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. 2020 Nov 20;99(47):e23315. doi: 10.1097/MD.0000000000023315

Association of elevated inflammatory markers and severe COVID-19

A meta-analysis

Pan Ji 1, Jieyun Zhu 1, Zhimei Zhong 1, Hongyuan Li 1, Jielong Pang 1, Bocheng Li 1, Jianfeng Zhang 1,∗
Editor: Bernhard Schaller1
PMCID: PMC7676531  PMID: 33217868

Abstract

Our study aimed to assess the existing evidence on whether severe coronavirus disease 2019 (COVID-19) is associated with elevated inflammatory markers.

The PubMed, Embase, Web of Science, Scopus, Chinese National Knowledge Infrastructure, WanFang, and China Science and Technology Journal databases were searched to identify studies published between January 1 and April 21, 2020 that assayed inflammatory markers in COVID-19 patients. Three reviewers independently examined the literature, extracted relevant data, and assessed the risk of publication bias before including the meta-analysis studies.

Fifty-six studies involving 8719 COVID-19 patients were identified. Meta-analysis showed that patients with severe disease showed elevated levels of white blood cell count (WMD: 1.15, 95% CI: 0.78–1.52), C-reactive protein (WMD: 38.85, 95% CI: 31.19–46.52), procalcitonin (WMD: 0.08, 95% CI: 0.06–0.11), erythrocyte sedimentation rate (WMD: 10.15, 95% CI: 5.03–15.46), interleukin-6 (WMD: 23.87, 95% CI: 15.95–31.78), and interleukin-10 (WMD: 2.12, 95% CI: 1.97–2.28). Similarly, COVID-19 patients who died during follow-up showed significantly higher levels of white blood cell count (WMD: 4.11, 95% CI: 3.25–4.97), C-reactive protein (WMD: 74.18, 95% CI: 56.63–91.73), procalcitonin (WMD: 0.26, 95% CI: 0.11–0.42), erythrocyte sedimentation rate (WMD: 10.94, 95% CI: 4.79–17.09), and interleukin-6 (WMD: 59.88, 95% CI: 19.46–100.30) than survivors.

Severe COVID-19 is associated with higher levels of inflammatory markers than a mild disease, so tracking these markers may allow early identification or even prediction of disease progression.

Keywords: Coronavirus disease 2019, inflammatory marker, meta-analysis, severe disease

1. Introduction

The outbreak of coronavirus disease 2019 (COVID-19) in December 2019, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), poses a severe threat to global public health. Data from the World Health Organization indicate that as of April 26, 2020, there were more than 2 million confirmed COVID-19 infections and nearly 200,000 COVID-19 deaths in 208 countries or territories.[1] The number of new cases continues to rise rapidly worldwide, which poses a significant challenge to public health.[1] While the disease is mild or even asymptomatic in most patients, and usually self-resolves without the need for hospitalization, it can rapidly and unpredictably progress to a severe form requiring hospitalization and intensive care.

Single-center studies suggest that numerous inflammation markers are elevated in patients in the intensive care unit[2] or patients with severe disease[3–5] relative to patients with milder conditions. These markers include leukocyte count, procalcitonin level (PCT), C-reactive protein (CRP), interleukin-6 (IL-6), and interleukin-10 (IL-10). A meta-analysis also suggested that patients with increased PCT are nearly 5-fold more likely to have severe infection.[6]

Although several studies have suggested that severe disease may be associated with elevated WBC count, CRP, PCT, and IL-6,[5,7–9] the results across these studies are not entirely consistent. So far, it is unclear whether inflammatory markers are significantly higher in patients with severe COVID-19 than in those with mild disease. Therefore, to gain a clearer picture of the potential association between inflammatory markers and severe COVID-19, we meta-analyzed the relevant literature. The results may provide a basis for detecting or even predicting disease progression quickly enough to improve prognosis.

