Severity-onset prediction of COVID-19 via artificial-intelligence analysis of multivariate factors
© 2023 The Authors. Published by Elsevier Ltd..
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 time-dependent 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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:9 |
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| Enthalten in: |
Heliyon - 9(2023), 8 vom: 12. Aug., Seite e18764 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Fu, Yu [VerfasserIn] |
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| Links: |
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| Themen: |
Artificial-intelligence analysis |
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| Anmerkungen: |
Date Revised 15.08.2023 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1016/j.heliyon.2023.e18764 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a 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 time-dependent 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Artificial-intelligence analysis | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a Severe disease onset prediction | |
| 700 | 1 | |a Zeng, Lijiao |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Pilai |e verfasserin |4 aut | |
| 700 | 1 | |a Liao, Mingfeng |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Jialu |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Mingxia |e verfasserin |4 aut | |
| 700 | 1 | |a Shi, Qinlang |e verfasserin |4 aut | |
| 700 | 1 | |a Xia, Zhaohua |e verfasserin |4 aut | |
| 700 | 1 | |a Ning, Xinzhong |e verfasserin |4 aut | |
| 700 | 1 | |a Mo, Jiu |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Ziyuan |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Zigang |e verfasserin |4 aut | |
| 700 | 1 | |a Yuan, Jing |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Lifei |e verfasserin |4 aut | |
| 700 | 1 | |a He, Qing |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Qikang |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Lei |e verfasserin |4 aut | |
| 700 | 1 | |a Liao, Yuhui |e verfasserin |4 aut | |
| 700 | 1 | |a Qiao, Kun |e verfasserin |4 aut | |
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