Meta-Analysis Open Access
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Methodol. Sep 20, 2024; 14(3): 92983
Published online Sep 20, 2024. doi: 10.5662/wjm.v14.i3.92983
Anticoagulant use before COVID-19 diagnosis prevent COVID-19 associated acute venous thromboembolism or not: A systematic review and meta-analysis
Kinza Iqbal, Taha Bin Arif, Syeda Kisa Batool Naqvi, Muhammad Mehdi, Pankaj Kumar, Ayman Iqbal, Jawad Ahmed, Department of Internal Medicine, Dow Medical College, Karachi 74200, Pakistan
Akshat Banga, Department of Internal Medicine, Sawai Man Singh Medical College, Jaipur 302004, India
Sawai Singh Rathore, Department of Internal Medicine, Dr. Sampurnanand Medical College, Jodhpur 342003, Rajasthan, India
Abhishek Bhurwal, Department of Gastroenterology and Hepatology, Rutgers Robert Wood Johnson School of Medicine, New Brunswick, NJ 08901, United States
Mitali Madhu Salklan, Department of Internal Medicine, Pandit Bhagwat Dayal Sharma Post Graduate Institute of Medical Sciences, Rohtak 124001, Haryana, India
Nikhil Sharma, Vikas Bansal, Juan Pablo Domecq, Department of Nephrology and Hypertension, Mayo Clinic, Rochester, MN 55905, United States
Amos Lal, Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN 55905, United States
Rahul Kashyap, Department of Research, Wellspan Health, York, PA 17403, United States
ORCID number: Kinza Iqbal (0000-0001-5460-4406); Akshat Banga (0000-0003-2626-8219); Taha Bin Arif (0000-0003-4879-560X); Sawai Singh Rathore (0000-0003-1954-094X); Abhishek Bhurwal (0000-0002-3886-7537); Syeda Kisa Batool Naqvi (0000-0002-2049-5739); Muhammad Mehdi (0000-0002-6799-2548); Pankaj Kumar (0000-0002-5580-9144); Mitali Madhu Salklan (0009-0002-1139-5725); Ayman Iqbal (0000-0002-0993-6396); Jawad Ahmed (0000-0001-5651-1916); Nikhil Sharma (0000-0002-3718-6967); Amos Lal (0000-0002-0021-2033); Rahul Kashyap (0000-0002-4383-3411); Vikas Bansal (0000-0001-6047-5559); Juan Pablo Domecq (0000-0002-8540-9862).
Co-first authors: Kinza Iqbal and Akshat Banga.
Co-corresponding authors: Vikas Bansal and Juan Pablo Domecq.
Author contributions: Iqbal K and Bansal V contributed equally to defining the study outline and manuscript writing; Data review and collection were done by Banga A, Iqbal A, Ahmed J, Iqbal K, Sharma N, Mehdi M, Kumar P, and Rathore SS; Statistical analysis was done by Banga A, Iqbal K, and Bansal V; Study design and critical review are done by Banga A, Lal A, Domecq JP, Kashyap R, and Bansal V; Iqbal K and Bansal V are the co-corresponding authors of the paper, taking responsibility for the integrity of the work from inception to the published article; all authors contributed to the article and approved the submitted version. Bansal V and Domecq JP contributed equally to this work and are considered co- corresponding authors.
Conflict-of-interest statement: Dr. Bansal has nothing to disclose.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
Corresponding author: Vikas Bansal, MBBS, Associate Research Scientist, Department of Nephrology and Hypertension, Mayo Clinic, 200 1st St SW, Rochester, MN 55905, Rochester, MN 55905, United States.
Received: February 15, 2024
Revised: April 24, 2024
Accepted: May 11, 2024
Published online: September 20, 2024
Processing time: 131 Days and 10.3 Hours


Coagulopathy and thromboembolic events are associated with poor outcomes in coronavirus disease 2019 (COVID-19) patients. There is conflicting evidence on the effects of chronic anticoagulation on mortality and severity of COVID-19 disease.


To summarize the body of evidence on the effects of pre-hospital anticoagulation on outcomes in COVID-19 patients.


A Literature search was performed on LitCovid PubMed, WHO, and Scopus databases from inception (December 2019) till June 2023 for original studies reporting an association between prior use of anticoagulants and patient outcomes in adults with COVID-19. The primary outcome was the risk of thromboembolic events in COVID-19 patients taking anticoagulants. Secondary outcomes included COVID-19 disease severity, in terms of intensive care unit admission or invasive mechanical ventilation/intubation requirement in patients hospitalized with COVID-19 infection, and mortality. The random effects models were used to calculate crude and adjusted odds ratios (aORs) with 95% confidence intervals (95%CIs).


Forty-six observational studies met our inclusion criteria. The unadjusted analysis found no association between prior anticoagulation and thromboembolic event risk [n = 43851, 9 studies, odds ratio (OR)= 0.67 (0.22, 2.07); P = 0.49; I2 = 95%]. The association between prior anticoagulation and disease severity was non-significant [n = 186782; 22 studies, OR = 1.08 (0.78, 1.49); P = 0.64; I2 = 89%]. However, pre-hospital anticoagulation significantly increased all-cause mortality risk [n = 207292; 35 studies, OR = 1.72 (1.37, 2.17); P < 0.00001; I2 = 93%]. Pooling adjusted estimates revealed a statistically non-significant association between pre-hospital anticoagulation and thromboembolic event risk [aOR = 0.87 (0.42, 1.80); P = 0.71], mortality [aOR = 0.94 (0.84, 1.05); P = 0.31], and disease severity [aOR = 0.96 (0.72, 1.26); P = 0.76].


Prehospital anticoagulation was not significantly associated with reduced risk of thromboembolic events, improved survival, and lower disease severity in COVID-19 patients.

