suspected COVID-19 (SCOVID) and
age and sex matched patients with SAB and patients hospitalized for any non-infectious
condition (NIC).
Results: IL-6 was elevated at admission in the SARS-CoV-2 cohort but at lower levels
compared to matched SAB patients. Median (IQR) IL-6 at admission was 73.89 pg/mL
(30.9, 126.39) in SARS-CoV-2 compared to 92.76 pg/mL (21.75, 246.55) in SAB
(p=0.017); 12.50 pg/mL (3.06, 35.77) in patients with NIC; and 95.51 pg/mL (52.17,
756.67) in SCOVID. Median IL-6 and CRP levels decreased between admission and
discharge timepoints. This reduction was amplified in patients treated with remdesivir and/
Frontiers in Immunology | www.frontiersin.org March 2021 | Volume 12 | Article 6460951
Edited by:
Christopher J. A. Duncan,
Newcastle University, United Kingdom
Reviewed by:
Johan Van Weyenbergh,
KU Leuven, Belgium
Anthony Jaworowski,
RMIT University, Australia
*Correspondence:
Ana Copaescu
ana.copaescu@gmail.com
†
These authors share first authorship
‡
These authors share
senior authorship
Specialty section:
This article was submitted to
Viral Immunology,
a section of the journal
Frontiers in Immunology
Received: 25 December 2020
Accepted: 25 February 2021
Published: 17 March 2021
Citation:
Copaescu A, James F, Mouhtouris E,
Vogrin S, Smibert OC, Gordon CL,
Drewett G, Holmes NE and
Trubiano JA (2021) The Role of
Immunological and Clinical Biomarkers
to Predict Clinical COVID-19 Severity
and Response to Therapy—A
Prospective Longitudinal Study.
Front. Immunol. 12:646095.
doi: 10.3389/fimmu.2021.646095
ORIGINAL RESEARCH
published: 17 March 2021
doi: 10.3389/fimmu.2021.646095

or dexamethasone. CRP and bedside vital signs were the strongest predictors of COVID-
19 severity.
Conclusions: Knowledge of the kinetics of IL-6 did not offer enhanced predictive value for
disease severity in COVID-19 over common investigations such as CRP and vital signs.
Keywords: SARS-CoV-2, interleukin-6, C-reactive protein, cytokine storm, Staphylococcus aureus bacteraemia,
sepsis, acute respiratory distress syndrome
INTRODUCTION
There has been increasing interest surrounding the function of
interleukin-6 (IL-6) and other laboratory markers in severe acute
respiratory syndrome coronavirus 2 (SARS-CoV-2) infection,
including the role in predicting disease severity, monitoring
response to therapy, and similarities with other cytokine storm
(CS) disease states (1–4). Heterogeneity in SARS-CoV-2 study
design and definitions of disease severity have limited advances
in understanding the clinical implications for IL-6 and other
inflammatory and clinical makers in coronavirus disease 2019
(COVID-19) (4). Further, IL-6 kinetics in SARS-CoV-2 and
comparisons with other infective syndromes with CS, such as
bacterial sepsis, have not been extensively undertaken.
METHODS
Participants and Setting
Adults aged ≥18 years hospitalized with suspected or confirmed
SARS-CoV-2, no known hypersensitivity to tocilizumab and no
active pulmonary tuberculosis, were enrolled sequentially in this
single-center prospective cohort study between May 15
th
2020
and August 21
st
2020 at Austin Health, Melbourne, Australia.
Definitions
Severe disease was defined as requirement for supplemental
oxygen for ≥24 hours and a Sp02 ≤94% on room air and/or
admission to intensive care unit (ICU), adapted from the
National Institutes of Health (NIH) criteria for disease severity
(5). Treatment for COVID-19 was as per hospital approved
treatment protocol. Patients that required oxygen received
dexamethasone, remdesivir was utilized in those that had
oxygenation < 94% and were early in disease and tocilizumab
was only considered on a case by case basis in intubated patients
with evidence of cytokine storm (4) and no occult sepsis
(negative procalcitonin). A 5-day course of remdesivir and/or
dexamethasone for up to 10 days was administered to patients
with severe disease starting on the 23
rd
of June 2020.
Objectives
The primary objective of the study was to describe the kinetics of
IL-6 and other biomarkers during SARS-CoV-2 infection.
Secondary objectives were to describe the association of these
markers with (1) disease severity (defined as ICU admission,
oxygen therapy or a composite of both), (2) treatment
with dexamethasone and/or remdesivir, and (3) other disease
states associated with CS such as Staphylococcus aureus
bacteraemia (SAB).
Data Collection and Cohorts
Standard baseline demographic and clinical characteristics,
laboratory parameters (including C-reactive protein (CRP),
lymphocyte count, ferritin, lactate dehydrogenase (LDH) and
D-dimer) and COVID-related treatment data (requirement for
oxygen therapy and/or intubation, drug therapy with
dexamethasone and/or remdesivir) were gathered. Serum
samples were collected at four timepoints: (1) hospital
admission, (2) 24 to 48 hours post dexamethasone and/or
remdesivir, (3) 7 to 14 days post-treatment, and (4) discharge
(Figure 1).
