Your privacy, your choice

We use essential cookies to make sure the site can function. We also use optional cookies for advertising, personalisation of content, usage analysis, and social media.

By accepting optional cookies, you consent to the processing of your personal data - including transfers to third parties. Some third parties are outside of the European Economic Area, with varying standards of data protection.

See our privacy policy for more information on the use of your personal data.

for further information and to change your choices.
Skip to main content
Download PDF

The association between mortality due to COVID-19 and coagulative parameters: a systematic review and meta-analysis study

BMC Infectious Diseases volume 24, Article number: 1373 (2024) Cite this article

Abstract

Aims and objectives

This systematic review and meta-analysis study evaluated the association between mortality due to COVID-19 and coagulative factors.

Methods

A systematic search was conducted on electronic databases including PubMed, Scopus, and the Web of Science from the beginning of the pandemic until October 2024 to identify relevant studies on COVID-19 patients and their laboratory findings related to coagulation markers and mortality outcome. Eligibility criteria were defined based on the PICO framework, and data extraction was performed by two authors independently using a standardized sheet. Statistical analysis was accomplished using the random effects model, and heterogeneity among studies was assessed using the I2 test. R and RStudio were used for statistical analysis and visualization.

Results

Our systematic literature search yielded 6969 studies, with 48 studies meeting the inclusion criteria for our meta-analysis. The mean platelet count was significantly lower in deceased COVID-19 patients compared to survivors (20.58), while activated partial thromboplastin time (aPTT) and fibrinogen levels did not show significant differences. The pooled mean difference of D-Dimer, International Normalized Ratio (INR), and prothrombin time (PT) were significantly lower in survived patients (-2.45, -0.10, and -0.84, respectively). These findings suggest that platelet count, D-Dimer, INR, and PT may serve as potential indicators of mortality in COVID-19 patients.

Conclusion

The results of our systematic review and meta-analysis revealed a significant reduction in the pooled platelet count among deceased individuals when compared to survivors. However, no significant distinctions were observed in the pooled mean activated aPTT and fibrinogen levels between the deceased and survivor groups. On the other hand, there were noticeable variations in the pooled estimated mean of INR, PT, and D-Dimer levels, with significantly higher values in the deceased group compared to those who survived.

Peer Review reports

Background

The global impact of the COVID-19 pandemic caused by the SARS-CoV-2 virus has been profound, resulting in a significant loss of lives in a relatively short period. SARS-CoV-2 which belongs to the coronavirus family, renowned for its ability to induce respiratory infections. Notably, COVID-19 has been linked to coagulopathy, leading to the development of intravascular thrombi and fibrinogen deposition [1,2,3]. Extensive research has demonstrated that COVID-19 infection triggers the activation and dysregulation of the coagulation system, leading to abnormalities in various coagulation parameters. Crucially, COVID-19-associated coagulopathy exhibits unique characteristics that distinguish it from other coagulation disorders. Initial reports from Wuhan, China, have highlighted elevated levels of D-dimers and fibrinogen, accompanied by alterations in platelet activation. These findings suggest a state of hypercoagulability in individuals with COVID-19 [4,5,6,7,8,9,10].

Severe cases of COVID-19 have been found to fulfil the criteria for disseminated intravascular coagulation (DIC), signifying a serious and potentially life-threatening complication associated with the disease [11,12,13,14]. However, recent investigations have revealed that COVID-19-associated coagulopathy differs from coagulopathic disorders caused by bacterial infections and other disorders. Specifically, individuals with COVID-19 often exhibit increased D-dimer and fibrinogen levels, while prothrombin time and platelet count may not exhibit significant changes during the early stages of the disease. Given the observed coagulation abnormalities, the early detection of elevated coagulation biomarkers becomes paramount for risk stratification in COVID-19 patients [15,16,17,18,19,20]. Thromboembolic complications have emerged as potential causes of clinical deterioration in severe cases, even after patients have been discharged from the hospital. This underscores the significance of vigilant monitoring of coagulation status in individuals with COVID-19 [21,22,23].

To assess the reliability of risk stratification based on early coagulation parameters and its impact on mortality and disease outcome, we conducted a comprehensive meta-analysis of published research worldwide. By scrutinizing data from various clinical studies, we aim to gain deeper insights into coagulation parameters in individuals with COVID-19.

Methods and materials

The present study was conducted based on the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline 2020 [24] (see Supplemental Table 1).

Search strategy

A systematic search was accomplished on electronic databases such as Web of Science, Scopus, and PubMed from the beginning of the pandemic until October 2024. The search strategy included a combination of relevant medical subject heading (MeSH) terms and relevant keywords for (“Covid-19” OR “Sars-Cov-2” OR “Coronavirus”) AND (“Laboratory findings” OR “Coagulation” OR “Biochemical markers”) AND (“Mortality”, “Survival”, “Death”, “Outcome”) (see supplemental Table 2).

Eligibility criteria

We defined our eligibility criteria based on the PICO framework: (P) Population: COVID patients (confirmed by serum antibody tests, polymerase chain reaction, or clinical symptoms after close exposure to proven cases of COVID) who deceased (I) Intervention/Exposure: Laboratory and coagulative measures. (C) Control: COVID patients who survived (O) Outcome: Mortality. The exclusion criteria were defined as: absence of laboratory findings, not reporting mortality as outcome, lack of individual data, reporting only patients with confounding conditions such as warfarin usage, pregnant women, cancer, coagulopathy disorder. Furthermore, articles in non-English language were excluded. The kappa statistic was used to measure inter-rater reliability in screening and selecting the eligible studies and the results were interpreted as less than 0.2 represents no agreement; 0.21–0.29 represents minimal agreement; 0.40–0.59 represents weak agreement; 0.60–0.79 represents moderate agreement; 0.80–0.90 represents strong agreement; greater than 0.9 represents almost perfect agreement [25].

Data extraction and outcome measures

The methodological quality evaluation of the included articles was assessed by the Newcastle–Ottawa Scale (NOS) checklist [26]. The quality scores were classified as follows: very good (score of 9–10), good (score of 7–8), satisfactory (score of 5–6), or unsatisfactory (score of 0–5). The data extraction using a standardized sheet was performed by two independent authors. A third-party discussion was done when any disagreement occurred. The standardized sheet included: authors’ name, year of publication, total number of participants, mean age of the participants, design of the study, and country of the study along with mean platelet count and its standard deviation (SD) and total number, mean prothrombin time (PT) and its standard deviation (SD) and total number, mean activated partial thromboplastin time (aPTT) and its standard deviation (SD) and total number, mean D-Dimer level and its standard deviation (SD) and total number, mean fibrinogen level and its standard deviation (SD) and total number, and mean International Normalized Ration (INR) and its standard deviation (SD) and total number. The aforementioned variables were extracted in two groups: the survivors and the deceased.

