Evidence map›Paper›PMID 37489620›Full record

ArticleAnnals of medicine2023

Prehospital physiological parameters related illness severity scores can accurately discriminate the severe/critical state in adult patients with COVID-19.

Chen Li, Kaili Wang, Liang Wu, Bing Song, Junyuan Tan, Haibin Su

Abstract read
In one paragraph

Article in Annals of medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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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

6 authors.

Chen LiDepartment of Hepatology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China.
Kaili WangDepartment of Hepatology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China.
Liang WuDepartment of Hepatology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China.
Bing SongDepartment of Infectious Diseases, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China.
Junyuan TanMedical Service Department, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China.
Haibin SuDepartment of Hepatology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWhether the National Early Warning Score 2 (NEWS2) can effectively discriminate the severe/critical state of patients with coronavirus disease 2019 (COVID-19) at the prehospital stage remains unknown. We aimed to assess the performance of NEWS2 in rapidly discriminating severe/critical COVID-19 and its relationship with prehospital medical services.

methodsSix illness severity scores of 414 patients were calculated at the prehospital stage. Receiver operating characteristic curves were generated to explore the ability of these scores to discriminate severe/critical patients from mild/moderate patients. A logistic regression analysis was conducted to evaluate independent predictors associated with severe/critical state.

resultsThe age, numbers of comorbidities, prehospital care workload, consumption of medical human resources, and illness severity scores of severe/critical patients were higher than those of mild/moderate patients (

conclusionsPrehospital NEWS2 can accurately and rapidly discriminate severe/critical COVID-19 during the Omicron variant wave. High levels of NEWS2 indicate an increase in prehospital care workload and consumption of medical human resources.

Indexed as

COVID-19Emergency Medical ServicesAdultAgedHumansPatient AcuitySARS-CoV-2coronavirus disease 2019National early warning score 2physiological parametersprehospitalseverity

Identifiers

PMID37489620
PMCPMC10392258

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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.