key: cord-333581-icp0xwhx
authors: Aziz, Muhammad; Haghbin, Hossein; Lee-Smith, Wade; Goyal, Hemant; Nawras, Ali; Adler, Douglas G.
title: Gastrointestinal predictors of severe COVID-19: systematic review and meta-analysis
date: 2020-07-30
journal: Ann Gastroenterol
DOI: 10.20524/aog.2020.0527
sha: 
doc_id: 333581
cord_uid: icp0xwhx

BACKGROUND: COVID-19 pandemic has created a need to identify potential predictors of severe disease. We performed a systematic review and meta-analysis of gastrointestinal predictors of severe COVID-19. METHODS: An extensive literature search was performed using PubMed, Embase, Web of Science and Cochrane. Odds ratio (OR) and mean difference (MD) were calculated for proportional and continuous outcomes using a random-effect model. For each outcome, a 95% confidence interval (CI) and P-value were generated. RESULTS: A total of 83 studies (26912 patients, mean age 43.5±16.4 years, 48.2% female) were included. Gastrointestinal predictors of severe COVID-19 included the presence of diarrhea (OR 1.50, 95%CI 1.10-2.03; P=0.01), elevated serum aspartate aminotransferase (AST) (OR 4.00, 95%CI 3.02-5.28; P<0.001), and elevated serum alanine aminotransferase (ALT) (OR 2.54, 95%CI 1.91-3.37; P<0.001). Significantly higher levels of mean AST (MD 14.78 U/L, 95%CI 11.70-17.86 U/L; P<0.001), ALT (MD 11.87 U/L, 95%CI 9.23-14.52 U/L; P<0.001), and total bilirubin (MD 2.08 mmol/L, 95%CI 1.36-2.80 mmol/L; P<0.001) were observed in the severe COVID-19 group compared to non-severe COVID-19 group. CONCLUSION: Gastrointestinal symptoms and biomarkers should be assessed early to recognize severe COVID-19.

COVID-19, caused by SARS-CoV-2, has become a worldwide pandemic imposing a significant burden on healthcare systems around the globe. The virus causes a variety of manifestations, including pneumonia, acute respiratory distress syndrome, shock, sepsis, and death [1] . Currently, no specific therapy (preventive or therapeutic) is available for this disease [2] . Symptomatically, the virus leads to fever, fatigue, cough, shortness of breath, myalgias, arthralgias, nasal congestion, runny nose, sore throat, nausea/vomiting, and diarrhea [1] . The virus further causes laboratory abnormalities, including derangements of white cell count, platelet count, C-reactive protein, procalcitonin, lactate dehydrogenase, aspartate aminotransferase (AST), alanine aminotransferase (ALT), total bilirubin (TB), creatinine, and D-dimer [1] . The pandemic nature of this disease necessitates emergent and early recognition of symptomatic patients to identify those at most severe risk and to provide supportive measures as needed, up to and including mechanical ventilation.

Gastrointestinal parameters (symptoms and laboratory findings) have been reported in the literature among patients with COVID-19 [3] , but there is little comprehensive information regarding gastrointestinal symptoms in these patients. We performed a systematic review and meta-analysis A comprehensive literature search was performed from January 1 st , 2020, to May 31 st , 2020, using the following databases: PubMed/Medline, Embase, Cochrane, Web of Science. The search strategy, using a predeveloped vocabulary for COVID-19 [4] , was created by an experienced librarian (WLS) and crosschecked by another reviewer (MA). An example search strategy using EMBASE is highlighted in Supplementary Table 1. Article screening and data extraction was performed by 2 independent reviewers (MA and HH) and any discrepancies in screening/extraction were resolved through mutual discussion. Interobserver agreement was evaluated using % of agreement and Cohen's Kappa (Κ) statistic. Articles were selected if they reported data on COVID-19 patients with respect to gastrointestinal symptoms (diarrhea, abdominal pain, and nausea/vomiting) or laboratory findings (serum AST, ALT, or TB). We excluded articles if the data of interest were not reported or the article had not undergone a peer-review process. We further excluded case reports and retrospective studies/case series reporting <10 cases. We used the bibliography of the finalized articles to further broaden our literature search. We did not restrict our search according to language.

Severe COVID-19 was defined as respiratory distress (rate ≥30 /min, oxygen saturation ≤93% at rest and/or PaO 2 /FiO 2 ≤300 mmHg) [1] , intensive care unit (ICU) admission, and/or death. Laboratory data (mean serum AST, ALT and TB) were reported based on the local laboratory's reference parameters for each study. Symptoms (diarrhea and nausea/vomiting) were reported based on initial presentation.