Statistical analysis and data synthesis

The pooled mean difference was calculated using random effects model and Hedges’ g along with SD estimation. For assessing the heterogeneity of the included studies, the I2 (I square) test was used. Moreover, < 25% considered low, 25–75% considered moderate, and > 75% considered high heterogeneity. Additionally, the leave-one-out sensitivity analysis was conducted to assess robustness of findings. The Mantel–Haenszel method and random effects model was used for pooling the effect sizes and SD was consequently calculated. For testing the overall significance of the random model, z-test was performed Potential publication bias was graphically assessed by creating funnel plots for each of the aforementioned groups. Furthermore, subgroup analysis based on patients' average age (> 60 or ≤ 60 years) and meta-regression was performed to find the source of heterogeneity. Two independent reviewers evaluated the certainty of synthesized evidence using GRADE guidelines [27]. R (R Foundation for Statistical Computing, Vienna, Austria) and RStudio (RStudio, Inc., Boston, MA) were used for the statistical analysis and creating forest and funnel plots.

Results

Study selection and characteristics

Our systematic search of the literature obtained 6969 studies, primarily. After removing the duplicates (n = 2580), 4389 studies were screened based on their title and abstract. Finally, 189 studies were included for full-text evaluation. Based on our inclusion and exclusion criteria, 48 studies [22, 28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74] were included in our final meta-analysis model (Fig. 1). The agreement between authors was moderate for the screening and selection process (κ = 0.70). The detailed quality assessment of the included studies is presented in supplemental Table 3. Supplemental Tables 4–6 represent characteristics of included articles and exact values of coagulative parameters of survived and deceased cases.

Fig. 1
figure 1

PRISMA flow diagram of the systematic search and study selection

Full size image

Results of syntheses

Based on our meta-analysis, the mean difference between Platelet count among the survivors and the deceased was 20.58 (95% CI: 12.28 to 28.88; P = < 0.01) based on the random effects model and was significantly (p-value < 0.01) lower among the deceased (Fig. 2). The between-study heterogeneity was high (I2 = 80%). Results of the Leave-one-out by sensitivity analysis method showed no difference from excluding any studies (Supplemental Fig. 1). Current meta-analysis indicated the mean difference between aPTT count among the survivors and the deceased was not statistically significant (MD: -0.609; 95% CI: -1.66 to 0.45; P = 0.24) based on the random effects model (Fig. 3). High heterogeneity (I2 = 95.3%) was observed between the included studies. Leave-one-out analysis indicated no significant difference by and studies' omission (Supplemental Fig. 2). Meta-analysis of D-dimer levels revealed that the average D-dimer counts were significantly lower in the survived compared to non-survived patients (MD: -2.45; 95% CI: -3.24 to -1.66; P < 0.01) (Fig. 4). The included studies were highly heterogeneous (I2 = 92.5%). The pooled effect and heterogeneity were not statistically changed by removing any studies using the leave-one-out method (Supplemental Fig. 3). Meta-analysis of the difference in fibrinogen levels among survival and deceased showed a non-significant mean difference between the two groups (MD: -0.25; 95% CI: -0.62 to 0.11; P = 0.16) (Fig. 5). The between-study heterogeneity was markedly high (I2 = 91%). Further sensitivity analysis using the leave-one-out method indicated that removing any studies did not significantly alter the polled results (Supplemental Fig. 4). The meta-analysis results based on the random effects showed significantly higher INR count in deceased than in the survived patients (MD: -0.10; 95% CI: -0.14 to -0.05; P < 0.01) (Fig. 6). High between-study heterogeneity (I2 = 93.9%%) in the included studies was observed. Furthermore, the Leave-one-out sensitivity analysis resulted in no significant difference by removing any selected studies (Supplemental Fig. 5). The findings of the meta-analysis indicated significantly lower PT levels in the survived compared to the deceased (MD: -0.84; 95% CI: -1.09 to -0.59; P < 0.01) (Fig. 7). The included articles were moderately heterogeneous (I2 = 73.5%). Sensitivity analysis by the leave-one-out method did not reveal any significant change in pooled estimate results or heterogeneity by any studies' omission (Supplemental Fig. 6).

Fig. 2
figure 2

The mean difference between Platelet count among the survivors and the deceased was 20.58 and was significantly higher among the survivors

Full size image
Fig. 3
figure 3

The mean difference between aPTT count among the survivors and the deceased was -0.60 and was not significantly lower among the deceased

Full size image
Fig. 4
figure 4

The mean difference between D-Dimer count among the survivors and the deceased was -2.45 and was significantly lower among the survived

Full size image
Fig. 5
figure 5

The mean difference between Fibrinogen count among the survivors and the deceased was -0.25 and was not significantly lower among the deceased

Full size image
Fig. 6
figure 6

The mean difference between INR count among the survivors and the deceased was -0.10 and was significantly lower among the survived

Full size image
Fig. 7
figure 7

The mean difference between PT count among the survivors and the deceased was -0.84 and was significantly lower among the survived

Full size image

Subgroup analyses

Subgroup analyses were performed based on age (Figure Supplemental 7–12). The tests of between-group differences were non-significant for all variables except for fibrinogen (P value = 0.02) and INR (P value < 0.01). In both Fibrinogen and INR analyses, only a subgroup of studies with an average age of less than 60 years showed significant mean differences between survived and non-survived cases (Figure Supplemental 10 and 11). Furthermore, the between-study heterogeneity within each group remained in the same category as the overall heterogeneity for all variables.

Meta-regression

Meta-regression analysis with year of publication, sample size, and age was performed for all variables and presented in supplemental Table 4. Findings of platelet, PT, D-dimer, INR and aPTT meta-regression were non-significant. However, the age in fibrinogen analysis significantly accounted for heterogeneity.

Certainty of evidence

Supplemental Table 5 shows a detailed investigation of the quality of polled results. The certainty was low in platelet, aPTT, INR, and PT results and very low in D-dimer and fibrinogen findings.

Risk of publication bias

The risk of publication bias was assessed by visual investigation and statistical tests (Figure Supplemental 13–18). The funnel plots of platelet, aPTT, Fibrinogen, and INR meta-analyses were symmetrical, and statistical tests did not demonstrate a significant risk of publication bias. In contrast, funnel plots of DD and PT analyses were asymmetrical, representing probable risk of publication bias confirmed by the Begg test (P values < 0.05).

Discussion

According to the results of our systematic review and meta-analysis study, the estimated pooled platelet count was significantly lower among the deceased compared to the survivors. Also, the pooled mean aPTT and fibrinogen levels did not show any significant difference among the deceased and the survivors. The pooled estimated mean of INR, PT, and D-Dimer was significantly higher among the deceased compared to those who survived.

Platelets play a critical role in maintaining haemostasis and contribute to thrombo-inflammatory processes during viral infections. Changes in platelet production or destruction at various stages of viral infection can lead to coagulation imbalances, resulting in pro-thrombotic events or platelet dysfunction and bleeding risks. Thrombocytopenia (low platelet count) has emerged as a significant marker of COVID-19 severity and mortality [75,76,77,78]. Thrombocytopenia has diverse reasons and may include early suppression of platelet production, damage to bone marrow, and hemo-phagocytosis [79,80,81,82,83].

In the convalescent phase of COVID-19, some patients may experience reactive thrombocytosis following initial thrombocytopenia. The increase in immature reticulated platelets observed in COVID-19 patients may impact the effectiveness of anti-platelet therapies [84,85,86,87,88]. Changes in platelet parameters and reactivity have been associated with severe COVID-19 infections. Moreover, studies have reported dysfunction of megakaryocytes (platelet progenitors) in COVID-19 patients, with elevated and abnormal megakaryocytes found in various organs, including the lungs, heart, brain, and bone marrow. The lungs appear to play a significant role in platelet biogenesis, and hypoxia-induced thrombocytopenia is linked to reduced lung megakaryocytes and impaired platelet generation in the lungs [89,90,91,92,93,94,95].

