key: cord-0847289-zgrgqvfi
authors: Zhao, Runzhen; Su, Zhenlei; Komissarov, Andrey A.; Liu, Shan-Lu; Yi, Guohua; Idell, Steven; Matthay, Michael A.; Ji, Hong-Long
title: Associations of D-Dimer on Admission and Clinical Features of COVID-19 Patients: A Systematic Review, Meta-Analysis, and Meta-Regression
date: 2021-05-07
journal: Front Immunol
DOI: 10.3389/fimmu.2021.691249
sha: a43641e939b038d81eb473b1c0f1ba5c0d285991
doc_id: 847289
cord_uid: zgrgqvfi

BACKGROUND: Dynamic D-dimer level is a key biomarker for the severity and mortality of COVID-19 (coronavirus disease 2019). How aberrant fibrinolysis influences the clinical progression of COVID-19 presents a clinicopathological dilemma challenging intensivists. METHODS: We performed meta-analysis and meta regression to analyze the associations of plasma D-dimer with 106 clinical variables to identify a panoramic view of the derangements of fibrinolysis in 14,862 patients of 42 studies. There were no limitations of age, gender, race, and country. Raw data of each group were extracted separately by two investigators. Individual data of case series, median and interquartile range, and ranges of median or mean were converted to SDM (standard deviation of mean). FINDINGS: The weighted mean difference of D-dimer was 0.97 µg/mL (95% CI 0.65, 1.29) between mild and severe groups, as shown by meta-analysis. Publication bias was significant. Meta-regression identified 58 of 106 clinical variables were associated with plasma D-dimer levels. Of these, 11 readouts were negatively related to the level of plasma D-dimer. Further, age and gender were confounding factors. There were 22 variables independently correlated with the D-dimer level, including respiratory rate, dyspnea plasma K(+), glucose, SpO2, BUN (blood urea nitrogen), bilirubin, ALT (alanine aminotransferase), AST (aspartate aminotransferase), systolic blood pressure, and CK (creatine kinase). INTERPRETATION: These findings support elevated D-dimer as an independent predictor for both mortality and complications. The identified D-dimer-associated clinical variables draw a landscape integrating the aggregate effects of systemically suppressive and pulmonary hyperactive derangements of fibrinolysis, and the D-dimer-associated clinical biomarkers, and conceptually parameters could be combined for risk stratification, potentially for tracking thrombolytic therapy or alternative interventions.

The sustained COVID-19 pandemic has oversaturated the emergency and intensive critical care resources globally. Hypercoagulability has been evidenced in most critically ill patients by elevated D-dimer and fibrin degradation products (FDP), a decrease in platelet count, an incremental increase in the prothrombin time, and a rise in fibrinogen (1) (2) (3) (4) (5) (6) (7) (8) (9) . Of these, patients with increased D-dimer are more vulnerable to worsen clinical consequences of COVID-19, with more severe complications, including requirements of ICU support (1) (2) (3) (4) (5) (6) (7) (8) (9) .

Thromboembolism of COVID-19 patients is the fatal sequelae of hypercoagulation and fibrinolytic abnormalities. Pulmonary embolism (PE) and deep vein thrombosis (DVT) can cause respiratory failure in severely ill patients with COVID-19 (10) (11) (12) (13) (14) . Postmortem pathology shows that small fibrinous thrombi in small pulmonary arterioles are very common. Activation of the coagulation cascade is further supported by endothelial tumefaction, pulmonary megakaryocytes in the capillaries, and endotheliitis (15) (16) (17) (18) (19) . Elevated D-dimer is an indicator of the activation of the fibrinolysis system and removal of clots or extravascular collections of fibrin by plasmin. Compared with the consistent coagulopathy, however, the clinical ramifications of deranged fibrinolysis are not well studied and reviewed systematically.