We compared the SARS-CoV-2 group with three distinct
inpatient cohorts with potentially varied cytokine storm disease
states: (1) patients with SAB; (2) patients hospitalized for any
non-infectious conditions (NIC); and (3) patients with suspected
COVID-19 (SCOVID) concurrently recruited with the SARS-
CoV-2 patients from Austin Hospital. The patients with
SCOVID had a minimum of 2 negative COVID-19 tests upon
hospital admission as well as repeated testing depending on their
clinical evolution. The SARS-CoV-2 patients were age and sex
matched with previously described SAB (6) and NIC (7) cohorts
(Figure 1). No other demographic information was available for
the patients recruited in theses cohorts. The serum samples for
the SAB and NIC cohorts were stored since their initial
recruitment in accordance with well-known laboratory storing
procedures at -80°C ensuring adequate back-up capacity for the
freezers under the supervision of trained personnel and with
ongoing alarm systems designed to monitor the temperature.
Serum IL-6 from each patient cohort was quantified using an
enzyme-linked immunosorbent assay (ELISA) assay (Crux
Biolabs®, Australia) following manufacturers’instructions (8)
and read at a wavelength of 450 nm with a FLUOstar Optima
plate reader (BMG labtech®).
Statistical Analysis
Categorical variables are reported as counts and percentages;
continuous variables as medians (interquartile range, IQR). IL-6
Abbreviations: ARDS, acute respiratory distress syndrome; CS, cytokine storm;
CRP, C-reactive protein; COVID-19, coronavirus disease 2019; ICU, intensive
care unit; IL-6, interleukin-6; NIC, non-infectious condition; SAB, Staphylococcus
aureus bacteraemia; SCOVID, suspected COVID-19; SARS-CoV-2, severe acute
respiratory syndrome coronavirus 2.
Copaescu et al. Biomarkers to Predict Clinical COVID-19 Severity
Frontiers in Immunology | www.frontiersin.org March 2021 | Volume 12 | Article 6460952

FIGURE 1 | Study design outlining patient cohorts and time points for sample collection. SAB and NIC cohorts are age and sex matched with the SARS-CoV-2
cohort. PCR, polymerase chain reaction; NIC, non-infectious condition; SAB, Staphylococcus aureus bacteraemia; SARS-CoV-2, severe acute respiratory
syndrome coronavirus.
TABLE 1 | Patient characteristics of SARS-CoV-2 positive cohort.
Characteristics COVID (n = 55)
All patients (n = 55) ICU Admission (n = 15) Supplemental O
2
(n = 25) y
Age (years), (median, IQR) 58 (40; 70) 59 (50; 69) 66 (52; 71)
Sex (M:F) 31:24 10:5 17:8
Ethnicity
(no.; %)
ATSI (1; 1.8%)
African (4; 7.3%)
Caucasian (31; 56.4%)
East Asian (4; 7.3%)
Indo-Asian (2; 3.6%)
Other (1; 1.8%)
Unknown (12; 21.8%)
African (3; 20%)
Caucasian (8; 53.3%)
Indo-Asian (1; 6.7%)
Other (1; 6.7%)
Unknown (2; 13.3%)
African (2; 8%)
Caucasian (17; 68%)
East Asian (1; 4%)
Indo-Asian (2; 4%)
Other (1; 4%)
Unknown (3; 12%)
Smoking status
(no./total no.; %)
Smoker (4/50; 8%)
Ex-smoker (6/50;12%)
Non-smoker (40/50; 80%)
Smoker (1/13; 7.7%)
Ex-smoker (1/13; 7.7%)
Non-smoker (11/13; 84.6%)
Smoker (1/21; 4.8%)
Ex-smoker (4/21; 19%)
Non-smoker (16/21; 76.2%)
Comorbidities (no.; %)
Hypertension 20; 36.4% 6; 40% 14; 56%
Cardiac disease 7; 12.7% 2; 13.3% 5; 20%
Chronic respiratory disease 13; 23.6% 5; 33.3% 9; 36%
Chronic renal or liver disease 1; 1.8% 0; 0% 0; 0%
Diabetes 15; 27.3% 5; 33.3% 10; 40%
Immunosuppression ♣5; 9.1% 2; 13.3% 3; 12%
Pregnancy 1; 1.8% 0; 0% 0; 0%
ACEI/ARB use 10; 18.2% 2; 13.3% 7; 28%
Clinical characteristics
Charlson comorbidity index ♦
(median, IQR)
1 (0;3) 1 (1;2) 2 (1;3)
COVID-MATCH65 Score F
(median, IQR)
3.5 (2.5;5) 4 (3.5;5) 4.5 (3.5;5)
Latency presentation recruitment ▽(days), (median, IQR) 7 (5;10) 7 (5;10) 7 (5;9)
Length of hospital stay (days)
(median, IQR)
6 (3;13) 17 (9;27) 13 (6;21)
Death (no.; %) 2; 3.6% 0; 0% 2; 8%
ACEI, angiotensin-converting-enzyme inhibitors; ARB, angiotensin II receptor blockers; ATSI, Aboriginal or Torres Strait Islander; ICU, intensive care unit; SARS-CoV-2, severe acute
respiratory syndrome coronavirus 2.
