Baseline Interleukin-6 Level Predicts Risk of Severe COVID-19: A Two-Center, Retrospective Study

doi:10.21203/rs.3.rs-68501/v1

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Baseline Interleukin-6 Level Predicts Risk of Severe COVID-19: A Two-Center, Retrospective Study

https://doi.org/10.21203/rs.3.rs-68501/v1

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Background:The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic has affected almost every country. Interleukin-6 (IL-6), a cytokine secreted by CD4+ T cell, has been shown to be a reliable marker of disease severity and a useful parameter for monitoring progression of coronavirus disease-2019 (COVID-19). However its value as a predictor of severe disease has not been assessed.

Methods:A total of 160 laboratory-confirmed COVID-19 patients admitted to two hospitals were enrolledand separated into two groups according to whether or not they progressed to develop severe illness. Demographic and clinical characteristics at admission were compared between the groups.

Results: Patients who developed severe COVID-19 had significantly higher baseline IL-6 levels than patients who had mild disease course in hospital (P< 0.001). Patients were further grouped according to quartiles of IL-6 level. The cumulative incidence of severe illnesswas significantly higher in the third and fourth quartiles groups than in the first quartile group (55% vs. 15% and 80% vs. 15%, respectively;bothP< 0.001). In multivariate logistic regression analysis, the risk for developing severe disease was markedly higher in the highest IL-6 quartile than in the lowest quartile (odds ratio: 14.95; 95% confidence interval: 3.65–61.30; P< 0.001). Receiver operating characteristic curve analysis of potential predictive variables showed the area under the curve to be largest for baseline IL-6, with the value of 5.20 pg/mL having the best balance of sensitivity and specificity for predicting risk of severe COVID-19.

Conclusion: Serum baseline IL-6 appears to be a reliable predictor of risk of severe COVID-19. Early intervention may be advisable in patients with serum IL-6 levels >5.20 pg/mL, even if initial symptoms are mild.

Immunology

Allergy & Immune Disorders

SARS-CoV-2

COVID-19

Interleukin-6

Predictive factor

In the eight months since the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) was identified as the pathogen of anovel pneumonia in Wuhan, China, the organism has spread rapidly across the world [1, 2]. As of 9 August 2020, a total of 19,462,112 COVID-19 cases have beenconfirmed worldwide, and 722,285 patients havedied. Disease severity varies widely, with mortality being much higher in patients with severe COVID-19 than in those with mild illness[3]. Amethod for rapid triage of patients is therefore urgently needed. Interleukin-6 (IL-6), a cytokine secreted by CD4 + T cell,has been shown to be a reliable marker ofdisease severity and a useful parameter for monitoring COVID-19 progression [4–6], but its value for predicting risk of severe illness has not been examined. The objective of this retrospective study was to determinewhether serum IL-6 level at admission can serve as a predictor of risk for severe COVID-19 in hospitalized patients.

Demographic and clinical characteristics

A total of 160 patients (58.8% males) from two hospitals were included in this study. The median age was 53.8 years. Comorbidities were present in 59/160 (36.9%) patients and included hypertension (37, 23.1%), cardio-cerebrovascular disease (26, 16.3%), diabetes mellitus (15, 9.4%), chronic pulmonary disease (9, 5.6%), chronic renal disease (4, 2.5%), and cancer (5, 3.1%). Computed tomography (CT) scans revealed multilobar pulmonary infiltrates in 128/160 (80.0%) patients (Table 1). Neutrophil count, neutrophil-to-lymphocyte ratio (NLR), neutrophil-to-CD8 + T cell ratio (N8R), and serum IL-6 and lactate dehydrogenase (LDH) levels were significantly higher in the SC group than in the MS group, while lymphocyte count, CD4 + T cell count, CD8 + T cell count, B cell count, and NK cell count were significantly lower (all P < 0.05; Table 1).