Increased D-dimer level has not consistently been observed by all COVID-19 clinical studies, although it is a broadly applied biomarker for prognosis and outcomes of anti-thrombosis (20) . The current explanations for the elevated D-dimer in critically ill patients are multiple, including "suppression of fibrinolysis", "secondarily hyperactive fibrinolysis", "consumption of fibrinolysis", "fibrinolysis resistance", and "fibrinolysis shutdown" (21, 22) . To restore the coagulopathic changes of COVID-19, two diametrically different therapeutic regimes are in practice: fibrinolytic (alteplase-tPA) (10, 11, (23) (24) (25) (26) (27) (28) and antifibrinolytic therapies (nafamostat and tranexamic acid (TXA)) (29) (30) (31) . It is therefore imperative to clarify the role of pathophysiologic derangements of fibrinolysis in clinical outcomes that occur in COVID-19 patients. We, therefore, performed both meta-analyses and meta-regressions to explore the relationships between the plasma D-dimer level on admission with demographics, laboratory tests, fatal cardiopulmonary function, radiology, interventions, complications, and outcomes.

We conducted a systematic review of the literature in accordance with the methods recommended in the PRISMA guidelines ( Figure 1 ).

Two independent investigators searched the potential studies in the NCBI PubMed, EMBASE, Scopus, Web of Science, Google Scholar, and some preprint platforms, including the medRxiv, Preprint, and bioRxiv. The search strategy was (D-dimer OR fibrin OR proteolytic OR fibrinolysis OR coagulation OR thrombin OR platelet OR plasmin OR tPA OR fibrinolytic OR thrombolytic) AND (COVID-19 OR 2019-nCoV OR SARS-nCoV OR Wuhan OR SARS-CoV-2). Sorted COVID-19 and SARS-CoV-2 preprints were screened if available. The hits were limited to publication in the year 2019-2020. Studies published in some high-impact journals focusing on the fibrinolysis and coagulation systems were summarized ( Table 1) . Related articles published during the preparation of this manuscript were discussed.

All eligible studies meeting the following criteria were included: 1) the species was human, 2) the publications were original clinical investigations, and 3) the results were presented as or could be converted or digitized to mean ± SD (SDM) or percentage. Studies were excluded if they were: 1) reviews or editorial, single case reports, commentaries, or preclinical studies; 2) results that could not be converted or digitized to SDM or percentage; and 3) full articles or clinical data that were not available. Raw data of each sub-group at admission were extracted by ZLS and RZZ. Individual data of case series (2, 44) , median and interquartile range (1, 4-8, 13, 33, 35, 39, 42, 43, 46, 48, 49, 52-54, 56, 57, 61, 62) , and ranges of median or mean (9, 45, 63) were converted to SDM as described previously (64, 65) . Percentages and SDMs were extracted directly from studies if available.

To perform meta-analysis with the STATA v.16.1, the studies with two groups or more were pooled to compute weighted mean differences (WMD) and 95% confidence intervals (95% CI) (64) . Different units and methodologies were converted to unified ones; for example, ng/ml, mcg/ml, µ;g/L for D-dimer were computed to µ;g/mL for all studies. The stages of COVID-19 were defined by the original studies mostly based on the WHO Interim Guidance. Publication bias between selected studies was assessed with both the Egger's and Begg's tests using the metabias program. The stability of the results was confirmed by the random-effects trim and fill analyses using the metatrim program.

hospitalization, outcomes, and complications were analyzed. D-dimer was considered as a covariate of other clinical variables. The standard errors of D-dimer were used to indicate the withinstudy variability, and the random-effect ReML method was applied.

If observations were lesser than six, the results of this parameter were removed. All defined complications/diagnoses, i.e., ARDS, DIC, sepsis, were originally reported by the included studies.

Following the PRISMA guideline, we included 42 key studies for meta-analysis and meta-regression ( Figure 1 ). The demographic features, D-dimer, ARDS, and mortality were summarized in Table  1 . In total, there were 14,862 laboratory-confirmed patients: 31 studies from China (5,961cases), 5 from the USA (8,525 cases), 3 from Italy (62 cases), and 1 from Ireland (83 cases), France (150 cases), and Brazil (81 cases), respectively. The age was from 1 to 98year-old. The races included Asian, African, Caucasian, and mixed with an incidence of ARDS ranged from 0 to 100%, and a fatality ranged from 0 to 95.3%. D-dimer level ranged from 0 to 35.7 µ;g/ mL, and 9 studies reported a normal value (< 0.5 µ;g/mL).

