Evidence map›Paper›PMID 36636572›Full record

ArticleWorld journal of emergency medicine2022

Exploratory COVID-19 death risk score based on basic laboratory tests and physiological clinical measurements.

Gui-Ying Dong, Fei-Fei Jin, Qi Huang, Chun-Bo Wu, Ji-Hong Zhu, Tian-Bing Wang

Open access · bronzeAbstract read
In one paragraph

Article in World journal of emergency medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.1field-weighted citation impact, top 56% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. 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

6 authors at 2 institutions in 1 country.

Gui-Ying DongEmergency Department, Peking University People's Hospital, Beijing 100044, China.
Fei-Fei JinTrauma Center, Peking University People's Hospital, Key Laboratory of Trauma and Neural Regeneration (Peking University), Ministry of Education, Beijing100044, China.
Qi HuangDepartment of Endocrinology and Metabolism, Peking University People's Hospital, Beijing100044, China.
Chun-Bo WuEmergency Department, Peking University People's Hospital, Beijing 100044, China.
Ji-Hong ZhuEmergency Department, Peking University People's Hospital, Beijing 100044, China.
Tian-Bing WangTrauma Center, Peking University People's Hospital, Key Laboratory of Trauma and Neural Regeneration (Peking University), Ministry of Education, Beijing100044, China.
Peking University People's Hospital · CNPeking University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn the event of a sudden shortage of medical resources, a rapid, simple, and accurate prediction model is essential for the 30-day mortality rate of patients with COVID-19.

methodsThis retrospective study compared the characteristics of the survivals and non-survivals of 278 patients with COVID-19. Logistic regression analysis was performed to obtain the "COVID-19 death risk score" (CDRS) model. Using the area under the receiver operating characteristic (AUROC) curve and Hosmer-Lemeshow goodness-of-fit test, discrimination and calibration were assessed. Internal validation was conducted using a regular bootstrap method.

resultsA total of 63 (22.66%) of 278 included patients died. The logistic regression analysis revealed that high-sensitivity C-reactive protein (hsCRP; odds ratio [OR]=1.018), D-dimer (OR=1.101), and respiratory rate (RR; OR=1.185) were independently associated with 30-day mortality. CDRS was calculated as follows: CDRS=-10.245+(0.022×hsCRP)+(0.172×D-dimer)+(0.203×RR). CDRS had the same predictive effect as the sequential organ failure assessment (SOFA) and "confusion, uremia, respiratory rate, blood pressure, and age over 65 years" (CURB-65) scores, with AUROCs of 0.984 for CDRS, 0.975 for SOFA, and 0.971 for CURB-65, respectively. And CDRS showed good calibration. The AUROC through internal validations was 0.980 (95% confidence interval [CI]: 0.965-0.995). Regarding the clinical value, the decision curve analysis of CDRS showed a net value similar to that of CURB-65 in this cohort.

conclusionCDRS is a novel, efficient and accurate prediction model for the early identification of COVID-19 patients with poor outcomes. Although it is not as advanced as the other models, CDRS had a similar performance to that of SOFA and CURB-65.

Indexed as

30-day mortalityCOVID-19Prediction model

Identifiers

PMID36636572
PMCPMC9807385
OpenAlexW4296280886

What OpenQuestion holds

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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.