The SARS-CoV-2 virus has been implicated in up-regulating megakaryocyte progenitors and elevating circulating megakaryocytes in severe COVID-19, possibly through infection of early megakaryocyte progenitors. This suggests the possibility of viral RNA transfer from megakaryocytes to platelets and circulating plasma extracellular vehicles (EVs) [96,97,98,99,100]. Notably, plasma SARS-CoV-2 RNA has been strongly associated with increased mortality in COVID-19 patients. However, there is a gap in our knowledge of platelet non-canonical function and regeneration, so other research is essential to comprehensively grasp COVID-19 pathogenesis and identify increased thromboembolic risk [96,97,98,99,100,101,102,103,104,105,106].

Publication bias and variability in cut-off values for D-dimer (DD) measurement across studies may influence reported outcomes. Haemostasis tests, including D-dimers, may have limited usefulness because there is some differences in methodologies, antibody origin, detection methods, calibrators, and diagnostic thresholds among different laboratories.

Autopsy findings have shown that increased DD levels can be linked to fibrin deposits in the pulmonary extravascular space and alveoli. However, these elevations may not always be specific to intravascular fibrin formation. Elevated DD levels, along with increased neutrophil counts, have been identified as predictors of pulmonary embolism in COVID-19 patients. DD and fibrinogen (FIB) levels are elevated in both COVID-19 and thromboembolism, making them non-specific and unhelpful as single tests to distinguish between these conditions [107,108,109,110]. The lack of specificity might be attributed to the involvement of various systems, such as endothelial cells, complement activation, and hypo-fibrinolysis, in the abnormal coagulation processes during COVID-19, leading to changes that routine tests may not capture. Initial reports from China indicated a decreased activated partial thromboplastin time (APTT) as a marker of hypercoagulation [111,112,113,114]. However, later studies reported prolonged APTT, which could suggest deficiencies in clotting factors or the presence of inhibitors, such as heparin therapy. Another explanation could be the presence of antiphospholipid antibodies (aPL) or lupus anticoagulant (LA) which is observed in patients affected by COVID-19. Prolonged APTT may also be influenced by increased heparin therapy usage, especially in severe cases of COVID-19 [55, 115,116,117].

There are conflicting reports regarding fibrinogen levels and COVID-19 severity. Some studies show high fibrinogen levels in COVID-19 patients, while others report alternative findings. Severe COVID-19 infection may induce fibrinolysis shutdown, contributing to high levels of D-dimers and fibrinogen. However, other reports suggest increased plasmin-antiplasmin complexes and mild consumption coagulopathy in COVID-19 patients [118,119,120,121,122,123]. Tissue-type plasminogen activator elevation has also been found in severe COVID-19 cases. Fibrinogen plays a crucial role in linking coagulation, complement system, and inflammation in COVID-19. Recent post-mortem studies have identified microvascular thrombi in multiple organs, indicating a hypercoagulable state with impaired fibrinolysis. However, the association between abnormal visco-elastometric testing (VET) patterns and clinical outcomes has not been demonstrated in patients affected by COVID-19 [124,125,126,127,128].

Routine coagulation tests which are taken at the time of admission can be helpful in differentiating severe and non-severe COVID-19 patients. The mechanisms of fibrinolysis shutdown may involve reduced fibrinolysis factors (plasminogen) and elevated inhibitors of fibrinolysis (such as α2-antiplasmin and plasminogen activator inhibitor PAI-1). Longitudinal observational studies are crucial for understanding COVID-19 infection dynamics and disease outcomes. Daily changes in relevant haemostasis parameters, including D-dimers and PT, have been observed in severe COVID-19 patients. However, it's essential to consider the variability in disease progression and admission timepoints when interpreting study results.

The current study has certain constraints. Included studies may have used different methods and kits for performing coagulation tests. Furthermore, the timing of tests could vary from study to study, while coagulation parameters may change throughout the disease. Additionally, the vaccination may affect the coagulation tests [129], and since study publications varied from 2020 to 2023, some of the differences may have contributed to vaccinations. Likewise, several underlying conditions, such as cancers, pregnancy, and receiving anticoagulants, may alter the results of coagulation tests. Unfortunately, detailed analyses on each condition were not allowed due to the incomplete reports of patients' conditions in the included articles, and readers should interpret our findings with caution. Further investigation, considering all the confounding factors mentioned, should be necessary to evaluate the findings of the current study.

Conclusion

The outcomes of our systematic review and meta-analysis indicate a significant decrease in the estimated pooled platelet count in deceased individuals compared to survivors. However, no significant differences were found in the pooled mean aPTT and fibrinogen levels between the deceased and survivor groups. Conversely, there were notable variations in the pooled estimated mean of INR, PT, and D-Dimer levels, which were significantly higher in the deceased group compared to those who survived.

Data availability

Data would be available based upon an eligible request to the corresponding author.

Abbreviations

DIC:

Disseminated intravascular coagulation

PRISMA:

Preferred Reporting Items for Systematic reviews and Meta-Analyses

MeSH:

Medical subject heading

PICO:

Population, Intervention, Comparison, Outcome

SD:

Standard deviation

aPTT:

Activated partial thromboplastin time

PT:

Prothrombin time

INR:

International Normalized Ration

DD:

D-dimer

EVs:

Extracellular vehicles

FIB:

Fibrinogen

aPL:

Antiphospholipid antibodies

LA:

Lupus anticoagulant

VET:

Visco-elastometric testing

References

  1. Wolny M, Dittrich M, Knabbe C, Birschmann I. Immature platelets in COVID-19. Platelets. 2023;34(1):2184183.

    Article  PubMed  Google Scholar 

  2. Russell L, Weihe S, Madsen EK, Hvas CL, Leistner JW, Michelsen J, et al. Thromboembolic and bleeding events in ICU patients with COVID-19: a nationwide, observational study. Acta Anaesthesiol Scand. 2023;67(1):76–85.

    Article  PubMed  Google Scholar 

  3. Rurua M, Ratiani L, Sanikidze T, Machvariani K, Pachkoria E, Ormocadze G, et al. Impact of the angiotensin-converting enzyme (ACE) inhibitors on the course of the septic shock developed during covid-19 and other severe respiratory infections in presence of hyperferritinemia. Georgian Med News. 2023;337:110–7.

    Google Scholar 

  4. Shoaee S, Rezaie F, Payab M, Bakhtiari F, Heydari M-H. Experiences from the management of COVID-19 pandemic in a nursing home in Iran (March–April, 2020). J Diabetes Metab Disord. 2022;21(1):1195–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Zhu G, Modepalli S, Anand M, Li H. Computational modeling of hypercoagulability in COVID-19. Comput Methods Biomech Biomed Engin. 2023;26(3):338–49.

