Prognostic Impact of Coagulopathy in Patients with COVID-19: a Meta-analysis of 35 Studies and 6427 Patients

Abstract

Coronavirus Disease 2019 (COVID-19) is a highly contagious disease that appeared in China in December 2019. Several patients with severe COVID-19 infection can develop a coagulopathy according to the ISTH-criteria for disseminated intravascular coagulopathy (DIC). We conducted a meta-analysis of all available studies on COVID-19 to explore the impact of coagulopathy on severe illness and mortality. An electronic search was performed within PubMed, Google Scholar and Scopus electronic databases. The primary endpoint was the difference of D-dimer values between Non-Severe vs Severe disease and Survivors vs Non-Survivors. The primary analysis showed that mean d-dimer is significantly higher in COVID-19 patients with severe disease than in those without (SMD -2.15 [-2.73 to -1.56], I ² 98%, P <0.0001). Additional analysis of platelet count showed lower levels of mean PLT in Severe patients than those observed in the Non-Severe patients (SMD 0.77 [0.32 to 1.22], I ² 96%, P <0.001). Interestingly, longer mean PT was found in Severe group (SMD -1.34 [-2.06 to -0.62], I ² 98%, P <0.0002) compared to Non-Severe group. In conclusion, the results of the present meta-analysis, the largest and most comprehensive to date, demonstrate that Severe COVID-19 infection is associated with higher D-dimer values, lower platelet count and prolonged PT.

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Prognostic Impact of Coagulopathy in Patients with
COVID-19: a Meta-analysis of 35 Studies and 6427
Patients
Alberto Polimeni
Division of Cardiology and Center for Cardiovascular Research, Department of Medical and Surgical
Sciences, Magna Graecia University, Catanzaro, Italy
Isabella Leo
Division of Cardiology, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro,
Italy
Carmen Spaccarotella
Division of Cardiology and Center for Cardiovascular Research, Department of Medical and Surgical
Sciences, Magna Graecia University, Catanzaro, Italy
Annalisa Mongiardo
Division of Cardiology, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro,
Italy
Sabato Sorrentino
Division of Cardiology and Center for Cardiovascular Research, Department of Medical and Surgical
Sciences, Magna Graecia University, Catanzaro, Italy
Jolanda Sabatino
Division of Cardiology and Center for Cardiovascular Research, Department of Medical and Surgical
Sciences, Magna Graecia University, Catanzaro, Italy
Salvatore De Rosa
Division of Cardiology and Center for Cardiovascular Research, Department of Medical and Surgical
Sciences, Magna Graecia University, Catanzaro, Italy
Ciro Indol (  indol@unicz.it )
Division of Cardiology and Center for Cardiovascular Research, Department of Medical and Surgical
Sciences, Magna Graecia University, Catanzaro, Italy
Systematic Review
Keywords: COVID-19, coagulopathy, meta-analysis, D-dimer values, platelet count
DOI: https://doi.org/10.21203/rs.3.rs-31142/v1
License:   This work is licensed under a Creative Commons Attribution 4.0 International License.  Read
Full License