COVID-19, as a newly emerging infectious disease, the included clinical studies had divergent designs. We compared the D-dimer level between mild and severe groups of 23 studies (Figure 2A) . Only a small increase of D-dimer (normal range <0.5 µ;g/mL), 0.97 µ;g/mL (95% CI 0.65, 1.29) was observed in the relatively severe group with significant publication bias (92.5%), which was corroborated by both the Begg's (P=0.009) and Egger's tests (P<0.001) ( Figure 2B ) and the random-effects filled funnel plot ( Figure 2C , P<0.001). Similarly, significant variations caused by retrospective studies and case series were observed for both age ( Figure S1 ) and mortality ( Figure S2 ). Thus, it was considered to be inappropriate to perform meta-analysis without well-designed RCT (randomized controlled trials) studies. Instead, we hypothesized that D-dimer could serve as a critical covariate for clinical features.

We performed meta-regression to detect the potential associations of D-dimer with 36 demographic characteristics of COVID-19 patients (Table S2) . Of these, preexisting medical conditions, including any comorbidity, hypertension, diabetes, chronic lung diseases, and cerebrovascular diseases, were positively associated with D-dimer (P<0.05). Age, gender, blood pressure, and dyspnea/tachypnea positively correlated with D-dimer (P<0.05, Table 2 ). Moreover, the percentage of female and diastolic pressure was negatively correlated to D-dimer. Subgroup analysis showed a cutoff age was <65 years in the studies with two groups (<65 or ≥65), and further <50 in the studies with four groups (<50, ≥50, <60, ≥60, and ≥70) for a negative coefficient value (P<0.05, Table S3 ).

To analyze D-dimer's correlation with 62 laboratory tests and radiological readouts, meta-regression was conducted for individual variables and summarized in Table S4 . There were 32 laboratory tests significantly associated with D-dimer (Table S4 ). In addition, FDP tended to associate with D-dimer levels (P=0.058, N=10). These tests could be roughly grouped as 1) inflammation and tissue injury markers, 2) acute lung injury markers, 3) acute kidney injury markers, 4) acute liver injury markers, and 5) cardiac/ skeletal muscle injury markers ( Table 2) . 

To evaluate the relationship between interventions and D-dimer, we associated 11 therapies with the D-dimer level (Tables S5 and 2) . Interestingly, mechanical ventilation was positively associated with D-dimer (N=71, P<0.001, Figure 3 ). Immune enhancement therapy (P=0.084, N=24) was also found to correlate with Ddimer if additional observations were available to increase the sample size.

To examine if D-dimer is an independent risk factor for deadly complications, we analyzed the dependence of fatal organ injury and systemic disorders using the metareg program. The results were summarized in Tables S6 and 2. Acute lung (ALI/ARDS), heart, kidney, and liver injuries were significantly associated with D-dimer (P<0.05, Figure 3 ). In addition, four systemic complications, i.e., sepsis, secondary infection, disseminated intravascular coagulation (DIC), and coagulopathy, showed significant associations with D-dimer. Acute brain injury and acidosis showed a tendency to associate with the D-dimer. Together, both acute fatal organ injury and systemic complications could be predicted by D-dimer.

Given the correlation of D-dimer with the demographic features, abnormal laboratory tests, interventions, and severe complications, we hypothesized that D-dimer is an independent indicator for these disease progression (CURB 65 score, onset to admission, onset to dyspnea), hospitalization (discharged, time is taken to turn SARS-CoV-2 PCR negative), and mortality. We analyzed the correlation of D-dimer and these clinical readouts and summarized in Tables S7 and 2. D-dimer was positively associated with the severity of lung injury (CURB 65), the days from the onset to admission, onset to dyspnea, time is taken to be PCR negative, and overall mortality ( Figure 3 ). In contrast, the discharge rate was negatively related to D-dimer, demonstrating D-dimer's capability to serve as a prognostic variate for outcomes.

Age and gender have been identified as preexisting medical conditions associated with COVID-19 resulting in higher mortality (3, 35, 62) . We eliminated their effects on the association of D-dimer with 61 identified variables in bivariate meta-regression analyses. Age was a significant confounding factor of 27 variables, and 26 variables were still associated with the D-dimer independent of age (Table S8 ). In contrast, 31 variables were disassociated with the D-dimer. By comparison with age, the male gender was a much weaker confounding covariate (Table S9) . Males were significantly associated with 11 variables and results in the dissociation of 14 variables with Ddimer. The association of 28 and 42 variables with D-dimer was still significant after considering age and male as a covariate, respectively ( (Figure 4 ).

