Evidence map›Paper›PMID 36540720›Full record

ArticleCardiology discovery2022

A New Coronavirus Estimation Global Score for Predicting Mortality During Hospitalization in Patients with COVID-19.

Hesong Zeng, Xingwei He, Wanjun Liu, Jing Kan, Liqun He, Jinhe Zhao, Cynthia Chen, Junjie Zhang, Shaoliang Chen

Open access · diamondAbstract read
In one paragraph

Article in Cardiology discovery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 45% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. 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

9 authors at 5 institutions in 2 countries.

Hesong ZengDivision of Cardiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, Hubei 430030, China.
Xingwei HeDivision of Cardiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, Hubei 430030, China.
Wanjun LiuDivision of Cardiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, Hubei 430030, China.
Jing KanDivision of Cardiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu 210006, China.
Liqun HeDivision of Cardiology, Wuhan First Hospital, Wuhan, Hubei 430022, China.
Jinhe ZhaoDivision of Cardiology, Tianyou Hospital affiliated to Wuhan University of Science & Technology, Wuhan, Hubei 430064, China.
Cynthia ChenMailman School of Public Health, Columbia University, New York, New York 10027, USA.
Junjie ZhangDivision of Cardiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu 210006, China.
Shaoliang ChenDivision of Cardiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, Hubei 430030, China.
Nanjing Medical University · CNTongji Hospital · CNColumbia University · USWuhan Third Hospital · CNWuhan University of Science and Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Coronavirus disease 2019 (COVID-19) exists as a pandemic. Mortality during hospitalization is multifactorial, and there is urgent need for a risk stratification model to predict in-hospital death among COVID-19 patients. Here we aimed to construct a risk score system for early identification of COVID-19 patients at high probability of dying during in-hospital treatment. Methods: In this retrospective analysis, a total of 821 confirmed COVID-19 patients from 3 centers were assigned to developmental ( Results: The CORE-G score consisted of 18 variables (5 demographics, 2 symptoms, and 11 laboratory measurements) with a sum of 69.5 points. Goodness-of-fit tests indicated that the model performed well in the developmental group ( Conclusions: The CORE-G score provides an estimate of the risk of in-hospital death. This is the first step toward the clinical use of the CORE-G score for predicting outcome in COVID-19 patients.

Indexed as

Coronavirus estimation global scoreCOVID-19Goodness-of-fitHospital mortalityReceiver operating characteristicsRisk stratification

Identifiers

PMID36540720
PMCPMC9749948
OpenAlexW4283359863

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.