Evidence map›Paper›PMID 33969085›Full record

ArticleWorld journal of clinical cases2021

Clinical diagnosis of severe COVID-19: A derivation and validation of a prediction rule.

Ming Tang, Xia-Xia Yu, Jia Huang, Jun-Ling Gao, Fu-Lan Cen, Qi Xiao, Shou-Zhi Fu, Yang Yang, Bo Xiong, Yong-Jun Pan and 4 more

Open access · diamondAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in World journal of clinical cases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.8field-weighted citation impact, top 28% 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

7 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. 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

14 authors at 7 institutions in 2 countries.

Ming TangDepartment of Critical Care Medicine, Shenzhen Third People's Hospital, The Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen 518114, Guangdong Province, China.
Xia-Xia YuSchool of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, Guangdong Province, China.
Jia HuangDepartment of Critical Care Medicine, Shenzhen Third People's Hospital, The Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen 518114, Guangdong Province, China.
Jun-Ling GaoBuddhism and Science Research Laboratory, Centre of Buddhist Studies, The University of Hong Kong, Hong Kong 999077, China.
Fu-Lan CenDepartment of Critical Care Medicine, Shenzhen Third People's Hospital, The Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen 518114, Guangdong Province, China.
Qi XiaoDepartment of Critical Care Medicine, Shenzhen Third People's Hospital, The Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen 518114, Guangdong Province, China.
Shou-Zhi FuDepartment of Critical Care Medicine, Wuhan Third Hospital, Wuhan 433304, Hubei Province, China.
Yang YangShenzhen Key Laboratory of Pathogen and Immunity, National Clinical Research Center for Infectious Disease, State Key Discipline of Infectious Disease, Shenzhen Third People's Hospital, The Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen 518114, Guangdong Province, China.
Bo XiongDepartment of Pediatrics, Wuhan Asia General Hospital, Wuhan 430022, Hubei Province, China.
Yong-Jun PanDepartment of Critical Care Medicine, Southern University of Science and Technology Hospital, Shenzhen 518055, Guangdong Province, China.
Ying-Xia LiuDepartment of Critical Care Medicine, Shenzhen Third People's Hospital, The Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen 518114, Guangdong Province, China.
Yong-Wen FengDepartment of Critical Care Medicine, The Second People's Hospital of Shenzhen, Shenzhen 518035, Guangdong Province, China.
Jin-Xiu LiDepartment of Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha 410011, Hunan Province, China.
Yong LiuShenzhen Hospital, Southern Medical University, Shenzhen 518000, Guangdong Province, China. liuyongjoy@outlook.com.
Southern University of Science and Technology · CNCentral South University · CNShenzhen Second People's Hospital · CNShenzhen University Health Science Center · CNUniversity of Hong Kong · HKWuhan Third Hospital · CNWuhan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe widespread coronavirus disease 2019 (COVID-19) has led to high morbidity and mortality. Therefore, early risk identification of critically ill patients remains crucial.

aimTo develop predictive rules at the time of admission to identify COVID-19 patients who might require intensive care unit (ICU) care.

methodsThis retrospective study included a total of 361 patients with confirmed COVID-19 by reverse transcription-polymerase chain reaction between January 19, 2020, and March 14, 2020 in Shenzhen Third People's Hospital. Multivariate logistic regression was applied to develop the predictive model. The performance of the predictive model was externally validated and evaluated based on a dataset involving 126 patients from the Wuhan Asia General Hospital between December 2019 and March 2020, by area under the receiver operating curve (AUROC), goodness-of-fit and the performance matrix including the sensitivity, specificity, and precision. A nomogram was also used to visualize the model.

resultsAmong the patients in the derivation and validation datasets, 38 and 9 participants (10.5% and 2.54%, respectively) developed severe COVID-19, respectively. In univariate analysis, 21 parameters such as age, sex (male), smoker, body mass index (BMI), time from onset to admission (> 5 d), asthenia, dry cough, expectoration, shortness of breath, asthenia, and Rox index < 18 (pulse oxygen saturation, SpO

conclusionThe proposed predictive model had great potential in severity prediction of COVID-19 in the ICU. It assisted the ICU clinicians in making timely decisions for the target population.

Indexed as

Clinical decision rulesCommunicable diseasesCOVID-19NomogramsPrognosis

Identifiers

PMID33969085
PMCPMC8080753
OpenAlexW3157427725

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.