Page 2/22

Page 3/22
Abstract
Coronavirus Disease 2019 (COVID-19) is a highly contagious disease that appeared in China in December
2019. Several patients with severe COVID-19 infection can develop a coagulopathy according to the ISTH-
criteria for disseminated intravascular coagulopathy (DIC). We conducted a meta-analysis of all available
studies on COVID-19 to explore the impact of coagulopathy on severe illness and mortality. An electronic
search was performed within PubMed, Google Scholar and Scopus electronic databases. The primary
endpoint was the difference of D-dimer values between Non-Severe vs Severe disease and Survivors vs Non-
Survivors. The primary analysis showed that mean d-dimer is signicantly higher in COVID-19 patients with
severe disease than in those without (SMD -2.15 [-2.73 to -1.56], I2 98%, P <0.0001). Additional analysis of
platelet count showed lower levels of mean PLT in Severe patients than those observed in the Non-Severe
patients (SMD 0.77 [0.32 to 1.22], I2 96%, P <0.001). Interestingly, longer mean PT was found in Severe group
(SMD -1.34 [-2.06 to -0.62], I2 98%, P <0.0002) compared to Non-Severe group. In conclusion, the results of
the present meta-analysis, the largest and most comprehensive to date, demonstrate that Severe COVID-19
infection is associated with higher D-dimer values, lower platelet count and prolonged PT.
Introduction
Coronavirus Disease 2019 (COVID–19), caused by a coronavirus named “severe acute respiratory syndrome
coronavirus 2” (SARS-CoV–2) is a contagious disease that appeared in Wuhan (China) in December 2019
and spread quickly around the whole world[1].
The most common symptoms of COVID–19 are fatigue, fever, nasal congestion and cough. Nevertheless,
about 1/5 people can progress rapidly and develop breathing diculties, requiring hospitalization, septic
shock, metabolic acidosis and coagulopathy[2].
Although the symptoms are usually mild, especially in young adults and children, COVID–19 can be highly
deadly and lethal, especially in high-risk patients with underlying conditions such as hypertension, heart
disease or diabetes. Therefore, it is mandatory to identify potential risk factors for predicting disease
progression and severity. Several patients with severe COVID–19 infection can develop a coagulopathy
according to the criteria for disseminated intravascular coagulopathy (DIC) with fulminant activation of
coagulation[3] (Figure 1).
D-dimer is a brin degradation product of crosslinked brin and can be considered a biomarker of
brinolysis and activation of coagulation[4]. D-Dimer has been found increased in COVID–19 patients[5], and
recently Zhou et al. demonstrated that the d-dimer levels on admission greater than 1 μg/mL were
associated with an increase of in-hospital death[6]. Moreover, virus-induced inammation also may
contribute to increase in blood coagulability. Thus, the data related to coagulation parameters in different
stages of COVID–19 disease may be of paramount importance to consider therapeutic prophylaxis or
anticoagulation.

Page 4/22
Thus, this study aims to summarize all available data on coagulation parameters in COVID–19 patients and
to perform a meta-analysis to assess the impact of coagulopathy in different stages of COVID–19 disease.
Methods
Search strategy and study selection.
An electronic search was performed within PubMed, Google Scholar
and Scopus electronic databases between December 2019 (rst conrmed Covid–19 case) up to April 6th,
2020. The following keywords were used for the search: “laboratory” or “coagulation” and “COVID–19” or
“Coronavirus” or “SARS-CoV–2”. The English language was a limiting criterium for our analysis. All reports,
including the search terms, were independently screened by two investigators for relevance and eligibility
(I.L. and A.P.). Additionally, references from relevant articles were also manually scanned for additional
studies. Where data were not available in the published study reports, authors were contacted, whenever
possible, to supply missing information by email. The authors discussed their evaluation, and any
disagreement was resolved through discussion and re-reading.
Inclusion and Exclusion Criteria.
Studies were considered eligible if the following statements were applying
a) they involved a study population with COVID–19 conrmed infection; b) studies that stratify the risk of
severe or fatal COVID–19; c) they reported information on the difference of D-dimer values between two
groups. Exclusion criteria were (just one was sucient for study exclusion): non-original articles or articles
with the number of patients less than 10, a duplicate publication with the same endpoint, endpoint measure
not specied.
Endpoints.
The primary endpoint was the difference of D-dimer values between Non-Severe vs Severe
disease and Survivors vs Non-Survivors. Moreover, results on additional coagulation parameters (platelets
count, prothrombin time, activated partial thromboplastin time) were also analyzed.
Data Abstraction and Management.
Baseline characteristics and laboratory data were abstracted from the
single studies through carefully scanning of the full article by two independent reviewers (I.L. and AP).
Divergences were resolved by consensus. Moreover, the following data were extracted: year of publication,
location, number of study patients, source type, peer-review process, study design, study groups. Selection
and data abstraction were performed according to the MOOSE (Meta-analyses Of Observational Studies in
Epidemiology) and PRISMA Checklist (Supplemental Table 1–2). The quality analysis of the selected studies
was performed using the Agency for Healthcare Research and Quality (AHRQ) for cross-sectional study form
(Supplemental Table 3).
Statistical analysis.
Mean and standard deviation were calculated from median and interquartile range (IQR),
according to the formula reported by Wan X. et al.[7] The summary measure used was the Standardized
mean difference (SMD) with 95% condence. Random-effects meta-analysis was used because high
variability between studies was expected. Heterogeneity was evaluated using the I2 statistic. Cut-off values
of 25%, 50%, and 75% indicated low, moderate, and high heterogeneity, respectively. Next, to explore potential
sources of heterogeneity, we conducted a subgroup analysis between peer-reviewed/non-peer-reviewed
articles. Finally, sensitivity analyses were performed by systematically removing each study, in turn, to