    Article  PubMed  Google Scholar 

  6. Zaghloul MS, Jammeh M, Gibson A, Luo S, Chadwick-Mansker K, Liu Q, et al. Chronic anti-coagulation therapy reduced mortality in patients with high cardiovascular risk early in COVID-19 pandemic. Thromb J. 2023;21(1):14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Xing Y, Li Y, Feng L, Huo R, Ma X, Dong Y, et al. Predictors of COVID-19 Severity in Elderly Patients Infected by Omicron in China, 18 December 2022-5 February 2023. Infect Drug Resist. 2023;16:4505–18.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Tuculeanu G, Barbu EC, Lazar M, Chitu-Tisu CE, Moisa E, Negoita SI, Ion DA. Coagulation disorders in sepsis and COVID-19-two sides of the same coin? a review of inflammation-coagulation crosstalk in bacterial sepsis and COVID-19. J Clin Med. 2023;12(2):601.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Smail SW, Babaei E, Amin K. Hematological, inflammatory, coagulation, and oxidative/antioxidant biomarkers as predictors for severity and mortality in COVID-19: a prospective cohort-study. Int J Gen Med. 2023;16:565–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Smadja DM, Gendron N, Philippe A, Diehl JL, Ochat N, Bory O, et al. Fibrin monomers evaluation during hospitalization for COVID-19 is a predictive marker of in-hospital mortality. Front Cardiovasc Med. 2023;10:1001530.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Moscucci F, Gallina S, Bucciarelli V, Aimo A, Pelà G, Cadeddu-Dessalvi C, et al. Impact of COVID-19 on the cardiovascular health of women: a review by the Italian Society of Cardiology Working Group on ‘gender cardiovascular diseases.’ J Cardiovasc Med (Hagerstown). 2023;24(Suppl 1):e15–23.

    PubMed  Google Scholar 

  12. Mayne ES, George JA, Louw S. Assessing biomarkers in viral infection. Adv Exp Med Biol. 2023;1412:159–73.

    Article  CAS  PubMed  Google Scholar 

  13. Malkoc A, GnanaDev R, Botea L, Jeney A, Glover K, Retamozo M, et al. A Comparative analysis of critical limb ischemia in the intensive care unit since the COVID-19 pandemic. Ann Vasc Surg. 2023;90:39–47.

    Article  PubMed  Google Scholar 

  14. Lippi G, Mullier F, Favaloro EJ. D-dimer: old dogmas, new (COVID-19) tricks. Clin Chem Lab Med. 2023;61(5):841–50.

    Article  CAS  PubMed  Google Scholar 

  15. Mortazavi H, Sadeghian A, Hazrati P, Heydari M-H, Madihi S. Oral hemorrhagic blister and its possible related factors: analyzes of reported cases in the literature. J Oral and Maxillofac Surg Med Pathol. 2023;35(4):358–67.

    Article  Google Scholar 

  16. Krinsky N, Sizikov S, Nissim S, Dror A, Sas A, Prinz H, et al. NETosis induction reflects COVID-19 severity and long COVID: insights from a 2-center patient cohort study in Israel. J Thromb Haemost. 2023;21(9):2569–84.

  17. Korb VG, Schultz IC, Beckenkamp LR, Wink MR. A systematic review of the role of purinergic signalling pathway in the treatment of COVID-19. Int J Mol Sci. 2023;24(9):7865.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Kole C, Stefanou Ε, Karvelas N, Schizas D, Toutouzas KP. Acute and post-acute COVID-19 cardiovascular complications: a comprehensive review. Cardiovasc Drugs Ther. 2024;38(5):1017-32.

  19. Khreefa Z, Barbier MT, Koksal AR, Love G, Del Valle L. Pathogenesis and Mechanisms of SARS-CoV-2 Infection in the Intestine, Liver, and Pancreas. Cells. 2023;12(2):262.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Khatib Y, Samele R, Chaudhari J, Khare M, Gupte P, Sable Y, Shah V. Hematological and other laboratory parameter changes in COVID-19 patients. J Assoc Phys India. 2023;71(4):11–2.

    Google Scholar 

  21. Idrissi A, Lekfif A, Amrani A, Yacoubi A, Yahyaoui A, Belmahi S, et al. Biomarkers predicting poor prognosis in covid-19 patients: a survival analysis. Cureus. 2023;15(1):e33921.

    PubMed  PubMed Central  Google Scholar 

  22. Huyut MT, Huyut Z. Effect of ferritin, INR, and D-dimer immunological parameters levels as predictors of COVID-19 mortality: a strong prediction with the decision trees. Heliyon. 2023;9(3):e14015.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Hojker M, Tršan J, Tršan U, Gale A, Jerman A, Košuta D. Predictive value of inflammatory and coagulation biomarkers for venous thromboembolism in COVID-19 patients. Clin Hemorheol Microcirc. 2023;83(4):387–95.

    Article  CAS  PubMed  Google Scholar 

  24. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, The PRISMA, et al. statement: an updated guideline for reporting systematic reviews. BMJ. 2020;2021:372.

    Google Scholar 

  25. McHugh ML. Interrater reliability: the kappa statistic. Biochem Med (Zagreb). 2012;22(3):276–82.

    Article  PubMed  Google Scholar 

  26. GA Wells BS, D O'Connell, J Peterson, V Welch, M Losos, P Tugwell. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. Available from: https://www.ohri.ca/programs/clinical_epidemiology/oxford.asp.

  27. Balshem H, Helfand M, Schünemann HJ, Oxman AD, Kunz R, Brozek J, et al. GRADE guidelines: 3. Rating the quality of evidence. J Clin Epidemiol. 2011;64(4):401–6.

    Article  PubMed  Google Scholar 

  28. Aljohani FD, Khattab A, Elbadawy HM, Alhaddad A, Alahmadey Z, Alahmadi Y, et al. Prognostic factors for predicting severity and mortality in hospitalized COVID-19 patients. J Clin Lab Anal. 2022;36(3):e24216.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Amado LA, Coelho WLDCNP, Alves ADR, Carneiro VCDS, Moreira ODC, de Paula VS, et al. Clinical profile and risk factors for severe COVID-19 in hospitalized patients from rio de janeiro, brazil: comparison between the first and second pandemic waves. J Clin Med. 2023;12(7):2568.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Chopra P, Sehgal T, Yadav R, Meena S, Maitra S, Soni KD, et al. A combination of inflammatory and hematological markers is strongly associated with the risk of death in both mild and severe initial disease in unvaccinated individuals with COVID-19 infection. Electron J Int Federation Clin Chem Lab Med. 2023;34(1):42–56.

    CAS  Google Scholar 

  31. Citu C, Burlea B, Gorun F, Motoc A, Gorun OM, Malita D, et al. predictive value of blood coagulation parameters in poor outcomes in covid-19 patients: a retrospective observational study in Romania. J Clin Med. 2022;11(10):2831.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Du RH, Liang LR, Yang CQ, Wang W, Cao TZ, Li M, et al. Predictors of mortality for patients with COVID-19 pneumonia caused by SARS-CoV-2: a prospective cohort study. Eur Respir J. 2020;55(5):2000524.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Fan H, Zhang L, Huang B, Zhu M, Zhou Y, Zhang H, et al. Cardiac injuries in patients with coronavirus disease 2019: Not to be ignored. Int J Infect Dis. 2020;96:294–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Gayam V, Chobufo MD, Merghani MA, Lamichhane S, Garlapati PR, Adler MK. Clinical characteristics and predictors of mortality in African-Americans with COVID-19 from an inner-city community teaching hospital in New York. J Med Virol. 2021;93(2):812–9.

