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HomeHealthCovid-19Elevated D-dimer Is Associated with Severity of COVID-19

Elevated D-dimer Is Associated with Severity of COVID-19

Abstract

With the rapid increase of COVID-19 cases, identifying case severity has become a critical issue for hospital admission and intensive care treatment. Given that pre-existing comorbidities play a significant role in the severity, emerging evidence indicates coagulopathy becomes an independent condition that causes respiratory distress in COVID-19.

In this metanalysis, relevant literature reporting D-dimer, a coagulation byproduct, in COVID-19 cases were synthesized and statistically analyzed to test if the D-dimer level can predict case severity and mortality.

The analysis found that D-dimer levels were higher in non-survivors/severe than in-survivors/non-severe, (MD 0.64, 95% CI 0.52 to 0.75; participants = 5957, I2 = 98%). Subgroup analysis showed MD between non-survivors and survivors was MD 3.48 μg/mL (95% CI 2.69 to 4.27; participants = 1799; studies = 7; I2 = 86%) with Z-score 8.64, p<0.0001. In meta-regression, a significant correlation was observed between increased plasma mean D-dimer level with increased proportion case severity (P=0.046) and mortality (P=0.009).

Overall, the study found that the D-dimer level index can be a predictor of risk for case severity and mortality in COVID-19 patients. The test is rapid and inexpensive and can help clinicians prioritize medical care other than deciding therapeutic options for clinical goals.

Keywords: COVID-19, SARS-CoV-19, D-dimer, Coagulopathy, risk, management.

Introduction

Novel coronavirus disease, also known as COVID-19 caused by SARS‐CoV‐2, has triggered colossal suffering for humanity since its outbreak in mid-December 2019, in Wuhan, China. As of June 15, 2020, there have been more than 8 million confirmed cases and 439,051 deaths worldwide, with a case fatality rate of over 5.4% so far.

The clinical features manifested to this disease primarily include lower respiratory tract illness with fever, dry cough, shortness of breath. Given that preexistence comorbidities such as hypertension, diabetes, cardiac diseases may play a great role in the severity of the disease, it has been hypothesized that SARS‐CoV‐2 induced intravascular lung thrombosis and its progression and evolution could induce the fast worsening of the clinical condition of the patients, which may eventually result in death [1].

D-dimer is a byproduct of blood coagulation and break-down processes. An increased level of plasma D-dimer indicates the presence of significant blood clot formation and its degrading in the human body [2]. Higher the level of D-dimer greater is the possibility of deep vein thrombosis (DVT) or pulmonary embolism (PE) [3], the conditions that have been manifested in many COVID-19 patients in an included study [4] and beyond. Thus, a meta-analysis was performed to investigate if the measurement of the D-dimer level could predict the progression and severity of the disease.

Here, we presented evidence that the plasma D-dimer level could be closely linked to the severity and mortality of COVID-19 infection.

Methods

A comprehensive literature search was conducted in PubMed, ScienceDirect, and Google Scholar. Keyword ‘D-dimer” was used in combination with “novel coronavirus, COVID-19”, “2019-nCoV”, “SARS-CoV-2,” without applying language restriction and dated up to May 31, 2020. After In the analysis, a random-effect model was applied to allow for heterogeneity in individual studies.

Pooled results of proportion at their respective 95% confidence intervals (CI) presented in the analysis were calculated using the DerSimonian–Laird method. The Inverse-Variance method was used for the comparison of mean difference (MD) with 95% confidence interval (CI), and the Random Effects model was used for calculation. Heterogenicity was evaluated with Q-test and I2 statistics. Data that was not expressed as mean and standard deviation was extrapolated from the sample size, median, and interquartile range (IQR) using Hozo’s calculation method [5].

The meta-analysis was performed using RevMan version 5.4. A meta-regression was performed on Open- Open Meta Analyst (CEBM, University of Oxford, Oxford, UK) pulling all literature in Endnote X6 software, duplicates were removed. Then, reviewing the title and abstract, the literature was shortlisted. Original cohort studies reporting D-dimer level separately in COVID-19 patients in two cohorts, 1) severe vs. non-severe, and 2) survivors vs. non-survivors were finally included in the meta-analysis. Case reports, comments, letters to the editors, reviews were excluded.