Page 5/22
explore its effect on outcome as previously described [8,9]. Publication bias was evaluated by the Egger test.
Forest plots were used to graphically display the results of the meta-analysis, as already previously
described [10,11]. All Analyses were performed using R Statistical Software (version 3.6.3; R Foundation for
Statistical Computing, Vienna, Austria).
Results
Search results.
Our search retrieved a total of 3439 entries, which were reduced to 3252 studies after
duplicates removed. After the screening of 322 records, 290 studies were then excluded because they were
not related to our research question. In the assessment of eligibility, further 20 studies were excluded
because of: duplicate publication; outcome not reported; not original articles. Finally, a total of 35 studies
were available for the analysis, including 6427 patients [5,6,12–44]. The study selection procedure is reported
in detail in gure 2.
Data on Included Studies.
Since randomized trials were not currently available, only retrospective studies
were included in the present meta-analysis. Table 1 summarizes the most relevant characteristics of the
selected studies. Sixteen studies were peer-reviewed [5,6,12–25], nineteen were non-peer-reviewed [26–44]. Not
surprisingly, quality assessment revealed a non-high study quality (Supplemental Table 1). Across the
studies, patients were predominantly male and approximately one-fourth of patients had a history of
cardiovascular disease. More details on patients’ characteristics are provided in table 2.
Meta-analysis results
The primary analysis showed that mean d-dimer is signicantly higher in COVID–19 patients with severe
disease than in those without (SMD –2.15 [–2.73 to –1.56], I2 98%, P <0.0001) (Figure 3, panel A). Similarly,
we found a much higher mean d-dimer in Non-Survivors compared to Survivors (SMD –2.91 [–3.87 to –
1.96], I2 98%, P <0.0001) (Figure 3, panel B).
Additional analysis of platelet count showed lower levels of mean PLT in Severe patients than those
observed in the Non-Severe group (SMD 0.77 [0.32 to 1.22], I2 96%, P <0.001) (Figure 4, panel A).
Of note, a similar result was observed even when Non-Survivors were compared to Survivors (SMD 1.84 [1.16
to 2.53], I2 97%, P <0.0001) (Figure 4, panel D).
Interestingly, longer mean PT was found in both Severe (SMD –1.34 [–2.06 to –0.62], I2 98%, P
<0.0002) (Figure 4, panel C) and Non-Survivors groups (SMD –1.61 [–2.69 to –0.54], I2 98%, P
<0.003) compared to Non-Severe and Survivor patients.
Whether, no statistically signicant differences were found in mean aPPT in both Non- Severe/Severe (SMD
0.39 [–0.33 to 1.12], I2 98%, P = 0.28) and Survivors/Non-Survivors (SMD
0.58 [–0.42 to 1.58], I2 97%, P = 0.26)(Figure 4, panels C-F).