Data extraction was performed using Microsoft Excel (Microsoft, Redmond, Wash, USA). Continuous variables (using mean and standard deviation [SD] ) and proportional variables (using event and total patients) were compared using the DerSimonian-Laird approach or a random-effects model. The fixed effect model was used as a sensitivity tool; however, given the presumed heterogeneity of study data from diverse sources and clinical settings, the randomeffects model was considered more appropriate and results were reported using that approach [5, 6] . The mean and SD were calculated from median and interquartile range where applicable. Results are displayed using forest plots for each summary estimate, i.e., mean difference (MD) and odds ratio (OR) for continuous and proportional variables, respectively. A 95% confidence interval (CI), P-value (<0.05 was considered statistically significant), and study heterogeneity using I 2 statistic (>50% was considered as substantial heterogeneity) were calculated for each outcome [7] . Subgroup analysis was performed based on the definition of severe COVID-19 (respiratory distress, ICU admission, and death) if at least 3 studies reported the outcome. Sensitivity analysis using leave-one-out metaanalysis was performed and point estimates were generated. Meta-regression was attempted to assess the impact of moderator variables on study outcomes. The moderator variables assessed included female proportions in each study, region of study (Asia, Europe, North America, South America), and number of centers in each study (single center, dual center, multicenter). The statistical analysis was performed using Open Meta Analyst (CEBM, University of Oxford, Oxford, United Kingdom) and Comprehensive Meta-Analysis (BioStat, Englewood, NJ, USA).

We utilized the Quality in Prognostic Studies (QUIPS) tool for assessing the risk of bias in the observational studies [8] . Publication bias was assessed qualitatively by visualizing the funnel plot and quantitatively using Egger's regression analysis. We adhered to "preferred reporting items for systematic reviews and meta-analyses (PRISMA)" guidelines for the purposes of this manuscript.

Using the search strategy defined above, a total of 1525 records were generated. After the inclusion/exclusion criteria had been applied, a total of 83 published studies (all observational) remained that reported data on gastrointestinal symptoms and/or laboratory findings ( Fig. 1) [1, 3, . All studies included laboratory-confirmed COVID-19 patients. The percentage of agreement was >90% for both screening and data extraction and corresponding Κ values of 0.72 and 0.69 (substantial agreement), respectively, were noted. Of the 83 included studies, 42 reported data on disease severity with respect to symptoms and/or lab findings.

Study details and demographics of included patients are highlighted in Table 1 . Based on region, 70 studies originated from Asia, 8 from North America, 1 from South America, and 4 from Europe. The study duration was from December 11 th through May 5 th , 2020. Based on the number of centers reporting data, 17 studies were multicenter, 6 were dualcenter, 57 were single-center, and 3 studies failed to mention the center from where the data originated. A total of 26,912 patients were included across these 83 studies. The patients' mean age was 43.5±16.4 years and the female proportion was 48.2%. 

The overall prevalence of diarrhea on admission among the study population was 13.0% (95%CI 10.8-15.5%; I 2 =95.1%). Based on region, the following prevalences were noted: Europe 16.8% (95%CI 2.9-57.8%; I 2 =98.0%), North America 26.2% (95%CI 20.1-33.3%; I 2 =90.6%), and Asia 11.5% (95%CI 9.5-13.9%; I 2 =91.8%). The overall prevalence of nausea/ vomiting on admission among the study population was 9.5% (95%CI 7.9-11.4%; I 2 =92.6%). Based on region, the following prevalences were noted: Europe 8.9% (95%CI 2.1-30.4%; I 2 =94.1%), North America 18.7% (95%CI 14.6-23.6%; I 2 =83.9%), and Asia 7.7% (95%CI 5.9-9.9%; I 2 =91.6%).

The prevalence of abnormal AST findings on admission was 27 .7%). The prevalence of abnormal TB levels on admission was 10.6% (95%CI 5.0-21.0%; I 2 = 97.1%). All studies that reported abnormal TB were from Asia.

The odds of patients with diarrhea having severe disease were significantly greater compared to those without diarrhea (26 studies, OR 1.50, 95%CI 1.10-2.03; P=0.01; I 2 =54.1%) ( Fig. 2A Liu, et al [12] Zhang, et al [27] Wang, et al [40] Zhou, et al [44] Chen, et al [48] Nobel, et al [67] Pan, et al [69] Wang, et al [77] Wang, et al [30] Yang , et al [83] Zhang, et al [85] 

Elevated serum AST levels in patients were evaluated in 16 studies and greater odds of disease severity were noted compared to patients without elevated AST (OR 4.00, 95%CI 3.02-5.28; P<0.001; I 2 =40.4%) (Fig. 3A) . The results were consistent on leave-one-out meta-analysis (OR 3.64-4.14) as well as subgroup analysis for disease severity defined based on respiratory distress ( Only 5 studies evaluated elevated serum TB levels in association with disease severity, and elevated TB was associated with severe disease (OR 2.09, 95%CI 1.36-3.21; P=0.001; I 2 =17.5%) (Fig. 3C ). Leave-one-out meta-analysis demonstrated a consistent association (OR 1.89-2.51). A subgroup analysis and meta-regression were not possible because of the low number of studies.