Table 1 Characteristics of the studies included in the meta-analysis 
StudiesNumber of patients (#n)Sex (Male %)AgeOutcomes, #n (diseased-severe/non-severe)  Non-survivors/Severe (%)Country
Wang, He et al. 2020 [6]3394969 (65-76)Dead/Survival, 65/27419.2China
Tang, Li et al. 2020 [7]18353.654.1 ± 16.2Non-survivors/survivors, 21/16211.5China
Chen, Wu et al. 2020 [8]2746262 (44.0-70.0Death/Recovered, 113/16141.2China
Wang, Lu et al. 2020 [9]3445264 (52-72)Non-survivor/Survivor, 133/21138.7China
Tang, Bai et al. 2020 [4]44959.765.1 ± 12.0Non-survivor/Survivors, 134/31529.8China
Zhang, Liu et al. 2020 [10]1957.973 (38–91)Non-survivors/Survivor     8/1142.1China
Zhou, Yu et al. 2020 [11]1916256 (46-–67)Non-survivors/Survivors, 54/13728.3China
Chen, Zheng et al. 2020 [12]14554.945.3 (±13.6)Severe/Non-severe, 43/10229.7China
Fu, Kong et al. 2020 [14]756046.6 ± 14Severe/Mild, 16/5921.3China
Wan, Xiang et al. 2020 [15]13553.347 (36-55)Severe/Mild, 40/9536.3China
Zhang, Dong et al. 2020 [16]14050.757 (25-87)Severe/Non-severe, 58/8219.5China
Zhang, Hu et al. 2020 [17]22148.955.0 (39.0–66.5)Severe/Non-severe, 55/16641.4China
Zhu, Cai et al. 2020 [18]12735.4350.90± 15.26Severe/Non-severe, 16/11124.9China
Petrilli, Jones et al. 2020 [19]272961.363 (51-74)Critical/Non-critical, 1739/99012.6USA
       

Results

The search retrieved 123 articles, of which 35 were duplicates. After reviewing the title and abstract, 42 articles were assessed for full-text review. Finally, 14 articles were found consistent for full inclusion criteria and were included in the qualitative synthesis and meta-analysis. The main characteristics of the included studies are summarized in Table 1. All studies were retrospective, thirteen studies were conducted in China, and one study was originated in the USA.  The median age of the participants ranged from 45.3 to 73 years, with a male proportion of 57.8%.

Figure 1 D-Dimer
Figure 1 Forest plot comparing mean difference of plasma D-dimer level between non-survivor/severe vs. survivors/non-severe COVID-19 patients in the eighteen included studies.

A total of 5371 participants were enrolled in the studies. However, D-dimer data was available for 5369 patients.  Among the studies, as many as seven studies involving 3570 participants reported D-dimer level in severe vs. non-severe COVID-19 cohort. An equal number of studies with 1799 participants assessed the level of D-dimer in the survivor vs. non-survivor cohort.

The prevalence of non-survivor/severe cases was 28.4 % (95% CI: 22.9-33.9%, P <0.001), wherein non-survivor cases accounted for 10.8% (95%CI: 8.6% 13.1%, P < 0.001), across the studies. The mean plasma D-dimer level in diseased/severe group was 1.77 µg/mL, which is significantly higher than the non-severe/survivors’ groups 0.53 µg/mL and the difference is significant (MD 0.64, 95% CI 0.52 to 0.75; participants = 5369; studies = 14; I2 = 98%).

Figure 2 D-Dimer
Figure 2 scatter plot demonstrating the association of plasma D-dimer and severity

In subgroup analysis, plasma D-dimer level in severe vs non-severe cohort, mean plasma D-dimer level in severe cases was 0.51 µg/mL, which is significantly (MD 0.19, 95% CI 0.12 to 0.26; participants = 3570; studies = 7; I2 = 97%) higher than the non-severe (0.29 µg/mL) patients. In survivors vs. non-survivor cohort, the mean plasma D-dimer level in diseased group was 4.69 µg/mL ( 2.12 to  21.1 µg/mL) which is 4.99 fold higher than survivors 0.94 µg/mL (0.3 to 4.16 µg/mL ) and the difference is statistically significant  (MD 3.48, 95% CI 2.69 to 4.27; participants = 1799; studies = 7; I2 = 86%)

Figure 3 D-Dimer
Figure 3 scatter plot demonstrating the association of plasma D-dimer and mortality

Further, meta-regression demonstrated increased mean D-dimer is closely related with an increased likelihood of severity (Q: 0.378 CI: 0.007 0.749, P= 0.046), Figure 2, and mortality (Q: 0.082, CI: 0.020 to 0.144, P= 0.009), Figure 3.