Key Words: Prior anticoagulation, COVID-19, Prehospital anticoagulation, Chronic anticoagulation, Mortality, Severity, Thromboembolic events

Core Tip: Coronavirus disease 2019 (COVID-19) patients are at an increased risk of developing thromboembolic events and hypercoagulable disorders, which are poor prognostic indicators of COVID-19 disease. The results of different observational studies on the effects of chronic anticoagulation on the mortality and severity of COVID-19 disease in infected patients are inconsistent. This systematic review and meta-analysis provide a comprehensive assessment of the risk of thromboembolic events, mortality, and severity of COVID-19 disease in patients on prehospital anticoagulation treatment.


Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged at the end of 2019 and was later termed coronavirus disease 2019 (COVID-19)[1]. While Acute Respiratory Distress Syndrome (ARDS) was the predominant complication associated with a potentially fatal outcome, the infection progression could also be influenced by other risk factors like diabetes, coronary artery disease, chronic kidney disease, metabolic syndrome, psychiatric and neurological complications[2-12]. Due to the complexity of these complications, a standardized treatment approach for COVID-19 has yet to be universally agreed upon to guide healthcare professionals worldwide[10,13-15].

Studies have described that multi-organ involvement in COVID-19 is associated with endothelial dysfunction and thrombosis[16]. Moreover, COVID-19 has been observed to influence clotting system pathways and activate platelets, potentially resulting in vascular inflammation, hypercoagulability, and endothelial dysfunction[17]. As a result, thromboembolic events are found to be a common complication in these individuals, with an incidence rate of around 25% for Venous Thromboembolism (VTE)[16]. SARS-CoV-2-related coagulopathy is distinct from other causes of coagulopathy that result in VTE as it involves a significant element of inducing systemic inflammation and endothelitis, which might not be effectively addressed by conventional anticoagulant treatment methods[18]. Regardless, emerging research evidence suggests that anticoagulation therapy has shown effectiveness in COVID-19 patients, particularly in noncritically ill hospitalized cases[19].

Studies mostly emphasize using anticoagulation therapy during hospitalization; however, the relationship between pre-existing chronic anticoagulation due to non-COVID-19 indication and clinical outcomes in COVID-19-infected patients remains largely unknown. This meta-analysis aims to analyze the relationship between VTE risk in COVID-19 disease and prehospital anticoagulation and to study the impact of prehospital anticoagulation on COVID-19 disease severity and mortality.


The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines were followed in this systematic review and meta-analysis[20]. The protocol for this meta-analysis was registered on PROSPERO (CRD42022306893).

Data sources and literature search strategy

Two independent investigators (Iqbal K and Rathore SS) utilized databases, including LitCovid, WHO, and Scopus, to conduct a systemic literature search for relevant articles from December 2019 to June 30, 2023 (Supplementary Table 1). A combination of keywords, such as “anticoagulant”, “direct oral anticoagulants”, “DOAC”, “vitamin K antagonist”, “VKA”, “dabigatran”, “rivaroxaban”, “apixaban”, “edoxaban”, “warfarin”, “heparin”, “prehospital”, “prehospital”, “preadmission”, “pre-admission”, “chronic”, “long-term”, “pre-hospital”, and “prior”, were searched electronically[21].

Study selection and inclusion criteria

Duplicates within the articles retrieved from our systemic search were identified and eliminated using Endnote (Clarivate Analytics, Thomson Reuters Corporation, Philadelphia, Pennsylvania). Two independent researchers (Iqbal K and Rathore SS) screened the articles based on their titles, abstracts, and keywords. The articles were subsequently assessed for relevance through full-text screening. Data review and collection were done by Bhurwal A, Iqbal A, Ahmed J, Iqbal K, Sharma N, Mehdi M, Kumar P, and Rathore SS. References to the shortlisted articles were also screened for additional studies. Any disagreement was resolved by discussing within the group or through an independent reviewer’s (Bansal V) inputs. Articles that failed to fulfill the inclusion criteria were removed. For the meta-analysis, the inclusion criteria were: (1) All retrospective or prospective observational studies enrolling patients ≥ 18 years; (2) Studies that compared outcomes of COVID-19-infected patients who had been on anticoagulation before their COVID-19 diagnosis for any indication vs those that did not receive any prehospital anticoagulation; and (3) Studies including data (raw or adjusted) for at least one of the outcomes of interest. Exclusion criteria consisted of: (1) Studies without a control group; (2) Studies with no outcome of interest (new incidence of thromboembolic events, all-cause mortality, severity of COVID-19 (defined as per WHO Ordinal scale 6-9); and (3) Literature reviews or narrative reviews or case reports.

Our primary outcome of interest was the incidence of VTE in COVID-19-infected adults on chronic anticoagulation prior to the infection. Mortality and disease severity were considered secondary outcomes. COVID-19 disease severity was based on the WHO ordinal scale[22]. A WHO clinical progression scale score of 6-9 indicated severe COVID-19, often necessitating intensive care unit (ICU) admission. In cases where this scale was not used, the highest WHO ordinal scale event was used as a substitute for severe infection. If ICU admission rates were unavailable, intubation or non-invasive mechanical ventilation rates were considered to assess COVID-19 severity. The cases included SARS-CoV-2 positive hospitalized patients with prehospital use of anticoagulants. The controls were COVID-19-positive hospitalized patients with no history of prehospital anticoagulation. Both raw data and their adjusted estimates were derived for the meta-analysis. Estimates from studies that performed propensity-matched scoring were also considered as adjusted estimates.