♣The immunosuppression category includes patients that are known for any of the following conditions: transplant recipient, hematological or oncological malignancy (in the last 5 years),
corticosteroid use of more than 10 mg prednisolone equivalent per day, connective tissue or autoimmune condition and acquired immunode ficiency syndrome.
YPatients that required supplemental O
2
continuously for more than 24 hours during their admission.
♦The Charlson comorbidity index is age-adjusted.
FCOVID-MATCH65 Score is a clinical decision rule internally derived that has a high sensitivity (92.6%) and NPV (99.5%) for SARS-CoV-2 and can be used to aid COVID-19 risk
assessment and resource allocation for SARS-CoV-2 diagnostics. The resulting score ranges from 1 to 6.5 points with score ≤1 representing low risk for a positive test (9).
▽Time from symptoms presentation and study recruitment (days).
Copaescu et al. Biomarkers to Predict Clinical COVID-19 Severity
Frontiers in Immunology | www.frontiersin.org March 2021 | Volume 12 | Article 6460953

levels across time in the treatment groups were compared using
mixed effects linear regression, while linear regression was used
to compare between treatment groups. The outcome was log-
transformed and the results reported as exponentiated regression
coefficients (95% CI). Characteristics of matched cohorts
(COVID, SAB and NIC) were compared using sign-rank test
and McNemar test. IL-6 levels between cohorts (SAB vs COVID
and NIC vs COVID) were compared using paired t-test.
Univariable logistic regression was used for evaluation of
demographic, clinical and laboratory variables with
supplemental oxygen requirement. Results were expressed as
odds ratios (95% CI). Competing risk time to event analysis was
performed to measure the association of IL-6 and CRP with
oxygen supplementation. The measured treatment outcomes
were admission to ICU, oxygen therapy or a composite of
both. Discharge was taken as competing risk and IL-6/CRP
were separately entered as admission values and as time-
varying covariates. Results were reported as sub-hazard ratios
TABLE 2 | Clinical, laboratory characteristics and treatment information for SARS-CoV-2 positive patients.
All patients (n = 55) ICU Admission (n = 15) Supplemental O
2
(n = 25) Y
Patient reported clinical symptoms at baseline (no.; %)
Fever >38°C 26; 47.3% 9; 60.0% 14; 56.0%
Malaise/myalgia 32; 58.2% 8; 53.3% 14; 56.0%
Dyspnea 42; 76.4% 14; 93.3% 22; 88.0%
Cough 36; 65.5% 9; 60.0% 15; 60.0%
Coryza 9; 16.4% 2; 13.3% 1; 4.0%
Sore throat 16; 29.1% 2; 13.3% 2; 8.0%
Diarrhea 16; 29.1% 4; 26.7% 7; 28.0%
Other Headache (3; 0.05%) Headache (0; 0%) Headache (0; 0%)
Nausea and Vomiting Nausea and Vomiting Nausea and Vomiting
(2; 0.04%) (0; 0%) (1; 0.04%)
Pleuritic chest pain Pleuritic chest pain Pleuritic chest pain
(4; 0.07%) (0; 0%) (1; 0.04%)
Vital signs –baseline at hospital admission (median, IQR)
Temperature (°C) 37.8 (36.7; 38.5) 38.1 (37.1; 38.7) 38.3 (37.3; 38.8)
Respiratory rate 22 (20; 30) 35 (22; 38) 28 (22; 35)
Oxygen saturation (%) 95 (92; 98) 94 (90; 96) 92 (90; 94)
Pulse rate 96 (88; 106) 100 (92; 119) 100 (88; 115)
Blood pressure (mmHg) 118/72
(107/66; 130/81)
118/78
(100/60; 130/85)
120/69
(103/64; 130/80)
Laboratory Data –baseline (median, IQR)
WCC (x10
9
/L) 6 (4.4;8) 7.2 (4.4;8) 7.1 (4.3;8)
Lymphocytes (x10
9
/L) 0.8 (0.7; 1.1) 0.7 (0.5; 0.8) 0.8 (0.6; 1)
Neutrophils (x10
9
/L) 4.3 (2.9; 6) 5.8 (2.9; 6.6) 5.1 (2.9; 6.2)
Eosinophils (x10
9
/L) 0 (0;0) 0 (0;0) 0 (0;0)
Hemoglobin (g/L) 135 (123;146) 139 (125:145) 139 (127;145)
Platelet count (x10
9
/L) 202 (171;257) 194 (161;253) 194 (161;253)
Creatinine (mmol/L) 74 (60; 94) 82 (64; 95) 82 (69; 104)
Estimated GFR ♦90 (71;90) 86 (71;90) 79 (64;90)
Sodium (mmol/L) 139 (136;141) 138 (135;141) 139 (136;141)
Potassium (mmol/L) 4.1 (3.9;4.4); N=53 4 (4;4.4); N=13 4.1 (4;4.5); N=23
Bicarbonate (mmol/L) 25 (22; 27) 25 (24;27) 25 (24;27)
IL-6 (pg/ml) 73.9 (30.9;126.39) 56.6 (21.3;108.3) 73.9 (31.1;123.2)
CRP (mg/L) 65 (19.4; 135); N=53 135 (49.1; 223); N=15 113 (44.9; 196.5); N=24
Ferritin (mg/L) 438 (167; 864); N=50 1,084 (490; 1,570); N=15 645.5 (209; 1,311); N=24
D-dimer (mg/L) 635 (473; 972); N=53 1,394 (738; 2,505); N=15 969 (680; 1,542); N=25