2. Data and methods

2.1. Search strategy

According to the Preferred Reporting Items for Meta-Analyses of Observational Studies in Epidemiology Statement,[10] our meta-analysis was carried out. We selected relevant studies published between January 1, 2020 and April 21, 2020. The literature was systematically searched using 7 databases: PubMed, Embase, Web of Science, Scopus, Chinese National Knowledge Infrastructure, WanFang, and the China Science and Technology Journal Database. Only literature available online was included, and no language restriction was imposed. The following keywords were used, both separately and in combination, when searching each database: “Coronavirus,““2019-nCoV,” “COVID-19,” “SARS-CoV-2,” “IL-6,” “IL-8,” “IL-10,” “tumor necrosis factor-alpha (TNF-α),” “erythrocyte sedimentation rate (ESR),” “procalcitonin,” “C-reactive protein,” “ESR,” or “Laboratory finding.”

2.2. Study eligibility

A study was included in the meta-analysis if it had a cohort, case-control, or case series design involving more than 20 patients with confirmed COVID-19; if it contained patients with the mild, severe, or critical disease, or it contained survivor and nonsurvivor groups; and if it reported sufficient details about inflammatory markers. The diagnosis and severity classification was based on the New Coronavirus Pneumonia Prevention and Control Program in China or WHO interim guideline, and patients were grouped into different types such as mild, moderate, severe, and critical diseases. The mild and moderate diseases were defined as “mild,” while severe and critical patients were categorized as “severe” in this study. All analyses were based on previously published studies. Thus no ethical approval and patient consent are required.

2.3. Data extraction and quality assessment

Three reviewers independently screened the articles’ titles and abstract to assess whether they were eligible for inclusion. The following data were extracted from included studies into an Excel database: the first author's surname, the publication date of the article, study design, sample size, age, outcome indicators, and assessment of bias risk. A fourth reviewer resolved disagreements. When necessary, authors of the original studies were contacted to obtain further information or clarification.

The quality of included studies was assessed based on the Newcastle-Ottawa Scale guidelines.[11] Three reviewers assessed study quality, and differences were resolved through discussion. Studies scoring more than 6 out of the total possible 9 points were considered high quality.

2.4. Statistical analyses

For studies that reported continuous data as ranges or as medians and interquartile ranges, the means and standard deviation were calculated as described.[12] All meta-analyses were performed using STATA 12 (StataCorp, TX) and a significance definition of 2-tailed P < .05. We calculated the weighted mean difference (WMD) and 95% confidence interval (CI) for differences in continuous variables between patients with severe or mild COVID-19 and between all COVID-19 patients who survived or died follow-up. Heterogeneity between studies was analyzed using the χ2 test with significance set at P < .10 and was quantified using the I2 statistic. The fixed-effect model was utilized when there was no significant heterogeneity in the pooled data. Otherwise, a sensitivity analysis was used to identify the study or studies explaining most of the heterogeneity, then these studies were removed, and the remaining ones were meta-analyzed using a random-effects model. Publication bias was assessed using funnel plots, Egger regression asymmetry test, and Begg test.

3. Results

3.1. Literature screening and assessment

A total of 1417 records were identified from the various databases examined, and 35 additional records were identified from the Chinese Medical Journal Network. After a detailed assessment based on the inclusion criteria, 56 studies[2–5,8,9,13–62] involving 8719 COVID-19 patients were included in the meta-analysis (Fig. 1).

Figure 1.

Figure 1

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Flowchart of literature screening.

3.2. Characteristics of included studies

All studies included in the meta-analysis were conducted in China and published between February 6, 2020 and April 21, 2020. These retrospective studies examined Chinese patients distributed across 31 provinces. Follow-up data were reported for most patients. All studies received quality scores varying from 6 to 9 points, indicating high quality (Table 1).

Table 1.

Basic characteristics of included studies.

