Evidence map›Paper›PMID 38586383›Full record

ArticleHeliyon2024

Time-dependent CT score-based model for identifying severe/critical COVID-19 at a fever clinic after the emergence of Omicron variant.

Zhenchen Zhu, Ge Hu, Zhoumeng Ying, Jinhua Wang, Wei Han, Zhengsong Pan, Xinlun Tian, Wei Song, Xin Sui, Lan Song and 1 more

Abstract read
In one paragraph

Article in Heliyon, 2024. 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
–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

1 citing paper in PubMed.

  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

11 authors.

Zhenchen ZhuDepartment of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Ge HuMedical Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Zhoumeng YingDepartment of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Jinhua WangDepartment of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Wei HanDepartment of Epidemiology and Biostatistics, Institute of Basic Medicine Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Zhengsong PanDepartment of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Xinlun TianDepartment of Respiratory and Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Wei SongDepartment of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Xin SuiDepartment of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Lan SongDepartment of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Zhengyu JinDepartment of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rationale and objectives: The computed tomography (CT) score has been used to evaluate the severity of COVID-19 during the pandemic; however, most studies have overlooked the impact of infection duration on the CT score. This study aimed to determine the optimal cutoff CT score value for identifying severe/critical COVID-19 during different stages of infection and to construct corresponding predictive models using radiological characteristics and clinical factors. Materials and methods: This retrospective study collected consecutive baseline chest CT images of confirmed COVID-19 patients from a fever clinic at a tertiary referral hospital from November 28, 2022, to January 8, 2023. Cohorts were divided into three subcohorts according to the time interval from symptom onset to CT examination at the hospital: early phase (0-3 days), intermediate phase (4-7 days), and late phase (8-14 days). The binary endpoints were mild/moderate and severe/critical infection. The CT scores and qualitative CT features were manually evaluated. A logistic regression analysis was performed on the CT score as determined by a visual assessment to predict severe/critical infection. Receiver operating characteristic analysis was performed and the area under the curve (AUC) was calculated. The optimal cutoff value was determined by maximizing the Youden index in each subcohort. A radiology score and integrated models were then constructed by combining the qualitative CT features and clinical features, respectively, using multivariate logistic regression with stepwise elimination. Results: A total of 962 patients (aged, 61.7 ± 19.6 years; 490 men) were included; 179 (18.6%) were classified as severe/critical COVID-19, while 344 (35.8%) had a typical Radiological Society of North America (RSNA) COVID-19 appearance. The AUCs of the CT score models reached 0.91 (95% confidence interval (CI) 0.88-0.94), 0.82 (95% CI 0.76-0.87), and 0.83 (95% CI 0.77-0.89) during the early, intermediate, and late phases, respectively. The best cutoff values of the CT scores during each phase were 1.5, 4.5, and 5.5. The predictive accuracies associated with the time-dependent cutoff values reached 88% (vs.78%), 73% (vs. 63%), and 87% (vs. 57%), which were greater than those associated with universal cutoff value (all Conclusion: The time interval between symptom onset and CT examination should be tracked to determine the cutoff value for the CT score for identifying severe/critical COVID-19. The radiology score combining qualitative CT features and the CT score complements clinical factors for identifying severe/critical COVID-19 patients and facilitates timely hierarchical diagnoses and treatment.

Indexed as

Computed tomographyCOVID-19CT scoreFever clinicSARS-CoV-2

Identifiers

PMID38586383
PMCPMC10998101

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.