LDH (U/L) 278 (236; 366); N=33 402.5 (326; 525); N=10 363 (267; 525); N=15
Bilirubin (mmol/L) 8 (6.5;13); N=48 10 (7;15); N=13 8 (6;15); N=22
ALT (U/L) 28 (21;50); N=50 37 (21; 49); N=14 28.5 (20;47); N=24
AST (U/L) 65 (55;85); N=13 66 (55;189); N=3 62.5 (55;66); N=6
GGT (U/L) 42 (25;82) 63 (40;109) 55 (38;84)
Albumin (g/L) 35 (31; 37); N=53 31 (26; 36); N=15 32.5 (30; 36); N=24
Treatment (no.; %)
Mechanical ventilation 5 (9.1%) 5 (33.3%) 5 (20%)
Dexamethasone 28 (50.9%) 12 (80%) 22 (88%)
Remdesivir and dexamethasone 15 (27.3%) 8 (53.5%) 13 (52%)
Intravenous antibiotics 35 (63.6%) 15 (100%) 22 (88%)
Antifungals 3 (5.5%) 3 (20%) 3 (12%)
ALT, alanine aminotransferase; AST, aspartate transaminase; CRP, C-Reactive protein; GFR, glomerular filtration rate; GGT, gamma-glutamyl transferase; ICU, intensive care unit; IL-6,
Interleukin-6; LDH, lactate dehydrogenase; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; SpO2, oxygen saturation; WCC, white cell count.
YPatients that required supplemental O
2
continuously for more than 24 hours during their admission.
♦Estimated GFR was calculated using Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), units: ml/min/1.73.
Copaescu et al. Biomarkers to Predict Clinical COVID-19 Severity
Frontiers in Immunology | www.frontiersin.org March 2021 | Volume 12 | Article 6460954

(SHRs) with 95% CI. Stata/IC 16.1 was used for all analysis. The
study was approved by the local human research ethics
committee (Ref HREC/63201/Austin-20). All patients, their
legal representatives or their next of kin provided informed
consent for this study.
RESULTS
Cohort Characteristics
The baseline demographics and clinical characteristics for SARS-
CoV-2 positive cohort (n=55) are described in Table 1. The
laboratory values and treatment information are listed in Table 2.
Twenty-five (45%) of the patients required continuous
supplemental oxygen for ≥24 hours after SpO
2
fell below 94%
on room air and 15 (27%) were admitted to ICU. Two patients
died while in hospital. There were 28 patients who received
dexamethasone (50.9%); 15 who received remdesivir (27.3%)
and 15 (27.3%) who received both.
IL-6 and Biomarker Kinetics in SARS-CoV-
2-Infected Patients
The kinetics of exploratory biomarkers over time, stratified by
treatment group (dexamethasone, dexamethasone and
remdesivir, no treatment) can be visualized in Figure 2. IL-6
and CRP values decreased with time from admission across all
groups. Ferritin, LDH and D-dimer were marginally decreased
from admission to discharge but increased post-treatment for the
small numbers of patients in this group and lymphocytes were
slightly increased from admission to discharge (Figure 2). At
A
B
D
EF
C
FIGURE 2 | Laboratory data for SARS-CoV-2 positive patients (n=55) according to their treatment regimen and at different admission timepoints (median, IQR):
(A) Interleukin-6; (B) C-Reactive protein; (C) Lymphocytes; (D) Lactate dehydrogenase (LDH); (E) Ferritin level and (F) D-dimer.
Copaescu et al. Biomarkers to Predict Clinical COVID-19 Severity
Frontiers in Immunology | www.frontiersin.org March 2021 | Volume 12 | Article 6460955

admission, there was no difference in serum IL-6 levels between
those who received treatment and those who did not (Table 3).
Post-treatment levels of serum IL-6 halved in the remdesivir and
dexamethasone treatment group (p=0.023) (Table 3). At
discharge, IL-6 was 48% lower than at admission for those
who received remdesivir and dexamethasone (p=0.059) and
83% lower in the dexamethasone only group (p=0.003)
(Table 3).
IL-6 in SARS-CoV-2 Infected Patients
Compared With Other Disease States
Serum IL-6 at hospital admission in SARS-CoV-2 positive
patients was compared with the SCOVID, SAB and NIC
groups (Figure 3). IL-6 values are demonstrated in
Supplementary Tables 1 and 2. A mean difference of 72.7 pg/
ml (95% CI; 40.0, 105.3) was found between the NIC and SARS-
CoV-2 groups (p<0.001). Mean IL-6 was elevated in the SAB
group by 57.8 pg/ml (95% CI; 0.3, 115.2) over the SARS-CoV-2
group (p=0.049). The univariable associations with ICU
admission for SAB and SARS-CoV-2 positive cohorts are
illustrated in Table 4. Although CRP was associated with ICU
admission in both cohorts, IL-6 was associated with ICU
admission only in the SAB cohort.
