key: cord-0826799-3n8m0v66
authors: Khinda, Jaskaran; Janjua, Naveed Z.; Cheng, Shannon; van den Heuvel, Edwin R.; Bhatti, Parveen; Darvishian, Maryam
title: Association between markers of immune response at hospital admission and COVID‐19 disease severity and mortality: A meta‐analysis and meta‐regression
date: 2020-08-10
journal: J Med Virol
DOI: 10.1002/jmv.26411
sha: 93f57c2ff6736c74137f78de8462bd5b46ca43b8
doc_id: 826799
cord_uid: 3n8m0v66

To determine the utility of admission laboratory markers in the assessment and prognostication of COVID‐19, a systematic review and meta‐analysis was conducted on the association between admission laboratory values in hospitalized COVID‐19 patients and subsequent disease severity and mortality. Searches were conducted in MEDLINE, Pubmed, Embase, and the WHO Global Research Database from Dec 1, 2019 to May 1, 2020 for relevant articles. A random effects meta‐analysis was used to calculate the weighted mean difference (WMD) and 95% confidence interval (95% CI) for each of 27 laboratory markers. The impact of age and sex on WMDs was estimated using meta‐regression techniques for 11 markers. In total, 64 studies met inclusion criteria. The most marked WMDs were for neutrophils (ANC) at 3.82x10(9)/L (2.76, 4.87), lymphocytes (ALC) at ‐0.34x10(9)/L (‐0.45, ‐0.23), interleukin‐6 (IL‐6) at 32.59pg/mL (23.99, 41.19), ferritin at 814.14ng/mL (551.48, 1076.81), C‐reactive protein (CRP) at 66.11mg/L (52.16, 80.06), Ddimer at 5.74mg/L (3.91, 7.58), LDH at 232.41U/L (178.31, 286.52), and high sensitivity troponin I at 90.47pg/mL (47.79, 133.14) when comparing fatal to non‐fatal cases. Similar trends were observed comparing severe to non‐severe groups. There were no statistically significant associations between age or sex and WMD for any of the markers included in the meta‐regression. The results highlight that hyperinflammation, blunted adaptive immune response, and intravascular coagulation play key roles in the pathogenesis of COVID‐19. Markers of these processes are good candidates to identify patients for early intervention and, importantly, are likely reliable regardless of age or sex in adult patients. This article is protected by copyright. All rights reserved.

The disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2), known as coronavirus disease 2019 (COVID- 19) , is primarily a respiratory condition that can range from being asymptomatic to causing respiratory failure and other potentially fatal complications. 1 Approximately 20% of cases develop severe dyspnea due to an often-bilateral viral pneumonia that requires hospitalization. 2 The virus has caused a global pandemic with growing case numbers, but early studies of seroprevalence estimate that the proportion of infected individuals does not exceed 20% even in regions with large case burdens, leaving most of the population susceptible. 3, 4, 5 As such, hospitals across the world remain at risk of spikes in patient load that may stretch or exceed their capacity, thereby contributing to worsening COVID-19 morbidity and mortality.

As has been demonstrated with other health conditions, clinical tools incorporating laboratory parameters have been invaluable to the efficient use of health care resources and improvement of patient outcomes. 6 Such tools are often based on an understanding of disease pathophysiology, and in the case of COVID-19, cytokine storm syndrome and thromboinflammation have surfaced as central and interconnected factors in the development of severe and fatal illness. 7, 8, 9 These disease processes can be monitored using various biochemical and hematologic markers that are routinely measured at the time of hospitalization, potentially contributing to the accurate The following laboratory markers were considered in this study: Hemoglobin (Hb), white blood cell count (WBC), absolute neutrophil count (ANC), absolute lymphocyte count (ALC), platelet count (PLT), C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), ferritin, interleukin-6 (IL-6), interleukin-10 (IL-10), procalcitonin (PCT), albumin, total bilirubin, prothrombin time (PT), creatinine (Cr), blood urea nitrogen (BUN), activated partial thromboplastin time (APTT), Ddimer, lactate dehydrogenase (LDH), creatine kinase (CK), high sensitivity troponin I (hsTropI), troponin I (TropI), creatine kinase myocardial band (CKMB), alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), and gamma glutamyl transferase (GGT). This article is protected by copyright. All rights reserved.

