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. 2020 Jul 27;92(10):1825–1833. doi: 10.1002/jmv.26055

COVID‐19 and liver dysfunction: A systematic review and meta‐analysis of retrospective studies

Mohanad Youssef 1, Mohammad H Hussein 1, Abdallah S Attia 1, Rami M Elshazli 2, Mahmoud Omar 1, Ghassan Zora 1, Ashraf S Farhoud 1, Ahmad Elnahla 1, Areej Shihabi 1, Eman A Toraih 1,3, Manal S Fawzy 4,5, Emad Kandil 6,✉
PMCID: PMC7283797  PMID: 32445489

Abstract

Recently, Coronavirus Disease 2019 (COVID‐19) pandemic is the most significant global health crisis. In this study, we conducted a meta‐analysis to find the association between liver injuries and the severity of COVID‐19 disease. Online databases, including PubMed, Web of Science, Scopus, and Science direct, were searched to detect relevant publications up to 16 April 2020. Depending on the heterogeneity between studies, a fixed‐ or random‐effects model was applied to pool data. Publication bias Egger's test was also performed. Meta‐analysis of 20 retrospective studies (3428 patients), identified that patients with a severe manifestation of COVID‐19 exhibited significantly higher levels of alanine aminotransferase, aspartate aminotransferase, and bilirubin values with prolonged prothrombin time. Furthermore, lower albumin level was associated with a severe presentation of COVID‐19. Liver dysfunction was associated with a severe outcome of COVID‐19 disease. Close monitoring of the occurrence of liver dysfunction is beneficial in early warning of unfavorable outcomes.

Keywords: COVID‐19, liver function, meta‐analysis, outcome, SARS‐CoV‐2

Highlights

Liver dysfunction was associated with a severe outcome of COVID‐19

Sever cases of COVID‐19 patients have high serum levels of ALT, AST and bilirubin with prolonged prothrombin time.

Low serum albumin is associated with a severe presentation of COVID‐19

Close monitoring of the occurrence of liver dysfunction is beneficial in early warning of unfavorable COVID‐19 outcomes.

Abbreviations

ACE2

angiotensin‐converting enzyme 2

AKI

acute kidney injury

ALT

alanine aminotransferase

ARDS

acute respiratory distress syndrome

AST

aspartate aminotransferase

COVID‐19

Coronavirus Disease 2019

PT

prothrombin time

SARS‐CoV‐2

severe acute respiratory syndrome coronavirus 2

SMD

the standardized mean difference

TSA

trial sequential analysis

1. INTRODUCTION

In December 2019, a novel virus known as severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) was identified as a causative pathogen for a cluster of pneumonia initially detected in Wuhan City, China. 1 As of 3 May 2020, the World Health Organization has reported Worldwide 3 267 184 confirmed cases and 229 971 deaths. The United States has reported 1 067 127 confirmed cases and 57 406 deaths. 2

Coronavirus Disease 2019 (COVID‐19) is typically characterized by the symptoms of viral pneumonia, such as fever, fatigue, dry cough, anosmia, and headache, which may evolve to respiratory failure. 3 , 4 The pathogen, however, displays a wide range of severity causing difficulty in determining infection outcome. COVID‐19 may cause hepatic, intestinal, and respiratory diseases, and lead to respiratory distress syndrome, organ failure, and even death in severe cases. 5 , 6

Currently, studies about the relationship between underlying mechanisms of COVID‐19 and liver dysfunction are limited. COVID‐19 uses the angiotensin‐converting enzyme 2 (ACE2) as the binding site to enter the host cell in the lungs, kidneys, and heart. 7 Chai et al 8 found that both liver cells and bile duct cells express ACE2. However, the ACE2 expression of bile duct cells is much higher than that of liver cells. 9 These findings suggest that liver injury in patients with COVID‐19 may be the result of damage to bile duct cells. Various studies have reported the laboratory findings and the clinical characteristics associated with different degrees of liver dysfunction in patients with COVID‐19 disease. 10 , 15

However, to date, there is still limited research regarding the concomitant association between the COVID‐19 and the hepatobiliary system. Therefore, by meta‐analyzing data in the observational studies available so far, our study aimed to assess liver dysfunction among patients infected with SARS‐CoV‐2 to investigate the potential relationship between acute liver injury and COVID‐19.