Association Between IL-6 and Other
Biomarkers With Clinical Outcomes in
SARS-CoV-2 Infected Patients
No association between elevated baseline IL-6 and either
requirement for oxygen therapy, ICU admission or composite
outcomes was found on univariable analysis (Supplementary
Tables 3 and 4). An increased CRP of 10 mg/L increased odds of
oxygen therapy requirement by 13% (p=0.006), ICU admission
by 1% (p=0.014) and the composite outcome by 1% (p=0.003). Using time to event analysis, IL-6 did not appear to be associated
with requirement for oxygen therapy, ICU admission or a
composite endpoint of both outcomes (Table 5). In contrast,
increased CRP was still associated with oxygen therapy and the
composite endpoint, even when adjusted for predictors of the
outcome identified on logistic regression. After adjustment for
respiratory rate and SpO
2
, an increase of 10 mg/mL in CRP was
associated with a 5% increased risk of requirement for oxygen
therapy (p=0.013) and a 5% increased risk of the composite
outcome (p=0.025).
DISCUSSION
This study presents unique prospectively data with multiple
time-point sampling, assessing IL-6 and other inflammatory
and clinical biomarkers in response to an antiviral (i.e.
remdesivir) and an immunosuppressant (i.e. dexamethasone)
treatment –informing strategies to predicting clinical severity
and response to therapy. IL-6 is secreted by a plethora of immune
and stromal cells and exerts effects on a similarly broad array of
cellular targets translating into functional pleiotropy including
the synthesis of acute phase proteins in the liver, such as
C-reactive protein (CRP), a surrogate for IL-6 (10,11). CRP is
TABLE 3 | Impact of COVID-19 treatment on IL-6 values.
OR (95% CI) p - value
Effect of time
No treatment
Discharge vs admission 0.76 (0.47, 1.22) 0.258
Remdesivir and dexamethasone
Post treatment vs admission 0.52 (0.29, 0.91) 0.023
Discharge vs admission 0.52 (0.27, 1.03) 0.059
Dexamethasone
Post treatment vs admission 0.54 (0.18, 1.59) 0.266
Discharge vs admission 0.17 (0.05, 0.55) 0.003
Comparison between groups
Admission
Treatment vs no treatment 0.91 (0.52, 1.62) 0.756
Remdesivir (+dexamethasone vs
dexamethasone
1.45 (0.54, 3.86) 0.442
Post-treatment
Remdesivir (+dexamethasone) vs
dexamethasone
1.91 (0.62, 5.84) 0.242
Discharge
Treatment vs no treatment 0.45 (0.17, 1.20) 0.106
Remdesivir (+dexamethasone) vs
dexamethasone
3.03 (0.79, 11.71) 0.099
FIGURE 3 | Log-transformed IL-6 values (pg/ml) for SARS-CoV-2 positive
patients at baseline, SCOVID, SAB and a cohort of hospitalized patients for
any NIC. The SARS-CoV-2 positive patients were age and gender matched
with patients from SAB and NIC cohorts. CRP, C-Reactive protein; COVID,
coronavirus disease; IL-6, Interleukin-6; LDH, Lactic acid dehydrogenase;
NIC, non-infectious condition; SAB, Staphylococcus aureus bacteraemia;
SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; SCOVID,
suspected COVID-19.
Copaescu et al. Biomarkers to Predict Clinical COVID-19 Severity
Frontiers in Immunology | www.frontiersin.org March 2021 | Volume 12 | Article 6460956

frequently used in the clinical setting as a screening marker of
infection and/or inflammation (12).
Although there are reports in the literature that an increase in
IL-6 can correlate with disease severity in COVID-19 (4,13), our
study of a prospective SARS-CoV-2 cohort did not find that IL-6
levels offer a clinical utility for prediction of disease severity. We
noted a stronger association between simple laboratory parameters
(i.e. CRP) and bedside observations (i.e. SpO
2
and respiratory rate)
TABLE 4A | Admission characteristics and univariable model for association of ICU admission in SARS-CoV-2, Staphylococcus aureus bacteremia (SAB), SARS-CoV-2
positive and suspected COVID-19 (SCOVID) cohorts.
Factors SAB SARS-CoV-2 positive p-value YNIC ♦SCOVID
N5555 555
Female, no. (%) 25 (45%) 24 (44%) 0.564 25 (45%) 2 (40%)
Age, median (IQR) 58 (41, 70) 58 (40, 70) 0.309 58 (41, 70) 68 (54, 75)
CCI, median (IQR) 2 (0, 3) 1 (0, 3) 0.344 n/a n/a
ICU admission 14 (25%) 15 (27%) 0.835 n/a 0 (0%)
WCC, median (IQR) 10 (6.8, 15.6) 6 (4.4, 8) <0.001 n/a 7.6 (1.6, 10.9)
Neutrophils, median (IQR) 8.7 (5.5, 13.5) 4.3 (2.9, 6) <0.001 n/a 155.9 (41.1, 261.5)
CRP, median (IQR) 190.9 (99.7, 290) 65 (19.4, 135) <0.001 n/a 95.51 (52.17, 756.67)
IL-6, median (IQR) 92.76 (21.75, 246.55) 73.89 (30.9, 126.39) 0.017 12.50 (3.06, 35.77) 95.51 (52.17, 756.67)
YThe sign rank test was used for continuous values and McNemar’s test for categorical values.
