Severity-onset prediction of COVID-19 via artificial-intelligence analysis of multivariate factors

Fu, Yu1; Zeng, Lijiao1; Huang, Pilai1; Liao, Mingfeng1; Li, Jialu5; Zhang, Mingxia1; Shi, Qinlang1; Xia, Zhaohua1; Ning, Xinzhong1; Mo, Jiu5; Zhou, Ziyuan1; Li, Zigang3,4; Yuan, Jing1; Wang, Lifei1; He, Qing1; Wu, Qikang6; Liu, Lei1; Liao, Yuhui2; Qiao, Kun1

2023-08-01

10.1016/j.heliyon.2023.e18764

HELIYON   影响因子和分区

2405-8440

Progression to a severe condition remains a major risk factor for the COVID-19 mortality. Robust models that predict the onset of severe COVID-19 are urgently required to support sensitive decisions regarding patients and their treatments. In this study, we developed a multivariate survival model based on early-stage CT images and other physiological indicators and biomarkers using artificial-intelligence analysis to assess the risk of severe COVID-19 onset. We retrospectively enrolled 338 adult patients admitted to a hospital in China (severity rate, 31.9%; mortality rate, 0.9%). The physiological and pathological characteristics of the patients with severe and non-severe outcomes were compared. Age, body mass index, fever symptoms upon admission, coexisting hypertension, and diabetes were the risk factors for severe progression. Compared with the non-severe group, the severe group demonstrated abnormalities in biomarkers indicating organ function, inflammatory responses, blood oxygen, and coagulation function at an early stage. In addition, by integrating the intuitive CT images, the multivariable survival model showed significantly improved performance in predicting the onset of severe disease (mean timedependent area under the curve = 0.880). Multivariate survival models based on early-stage CT images and other physiological indicators and biomarkers have shown high potential for predicting the onset of severe COVID-19.

COVID-19 Severe disease onset prediction Artificial-intelligence analysis

SCI

英语

第一 ; 通讯

Special Fund of Foshan Summit Plan[2021YFC2302200] ; Training project of National Science Foundation for Outstanding/Excellent Young Scholars of Southern Medical University["81972019","21904145","82002253"] ; Regional Joint Fund of Natural Science Foundation of Guangdong Province[2021M691428] ; Guangdong Basic and Applied Basic Research Foundation["2020B019","2020B012","2020A015"] ; Fundamental Research Funds for the Central Universities[C620PF0217] ; Chen Jingyu Team of Sanming Project of Medicine in Shenzhen[2020A1515110529] ; Shenzhen Science and Technological Foundation[2020A1515010754] ; Guangdong Medical science foundation[2019MS134] ; Natural Science Foundation of China[SZSM201812058] ; Hospital Fund of Chinese Academy of Medical Sciences Cancer Hospital Shenzhen Hospital[JSGG20210901145200001] ; null[A2021413] ; null[22107045] ; null[E010221005]

Science & Technology - Other Topics

Multidisciplinary Sciences

WOS:001052317500001

CELL PRESS

Web of Science

1.Southern Univ Sci & Technol, Affiliated Hosp 2, Shenzhen Peoples Hosp 3, Dept Infect Dis,Dept Thorac Surg,Dept Radiol,Natl, Shenzhen, Peoples R China
2.Southern Med Univ, Dermatol Hosp, Mol Diag & Treatment Ctr Infect Dis, Guangzhou, Peoples R China
3.Peking Univ, Pingshan Translat Med Ctr, Sch Chem Biol & Biotechnol, Shenzhen Grad Sch,Shenzhen Bay Lab, Shenzhen, Peoples R China
4.Peking Univ, Sch Chem Biol & Biotechnol, Shenzhen Grad Sch, State Key Lab Chem Oncogen, Shenzhen, Peoples R China
5.HuaJia Biomed Intelligence, Dept Biostat, Shenzhen, Peoples R China
6.First Peoples Hosp Foshan, Dept Clin Lab, Foshan, Peoples R China

Fu, Yu,Zeng, Lijiao,Huang, Pilai,et al. Severity-onset prediction of COVID-19 via artificial-intelligence analysis of multivariate factors[J]. HELIYON,2023,9(8).

Fu, Yu.,Zeng, Lijiao.,Huang, Pilai.,Liao, Mingfeng.,Li, Jialu.,...&Qiao, Kun.(2023).Severity-onset prediction of COVID-19 via artificial-intelligence analysis of multivariate factors.HELIYON,9(8).

Fu, Yu,et al."Severity-onset prediction of COVID-19 via artificial-intelligence analysis of multivariate factors".HELIYON 9.8(2023).