Evidence map›Paper›PMID 38354691›Full record

ArticleAnnals of medicine2024

Associations of nirmatrelvir-ritonavir treatment with death and clinical improvement in hospitalized patients with COVID-19 during the Omicron wave in Beijing, China: a multicentre, retrospective cohort study.

Xiaobo Han, Chenglong Li, Xin Yuan, Junchang Cui, Zhihai Han, Jiguang Meng, Weiguo Zhao, Fei Xie, Kaifei Wang, Yuhong Liu and 15 more

Open access · goldAbstract readMulticenter Study
In one paragraph

Article in Annals of medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
2.3field-weighted citation impact, top 13% 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

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

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

25 authors at 5 institutions in 1 country.

Xiaobo HanCollege of Pulmonary and Critical Care Medicine, The Eighth Medical Center, Chinese PLA General Hospital, Beijing, China.
Chenglong LiNational Institute of Health Data Science, Peking University, Beijing, China.
Xin YuanPulmonary and Critical Care Medicine Department, The Fifth Medical Center, Chinese PLA General Hospital, Beijing, China.
Junchang CuiCollege of Pulmonary and Critical Care Medicine, The Eighth Medical Center, Chinese PLA General Hospital, Beijing, China.
Zhihai HanPulmonary and Critical Care Medicine Department, The Sixth Medical Center, Chinese PLA General Hospital, Beijing, China.
Jiguang MengPulmonary and Critical Care Medicine Department, The Fourth Medical Center, Chinese PLA General Hospital, Beijing, China.
Weiguo ZhaoCollege of Pulmonary and Critical Care Medicine, The Eighth Medical Center, Chinese PLA General Hospital, Beijing, China.
Fei XiePulmonary and Critical Care Medicine Department, The First Medical Center, Chinese PLA General Hospital, Beijing, China.
Kaifei WangPulmonary and Critical Care Medicine Department, The First Medical Center, Chinese PLA General Hospital, Beijing, China.
Yuhong LiuCollege of Pulmonary and Critical Care Medicine, The Eighth Medical Center, Chinese PLA General Hospital, Beijing, China.
Guoxin MuoCollege of Pulmonary and Critical Care Medicine, The Eighth Medical Center, Chinese PLA General Hospital, Beijing, China.
Na XiPharmacy Department, The Eighth Medical Center, Chinese PLA General Hospital, Beijing, China.
Mengli ZhengCollege of Pulmonary and Critical Care Medicine, The Eighth Medical Center, Chinese PLA General Hospital, Beijing, China.
Rentao WangCollege of Pulmonary and Critical Care Medicine, The Eighth Medical Center, Chinese PLA General Hospital, Beijing, China.
Kun XiaoCollege of Pulmonary and Critical Care Medicine, The Eighth Medical Center, Chinese PLA General Hospital, Beijing, China.
Wei ChenPulmonary and Critical Care Medicine Department, The Sixth Medical Center, Chinese PLA General Hospital, Beijing, China.
Junchen XiongPulmonary and Critical Care Medicine Department, The Sixth Medical Center, Chinese PLA General Hospital, Beijing, China.
Dahui ZhaoPulmonary and Critical Care Medicine Department, The First Medical Center, Chinese PLA General Hospital, Beijing, China.
Xinxin ZhangCollege of Pulmonary and Critical Care Medicine, The Eighth Medical Center, Chinese PLA General Hospital, Beijing, China.
Xinjie HanCollege of Pulmonary and Critical Care Medicine, The Eighth Medical Center, Chinese PLA General Hospital, Beijing, China.
Haibo ChengShandong Future Network Research Institute, Jiangsu Future Network Group Co., Ltd., Jiangsu, China.
Zhongkuo YuCollege of Pulmonary and Critical Care Medicine, The Eighth Medical Center, Chinese PLA General Hospital, Beijing, China.
Yinghan ShiCollege of Pulmonary and Critical Care Medicine, The Eighth Medical Center, Chinese PLA General Hospital, Beijing, China.
Wuxiang XiePeking University Clinical Research Institute, Peking University First Hospital, Beijing, China.
Lixin XieCollege of Pulmonary and Critical Care Medicine, The Eighth Medical Center, Chinese PLA General Hospital, Beijing, China.
Chinese PLA General Hospital · CNPLA Academy of Military Science · CNAnhui Medical University · CNPeking University · CNPeking University First Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe effectiveness of nirmatrelvir-ritonavir has mainly been shown in non-hospitalized patients with mild-to-moderate coronavirus disease 2019 (COVID-19). The real-world effectiveness of nirmatrelvir-ritonavir urgently needs to be determined using representative in-hospital patients with COVID-19 during the Omicron wave of the pandemic.

methodsWe performed a multicentre, retrospective study in five Chinese PLA General Hospital medical centers in Beijing, China. Patients hospitalized with COVID-19 from 10 December 2022 to 20 February 2023 were eligible for inclusion. A 1:1 propensity score matching was performed between the nirmatrelvir-ritonavir group and the control group.

results1010 recipients of nirmatrelvir-ritonavir and 1010 matched controls were finally analyzed after matching. Compared with matched controls, the nirmatrelvir-ritonavir group had a lower incidence rate of all-cause death (4.6/1000 vs. 6.3/1000 person-days,

conclusionAmong hospitalized patients with COVID-19 during the Omicron wave in Beijing, China, the early initiation of nirmatrelvir-ritonavir was associated with clinical benefits of lowering mortality and improving clinical recovery.

Indexed as

COVID-19LactamsLeucineNitrilesProlineRitonavirAntiviral AgentsBeijingChinaCOVID-19 Drug TreatmentHumansRetrospective StudiesAntiviral AgentsLactamsLeucinenirmatrelvirNitrilesProlineRitonavirall-cause deathclinical improvementCovid-19nirmatrelvir-ritonaviroff-label treatmentreal-world

Identifiers

PMID38354691
PMCPMC10868413
OpenAlexW4391824355

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.