♦Other demographic details were not collected for this cohort.
The SARS-CoV-2 positive patients were age and gender matched with patients from the SAB and NIC cohorts.
TABLE 4B | Association with ICU admission (logistic regression separately for SARS-CoV-2 positive patients and SAB).
SARS-CoV-2 positive SAB
OR
(95% CI)
p-value OR
(95% CI)
p-value
Female vs male 0.55 (0.16, 1.91) 0.349 2.81 (0.80, 9.92) 0.108
Age (increase of 1 year) 1.02 (0.98, 1.05) 0.364 1.01 (0.97, 1.04) 0.625
CCI, median (IQR) 0.90 (0.59, 1.39) 0.641 0.93 (0.68, 1.27) 0.652
WCC, median (IQR) 0.99 (0.92, 1.07) 0.879 0.96 (0.86 1.7) 0.437
Neutrophils, median (IQR) 1.15 (0.94, 1.41) 0.163 0.96 (0.86, 1.08) 0.505
CRP (increase for 10 units),
median (IQR)
1.13 (1.03, 1.23) 0.008 1.06 (1.00, 1.12) 0.034
IL-6 (increase for 10 units), median (IQR) 0.95 (0.87, 1.03) 0.232 1.07 (1.02, 1.12) 0.01
CCI, Charlson comorbidity index; CRP, C-reactive protein; ICU, intensive care unit; IL-6, interleukin-6; NIC, non-infectious conditions; SAB, Staphylococcus aureus bacteraemia; SARS-
CoV-2, severe acute respiratory syndrome coronavirus 2; SCOVID, suspected COVID-19; n/a, non-available data; WCC, white cell count.
TABLE 5 | Association of increased CRP and IL-6 values with outcome of severe disease (ICU admission, oxygen therapy or composite of both).
N total N with event Unadjusted Adjusted*
SHR (95% CI) p-value SHR (95% CI) p-value
ICU admission
IL-6 at baseline 47 9 0.91 (0.81, 1.03) 0.144 1.01 (0.96, 1.08) 0.649
IL-6^ 47 9 1.02 (0.97, 1.07) 0.360 0.86 (0.73, 1.01) 0.064
CRP at baseline 54 14 1.10 (1.05, 1.14) <0.001 1.05 (0.96, 1.15) 0.249
CRP^ 54 14 1.09 (1.05, 1.14) <0.001 1.06 (0.98, 1.14) 0.149
Oxygen therapy
IL-6 at baseline 51 21 1.00 (0.97, 1.03) 0.960 1.00 (0.96, 1.04) 0.973
IL-6^ 51 21 1.02 (0.99, 1.05) 0.270 1.03 (0.99, 1.06) 0.117
CRP at baseline 54 24 1.08 (1.04, 1.12) <0.001 1.05 (1.00, 1.10) 0.047
CRP^ 54 24 1.07 (1.03, 1.11) <0.001 1.05 (1.01, 1.10) 0.013
Composite outcome
IL-6 at baseline 46 18 1.00 (0.97, 1.04) 0.807 0.99 (0.94, 1.05) 0.795
IL-6^ 46 18 1.02 (0.99, 1.06) 0.136 1.02 (0.97, 1.07) 0.448
CRP at baseline 54 26 1.09 (1.05, 1.14) <0.001 1.06 (1.01, 1.11) 0.020
CRP^ 54 26 1.09 (1.05, 1.13) <0.001 1.05 (1.01, 1.10) 0.025
CRP, C-reactive protein; ICU, intensive care unit; Interleukin-6 (IL-6); SHR, sub-hazard ratio.
*Adjusted for respiratory rate (RR) (ICU admission), RR and SpO2 (oxygen therapy) and RR, SpO2 and age (composite outcome).
^Entered as a time-varying covariate.
Significant unadjusted values are shown in bold font.
Copaescu et al. Biomarkers to Predict Clinical COVID-19 Severity
Frontiers in Immunology | www.frontiersin.org March 2021 | Volume 12 | Article 6460957

with disease severity, over IL-6. In our cohort, IL-6, CRP, ferritin
and LDH were raised at hospital admission while lymphocytes were
reduced in line with previous reports (14–16). Further, in another
clinical prospective study on longitudinal immune profiling with a
median of two time points of peripheral blood collection, the
authors indicated an association between serum IL-6 at the time
of hospital admission and the severity of COVID-19, defined based
on the degree of respiratory failure (16). Similar, in a larger
retrospective longitudinal study (N=317), the authors showed the
same pattern of increased inflammatory markers within the initial
24 hours after admission as described in our study and previously
described study and correlation with disease severity for IL-6 more
than 50 pg/ml on multivariable logistic regression (17). Vultaggio
et al. (2020) found that in a cohort of 208 patients, 63 presenting
clinical deterioration (defined as oxygen therapy, ICU admission,
and death), IL-6 and CRP were predictors of negative outcomes in
the first 3 days after hospital admission (18). In another study,
maximal IL-6 (>80 pg/mL) and CRP (>97 mg/L) levels before
intubation showed the strongest association with the need for
mechanical ventilation in a cohort of COVID-19 hospitalized
patients (19). These findings along with the results from our
study support the use of CRP, a routinely available test, as a
reliable predictor of clinical outcome in SARS-CoV-2 positive
patients. Whilst, in our cohort, ferritin, D-dimer and LDH were
not useful to monitor response to COVID therapies but, as
discussed, were a marker of acute disease.