First author Publication date in 2020 n Single- or multicenter∗ Patient population Age†, yr Follow-up Diagnosis and severity criteria‡ Outcomes§ Quality score||
Huang CL[2] Feb 15 41 Single Mild and severe COVID-19 patients in Hubei Province 49 (41–58) Dec 2019 to Jan 2, 2020 WHO interim guideline ① 7
Wan SX[3] Apr 16 123 Single Mild and severe COVID-19 patients in Chongqing Three Gorges Central Hospital 43.1 ± 13.1/61.2 ± 15.6 Jan 26 to Feb 4 WHO interim guideline ⑥⑦ 8
Qin C[4] Mar 12 452 Single Mild and severe COVID-19 patients in Tongji Hospital Affiliated to Huazhong University of Science and Technology 52.1 ± 15.1/60.3 ± 13.4 Jan 10 to Feb 12 Trial fifth Edition ①②③④⑤⑥⑦ 8
Xiao KH[5] Feb 27 143 Single Mild, severe, and critically ill COVID-19 patients in Chongqing Three Gorges Central Hospital 45.1 ± 1.0 Jan 23 to Feb 28 Trial fifth Edition ⑤ 9
Yuan J[8] Mar 06 223 Single Mild and severe COVID-19 patients in Chongqing Public Medical Center 46.5 ± 16 Jan 24 to Feb 23 Trial sixth Edition ①③ 9
Fang XW[9] Feb 25 79 Single Mild and severe COVID-19 patients in Anhui Provincial Hospital 45 ± 16.6 Jan 22 to Feb 18 Trial sixth Edition ①② 6
Wei WX[13] Mar 18 95 Single Mild, severe, and critically ill COVID-19 patients in Chengdu Public Health Clinical Medical Center 45.5 ± 58.6/56.7 ± 39.8/66.3 ± 28.2 Jan 16 to Feb 18 Trial fifth Edition ①③ 7
Shi YL[14] Feb 27 164 Single Mild, severe, and critically ill COVID-19 patients in Guangzhou Eighth Peoples Hospital NR Jan to Feb Trial fifth Edition ①③ 8
Liu et al[15] Feb 28 79 Multi Mild and severe COVID-19 patients in 3 tertiary hospitals in Wuhan 38 (33,57) Dec 30 to Jan 15 Trial fourth Edition ①②③④ 7
Deng Y[16] Mar 20 225 Multi Survival and non-survival COVID-19 patients in 2 tertiary hospitals in Wuhan 43 ± 18/68 ± 9 Jan 1 to Feb 21 Survival and non-survival ①② 8
Zhou F[17] Mar 11 191 Multi Survival and nonsurvival COVID-19 patients in Wuhan Jinyintan Hospital and Wuhan Pulmonary Hospital 56 (46–67) Dec 2019 to Jan 31, 2020 WHO interim guideline ①③⑤ 8
Chen T[18] Mar 26 274 Single Survival and non-survival COVID-19 patients in Tongji Hospital Affiliated to Huazhong University of Science and Technology 62 (44–70) Dec 2019 to Feb 28, 2020 Survival and non-survival ①②③④⑤ 7
Wang Y[19] Apr 8 344 Single Survival and non-survival COVID-19 patients in Tongji Hospital Affiliated to Huazhong University of Science and Technology 52–72 Jan 25 to Feb 25 WHO interim guideline ①②③⑤ 7
Cheng KB[20] Mar 12 463 Single Mild and severe COVID-19 patients in Wuhan Jinyintan Hospital 15–90 Dec 2019 to Feb 06, 2020 Trial fifth Edition ①②③④ 7
Wang D[21] Feb 08 138 Single Mild and severe COVID-19 patients in Zhongnan Hospital of Wuhan University 56 (42–68) Jan 1 to Jan 28 WHO interim guideline ① 7
Liu M[22] Feb 17 30 Single Mild and severe COVID-19 patients in Jianghan University Affiliated Hospital 35 ± 8 Jan 10 to Jan 31 Trial fifth Edition ① 6
Zhong SH[23] Mar 26 62 Single Mild, severe, and critically ill COVID-19 patients in Hainan General Hospital 51.8 ± 13.5 Jan 21 to Feb 10 Trial third Edition ① 6
Guan W[24] Feb 06 1099 Multi Mild and severe COVID-19 patients in 552 Hospitals in 31 provinces 47.0 NR WHO interim guideline ① 9