Page 6/22
Subgroup and Sensitivity Analyses for the Primary Endpoint.
As both peer-reviewed and non-peer-reviewed studies were included in this analysis (Table 1), we performed
a subgroup analysis, revealing a similar result for both study types for the primary endpoint (peer-reviewed
SMD –1.90 [–2.95 to –0.84], I2 98%, P <0.001; non-peer-reviewed SMD -
2.34 [–3.0 to –1.68], I2 97%, P <0.0001)(Supplemental Figure 1, panels A-B).
Moreover, sensitivity analysis performed by the leave-one-out approach showed that no single study had a
substantial contribution to the pooled mean difference (Supplemental Figure 2, panels A-B).
Publication Bias.
No evidence of publication bias was found by Egger’s test. The P values were: P = 0.07 for d- dimer, 0.81 for
PLT, 0.13 for PT, and 0.10 for aPTT.
Discussion
The major nding of the present meta-analysis, the largest and most comprehensive to date, is that high
levels of D-Dimer are associated with a more severe prognosis and increased mortality in patients with
COVID–19. Finally, the mean platelet count is lower and mean prothrombin time more prolonged in Severe
and Non-Survivor Covid–19 patients, supporting the concept that patients infected by COVID–19 may be at
risk of developing disseminated intravascular coagulation (DIC). In fact, high d-dimer levels, low platelet
count and prolonged PT are critical parameters of ISTH Criteria for DIC[3] as showed in a recent study by
Tang and colleagues[17]. First, they showed that most of non-survivor patients with COVID–19 disease met
the criteria for DIC. Moreover, elevated D-dimer values were associated with a worse clinical outcome,
reecting coagulation activation from infection, marked inammation and multiorgan failure [45].
Recently, Lippi et al.[46] showed in a brief letter reporting a pooled analysis of 4 studies that D- dimer is
associated with the severity of COVID–19 disease. The mean difference of the four studies which reported
D-dimer values showed that they are signicantly higher in COVID–19 patients with severe disease than in
those with mild disease.
The obvious consideration is related to therapy with heparin to limit coagulopathy. Nonetheless, it is
paramount to stimulate local brinolysis to degrade pre‐existing brin in the lung. Hence, a nebulizer form of
tissue‐type plasminogen activator to treat COVID‐19 has been proposed recently [47].
Interestingly, a recent nding investigated the predictors of 28-day mortality in Severe COVID–19 patients
and the association between death and low molecular weight heparin (LMWH) therapy. They showed that
patients with elevated D-dimer values, prolonged PT and increased age presented a greater mortality at 28
days, while those with a higher platelet count had a lower 28-day mortality. Specically, the use of
anticoagulant therapy resulted in lower mortality in patients with severe coagulopathy with a SIC score ≥4
(LMWH: 40.0% vs No-LMWH: 64.2%,
p
= 0.03) or D-dimer >6-fold of the normal upper limit (32.8% vs 52.4%,
p

Page 7/22
= 0.02. Still, there was no overall benet between those who use heparin and those who do not. (30.3% vs
29.7%,
p
= 0.91) [17].
Although coagulopathy acknowledges various aetiological causes, our ndings suggest that the worsening
of coagulation parameters may indicate progressive severity of COVID–19 infection and may predict the
need for more aggressive critical care and treatment. Thus, patients in the Intensive Care Unit (ICU) should
have pharmacologic prophylaxis with heparin if there is not a caution. Consideration of clotting problems
and antithrombotic therapy in the daily COVID–19 management process is essential, rather than focusing
solely on the infection. Further, potential complications related to intravascular clotting should always be
taken into consideration in the presence of worsening clinical conditions. The risk of bleeding should always
be considered in individual patients when anticoagulant drugs are administered [48].
In conclusion, further studies to dene whether adjunctive antithrombotic drugs may be helpful to treat
patients properly with severe COVID–19 disease are still needed.
Limitations.
Our study has some limitations. First, in the absence of randomized clinical trials, our analysis
reported only data from retrospective and observational studies. Second, since there is signicant
heterogeneity, we used a random-effects model for all analyses. Third, the denition of the endpoints is
variable in the different studies. Thus, we performed a subgroup analysis (Severe/Non Severe, Non
Survivors/Survivors) to overcome this issue.
Conclusions
Results of the present meta-analysis, the largest and most comprehensive to date, demonstrate that Severe
COVID–19 infection is associated with higher D-dimer values, lower platelet count and prolonged PT. This
data suggests a possible role of disseminated intravascular coagulation in the pathogenesis of COVID–19
disease.
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Declarations
Competing interests: The author(s) declare no competing interests.
Funding: none
Authors' contributions: AP, CS, and SS designed the study and acquired, analysed, and interpreted data. IL,
AP and AM did the literature search and study selection procedures. JS, SDR and CI drafted the manuscript,
with critical revisions for important intellectual content from all authors.
Data availability: The datasets generated during and/or analysed during the current study are available from
the corresponding author on reasonable request
Acknowledgements: none
Tables

