key: cord-0692898-50usynmq
authors: Moutchia, J.; Pokharel, P.; Kerri, A.; McGaw, K.; Uchai, S.; Nji, M.; Goodman, M.
title: Clinical Laboratory Parameters Associated with Severe or Critical Novel Coronavirus Disease 2019 (COVID-19): A Systematic Review and Meta-analysis
date: 2020-04-29
journal: nan
DOI: 10.1101/2020.04.24.20078782
sha: 4ba1fd8993d1b505ada4e2184cfab956f4768abc
doc_id: 692898
cord_uid: 50usynmq

Background: To date, several clinical laboratory parameters associated with COVID-19 severity have been reported. However, these parameters have not been observed consistently across studies. The aim of this review was to assess clinical laboratory parameters which may serve as markers or predictors of severe or critical COVID-19 disease Methods: We conducted a systematic search of MEDLINE, Embase, Web of Science, CINAHL and Google Scholar databases from 2019 through April 18, 2020, and reviewed bibliographies of eligible studies, relevant systematic reviews, and the medRxiv pre-print server. We included hospital-based observational studies reporting clinical laboratory parameters of confirmed cases of COVID-19 and excluded studies having large proportions (>10%) of children and pregnant women. Two authors independently carried out screening of articles, data extraction and quality assessment. Meta-analyses were done using random effects model. Meta-median difference (MMD) and 95% confidence interval (CI) was calculated for each laboratory parameter. Results: Forty-five studies in 6 countries were included. Compared to non-severe COVID-19 cases, severe or critical COVID-19 disease was characterised by higher neutrophil count (MMD: 1.23 [95% CI: 0.58 to 1.88] x109 cells/L), and lower lymphocyte and CD4 counts with MMD (95% CI) of -0.39 (-0.47, -0.31) x109 cells/L and -204.9 (-302.6, -107.1) cells/l, respectively. Other notable results were observed for C-reactive protein (MMD: 36.97 [95% CI: 27.58, 46.35] mg/L), interleukin-6 (MMD: 17.37 [95% CI: 4.74, 30.00] pg/ml,), Troponin I (MMD: 0.01 [0.00, 0.02] ng/ml), and D-dimer (MMD: 0.65 [0.45, 0.85] mg/ml). Conclusions and Relevance: Relative to non-severe COVID-19, severe or critical COVID-19 is characterised by increased markers of innate immune response, decreased markers of adaptive immune response, and increased markers of tissue damage and major organ failure. These markers could be used to recognise severe or critical disease and to monitor clinical course of COVID-19.

Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) [1, 2] . Phylogenetically, SARS-CoV-2 sufficiently differs from other zoonotic coronaviruses, such as Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) and Middle East Respiratory Syndrome Coronavirus (MERS-CoV) introduced to humans in the past two decades [1, 3] . Disease resulting from infection with SARS-CoV-2 was first reported in Wuhan, China in December 2019, and the virus rapidly spread to other regions of the world thereafter [4, 5] . Given the scale of the outbreak, COVID-19 was declared a pandemic on March 12 2020 by the World Health Organization [6] .

As of April 19, 2020, there have been 2,394,291 confirmed cases in 185 countries/regions and 164,938 COVID-related deaths [7] .

Clinical features of infection with SARS-CoV-2 vary widely and have been classified as mild, severe or critical, with some persons remaining asymptomatic [8, 9] . Majority of SARS-CoV-2 infected persons display mild symptoms similar to a viral upper respiratory tract infection such as dry cough, fever, sore throat, nasal congestion, and muscle pain [8] [9] [10] . Severe COVID-19 is characterised by features of severe pneumonia such as dyspnoea, respiratory frequency ≥ 30 breaths per minute and blood oxygen saturation ≤ 93%, while critical COVID-19 is characterised by respiratory failure, septic shock, and/or multiple organ failure [8, 9] . Severe or critical COVID-19 is highly associated with mortality [11] . In a single-centre observational study of critical COVID-19 patients, up to 61% of critical COVID-19 patients and 94% of critical COVID-19 patients requiring mechanical ventilation died within 28 days of admission into the intensive care unit [12] .