This systematic review and meta-analysis follows the Preferred Reporting Items for Systematic review and Meta-Analysis (PRISMA) guidelines. 19 The quality of included studies was assessed by using the Institute of Health Economics (IHE) quality appraisal of case series studies checklist. 20 Although the checklist includes twenty critical appraisal items for quality assessment, only fifteen criteria were relevant to those included in this review, of which both reviewers agreed that seven were especially important to the risk of bias. These seven items were: Consecutive recruitment, reporting of patient characteristics, clear eligibility criteria, similar disease point at study entry, appropriate outcome measurement, sufficient follow-up, and estimates of random variability. 20 Based on reviewers' judgment, the risk of bias was categorized as low, medium, or high, if 0, 1, or ≥ 2 checklist items were marked as no or unclear, respectively.

To determine the potential of duplicate data from the studies selected for inclusion, we compared studies based on their research teams (e.g., authors list), study location (i.e., city and hospital), and reported study period. If two or more studies shared study sites and had overlapping study periods, one study was designated as a reference study and assigned a low duplicate risk and the others were considered high duplicate risk. Reference studies were selected based on length of study period, number of patients in the sample, and number of laboratory markers reported and had to be agreed upon by both reviewers. Furthermore, the risk of duplication was separately considered for each laboratory marker in those studies considered to be at high risk of duplicate reporting. Within a high duplicate risk study, laboratory markers not reported in the associated reference study were considered as low risk of duplication.

Reported means and SDs for laboratory parameters in each included study were used to estimate weighted mean differences (WMD) and 95% confidence intervals (95% CI) for severe versus non-severe and deceased versus surviving patients. In the absence of mean and SD values, sample sizes, medians, and measures of precision (i.e. IQR or range) were used to calculate mean and SD (Supplement 3 and 4). 21 These data were then pooled to provide overall WMDs and their 95% CIs using the DerSimonian and Laird random effects model. 22 To quantify heterogeneity, the I 2 (%) statistic was calculated as a measure of inconsistency. 23 I 2 thresholds of 25%, 50%, and 75%, indicated low, medium, and high levels of heterogeneity, respectively. 23 To assess the potential impact of duplicate data, as a sensitivity analysis, we excluded studies categorized as high risk of duplication. Additional sensitivity analyses were performed by excluding studies with high risk of bias and studies with confidence intervals not overlapping with the 95% CIs of the pooled estimates (i.e., "outlier studies") 11 . This article is protected by copyright. All rights reserved.

In order to account for type I error rate for multiple hypothesis testing (i.e., 27 and 23 laboratory markers for disease severity and mortality, respectively), Bonferroni correction was used to declare the significance levels of p-values. 24 The Bonferroni corrected p-value for the disease severity tests including overall estimates, sensitivity analysis by risk of duplicates, and sensitivity analysis by risk of bias was 0.002 (i.e., corrected p-value = 0.05/27) and for sensitivity analysis of outliers, where only 15 laboratory markers were tested, was 0.003 (i.e., corrected p-value = 0.05/15). The Bonferroni corrected p-value for the mortality analyses including overall estimates, sensitivity analysis by risk of duplicates, and sensitivity analysis by risk of bias was 0.002 (i.e., corrected p-value = 0.05/23), and for sensitivity analysis of outliers, where only 8 laboratory markers were tested, was 0.006 (i.e., corrected p-value = 0.05/8).

Finally, to assess the potential impact of age and sex on WMD variation when comparing severe to non-severe or fatal to non-fatal cases, univariate random effects meta-regression, using method of moments, was conducted on 11 laboratory markers, including ALC, ANC, WBC, Ddimer, PT, ferritin, IL-6, IL-10, CRP, ESR, and albumin. These markers were selected because they reflect the key pathogenetic mechanisms involved and may vary by age and sex. We calculated tests for covariates using a minimum of 10,000 Monte Carlo random permutations. 25 All statistical analyses were performed using STATA software (version 16.1, StataCorp, College Station, TX, USA). 26 

In total 15,314 studies were identified through systematic search and backward chaining ( Figure  I ). Following removal of duplicates and title/abstract screening, 527 studies remained for fulltext review. 64 studies remained for data extraction and analysis after excluding ineligible studies for the following reasons: irrelevant to study objectives (N = 292), wrong study outcomes (N = 68), ineligible study design (N = 30), wrong patient population (N =28), not available in full-text (N =23), duplicate of included study (N = 13), and not in English (N = 9). All included studies were case series.

The key characteristics of included studies are presented in Table I . Forty-nine studies reported severity outcomes 39-87 , fourteen reported mortality outcomes [27] [28] [29] [30] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] and one reported both mortality and severity. 31 COVID-19 severity was classified using Chinese National Commission of Health Guidelines in 31 studies, American Thoracic Society Guidelines for Community Acquired Pneumonia in three studies, oxygen saturation at rest in two studies, ICU admission in seven studies, ARDS in one study, cardiac injury in one study, IMV in two studies, and custom composite endpoints in three studies.

