Incidence and Impact of Disseminated Intravascular Coagulation in COVID-19

Background: Coronavirus disease 2019 (COVID-19) is a novel infectious disease, with signicant morbidity and mortality. This meta-analysis is to evaluate the prevalence of disseminated intravascular coagulation (DIC) in COVID-19 patients and to determine the association of DIC with the severity and prognosis of COVID-19. Methods: We searched the PubMed, EMBASE, and China National Knowledge Infrastructure (CNKI) database until August 12, 2020. The meta-analysis was performed using Stata 16.0 software. Results: 15 studies were included in our meta-analysis. The pooled analysis revealed that the incidence of COVID-19 patients developing DIC was 4% (95%: 2%-5%, P<0.001). In addition, DIC was more likely to occur in the death group (Log OR = 2.4, 95% CI: 1.58-3.21, P<0.001) with statistical signicance. Conclusions: DIC is associated with the severity and poor prognosis of COVID-19 patients. Therefore, attention should be paid to coagulation dysfunction in COVID-19 patients. Monitoring of coagulation indicators may improve the prognosis of COVID-19 inpatients.


Introduction
At the end of 2019, hospitals reported a cluster of cases with pneumonia of unknown cause in Wuhan, Hubei, China, attracting great attention nationally and worldwide. [1] researchers rapidly isolated a novel coronavirus (SARS-CoV-2, also referred to as 2019-nCoV) from con rmed infected pneumonia patients. Phylogenetic analysis shows that SARS-CoV-2 is a new member of the coronaviridae but is distinct from SARS-CoV and MERS-CoV. The World Health Organization (WHO) declared that COVID-19 has become a global health concern, causing severe respiratory tract infections in humans. [2,3] As of 22 Aug 2020, 22812491 laboratory-con rmed cases and 795132 deaths in 216 countries, regions, or territories have been documented [https://www.who.int (accessed 14 August 2020)].
Coronavirus disease 2019 (COVID- 19) is a viral infection that can result in cytokine storm, systemic in ammatory response and coagulopathy that is prognostic of poor outcomes. [4] In previous studies, SARS-CoV-1 were reported to be associated with thrombocytopenia, thrombocytosis, and prolonged activated partial thromboplastin time (APTT) [5]. Currently, accumulated evidence reveal that a coagulation disorder is often seen in COVID-19, and the incidence is higher in severe cases. [6] A broad range of laboratory coagulation parameter abnormalities was reported in patients with COVID-19 including alterations in D-dimer, prothrombin time (PT), brinogen, Fibrin degradation products (FDP), platelet count and Antithrombin III activity. [7] In addition, thrombotic complications in patients diagnosed with COVID-19 are emerging as important result that contribute to signi cant mortality. [8] Though accumulated evidence reveal that a coagulation disorder is often seen in COVID-19, the detailed incidence of DIC are not so common reported. As presented in different articles, the incidence of DIC varied widely among articles. Guan, W. J.'s analysis focusing on abnormal coagulation parameters revealed that 0.09% of patients with COVID-19 met DIC. [9] However, Chen,T. and his colleagues found that 7.7% of patients with COVID-19 had DIC. [10] In addition, There are many diagnostic criteria for DIC, including The JAAM-DIC 2016 score [11], ISTH overt-DIC score [12] and SIC score [13], which had different diagnostic effects on DIC, easily leading to misdiagnosis and missed diagnoses. The epidemiology of DIC among COVID-19 patients is currently based on small case series and retrospective studies. The current meta-analysis was also limited to China. As the epidemic is now raging in hundreds of countries around the world, the research results of cases limited to China cannot be applied to the global scope. This systematic review and meta-analysis focus on this gap in knowledge, helping rst-line healthcare providers' understanding of DIC incidence and mortality in COVID-19.Furthermore, increasing evidence to support DIC, a devastating systemic disorder is linked with severe COVID-19, prompting considerable concern. [14] However, the OR value of DIC for disease risk strati cation and prognosis assessment was still unclear, so another purpose was to evaluate the relationship between DIC and disease strati cation and prognosis.

Search Strategy
Our current systematic review and meta-analysis was reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Statement. We selected relevant studies published between Jan 1, 2020 and Aug 12, 2020, by searching them in PubMed, Embase, and CNKI. The terms for the literature search were combinations of "COVID-19", "2019-nCoV", "SARS-CoV-2", "2019 novel coronavirus", "Novel coronavirus 2019", and "severe acute respiratory syndrome coronavirus 2" with "Consumption Coagulopathy", "Disseminated Intravascular Coagulation", and "DIC". In conformity with the quality standards for reporting systematic reviews and meta-analyses of observational studies, two independent researchers (Zhou XH and Cheng ZP) screened retrieved articles. The researchers independently assessed full texts of articles deemed eligible for inclusion. All disagreements were resolved by discussion with a third reviewer (Hu Yu). The reference list of all identi ed documents was scrutinized to identify eligible studies.