We aimed to systematically analyze the relationships between circulating D-dimer level and clinical variables in critically ill COVID-19 patients. There is a range of plasma D-dimer levels on hospital admission. The directions of dynamically changed FDPs for hospitalized patients are different between discharged and deceased cohorts. Our meta-regression analysis revealed that plasma D-dimer is associated with comorbidities, demographics, some laboratory tests, radiology, hospitalization, complications, and outcomes. These results suggest that in addition to serving as an independent predictor for fatality, severity and could potentially serve as a marker for daily monitoring of thrombolytic therapy, D-dimer is a specific biomarker that interacts with other coagulation molecules, inflammatory cytokines, and markers for organ/tissue injury. Of note, the interplay of acute-phase proteins with fibrinogen and Ddimer suggests that infection-induced inflammation (cytokines and chemokines) initiates a state of hyperfibrinolysis. This notion is supported by D-dimer's disassociation with the entire coagulation panel (PT, APTT, factor VIII and XI, TAT) (66, 67) . In general, hyperfibrinolytic homeostasis maintains vascular patency and normal organ function under physiological conditions ( Figure  4B ). SARS-CoV-2 and co-bacterial infection initiate a hypercoagulable state followed by hyperfibrinolysis in COVID-19. If hyperfibrinolysis can counter excessive coagulopathy, then the patients could be protected against thrombosis. Otherwise, insufficient local hyperfibrinolysis in the lung of non-survivors will be exhausted. Age is associated with an increased D-dimer level in COVID-19 patients at admission as a covariate or independent prognostic marker for the outcomes of COVID-19. The cutoff value of Ddimer (0.5 µ;g/mL) is age-dependent for healthy cohorts (68, 69) . Our subgroup analysis is consistent with the concept that adults older than 50 approach the threshold of the D-dimer levels seen in normalcy ( Table S9) . The difference in D-dimer between men and women is minor in a healthy population (69) . D-dimer's positive association with the percentage of male patients in COVID studies suggests more severe cases in men than women when admitted.