Discussion

Coronaviruses are enveloped viruses, have a single-strand positive-sense RNA of 26 to 32 kb in length within its structures. The family, called Coronaviridae, has at least seven strains of coronavirus. Two of them have caused severe respiratory illness in humans in the past: SARS-CoV during 2002-2003, MARS-CoV 2013. In the middle of December, in 2019, the virus that caused an acute respiratory outbreak in Uhan, China, has been named SARS-CoV-2, and the disease was termed COVID-19 by WHO. Similar to SARS-CoV, SARS-CoV-2 primarily targets the respiratory system where the virus enters host epithelium cells via the ACE2 receptor.

With the rapid increase of infection, case severity determination has become an important component of providing effective medical care for COVID-19 patients. It helps clinicians to determine if the patient needs an urgent and intensive care facility. In the case of a limited facility, it may help clinicians prioritize the case who needs immediate care.

Also, determining severity helps clinicians to determine what medical equipment and medicine do the patient need for the clinical goal. Along with pre-existing comorbidity such as hypertension, diabetes, respiratory disease, routine blood profile/biochemistry/immunology analysis results such as increased white blood cell (WBC), decreased lymphocyte and platelet counts, higher IL-6 and IL-10 levels are associated with both severe and fatal COVID-19 cases, for review see [22].

In this review, the evidence provided shows that investigating the D-dimer level in COVID-19 infected patients could be an effective parameter to determine risk and severity in the infected patients. This meta-analysis, to the best of available information, is the first meta-analysis systematically comparing D-dimer abnormalities in either a severity or mortality cohort, and determining if there is a statistically significant relationship between the variables.

D-dimer is a byproduct of the active blood coagulation and degradation process. Activation of blood coagulation decreases blood flow, results in hypoxia leading to multiple organ failure and death, characteristic manifestations of severe and fatal COVID-19 cases. Also, in accordance with the observation that shows coagulopathy—development of miro-clots in lung tissues have been shown to be associated with COVID-19 disease severity [23], significantly greater increases for D-dimer was observed in non-survivors vs. survivors (MD 3.48 µg/mL) as compared to severe vs. non-severe cases (MD 0.19 µg/mL), respectively.

Both of the values are equally important for monitoring clinical prognosis in COVID-19 patients during hospital admission and throughout taking a medical decision. Given the observation—higher the D-dimer level greater is the proportion of severe and deceased cases, plasma D-dimer level, statistically, could be a significant predictor of clinical deterioration of cases leading to case severity and death in hospitalized Covid-19 cases.  

A possible mechanism involving SARS-CoV-2 in the coagulation activation is being investigated. It is believed that the virus causes injuries on the inner endothelial lining of blood vessels, which triggers the release and activation of coagulation factors. Other contributing factors include the release of various cytokines, inflammatory factors in response to a viral infection—the study mentioned [24].

Several limitations exist in this meta-analysis. The most important was the presence of high heterogenicity among the studies. This can be explained due to the different populations of the participants, the presence of different comorbidity, and variation in the follow-up process. Also, the laboratory value might have influenced the results, as different laboratories have different normal range settings based on the method and local data. These limitations need to be kept in mind to interpret the test results. Sample size, exploitation of data from median and range, strict selection criteria, and covering studies that originated in only China, and the USA are among the other limitations of the study.

Despite limitations, this study has shown that the D-dimer level could be a predictor of case severity and death as in both instances positive coefficient (Q) signifies there is a linear relationship between the variables when the value of D-dimer increases population in severe or mortal cases increases.

The finding of this study, therefore, may potentially be useful in creating a therapeutic intervention. At the time of hospital admission and during the treatment process, measurement of D-dimer could provide a therapeutic option, as in one study, Tang et al. reported that heparin treatment reduced mortality of COVID-19 patients with elevated D-dimer [4]. Developing a scoring system involving D-dimer would help clinicians select patients, at risk for developing severity and mortality. However, the conclusion of this study needs to be verified with more studies involving a larger sample size.

The author declares no conflict of interest.