The mean serum AST level was significantly higher in the severe group compared to the non-severe group (32 studies, MD 14.78 U/L, 95%CI 11.70-17.86 U/L; P<0.001; I 2 =97.5%) (Fig. 4A) . The leave-one-out meta-analysis was consistent with a point estimate (MD) ranging from 13. The mean serum ALT level was also significantly higher for the severe group compared to the non-severe group (31 studies, MD 11.87 U/L, 95%CI 9.23-14.51 U/L; P<0.001; I 2 =95.5%) (Fig. 4B) . The results were consistent on leave-one-out meta-analysis (MD 11.14-12.61 U/L) and subgroup analysis for severity based on respiratory distress ( On meta-regression, female proportions were inversely correlated with disease severity on the basis of mean TB level (P=0.03).

Based on QUIPS tools, most of the studies (n=63) were at risk of bias for failing to account for confounders, while the remaining (n=20) accounted for some confounders. Twenty studies lacked details of the statistical design (Supplementary Table 2 ). Visible asymmetry was observed on a funnel plot based on the symptom of diarrhea; however, Egger's regression did not reveal a significant publication bias (P=0.76) (Supplementary Fig. 1 ).

Our meta-analysis demonstrated significant correlations between gastrointestinal parameters (diarrhea, elevated serum ALT, AST and TB) and severe disease outcomes, i.e., respiratory distress, ICU admission, and/or death. Although the most frequent manifestation of COVID-19 is pneumonia, gastrointestinal signs/symptoms are seen in a significant number of patients and can be the presenting manifestations of the disease [90] . A systematic review by Cheung et al reported diarrhea and nausea/vomiting in 13% and 10% of COVID-19 patients, respectively [91] . We demonstrated a similar prevalence of diarrhea (13%) and nausea/vomiting (9.5%). We believe that the reported prevalence of diarrhea and nausea/vomiting is somewhat lower than in reality, as some of these patients only present with these symptoms and may not undergo COVID-19 testing because they do not fulfill local hospital or laboratory criteria.

The mechanism behind gastrointestinal symptoms is thought to be secondary to viral attachment and entry via angiotensin-Guar, et al [1] Huaig, et al [10] Chen, et al [3] Liu, et al [12] Chai, et al [ Chen, et al [3] Liu, et al [12] Zhang, et al [26] Zhang, et al [35] Wang, et al [40] Zhou, et al [44] Cai, et al [47] Hcng, et al [57] Vteng, et al [76] Yang, et al [83] Zhang, et al [85] Zhang, et al [86] Zhao, et al [87] converting enzyme 2 (ACE2), readily expressed in ileal and colonic epithelium [92] . This can explain symptoms such as diarrhea and nausea/vomiting. Furthermore, researchers have also identified viral RNA in the stool of patients infected with COVID-19, making diarrhea not only a marker for disease severity but a potential route of contagion [22] . Given the association of diarrhea with severe COVID-19 disease, based on our meta-analysis results, COVID-19 patients with diarrhea should be stratified into a high-risk group for developing severe disease as described above and managed accordingly. Several mechanisms have been postulated to explain the hepatotoxicity seen in COVID-19 patients. One possible mechanism of hepatotoxicity of COVID-19 is immune system activation. It has been shown that many of the respiratory viruses, including COVID-19, lead to an activation of cytotoxic T cells and Kupffer cells in the liver that eventually damage hepatocytes [93] . Another mechanism is the triggering of a "cytokine storm, " leading to a massive surge in mediators such as interleukin-6, associated with sepsis, multiorgan dysfunction and death [8, 94, 95] . Direct viral entry through the intestines and invasion of the portal system and, subsequently, cholangiocytes, is another hypothesized mechanism [96] . Lastly, drug-induced hepatotoxicity should also be considered, as currently researchers are investigating all possible therapeutic options [97] . We demonstrated significantly increased elevation of ALT, AST and TB in patients with severe COVID-19 compared to non-severe patients, which can be attributed to some or all of the aforementioned mechanisms.