    Article  CAS  PubMed  Google Scholar 

  35. Gromadzinski L, Zechowicz M, Moczulska B, Kasprzak M, Grzelakowska K, Nowek P, et al. Clinical characteristics and predictors of in-hospital mortality of patients hospitalized with COVID-19 infection. J Clin Med. 2023;12(1):143.

    Article  CAS  Google Scholar 

  36. Hartantri Y, Debora J, Widyatmoko L, Giwangkancana G, Suryadinata H, Susandi E, et al. Clinical and treatment factors associated with the mortality of COVID-19 patients admitted to a referral hospital in Indonesia. Lancet Reg Health Southeast Asia. 2023;11(C7):100167.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Hilda F, Liana P, Nurtjahyo A, Hudari H, Sari NP, Umar TP, et al. D-dimer as a sensitive biomarker of survival rate in patients with COVID-19. Eurasian J Med. 2022;54(3):219–24.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Kurien SS, David RS, Chellappan AK, Varma RP, Pillai PR, Yadev I. Clinical profile and determinants of mortality in patients with COVID-19: a retrospective analytical cross-sectional study in a tertiary care center in South India. Cureus. 2022;14(3):e23103.

    PubMed  PubMed Central  Google Scholar 

  39. László I, Berhés M, Tisza K, Miltenyi Z, Balázsfalvi N, Vaskó A, et al. The prognostic value of laboratory parameters referring to hemopoietic stress in patients with COVID-19-a single center experience. Signa Vitae. 2022;19(3):36–43.

  40. Li J, Xu G, Yu H, Peng X, Luo Y, Cao C. Clinical characteristics and outcomes of 74 patients with severe or critical COVID-19. Am J Med Sci. 2020;360(3):229–35.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Luo X, Zhou W, Yan X, Guo T, Wang B, Xia H, et al. Prognostic value of C-reactive protein in patients with coronavirus 2019. Clin Infect Dis. 2020;71(16):2174–9.

    Article  CAS  PubMed  Google Scholar 

  42. Muhammad R, Ogunti R, Ahmad B, Munawar M, Donaldson S, Sumon M, et al. Clinical characteristics and predictors of mortality in minority patients hospitalized with COVID-19 infection. J Racial Ethn Health Disparities. 2022;9(1):335–45.

    Article  PubMed  Google Scholar 

  43. Pan F, Yang L, Li Y, Liang B, Li L, Ye T, et al. Factors associated with death outcome in patients with severe coronavirus disease-19 (COVID-19): a case-control study. Int J Med Sci. 2020;17(9):1281–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Peiró ÓM, Carrasquer A, Sánchez-Gimenez R, Lal-Trehan N, Del-Moral-Ronda V, Bonet G, et al. Biomarkers and short-term prognosis in COVID-19. Biomarkers. 2021;26(2):119–26.

    Article  PubMed  Google Scholar 

  45. Pinheiro FD, Lopes LW, Dórea R, Araújo GRL, da Silva FAF, de Brito BB, et al. Epidemiological and clinical characteristics of COVID-19 in a Brazilian public hospital. World J Clin Cases. 2023;11(8):1761–70.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Poskurica M, Stevanović Đ, Zdravković V, Čekerevac I, Ćupurdija V, Zdravković N, et al. Admission predictors of mortality in hospitalized COVID-19 patients-a Serbian cohort study. J Clin Med. 2022;11(20):6109.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Raychaudhuri S, Pujani M, Menia R, Verma N, Singh M, Chauhan V, et al. COVID-19 associated coagulopathy in an indian scenario: a correlation with disease severity and survival status. Indian J Hematol Blood Transfus. 2022;38(2):341–51.

    Article  PubMed  Google Scholar 

  48. Riddell A, Chowdary P, Davenport A. The effect of SARS-Co-V2 infection on prothrombotic and anticoagulant factors in dialysis patients. Artif Organs. 2022;46(7):1328–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Sai F, Liu X, Li L, Ye Y, Zhu C, Hang Y, et al. Clinical characteristics and risk factors for mortality in patients with coronavirus disease 2019 in intensive care unit: a single- center, retrospective, observational study in China. Ann Palliat Med. 2021;10(3):2859–68.

    Article  PubMed  Google Scholar 

  50. Satici C, Demirkol MA, Sargin Altunok E, Gursoy B, Alkan M, Kamat S, et al. Performance of pneumonia severity index and CURB-65 in predicting 30-day mortality in patients with COVID-19. Int J Infect Dis. 2020;98:84–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Surme S, Tuncer G, Bayramlar OF, Takak H, Copur B, Yazla M, et al. Risk factors and predictors of 1-year overall mortality in patients with COVID-19. Haseki Tip Bulteni. 2022;60(5):439–46.

    Article  Google Scholar 

  52. Tang N, Bai H, Chen X, Gong J, Li D, Sun Z. Anticoagulant treatment is associated with decreased mortality in severe coronavirus disease 2019 patients with coagulopathy. J Thromb Haemost. 2020;18(5):1094–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Velasco-Rodríguez D, Alonso-Dominguez JM, Vidal Laso R, Lainez-González D, García-Raso A, Martín-Herrero S, et al. Development and validation of a predictive model of in-hospital mortality in COVID-19 patients. PLoS ONE. 2021;16(3):e0247676.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Violi F, Ceccarelli G, Cangemi R, Cipollone F, D’Ardes D, Oliva A, et al. Arterial and venous thrombosis in coronavirus 2019 disease (Covid-19): relationship with mortality. Intern Emerg Med. 2021;16(5):1231–7.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Wang H, Sun B, Li X, Wang Y, Yang Z. Clinical analysis of severe COVID-19 patients. Technol Health Care. 2022;30(S1):225–34.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Wang J, Zhang H, Qiao R, Ge Q, Zhang S, Zhao Z, et al. Thrombo-inflammatory features predicting mortality in patients with COVID-19: The FAD-85 score. J Int Med Res. 2020;48(9):300060520955037.

    Article  CAS  PubMed  Google Scholar 

  57. Wang L, He W, Yu X, Hu D, Bao M, Liu H, et al. Coronavirus disease 2019 in elderly patients: characteristics and prognostic factors based on 4-week follow-up. J Infect. 2020;80(6):639–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Wu C, Chen X, Cai Y, Xia J, Zhou X, Xu S, 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;180(7):934–43.

    Article  CAS  PubMed  Google Scholar 

  59. Yan Y, Yang Y, Wang F, Ren H, Zhang S, Shi X, et al. Clinical characteristics and outcomes of patients with severe covid-19 with diabetes. BMJ Open Diabetes Res Care. 2020;8(1):e001343.

    Article  PubMed  Google Scholar 

  60. Yang X, Li ZF, Wang BB, Pan YB, Jiang CY, Zhang XG, et al. Prognosis and antibody profiles in survivors of critical illness from COVID-19: a prospective multicentre cohort study. Br J Anaesth. 2022;128(3):491–500.