Table 1

Demographic and clinical characteristics of COVID-19 patients at admission
	All patients (N = 160)	MS (n = 88)	SC (n = 72)	P
Age, y, mean ± SD	53.8 ± 17.8	51.5 ± 16.5	56.5 ± 19.0	0.076
Sex, n (%)				0.923
Male	94 (58.8)	52 (59.1)	42 (58.3)
Female	66 (41.2)	36 (40.9)	30 (41.7)
Underlying disease, n (%)
Hypertension	37 (23.1)	18 (20.5)	19 (26.4)	0.376
Cardio-cerebrovascular disease	26 (16.3)	8 (9.1)	18 (25.0)	0.007
Diabetes	15 (9.4)	7 (8.0)	8 (11.1)	0.496
Chronic pulmonary disease	9 (5.6)	2 (2.3)	7 (9.7)	0.091
Chronic renal disease	4 (2.5)	0 (0.0)	4 (5.6)	0.084
Cancer	5 (3.1)	3 (3.4)	2 (2.8)	1.000
Radiographic findings, n (%)				< 0.001
Negative	12 (7.5)	12 (13.6)	0 (0.0)
Unilobar lesion	20 (12.5)	16 (18.2)	4 (5.6)
Multilobar lesion	128 (80.0)	60 (68.2)	68 (94.4)
Laboratory results, median (IQR)
Neutrophil count (× 10⁹/L)	3.24 (2.36–4.68)	2.96 (2.24–4.24)	3.70 (2.49–5.72)	0.024
Lymphocyte count (× 10⁹/L)	1.24 (0.84–1.70)	1.52 (1.07–1.92)	0.97 (0.58–1.38)	< 0.001
NLR	2.56 (1.69–4.32)	1.92 (1.56–3.07)	3.54 (2.32–8.90)	< 0.001
CD4 + T cell count (× 10⁶/L)	412 (251–706)	581 (372–810)	281 (168–484)	< 0.001
CD8 + T cell count (× 10⁶/L)	263 (149–400)	320 (217–479)	201 (98–294)	< 0.001
N8R	12.35 (6.59–25.80)	8.25 (6.24–18.71)	18.53 (9.22–48.47)	< 0.001
B cell count (× 10⁶/L)	152 (88–254)	198 (130–292)	119 (74–168)	< 0.001
NK cell count (× 10⁶/L)	166 (101–274)	183 (118–301)	147 (71–249)	0.024
IL-6 (pg/mL)	9.4 (3.03–27.6)	3.6 (2.2–13.8)	22.3 (9.4–43.6)	< 0.001
Alanine aminotransferase (U/L)	22 (16–33)	21 (15–33)	23 (18–33)	0.450
Lactate dehydrogenase (U/L)	205 (170–268)	193 (160–227)	240 (190–316)	< 0.001
Creatine kinase (U/L)	110 (76–180)	111 (76–157)	108 (75–212)	0.518
Blood urea nitrogen (mmol/L)	4.27 (3.43–5.31)	4.18 (3.44–4.83)	4.47 (3.43–6.18)	0.178
Serum creatinine (µmol/L)	65 (54–78)	66 (55–78)	64 (54–78)	0.689
MS, mild steady group; SC, severe change group; SD, standard deviation; NLR, neutrophil-to-lymphocyte ratio; N8R, neutrophil-to-CD8 + T cell ratio; IQR, interquartile range

Correlations between baseline IL-6 and other parameters

Figure 1 shows the correlations between baseline IL-6 and various parameters. IL-6 level was positively correlated with NLR (r = 0.378, P < 0.001), N8R (r = 0.334, P < 0.001), LDH level (r = 0.498, P < 0.001), and creatine kinaselevel (r = 0.206, P = 0.009),but negatively correlated with lymphocyte count (r = − 0.422, P < 0.001), CD4 + T cell count (r = − 0.440, P < 0.001), CD8 + T cell count (r = − 0.356, P < 0.001), and B cell count (r = − 0.383, P < 0.001).