Qian GQ[25] Mar 17 88 Multi Mild and severe COVID-19 patients in 5 hospitals in Zhejiang province 50 (36.5–57) Jan 20 to Feb 11 WHO interim guideline ①② 9
LI KH[26] Feb 15 41 Single Mild and severe COVID-19 patients in Hubei Province 49 (41–58) Dec 2019 to Jan 2, 2020 Trial fifth Edition ①②③ 7
Wan SX[27] Feb 29 83 Single Mild and severe COVID-19 patients in The Second Affiliated Hospital of Chongqing Medical University 45.5 ± 12.3 Jan to Feb WHO interim guideline ①②③ 8
Gao Y[28] Mar 21 135 Single Mild and severe COVID-19 patients in Chongqing Three Gorges Central Hospital 47 (36–55) Jan 23 to Feb 8 WHO interim guideline ①②③⑤ 6
Zhang JJ[29] Mar 17 43 Single Mild and severe COVID-19 patients in Fuyang Second People's Hospital 45 ± 7.7/43 ± 14 Jan 23 to Feb 2 trail version 3–5 ①②③⑧ 7
Chen W[30] Mar 17 91 Single Mild, severe, and critically ill COVID-19 patients in Jingmen First People's Hospital 41.6 ± 15.6 Dec 2019 to Feb 21, 2020 Trial seventh Edition ① 7
Li D[31] Mar 26 80 Single Mild and severe COVID-19 patients in tertiary hospitals in Zhuzhou 47.8 ± 19.5 Jan 20 to Feb 27 Trial fifth Edition ①②③④ 7
Li D[32] Apr 2 62 Single Mild, severe, and critically ill COVID-19 patients in Renmin Hospital of Wuhan University 49 ± 37/59 ± 31 Jan 31 to Feb 25 Trial sixth Edition ①②③ 6
Xiong J[33] Mar 03 89 Single Mild, severe, and critically ill COVID-19 patients in Renmin Hospital of Wuhan University 53 ± 16.9 Jan 17 to Feb 20 Trial sixth Edition ①②③ 7
Liu J[34] Mar 27 91 Single Mild, severe, and critically ill COVID-19 patients in Wuhan Children's Hospital NR Jan 25 to Feb 18 Trial fifth Edition ①③ 6
Gao W[35] Mar 31 90 Single Mild, severe, and critically ill COVID-19 patients in Beijing Youan Hospital 51.7 ± 18.6 Jan to Feb Trial sixth Edition ①②③ 7
Xie HS[36] Apr 2 79 Single Mild and severe COVID-19 patients in Wuhan Jinyintan Hospital 60 (48–66) Feb 2 to Feb 23 Trial sixth Edition ①②④ 7
Zhang YF[37] Apr 2 121 Single Mild and severe COVID-19 patients in Zhongnan Hospital of Wuhan University 43.9 ± 15/65 ± 1 Dec 2019 to Feb 22, 2020 Trial fifth Edition ① 7
Chen L[38] Feb 6 29 Single Mild, severe, and critically ill COVID-19 patients in Tongji Hospital Affiliated to Huazhong University of Science and Technology 26–79 Jan Trial fifth Edition ①⑤⑥⑦ 6
Peng YD[39] Mar 2 112 Single Mild and severe COVID-19 patients in western district of Union Hospital in Wuhan 62 (5567) Jan 20 to Feb 15 Trial sixth Edition ①③ 7
Chen M[40] Feb 27 54 Single Mild, severe, and critically ill COVID-19 patients in Hubei No. 3 People's Hospital 43.8–69 Jan 24 to Feb 8 Trial fifth Edition ①②③④ 6
Wan Q[41] Feb 24 153 Single Mild and severe COVID-19 patients in Chongqing Public Health Medical Center 43.4 ± 15/57.7 ± 13 Jan 26 to Feb 5 Trial fifth Edition ① 8
Li D[42] Mar 5 30 Single Mild and severe COVID-19 patients in Shenyang sixth people's hospital 21–72 Jan 22 to Feb 8 Trial fifth Edition ① 6
Ling Y[43] Mar 18 292 Single Mild and severe COVID-19 patients in Shanghai Public Health Clinical Center 48.7 ± 16/65.5 ± 16 Jan 20 to Feb 10 Trial fifth Edition ①②③④ 9
Bin YF[44] Feb 29 55 Single Mild and severe COVID-19 patients in Huangpi District Chinese Medicine Hospital of Wuhan 53.9 ± 17. 1 Jan 29 to Feb 16 Trial fifth Edition ① 6
Xia XT[45] Apr 7 63 Single Mild and severe COVID-19 patients in Hubei Provincial Hospital of Integrated Chinese and Western Medicine 62.3 ± 15.1/64.6 ± 14.9 Jan 26 to Feb 20 Trial fifth Edition ① 6