Currently, there is no approved cure for infection with SARS-CoV-2 and an effective vaccine is not yet available. Approximately 18% of diagnosed COVID-19 cases have severe or critical disease, and about 5% of diagnosed COVID-19 require intensive care management with or without mechanical ventilation [8, 13] . Consequently, there is substantial pressure on healthcare systems worldwide, particularly on intensive care units. As healthcare systems become further stretched by the increasing numbers of cases, identifying clinical laboratory parameters associated with severe and critical cases is crucial in helping clinicians triage patients appropriately and optimize use of the limited healthcare resources. Furthermore, as more clinical trials are being launched to test possible treatments for COVID-19, laboratory parameters associated with COVID-19 severity can aid in monitoring the clinical evolution of cases on trial drugs and serve as composite or secondary outcomes for these trials.

To date, changes in several clinical laboratory parameters have been linked to severity [4, [13] [14] [15] [16] . However, it is not clear if these changes are observed consistently across studies. With these considerations in mind, the objective of this systematic review and metaanalysis was to investigate which clinical laboratory parameters may be associated with severe or critical COVID-19 disease.

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We registered our study protocol with the International Prospective Register of Systematic Reviews (PROSPERO); registration number CRD42020176651 [17] . This review and metaanalysis was conducted and has been reported according to The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines [18, 19] .

This review and meta-analysis included observational studies reporting clinical laboratory parameters among patients with confirmed COVID-19. Cases were diagnosed using guidelines by either the World Health Organization or the China National Commission for Health [20, 21] .

The exposure of interest of this review was severe or critical COVID-19 and the comparator was non-severe COVID-19. According to the criteria defined by China National Health Commission, severe COVID-19 is characterised by dyspnoea, ≥ 30 breaths/minute, blood oxygen saturation ≤ 93%, arterial partial pressure of oxygen to fraction of inspired oxygen (PaO²/FiO²) ratio <300, and/or lung infiltrates >50% within 24-48 hours; and critical COVID-19 is characterised by respiratory failure, septic shock, and/or multiple organ failure [22] . Non-severe COVID-19 is defined by no or mild pneumonia [22] . We also considered COVID-19 cases requiring oxygen therapy, and COVID-19 cases admitted to intensive care units as severe or critical cases. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted April 29, 2020. We included only hospital-based studies and excluded reviews, opinion articles, and studies that did not report clinical laboratory parameters stratified by COVID-19 disease severity. Also, as children and pregnant women have different cut-off values for most clinical laboratory parameters compared to general adults, we excluded studies that examined populations with large proportions of children under 11 years of age and pregnant women to reduce clinical heterogeneity. We considered studies that included children, pregnant women along with the general adult population as eligible only if the proportion of children or pregnant women constituted less than 10%.

We conducted a systematic search of Ovid MEDLINE, Ovid Embase, Clarivate Analytics Web of Science Core Collection, EBSCO CINAHL and Google Scholar databases from 2019 through April 18, 2020. The search strategy used both controlled vocabulary and free text words relevant to COVID-19 and clinical laboratory parameters (see search strategy in Supplement S1). We also reviewed bibliographies of eligible studies, relevant systematic reviews to identify additional papers that were missed by the electronic search. Further, we performed a manual search of the medRxiv pre-print server to identify latest relevant studies that might still be undergoing peerreview. The search was limited to the years 2019-2020 and there was no limitation regarding language of publication.

Following deduplication of records retrieved during the systematic search, we exported retained articles into Covidence review manager to facilitate the screening of titles and abstracts, which was followed by a full text review to determine eligibility [23].

Two authors (JM and PP) independently carried out title and abstract screening and full text evaluation of all articles using the eligibility criteria listed in the previous section. The discrepancies in study selection were resolved through adjudication by a third author (KM). To avoid including data on the same patient populations more than once in the meta-analysis, we matched studies based on the location of the study (hospital, town) and the period over which data was collected. For two or more studies conducted at the same location over the same or . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted April 29, 2020. . https://doi.org/10.1101/2020.04.24.20078782 doi: medRxiv preprint overlapping periods, we included only the largest study, unless one of the smaller studies presented relevant information not included in the larger study.

Two authors (JM and PP) independently extracted, verified and summarized data from each study included in the meta-analysis. laboratory parameters when reported on continuous scales, and numbers (percentages) of cases above and below cut-off values when reported on categorical scales. Discrepancies in collected data were resolved by re-checking the primary studies until consensus was reached. For the studies which had unclear severity classification, the authors were contacted to seek additional clarification. Studies in the Chinese language were translated into English language by a Chinese native speaker. The extracted data were exported into R programming software.