Statistical analysis

We used Review Manager v.5.4 (The Nordic Cochrane Centre, The Cochrane Collaboration, 2014)[23] for unadjusted and adjusted analysis and Comprehensive Meta-Analysis software package 154 (Biostat, Englewood, NJ, United States)[24] for the meta-regression analysis. For the unadjusted analysis, we calculated the Mantel Haenszel crude odds ratios (ORs) using a random-effects model with a 95% confidence interval (95%CI). We also calculated the adjusted OR (aOR) with 95%CI to measure adjusted estimates. Adjusted estimates were used for the primary analysis to produce more reliable results as it considers the baseline differences between the two groups (prior anticoagulant users and non-users) that may influence the outcomes. In all cases, significance was defined by a P-value < 0.05, and all analyses utilized a random-effects model. Statistical heterogeneity was gauged among the included articles using the Higgins I2 statistics, and I2 > 50% was regarded as substantial heterogeneity. To reduce the inherent selection bias in observational studies, a subgroup analysis based on study design was performed[25].

We conducted univariate and multivariate meta-regression analyses with random effects (maximum likelihood approach) to examine potential study variations that could impact the effect magnitude. The age and gender distribution of the study sample, the study's country of origin, and the percentage of participants with comorbid conditions like obesity, diabetes, pulmonary illness, dyslipidemia, cardiovascular disease, and hypertension were all considered potential sources of variability. If covariates significantly (P < 0.05) altered the relationship between mortality or severity in COVID-19 hospitalized patients and prehospital anticoagulant usage, they were chosen for additional modeling. Two models were developed: one for mortality and the other for the severity of the condition. Then, using P = 0.05 as the cutoff threshold for elimination, preselected factors were included in a manual backward and stepwise multiple meta-regression analysis. P < 0.05 (P < 0.10 for heterogeneity) was used to define statistical significance. The meta-analysis and meta-regression tests were all two-tailed.

Risk of Bias and Quality assessment

Two investigators (Kumar P, Iqbal A) assessed the included studies’ methodological quality through the Newcastle-Ottawa Scale (Supplementary Table 2)[26]. The following categories were used to evaluate every study: (1) Study group selection; (2) Study group comparability; and (3) Determination of the desired outcomes (Supplementary Table 2). The publishing bias likelihood was tested using Egger regression methods, and the results were visually examined using funnel plots (Supplementary Figure 1). The degree of evidence certainty at the outcome level was evaluated using the GRADE pro profiler (GRADE working group, McMaster University, and Evidence Prime Inc; Supplementary Table 3)[27].


The initial database search produced 2056 articles of potential relevance. After eliminating the duplicates, 956 articles were filtered for appropriateness and relevancy using their titles, abstracts, and keywords. Of them, 86 full-text papers relevant to the manuscript's objectives were examined. The final analysis included 46 studies comprising 41 cohort studies (36 retrospective, 2 ambispective, and 3 prospective), three case-control studies, and one cross-sectional study. The PRISMA flowchart outlining the search process is shown in Figure 1. Table 1 provides an overview of the study characteristics of the included publications, and Table 2 shows the studies reporting adjusted estimates along with the covariates for which the estimates are adjusted.

