Evidence map›Paper›PMID 38343793›Full record

ArticleResearch square2024

Oral antivirals for COVID-19 among patients with cancer.

Dorra Guermazi, Panos Arvanitis, Kendra Vieira, Jeremy L Warner, Dimitrios Farmakiotis

Open access · greenAbstract readPreprint
In one paragraph

Article in Research square, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 1 country.

Dorra GuermaziBrown University.
Panos ArvanitisThe Warren Alpert Medical School of Brown University.
Kendra VieiraThe Warren Alpert Medical School of Brown University.
Jeremy L WarnerRhode Island Hospital.
Dimitrios FarmakiotisThe Warren Alpert Medical School of Brown University.
Brown University · USJohn Brown University · USRhode Island Hospital · US

Funding

Emerging Infectious Disease Scholars at Brown UniversityR25AI140490 · NIAID · BROWN UNIVERSITY · PI Silvia Shinpei Chiang · 2018 to 2026
$3.0M
The COVID-19 and Cancer Consortium (CCC19) RegistryU01CA287008 · NCI · RHODE ISLAND HOSPITAL · PI Sonya Reid, Jeremy L Warner · 2024 to 2026
$2.0M
NCI NIH HHS U01 CA287008NIAID NIH HHS R25 AI140490
6 · The paper itself

Abstract

Purpose: Immunocompromised individuals, such as those diagnosed with cancer, are at a significantly higher risk for severe illness and mortality when infected with SARS-CoV-2 (COVID-19) than the general population. Two oral antiviral treatments are approved for COVID-19: Paxlovid Methods: We evaluated the efficacy and safety of nirmatrelvir/ritonavir and molnupiravir in preventing severe illness and death using our database of 457 patients with cancer and COVID-19 from Brown University-affiliated hospitals. 67 patients received nirmatrelvir/ritonavir or molnupiravir and were compared to 56 concurrent controls who received no antiviral treatment despite being eligible to receive it. Results: Administration of nirmatrelvir/ritonavir or molnupiravir was associated with improved survival and lower 90-day all-cause and COVID-19-attributed mortality (p<0.05) and with lower peak O2 requirements (ordinal odds ratio [OR] 1.52, 95% confidence interval [CI] 0.92-2.56). Conclusion: Acknowledging the small size of our sample as a limitation, we concluded that early antiviral treatment might be beneficial to immunocompromised individuals, particularly those with cancer, when infected with SARS-CoV-2. Larger-scale, well-stratified studies are needed in this patient population.

Indexed as

cancerCOVID-19immunocompromisedmolnupiravirnirmatrelvir/ritonavir

Identifiers

PMID38343793
PMCPMC10854279
OpenAlexW4391165132

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.