Evidence map›Paper›PMID 38909101›Full record

ArticleScientific reports2024

An elastic net regression model for predicting the risk of ICU admission and death for hospitalized patients with COVID-19.

Wei Zou, Xiujuan Yao, Yizhen Chen, Xiaoqin Li, Jiandong Huang, Yong Zhang, Lin Yu, Baosong Xie

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

8 authors.

Wei Zou *Shengli Clinical Medical College of Fujian Medical University, Fuzhou, 350013, China.
Xiujuan Yao *Shengli Clinical Medical College of Fujian Medical University, Fuzhou, 350013, China.
Yizhen ChenShengli Clinical Medical College of Fujian Medical University, Fuzhou, 350013, China.
Xiaoqin LiShengli Clinical Medical College of Fujian Medical University, Fuzhou, 350013, China.
Jiandong HuangShengli Clinical Medical College of Fujian Medical University, Fuzhou, 350013, China.
Yong ZhangChongqing Nanpeng Artificial Intelligence Technology Research Institute Co., Ltd., Chongqing, 401123, China.
Lin YuChongqing Nanpeng Artificial Intelligence Technology Research Institute Co., Ltd., Chongqing, 401123, China.
Baosong XieShengli Clinical Medical College of Fujian Medical University, Fuzhou, 350013, China. xbaosong@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to develop and validate prediction models to estimate the risk of death and intensive care unit admission in COVID-19 inpatients. All RT-PCR-confirmed adult COVID-19 inpatients admitted to Fujian Provincial Hospital from October 2022 to April 2023 were considered. Elastic Net Regression was used to derive the risk prediction models. Potential risk factors were considered, which included demographic characteristics, clinical symptoms, comorbidities, laboratory results, treatment process, prognosis. A total of 1906 inpatients were included finally by inclusion/exclusion criteria and were divided into derivation and test cohorts in a ratio of 8:2, where 1526 (80%) samples were used to develop prediction models under a repeated cross-validation framework and the remaining 380 (20%) samples were used for performance evaluation. Overall performance, discrimination and calibration were evaluated in the validation set and test cohort and quantified by accuracy, scaled Brier score (SbrS), the area under the ROC curve (AUROC), and Spiegelhalter-Z statistics. The models performed well, with high levels of discrimination (AUROC

Indexed as

COVID-19HospitalizationIntensive Care UnitsAdultAgedChinaFemaleHospital MortalityHumansMaleMiddle AgedPrognosisRisk AssessmentRisk FactorsROC CurveSARS-CoV-2COVID-19Elastic net regressionPrediction models

Identifiers

PMID38909101
PMCPMC11193779

What OpenQuestion holds

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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.