2. METHODS

2.1. Literature search strategy

A comprehensive literature review of all qualifying studies was conducted to identify the association of COVID‐19 with acute liver injury based on Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines. 16 Two authors (RE, MY) independently screened the following medical electronic database: PubMed, Web of Science, Scopus, and Science direct for relevant data published up to 16 April 2020, using a combination of the following keywords and medical subjects headings (MeSHs): (“COVID‐19” OR “SARS‐CoV‐2” OR “severe acute respiratory syndrome coronavirus 2” OR “coronavirus SARS‐CoV‐2” OR “2019‐nCoV” OR “Wuhan coronavirus” OR “Wuhan pneumonia”) AND (“Liver” OR “Acute Liver injury” OR “Liver enzymes” Chronic Liver”) AND (“outcome” OR “survival” OR “mortality” OR “complications” Or “infection”). The reference list of previous studies and systematic reviews were also searched for identifying eligible studies. The identified records were screened for the inclusion criteria specified for the present systematic review and meta‐analysis.

2.2. Eligibility criteria

We applied the following criteria to all extracted studies: (a) Types of studies: observational, retrospective cohort, prospective case‐control, or clinical trials reporting laboratory features of COVID‐19 patients, including alanine aminotransferase (ALT), aspartate aminotransferase (AST), bilirubin, albumin, and prothrombin time (PT); (b) Subjects: diagnosed patients with COVID‐19, and (c) Severity: mild cases of COVID‐19 disease with patients that do not require extraordinary measures to manage the diseases and severe cases of COVID‐19 infection who developed COVID‐related complications such as acute respiratory distress syndrome (ARDS) and respiratory failure, or expired. Exclusion criteria were as follows: (a) duplicate data (b) case reports, series, abstract‐only articles, conference article and comment, editorials and expert opinions (c) studies with insufficient outcome data, and (d) preprints (articles in the peer‐review stage).

2.3. Data extraction

Data extraction was conducted by four authors (MY, GZ, AA, and AF). The process included using a two‐step approach: first, we screened titles and abstracts for eligibility according to the study objective, and second, we screened the full‐text article of relevant abstracts.

2.4. Quality assessment

The Newcastle‐Ottawa Scale was used for assessing the quality of eligible manuscripts. Publication bias was assessed with the Newcastle‐Ottawa Quality Assessment Scale cohort studies. 17

2.5. Pairwise comparison and heterogeneity assessment

The pooled estimates were extracted using RevMan version 5.3. Descriptive summary statistics in the form of mean, standard deviation, and range for continuous parametric measures were tabulated. Pairwise comparison between mild and severe COVID‐19 patients was performed. Overall pooled odds ratio (OR) or standardized mean difference (SMD) with 95% confidence intervals (CI) were estimated for categorical and quantitative variables, respectively. A Fixed‐effects model was employed unless significant heterogeneity was detected. In this case, the Random‐effects model has applied. 18 Heterogeneity was considered significant if the I2 value exceeds 50%, or its P value was less than .1.

Subgroup analyses by the location of the patients, publication date, sample size, and quality score were performed. Sensitivity analysis was carried out by removing one study each time, to reflect its effect size on the overall OR.

Publication bias was assessed via Begg's funnel plot and Egger's linear regression approach using Comprehensive Meta‐analysis software. 19 An asymmetric funnel‐shape or a P value less than .1 indicated significant bias. 20

2.6. Meta‐regression analysis

Meta‐regression analysis was employed using OpenMeta Analyst software, taking into consideration the following study characteristics; sample size, mean age of patients, percentage of males, city of the hospital, publication date, and quality score.

2.7. Trial sequential analysis

To evaluate the reliability of statistical appraisal of this meta‐analysis study, we used trial sequential analysis (TSA) software (version 0.9.5.10 beta) by merging several available sample sizes of applicable studies with the threshold of statistical influence to reduce the unintentional miscalculations and improve the strength of anticipations. We used two‐side trials and type I error with a calculated power of 5% and 80%. If the cumulative Z‐curve crosses the monitoring boundaries, no additional trials would be required. On the contrary, if the Z‐curve did not accomplish the boundary levels, the necessary threshold requires additional records to achieve a prominent significance.