Page 11/22
Table 1 - Characteristics of the selected studies


Page 12/22
STUDY YEAR LOCATION N SOURCETYPE PEER-
REVIEWED
STUDY
DESIGN
STUDY
GROUPS
Cai Q. et al.
[26]
2020 China 298 Journal
Article No Retrospect
ive
study
Non-
Severe
vs
Severe
Chen G.
et al.
[12]
2020 China 21 Journal
Article Yes Retrospect
ive
study
Moderat
e vs
Severe
Chen T. et
al.
[13] 2020 China 799 Journal
Article Yes Retrospect
ive
study
Deaths
vs
Recover
ed
Patients
Deng
Q. et
al.
[14]
2020 China 112 Journal
Article Yes Retrospect
ive
study
Non-
Severe
vs
Severe
Gao Y. et al.
[15]
2020 China 43 Journal
Article Yes Retrospect
ive
study
Mild vs
Severe
Han H. et al.
[16]
2020 China 94 Journal
Article Yes Retrospect
ive
study
Ordinary
vs
Severe/
Critical
Huang C.
et al.
[5]
2020 China 41 Journal
Article Yes Retrospect
ive
study
ICU care
vs Non-
ICU care
Huang H.
et al.
[27]
2020 China 125 Journal
Article No Retrospect
ive
study
Mild vs
Severe

Li J. et al.
[28]

2020

China

134 Journal
Article

No Retrospect
ive
study
Non-
Died Vs
Died
Moderat
e vs
Severe/
Critical
Li K. et al.
[29]
2020 China 102 Journal
Article No Retrospect
ive
study
Non-
survivor v
Survivor
Li Z. et al.
[30]
2020 China 193 Journal
Article No Retrospect
ive
study
Non-
Severe
vs
Severe
Liu Jiachen
g et
al.
[31]
2020 China 122 Journal
Article No Retrospect
ive
study
Common
vs
Severe
Liu Jing
et al.
[32]
2020 China 40 Journal
Article No Retrospect
ive
study
Mild vs
Severe
Lu H. et al.
[33]
2020 China 265 Journal
Article No Retrospect
ive
study
Mild/Mo
erate vs
Severe
Critically
Ill
Lu Z. et al.
[34]
2020 China 124 Journal
Article No Retrospect
ive
study
Discharg
ed vs
Death

Luo X. et al.
[35]

2020

China

403 Journal
Article

No Retrospect
ive
study
Recover
ed vs
Died,
Ordinary
vs
Severe/
Critical
Journal Retrospect Non-

Page 13/22
Ma K. et al.
[36]
2020 China 84 Article No ive
study
Severe
vs
Severe

Qian G.
et al.
[37]

2020

China

91

Journal
Article

No

Retrospect
ive
study

Mild vs
Severe
Tang N. et
al.
[17]
2020 China 449 Journal
Article Yes Retrospect
ive
study
Non-
survivor
vs
Survivor
Wan S. et
al.
[18]
2020 China 135 Journal
Article Yes Retrospect
ive
study
Mild vs
Severe
Wang D.
et al.
[19]
2020 China 138 Journal
Article Yes Retrospect
ive
study
ICU vs
Non-ICU
Wang K.
et al.
[38]
2020 China 305 Journal
Article No Retrospect
ive
study
Survivor
s vs
Non-
Survivor
s
Wang L.
et al.
[20]
2020 China 339 Journal
Article Yes Retrospect
ive
study
Survival
vs Dead

Wu C. et al.
[22]

2020

China

201

Journal
Article

Yes

Retrospect
ive
study
Patients
with
ARDS vs
Patients
without
ARDS,
Patients
Ali
ve
vs
Di
ed
Pa
tie
nt
s

Page 14/22

Wu J. et al.
[21]