Selection Criteria And Data Extraction
The inclusion criteria were as follows: (1) patients should be con rmed to have been infected with SARS-CoV-2 by laboratory detection or clinical diagnosis; (2) the full text of each article should be available; and (3) outcome should include the incidence of DIC. Meanwhile, the following selection criteria were used to exclude the studies: (1) duplicated studies, (2) studies with sample sizes smaller than 10, (3) studies that focus only on children or infants or pregnant woman, and (4) case reports, clinical guidelines, consensus documents, reviews, and systematic reviews.
Two authors (Zhou XH and Cheng ZP) independently extracted relevant information, including rst author, published journal, inclusion period, country, the number of COVID-19 patients, the mean or median age of patients, gender ratio, incidence of DIC. We also separate patients into groups of severe and nonsevere patients or groups of survivor and non-survivors for further analysis. The degree of severity of COVID-19 were admitted according to American Thoracic Society guidelines for community-acquired pneumonia by clinicians. [15] Data analysis All data analyses were performed using STATA 16.0 software. The Log OR, and relevant 95% CIs were used to estimate pooled results from studies. In case of no obvious heterogeneity (I 2 < 50% and P > 0.1 in the Q test), the xed-effects model was applied. Otherwise, the random-effects model was used. All P-values of ≤ 0.05 were considered to be signi cant statistically. In addition, we performed the Egger's regression test to analyze the publication bias. and we used the trim-and-ll method to eliminate the impact of the publication bias. Furthermore, we conducted a subgroup analysis of the incidence of DIC according to the diagnostic criteria of DIC.
The quality of each study was independently assessed by two participants using the Newcastle Ottawa Scale (NOS) [16]. NOS scores of at least six were considered high-quality literature, and those with higher NOS scores showed higher literature quality.

Literature search and screening
The database searches identi ed a total of 346 potentially relevant articles, including 137 in PubMed, 96 in EMBASE, and 113 in CNKI. Three articles were added after we read the literature we have searched. Of these articles, 84 were excluded due to duplication. After screening titles and abstracts, we further excluded 239 due to non-relevance. After full texts were carefully reviewed, 11 articles were removed for not reporting clinical features of COVID-19 or describing the incidence of DIC. Finally, the meta-analysis included 15 eligible articles. [7,9,10,[17][18][19][20][21][22][23][24][25][26][27][28] The ow diagram ( Fig. 1) illustrates the detailed procedure of literature search.

Characteristics Of Studies And Demographic Features
Ultimately, our analysis included 15 articles, mostly from China. Other studies came from European countries, such as Spain, Italy and France. and we summarize their demographic data in Table 1. The sample size of groups varied from 32 to 1099, and the median age was between 46.7 and 68.6 years old. The overall proportion of male ranged from 50.3-81.3%. Moreover, ve studies have described in detail the occurrence of DIC in survivors and deaths. [10,20,22,24,28] Two studies described ICU patients and non-ICU patients, [9,21] and two study described the incidence of DIC in patients with severe and non-severe disease. [24,27]All articles are of high quality because of NOS score no less than six. Detailed descriptions of the studies included are shown in Table 1.

Meta-analysis
Meta-analysis Thirteen articles with 3511 patients were analyzed for the incidence of DIC in the whole COVID-19 patients. Given the high statistical heterogeneity among the thirteen articles ((I 2 = 92.28% and P < 0.1 in the Q test)), a random-effects model was chosen. (Fig. 2) the incidence of DIC in whole COVID-19 patients was 4% (95%: 2%-5%, P < 0.001). In order to nd possible sources of heterogeneity, subgroup analysis was conducted according to different DIC diagnostic standards.
Of the thirteen articles, six studies used the ISTH Overt-DIC criteria, which indicated that DIC was considered present if the score was 5 or greater. [12] The heterogeneity of the subgroup using ISTH criteria was lower than the original analysis (I 2 = 85.5% vs I 2 = 92.28%), indicating that the differences in diagnostic methods is at least due to the high heterogeneity. The incidence of DIC in the subgroup using ISTH criteria was higher than the subgroup using non-ISTH criteria (proportion:5%; 95% CI 2%-8%, p < 0.001 vs proportion:3%; 95% CI 1%-5%, p = 0.01) (Additional le 1).
To exploring whether the occurrence of DIC predict risk strati cation and prognosis, subgroup analysis based on disease severity and outcome were also conducted. We found that DIC correlated with disease severity in patients with COVID-19 (Log OR = 1.71, 95% CI: 0.62-2.79, P < 0.001) with low heterogeneity (I 2 = 0% and P = 0.73 in the Q test), suggesting that DIC were signi cantly elevated in severe patients /ICU patients compared with Non-severe patients /Non-ICU patients. (Fig. 3A) The analysis of the occurrence of DIC in survivors and deaths is shown in Fig. 3B. DIC was more likely to occur in the death group (Log OR = 2.4, 95% CI: 1.58-3.21, P < 0.001) with statistical signi cance. The heterogeneity test result (I 2 = 35.84% and P = 0.18 in the Q test) indicated that the heterogeneity was low and the result was reliable.