Figure 1
Figure 1  PRISMA flow diagram.
Table 1 Study characteristic table for included studies.
Country of study
Study design
Total COVID-19 positive patients
Total patients with pre-admission anticoagulation
Type of anticoagulant
Duration of anticoagulant
Indication for anticoagulant Use
Definition Of Severity
Mean age ± SD (yr)
Female sex proportion (%)
Diabetes proportion (%)
Hypertension proportion (%)
Pulmonary disease proportion (%)
Arrhythmia proportion (%)
Ageno et al[28], 2021ItalyRetrospective observational studyInpatient hospitalized439643DOAC or VKA-AFNA-----56.4
Arachchillage et al[29], 2022United KingdomAmbispective cohort studyInpatient hospitalized5883963DOAC (rivaroxaban, apixaban, edoxaban, dabigatran, or VKA (warfarin)-VTE or heart disease or AFICU admission-44.8128.9647.1224.55-
Aslan et al[55], 2021TurkeyRetrospective cohort studyInpatient hospitalized171079DOAC (dabigatran, rivaroxaban, apixaban, edoxaban)-AF, venous thrombosisICU admission62
Bauer et al[71], 2021United StatesCase–control studyHospitalized and non-hospitalized patients1449---Admitted to the hospital/died54.7 ± 22.563173622-
Boari et al[68], 2020ItalyRetrospective cohort studyInpatient hospitalized25829---NA71.0 ± 13.832.92658.514-
Brouns et al[53], 2020NetherlandsRetrospective case-seriesInpatient hospitalized10118DOAC or VKA---------
Buenen et al[40], 2021NetherlandsCohort studyHospitalized and non-hospitalized patients497110DOAC or VKA--NA-36.220.552.126-
Chocron et al[41], 2021FranceRetrospective cohort studyInpatient hospitalized2878382DOAC or VKA-AF, VTEICU admission-40.330.375.3-24.8
Corrochano et al[30], 2022SpainRetrospective observational studyInpatient hospitalized1598155VKA (warfarin or acenocoumarol), DOAC (dabigatran, rivaroxaban, apixaban or edoxaban), or heparins--ICU admission66.5 (17.1)47.12050.8--
Covino et al[42], 2021ItalyRetrospective observational studyEmergency department18492DOAC or VKA1 monthAFNA84
Denas et al[43], 2021ItalyRetrospective observational studyHospitalized and non-hospitalized patients4697651VKA, NOAC (dabigatran, rivaroxaban, apixaban) or edoxaban6 monthsAFICU admission-45.624.187.3--
Fauvel et al[54], 2020FranceRetrospective observational studyInpatient hospitalized1240136VKA 47 (3.8), NOAC 78 (6.3), heparin 11 (0.9)--ICU admission and mechanical ventilation------
Flam et al[59], 2021SwedenProspective cohort studyInpatient hospitalized459402103703DOAC (dabigatran, apixaban, rivaroxaban or edoxaban)0–6 months [7394 (7.1%)]; 7–24 months [29012 (28.0%)]; > 24 months [67245 (64.8%)]AF and atrial flutterHospital admission and ICU admission------
Fröhlich et al[31], 2021GermanyRetrospective cohort studyInpatient hospitalized6637731DOAC or VKA6 monthsAFNA-502593-76
Fumagalli et al[44], 2022ItalyRetrospective cohort studyInpatient hospitalized17691DOAC or VKA-AFNA-48.333712111.9
Gülcü et al[32], 2022TurkeyRetrospective cohort studyInpatient hospitalized5575451DOAC (rivaroxaban, apixaban, edoxaban, dabigatran), and VKA (warfarin)--NA64
Hanif et al[33], 2020United StatesRetrospective cohort studyInpatient hospitalized5833DOAC (rivaroxaban, apixaban, edoxaban, and dabigatran), VKA (warfarin)--NA-34.5----
Harrison et al[34], 2021United StatesRetrospective cohort studyInpatient hospitalized1027132DOAC (rivaroxaban, apixaban, dabigatran), and VKA (warfarin)--NA-52.538.774.3-21.1
Ho et al[45], 2021United StatesRetrospective cohort studyHospitalized and non-hospitalized patients28076304VKA (warfarin) or DOAC (dabigatran)Within 3 months prior to SARS-Cov-2 diagnosis-ICU admission-
Hozayen et al[35], 2021United StatesProspective cohort studyOutpatient and inpatient5597160Enoxaparin, VKA (warfarin), DOACWithin 3 months prior to SARS-Cov-2 diagnosis-NA51 ± 2257.115.734.74.512
Iaccarino et al[72], 2021ItalyCross-sectional studyInpatient hospitalized2377125DOAC 6 monthsAF, mechanic valvularreplacement, pulmonary thromboembolism prophylaxisICU admission68.2 ± 0.3837.31859-4.7
Klok et al[61], 2020DenmarkRetrospective cohort studyIntensive care18417---NA------
Li et al[36], 2020ChinaAmbispective cohort studyInpatient hospitalized54716---Severe COVID-19 infection60
Lodigiani et al[73], 2020ItalyRetrospective cohort studyInpatient hospitalized38833DOAC or VKA--ICU admission66
Ménager et al[60], 2020FranceRetrospective cohort studyInpatient hospitalized829VKA (warfarin or acenocoumarol or fluindione)--NA88 (85–92)47.623.263.4-36.6
Middeldorp et al[62], 2020NetherlandsRetrospective cohort studyInpatient hospitalized19819--AFICU admission61± 1434----
Natali et al[63], 2020United StatesRetrospective case control studyInpatient hospitalized40022---NA-------
Olcott et al[46], 2021England.Retrospective cohort studyInpatient hospitalized30981DOAC or VKA--NA-47.9----
Parker et al[47], 2021United KingdomRetrospective cohort studyInpatient hospitalized1032164DOAC, VKA, LMWH, fondaparinuxTaking the anticoagulant for > 1 monthAF, VTE, metallic heart valve, LV thrombusICU admission71
Philipose et al[66], 2020United KingdomRetrospective cohort studyInpatient hospitalized46668---NA---50.228.1-
Reilev et al[64], 2020DenmarkCohort studyCommunity-managed and hospitalized11122577-At least one filled prescription within 6 months prior to the test date-ICU admission48
Rieder et al[37], 2022Multi-CountryRetrospective cohort studyHospitalized and outpatient1433334VKA or non-VKA DOAC (rivaroxaban, apixaban, edoxaban, dabigatran etexilate)-AFNA-39.831.286.5-25.4
Rivera-Caravaca et al[58], 2021International, HOPE COVID-19 RegistryRetrospective cohort studyInpatient hospitalized1002110DOAC or VKA-AF, VTE, mechanical heart valvesNA-40.831.282.118.3-
Rivera-Caravaca et al[38], 2021United States, TrinetxCohort studyHospitalized and outpatient2600613003DOAC (dabigatran, apixaban, rivaroxaban or edoxaban)1 yr-NA-48.437.971.918.348.1
Rodríguez-Molinero et al[67], 2020SpainRetrospective cohort studyInpatient hospitalized41834---Need for oxygen therapy through a nonrebreather mask or mechanical ventilation65.4 ± 16.643.123.7529.810.8
Rossi et al[57], 2020ItalyRetrospective observational studyOutpatient7026DOAC (eivaroxaban, apixaban, edoxaban, dabigatran)Regularly taken by the patient for at least 6 monthsAF, pulmonary embolism, or DVTNA-5025.761.415.7-
Russo et al[48], 2022ItalyRetrospective observational studyInpatient hospitalized46787DOAC (edoxaban, dabigatran, rivaroxaban, apixaban), VKA (warafrin)-AF, prosthetic heart valve, venous thromboembolismNA-33.325.37418.8-