2020

China

280

Journal
Article

Yes

Retrospe
ctive
study
Mild
a
n
d
M
o
d
e
r
a
t
e
t
y
p
e
Pati
ents vs
Severe
and
Criticall
y
ill type
Patient
Xu Y. et al.
[39]
2020 China 69 Journal
Article No Retrospe
ctive
study
Mild
cases vs
Severe
or
Critical
cases
Zeng J. et
al.
[40]
2020 China 419 Journal
Article No Retrospe
ctive
study
ICU vs
Non-ICU
Zhang F.
et al.
[41]
2020 China 48 Journal
Article No Retrospe
ctive
study
Survivor
s vs
Non-
Survivor
s
Zhang G.
et al.
[42]
2020 China 221 Journal
Article No Retrospe
ctive
study
Non-
Severe
vs
Severe
Zhang J.
et al.
[23]
2020 China 140 Journal
Article Yes Retrospe
ctive
study
Non-
Severe
vs
Severe
Zheng
C. et
al.
[24]
2020 China 55 Journal
Article Yes Retrospe
ctive
study
Non-
Severe
vs
Severe
Zheng
X. et
al.
[43]
2020 China 52 Journal
Article No Retrospe
ctive
study
Severe
vs
Common
Zhou F. et
al.
[6]
2020 China 191 Journal
Article Yes Retrospe
ctive
study
Survivor
s vs
Non-
Survivor
s
Zhou
Ying
et al.
[44]
2020 China 277 Journal
Article No Retrospe
ctive
Study
Non-
Severe
vs
Severe
ZhouYulong
et al.
[25]
2020 China 17 Journal
Article Yes Retrospe
ctive
Study
Non-
Aggravat
nvs
Aggravat
nGroup

Page 15/22

Table 2 - Baseline Patient’s Characteristics

Page 16/22
STUDY AGE
Mean±SD
MALE N
(%)
HYPERTE
NSI
ON
N
(%)
SMOKER
S N
(%)
DIABETE
S N (%)
CVD N (%) COPD N
(%)
Cai Q.
et al.
[26]
47 ± 4.6 149
(50.0) 38
(1
2.8
)
NA 19 (6.4) 11
(3.7) NA
Chen G.
et al.
[12]
56 ± 3.7 17
(81.0) 5
(2
3.8
)
NA 3 (14.3) NA NA
Chen T.
et al.
[13]
62 ± 4.3 171
(62.0) 97
(3
4.0
)
12
(4.0)
47
(17.0)
23
(8.0)
18
(7.0)
Deng Q.
et al.
[14]
65 ± 3.6 57
(50.9) 36
(3
2.1
)
NA 19
(17.0)
15
(13.4) 4 (3.6)
Gao Y.
et al.
[15]
43 ± 11.7 26
(60.0) 13
(3
0.2
)
NA 7 (16.3) 3
(69.7)
8
(18.6)
Han H.
et al.
[16]
NA NA NA NA NA NA NA
Huang C.
et al.
[5] 49 ± 4.2 30
(73.0) 6
(1
5.0
)
3 (7.0) 8 (20.0) 6 (8.0) 1 (2.0)
Huang H.
et al.
[27] 44 ± 18.5 63
(50.0) 20
(1
6.0
)
NA 8 (6.4) NA NA
Li J. et al.
[28]
61 ± 3.8 75
(56.0) 44
(3
2.8
)
22
(16.4)
34
(25.3)
59
(44.0)
11
(8.2)
Li K. et al.
[29]
5 7± 4.1 59
(58.0) 31
(3
0.0
)
7 (7.0) 15
(15.0)
4 (4.0) 2 (2.0)
Li Z. et al.
[30]
67 ± 3.5 95
(49.0) NA NA NA 70
(36.0) NA
Liu Jiacheng
et al.
[31] 62 ± 3.8 72
(59.0) 50
(4
1.0
)
5 (4.1) 15
(12.3)
2 (1.6) 2 (1.6)
Liu Jing
et al.
[32]
48 ± 13.9 15
(37.5) 6
(1
5.0
)
NA 6 (15.0) NA NA
Lu H.
et al.
[33]
NA NA 52
(1
9.6
)
NA 21 (7.9) 14
(5.3) 4 (1.5)
Lu Z. et al.
57 ± 12.6 61 41 17 14 15 6 (4.8)

Page 17/22
[34] (49.0) (3
3.0
)
(10.9) (11.2) (12.0)
Luo X.
et al.
[35]
56 ± 4.8 193
(47.9) 11
3
(2
8.0
)
29
(7.2)
57
(14.1)
36
(8.9)
28
(6.9)
Ma K. et
al.
[36] 48 ± 3.3 48
(57.1) 12
(1
4.3
)
7 (8.3) 10
(11.9)
5 (6.0) 5 (6.0)

Qian G.
et al.
[37]

50 ± 3.4

37
(40.7)

15
(1
6.4
)

NA

8 (8.8)

3 (3.3)