    Article  CAS  PubMed  Google Scholar 

  61. Zhang F, Xiong Y, Wei Y, Hu Y, Wang F, Li G, et al. Obesity predisposes to the risk of higher mortality in young COVID-19 patients. J Med Virol. 2020;92(11):2536–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, 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):1054–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Shrestha MR, Basnet A, Tamang B, Khadka S, Maharjan R, Maharjan R, et al. Analysis of altered level of blood-based biomarkers in prognosis of COVID-19 patients. PLoS ONE. 2023;18(8):e0287117.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Lipski D, Radziemski A, Wasiliew S, Wyrwa M, Szczepaniak-Chicheł L, Stryczyński Ł, et al. Assessment of COVID-19 risk factors of early and long-term mortality with prediction models of clinical and laboratory variables. BMC Infect Dis. 2024;24(1):685.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Ghareeb AAaA, Sazan Moffaq. Clinical & Laboratory markers as predictors, for severity and mortality in COVID-19. Polytechn J. 2023;13(1 A). https://doi.org/10.59341/2707-7799.1746.D.

  66. Alizad G, Ayatollahi AA, Shariati Samani A, Samadizadeh S, Aghcheli B, Rajabi A, et al. Hematological and biochemical laboratory parameters in COVID-19 patients: a retrospective modeling study of severity and mortality predictors. Biomed Res Int. 2023;2023:7753631.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Pertiwi D, Nisa M, Aulia AP, Rahayu. Hematological and Biochemical Parameters at Admission as Predictors for Mortality in Patients with Moderate to Severe COVID-19. Ethiop J Health Sci. 2023;33(2):193–202.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Gabriel M. C. Guimarães RF, Any C. Oliveira, Lilian S. Alves, Fabiana R. Carvalho, Katia L. Baptista,, Karina Y. Yaginuma HHKS, Jorge R. Almeida, Thalia Medeiros, Andrea A. Silva. Hematological and coagulation parameters as predictors of death by Coronavirus disease in hospitalized patients: a Brazilian follow-up study. Br J Pharmaceutical Sci. 2023;59:e21798.

  69. Ikiz F, Ak A. Investigation of the relationship between coagulation parameters and mortality in COVID-19 infection. Blood Sci. 2024;6(2):e00191.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Siavoshi F, Safavi-Naini SAA, Shirzadeh Barough S, Azizmohammad Looha M, Hatamabadi H, Ommi D, et al. On-admission and dynamic trend of laboratory profiles as prognostic biomarkers in COVID-19 inpatients. Sci Rep. 2023;13(1):6993.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Uzum Y, Turkkan E. Predictivity of CRP, albumin, and CRP to albumin ratio on the development of intensive care requirement, mortality, and disease severity in COVID-19. Cureus. 2023;15(1):e33600.

    PubMed  PubMed Central  Google Scholar 

  72. Kutlusoy S, Koca E. Routine laboratory parameters in estimating mortality and morbidity in COVID-19 diagnosed cases followed in the intensive care unit. Eur Rev Med Pharmacol Sci. 2023;27(12):5885–92.

    CAS  PubMed  Google Scholar 

  73. Ergenc Z, Ergenc H, Öztürk A, Kaya T, Nalbant A, Karacaer C, et al. The effect of thrombosis-related laboratory values on mortality in COVID-19 infection. Eur Rev Med Pharmacol Sci. 2023;27(6):2699–705.

    CAS  PubMed  Google Scholar 

  74. Dwivedi T, Raj A, Das N, Gupta R, Gupta N, Tiwari P, et al. The Evaluation of laboratory parameters as predictors of disease severity and mortality in COVID-19 patients: a retrospective study from a tertiary care hospital in India. Cureus. 2023;15(6):e40273.

    PubMed  PubMed Central  Google Scholar 

  75. Harky A, Ala’Aldeen A, Butt S, Duric B, Roy S, Zeinah M. COVID-19 and multiorgan response: the long-term impact. Curr Probl Cardiol. 2023;48(9):101756.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Hakoshima M, Kitakaze K, Adachi H, Katsuyama H, Yanai H. Clinical, hematological, biochemical and radiological characteristics for patients with splenic infarction: case series with literature review. J Clin Med Res. 2023;15(1):38–50.

    Article  PubMed  PubMed Central  Google Scholar 

  77. Gupta V, Acharya S, Keerti A. Common coagulopathies associated With COVID-19 patients. Cureus. 2023;15(4):e38067.

    PubMed  PubMed Central  Google Scholar 

  78. Gupta B, Chandrakar S, Gupta N, Jain G. Nebulized heparin to reduce COVID-19-induced acute lung injury: a prospective observational study. Indian J Crit Care Med. 2023;27(3):222–4.

    Article  PubMed  PubMed Central  Google Scholar 

  79. Girard TJ, Antunes L, Zhang N, Amrute JM, Subramanian R, Eldem I, et al. Peripheral blood mononuclear cell tissue factor (F3 gene) transcript levels and circulating extracellular vesicles are elevated in severe coronavirus 2019 (COVID-19) disease. J Thromb Haemost. 2023;21(3):629–38.

    Article  PubMed  Google Scholar 

  80. Ghanem HB, Elderdery AY, Alnassar HN, Aldandan HA, Alkhaldi WH, Alfuhygy KS, et al. Study of coagulation disorders and the prevalence of their related symptoms among COVID-19 patients in Al-Jouf Region, Saudi Arabia during the COVID-19 pandemic. Diagnostics (Basel). 2023;13(6):1085.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Ghanbari EP, Jakobs K, Puccini M, Reinshagen L, Friebel J, Haghikia A, et al. The role of NETosis and complement activation in COVID-19-associated coagulopathies. Biomedicines. 2023;11(5):1371.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Gashimova NR, Pankratyeva LL, Bitsadze VO, Khizroeva JK, Tretyakova MV, Grigoreva KN, et al. Inflammation and Immune Reactions in the Fetus as a Response to COVID-19 in the Mother. J Clin Med. 2023;12(13):4256.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Gardinassi LG, Servian CDP, Lima GDS, Dos Anjos DCC, Gomes Junior AR, Guilarde AO, et al. Integrated metabolic and inflammatory signatures associated with severity of, fatality of, and recovery from COVID-19. Microbiol Spectr. 2023;11(2):e0219422.

    Article  PubMed  Google Scholar 

  84. Fanning JP, Weaver N, Fanning RB, Griffee MJ, Cho SM, Panigada M, et al. Hemorrhage, disseminated intravascular coagulopathy, and thrombosis complications among critically Ill patients with COVID-19: an international COVID-19 critical care consortium study. Crit Care Med. 2023;51(5):619–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. El Mawla Z, El Saddik G, Zeineddine M, Hassoun M, El Hajj T. Cerebrovascular disease in patients with COVID-19 infection: a case series from Lebanon. Ann Med Surg (Lond). 2023;85(7):3701–8.

