Evidence map›Paper›PMID 35275943›Full record

SynthesisPloS one2022

A look back at the first wave of COVID-19 in China: A systematic review and meta-analysis of mortality and health care resource use among severe or critical patients.

Mengmeng Zhang, Peng Hu, Xiaowei Xu, Jingwen Ai, Yang Li, Yun Bao, Wimonchat Tangamornsuksan, Alain Chan, Shelley Xie, Hao Hu and 3 more

Open access · goldAbstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.0field-weighted citation impact, top 24% 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, 10 citations in OpenAlex.

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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

13 authors at 8 institutions in 5 countries.

Mengmeng ZhangDepartment of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.
Peng HuDepartment of Infectious Disease, The Second Hospital Affiliated to Chongqing Medical University, Chongqing, China.
Xiaowei XuDepartment of Infectious Disease, The First Hospital Affiliated to Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Jingwen AiDepartment of Infectious Disease, Huashan Hospital, Fudan University, Shanghai, China.
Yang LiDepartment of Infectious Disease, Huashan Hospital, Fudan University, Shanghai, China.
Yun BaoInstitute of Clinical Research and Evidence-Based Medicine, Gansu Provincial Hospital, Lanzhou, Gansu, China.
Wimonchat TangamornsuksanDepartment of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.
Alain ChanChina Medical Affairs, Gilead Sciences.
Shelley XieChina Medical Affairs, Gilead Sciences.
Hao HuGlobal Value and Access, Gilead Sciences.
Shuting LiangGlobal Medical Affairs Research, Gilead Sciences.
Wenhong ZhangDepartment of Infectious Disease, Huashan Hospital, Fudan University, Shanghai, China.
Feng XieDepartment of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.ORCID 0000-0003-3454-6266
Fudan University · CNImpact · CAChulabhorn Hospital · THDalian Medical University · CNGansu Provincial Hospital · CNGilead Sciences (Australia) · AUGilead Sciences (United States) · USZhejiang University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo investigate the mortality and health care resource use among patients with severe or critical coronavirus disease of 2019 (COVID-19) in the first wave of pandemic in China.

methodsWe performed a systematic review and meta-analysis to investigate the mortality, discharge rate, length of hospital stay, and use of invasive ventilation in severe or critical COVID-19 cases in China. We searched electronic databases for studies from China with no restrictions on language or interventions patients received. We screened records, extracted data and assessed the quality of included studies in duplicate. We performed the meta-analysis using random-effect models through a Bayesian framework. Subgroup analyses were conducted to examine studies by disease severity, study location and patient enrolment start date. We also performed sensitivity analysis using various priors, and assessed between-study heterogeneity and publication bias for the primary outcomes.

resultsOut of 6,205 titles and abstracts screened, 500 were reviewed in full text. A total of 42 studies were included in the review, of which 95% were observational studies (n = 40). The pooled 28-day and 14-day mortalities among severe or critical patients were 20.48% (7,136 patients, 95% credible interval (CrI), 13.11 to 30.70) and 10.83% (95% CrI, 6.78 to 16.75), respectively. The mortality declined over time and was higher in patients with critical disease than severe cases (1,235 patients, 45.73%, 95% CrI, 22.79 to 73.52 vs. 3,969 patients, 14.90%, 95% CrI, 4.70 to 39.57) and patients in Hubei compared to those outside Hubei (6,719 patients, 26.62%, 95% CrI, 13.11 to 30.70 vs. 244 patients, 5.88%, 95% CrI 2.03 to 14.11). The length of hospital stay was estimated at 18.48 days (6,847 patients, 95% CrI, 17.59 to 21.21), the 28-day discharge rate was 50.48% (3,645 patients, 95% CrI, 26.47 to 79.53), and the use of invasive ventilation rate was 13.46% (4,108 patients, 95% CrI, 7.61 to 22.31).

conclusionsOur systematic review and meta-analysis found high mortality among severe and critical COVID-19 cases. Severe or critical COVID-19 cases consumed a large amount of hospital resources during the outbreak.

Indexed as

COVID-19Critical CareLength of StayPandemicsSARS-CoV-2ChinaCritical IllnessHumansSeverity of Illness Index

Identifiers

PMID35275943
PMCPMC8916647
OpenAlexW4220820814

What OpenQuestion holds

Textmetadata
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.