3. RESULTS

3.1. Characteristics of the included studies

Following the removal of duplicates (n = 1870), our database search identified 2582 unique citations, of which 186 full‐text articles were assessed. A total of 20 eligible retrospective cohort studies, including 3428 positively confirmed COVID‐19 patients, were enrolled in the current meta‐analysis. The workflow of the process of study selection is demonstrated in Figure 1. All articles were published during the period between 30 January and 16 April 2020. Most of them were from Wuhan city (13), three from Zhejiang, one from Guangdong, one from Hubei, one from Guangdong, and one from Anhui. As depicted in Table 1, the sample size of studies ranged from 21 to 651 cohorts. The mean age of patients was 53.8 years, and 57.8% were men. In the included studies, the severe disease was detected in 36.2% of patients and the average survival rate was 72.18%. All studies except for three scored more than 5 on the scale. Two studies scored a three, and one study scored a two.

Figure 1.

Figure 1

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The workflow of the selection process

Table 1.

Characteristics of the included studies

Author Date of publication Year Ref Journal name Study Country City Sample Size Mean age Male/female Severe cases (%) Survival rate (%) Quality score
Huang 4 30 Jan 2020 [4] The Lancet Retro China Wuhun 41 49 30/11 31.7 85.37 7
Wang 21 7 Feb 2020 [21] JAMA Retro China Wuhun 138 58.5 75/63 26.1 NA 5
Yang 22 21 Feb 2020 [22] The Lancet Retro China Wuhun 52 58.25 35/17 61.5 38.46 6
Liu 23 28 Feb 2020 [23] Chin Med J (Engl) Retro China Wuhun 78 51.5 39/39 14.1 NA 5
Ruan 24 3 Mar 2020 [24] Intensive Care Med Retro China Wuhun 150 58.5 102/48 45.3 54.67 5
Zhou 25 11 Mar 2020 [25] The Lancet Retro China Wuhun 191 60.5 119/72 28.3 71.73 8
Gao 11 13 Mar 2020 [11] J Med Virol Retro China Anhui 43 44 26/17 34.9 NA 5
Wu 14 13 Mar 2020 [14] JAMA Intern Med Retro China Wuhun 201 53.25 128/73 41.8 78.11 5
Zhang 15 15 Mar 2020 [15] Int J Infect Dis Retro China Zhejiang 645 40.77 328/317 88.8 NA 7
Mo 10 16 Mar 2020 [10] Clin Infect Dis Retro China Wuhun 155 53.5 86/69 54.8 NA 5
Wang 26 16 Mar 2020 [26] Clin Infect Dis Retro China Wuhun 69 53.75 32/37 20.3 92.75 8
Chen 12 17 Mar 2020 [12] BMJ Retro China Wuhun 274 59.5 171/103 41.2 58.76 8
Qian 27 17 Mar 2020 [27] QJM Retro China Zhejiang 91 57.5 37/54 9.9 NA 3
Qu 28 17 Mar 2020 [28] J Med Virol Retro China Guangdong 30 54.72 NA 10.0 NA 2
Deng 29 20 Mar 2020 [29] Chin Med J (Engl) Retro China Wuhun 225 54.5 124/101 48.4 51.56 7
Wan 30 21 Mar 2020 [30] J Med Virol Retro China Chongqing 135 50 72/63 29.6 99.26 8
Jin 31 24 Mar 2020 [31] Gut Retro China Zhejiang 651 45.61 331/320 11.4 NA 8
Chen 13 27 Mar 2020 [13] J Clin Invest Retro China Wuhun 21 56.5 17/4 52.4 80.95 6
Pan 32 14 Apr 2020 [32] Am J Gastroenterol Retro China Hubei 204 52.9 107/97 50.5 82.35 7
Zhou 33 16 Apr 2020 [33] J Infect Retro China Wuhun 34 65 17/17 23.5 NA 3
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Abbreviations: Ref, reference number; Retro, retrospective.