Risk Of Publication Bias
Egger's test indicates statistical signi cance (p < 0.001). Therefore, we addressed the potential publication bias by the trim-and-ll method. The result, which included 13 observational studies and three imputed studies, showed that the incidence of COVID-19 patients developing DIC was 2.7% (95%: 0.7%-4.8%). The funnel plot after applying the trim-and-ll method is shown in Fig. 4.

Discussion
COVID-19, caused by SARS-CoV-2, is rapidly spreading to many countries around the world, posing a critical threat to global health. [29] In this comprehensive meta-analysis, we combined the outcomes of multiple centers and illuminate the incidence DIC in COVID-19 patients, as well as in different subgroups.
Currently, studies have provided evidence that COVID-19 is commonly accompanied with excessive in ammation. [9]One of the most important clinical features of the infection is a profound coagulopathy. In clinical practice, some thrombotic complications described in patients with increasing frequency, including strokes, deep vein thrombosis, myocardial infarction, pulmonary embolism, as well as Disseminated intravascular coagulation (DIC). [17,30,31] In a recent cohort study, 71.4% of non-survivors and 0.6% survivors met the ISTH criteria of disseminated intravascular coagulation. [30] Therefore, coagulation dysfunction may be a major cause of death in severe COVID-19 patients, and monitoring coagulation and anticoagulation biomarkers, such as D-dimer and brin degradation product (FDP) levels, longer prothrombin time and activated partial thromboplastin time, is necessary and helpful for the early diagnosis and a timely intervention of DIC. [32] Evidence has shown the application of heparin sodium and LMWH inhibits blood coagulation, reduces in ammation, and inhibits platelet aggregation, thereby preventing thrombosis and delaying coagulopathy progression to DIC in high-risk patients. [13,17,27]However, when and how to applying preventive anticoagulation therapy remains unclear.
In this study, our meta-analysis showed that 4% of the COVID-19 patients were complicated by DIC, and the incidence was higher in non-survivors than in survivors, which indicated that complication with DIC tends to be associated with enhanced risk of severe COVID-19, even the mortality. In addition, the clinical classi cation of patient severity is often associated with ICU management. We integrated ICU/non-ICU patients and severe/non-severe patients for metaanalysis, further verifying that DIC was a risk factor for aggravation of the disease. As discussed, the development of coagulation test abnormalities seen in SARS-CoV-2-infected patients is most likely a result of the profound in ammatory response. [33] Therefore, we can combine in ammatory markers (interleukin-6, C-reactive protein (CRP) and procalcitonin) with coagulation indicators to evaluate the coagulation function of patients. Moreover, clinical heterogeneity between studies is noteworthy, our study was limited by variable diagnostic criteria for DIC, bias of which we mitigated through subgroup analysis. However, the heterogeneity that still exists is determined by many factors, for example, the included studies apply different criteria for inclusion of cases, which may in the future enable more detailed analyses.

Study Limitations
The number of studies included was limited in terms of sample size, data availability, and methodologic quality. Given that the included studies are all from China and European countries, factors such as virus strain types, medical levels, races, etc., may affect the results. It will be better to include more studies with a broad geographic scope, to get a more comprehensive understanding of COVID-19-associated DIC. Most published literatures are observational studies, making it di cult to con rm causality between COVID-19 and DIC. In addition, patient overlap is possible between a few of the studies. As such, as more data from more regions becomes available. This should be further evaluated in future studies.

Conclusion
In the current pandemic, prevention and control of COVID-19 remains paramount. This meta-analysis showed DIC was associated with increased mortality in COVID-19 pneumonia. Therefore, assessment and optimal management of DIC biomarkers may signi cantly avoid further disease progression in patients with COVID-19.   Forest plot of incidence of DIC.

Figure 3
The Log risk ratio of DIC in survivors compared with deaths and severe patients compared with Non-severe patients.