    Article  PubMed  Google Scholar 

  86. Eichhorn T, Huber S, Weiss R, Ebeyer-Masotta M, Lauková L, Emprechtinger R, et al. Infection with SARS-CoV-2 is associated with elevated levels of IP-10, MCP-1, and IL-13 in sepsis patients. Diagnostics (Basel). 2023;13(6):1069.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Dong W, Wang J, Tian L, Zhang J, Settles EW, Qin C, et al. Factor Xa cleaves SARS-CoV-2 spike protein to block viral entry and infection. Nat Commun. 2023;14(1):1936.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Devaux CA, Camoin-Jau L. Molecular mimicry of the viral spike in the SARS-CoV-2 vaccine possibly triggers transient dysregulation of ace2, leading to vascular and coagulation dysfunction similar to SARS-CoV-2 infection. Viruses. 2023;15(5):1045.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Sofi-Mahmudi A, Masinaei M, Shamsoddin E, Tovani-Palone MR, Heydari M-H, Shoaee S, et al. Global, regional, and national burden and quality of care index (QCI) of lip and oral cavity cancer: a systematic analysis of the Global Burden of Disease Study 1990–2017. BMC Oral Health. 2021;21(1):558.

    Article  PubMed  PubMed Central  Google Scholar 

  90. Chen YE, Ren FL, Gu X, Zhang HJ, Li WJ, Yang H, Shang FQ. Clinical value of platelets and coagulation parameters in predicting the severity of delta variant SARS-CoV-2. Pathobiology. 2023;90(4):241–50.

  91. Ceriz T, Lagarteira J, Alves SR, Carrascal A, Terras AR. Disseminated intravascular coagulation in COVID-19 setting: a clinical case description. Cureus. 2023;15(6):e39941.

    PubMed  PubMed Central  Google Scholar 

  92. Busch MH, Timmermans S, Van Kuijk SMJ, Aendekerk JP, Ysermans R, Van Doorn DPC, et al. Thrombin formation via the intrinsic coagulation pathway and von Willebrand factor reflect disease severity in COVID-19. Haematologica. 2023;108(5):1417–22.

    Article  CAS  PubMed  Google Scholar 

  93. Boswell MT, Maimela T, Hameiri-Bowen D, Riley G, Malan A, Steyn N, et al. COVID-19 severity and in-hospital mortality in an area with high HIV prevalence. South Afr J HIV Med. 2023;24(1):1412.

    Article  PubMed  PubMed Central  Google Scholar 

  94. Borulu F, Erkut B, Unlu Y. SARS-CoV-2 infection-associated thoraco-abdomino-iliac thrombosis in a patient without cardiac and systemic co-morbidity. Cardiovasc J Afr. 2023;34(2):114–6.

    Article  PubMed  PubMed Central  Google Scholar 

  95. Bolek T, Samoš M, Jurica J, Stančiaková L, Péč MJ, Škorňová I, et al. COVID-19 and the response to antiplatelet therapy. J Clin Med. 2023;12(5):2038.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Balaji Easwaran V, Satarker S, TVG, John J, Veedu AP, George KT, et al. Expediting molecular translational approach of mesenchymal stem cells in COVID-19 treatment. Curr Stem Cell Res Ther. 2023;18(5):653–75.

    Article  PubMed  Google Scholar 

  97. Badaras I, Laučytė-Cibulskienė A. Vascular aging and COVID-19. Angiology. 2023;74(4):308–16.

    Article  CAS  PubMed  Google Scholar 

  98. Antoniello A, Brophy A, Opsha Y. Evaluation of hospitalized patient outcomes in COVID-19 infection for continued versus discontinued use of preadmission antiplatelet regimen. J Pharm Pract. 2023;36(3):508–13.

    Article  PubMed  Google Scholar 

  99. Amatya I, Regmi PR, Adhikari G, Pokhrel B, Baniya A, Dangol A. Pulmonary embolism at CT pulmonary angiography in patients with COVID-19 at a tertiary care center in Nepal: a cross-sectional study. Ann Med Surg (Lond). 2023;85(5):1661–6.

    Article  PubMed  Google Scholar 

  100. Aliani C, Rossi E, Luchini M, Calamai I, Deodati R, Spina R, et al. Automatic COVID-19 severity assessment from HRV. Sci Rep. 2023;13(1):1713.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Shoaee S, Saeedi Moghaddam S, Masinaei M, Sofi-Mahmudi A, Hessari H, Heydari M-H, et al. Trends in dental caries of deciduous teeth in Iran: a systematic analysis of the national and sub-national data from 1990 to 2017. BMC Oral Health. 2022;22(1):634.

    Article  PubMed  PubMed Central  Google Scholar 

  102. Abdelaal A, Abu-Elfatth A, Bakkar LM, El-Azeem HGA, Hetta HF, Badawy ER. Assessment of COVID -19 associated coagulopathy and multiple hemostatic markers: a single center study in Egypt. Infection. 2023;51(3):655–64.

    Article  CAS  PubMed  Google Scholar 

  103. Zhu FF, Gu BB, Jin YJ, Yao L, Zhou L, Zou D, et al. Risk factors for radiological progression within admissive one week in the hospitalized COVID-19 omicron variant-infected patients. Infect Drug Resist. 2022;15:7127–37.

    Article  PubMed  PubMed Central  Google Scholar 

  104. Zhu A, Zakusilo G, Lee MS, Kim J, Kim H, Ying X, et al. Laboratory parameters and outcomes in hospitalized adults with COVID-19: a scoping review. Infection. 2022;50(1):1–9.

    Article  CAS  PubMed  Google Scholar 

  105. Zhang Z, Lin F, Liu F, Li Q, Li Y, Zhu Z, et al. Proteomic profiling reveals a distinctive molecular signature for critically ill COVID-19 patients compared with asthma and chronic obstructive pulmonary disease. Int J Infect Dis. 2022;116:258–67.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Zendehdel A, Jamalimoghadamsiahkal S, Arshadi M, Godarzi F, S SH, Hekmat H, et al. Survival analysis of COVID-19 patients based on different levels of D-dimer and coagulation factors. Biomed Environ Sci. 2022;35(10):957–61.

    CAS  PubMed  PubMed Central  Google Scholar 

  107. Yulistiani, Neldi V, Suprapti B, Nur RA. Efficacy and safety of anticoagulants for COVID-19 patients in the intensive care unit: a systematic review and meta-analysis. J Pharm Pharm Sci. 2022;25:274–84.

    Article  CAS  PubMed  Google Scholar 

  108. Yuan Y, Wang G, Chen X, Ye XL, Li XK, Li R, et al. Thrombocytopenia and increased risk of adverse outcome in COVID-19 patients. PeerJ. 2022;10:e13608.

    Article  PubMed  PubMed Central  Google Scholar 

  109. Yoo EH, Chang SH, Song DY, Lee CH, Cheong GY, Park S, et al. Comprehensive laboratory data analysis to predict the clinical severity of coronavirus disease 2019 in 1,952 patients in Daegu. Korea Ann Lab Med. 2022;42(1):24–35.

    Article  CAS  PubMed  Google Scholar 

  110. Yıldırım Ayaz E, Coşkun Z, Kaplan M, Bulut AS, Yeşildal M, Ankaralı H, et al. Comparison of initial CT findings and CO-RADS stage in COVID-19 patients with PCR, inflammation and coagulation parameters in diagnostic and prognostic perspectives. J Belg Soc Radiol. 2022;106(1):67.