3.2. Pooled analysis of laboratory findings

Table 2 summarizes pairwise comparison, heterogeneity analysis, and publication bias of the meta‐analysis. Patients who had severe presentations of COVID disease had higher levels of AST (SMD = 0.36; 95% CI = 0.27; 0.44; P < .001), ALT (SMD = 0.44; 95% CI = 0.35, 0.52; P < .001), bilirubin (SMD = 0.40; 95% CI = 0.31, 0.50; P < .001), and PT (SMD = 0.69; 95% CI = 0.57, 0.81; P < .001). In contrast, lower albumin level was associated with severe presentation (SMD = −0.68; 95% CI = −0.7, −0.58; P < .001) (Figure S1). Apart from ALT data, significant heterogeneity was detected in laboratory results. Subgroup analysis by the origin of the hospital, publication date, sample size, and quality score of the studies failed to resolve the obvious heterogeneity.

Table 2.

Summarizing results of pooled estimates of liver function tests and clinical parameters of chronic liver patients

Characteristics Number studies Sample size Test of association Effect size Heterogeneity Publication bias
Total Mild Severe Method Model Estimate 95% CI P value I 2 P value P (Egger's)
Laboratory tests
ALT 19 3376 1953 1423 SMD, IV Fixed 0.35 0.27, 0.43 .073 34.13% <.001 0.279
AST 19 3376 1953 1423 SMD, IV Random 0.44 0.17, 0.70 <.001 88.80% .001 0.940
Bilirubin 11 2512 1365 1147 SMD, IV Random 0.41 0.20, 0.62 <.001 75.02% <.001 0.773
Albumin 11 2605 1434 1171 SMD, IV Random −0.84 −1.20, −0.48 <.001 91.4% <.001 0.204
PT 10 1300 799 501 SMD, IV Random 0.62 0.32, 0.91 <.001 81.34% <.001 0.512
Comorbidities
Hypertension 13 2141 1024 1117 OR, M‐H Fixed 2.36 1.86, 3.01 .30 14.01% <.001 0.976
Chronic kidney dis 7 1675 690 985 OR, M‐H Fixed 7.28 3.25, 16.26 .54 0.00% <.001 0.279
Diabetes 14 2193 1044 1149 OR, M‐H Fixed 2.72 2.05, 3.60 .05 41.58% <.001 0.453
Cardiovascular dis 12 2327 1086 1241 OR, M‐H Random 5.11 2.03, 12.83 <.001 77.27% <.001 0.061
Chronic liver dis 9 1659 685 974 OR, M‐H Fixed 1.17 0.66, 2.06 .87 0.00% .58 0.824
Malignancy 12 2132 990 1142 OR, M‐H Fixed 2.20 1.28, 3.77 .90 0.00% .004 0.890
Cerebrovascular dis 5 769 435 334 OR, M‐H Fixed 5.73 2.52, 13.04 .20 32.59% <.001 0.041
Treatment
Antiviral 10 1685 1002 683 OR, M‐H Random 0.70 0.42, 1.16 .16 62.06 <.001 0.52
Antibiotics 7 1991 1387 604 OR, M‐H Random 2.13 0.86, 5.29 .10 81.93 <.001 0.64
Glucocorticoids 13 2981 1651 1330 OR, M‐H Random 3.17 2.03, 4.97 <.001 73.41 <.001 0.49
Immunoglobulins 6 1101 605 496 OR, M‐H Random 2.75 1.09, 6.94 .032 89.10 <.001 0.32
Outcomes
ARDS 9 2204 1230 974 OR, M‐H Random 18.84 5.39, 65.87 <.001 89.58% <.001 0.106
AKI 6 1300 516 784 OR, M‐H Random 7.20 1.38, 37.74 .003 71.59% <.001 0.511
Sepsis 5 1259 488 771 OR, M‐H Random 21.19 4.21, 106.73 .085 50.99% <.001 0.680
Acute liver injury 2 1296 649 647 OR, M‐H Fixed 1.93 1.11, 3.34 .60 0.00% .001 NA
Myocardial injury 3 464 334 130 OR, M‐H Random 11.19 0.44, 285.9 <.001 90.44% <.001 0.408
Mortality 11 1563 922 641 OR, M‐H Random 55.22 12.62, 241.66 <.001 90.82% <.001 0.282
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Abbreviations: AKI, acute kidney injury; ALT, alanine transaminase; ARDS, acute respiratory distress syndrome; AST, aspartate aminotransferase; CI, confidence interval; I2, the ratio of true heterogeneity to total observed variation; IV, inverse variance; M‐H, Mantel‐Haenszel; OR, odds ratio; PT, prothrombin time; SMD, standardized mean difference.

