Evidence map›Paper›PMID 36848657›Full record

ArticleBlood2023

Effectiveness of nirmatrelvir plus ritonavir treatment for patients with chronic lymphocytic leukemia during the Omicron surge.

Tamar Tadmor, Hilel Alapi, Lior Rokach

Open access · bronzeAbstract read
In one paragraph

Article in Blood, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
2.9field-weighted citation impact, top 9% 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

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.

  1. Pooled it
  2. Observational
  3. Article
  4. Review
  5. Review
  6. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors at 3 institutions in 1 country.

Tamar TadmorHematology Unit, Bnai Zion Medical Center, The Ruth and Bruce Rappaport Faculty of Medicine, Technion, Haifa, Israel.ORCID 0000-0002-3435-8612
Hilel AlapiMaccabi Institute for Research and Innovation, Maccabi Healthcare Services, Tel Aviv, Israel.
Lior RokachDepartment of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel.ORCID 0000-0002-6956-3341
Ben-Gurion University of the Negev · ILBnai Zion Medical Center · ILMaccabi Institute for Health Services Research · IL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Patients with chronic lymphoid leukemia (CLL), even in the Omicron era and after vaccination, suffer from persistent COVID-19 infection, higher complications, and mortality compared with the general population. In this study, we evaluated retrospectively the effectiveness of nirmatrelvir + ritonavir among 1080 patients with CLL who were infected with severe acute respiratory syndrome coronavirus 2. Nirmatrelvir administration was associated with a reduction in COVID-19-related hospitalization or death by day 35. Specifically, the rate of COVID-19-related hospitalization or death in the treated group compared with the untreated group was 4.8% (14 out of 292) vs 10.2% (75 out of 733), respectively. Moreover, we report a 69% relative risk reduction in COVID-19-related hospitalization or death in patients with CLL at the age of ≥65 years. Multivariate analysis indicates that patients aged >65 years, patients who received heavy treatment (>2 previous treatments), patients with recent hospitalizations, intravenous immunoglobulin (IVIG) treatment, and comorbidity had significant improvement outcomes after treatment with nirmatrelvir.

Indexed as

COVID-19Leukemia, Lymphocytic, Chronic, B-CellAntiviral AgentsCOVID-19 Drug TreatmentHumansRetrospective StudiesRitonavirAntiviral AgentsRitonavir

Identifiers

PMID36848657
PMCPMC10158583
OpenAlexW4322489298

What OpenQuestion holds

Textmetadata
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.