Evidence map›Paper›PMID 33933168›Full record

ArticleBiomarker research2021

Empiric use of anticoagulation in hospitalized patients with COVID-19: a propensity score-matched study of risks and benefits.

Bo Yu, Victor Perez Gutierrez, Alex Carlos, Gregory Hoge, Anjana Pillai, J Daniel Kelly, Vidya Menon

Open access · goldAbstract read
In one paragraph

Article in Biomarker research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 3 pooled it
2.3field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 3 syntheses or guidelines pooled it, 37 citations in OpenAlex.

  1. Pooled it
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  3. Risk Factors for Venous Thromboembolism in Severe COVID-19: A Study-Level Meta-Analysis of 21 Studies.International journal of environmental research and public health · 2021
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Bo YuDepartment of Medicine, New York City Health + Hospitals, Lincoln Medical Center, Bronx, New York, USA.
Victor Perez GutierrezDepartment of Medicine, New York City Health + Hospitals, Lincoln Medical Center, Bronx, New York, USA.
Alex CarlosDepartment of Medicine, New York City Health + Hospitals, Lincoln Medical Center, Bronx, New York, USA.
Gregory HogeDepartment of Medicine, New York City Health + Hospitals, Lincoln Medical Center, Bronx, New York, USA.
Anjana PillaiDepartment of Medicine, New York City Health + Hospitals, Lincoln Medical Center, Bronx, New York, USA.
J Daniel KellyDepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, USA.
Vidya MenonDepartment of Medicine, New York City Health + Hospitals, Lincoln Medical Center, Bronx, New York, USA. menonv@nychhc.org.
Lincoln Medical Center · USUniversity of California, San Francisco · US

Funding

Understanding the epidemiology and natural history of unrecognized Ebola virus infectionK23AI146268 · NIAID · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI KELLY, JOHN DANIEL · 2019 to 2022
$748k
NIAID NIH HHS K23 AI146268NIAID NIH HHS L30 AI126521
6 · The paper itself

Abstract

backgroundHospitalized patients with COVID-19 demonstrate a higher risk of developing thromboembolism. Anticoagulation (AC) has been proposed for high-risk patients, even without confirmed thromboembolism. However, benefits and risks of AC are not well assessed due to insufficient clinical data. We performed a retrospective analysis of outcomes from AC in a large population of COVID-19 patients.

methodsWe retrospectively reviewed 1189 patients hospitalized for COVID-19 between March 5 and May 15, 2020, with primary outcomes of mortality, invasive mechanical ventilation, and major bleeding. Patients who received therapeutic AC for known indications were excluded. Propensity score matching of baseline characteristics and admission parameters was performed to minimize bias between cohorts.

resultsThe analysis cohort included 973 patients. Forty-four patients who received therapeutic AC for confirmed thromboembolic events and atrial fibrillation were excluded. After propensity score matching, 133 patients received empiric therapeutic AC while 215 received low dose prophylactic AC. Overall, there was no difference in the rate of invasive mechanical ventilation (73.7% versus 65.6%, p = 0.133) or mortality (60.2% versus 60.9%, p = 0.885). However, among patients requiring invasive mechanical ventilation, empiric therapeutic AC was an independent predictor of lower mortality (hazard ratio [HR] 0.476, 95% confidence interval [CI] 0.345-0.657, p < 0.001) with longer median survival (14 days vs 8 days, p < 0.001), but these associations were not observed in the overall cohort (p = 0.063). Additionally, no significant difference in mortality was found between patients receiving empiric therapeutic AC versus prophylactic AC in various subgroups with different D-dimer level cutoffs. Patients who received therapeutic AC showed a higher incidence of major bleeding (13.8% vs 3.9%, p < 0.001). Furthermore, patients with a HAS-BLED score of ≥2 had a higher risk of mortality (HR 1.482, 95% CI 1.110-1.980, p = 0.008), while those with a score of ≥3 had a higher risk of major bleeding (Odds ratio: 1.883, CI: 1.114-3.729, p = 0.016).

conclusionEmpiric use of therapeutic AC conferred survival benefit to patients requiring invasive mechanical ventilation, but did not show benefit in non-critically ill patients hospitalized for COVID-19. Careful bleeding risk estimation should be pursued before considering escalation of AC intensity.

Identifiers

PMID33933168
PMCPMC8087886
OpenAlexW3131028610

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.