The 50 severity studies contributed a total of 11173 patients, of which 7845 were from at least 522 hospitals across China, 85 were from at least 4 hospitals in Singapore, 3315 were from at least 7 hospitals in the United States, and 40 were from one hospital in Germany. The 15 mortality studies contributed a total of 2525 patients from 6 hospitals in Wuhan, Hubei, China. Blood samples were collected within two days of hospital admission in all but two studies. 33,60 15 This article is protected by copyright. All rights reserved.

severity studies 46,52,57, [59] [60] [61] [62] [70] [71] [72] 76, 78, 79, 88, 89 and two mortality studies 35,41 reported median time from symptom onset to hospital admission, which ranged from 3.5 52 to 12 35 days. There were significant differences in time to admission for three severity studies 60, 70, 89 and one mortality study (Table I) . 41

A summary of meta-analysis results for severity is presented in Table II . In total, 27 laboratory markers were analyzed for associations with disease severity. Only Hb, PLT, Cr, APTT, TropI, CKMB, ALP, and GGT did not achieve statistical significance. Among markers involved in a complete blood count (CBC), the WMDs of WBC and ANC in patients with severe vs. those with non-severe disease were 1.23x10 9 (0.85, 1.60) and 1.49x10 9 (0.96, 2.01) cells/L, respectively. ALC and PLT showed associations in the opposite direction, with WMDs of -0.30x10 9 (-0.37, -0.24) and -16.69x10 9 (-35.35, 1.96) cells/L, respectively. Among inflammatory markers, the most pronounced difference was for ferritin at 423.13ng/mL (281.41, 582.85). Other inflammatory markers including IL-6 and IL-10 also showed statistically significant associations. Among markers for tissue damage the most marked differences were for LDH, CK, and hsTropI at 120.31U/L (93.50, 147.12), 45.33U/L (18.60, 72.07), and 11.07pg/mL (3.64, 18.50), respectively. TropI did not reach statistical significance at a WMD of 0.04ng/mL (-0.01, 0.09), but was only reported in three studies (Table II) .

A summary of meta-analysis results for mortality is presented in Table III . In general, the trends in WMDs were the same as for severity, but with larger absolute differences. Of the 22 laboratory markers that were analyzed for associations with mortality, only Hb, ESR, and APTT did not achieve statistical significance. Among the CBC markers, the WMDs of WBC, ANC, and PLT in patients who died vs. those that survived were 3.49x10 9 (2.71, 4.27), 3.82x10 9 (2.76, 4.87), and -43.41x10 9 (-54.55, -32.27) cells/L, respectively. The WMD for ALC was -0.34x10 9 (-0.45, -0.23) cells/L, which is similar to the value seen for severity. Among the inflammatory markers and acute phase reactants, ferritin showed the most marked elevation at 814.14ng/mL (551.48, 1076.81). CRP, ESR, IL-6, IL-10, and PCT also showed positive associations. Of the liver, coagulation and renal function tests, D-dimer was the most markedly elevated at 5. 

Meta-regressions were conducted for age and sex as described in the methods section. All associations were non-significant when compared to Bonferroni corrected significance levels (data not provided). This article is protected by copyright. All rights reserved.

Of the 64 included studies, 17, 31 and 16 were assigned high, medium, and low risks of bias, respectively (Table I) . Insufficient or unclear follow-up durations and non-consecutive recruitment were the most common shortcomings among high risk studies. When studies at high risk of bias were excluded for sensitivity analysis, final estimates and statistical significance were unchanged, except for IL-6 in association with disease severity; the WMD dropped from 12.25pg/mL (7.00, 17.50) to 2.58pg/mL (-1.53, 6.69) (Supplement 5). A total of 27 studies were assessed to be at high risk of duplication (Table I) . Excluding these studies resulted in loss of statistical significance for IL-6 (WMD = 8.37pg/mL; 2.76, 13.99) and CK (WMD = 27.65U/L; 10.19, 45.11) in association with severity, and for IL-6 (WMD = 46.34pg/mL; 4.35, 88.33) and ALT (WMD = 4.60U/L; 1.03, 8.17) in association with mortality (Supplement 5 and 6). Excluding outlier studies (8 among studies of mortality and 15 among studies of severity) reduced heterogeneity but had minimal impact on overall estimates, except for Cr and CKMB in association with disease severity, which achieved statistical significance at 6.69uM (3.31, 10.06) and 2.31U/L (0.61, 4.02), respectively (Supplement 5).