Chen C[46] Mar 6 150 Single Mild and severe COVID-19 patients in Tongji Hospital Affiliated to Huazhong University of Science and Technology 59 ± 16 Jan to Feb Trial sixth Edition ① 6
Liu SJ[47] Apr 2 342 Single Mild, severe, and critically ill COVID-19 patients in Ezhou Central Hospital NR Jan 23 to Feb 12 Trial sixth Edition ①②③④ 7
An W[48] Apr 16 110 Single Survival and nonsurvival COVID-19 patients in Hubei No.3 People’ s Hospital of Jianghan University NR Jan 24 to Feb 19 Current trail version ①③④ 7
Feng Y[49] Apr 10 476 Multi Mild, severe, and critically ill COVID-19 patients from 3 hospitals in Wuhan, Shanghai and Anhui NR Jan 1 to Feb 15 Trial fifth Edition ①②③④ 8
Cai QX[50] Apr 2 298 Single Mild and severe COVID-19 patients in The Third People's Hospital of Shenzhen 42.7 ± 18.7/61.5 ± 7.6 Jan 11 to Feb 6 WHO interim guideline ①②③④⑤ 6
Zheng F[51] Mar 161 Single Mild and severe COVID-19 patients in Changsha First Hospital 40.7 ± 15.0/56.5 ± 15.2 Jan 17 to Feb 7 Trial fifth Edition ①② 7
Tang JS[52] Apr 13 40 Single Mild, severe, and critically ill COVID-19 patients in The Ninth People's hospital of DongGuan NR Jan to Feb Trial sixth Edition ①③⑤ 8
Zheng YL[53] Apr 10 99 Single Mild and severe COVID-19 patients in Chengdu Public Health Clinical Medical Center 42.5 ± 15.1/63.8 ± 16.5 Jan 16 to Feb 20 Trial fifth Edition ①② 6
Wang L[54] Mar 30 339 Single Survival and non-survival COVID-19 patients in Renmin Hospital of Wuhan University 68.7 ± 7.5/76.3 ± 9.9 Jan 1 to Feb 6 Trial sixth Edition ①③⑤ 7
Ruan QR[55] Apr 6 150 Multi Mild and severe COVID-19 patients in Wuhan Jinyintan Hospital and Tongji Hospital Affiliated to Huazhong University of Science and Technology 58.3 ± 27.9/54.3 ± 50.0 NR Survival and nonsurvival ①②⑤ 8
Tu WJ[56] Apr 6 174 Single Survival and non-survival COVID-19 patients in Zhongnan Hospital of Wuhan University 50.0 ± 18.7/71.3 ± 21.6 Jan 3 to Feb 24 Survival and non-survival ①⑤ 8
Chen XH[57] Apr 17 48 Single Mild and severe COVID-19 patients in General Hospital of Central Theater Command 52.8 ± 14.2/63.7 ± 15.2/79.6 ± 12.6 Jan 1 to Feb 19 Trial sixth Edition ①③⑤ 9
Ma J[58] Apr 13 37 Single Mild and severe COVID-19 patients in Renmin Hospital of Wuhan University 61.0 ± 4.9/66.5 ± 9.6 Jan 1 to Mar 30 Trial seventh Edition ①⑤ 7
Wang ZL[59] Mar 16 69 Single Mild and severe COVID-19 patients in western district of Union Hospital in Wuhan 40.0 ± 14.5/69.8 ± 12.4 Jan 16 to Jan 29 Trial third Edition ①②③④⑤⑥⑦ 7
Zou QX[60] Apr 21 50 Single Mild, severe, and critically ill COVID-19 patients in Sino-French New City Campus of Tongji Hospital Affiliated to Huazhong University of Science and Technology 62.3 ± 10.6/65.8 ± 11.1/72.4 ± 8.5 Feb to Mar Trial sixth Edition ①⑦ 6
He XW[61] Apr 21 56 Single Survival and non-survival COVID-19 patients in Sino-French New City Campus of Tongji Hospital Affiliated to Huazhong University of Science and Technology 64.8 ± 12.3/69.7 ± 13.0 Feb 3 to Feb 24 Trial seventh Edition ①④ 8
Zuo FT[62] Apr 14 50 Single Mild and severe COVID-19 patients in Nanyang Central Hospital 46.6 ± 16.3/53.7 ± 10.1 Jan 19 to Mar 20 Trial fifth Edition ①②③ 8
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∗

