Evidence map›Paper›PMID 37735372›Full record

ArticleBMC infectious diseases2023

Reconstructing the cytokine view for the multi-view prediction of COVID-19 mortality.

Yueying Wang, Zhao Wang, Yaqing Liu, Qiong Yu, Yujia Liu, Changfan Luo, Siyang Wang, Hongmei Liu, Mingyou Liu, Gongyou Zhang and 5 more

Open access · goldAbstract read
In one paragraph

Article in BMC infectious diseases, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 83% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

15 authors at 3 institutions in 1 country.

Yueying WangCollege of Computer Science and Technology, Jilin University, 130012, Changchun, China.
Zhao WangCollege of Software, Jilin University, 130012, Changchun, China.
Yaqing LiuCollege of Computer Science and Technology, Jilin University, 130012, Changchun, China.
Qiong YuDepartment of Epidemiology and Biostatistics, School of Public Health, Jilin University, 130021, Changchun, Jilin Province, China.
Yujia LiuCollege of Software, Jilin University, 130012, Changchun, China.
Changfan LuoCollege of Software, Jilin University, 130012, Changchun, China.
Siyang WangCollege of Software, Jilin University, 130012, Changchun, China.
Hongmei LiuSchool of Biology and Engineering, Guizhou Medical University, 550025, Guiyang, Guizhou, China.
Mingyou LiuSchool of Biology and Engineering, Guizhou Medical University, 550025, Guiyang, Guizhou, China.
Gongyou ZhangSchool of Biology and Engineering, Guizhou Medical University, 550025, Guiyang, Guizhou, China.
Yusi FanCollege of Software, Jilin University, 130012, Changchun, China.
Kewei LiCollege of Computer Science and Technology, Jilin University, 130012, Changchun, China.
Lan HuangCollege of Computer Science and Technology, Jilin University, 130012, Changchun, China.
Meiyu DuanCollege of Computer Science and Technology, Jilin University, 130012, Changchun, China. dmy235813@163.com.
Fengfeng ZhouCollege of Computer Science and Technology, Jilin University, 130012, Changchun, China. FengfengZhou@gmail.com.
Jilin University · CNGuiyang Medical University · CNJilin Province Science and Technology Department · CN

Funding

Fundamental Research Funds for the Central Universities JLUJilin Provincial Key Laboratory of Big Data Intelligent Computing 20180622002JCJilin Senior and Junior Technological Innovation Team 20210509055RQNational Natural Science Foundation of China 62072212
6 · The paper itself

Abstract

backgroundCoronavirus disease 2019 (COVID-19) is a rapidly developing and sometimes lethal pulmonary disease. Accurately predicting COVID-19 mortality will facilitate optimal patient treatment and medical resource deployment, but the clinical practice still needs to address it. Both complete blood counts and cytokine levels were observed to be modified by COVID-19 infection. This study aimed to use inexpensive and easily accessible complete blood counts to build an accurate COVID-19 mortality prediction model. The cytokine fluctuations reflect the inflammatory storm induced by COVID-19, but their levels are not as commonly accessible as complete blood counts. Therefore, this study explored the possibility of predicting cytokine levels based on complete blood counts.

methodsWe used complete blood counts to predict cytokine levels. The predictive model includes an autoencoder, principal component analysis, and linear regression models. We used classifiers such as support vector machine and feature selection models such as adaptive boost to predict the mortality of COVID-19 patients.

resultsComplete blood counts and original cytokine levels reached the COVID-19 mortality classification area under the curve (AUC) values of 0.9678 and 0.9111, respectively, and the cytokine levels predicted by the feature set alone reached the classification AUC value of 0.9844. The predicted cytokine levels were more significantly associated with COVID-19 mortality than the original values.

conclusionsIntegrating the predicted cytokine levels and complete blood counts improved a COVID-19 mortality prediction model using complete blood counts only. Both the cytokine level prediction models and the COVID-19 mortality prediction models are publicly available at http://www.healthinformaticslab.org/supp/resources.php .

Indexed as

COVID-19Area Under CurveCytokinesHumansLinear ModelsPrincipal Component AnalysisCytokinesComplete blood countsCOVID-19Cytokine predictionModel-adjusted cytokineMortality prediction

Identifiers

PMID37735372
PMCPMC10514938
OpenAlexW4386927344

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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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.