A limitation of the study in predicting severity of COVID-19 was
the small sample size available in a low prevalence setting as well as
the absence of confounders for most of the control groups (especially
NIC). Our unique perspective of comparing IL-6 values among
patients with other diseases, in particular those with an associated CS
phenotype, provides insight into the underlying pathophysiology of
SARS-CoV-2. CS is a somewhat controversial disease state with
hyper-cytokinemia including IL-6 as key features (20). In a recent
study from the Netherlands, the authors showed that IL-6 values
were lower in patients with COVID-19 and acute respiratory distress
syndrome (ARDS) when compared to patients with septic shock
with ARDS or septic shock without ARDS (21). This was supported
by our research, illustrating lower IL-6 values in the SARS-CoV-2
positive patients compared to patients with SAB. The idea that CS is
not a prominent feature of severe COVID-19 as previously thought
is growing in popularity and is supported by our study. Further
examination of novel biomarkers in COVID-19 is required. In the
interim, the use of routine tools such as CRP and bedside vital signs
may offer the most reliability for clinical prediction.
CONCLUSIONS
•IL-6 levels in COVID-19 are elevated early in disease, although
lower compared to other cytokine storm states.
•IL-6 levels follow the response to novel COVID-19 therapies,
however do not offer a clinical advantage over C-reactive
protein and bedside observations in predicting severe disease.
DATA AVAILABILITY STATEMENT
The original contributions presented in the study are included in
the article/Supplementary Material. Further inquiries can be
directed to the corresponding author.
ETHICS STATEMENT
The study was approved by the local human research ethics
committee (Ref HREC/63201/Austin-20). All patients, their legal
representatives or their next of kin provided oral rather than
written consent for this study due to COVID-19 restrictions.
AUTHOR CONTRIBUTIONS
AC and FJ did the literature review and wrote the manuscript
draft. AC and EM were responsible for the laboratory
manipulations and data analysis. SV was the statistician
responsible for this project. OS, CG, and GD were responsible
for patient recruitment, sample follow-up and database entry.
NH and JT proposed the study design and manuscript structure.
All authors reviewed the current manuscript for important
scientificcontentandmadesignificant contribution to the
different sections. All authors contributed to the article and
approved the submitted version.
FUNDING
This study was supported by unrestricted funding from Austin
Health Fundraising. JT was supported by the Austin Medical
Research Foundation (AMRF) and by a National Health and
Medical Research Council (NHMRC) postgraduate scholarship
(GNT 1139902) and Royal Australian College.of Physicians
Research Establishment Fellowship.
ACKNOWLEDGMENTS
We thank our research assistant, Mona Saade, and clinical nurse
specialists, Wendy Stevenson and Kerryn McInnes for their
assistance in sample coordination.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found online
at: https://www.frontiersin.org/articles/10.3389/fimmu.2021.
646095/full#supplementary-material
Copaescu et al. Biomarkers to Predict Clinical COVID-19 Severity
Frontiers in Immunology | www.frontiersin.org March 2021 | Volume 12 | Article 6460958

REFERENCES
1. Ruan Q, Yang K, Wang W, Jiang L, Song J. Clinical predictors of mortality
due to COVID-19 based on an analysis of data of 150 patients from Wuhan,
China. Intensive Care Med (2020) 46(5):846–8. doi: 10.1007/s00134-020-
05991-x
2. Chen G, et al. Clinical and immunologic features in severe and moderate
Coronavirus Disease 2019. J Clin Invest (2020). doi: 10.1101/
2020.02.16.20023903
3. Laguna-Goya R, Utrero-Rico A, Talayero P, Lasa-Lazaro M, Ramirez-
Fernandez A, Naranjo L, et al. IL-6-based mortality risk model for
hospitalized patients with COVID-19. J Allergy Clin Immunol (2020) 146
(4):799–807 e799. doi: 10.1016/j.jaci.2020.07.009
4. Copaescu A, Smibert O, Gibson A, Phillips EJ, Trubiano JA. The role of IL-6
and other mediators in the cytokine storm associated with SARS-CoV-2
infection. J Allergy Clin Immunol (2020) 146(3):518–34.e1. doi: 10.1016/
j.jaci.2020.07.001
5. National Institutes of Health (NIH). Management of Persons with COVID-19
National Institutes of Health (NIH). (2020). Available at: https://www.
covid19treatmentguidelines.nih.gov/overview/management-of-covid-19/,
[cited 2020 7/10].