Two authors (AK and MN) independently carried out quality assessment of each article using National Institutes of Health (NIH) study quality assessment tools for observational cohort and cross-sectional studies, and for case series studies [24] . These tools were used to evaluate the risk of bias and to assess the overall validity of reported results. Each study was assessed using all elements of the relevant tool, and an overall judgement was made by considering the responses to the various elements. An overall rating of poor quality translates to a high risk of bias, and an overall rating of good quality translates to a low risk of bias [24] . The final decision for each study was made through professional judgement and by consensus among the authors. We evaluated the impact of studies with a high risk of bias by doing sensitivity analysis using the Leave-One-Out method [25] .

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Where clinical laboratory parameters were measured on a continuous scale, we pooled median differences from each study using the quantile estimation method [26] . The result of this analysis was expressed as a meta-median difference (MMD) accompanied by a corresponding 95 % confidence interval (CI). We preferred median differences over mean differences because clinical laboratory indices are usually skewed, and mean values could be influenced by outlier values, particularly in small samples. We performed a sensitivity analysis by pooling mean differences from each study using inverse variance weighting. Where the studies reported only median (IQR or total range) values, we computed mean (SD) using methods previously described [27, 28] .

Where clinical laboratory parameters were measured on a categorical scale, we computed prevalence ratios for each study using counts of events in the exposure and comparator group and calculated meta-prevalence ratios (MPR) and the 95% CIs using the Mantel-Haenszel method.

Meta-analysis was conducted using random effect models. We assessed clinical heterogeneity (age distribution, comorbidities criteria of severity) and study methodological heterogeneity (timing of blood sample collection) and considered the potential impact of these factors on the meta-analysis results. We assessed statistical heterogeneity using Cochran's Q test and calculated the I² statistic, which was interpreted using cut-offs of 25%, 50%, and 75% for low, moderate, and substantial heterogeneity, respectively. We performed influence analysis using the Leave-One-Out-method to identify studies that have a high influence on our results [25] . Additional sensitivity analyses were performed by excluding 'outlier' studies. A study-specific estimate was considered an outlier if its confidence interval did not overlap with the confidence interval of the meta-estimate [25].

To detect possible publication bias, funnel plots were constructed for the 4 laboratory parameters with the highest number of individual studies. Egger's test was carried out to assess statistical symmetry of the plots.

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The copyright holder for this preprint this version posted April 29, 2020. . https://doi.org/10.1101/2020.04.24.20078782 doi: medRxiv preprint Statistical analyses were done using R programming software and in the 'meta', 'metafor', 'dmetar' and 'metadian' packages [29] .

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We identified 3,779 studies through database searching and from other sources ( Figure 1 ). After removing duplicates, 1722 unique records were screened, and of those, 1398 were removed after title and abstract review. Additional 257 records were excluded due to lack of COVID-19 severity classification, lack of laboratory parameter records or ineligible study design. Of the 67 remaining studies, another 22 were excluded because they used data from the same locations or covered overlapping periods (see Supplement S2). A total of 45 studies were retained for metaanalyses.

The characteristics of included studies are detailed in Table 1 The median (or mean) age of patients in the included studies ranged from 35 years to 67 years, and the proportion of male patients ranged from 30% to 81%. Patients in the included studies had varying proportions of comorbidities such as hypertension, diabetes and cancer as detailed in Table 2 , and Forest plots and Leave-One-Out analysis are displayed in supplement S4.

As pertains to haematological parameters, the majority of studies reported higher white cell count and higher neutrophil count in severe or critical COVID-19 patients relative to non-severe 

The threats to validity in this meta-analysis fall into two broad categories: risk of bias in individual studies, and publication bias across the body of literature. Assessments of these two categories of threat to validity are presented below.

Using the NIH study quality assessment tools, 28 studies (62.2%) were rated as having a low risk of bias, 14 studies (31.1%) were rated as having a medium risk of bias, and 3 studies (6.7%) were rated as having a high risk of bias. The majority of studies had a clearly defined study objective (97.8%), a well-defined study population (100%), and had comparable subjects (100%). In contrast, no study provided a sample size calculation or power description. All the studies were rated as having a high risk of bias for the element assessing a temporal sequence between the laboratory measure and disease severity, and none of the reported results was adjusted for potential confounding (Figure 2 and supplement S5). Our results did not markedly differ in sensitivity analyses after excluding studies with a high risk of bias.