Ruzhentsova et al[56], 2021ItalyRetrospective cohort studyOutpatient7626DOAC (rivaroxaban, apixaban, dabigatran)--NA-56.626.377.6-36.8
Schiavone et al[74], 2021ItalyRetrospective cohort studyInpatient hospitalized84465DOAC or VKA--ICU admission63.4 ± 16.138.316.645.17.49.2
Sivaloganathan et al[49], 2020United KingdomCase-control studyInpatient hospitalized18031VKA (warfarin), DOAC (dabigatran, rivaroxaban, apixaban), or LMWH--ICU admission------
Spiegelenberg et al[50], 2021NetherlandsRetrospective cohort studyInpatient hospitalized1154190DOAC or VKA-AF, VTE, mechanical valve replacement, cardiac arrest in history, or unknown (5%)ICU admission-32.227.353.826.1-
Tehrani et al[69], 2021SwedenRetrospective cohort studyInpatient hospitalized25549---NA66 ± 1741315413-
Togano et al[39], 2021JapanRetrospective cohort studyInpatient hospitalized4026105VKA (warfarin), DOAC (dabigatran, rivaroxaban, apixaban, or edoxaban)--Mechanical ventilation/ supplemental oxygen/SPO2 ≤94% on room air/tachypnea52.0
Tremblay et al[70], 2020United StatesRetrospective cohort studyHospitalized and ambulatory patients3772241---Intubation-mechanical ventilation56.6 (18.2)45.2--14.9-
van Haaps et al[51], 2021DenmarkRetrospective cohort studyInpatient hospitalized3006445DOAC, VKA, LMWH--ICU admission-35.137.859.58.3-
Wargny et al[65], 2021FranceRetrospective cohort studyInpatient hospitalized2796501---NA69.7 ± 13.236.310076.89.6-
Table 2 Studies reporting adjusted estimates and the factors for which they are adjusted.
Ref.OutcomeAdjusted estimates
Ageno et al[28], 2021MortalityAge, gender, and heparin use after admission, history of acute MI, T2D, HTN, cancer, COPD, renal function and CRP at hospital entry
Arachchillage et al[29], 202290-day mortality, thrombosis, and ICU admissionAge, gender, BMI, antiplatelet treatment prior to admission, autoimmune disease, malignancy, hypercholesterolaemia, heart disease, T2D, smoking status, liver disease, lung disease, existing renal failure and whether renal failure was dialysis dependent
Aslan et al[55], 2021In-hospital mortalityAge, male gender, T2D, ferritin, d-dimer, neutrophil, lymphocyte, creatinine, CRP, SaO2, procalcitonin, DOAC, HTN, HF, AF, CAD, COPD, systolic blood pressure and hematocrit in univariable logistic regression analysis
Buenen et al[40], 2021All-cause mortality within 30 dAge, sex, symptom duration, home medication, and comorbidities
Covino et al[42], 2021All-cause in-hospital death.Age, sex, comorbidity (categorized as CCI < 3 or CCI ≥ 3), and illness severity at admission (categorized as NEWS < 6 or NEWS ≥ 6)
Gülcü et al[32], 2022In-hospital all-cause mortalityAge, gender, HTN, DM, HF, CAD, eGFR, albumin, CRP, D-dimer, hemoglobin, platelet count, LDH, and oxygen saturation variables
Bauer et al[71], 2020SeverityAge and gender
Ménager et al[60], 20207-day mortalityAge, sex, severe undernutrition, T2D, HTN, prior MI, HF, prior stroke and/or TIA, CHA2DS2-VASc score, HAS-BLED score, and eGFR
Philipose et al[66], 2020MortalityAge and gender
Rieder et al[37], 2022COVID-19 related mortalityAge, gender, BMI and smoking status, the phase of disease at diagnosis, solid tumor, AF, CAD, prior MI, peripheral artery disease, HTN, cerebrovascular disease, and T2D
Rivera-Caravaca et al[38], 2021Mortality and the composite of any thrombotic or thromboembolic eventAge, gender and ethnicity, all the included comorbidities
Rodríguez-Molinero et al[67], 2020MortalityAge, sex, obesity, and corticosteroids
Russo et al[48], 2022MortalityAge, arterial HTN, T2D, CAD, HF, previous stroke
Schiavone et al[74], 2021MortalityAge > 65 years, male gender, CAD, CKD, COPD, HF, OAC, PaO2/FiO2, hydroxychloroquine, tocilizumab, antivirals, heparin
Spiegelenberg et al[50], 2021All-cause in hospital mortality and ICU admissionAge, sex, body mass index, active malignancy, COPD, T2D, HTN, CAD, MI, HF, non-ischaemic cardiomyopathy, previous heart surgery, electronic heart device, cerebrovascular accident, peripheral artery disease, immunosuppressive medication, no ICU policy
Togano et al[39], 2021Severity (mechanical ventilation/ supplemental oxygen/SpO2 ≤ 94% on room air/tachypnea)Age, gender, BMI, smoking, alcohol consumption, myocardial infarction/congestive heart failure, peripheral artery disease, cerebrovascular disease, dementia, paralysis, COPD, liver dysfunction, hypertension, hyperlipidemia, diabetes, obesity, leukemia, lymphoma, immunosuppression
Tremblay et al[70], 2020All-cause mortality, mechanical ventilationAge, gender, race, CCI and obesity
van Haaps et al[51], 202121-day all-cause mortality and ICU admissionAge, gender, T2D, HTN, CKD, asthma, obesity, time in pandemic, center, chronic cardiac disease, malignancy, liver disease, dementia, organ transplant, autoimmune disorder, and rheumatic disorder
Wargny et al[65], 2021Death within 28 dAge
Harrison et al[34], 202121-day all-cause in hospital mortalityAge, gender, and confounding variables
Iaccarino et al[72], 2021Mortality, ICU admissionAge, multimorbidity (combined in the CCI score), and gender
Hozayen et al[35], 2021MortalityAge, sex, self-identified race/ethnicity (as a proxy for social, not biological risk factors), Elixhauser comorbidity score, and the presence/absence of any cardiovascular, immunological or hematological comorbidities
Ho et al[45], 2021ICU admission, VTE, and mortality between date of SARS-CoV-2 diagnosis and 45 d after diagnosisAge, sex, race/ethnicity, body mass index, CCI, HTN, T2D, and smoking history as well as the week of SARS-CoV-2 diagnosis
Denas et al[43], 2021ICU admission and all-cause mortalityAge, sex, HF, HTN, cancer, T2D, history of stroke/TIA, previous bleeding, history of MI, peripheral artery disease, abnormal renal function, abnormal hepatic function, use of antiplatelet drugs, NSAIDs and statin use
Corrochano et al[30], 2022All-cause mortality and ICU admissionSex, age, CCI, and antithrombotic therapy
Chocron et al[41], 2021In-hospital mortality and ICU admissionSex, age, cardiovascular comorbidities (history of HTN, dyslipidemia, BMI, T2D, and current smoking), plasma creatinine level (µmol/L), CRP (mg/L), fraction of inspired oxygen, the degree of pulmonary lesions with ground-glass opacities and areas of consolidation, and the use of in-hospital anticoagulation (preventive low or high dose and therapeutic dose)