6. Holmes NE, Robinson JO, van Hal SJ, Munckhof WJ, Athan E, Korman TM,
et al. Morbidity from in-hospital complications is greater than treatment
failure in patients with Staphylococcus aureus bacteraemia. BMC Infect Dis
(2018) 18(1):107. doi: 10.1186/s12879-018-3011-2
7. Holmes N, Lemmens N, Robinson JO, van Hal S, Munckhof W, Athan E,
et al. [P096] ANTI-STAPHYLOCOCCAL ANTIBODY PROFILE IN
AUSTRALIAN PATIENTS WITH Staphylococcus aureus BACTERAEMIA.
In: International Symposium on Staphylococci and Staphylococcal Infections.
Copenhagen, Denmark (2018). Available at: https://isssi2018.org/fileadmin/
user_upload/ISSI2018/ISSSI2018_programme_abstract_book_web5.pdf
8. elisatkit.com. (2017). Product datasheet - Human IL-6 ELISA Kit. [cited 2020
6/10]. Available at: https://elisakit.com/wp-content/uploads/2018/06/0012-
Hu-IL-6-.pdf.
9. Trubiano A, Vogrin S, Smibert O, Marhoon N, Alexander A, Chua K, et al.
COVID-MATCH65 –A prospectively derived clinical decision rule for severe
acute respiratory syndrome coronavirus 2. medRxiv (2020). doi: 10.1371/
journal.pone.0243414
10. Tanaka T, Kishimoto T. The biology and medical implications of interleukin-6.
Cancer Immunol Res (2014) 2(4):288–94. doi: 10.1158/2326-6066.CIR-14-0022
11. Uciechowski P, Dempke WCM. Interleukin-6: A Masterplayer in the
Cytokine Network. Oncology (2020) 98(3):131–7. doi: 10.1159/000505099
12. Faix JD. Biomarkers of sepsis. Crit Rev Clin Lab Sci (2013) 50(1):23–36. doi:
10.3109/10408363.2013.764490
13. Del Valle DM, Kim-Schulze S, Huang HH, Beckmann ND, Nirenberg S, Wang
B, et al. An inflammatory cytokine signature predicts COVID-19 severity and
survival. Nat Med (2020) 26(10):1636–43. doi: 10.1038/s41591-020-1051-9
14. Henry BM, de Oliveira MHS, Benoit S, Plebani M, Lippi G. Hematologic,
biochemical and immune biomarker abnormalities associated with severe illness
and mortality in coronavirus disease 2019 (COVID-19): a meta-analysis. Clin
Chem Lab Med (2020) 58(7):1021–8. doi: 10.1515/cclm-2020-0369
15. Terpos E, Ntanasis-Stathopoulos I, Elalamy I, Kastritis E, Sergentanis TN,
Politou M, et al. Hematological findings and complications of COVID-19. Am
J Hematol (2020) 95(7):834–47. doi: 10.1002/ajh.25829
16. Mann ER, Menon M, Knight SB, Konkel JE, Jagger C, Shaw TN, et al.
Longitudinal immune profiling reveals key myeloid signatures associated with
COVID-19. Sci Immunol (2020) 5(51). doi: 10.1126/sciimmunol.abd6197
17. Zeng Z, Yu H, Chen H, Qi W, Chen L, Chen G, et al. Longitudinal changes of
inflammatory parameters and their correlation with disease severity and
outcomes in patients with COVID-19 from Wuhan, China. Crit Care
(2020) 24(1):525. doi: 10.1186/s13054-020-03255-0
18. Vultaggio A, Vivarelli E, Virgili G, Lucenteforte E, Bartoloni A, Nozzoli C,
et al. Prompt Predicting of Early Clinical Deterioration of Moderate-to-Severe
COVID-19 Patients: Usefulness of a Combined Score Using IL-6 in a
Preliminary Study. J Allergy Clin Immunol Pract (2020) 8(8):2575–81.e2.
doi: 10.1016/j.jaip.2020.06.013
19. Herold T, Jurinovic V, Arnreich C, Hellmuth J, von Bergwelt-Baildon M,
Klein M, et al. Level of IL-6 predicts respiratory failure in hospitalized
symptomatic COVID-19 patients. medRxiv (2020). doi: 10.1101/
2020.04.01.20047381
20. Tisoncik JR, Korth MJ, Simmons CP, Farrar J, Martin TR, Katze M. Into the
eye of the cytokine storm. Microbiol Mol Biol Rev (2012) 76(1):16–32. doi:
10.1128/MMBR.05015-11
21. Kox M, Waalders NJB, Kooistra EJ, Gerretsen J, Pickkers P. Cytokine Levels in
Critically Ill Patients With COVID-19 and Other Conditions. JAMA (2020).
doi: 10.1001/jama.2020.17052
Conflict of Interest: The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
Copyright © 2021 Copaescu, James, Mouhtouris, Vogrin, Smibert, Gordon, Drewett,
Holmes and Trubiano. This is anopen-access article distributed under the terms of the
Creative Commons Attribution License (CC BY). The use, distribution or
reproduction in other forums is permitted, provided the original author(s) and the
copyright owner(s) are credited and that the original publication in this journal is
cited, in accordance with accepted academic practice. No use, distribution or
reproduction is permitted which does not comply with these terms.
Copaescu et al. Biomarkers to Predict Clinical COVID-19 Severity
Frontiers in Immunology | www.frontiersin.org March 2021 | Volume 12 | Article 6460959