Hemostatic Changes in Patients with COVID-19: A Meta-Analysis with Meta-Regressions

Minno, Matteo Nicola Dario Di; Calcaterra, Ilenia; Lupoli, Roberta; Storino, Antonio; Spedicato, Giorgio Alfredo; Maniscalco, Mauro; Minno, Alessandro Di; Ambrosino, Pasquale

doi:10.3390/jcm9072244

Cited by 41 publications

(44 citation statements)

References 149 publications

(357 reference statements)

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“…Results of raised D-Dimer levels, in the present study, are consistent with different studies that showed higher D-Dimer levels in expired COVID-19 patients compared to survive. 17,[26][27][28][29][30] Elevated Ddimer level is a risk factor for the development of DVT or pulmonary embolism and can predict both severity and mortality. 2,31,32 British Thoracic Society, based on risk stratification, suggested prophylactic low dose heparin in COVID patients with D-dimer more than 3000 ng/ml, while D-dimer more than 1000 ng/ml is an independent risk factor for the critical disease.…”

Section: Discussionmentioning

confidence: 99%

Predictive Efficacy of Haematological Biomarkers in COVID-19 infection

Ikram¹

2023

JRMC

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Introduction: The ongoing Corona Virus Pandemic is linked with high rates of morbidity and mortality globally. Early and effective predictors of clinical outcomes are urgently required to develop effective management protocols.Objective: To investigate the predictive efficacy of haematological biomarkers in Covid -19 infectionMethods: Blood samples were drawn from COVID‐19 infected pneumonia patients. Baseline clinical information was collected and quantification of hemostatic variable was done. Laboratory data both groups expired and recovered were compared using t- test, Mann Whitney- U test, chi squared-test and Kruskal Wallis test. Multivariate regression analysis was performed to determine the independent contribution of haematological variable in Covid19 related mortality. Receiver operating characteristic curve were drawn to find the predictive efficacy of significantly related parameters.Results: Out of 191 patients 68.1% were male. D‐dimer (median 800 ng/mL; IQR 200‐3200) and NLR (median 10.40; IQR 3.20-22.80) were found to be predominantly raised and significantly correlated with Covid mortality in multivariate regression analysis. The optimum cutoff value of D-dimer to predict in-hospital mortality was 450 ng/ml with a sensitivity of 71.6% and a specificity of 95.8%. The optimum cutoff value of NLR to predict in-hospital mortality was 5.450 with a sensitivity of 70.1% and a specificity of 71%.Conclusion: D-dimer and NLR could be used as the signiﬁcant indicators in predicting the mortality of COVID-19.

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Section: Discussionmentioning

confidence: 99%

Predictive Efficacy of Haematological Biomarkers in COVID-19 infection

Ikram¹

2023

JRMC

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“…The association of COVID-19 infection severity with changes in hemostatic parameters was explored in a meta-analysis of 60 studies comparing 5487 patients with severe and 9670 patients with mild COVID-19 infection. 42 The meta-analysis found a higher prothrombin time (PT) (standardized mean difference-SMD: 0.41), D-dimer (SMD: 0.67), and fibrinogen values (SMD: 1.84), with lower platelet count (SMD: -0.74) among severe infection patients. Analysis of 25 studies on 1511 COVID-19 non-survivors and 6287 survivors showed higher PT (SMD: 0.67) and D-Dimer values (SMD: 3.88), with lower platelet count (SMD: -0.60) among non-survivors.…”

Section: Hypercoagulable Statementioning

confidence: 99%

COVID-19 Infection: Viral Macro- and Micro-Vascular Coagulopathy and Thromboembolism/Prophylactic and Therapeutic Management

Manolis¹,

Manolis

et al. 2020

J Cardiovasc Pharmacol Ther

110

100

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Coronavirus-2019 (COVID-19) predisposes patients to arterial and venous thrombosis commonly complicating the clinical course of hospitalized patients and attributed to the inflammatory state, endothelial dysfunction, platelet activation and blood stasis. This viral coagulopathy may occur despite thromboprophylaxis and raises mortality; the risk appears highest among critically ill inpatients monitored in the intensive care unit. The prevalence of venous thromboembolism in COVID-19 patients has been reported to reach ∼10-35%, while autopsies raise it to nearly 60%. The most common thrombotic complication is pulmonary embolism, which though may occur in the absence of a recognizable deep venous thrombosis and may be due to pulmonary arterial thrombosis rather than embolism, resulting in thrombotic occlusion of small- to mid-sized pulmonary arteries and subsequent infarction of lung parenchyma. This micro-thrombotic pattern seems more specific for COVID-19 and is associated with an intense immuno-inflammatory reaction that results in diffuse occlusive thrombotic micro-angiopathy with alveolar damage and vascular angiogenesis. Furthermore, thrombosis has also been observed in various arterial sites, including coronary, cerebral and peripheral arteries. Biomarkers related to coagulation, platelet activation and inflammation have been suggested as useful diagnostic and prognostic tools for COVID-19-associated coagulopathy; among them, D-dimer remains a key biomarker employed in clinical practice. Various medical societies have issued guidelines or consensus statements regarding thromboprophylaxis and treatment of these thrombotic complications specifically adapted to COVID-19 patients. All these issues are detailed in this review, data from meta-analyses and current guidelines are tabulated, while the relevant mechanisms of this virus-associated coagulopathy are pictorially illustrated.

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“…Therefore, other auxiliary tests combining clinical, molecular, and radiological features would be beneficial for diagnosis. In clinical, COVID-19 is mostly characterized by inflammatory exudative pulmonary lesions with symptoms including fever, dry cough, headache, myalgia, breathlessness, and asthenia [14,15]. Although clinical tests are essential for diagnosis, the symptoms of patients at the early stage of COVID-19 could not be differentiated from common viral pneumonia.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] A Systematic Analysis on COVID‐19 Patients in Inner Mongolia Based on Dynamic Monitoring

Lan

Lin

et al. 2021

BioMed Research International

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COVID-19 has spread globally with over 90,000,000 incidences and 1,930,000 deaths by Jan 11, 2021, which poses a big threat to public health. It is urgent to distinguish COVID-19 from common pneumonia. In this study, we reported multiple clinical feature analyses on COVID-19 in Inner Mongolia for the first time. We dynamically monitored multiple clinical features of all 75 confirmed COVID-19 patients, 219 pneumonia patients, and 68 matched healthy people in Inner Mongolia. Then, we studied the association between COVID-19 and clinical characteristics, based on which to construct a novel logistic regression model for predicting COVID-19. As a result, among the tested clinical characteristics, WBC, hemoglobin, C-reactive protein (CRP), ALT, and Cr were significantly different between COVID-19 patients and patients in other groups. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was 0.869 for the logistic regression model using multiple factors associated with COVID-19. Furthermore, the CRP reaction showed five different time-series patterns with one-peak and double-peak modes. In conclusion, our study identified a few clinical characteristics significantly different between COVID-19 patients and others in Inner Mongolia. The features can be used to establish a reliable logistic regression model for predicting COVID-19.

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Hemostatic Changes in Patients with COVID-19: A Meta-Analysis with Meta-Regressions

Cited by 41 publications

References 149 publications

Predictive Efficacy of Haematological Biomarkers in COVID-19 infection

Predictive Efficacy of Haematological Biomarkers in COVID-19 infection

COVID-19 Infection: Viral Macro- and Micro-Vascular Coagulopathy and Thromboembolism/Prophylactic and Therapeutic Management

[Retracted] A Systematic Analysis on COVID‐19 Patients in Inner Mongolia Based on Dynamic Monitoring

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