3.3. Pooled analysis of comorbidities

The analysis showed that patients with hypertension (OR = 2.37; 95% CI = 1.86‐3.01; P < .001), chronic kidney disease (OR = 7.28; 95% CI = 3.26‐16.26; P < .001), and diabetes (OR = 2.72; 95% CI = 2.06‐3.61; P < .001) were nearly twofold more risk to develop severe presentation of COVID‐19. Patients with underlying cardiovascular disease or cerebrovascular disease were five‐times more liable to develop severe phenotype (OR = 5.11; 95% CI = 2.04‐12.83; P < .0001 and OR = 5.73; 95% CI = 2.52‐13.04; P < .0001, respectively). Cancer patients also exhibited severe manifestations of the disease (OR = 2.20; 95% CI = 1.28‐3.78; P = .004) (Figure S2). Apart of cardiovascular disease, homogeneity between studies was detected.

3.4. Pooled analysis of treatment

A total of 17 studies reported treatment to be administered to COVID‐19 patients. On comparison between the two groups, severe patients were nearly three times more likely to receive steroids (OR = 3.17; 95% CI = 3.02‐4.97; P < .001) and immunoglobulins (OR = 2.75; 95% CI = 1.09‐6.94; P = .032). Sensitivity analysis revealed that the studies of Wang 21 and Zhang 15 contributed in the significant heterogeneity observed in treatment results (Table 2).

3.5. Pooled analysis of COVID‐19 outcomes

Our analysis confirmed that patients with severe COVID‐19 disease had higher odds of developing ARDS (OR = 18.84; 95% CI = 5.39‐65.87; P < .0001) and sepsis (OR = 21.19; 95% CI = 4.21‐106.7; P < .001). Similarly, acute liver injury (OR = 1.93; 95% CI = 1.12‐3.34; P = .001) and acute kidney injury (OR = 7.2; 95% CI = 1.38‐37.74; P < .001) were more prevalent among patient with severe disease. Moreover, our analysis revealed that mortality was more likely to occur among patients with severe COVID‐19 patients (OR = 55.22; 95% CI = 12.62‐241.66; P < .001) (Figure S3). Considerable heterogeneity was observed for the outcomes. Meta‐regression analysis for study characteristics showed higher odds of mortality in articles involving Wuhun hospitals (coefficient = 4.30; 95% CI = 3.07‐5.54; P < .001) (Table S1).

3.6. Publication bias

The funnel plot of laboratory and clinical parameters is shown in Figure S4. Egger's test showed no publication bias for all variables (P > .1) except for two; cardiovascular and cerebrovascular diseases (P = .061 and .041) (Table 2).

3.7. Trial sequential analysis

We applied TSA on mortality rate available among all eligible articles of COVID‐19 patients with a mild and severe exhibition and indicated that the cumulative Z‐curve transverses the monitoring boundaries before reaching the required sample size and achieving considerable significant and so no further studies are necessary (Figure 2).

Figure 2.

Figure 2

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Trial sequential analysis for mortality

4. DISCUSSION

Our meta‐analysis including 3428 subjects from 20 retrospective studies explored the potential relationship between liver injury and the severity of COVID‐19 disease. We found that liver dysfunction seemed to be higher in patients with severe outcomes from COVID‐19 infection.