All studies were retrospective.

†

Reported as range, mean ± SD, or median (interquartile range).

‡

Version of New Coronavirus Pneumonia Prevention and Control Program in China, or WHO interim guideline.

§

NR = not reported. ① WBC = white blood cell count, ② CRP = C-reactive protein, ③ PCT = procalcitonin, ④ ESR = erythrocyte sedimentation rate, ⑤ IL-6 = interleukin-6, ⑥ IL-10 = interleukin-10, ⑦ TNF-α = tumor necrosis factor-α.

||

Score based on the Newcastle-Ottawa Scale guidelines.[11]

3.3. Meta-analysis

3.3.1. Inflammatory markers

Pooled results revealed that patients with severe disease showed significantly higher (WMD: 1.15, 95% CI: 0.78–1.52), CRP (WMD: 38.85, 95% CI: 31.19–46.52), PCT (WMD: 0.08, 95% CI: 0.06–0.11), erythrocyte sedimentation rate (WMD: 10.15, 95% CI: 5.03–15.46), IL-6 (WMD: 23.87, 95% CI: 15.95–31.78), and IL-10 (WMD: 2.12, 95% CI: 1.97–2.28) (Figs. 2–5, Table 2). In contrast, the 2 groups showed similar TNF-α levels.

Figure 2.

Figure 2

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Meta-analysis of the difference in white blood cell count (×1012/L) between patients with mild or severe COVID-19. WMD = weighted mean difference, COVID-19 = coronavirus disease 2019.

Figure 5.

Figure 5

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Meta-analysis of the difference in PCT (ng/mL) between COVID-19 patients with mild or severe disease. WMD = weighted mean difference. COVID-19 = coronavirus disease 2019, PCT = procalcitonin.

Table 2.

Meta-analysis of inflammatory marker levels in Chinese COVID-19 patients.

Heterogeneity Meta-analysis
Marker No. studies No. patients P I2 Model∗ WMD (95%CI) P
Mild versus severe disease
 WBC, × 10 9/L 37 8973 <.001 77.0% R 1.15 (0.78,1.52) <.001
 CRP, mg/L 34 4910 <.001 93.0% R 38.85 (31.19,46.52) <.001
 PCT, ng/mL 27 4250 <.001 90.3% R 0.08 (0.06,0.11) <.001
 ESR, mm/h 11 2684 <.001 75.7% R 10.25 (5.03,15.46) <.001
 IL-6, pg/mL 11 1359 <.001 93.1% R 23.87 (15.95,31.78) <.001
 IL-10, pg/mL 4 673 .791 0.0% F 2.12 (1.97,2.28) <.001
 TNF-α, pg/mL 5 723 <.001 91.1% R 0.20 (−0.60,1.01) .622
Nonsurvivors versus survivors
 WBC, × 1012/L 4 1034 .057 60.1% R 4.11 (3.25,4.97) <.001
 CRP, mg/L 7 1522 <.001 76.4% R 74.18 (56.63,91.73) <.001
 PCT, ng/mL 4 1067 <.001 93.2% R 0.26 (0.11,0.42) .001
 ESR, mm/h 3 440 .902 0.0% F 10.94 (4.79,17.09) <.001
 IL-6, pg/mL 5 1322 <.001 98.0% R 59.88 (19.46,100.30) .004
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CI = confidence interval, CRP = C-reactive protein, ESR = erythrocyte sedimentation rate, IL-10 = interleukin-10, IL-6 = interleukin-6, PCT = procalcitonin, TNF-α = tumor necrosis factor-α, WBC = white blood cell count, WMD = weighted mean difference.