References

1.   Saba, L. and N. Sverzellati, Is COVID Evolution Due to Occurrence of Pulmonary Vascular Thrombosis? J Thorac Imaging, 2020. https://www.ncbi.nlm.nih.gov/pubmed/32349055

2.  Bounds, E.J., et al., D Dimer, in StatPearls. 2020: Treasure Island (FL).

3.  Lippi, G., et al., Causes of elevated D-dimer in patients admitted to a large urban emergency department. Eur J Intern Med, 2014. 25(1): p. 45-8.

4.  Tang, N., et al., Anticoagulant treatment is associated with decreased mortality in severe coronavirus disease 2019 patients with coagulopathy. J Thromb Haemost, 2020. 18(5): p. 1094-1099.

5.  Hozo, S.P., et al., Estimating the mean and variance from the median, range, and the size of a sample. BMC Med Res Methodol, 2005. 5: p. 13.

6.  Wang, L., et al., Coronavirus disease 2019 in elderly patients: Characteristics and prognostic factors based on 4-week follow-up. J Infect, 2020. 80(6): p. 639-645.

7.  Tang, N., et al., Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia. J Thromb Haemost, 2020. 18(4): p. 844-847.

8.   Chen, T., et al., Clinical characteristics of 113 deceased patients with coronavirus disease 2019: retrospective study. BMJ, 2020. 368: p. m1091.

9.   Wang, Y., et al., Clinical Course and Outcomes of 344 Intensive Care Patients with COVID-19. Am J Respir Crit Care Med, 2020. 201(11): p. 1430-1434.

10.  Zhang, J., et al., The clinical data from 19 critically ill patients with coronavirus disease 2019: a single-centered, retrospective, observational study. Z Gesundh Wiss, 2020: p. 1-4.

11.   Zhou, F., et al., Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet, 2020. 395(10229): p. 1054-1062.

12.   Chen, Q., et al., Clinical characteristics of 145 patients with corona virus disease 2019 (COVID-19) in Taizhou, Zhejiang, China. Infection, 2020. 48(4): p. 543-551.

13.  Chen, G., et al., Clinical and immunological features of severe and moderate coronavirus disease 2019. J Clin Invest, 2020. 130(5): p. 2620-2629.

14.  Fu, J., et al., The clinical implication of dynamic neutrophil to lymphocyte ratio and D-dimer in COVID-19: A retrospective study in Suzhou China. Thromb Res, 2020. 192: p. 3-8.

15.  Wan, S., et al., Clinical features and treatment of COVID-19 patients in northeast Chongqing. J Med Virol, 2020. 92(7): p. 797-806.

16.  Zhang, J.J., et al., Clinical characteristics of 140 patients infected with SARS-CoV-2 in Wuhan, China. Allergy, 2020. 75(7): p. 1730-1741.

17.  Zhang, G., et al., Clinical features and short-term outcomes of 221 patients with COVID-19 in Wuhan, China. J Clin Virol, 2020. 127: p. 104364.

18. Zhu, Z., et al., Clinical value of immune-inflammatory parameters to assess the severity of coronavirus disease 2019. Int J Infect Dis, 2020. 95: p. 332-339.

19. Petrilli, C.M., et al., Factors associated with hospital admission and critical illness among 5279 people with coronavirus disease 2019 in New York City: prospective cohort study. BMJ, 2020. 369: p. m1966.

20.  Wu, C., et al., Risk Factors Associated With Acute Respiratory Distress Syndrome and Death in Patients With Coronavirus Disease 2019 Pneumonia in Wuhan, China. JAMA Intern Med, 2020.

21.  Yan, Y., et al., Clinical characteristics and outcomes of patients with severe covid-19 with diabetes. BMJ Open Diabetes Res Care, 2020. 8(1).

22.  Henry, B.M., et al., Hematologic, biochemical and immune biomarker abnormalities associated with severe illness and mortality in coronavirus disease 2019 (COVID-19): a meta-analysis. Clin Chem Lab Med, 2020. 58(7): p. 1021-1028.

23.  Fogarty, H., et al., COVID19 coagulopathy in Caucasian patients. Br J Haematol, 2020. 189(6): p. 1044-1049.

24.  Willyard, C., Coronavirus blood-clot mystery intensifies. Nature, 2020. 581(7808): p. 250.

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