In this systematic review and meta-analysis, we observed significant differences in many admission laboratory values between severe and non-severe, and fatal and non-fatal cases of COVID-19. While the WMDs for most parameters were statistically significant, those that were most pronounced, and thus those would be most clinically useful, are markers of overactive inflammatory response, blunted adaptive response, intravascular coagulation, and cell death, reflecting the pathophysiology of severe and fatal COVID-19. 7, 8, 9 In support of the role that hyperinflammation plays in COVID-19, the levels of all included acute phase reactants were significantly altered at admission when comparing severe to non-severe and fatal to non-fatal cases. Among the acute phase proteins, the largest difference was observed for ferritin, which is driven by interleukin-18 (IL-18). 91 While IL-18 was not reported in any of the included studies, we expect elevated levels. CRP was also markedly elevated, and albumin was decreased, both of which are acute phase reactions driven by IL-6, a major proinflammatory cytokine. 92 As expected by the derangements in acute phase reactants, IL-6 levels were increased and were more prominent for fatal than for severe cases.

Inflammation is a major component of innate immunity and is typically a transient initial response to any pathogen or injury, eventually subsiding and being replaced by a focused immune response when the trigger is infectious. 93 The inflammatory response is driven and sustained by numerous proinflammatory cytokines and chemokines including IL-6, TNFα, and CXCL10, which are not only elevated in COVID-19, but have also been implicated in the pathogenesis of disease caused by the related respiratory coronaviruses SARS-CoV and MERS-CoV. 9, 93 As such, a prolonged hyperinflammatory state caused by dysregulated release of This article is protected by copyright. All rights reserved.

proinflammatory cytokines, known as cytokine storm syndrome, is thought to be central to the pathogenesis of severe and fatal COVID- 19. 94 While T-lymphocytes are usually the major producers of many cytokines including IL-6 95 , ALC was decreased for both severe and fatal disease, consistent with hypotheses proposing alternate major sources of cytokines in COVID-19. 96 In fact, even in patients with relatively mild illness, lymphopenia is a common and characteristic feature of COVID-19, suggesting that the adaptive immune response is blunted and may be delayed or insufficient. 31, 39, 41, 42, 44, 55, 56, 64, 88 One possible explanation is that, like a number of other viruses 97 , SARS-CoV2 may directly infect lymphocytes. SARS-CoV2 relies on angiotensin converting enzyme 2 (ACE2) for cellular entry 98 , and it has been reported that a small proportion of lymphocytes are ACE2 positive. 99 Another possibility is that inflammatory cytokines such as IL-6 induce chemotaxis of lymphocytes to lymphoid organs, thus reducing circulating concentrations. 100 Functional exhaustion of lymphocytes due to SARS-CoV2-induced inhibitory cytokines such as IL-10, which was significantly elevated in our analysis, has also been suggested. 96, 101 However, IL-10 is an important anti-inflammatory cytokine that may in fact not be elevated enough to combat inflammation in fatal COVID- 19. 102 In contrast to ALC, ANC was elevated with a more pronounced difference observed for fatal than for severe illness. Neutrophils play a major role in inflammation and are not typically elevated in viral infections. However, in COVID-19, not only are their concentrations increased, but they have been suggested to be major producers of proinflammatory cytokines 103, 104 and to contribute to the development of acute respiratory distress syndrome (ARDS) through the formation of neutrophil extracellular traps (NETs) 103, 105 and direct tissue infiltration causing vascular leakage. 106 On the other hand, neutrophilia is classically a marker of bacterial infection; thus, it is possible that the observed elevations in ANC seen in severe/fatal COVID-19 reflect bacterial super-infection contributing to severe illness. However, procalcitonin, which is a more specific marker of bacterial infection 107 , has been reported to fall within normal reference ranges even in patients with fatal illness 27, 28, [33] [34] [35] [38] [39] [40] [41] , suggesting that this explanation may not be sufficient. In SARS-CoV, neutrophilia is an independent predictor of severe illness and is associated with hypersensitivity pneumonitis. 108 Hence, similar mechanism might be plausible for the neutrophilia seen in severe/fatal COVID-19.

