Evidence map›Paper›PMID 37469290›Full record

SynthesisJournal of global health2023

Nirmatrelvir plus ritonavir remains effective in vaccinated patients at risk of progression with COVID-19: A systematic review and meta-analysis.

Huamin Li, Huairong Xiang, Bei He, Qizhi Zhang, Wenxing Peng

Abstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in Journal of global health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 3 pooled it
–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

4 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Review
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

5 authors.

Huamin LiDepartment of Pharmacy, the Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
Huairong XiangDepartment of Pharmacy, the Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
Bei HeDepartment of Pharmacy, the Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
Qizhi ZhangDepartment of Pharmacy, the Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
Wenxing PengDepartment of Pharmacy, the Second Xiangya Hospital, Central South University, Changsha, Hunan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The efficacy of nirmatrelvir plus ritonavir (NMV-r) for vaccinated COVID-19 patients at high risk of progression is not adequately recognised. To address this gap, we conducted a systematic review and meta-analysis of current literature. Methods: We searched PubMed, Web of Science, Embase, Cochrane Library, and medRxiv for articles published up to 8 January 2023 on NMV-r in outpatients. At least two researchers screened articles, extracted data, and assessed the quality of selected studies. We evaluated the results via risk ratios (RRs) with 95% confidence intervals (CIs) and tested for heterogeneity using I Results: We included seven observational cohort studies comprising 224 238 vaccinated patients. According to our meta-analysis, NMV-r reduced 47% incidence of all-cause death or hospitalisation within 30 days for vaccinated patients (RR = 0.53; 95% CI = 0.40-0.70; I Conclusions: We found positive evidence for the use of NMV-r for vaccinated patients at high-risk of progression with mild to moderate COVID-19. However, large-scale RCTs are needed to confirm these findings. Registration: PROSPERO CRD42023391349.

Indexed as

COVID-19COVID-19 Drug TreatmentHospitalizationHumansRitonavirRitonavir

Identifiers

PMID37469290
PMCPMC10357131

What OpenQuestion holds

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Registered trials

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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.