∗

R means random model; F means fixed model.

Figure 3.

Figure 3

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Meta-analysis of the difference in CRP (mg/L) between COVID-19 patients with mild or severe disease. WMD = weighted mean difference, COVID-19 = coronavirus disease 2019, CRP = C-reactive protein.

Figure 4.

Figure 4

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Meta-analysis of the difference in IL-6 (pg/mL) between COVID-19 patients with mild or severe disease. WMD = weighted mean difference, COVID-19 = coronavirus disease 2019, L-6 = interleukin-6.

Eight studies[16–19,48,54,56,61] comparing 543 COVID-19 patients who died during follow-up with 1713 who remained alive during the same period found that on admission, patients who subsequently died showed significantly higher white blood cell count (WMD: 4.11, 95% CI: 3.25–4.97), CRP (WMD: 74.18, 95% CI: 56.63–91.73), PCT (WMD: 0.26, 95% CI: 0.11–0.42), erythrocyte sedimentation rate (WMD: 10.94, 95% CI: 4.79–17.09), and IL-6 (WMD: 59.88, 95% CI: 19.46–100.30) (Table 2).

3.3.2. Sensitivity analysis

For most of the outcomes described in Section 3.3.1, there was heterogeneity in the pooled data, with I2 ranging from 60.1% to 98.5%. Therefore we repeated each meta-analysis after excluding 1 study at a time. We found that the results did not change substantially (Fig. 6), suggesting our original meta-analyses’ reliability.

Figure 6.

Figure 6

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Sensitivity analysis of white blood cell count between patients with mild or severe COVID-19. COVID-19 = coronavirus disease 2019.

4. Discussion

Inflammation markers can appear elevated in infected individuals, including those infected with SARS-CoV-2.[54] Previous work suggested that the magnitude of the elevation WBC count, CRP, PCT, and IL-6 may relate to the severity of the resulting COVID-19.[17,59] The National Health Commission of the People's Republic of China included elevated inflammatory factors such as IL-6 and CRP as potential early warning indicators of severe disease in its widely used “COVID-19 diagnosis and treatment plan (for version 7).”[63] While these considerations imply that monitoring levels of inflammatory markers may help identify progression to severe disease, the literature has not been entirely consistent on which markers may be useful in this regard. For example, at least 2 studies found white blood cell count is similar or even lower in severe disease than in mild disease[23,44] in severe disease, yet other studies found the same marker to be higher in severe disease.[32,35] To help clarify the inflammatory markers whose elevation may signal severe COVID-19, we meta-analyzed the relevant literature from January 1, 2020, at the beginning of what would quickly become a global pandemic. Our analysis of 56 studies[2–5,8,9,13–62] involving 8719 patients with confirmed COVID-19 suggests that WBC, CRP, PCT, ESR, and IL-6 are significantly higher with mild disease, and higher in those who die during follow-up than in those who survive. It is also noteworthy that our results are also in keeping with those of previous studies,[6,64,65] but our study included larger sample size and the analyzed inflammation markers that are more comprehensive. However, there is no insufficient evidence that shows a ranking for inflammatory markers in terms of correlation with the severity of COVID-19. These findings justify the monitoring of inflammatory markers to detect COVID-19 progression as early as possible for timely intervention.

Our results are consistent with the idea that IL-6 levels positively correlate with COVID-19 severity,[38] with levels in critically ill patients exceeding those with the milder disease by up to 10 times.[38] Our results are also consistent with a positive correlation between IL-6 levels and the risk of mortality.[57] IL-6 has strong pro-inflammatory effects.[66–68] Increases in IL-6 also trigger increases in PCT, which may explain why both are significantly higher in severe COVID-19.[69] The reason for there is no significant difference in TNF-α levels between mild and severe groups is unclear, but it may be related to the inhibition of Th2 cells involved in humoral immunity in the early stage of infection[3] or the sample size is small, and the results are not representative.