Our results were in agreement with a previous study review. 36 Previously, liver injury has been reported as an important risk factor for severe outcome and death in SARS and Middle East Respiratory Syndrome. 35 , 37 , 39

Patients in our study who had severe presentations of COVID‐19 disease had higher levels of AST, ALT, bilirubin, and lower albumin levels. Our results are consistent with recent studies on COVID‐19 disease that showed that the incidence of liver injury ranged from 58% to 78%, mainly indicated by elevated AST, ALT, and total bilirubin levels accompanied by slightly decreased albumin levels. 40 , 41 In a recent study, Guan et al 42 documented that higher serum levels of AST were observed in nearly 18% of patients with nonsevere COVID‐19 disease and approximately 56% of patients with severe COVID‐19 infection. Moreover, in that study, higher serum levels of ALT were also observed in nearly 20% of patients with nonsevere COVID‐19 presentation, and approximately 28% of patients with severe COVID manifestation. 42 Similar findings in Huang et al 4 were also observed, where patients with severe COVID‐19 features had an increased incidence of liver injury.

Postmortem liver biopsies specimens were observed in deceased COVID‐19 patients. The findings showed mild lobular and portal activity along with microvascular stenosis, indicating the injury could have been caused by either COVID‐19 disease or drug‐induced liver injury. 3 Similar to the treatment of SARS, steroids, antivirals, and antibiotics are widely used for the treatment of COVID‐19. 34 , 43 , 44 These drugs are all potential causes of liver injury during COVID‐19 treatment but have not yet been evident. 22 A recent study reported that the liver injury observed in COVID‐19 patients might be caused by lopinavir, which is used as an antiviral for the treatment of SARS‐CoV‐2 infection. 45 It is worth noting that the specific underlying causes of liver injury and elevated levels of liver enzymes in COVID‐19 patients are still limited. However, collectively the proposed mechanisms might include “hyperactivated immune responses and cytokine storm‐related systemic inflammation, psychological stress, drug toxicity, and progression of pre‐existing liver diseases” as detailed by Li and Fan. 46

Further studies are needed to investigate the mechanisms of liver dysfunction in COVID‐19 disease as a direct outcome of infection and the possible effects that treatment has on the liver.

Limitations of our study include the following; First, all the studies included in this meta‐analysis used a case‐control or cohort design, which are susceptible to recall and selection biases. Second, we could not distinguish if the liver dysfunction in COVID‐19 patients was an acute liver injury or exacerbated chronic liver disease. Last, the enrolled studies focused on Chinese patients, which restricted a more precise estimation of liver dysfunction in the context of other races.

5. CONCLUSIONS

In this meta‐analysis, we comprehensively analyzed liver dysfunction in accordance with the severity of clinical outcomes in COVID‐19 patients. Liver dysfunction was associated with severe COVID‐19 infection. Patients presented with abnormal liver function tests are at higher risk of severe clinical outcomes. Close monitoring of the presence of liver dysfunction may be beneficial as an early indicator of worse outcomes. This may serve to better prepare the treatment of patients.

CONFLICT OF INTERESTS

The authors declare that there are no conflict of interests.

AUTHOR CONTRIBUTIONS

MY, ASA, RE: study design; MY, ASA, MO, GZ, AE, AS: study identification and data extraction; MH, RE, EAT: statistical analysis; MH, RE, EAT, MSF: data interpretation; MY, ASA, RE, MO, GZ, AF, EAT, MSF: original draft preparation. All authors revised and approved the final version of the manuscript.

Supporting information

Supplementary Information

Click here for additional data file. (4.2MB, docx)

Youssef M, Hussein M, Attia AS, et al. COVID‐19 and liver dysfunction: A systematic review and meta‐analysis of retrospective studies. J Med Virol. 2020;92:1825–1833. 10.1002/jmv.26055

Contributor Information

Mohammad H Hussein, Email: mhussein1@tulane.edu.

Abdallah S Attia, Email: aattia@tulane.edu.

Rami M Elshazli, Email: Relshazly@horus.edu.eg.

Mahmoud Omar, Email: Mahmoud.omar@waimg.org.

Ghassan Zora, Email: gzora@tulane.edu.

Ashraf S Farhoud, Email: afarhoud@tulane.edu.

Ahmad Elnahla, Email: aelnahla@tulane.edu.

Eman A Toraih, Email: etoraih@tulane.edu.

Manal S Fawzy, Email: manal_mohamed@med.suez.edu.eg.

Emad Kandil, Email: ekandil@tulane.edu.

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