Out of the 46 studies included in our meta-analysis, 26 studies[28-54] described using both direct oral anticoagulants (DOACs) and vitamin K antagonists (VKAs). Five studies[55-59] had only DOAC as the prehospital anticoagulant, while a single study by Ménager et al[60] included patients only on VKAs. The anticoagulant type was unspecified in the remaining 13 studies[36,61-72]. In the 32 studies[28-35,37,39-51,53-60,73,74] studying DOACs Apixaban, Dabigatran, Edoxaban, and Rivaroxaban were the most commonly used, while on the other hand, VKAs were represented mostly by Warfarin. However, in two studies[30,60], Acenocoumarin was also taken into consideration. Supplemental prehospital use of other anticoagulants, including LMWH, Fondaparinux, and Enoxaparin, was observed in five studies[30,35,47,49,51].

Quality assessment and publication bias

The included studies’ methodological quality assessment demonstrated that 21 studies[25,28,30,34,36,38,39,43,44,50,54,56,58-60,63,66,68,69,71,72,73] had a high quality with very low risk of bias, while 24 studies[29,31-33,35,37,40-42,46-49,51,55,57,62,64,65,67,70,73,74] were of moderate quality (Supplementary Table 2). Out of the 24 studies with moderate quality, 11 studies[29,33,35,37,41,46-49,52,62] had an unclear risk of bias (not enough information to make a clear judgment), and the remaining 13 studies[31,32,40,46,55,57,64,65,67,70,73,74] had a low risk of bias, but some other potential flaws. Therefore, every study was approved for inclusion in the quantitative analysis. A single study was of poor quality and potentially high risk of bias[53]. Supplementary Figure 1 displays the publishing bias funnel plots. The results showed no discernible publication bias, and Supplementary Figure 1 displays the various P-values from Egger's regression test. We also evaluated the certainty of evidence at the outcome level using GRADE pro profiler (GRADE working group, McMaster University, and Evidence Prime Inc; Supplementary Table 3)[20].

Primary outcome

Thromboembolic event rate: A total of nine studies[29,30,37,40,45,50,54,60,70] evaluated the unadjusted risk, while six studies[29,45,50,54,58,61]calculated the adjusted risk between prehospital anticoagulation and new thromboembolic events in COVID-19 infected patients. Three studies[45,50,62] defined thromboembolic events as VTE, while three others[29,30,37] included both arterial and venous thromboembolic events, and the remaining three[40,70] did not clarify their definition of thromboembolic events.

Overall, anticoagulation prior to COVID-19 diagnosis suggested a reduction in the unadjusted risk of new VTE in adult patients in these nine studies but did not attain statistical significance [n = 43851, OR = 0.67 (0.22, 2.07); P = 0.49; I2 = 95%; Figure 2A]. Similarly, in the predefined subgroup analysis, the use of VKA[40,50,54] [2658 participants, OR = 0.32 (0.05, 1.98); P = 0.22; I2 = 63%; Supplementary Figure 2A] or DOAC[40,50,54] [2699 participants, OR = 0.36 (0.10, 1.25); P = 0.11; I2 = 51%; Supplementary Figure 2B] or any anticoagulants [40960 participants, OR = 1.03 (0.26, 4.08); P = 0.97; I2 = 96%; Supplementary Figure 2C] did not show a statistical significant reduction of new VTE event risk associated with COVID-19 infection (P value for subgroup differences = 0.70, I2 = 0%). Our prespecified adjusted meta-analysis on six studies[29,45,50,54,61,74] did not achieve statistical significance in the lower odds of thromboembolic events in patients suffering from COVID-19 [aOR = 0.87 (0.42, 1.80); P = 0.71; I2 = 99%; Figure 3A].

Figure 2
Figure 2 Unadjusted meta-analysis for mortality, severity thromboembolic events in prehospital use of anticoagulants vs control cohort in COVID-19. A: Unadjusted thromboembolic events in prehospital use of anticoagulants vs control cohort; B: Unadjusted mortality in prehospital use of anticoagulants vs control cohort; C: Unadjusted severity in prehospital use of anticoagulants vs control cohort.
Figure 3
Figure 3 Adjusted meta-analysis for mortality, severity thromboembolic events in prehospital use of anticoagulants vs control cohort in COVID-19. A: Adjusted thromboembolic events in prehospital use of anticoagulants vs control cohort; B: Adjusted mortality in prehospital use of Anticoagulants vs control cohort; C: Adjusted severity in prehospital use of Anticoagulants vs control cohort.
Secondary outcomes

Mortality: A total of 35[28-35,37,38,40,41,43,44,46-48,50,51,53,55-57,59,60,63-70,72,74] studies reported mortality in prior anticoagulant users (2388 deaths out of 110975 patients; 2.1%) vs non-users (9457 deaths out of 96317 patients; 9.8%). Prior anticoagulation was found to significantly increase the risk of mortality in COVID-19 patients in the unadjusted analysis [OR = 1.72 (1.37, 2.17); P < 0.00001; I2 = 93%; Figure 2B). Subgroup analysis by the type of anticoagulation medication showed that prior VKA use [19747 participants, OR = 1.91 (1.20, 3.06); P = 0.007; I2 = 83%; Supplementary Figure 3A] and any anticoagulant use [43643 participants, OR = 1.88 (1.40, 2.52); P < 0.00001; I2 = 94%; Supplementary Figure 3B] was associated with an increased mortality risk. However, prior use of DOAC [22374 participants, OR = 1.42 (0.95, 2.12); P = 0.08; I2 = 87%; Supplementary Figure 3C] and the adjusted estimates of 28 studies[28,30,32,34,35,37,38,40-45,48,50,51,53,55,57,60,61,65-67,70,72,74] [aOR = 0.94 (0.84, 1.05); P = 0.31; I2 = 65%; Figure 3B] revealed no statistically significant association between prehospital anticoagulation and mortality in patients hospitalized for COVID-19.