Association between baseline IL-6 and risk of severe COVID-19

After the patients were separatedinto IL-6 quartile subgroups, the incidenceof severe illness was found to increase with increase in IL-6 level. The cumulative incidence was significantly higher in the third and fourth quartile subgroups than in the first quartile subgroup (55% vs. 15% and 80% vs. 15%, respectively; bothP < 0.001; Fig. 2A). Duration of hospitalization was also significantly longer in the higher quartile patient groups (all P < 0.01; Fig. 2B). The comorbidity-adjusted odds for severe illness increased significantly with increase inIL-6 level (Table 2; Model 1The trend did not change even after adjustment for age, sex, and comorbidityin multivariate logistic regression (Model 2) or after further adjustment for leukocyte subgroups (Model 3).

Table 2

Risk for severe COVID-19 in patients grouped according to quartiles of baseline IL-6
IL-6 (pg/mL)	Number of patients	Number of severe events	Model 1: OR (95% CI)	P for trend	Model 2: OR (95% CI)	P for trend	Model 3: OR (95% CI)	P for trend
< 3.1	40	6	1		1		1
3.1–9.4	40	12	2.84 (0.91–8.87)		3.04 (0.95–9.71)		3.89 (0.97–15.53)
9.5–27.6	40	22	8.06 (2.60–24.98)		9.33 (2.89–30.10)		6.22 (1.60–24.15)
> 27.6	40	32	24.81 (7.29–84.41)	< 0.001	26.62 (7.52–94.25)	< 0.001	14.95 (3.65–61.30)	< 0.001
OR, odds ratio; CI, confidence interval; IL-6, interleukin-6.
Model 1: Adjustment made for comorbidities; Model 2: Adjustment made age, sex, and comorbidities; Model 3: Adjustment made for age, sex, comorbidities, neutrophil count, lymphocyte count, CD4 + T cell count, CD8 + T cell count, B cell count, and NK cell count.

Comparison of predictive values of different variables

Baseline IL-6 level, neutrophil count, NLR, and N8R were assessed for ability to predict risk of severe disease. In ROC analysis,the AUC for IL-6 (0.798, 95% CI: 0.727–0.857) was significantly larger than the AUC for neutrophil count (0.604, 95%CI: 0.523–0.680) and N8R (0.688, 95%CI: 0.610–0.759). The AUC for IL-6 was not significantly different from the AUC of NLR (0.720, 95%CI: 0.644–0.788). The optimal cutoff values of the different variables were as follows:IL-6, 5.20 pg/mL (sensitivity: 91.67%, specificity: 59.09%), neutrophil count, 4.67 × 10⁹/L (sensitivity: 36.11%, specificity: 84.09%), NLR,2.74 (sensitivity: 66.67%, specificity: 72.73%),and N8R,25.42 (sensitivity: 43.06%, specificity: 89.77%) (Table 3).

Table 3

Optimal cutoff values of potential predictors of severe COVID-19 and their sensitivity and specificity
	IL-6 (pg/mL)	Neutrophil count (× 10⁹/L)	NLR	N8R
Cutoff value	5.20	4.67	2.74	25.42
Sensitivity (%)	91.67	36.11	66.67	43.06
Specificity (%)	59.09	84.09	72.73	89.77
Area under ROC curve (95% CI)	0.798 (0.727–0.857)	0.604^# (0.523–0.680)	0.720 (0.644–0.788)	0.688^# (0.610–0.759)
ROC, receiver operating characteristic; CI, confidence interval; NLR, neutrophil-to-lymphocyte ratio; N8R, neutrophil-to-CD8 + T cell ratio.
^#P < 0.05 vs. IL-6

The COVID-19 pandemic poses a great challenge to human society. Although case mortality for COVID-19 is relatively low, it has so far caused far more deaths than severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) combined [7, 8]. Becausemortality is very high inpatients with severe COVID-19 [3], it is essential to find reliable early predictors ofrisk for severe illness.