Soluble fibrinogen is synthesized in the liver (1.7-5g/d) primarily and others, including the bone marrow, brain, lung, and gastrointestinal epithelium ( Figure 5A ). It mainly distributes in the plasma (75%), interstitial fluid (16%), platelets, and lymph (70, 71) . IL6 and other proinflammatory cytokines/chemokines, steroids, and miRNAs upregulate the fibrinogen synthesis up to 10-fold during the acute phase of injury and infection. Fibrinogen (2-3%) can be turned over to fibrin monomers by thrombin and cleaved by plasmin, and the process termed fibrinogenolysis (70, 72) . Fibrinogen degradation products (FgDP) are 2-or 3-fold that of plasma D-dimer but with a shorter half lifetime (2.8 h vs 16 h for D-dimer) (70, 72) . Crosslinked fibrin is formed in the presence of FXIIIa. At the endothelial cell surface of injured blood vessels, fibrin(ogen) "glues" the plugs formed by the aggregation of platelets to develop thrombi (clots). Excessive fibrin deposition and inflammation activate endothelial cells to produce tPA and urokinase plasminogen activator (uPA) (70, 71) . Either tPA or uPA is capable of cleaving hepatocyte-derived plasminogen to activate plasmin. Plasmin proteolytically cleaves fibrin within the thrombi into FDP and D-dimers, the end products of fibrinolysis. Given the very short half lifetime (in seconds or minutes) of CI, confidence interval; Obs, the number of groups from the included studies; WBC, white blood cells; CK, creatine kinase; CK-MB, creatine kinase myocardial band; ALT, alanine aminotransferase; AST, aspartate aminotransferase; BUN, blood urine nitrogen; eGFR, estimated glomerular filtration rate; LDH, lactate dehydrogenase; PCT, Procalcitonin; CRP, C-reactive protein; ESR, erythrocyte sedimentation rate; IL2R, interleukin 2 receptor; TNFa, tissue necrosis factor a; CURB 65, CURB-65 Severity Score; ARDS, acute respiratory distress syndrome; TE, thromboembolism; DIC, disseminated intravascular coagulation. † include the range of both male and female. P < 0.05 after removing the confounding variable age (*) or male ( §) using bivariate regression. Frontiers in Immunology | www.frontiersin.org May 2021 | Volume 12 | Article 691249 endogenous thrombin, tPA, uPA, and plasmin, and the overwhelming antithrombin, plasminogen activator inhibitor 1 (PAI-1), and plasmin inhibitors with a much longer lifetime in the plasma (73) , the primary cleavage of plasminogen and fibrin may predominately take place at the surface of clots. Eventually, FgDP and FDP (D-dimer) will be catabolized in the liver and captured by the reticuloendothelial system and excreted to the bile (70, 71) . Another clearance pathway is via the kidney to excreted to urine (70, 71) . The D-dimer assay has routinely been applied for excluding PE, deep venous thromboembolism, and DIC, as well as a marker for monitoring the effects of fibrinolytic/ thrombolytic therapy. Our regression analysis reveals that elevated D-dimer is associated with a broad spectrum of immune responses to SARS-CoV-2 infection, including increased pro-inflammatory cytokines (IL2R, IL6, IL8, and TNFa), acute phase proteins (CRP/C-reactive protein, fibrinogen, ferritin, and albumin), and inflammation indicators (ESR-erythrocyte sedimentation rate, PCT-procalcitonin, globulin, white blood cells, neutrophil, lymphocyte, and CD4 + & CD8 + T cells). Moreover, the days for reversion of the PCR test to negative is related to the D-dimer level. These correlations support the concept that interactions between high levels of circulating cytokines and hyperfibrinolysis may be functionally correlated. The binding of the spike proteins of SARS-CoV-2 to the ACE2 receptor in host respiratory epithelial cells downregulates the protective ACE2/Ang1-7/Mas axis, leading to increased expression of PAI-1 (74) . Airway and lung epitheliitis releases pro-inflammatory cytokines to attract leukocytes from the blood. Moreover, infiltrated immune cells are activated and unleashed to attach normal lung tissues by releasing overwhelming cytokines. These cytokines upregulate positive acute-phase protein (i.e., fibrinogen), TF, and trypsin expression and inhibit negative proteins (i.e., albumin). Trypsinactivated matrix metalloproteinases break down the basolateral membrane and interstitial extracellular matrix. Further, endotheliopathy occurs in infected capillaries to initiate a local hypercoagulable state. The kinin-bradykinin is activated by IL6 to stimulate tPA expression in endothelial cells (74) . Deposition of fibrin (clotting) activates endothelial cells to express more IL8, which suppresses clot lysis time (75) . This, combined with the hypoxia-causing eryptosis, maybe the reason for IL8 associating with high mortality. ESR is associated with the severity of COVID-19 patients (76) . In addition to carrying oxygen, erythrocyte-bound streptokinase and tPA break down the clots. Extrathyroidal produced PCT, if maintained at an elevated level by cytokines, is an indicator of poor outcomes of COVID-19 (77) . It has been used as a marker for co-bacterial infection in septic shock and influenza. Neutrophil extracellular traps (NETs) may contribute to organ damage and promote thrombosis and fibrinolysis via elastase, so do lymphocytes/macrophages. Hepatocyte-synthesized globulins and albumin are involved in liver function, coagulation, and anti-inflammation. Their reduced levels by vast consumption predict a poor outcome. Albumin acts as an anticoagulant and antiplatelets and increases vascular permeability (78) , which seems to explain the link between elevated D-dimer and hypoalbuminemia ( Figure 5B ). The association of laboratory tests and complications can be explained by cell death. The lysis of platelets, erythrocytes, and other cells may contribute to higher serum K + levels and reduced hemoglobin. This is supported by the association of D-dimer with LDH level and serum K + and hemoglobin. At the organ level, D-dimer is associated with acute lung injury/ARDS, including hypoxia (respiratory rate, dyspnea, SpO 2 , CURB 65, and the onset of dyspnea). As a key early target organ of SARS-CoV-2 infection, both intra-and extravascular fibrin degradation in the alveolar sacs and interstitium could be the major site responsible for these associations. Hypoxia occurs in patients with the lungs with diffuse alveolar damage, a result in part of fibrin deposition and degradation. Moreover, mechanical ventilation with positive pressure could facilitate the retention of extravascular generated D-dimers. Associations of D-dimer with liver, kidney, and cardiac injury have been reported for other diseases (15, 17, 18, 79, 80) . In addition, Pancreatic function and or metabolism of glucose may be dysfunctional, as shown by the association of D-dimer and blood glucose level. Our results demonstrate that D-dimer may serve as a biomarker to predict the damage of these organs by COVID-19 infection. Furthermore, the associations of D-dimer with systemic complications, including bacterial co-infection, septic shock, DIC, and thromboembolic events in large blood vessels, indicate high incidences and poor outcomes in critically ill patients.