In addition to causing localized damage at sites of inflammation, prolonged activation of neutrophils may also contribute to systemic damage in other ways. There were significant abnormalities in Ddimer, PLT, and PT, three of the analyzed coagulation parameters. The largest difference was for Ddimer, which is a fibrin degradation product indicative of intravascular thrombosis. The significant elevation in PT and decrease in PLT is likely due to the development of consumptive coagulopathy, as clotting factors and platelets are used up in forming microthrombi. Elevated Ddimer in severe and fatal COVID-19 may be explained by NETs, which can play a major role in the formation of intravascular thrombi. 103, 109 In addition, inflammatory cytokines such as IL-6 have procoagulant effects which contribute to an inflammation-induced hypercoagulable state known as thromboinflammation 7 , reinforcing the connection between the innate immune system and thrombosis. Once ARDS has developed and a patient becomes hypoxemic, thrombosis may also be promoted via a hypoxia-inducible factor mediated pathway. 37, 110 Tissue damage is an inevitable and unsurprising result of the disease processes described, as evidenced by significant increases in most markers of tissue damage that were analyzed. LDH is a ubiquitous intracellular enzyme that was markedly elevated in both severe and fatal cases. CK, which is highly expressed in skeletal muscle and the aminotransferases, which are expressed in hepatocytes, were also significantly elevated, but to a lesser degree than LDH. Cardiac troponin I, a marker of heart muscle damage, was also markedly increased, especially in fatal illness. While hypoxemia and shock are the most likely causes of myocardial damage, it is possible that direct infection of cardiomyocytes by SARS-CoV2 111 plays a role in some cases. This hypothesis is given plausibility by the presence of ACE2 on cardiomyocytes 112 and case reports of COVID-19 associated myocarditis. 113 Meta-regression analyses conducted for age and sex on key markers involved in inflammation, poor adaptive response, and intravascular coagulation did not show any significant associations between age or sex and observed marker levels. This is an important result reinforcing the utility of these markers for predicting disease severity among all adults, as males and the elderly have been overrepresented among severe cases. 2

To our knowledge, this is the first meta-analysis that assessed COVID-19 disease severity and mortality in association with laboratory markers and included a meta-regression for age and sex. In addition, the potential impacts of duplicate reporting and important sources of bias were considered. Rather than simply exclude duplicate studies, as was done in a previous systematic review 11 , we conducted a sensitivity analysis showing that exclusion had little impact on overall results. Despite these strengths, there are multiple limitations with our study. When not available, we estimated means and standard deviations from reported medians, IQRs, and ranges. Estimates from studies with small sample sizes can be imprecise, contributing to greater heterogeneity. Furthermore, we did not assess the risk of publication bias in this study. Due to the pandemic nature of COVID-19, most published studies on clinical outcomes, especially during the first months, were small case-reports and case-series. Hence, it is unlikely that small studies reporting on COVID-19 disease severity and mortality remained unpublished because of null and/or nonsignificant results. There are also limitations to the dataset. For example, our entire mortality dataset and 70% of the severity dataset is from China, potentially limiting the generalizability of the results. Additionally, 42 out of 64 studies had unreported or insufficient follow-up, which could bias the results by incorrectly classifying a patient as non-severe or living, only to develop more severe disease after the follow-up period. This requires updating analyses once more data becomes available outside of China. Diverse classification schemes for disease severity among different studies potentially contributed to high levels of observed heterogeneity. Another potential contributor to heterogeneity is that time to hospitalization was unreported in all but 17 This article is protected by copyright. All rights reserved.

studies, however we assume most patients were hospitalized shortly after developing severe respiratory symptoms such as dyspnea.

The associations between markers of inflammation (ANC, IL-6, ferritin, CRP, albumin), poor adaptive immune response (ALC), intravascular coagulation (Ddimer), and tissue damage (LDH, hsTropI) observed with severe and fatal disease in this meta-analysis not only support the key roles of these processes in COVID-19, but also provide evidence that there are identifiable biochemical and hematologic differences that exist between severe and non-severe, and fatal and non-fatal cases before the development of potentially lethal complications such as ARDS. While these disease processes are certainly not unique to COVID-19, they appear to be key pathways involved in the development of severe/fatal disease and can all be connected to hyperinflammation and cytokine storm. Importantly, the results of the meta-regression suggest that these markers are likely reliable regardless of age or sex in adult patients. Assessment of these markers at admission contributes both to an understanding of the disease mechanisms involved, as well as guiding attempts at predicting severe illness, thus allowing for identification of patients likely to benefit from early interventions. There are no widely accepted disease prediction models yet for COVID-19 114 , but accurate tools will likely need to incorporate markers of the main pathogenetic pathways involved: inflammation, blunted adaptive response, and thrombosis. These pathways are likely also ideal targets for therapy, such as the IL-6 inhibitor tocilizumab to target inflammation 115 , or heparin to target coagulation. 37 The results also indicate that further research is warranted in the utility of different immunomodulators and anticoagulants in the treatment of COVID-19.

No potential conflicts of interest were disclosed by the authors. 

The data that support the findings of this study are available from the corresponding author upon reasonable request. 

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Setting and This article is protected by copyright. All rights reserved. 

This article is protected by copyright. All rights reserved.