Previous studies in patients with severe COVID-19 have shownchanges in immune-inflammation parameters, e.g., low counts of lymphocytes and its subgroups, high neutrophil counts, high NLR, and elevated serum IL-6 level [4, 6, 9–11]; these changes were attributed to the severe and aberrant host immune response [12]. In our study, we found similar changes in patients who developed severe disease versus those who had mild disease:lymphocytes and all its subsets were significantly lower in the SC group, while neutrophil, NLR, N8R, and IL-6 were significantly higher (all P < 0.05). Reduction in T lymphocyte counts may due to the direct invasion by SARS-CoV-2, similar to the mechanism reported in MERS-CoV infection [13]. In addition, autoimmune antibodies triggered by the viruscould decrease production and differentiation of T lymphocytes and other lymphocytes viainhibition of growthand suppression of hematopoiesis[11, 14]. Elevation of neutrophil counts usually indicates an inflammatory response, while decrease in lymphocyte counts suggests damage to the immune system; therefore, as the results of our study suggest, high NLR and N8R can serve as diagnostic markers for severe COVID-19 [9] and also be useful predictors of severe illness.

Huang et al.were the first to suggest that a cytokine storm could be associated with severe disease [1]; this was confirmed by subsequent studies [15, 16]. IL-6 is a multifunctional cytokine involved in cell signaling and regulation of immune cells, and usually shows more significant serum levels changes than other cytokinesin inflammatory disease [17, 18]. IL-6 reflects the intensity of inflammatory response in the body. Liu et al. and Zhe et al. have demonstrated that IL-6 plays a pivotal role in the severity of COVID-19 and thereforehas value for monitoring the progress of severely ill patients [4, 5]. In our study, we found the cumulative incidence of severe illnessto be related to the baseline IL-6 level. Patients in the higher IL-6 quartile groups were significantly more likely to develop severe illness (Fig. 2A) and to need longer hospitalization (Fig. 2B). Since the severity of the disease corresponded to the intensity of inflammation, former phenomena could be explained.

Comorbidities such as diabetes and cancer can affect baseline IL-6 level [19, 20]; we therefore used logistic regression models to adjust for the effect of potential confounders. However, the significant association between baseline IL-6 level andrisk of severe illnesspersisted even after adjustment for all potential confounders (Models1–3; Table 2).

Neutrophil count, NLR, N8R, and IL-6 level have all been previously shown to be useful prognostic indicatorsin severe COVID-19 [4–6, 9]. We performed ROC analysis to identify the variable with the best predictive value. The AUC of IL-6 was significantly larger than those of neutrophil count and N8R. The AUC of IL-6 was comparable to that of NLR, which is currently widely accepted as a predictor of outcome in patients with COVID-19. However, the sensitivity of IL-6 was much higher than that of NLR (91.67% vs.66.67%). For any predictive or screening index, sensitivity is more important than specificity;the higher the sensitivity, the fewer the false-negative results. Thus,in addition to being a useful parameter for monitoring progression of disease in patients with COVID-19, baseline IL-6 could be a powerful predictor of risk of severe disease.

This study had some limitations. First, this was a retrospective analysis of a small sample and some bias is inevitable. Second, because of inconsistencies in data recording between the two centers, some clinical parameters could not be included in the analysis.

Baseline IL-6 levelappears to be areliable predictor of the risk for developing severe COVID-19. In patients with mild COVID-19 features on admission but serum IL-6 level > 5.20 pg/mL, early interventionmight helpprevent progression to severe disease.

Data source and collection

This retrospective observational study was jointly performed by the Fourth Affiliated Hospital, School of Medicine, Zhejiang University, and the General Hospital of the Central Theater Command, and was approved by the Ethics Committees of both institutions (No. K20200027 and No. [2020]017 − 1, respectively).

A total of 160 patients hospitalized for treatment of COVID-19 between January 10, 2020,and March 9, 2020, were included in this retrospective study. COVID-19 was diagnosed according to the Chinese National Health Committee guidelines (version 7) [21]. The patients were separated into two groups according to the course of the disease during hospital stay: the mild steady (MS) group (n = 88), comprising patients who had mild symptoms on admission and continued to have mild disease until recovery, and the severe change (SC) group, comprising patients who developed severe illness during hospitalization (n = 72). Severe illness was defined as COVID-19 with any of the following: 1) shortness of breath, with respiratory rate ≥ 30/min; 2) oxygen saturation by pulse oximeter ≤ 93% in resting state; 3) ratio of partial pressure of arterial oxygen (PaO₂) to fraction of inspired oxygen (FiO₂) ≤ 300 mm Hg (1 mm Hg = 0.133 kPa); or 4) increase in lesion size by ≥ 50% over 24–48hourson lung imaging. Data on demographic characteristics, comorbidities, radiographic findings, and laboratory tests on admission, and duration of hospitalization, were extracted from the electronic medical records and compared between the groups. All data were cross-checked for consistency and accuracy before analysis. For a closer exploration of the association between baseline IL-6 and risk of severe illness, we also grouped patients by IL-6 quartiles.