Our results showed a strong positive association of elevated Ddimer on admission with mortality, indicating the prognostic value of an elevated D-dimer for the high risk of death. This is further corroborated by the positive correlation between D-dimer and days from onset to admission, the need for ventilation and the days are taken for PCR test reversion to negative. Another line of supportive evidence is the adverse relation of D-dimer and discharge probability. This data shows that prompt admission and clearance of the SARS-CoV-2 virus may alleviate the severity and reduce fatal events by preventing hyperfibrinolysis and inflammation. D-dimer, as a prognostic marker, is supported by other reviews (81) (82) (83) (84) and clinical studies (1, 62) . Patients with severe COVID-19 maybe those who were ill for a longer period than mild controls. This is supported by the association of the Ddimer level and the days from onset to admission ( Table 2) . Alternatively, the progression of the disease may be much quicker in critically ill patients at admission.

We have systematically reviewed the literature using metaregressions and meta-analyses to define associations between elevated D-dimer and clinical multi-variants for the first time. Our study demonstrates: 1) D-dimer and other clinical variables' associations indicate either a relationship that could be cause-effect or indirect. 2) Few D-dimer-associated variables have been confirmed or could be prognostic biomarkers for developing fatal events and in-hospital mortality. 3) Extremely elevated plasma Ddimer seems to be the consequence of hyperfibrinolysis predominately in the pulmonary capillaries and other organs. 4) A dynamic increase in the D-dimer level may be associated with thromboembolism and higher fatality, while we infer that a continuous decline by daily testing will generally lead to recovery.

Because COVID-19 is a newly emerging disease, most of the included studies are descriptive, case series, retrospective, singlecenter, and observational. Most of the studies included are from China/Asia, where the pandemic was discovered. Diversity in cohorts exists from children to seniors associated with variant comorbidities and complications. Inconsistent grouping strategies between studies pose a challenge for meta-analysis, for example, ICU vs non-ICU, ARDS vs non-ARDS, control vs COVID-19, survivor vs non-survivor, severe vs non-severe, VTE vs non-VTE, death vs recovered, mild vs moderate, moderate vs severe, normal vs abnormal D-dimer, etc. We cannot exclude the potential pre-and post-test deviations regarding the methodology for D-dimer assays. Meta-regression but not meta-analysis may partially mitigate these deviations. Also, a cutoff of D-dimer level could not be computed. Of note, D-Dimer level on admission could not be a biomarker associated with all clinical readouts.

We systematically analyzed the associations of D-dimer on admission with more than 100 clinical readouts. Our results demonstrate that elevated D-dimer could be inter-regulated by a spectrum of clinical variables, including preexisting conditions, inflammation, organ injury, abnormal glucose, complications, and outcomes. The clinical relevance of elevated D-dimer may be multifaceted.

H-LJ, ZS, and RZ were responsible for searching the literature, reviewing extracted data, meta-analyzing data, and preparing drafts of this manuscript. H-LJ, SI, AK, S-LL, MM, and GY improved the final manuscript, and all authors approved submission.

This study was funded in part by the grants: HL87017 (HLJ), HL095435 (MAM), HL134828 (MAM), HL130402 (AK & SI), AI112381(SLL), AI150473 (SLL), HL154103 (SI & AK), and HL14285301 (SI). The funders had no role in study design, data ex-traction, data analysis, data interpretation, or writing of this study.

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We thank Mr. Cong Liu, Ms. Bingxin Guo, and Ms. Qihang Yin for their technical support at the beginning of searching the literature.

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2021. 691249/full#supplementary-material