The symmetry of funnel plots obtained from the 4 laboratory parameters with the highest number of individual studies was assessed using Egger's test. The symmetrical funnel plots for Creactive protein (p: 0.155) and creatinine (p: 0.415) suggested no evidence of publication bias whereas asymmetrical funnel plot for white cell count (p: 0.004) and lymphocyte count (p: 1 2 0.005) indicated significant influence of smaller studies, which may be indicative of publication bias ( Figure 3 ). Important to note that Egger's test may not be robust for C-reactive protein, white cell count and lymphocyte parameters due to substantial heterogeneity (I 2 >75%).

In sensitivity analyses excluding outlier studies, statistical heterogeneity was reduced, and the meta-estimate of most laboratory parameters were not markedly altered. In sensitivity analysis using mean differences (supplement S6), there was substantial heterogeneity for most laboratory parameters, and the associations observed from using median differences persisted.

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The copyright holder for this preprint this version posted April 29, 2020. . https://doi.org/10.1101/2020.04.24.20078782 doi: medRxiv preprint Discussion COVID-19 is a rapidly evolving pandemic with significant global morbidity and mortality. The aim of this meta-analysis was to investigate which clinical laboratory parameters may be associated with severe or critical COVID-19 disease. Out of the 39 clinical laboratory parameters evaluated, we found that derangements in 36 clinical laboratory parameters were significantly associated with severe or critical COVID-19. Whilst some of the observed associations may not be clinically relevant, certain, more pronounced laboratory abnormalities may have important clinical implications. Markers of an overactive innate immune system such as markedly elevated neutrophil-to-lymphocyte ratio (NLR), IL-6, serum ferritin and C-reactive protein, and markers of a deficient adaptive immune system such as lymphocytes and CD4 count could help recognise potential severe infections during triage, while markers of organ failure could be helpful in monitoring evolution of hospitalised COVID-19 patients.

Following infection with a virus, the innate immune system in activated. This early response is nonspecific and serves to limit virus multiplication during the acute phase [33] . The adaptive immune system is activated a few days later and is responsible for a more specific response, which is immunomodulatory (via engagement of helper T cells and regulatory T cells) and produces 'immunological memory' [33] . Elevated lymphocyte count is commonly found in most viral infections, and the magnitude and quality of T cell responses may determine the fate of these infections [34, 35] . Failure to mount an appropriate adaptive immune response means the innate immune response remains continuously stimulated with deleterious effects on the lungs and other organs. We found that severe or critical COVID-19 patients had increased markers of innate immune system activity compared to patients with non-severe disease. This is evidenced in the significantly higher levels of neutrophils, IL-6, and acute phase reaction markers such as CRP, ESR and serum ferritin, as well as decreased concentrations of albumin and prealbumin.

Severe or critical COVID-19 patients also exhibited defective adaptive immune response evidenced by significantly lower levels of lymphocytes and their subsets (CD3, CD4, CD8). CD4 count is currently being used to define severe cases of HIV infection [36] . In the case of HIV, the virus directly infects CD4 cells using the envelope glycoprotein gp120. Various authors have suggested that SARS-CoV-2 could deplete lymphocytes directly by infecting T lymphocytes, or indirectly through lymphocyte apoptosis induced by persistent elevated inflammatory cytokines [15, 37, 38] . Since severe COVID-19 patients display reduced lymphocyte count, it is likely that . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted April 29, 2020. patients before their disease severity was known. In addition, many studies from China reported on overlapping patient populations. While we tried to exclude studies that relied on the same data, it is possible that some of the associations examined in this meta-analysis were based on non-independent observations. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted April 29, 2020. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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Author Contributions: JM and PP had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. 

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The copyright holder for this preprint this version posted April 29, 2020. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted April 29, 2020. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted April 29, 2020. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted April 29, 2020. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted April 29, 2020. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted April 29, 2020. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted April 29, 2020. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted April 29, 2020. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted April 29, 2020. is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted April 29, 2020. . https://doi.org/10.1101/2020.04.24.20078782 doi: medRxiv preprint . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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