COVID-19 disease severity: Overall, 22 studies[30,31,33,36,39,41,43,47-51,55,56,59,62,64,67,70,72,73,74] documented the association between prehospital anticoagulation and COVID-19 infection severity. ICU admission was the surrogate marker of COVID-19 severity in 15 studies, while mechanical ventilation was used in five studies, and one study used ARDS development during hospitalization. Six hundred seventy-one patients out of 103,703 who received anticoagulants before COVID-19 diagnosis developed severe COVID-19 infection, while 6155 out of 78890 non-users progressed to severe COVID-19 illness. We derived no statistically significant association between prehospital anticoagulation and COVID-19 disease severity [OR = 1.08 (0.78, 1.49); P = 0.64; I2 = 89%; Figure 2C]. On carrying out a subgroup analysis based on the type of anticoagulants, VKAs [6947 participants, OR = 1.26 (0.57,2.77); P = 0.57; I2 = 82%; Supplementary Figure 4A], DOACs [149564 participants, OR = 1.12 (0.58, 2.15); P = 0.74; I2 = 88%; Supplementary Figure 4B], and any anticoagulant use [36854 participants, OR = 1.07 (0.72, 1.58); P = 0.75; I2 = 89%; Supplementary Figure 4C] all reported non-significant association with severe COVID-19 disease. Similarly, prehospital anticoagulation was not significantly associated with COVID-19 disease severity in the adjusted analysis [12 studies[29,30,39,41,43,45,50,51,59,70-72]; aOR = 0.96 (0.72, 1.26); P = 0.76; I2 = 80%; Figure 3C].

Multivariate meta-regression model for mortality outcome

To account for variations in the correlation between mortality and prehospital anticoagulant use, a multivariate meta-regression was performed. The findings demonstrated that when considered collectively, the proportion of age, diabetes, female gender, hypertension, and pulmonary diseases was significant. These variables accounted for R2 = 90% of the difference in mortality between studies (Figure 4A).

Figure 4
Figure 4 Meta-regression analyses. A: Mortality meta-regression analysis; B: Severity meta-regression analysis.
Multivariate meta-regression model for severity outcome

Multivariate meta-regression was employed to take into consideration variations in the correlation between COVID-19 severity and prehospital anticoagulation. Together, age, female gender, and the proportion of hypertension, pulmonary disease, and diabetes were found to be significant covariates. Figure 4B demonstrates that these factors, all together, explained R2 = 100% for heterogeneity in severity among the included studies.


Our analysis offered a thorough examination of the risk of thromboembolic events, mortality, and severity of COVID-19 disease when anticoagulant medication was used prior to hospitalization. The meta-analysis identified no significant association between prehospital anticoagulation and decreased thromboembolic events risk and reduced COVID-19 disease severity. Though the unadjusted analysis exhibited a rise in mortality risk of COVID-19 patients with prior anticoagulation, adjusting the estimates revealed no significant difference in the odds of thromboembolic events, mortality, and severity between COVID-19 infected patients on prehospital anticoagulation and those without antecedent anticoagulation treatment.

Literature review and analysis of existing research

VTE: Coagulopathy and thromboembolic events are described as independent poor prognostic indicators in COVID-19[58,75]. Hospitalized COVID-19 patients tend to have a higher incidence of VTE than individuals with other illnesses[76], yet the exact mechanism of hypercoagulability remains unclear. COVID-19 infection can induce hyperinflammation[77], leading to endothelial dysfunction[78-80], platelet activation, blood stasis[58], and microvascular inflammation[81], all of which can influence consequent respiratory distress and other organ dysfunctioning[78-80]. This acute inflammatory state also increases the arterial and venous TE risk[61].

While ample evidence supports anticoagulation benefits during hospitalization[82-84], the role of prehospital anticoagulation before COVID-19 diagnosis, particularly in thromboembolic event incidence, lacks sufficient data. Despite anticoagulation, people on long-term oral anticoagulation therapy that started prior to COVID-19 infection may be more prone to thrombosis because of the existing SARS-CoV-2 infection[45]. Initial research suggests potential negative effects of VKAs on COVID-19[50]. SARS-CoV-2 infected individuals have reduced extra-hepatic vitamin K stores, which could be further decreased with VKAs[50]. However, studies have produced conflicting results regarding prehospital anticoagulation's impact on COVID-19 patients' thromboembolic risk, with some studies showing increased thromboembolic event risk[58]. In contrast, others observed benefits from prehospital anticoagulation in reducing COVID-related TE risk[29,50]. However, as shown in both unadjusted and adjusted analyses, our study did not find a significant reduction in thromboembolic events among COVID-19 patients receiving prehospital anticoagulation.

Mortality: Diverse study outcomes exist on chronic anticoagulation's impact on COVID-19 mortality. Tremblay et al[70] noted higher mortality among prehospital anticoagulated COVID-19 patients, yet non-significance emerged after age, sex, race, obesity, and Charlson Comorbidity Index adjustment. Rivera-Caravaca et al[58] reported higher all-cause mortality rates in prehospital DOAC-treated patients, remaining significant post-adjustment. Conversely, studies like Fumagillin et al’s[44] observed lower mortality rates in pre-COVID-19 anticoagulated patients[41,43,44,68]. Our research aligns with studies[41-51,69] and an earlier meta-analysis published by Kamel et al[85], revealing no substantial prehospital anticoagulation-associated mortality impact. Unlike Kamel et al[85], our study comprehensively explores pre-admission anticoagulation effects on VTE risk, severity of disease, and mortality in COVID-19.