The increase in inflammatory markers seen with severe COVID-19 is reminiscent of increases in similar markers during infection. For example, upon bacterial infection, PCT is released into the circulation, and elevated levels in peripheral blood correlate with infection severity.[5] Tan et al[70] found that CRP increased significantly in the initial stage of severe COVID-19 infection, while there was no significant difference in CT imaging between the severe group and the mild group. Research is needed to clarify to what extent the increases in inflammatory markers are caused directly by SARS-CoV-2 or reflect comorbidities such as hypertension, diabetes mellitus, and other chronic diseases that, like infectious diseases, trigger a chronic proinflammatory state. Patients with such comorbidities are more likely to develop severe COVID-19 than patients who are otherwise healthy,[71] at least partly because such conditions weaken the innate immune response, increasing the risk of SARS-CoV-2 infection.[70]

Our results suggest that monitoring inflammatory markers may serve as an early warning system for progression to severe COVID-19. Simultaneously, monitoring levels of IL-6, CRP, and PCT can allow early detection of bacterial infections, which may reduce overprescription of antibiotics for patients who do not need them and trigger early antibiotic therapy to prevent sepsis and other severe conditions.[72]

Although our meta-analysis rigorously analyzed data from a large sample of COVID-19 patients, our results are limited by the heterogeneity observed across studies, such as in the disease course and severity, reflecting the difficulties of standardizing methods during an emerging epidemic. We could not control for these and other potential confounders because all studies in our meta-analysis were retrospective. Due to the nature of reporting in the emerging outbreak, we did not perform a risk of bias assessment and presume it to be high across studies, which should be considered when interpreting results.

5. Conclusion

In summary, current evidence showed that higher levels of inflammatory markers such as WBC, CRP, PCT, ESR, IL-6, and IL-10 are associated with the severity of COVID-19 and thus could be used as significant prognostic factors of the disease.

Author contributions

Zhimei Zhong, Hongyuan Li, Jielong Pang, and Bocheng Li collected and analyzed the data. Jianfeng Zhang acquired the funding. Pan Ji and Jieyun Zhu designed the study and wrote the first draft of the manuscript. Jianfeng Zhang designed and supervised the study, and finalized the manuscript, which all authors read and approved.

Conceptualization: Pan Ji, Jielong Pang, Bocheng Li.

Data curation: Zhimei Zhong, Hongyuan Li, Jielong Pang.

Formal analysis: Pan Ji, Zhimei Zhong.

Funding acquisition: Jianfeng Zhang.

Investigation: Zhimei Zhong.

Methodology: Pan Ji, Jielong Pang, Bocheng Li, Jianfeng Zhang.

Resources: Jianfeng Zhang.

Software: Jieyun Zhu, Hongyuan Li, Bocheng Li.

Supervision: Jianfeng Zhang.

Validation: Jianfeng Zhang.

Writing – original draft: Pan Ji, Jieyun Zhu.

Writing – review & editing: Jianfeng Zhang.

Footnotes

Abbreviations: CI = confidence interval, COVID-19 = coronavirus disease 2019, CRP = C-reactive protein, ESR = erythrocyte sedimentation rate, IL-10 = interleukin-10, IL-6 = interleukin-6, = not reported, PCT = procalcitonin, TNF-α = tumor necrosis factor-α, WBC count = white blood cell count, WMD = weighted mean difference.

How to cite this article: Ji P, Zhu J, Zhong Z, Li H, Pang J, Li B, Zhang J. Association of elevated inflammatory markers and severe COVID-19: a meta-analysis. Medicine. 2020;99:47(e23315).

PJ and JZ contributed equally to this work.

This study was supported by grants from the National Natural Science Foundation of China (81960343), the Emergency Science and Technology Brainstorm Project for the Prevention and Control of COVID-19, which is part of the Guangxi Key Research and Development Plan (GuikeAB20058002), and the High-level Medical Expert Training Program of Guangxi “139” Plan Funding (G201903027).

The authors have no conflicts of interest to disclose.

All data generated or analyzed during this study are included in this published article [and its supplementary information files].

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