Several limitations exist with our analysis. The most notable was the lack of high quality randomized controlled trials and cohort studies. We relied on data from observational studies that reported admission data. Observational studies have their own inherent biases that limit data interpretation, including selection, recall, and confounding bias. It is difficult to establish a temporal relation between cause and event using observational studies, as there is no follow up. However, as we reported admission data, we propose screening and riskstratifying individuals, based on their admission laboratory findings and symptoms, into severe and non-severe categories. We were not able to account for factors such as comorbidities, timing of hospitalization and routine home medications. We were also not able to account for these related gastrointestinal symptoms due to lack of stratified data. Lastly, given that the major manifestations of COVID-19 are respiratory symptoms (cough, shortness of breath, sputum production) and fever, gastrointestinal symptoms may have been underreported.

Despite the limitations, our analysis combines data from a large number of studies with a robust number of patients. We used admission data to avoid potential heterogeneity introduced by other factors, such as in-hospital medications, nosocomial infections, intubation, etc. The results of our study were consistent on both subgroup and sensitivity analysis. Furthermore, we provided subgroup prevalence based on region, i.e., Asia, Europe and North America where applicable.

In conclusion, patients presenting with diarrhea or elevated ALT, AST and/or TB and diagnosed with COVID-19 should be stratified into a high-risk group for developing severe disease outcomes (i.e., respiratory distress, ICU admission, and/or death) and managed appropriately. • We performed a comprehensive systematic review and meta-analysis of the available literature through May 31 st , 2020 to assess these manifestations with respect to disease severity • Our results indicate that diarrhea, abnormal ALT, AST and TB were associated with severe disease (intensive care unit admission, respiratory distress, and/or mortality) • Based on the current study results, patients with these manifestations should be stratified as highrisk and managed appropriately

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OR indigestion* OR dysmotilit* OR nausea* OR vomit* OR emesis* OR hematemesis* OR 'abdominal pain*' OR amylase OR lipase OR alt OR 'alanine aminotransferase*' OR ast OR 'aspartate aminotransferase*' OR bilirubin OR 'alk phos' OR 'alkaline phosphatase*' OR cea OR 'carcinoembryonic antigen*' OR 'ca19 9' OR 'carbohydrate antigen 19 9' OR ggt OR 'γ glutamyltransferase*' OR 'gamma glutamyltransferas3*' OR 'y glutamyltransferase*' OR 'fecal calprotectin*' OR 'fecal leukocyte*' 1858951 2. (('coronavirinae'/exp OR 'coronavirus infection'/de OR coronavirus*:ti,ab,kw OR 'corona virus*':ti,ab,kw OR 'pneumonia virus*':ti,ab,kw OR cov:ti,ab,kw OR ncov:ti,ab,kw) AND (outbreak:ti,ab,kw OR wuhan:ti,ab,kw) OR covid19:ti,ab,kw OR

#3 NOT ('conference abstract'/it OR 'editorial'/it OR 'review'/it OR 'short survey'/it)

/de OR 'animal experiment'/de OR 'animal model'/de OR 'animal tissue'/de OR 'meta analysis'/de OR 'practice guideline'/de OR 'systematic review

/de OR 'animal experiment'/de OR 'animal model'/de OR 'animal tissue'/de OR 'meta analysis'/de OR 'practice guideline'/de OR 'systematic review'/de) AND

*Searches for human or excluding nonhuman or animal studies were inconsistently indexed, so were abandoned as a strategy

State the process for selecting studies (i.e., screening, eligibility, included in systematic review, and, if applicable, included in the meta-analysis).

Data collection process 10 Describe method of data extraction from reports (e.g., piloted forms, independently, in duplicate) and any processes for obtaining and confirming data from investigators.

Data items 11 List and define all variables for which data were sought (e.g., PICOS, funding sources) and any assumptions and simplifications made.6 (Contd...)

12 Describe methods used for assessing risk of bias of individual studies (including specification of whether this was done at the study or outcome level), and how this information is to be used in any data synthesis. Table 2 Results of individual studies 20 For all outcomes considered (benefits or harms), present, for each study: (a) simple summary data for each intervention group (b) effect estimates and confidence intervals, ideally with a forest plot. consider their relevance to key groups (e.g., healthcare providers, users, and policy makers).

Limitations 25 Discuss limitations at study and outcome level (e.g., risk of bias), and at review-level (e.g., incomplete retrieval of identified research, reporting bias).14 Conclusions 26 Provide a general interpretation of the results in the context of other evidence, and implications for future research.

FUNDING Funding 27 Describe sources of funding for the systematic review and other support (e.g., supply of data); role of funders for the systematic review.