    Article  PubMed  PubMed Central  Google Scholar 

  111. Xu Z, Zhang Y, Zhang C, Xiong F, Zhang J, Xiong J. Clinical features and outcomes of COVID-19 patients with acute kidney injury and acute kidney injury on chronic kidney disease. Aging Dis. 2022;13(3):884–98.

    Article  PubMed  PubMed Central  Google Scholar 

  112. Xu X, Feng Y, Jia Y, Zhang X, Li L, Bai X, Jiao L. Prognostic value of von Willebrand factor and ADAMTS13 in patients with COVID-19: a systematic review and meta-analysis. Thromb Res. 2022;218:83–98.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Wu S, Xu Y, Zhang J, Ran X, Jia X, Wang J, et al. Longitudinal serum proteome characterization of COVID-19 patients with different severities revealed potential therapeutic strategies. Front Immunol. 2022;13:893943.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Wu M, Zou ZY, Chen YH, Wang CL, Feng YW, Liu ZF. Severe COVID-19-associated sepsis is different from classical sepsis induced by pulmonary infection with carbapenem-resistant klebsiella pneumonia (CrKP). Chin J Traumatol. 2022;25(1):17–24.

    Article  CAS  PubMed  Google Scholar 

  115. Wen C, Shi G, Liu W, Zhang H, Lin G, Chen H. COVID-19 in a child with transposition of the great arteries S/P fontan palliation: a case report and literature review. Front Cardiovasc Med. 2022;9:937111.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Wang J, Lu Z, Jin M, Wang Y, Tian K, Xiao J, et al. Clinical characteristics and risk factors of COVID-19 patients with chronic hepatitis B: a multi-center retrospective cohort study. Front Med. 2022;16(1):111–25.

    Article  PubMed  Google Scholar 

  117. Wang J, Choy KW, Lim HY, Ho P. Impaired fibrinolytic potential predicts oxygen requirement in COVID-19. J Pers Med. 2022;12(10):1711.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Volod O, Wegner J. Viscoelastic testing in the management of adult patients on mechanical circulatory support devices with focus on extracorporeal membrane oxygenation. Semin Thromb Hemost. 2022;48(7):814–27.

    Article  PubMed  Google Scholar 

  119. Vatansev H, Karaselek MA, Yılmaz R, Küççüktürk S, Topal A, Yosunkaya Ş, et al. Evaluation of coagulation with TEG in patients diagnosed COVID-19. Turk J Med Sci. 2022;52(1):1–10.

    PubMed  Google Scholar 

  120. Vahdat S. A review of pathophysiological mechanism, diagnosis, and treatment of thrombosis risk associated with COVID-19 infection. Int J Cardiol Heart Vasc. 2022;41:101068.

    PubMed  PubMed Central  Google Scholar 

  121. Ueland T, Hausberg I, Mørtberg TV, Dahl TB, Lerum TV, Michelsen A, et al. Anti-PF4/polyanion antibodies in COVID-19 patients are associated with disease severity and pulmonary pathology. Platelets. 2022;33(4):640–4.

    Article  CAS  PubMed  Google Scholar 

  122. Tiwari L, Gupta P, N Y, Banerjee A, Kumar Y, Singh PK, et al. Clinicodemographic profile and predictors of poor outcome in hospitalised COVID-19 patients: a single-centre, retrospective cohort study from India. BMJ Open. 2022;12(6):e056464.

    Article  PubMed  Google Scholar 

  123. Theofilis P, Vordoni A, Kalaitzidis RG. COVID-19 and kidney disease: a clinical perspective. Curr Vasc Pharmacol. 2022;20(4):321–5.

    Article  CAS  PubMed  Google Scholar 

  124. Nugroho J, Wardhana A, Rachmi DA, Mulia EPB, A’yun MQ, et al. Simple coagulation profile as predictor of mortality in adults admitted with COVID-19: a meta-analysis. Arch Clin Infect Dis. 2021;16(5):e115442.

    Article  CAS  Google Scholar 

  125. Len P, Iskakova G, Sautbayeva Z, Kussanova A, Tauekelova AT, Sugralimova MM, et al. Meta-analysis and systematic review of coagulation disbalances in COVID-19: 41 studies and 17,601 patients. Front Cardiovasc Med. 2022;9:794092.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Zhang A, Leng Y, Zhang Y, Wu K, Ji Y, Lei S, Xia Z. Meta-analysis of coagulation parameters associated with disease severity and poor prognosis of COVID-19. Int J Infect Dis. 2020;100:441–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Teimury A, Khameneh MT, Khaledi EM. Major coagulation disorders and parameters in COVID-19 patients. Eur J Med Res. 2022;27(1):25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  128. Lin J, Yan H, Chen H, He C, Lin C, He H, et al. COVID-19 and coagulation dysfunction in adults: a systematic review and meta-analysis. J Med Virol. 2021;93(2):934–44.

    Article  CAS  PubMed  Google Scholar 

  129. Sekulovski M, Mileva N, Vasilev GV, Miteva D, Gulinac M, Peshevska-Sekulovska M, et al. Blood coagulation and thrombotic disorders following SARS-CoV-2 infection and COVID-19 vaccination. Biomedicines. 2023;11(10):2813.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

Not received.

Author information

Authors and Affiliations

  1. Department of Pathology, School of Medicine, IKHC, Teheran University of Medical Sciences, Tehran, Iran

    Alireza Abdollahi & Samaneh Salarvand

  2. Department of Cardiology, Tehran University of Medical Sciences, Tehran, Iran

    Saeed Nateghi

  3. School of Medicine, Department of Obstetrics and Gynecology, Tehran University of Medical Sciences, Tehran, Iran

    Zahra Panahi

  4. Department of Urology, Sina Hospital Tehran University of Medical Sciences, Tehran, Iran

    Seyed Hassan Inanloo

  5. School of Medicine, Tehran University of Medical Sciences, Tehran, Iran

    Seyed Morteza Pourfaraji

Authors
  1. Alireza Abdollahi
    View author publications

    Search author on:PubMed Google Scholar

  2. Saeed Nateghi
    View author publications

    Search author on:PubMed Google Scholar

  3. Zahra Panahi
    View author publications

    Search author on:PubMed Google Scholar

  4. Seyed Hassan Inanloo
    View author publications

    Search author on:PubMed Google Scholar

  5. Samaneh Salarvand
    View author publications

    Search author on:PubMed Google Scholar

  6. Seyed Morteza Pourfaraji
    View author publications

    Search author on:PubMed Google Scholar

Contributions

S.N. and Z.P. designed the study and participated in the search and screening sections. A.A., S.H.I., and S.M.P. extracted and analysed data. S.S. and S.M.P. wrote the manuscript. All authors revised and modified the final manuscript.

Corresponding author

Correspondence to Samaneh Salarvand.

Ethics declarations

Ethics approval and consent to participate

The protocol for this systematic review was approved by the ethics committee at IKHC ethics committee (Ethics code: IR.TUMS.IKHC.REC.1399.102).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abdollahi, A., Nateghi, S., Panahi, Z. et al. The association between mortality due to COVID-19 and coagulative parameters: a systematic review and meta-analysis study. BMC Infect Dis 24, 1373 (2024). https://doi.org/10.1186/s12879-024-10229-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12879-024-10229-y

Keywords

BMC Infectious Diseases

ISSN: 1471-2334

Contact us