Severity: Divergent results have been observed regarding the effect of anticoagulation on the severity of COVID-19[47,48,52,64,66,74]. Aslan et al[55] revealed significantly higher ICU admission, ventilation, oxygen therapy, and mortality rates with DOAC use. Yet, Corrochano et al’s[30] study found ICU admission reduction with prehospital anticoagulation after age, sex, and CCI adjustment. Similarly, Iaccarino et al[72] linked oral anticoagulants to ICU admission risk reduction. However, their cross-sectional design for hypothesis generation might influence outcomes[33].

Assessment of potential biases

The existence of selection bias and confounding bias is one possible explanation for inconsistent results among studies[29,30,49,55,58], as some studies include patients with cardiovascular or coagulation issues (poorer premorbid state). Certain studies neglect the effects of other medications during hospitalization[84]. Another reason can be the nature of the study, as observational and retrospective studies limit the causal inference[27-30,55], even though propensity score matching efforts[58], as propensity scoring is dependent on covariates and confounders are not included in the scoring, resulting in a potential bias. Small sample size also introduces bias[28,55]. The focus on hospitalized patients can hamper the generalizability of milder and asymptomatic COVID-19 cases. Similarly, heterogeneous sample sizes impact data collection and yield inconsistent results. Socio-demographics, like age and gender, may also influence drug effects on severity and mortality.

Possible mechanisms underlying the findings

A hypercoagulative state is promoted by COVID-19-induced coagulative abnormalities and alterations in prothrombotic factors, such as enhanced factor VIII, plasma fibrinogen, microparticles, and increased platelet activation[86-89]. This hypercoagulative state, known as COVID-19-associated coagulopathy (CAC), differs from acute disseminated intravascular coagulation (DIC) in presentation, involving thrombosis instead of bleeding[90]. Although D-dimer levels are elevated in both CAC and DIC, additional coagulation factors differentiate CAC (higher levels of Von Willebrand factor (vWF) antigen, Factor VIII activity, and fibrinogen) from DIC (lower levels of fibrinogen, reduced Factor VIII activity, and decreased VWF)[88-91].

Evidence suggests coagulation factor Xa's role in virus entry by cleaving SARS Spike proteins, which certain anticoagulants inhibit, potentially affecting viral fusion with ACE-2 receptors[10]. Ageno et al[28] used this concept to show DOACs' potentially higher antiviral efficacy over VKAs, though with limitations. Meanwhile, in our meta-analysis, DOAC-treated cases showed more severe disease progression than VKA-treated ones.

The underlying pathophysiology of endothelial damage by SARS-COV-2 is thought to be related to the renin-angiotensin-aldosterone system (RAAS). Virus-host cell binding via ACE-2 receptors raises disease severity risk. Thus, ACEI and ARBs (RAAS inhibitors) could heighten disease severity by increasing ACE2 receptor expression[16]. Anticoagulants, not affecting ACE2 receptors, might explain the non-significant enhancement in disease severity, as seen in our meta-analysis with no prehospital anticoagulation-COVID-19 severity correlation.

Clinical implications and future directions

Our study has significant clinical implications. Monitoring coagulation function in ICU patients using repeated platelet count, prothrombin time, and D-dimer measurements is vital. Elevated serum D-dimer predicts VTE and is a prognostic tool for COVID-19 risk stratification[92,93]. High D-dimer or rapid respiratory decline may indicate suspected VTE. Middeldorp et al[62] noted significantly higher D-dimer levels in ICU-admitted COVID-19-infected patients, regardless of chronic prehospital anticoagulation. Our analysis found that chronic anticoagulation does not significantly reduce the risk of new thromboembolic events in COVID-19 patients. This suggests that prior anticoagulation does not protect against COVID-19-related thromboembolic events. Due to the controversial effects of anticoagulant therapy on COVID-19 severity, further studies are needed.

Strengths and limitations

This meta-analysis's strengths include a sizable sample size dispersed across several nations and moderate to high-quality studies included according to the New-Castle Ottawa Scale. However, there are notable limitations. First, the retrospective nature of most included research limits the generalizability of the conclusions, highlighting the need for expansive prospective investigations. Second, while adjusted estimates were prioritized in the original analysis, selection bias and the confounding impact typical of observational research cannot be completely eliminated. Furthermore, inconsistency existed in the definition of severity among the included articles. Lastly, data gaps regarding prehospital anticoagulation specifics—duration, indication, type, and dosage—in some studies impeded comprehensive analysis.


The current meta-analysis concludes that prehospital anticoagulation does not significantly correlate with reduced COVID-19-related thromboembolic events, enhanced survival, or lowered disease severity risk. This aligns with recent guidelines advocating prophylactic anticoagulant use in COVID-19 patients, irrespective of VTE risk. To gain deeper insights and robust evidence, we suggest well-designed prospective studies and randomized trials investigating the impact of prior anticoagulant usage on thromboembolic risk, mortality, and disease severity in COVID-19 cases. Furthermore, a thorough exploration of the reasons behind the limited efficacy of chronic anticoagulants in severe infections is warranted. Future research should focus on determining personalized VTE risks for COVID-19 patients, uncovering underlying pathogenic pathways, and identifying optimal anticoagulant interventions for VTE prevention.


Data from this study was presented as an abstract format for the SCCM 2023 Critical Care Congress in San Francisco, California, United States.


Provenance and peer review: Invited article; Externally peer reviewed.

Peer-review model: Single blind

Specialty type: Medical laboratory technology

Country of origin: United States

Peer-review report’s classification

Scientific Quality: Grade C

Novelty: Grade B

Creativity or Innovation: Grade B

Scientific Significance: Grade B

P-Reviewer: Adam CA, Romania S-Editor: Lin C L-Editor: A P-Editor: Zhang L

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