NA
Tang N.
et
al.
[17]
65 ± 12.0 268
(59.7) 17
7
(3
9.4
)
NA 93
(20.7)
41
(9.1) NA
Wan S. et al.
[18] 47 ± 3.1 72
(53.3) 13
(9.
6)
9 (6.7) 12 (8.9) 7 (5.2) 0 (0)
Wang D.
et al.
[19]
56 ± 4.3 75
(54.3) 43
(3
1.2
)
NA 14
(10.1)
20
(14.5) 4 (2.9)
Wang K.
et al.
[38]
47 ± 15.1 142
(53.4) 45
(1
4.8
)
NA 31
(10.2)
NA NA
Wang L.
et al.
[20]
69 ± 1.8 166
(49.0) 13
8
(4
0.8
)
NA 54
(16.0)
21
(15.7)
21
(6.2)
Wu C.
et al.
[22]
51 ± 2.8 128
(63.7) 39
(1
9.4
)
NA 22
(10.9)
8 (4.0) 5 (2.5)
Wu J. et
al.
[21] 43 ± 19.0 151
(53.9) NA NA NA NA 1
(0.36)
Xu Y.
et al.
[39]
57 ± 6.5 35
(50.7) NA 5 (7.2) NA NA NA
Zeng J. et al.
46 ± 3.8 198 60
(1
4.3
)
NA 24 (5.7) 18 5 (1.2)


Page 18/22
[40] (47.2)    (4.2) 
Zhang F.
et al.
[41]
70 ± 13.3 60
(68.9) 32
(51.8)
NA 10
(17.3)
13
(14.5) NA
Zhang G.
et al.
[42] 55 ± 4.5 108
(48.9) 54
(24.4)
NA 22
(10.0)
22
(10.0) 6 (2.7)
Zhang J.
et al.
[23]
55 ± 10.0 71
(50.7) 42
(30.0)
NA 17
(12.1)
7 (5.0) 2 (1.4)
Zheng C.
et al.
[24]
59 ± 9.5 24
(43.6) N
A
NA NA NA NA
Zheng X.
et al.
[43]
51 ± 15.9 23
(44.2) 12
(23.1)
NA 6 (11.5) 3 (5.8) 2 (3.8)
Zhou F.
et al.
[6] 56 ± 3.5 119
(62.0) 58
(30.0)
11
(6.0)
11
(19.0)
15
(8.0) 6 (3.0)
Zhou Ying
et al.
[44] 53 ± 15.3 170
(45.0) 133
(35.2)
NA 84
(22.2)
23
(6.1) 6 (1.6)
Zhou
Yulong
et al.
[25]
42 ± 14 .0 6
(35.0) N
A
NA NA NA NA

Figures

Page 19/22
Figure 1
Pathogenesis of Disseminated intravascular Coagulation. DIC is characterized by systemic activation of
blood coagulation, which results in generation and deposition of brin, leading to microvascular thrombi

Page 20/22
contributing to multi-organ dysfunction. Furthermore, consumption of clotting factors and platelets can
result in life-threatening hemorrhage.
Figure 2
Flowchart Depicting Literature Review and Study Selection

Page 21/22
Figure 3
Forest plots of the standardized mean difference in d-dimer levels. Panel A. Non severe vs Severe patients.
The black squares represent the pooled standardized mean difference effect size for each analysis while the
left and right extremes of the squares represent the corresponding 95% condence intervals for the pooled
standardized mean difference effect size for each analysis. All analyses are based on a random-effects

Page 22/22
model. Panel B. Survivors vs Non-Survivors. The black squares represent the pooled standardized mean
difference effect size for each analysis while the left and right extremes of the squares represent the
corresponding 95% condence intervals for the pooled standardized mean difference effect size for each
analysis. All analyses are based on a random-effects model
Figure 4
Forest plots of the standardized mean difference in platelets count (PLT), prothrombin time (PT) and
activated partial thromboplastin time (aPTT). Panel A-B-C. Forest plots of the standard mean difference in
PLT count, PT and aPTT between Non Severe and Severe patients. Panel D-E-F. Forest plots of the standard
mean difference in PLT count, PT and aPTT between Survivors and Non-Survivors.
Supplementary Files
This is a list of supplementary les associated with this preprint. Click to download.
Supplementarymaterial.pdf