Evidence map›Paper›PMID 38961438›Full record

ArticleBMC pulmonary medicine2024

Development and validation of a scoring system to predict the mortality of hospitalized patients with SARS-CoV-2 Omicron: a nationwide, multicentre study.

Wanru Guo, Xiaomeng Li, Cheng Ding, Xiahong Dai, Shuai Wu, Yunzhen Shi, Yongjun Jiang, Yukun Chang, Zhidan Zhang, Shiyang Liu and 7 more

Abstract readMulticenter StudyValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC pulmonary medicine, 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

17 authors.

Wanru Guo *State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, National Medical Center for Infectious Diseases, Zhejiang University School of Medicine, Hangzhou, China.
Xiaomeng Li *State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, National Medical Center for Infectious Diseases, Zhejiang University School of Medicine, Hangzhou, China.
Cheng Ding *State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, National Medical Center for Infectious Diseases, Zhejiang University School of Medicine, Hangzhou, China.
Xiahong DaiDepartment of Infectious Diseases, Hospital Affiliated to Zhejiang Shuren University Shulan International Medical College, Shulan, Hangzhou, China.
Shuai WuDepartment of Infectious Diseases, Shenzhen Third People's Hospital, Shenzhen, China.
Yunzhen ShiDepartment of Infectious Diseases, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China.
Yongjun JiangKey Laboratory of AIDS Immunology (China Medical University), National Clinical Research Center for Laboratory Medicine, National Health Commission (NHC), The First Hospital of China Medical University, Shenyang, China.
Yukun ChangKey Laboratory of AIDS Immunology (China Medical University), National Clinical Research Center for Laboratory Medicine, National Health Commission (NHC), The First Hospital of China Medical University, Shenyang, China.
Zhidan ZhangDepartment of Infectious Disease, The First Affiliated Hospital of China Medical University, Shenyang, China.
Shiyang LiuInstitute of Infectious Diseases, Fifth Medical Center of People's Liberation Army General Hospital, Beijing, China.
Lei MaCenter of Liver Diseases Division 3, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
Yu ZhangCenter of Liver Diseases Division 3, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
Tong ZhaoDepartment of Hepatology, Qilu Hospital of Shandong University, Ji'nan, China.
Wenjuan HuState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, National Medical Center for Infectious Diseases, Zhejiang University School of Medicine, Hangzhou, China.
Jiafeng XiaState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, National Medical Center for Infectious Diseases, Zhejiang University School of Medicine, Hangzhou, China.
Yanwan ShangguanInfection Control Department, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Kaijin XuState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, National Medical Center for Infectious Diseases, Zhejiang University School of Medicine, Hangzhou, China. zdyxyxkj@zju.edu.cn.

Funding

Fundamental Research Funds for the Central Universities 2022ZFJH003National Key Research and Development Program of China 2021YFC2301800
6 · The paper itself

Abstract

backgroundThe Omicron variant broke out in China at the end of 2022, causing a considerable number of severe cases and even deaths. The study aimed to identify risk factors for death in patients hospitalized with SARS-CoV-2 Omicron infection and to establish a scoring system for predicting mortality.

methods1817 patients were enrolled at eight hospitals in China from December 2022 to May 2023, including 815 patients in the training group and 1002 patients in the validation group. Forty-six clinical and laboratory features were screened using LASSO regression and multivariable logistic regression.

resultsIn the training set, 730 patients were discharged and 85 patients died. In the validation set, 918 patients were discharged and 84 patients died. LASSO regression identified age, levels of interleukin (IL) -6, blood urea nitrogen (BUN), lactate dehydrogenase (LDH), and D-dimer; neutrophil count, neutrophil-to-lymphocyte ratio (NLR) as associated with mortality. Multivariable logistic regression analysis showed that older age, IL-6, BUN, LDH and D-dimer were significant independent risk factors. Based on these variables, a scoring system was developed with a sensitivity of 83.6% and a specificity of 83.5% in the training group, and a sensitivity of 79.8% and a sensitivity of 83.0% in the validation group.

conclusionsA scoring system based on age, IL-6, BUN, LDH and D-dime can help clinicians identify patients with poor prognosis early.

Indexed as

COVID-19SARS-CoV-2AdultAgedAge FactorsBlood Urea NitrogenChinaFemaleFibrin Fibrinogen Degradation ProductsHospitalizationHumansL-Lactate DehydrogenaseLogistic ModelsMaleMiddle AgedNeutrophilsFibrin Fibrinogen Degradation Productsfibrin fragment DL-Lactate DehydrogenaseCOVID-19MortalityMulticentre studySARS-CoV-2 Omicron variantScoring system

Identifiers

PMID38961438
PMCPMC11223413

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