Statistical analysis

Continuous variables were expressed as means(± standard deviation) or medians (and interquartile ranges), and compared using the Student’s t-test orthe Mann–Whitney U test. Categorical variables were expressed as counts and percentages and compared using thechi-square test or Fisher exact test. Correlations between IL-6 and other variables were analyzed by Spearman correlation analysis. One-way analysis of variance (ANOVA) with Bonferroni correction was used to compare the duration of hospitalization between IL-6 quartilepatient subgroups. Multivariate logistic regression analysis was performed to assess the association between IL-6 level and risk of severe COVID-19. Odds ratios (OR) and 95% confidence intervals (95% CI) were calculated. To compare the predictive values of different laboratory parameters, receiver operating characteristic (ROC) curves were plotted, and the areas under the curves (AUC) were compared. The Youden index was used to find the optimal cutoff values of each parameter forpredicting future severe illness. Statistical analysis was performed using SPSS 20.0 (IBM Corp., Armonk, NY, USA).Statistical significance was at P ≤ 0.05 for all tests.

SARS-CoV-2: Severe acute respiratory syndrome coronavirus-2; COVID-19: Coronavirus disease-2019; IL-6: Interleukin-6; CT: Computed tomography; NLR: Neutrophil-to-lymphocyte ratio; N8R: Neutrophil-to-CD8+ T cell ratio; LDH: Lactate dehydrogenase; MERS: Middle East respiratory syndrome; MS: Mild steady; SC: Severe change; OR: Odds ratios; CI: Confidence intervals; ROC: Receiver operating characteristic; AUC: Areas under the curves

Acknowledgements

We thank all the medical staff of the two institutions for their hard work and great efforts during the outbreak of COVID-19.

Authors’ contributions

WGZ, YPW and JGW designed the research. YPL, BL and PX collected the data. WGZ, YPW and DWC contributed to analysis and interpretation of the data. YPL, BD and JGW interpreted the results and wrote the manuscripts. All authors have read and approved the manuscript。

Funding

This study was supported by the Science and Technology Project of Jinhua (No.2020XG-37) and the Medical Science and Technology Project of Zhejiang Province (No.2020ZH022).

Availability of data and materials

The datasets analysed during the current study are available from thecorresponding author on reasonable request.

Ethics approval and consent to participate

All participants provided written informed consent for the use oftheir information.This r study wasapproved by the Ethics Committees ofThe Fourth Affiliated Hospital, School of Medicine, Zhejiang University, and the General Hospitalof the Central Theater Commandboth.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interest.

Author details

¹Department of Clinical Laboratory, The Fourth Affiliated Hospital, School of Medicine, Zhejiang University,Yiwu, China. ²Department of Infectious Diseases,The Fourth Affiliated Hospital, School of Medicine, Zhejiang University, Yiwu, China. ³Department of Medical Laboratory Center, General Hospital of the Central Theater Command, Wuhan, China. ⁴Department of Blood Transfusion, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.

Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395(10223):497–506.
World Health Organization. Coronavirus Disease (COVID-2019) Situation Reports. 2020; https://www.who.int/emergencies/diseases/novel-coronavirus-2019.
Li X, Xu S, Yu M, et al. Risk factors for severity and mortality in adult COVID-19 inpatients in Wuhan. J Allergy ClinImmunol. 2020;146(1):110–8.
Liu T, Zhang J, Yang Y, et al. The role of interleukin-6 in monitoring severe case of coronavirus disease 2019. EMBO Mol Med. 2020;12(7):e12421.
Zhu Z, Cai T, Fan L, et al. Clinical value of immune-inflammatory parameters to assess the severityof coronavirus disease 2019. Int J Infect Dis. 2020;95:332–9.
Liu F, Li L, Xu M, Wu J, Luo D, Zhu Y, et al. Prognostic Value of interleukin-6, C-reactive Protein, and Procalcitonin in Patients With COVID-19. J ClinVirol. 2020;127:104370.
Centers for Disease Control and Prevetion. Frequently asked questions about SARS. 2019-07-24; https://www.cdc.gov/sars/about /faq.html.
World Health Organization. Middle East respiratory syndrome coronavirus (MERS-CoV). 2019-11-30; https://www.who.int/emerg encie s/mers-cov/en.
Liu J, Li S, Liu J, et al. Longitudinal characteristics of lymphocyte responses and cytokine profiles in the peripheral blood of SARS-CoV-2 infected patients.EBioMedicine. 2020;55:102763.
Liu R, Wang Y, Li J, et al. Decreased TCell Populations Contribute to the Increased Severity of COVID-19. ClinChimActa. 2020;508:110–4.
He R, Lu Z, Zhang L, et al. The clinical course and its correlated immune status in COVID-19pneumonia. J ClinVirol. 2020;127:104361.
Zhou Y, Fu B, Zheng X, et al. Aberrant pathogenic GM-CSF + T cells and inflammatory CD14 + CD16 + monocytes in severe pulmonary syndrome patients of a new coronavirus. medRxiv. 2020. https://doi.org/10.1101/2020.02.12.945576.
Chu H, Zhou J, Wong BH-Y, et al. Middle East Respiratory Syndrome Coronavirus Efficiently InfectsHuman Primary T Lymphocytes and Activates the Extrinsic and Intrinsic Apoptosis Pathways. J InfectDis. 2020;213(26):904–14.
Yang M, Li CK, Li K, et al. Hematological Findings in SARS Patients and Possible Mechanisms (Review). Int J Mol Med. 2004;14(2):311–5.
Chen L, Liu HG, Liu W, et al. Analysis of clinical features of 29 patients with 2019 novel coronavirus pneumonia.ZhonghuaJie. He He Hu Xi ZaZhi. 2020;43(3):203–8.
Xu Z, Shi L, Wang Y, et al. Pathological findings of COVID-19 associated with acute respiratory distress syndrome. Lancet Respir Med. 2020;8(4):420–2.
Taniguchi K, Karin M. IL-6 and related cytokines as the critical lynchpins between inflammation and cancer.SeminImmunol. 2014;26(1):54–74.
Tang Y, Liao C, Xu X, et al. Th1/Th2 cytokine profiles in G+/G- bacteremia in pediatric hematology/oncology patients. Pediatr Blood Cancer. 2012;58(1):50–4.
Landers-Ramos RQ, Blumenthal JB, Prior SJ. Serum IL-6 and sIL-6R in type 2 diabetes contribute to impaired capillary-like network formation. J ApplPhysiol (1985). 2019;127 (2):385 – 92.
Stoll JR, Vaidya TS, Mori S, et al. Association of IL-6 and TNF-α with mortality in hospitalized cancer patients. J Am AcadDermatol. 2020. https://doi.org/10.1016/j.jaad.2020.03.010.
National Health and Health Commission of the people's Republic of China. Diagnosis and Treatment of Pneumonia of New Coronavirus Infection (Trial Version 7). 2020-03-03; http://www.nhc.gov.cn/yzygj/s7653p/202003/46c9294a7dfe4cef80dc7f5912eb1989.shtml.

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Baseline Interleukin-6 Level Predicts Risk of Severe COVID-19: A Two-Center, Retrospective Study

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Abstract

Figures

Background

Result

Demographic and clinical characteristics

Correlations between baseline IL-6 and other parameters

Association between baseline IL-6 and risk of severe COVID-19

Comparison of predictive values of different variables

Discussion

Conclusion

Methods

Data source and collection

Statistical analysis

Abbreviations

Declarations

References

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