Time-dependent CT score-based model for identifying severe/critical COVID-19 at a fever clinic after the emergence of Omicron variant
© 2024 The Authors. Published by Elsevier Ltd..
Rationale and objectives: The computed tomography (CT) score has been used to evaluate the severity of COVID-19 during the pandemic; however, most studies have overlooked the impact of infection duration on the CT score. This study aimed to determine the optimal cutoff CT score value for identifying severe/critical COVID-19 during different stages of infection and to construct corresponding predictive models using radiological characteristics and clinical factors.
Materials and methods: This retrospective study collected consecutive baseline chest CT images of confirmed COVID-19 patients from a fever clinic at a tertiary referral hospital from November 28, 2022, to January 8, 2023. Cohorts were divided into three subcohorts according to the time interval from symptom onset to CT examination at the hospital: early phase (0-3 days), intermediate phase (4-7 days), and late phase (8-14 days). The binary endpoints were mild/moderate and severe/critical infection. The CT scores and qualitative CT features were manually evaluated. A logistic regression analysis was performed on the CT score as determined by a visual assessment to predict severe/critical infection. Receiver operating characteristic analysis was performed and the area under the curve (AUC) was calculated. The optimal cutoff value was determined by maximizing the Youden index in each subcohort. A radiology score and integrated models were then constructed by combining the qualitative CT features and clinical features, respectively, using multivariate logistic regression with stepwise elimination.
Results: A total of 962 patients (aged, 61.7 ± 19.6 years; 490 men) were included; 179 (18.6%) were classified as severe/critical COVID-19, while 344 (35.8%) had a typical Radiological Society of North America (RSNA) COVID-19 appearance. The AUCs of the CT score models reached 0.91 (95% confidence interval (CI) 0.88-0.94), 0.82 (95% CI 0.76-0.87), and 0.83 (95% CI 0.77-0.89) during the early, intermediate, and late phases, respectively. The best cutoff values of the CT scores during each phase were 1.5, 4.5, and 5.5. The predictive accuracies associated with the time-dependent cutoff values reached 88% (vs.78%), 73% (vs. 63%), and 87% (vs. 57%), which were greater than those associated with universal cutoff value (all P < 0.001). The radiology score models reached AUCs of 0.96 (95% CI 0.94-0.98), 0.90 (95% CI 0.87-0.94), and 0.89 (95% CI 0.84-0.94) during the early, intermediate, and late phases, respectively. The integrated models including demographic and clinical risk factors greatly enhanced the AUC during the intermediate and late phases compared with the values obtained with the radiology score models; however, an improvement in accuracy was not observed.
Conclusion: The time interval between symptom onset and CT examination should be tracked to determine the cutoff value for the CT score for identifying severe/critical COVID-19. The radiology score combining qualitative CT features and the CT score complements clinical factors for identifying severe/critical COVID-19 patients and facilitates timely hierarchical diagnoses and treatment.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
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| Enthalten in: |
Heliyon - 10(2024), 7 vom: 15. Apr., Seite e27963 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhu, Zhenchen [VerfasserIn] |
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| Links: |
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| Themen: |
COVID-19 |
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| Anmerkungen: |
Date Revised 09.04.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1016/j.heliyon.2024.e27963 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM370757971 |
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| 245 | 1 | 0 | |a Time-dependent CT score-based model for identifying severe/critical COVID-19 at a fever clinic after the emergence of Omicron variant |
| 264 | 1 | |c 2024 | |
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| 500 | |a Date Revised 09.04.2024 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © 2024 The Authors. Published by Elsevier Ltd. | ||
| 520 | |a Rationale and objectives: The computed tomography (CT) score has been used to evaluate the severity of COVID-19 during the pandemic; however, most studies have overlooked the impact of infection duration on the CT score. This study aimed to determine the optimal cutoff CT score value for identifying severe/critical COVID-19 during different stages of infection and to construct corresponding predictive models using radiological characteristics and clinical factors | ||
| 520 | |a Materials and methods: This retrospective study collected consecutive baseline chest CT images of confirmed COVID-19 patients from a fever clinic at a tertiary referral hospital from November 28, 2022, to January 8, 2023. Cohorts were divided into three subcohorts according to the time interval from symptom onset to CT examination at the hospital: early phase (0-3 days), intermediate phase (4-7 days), and late phase (8-14 days). The binary endpoints were mild/moderate and severe/critical infection. The CT scores and qualitative CT features were manually evaluated. A logistic regression analysis was performed on the CT score as determined by a visual assessment to predict severe/critical infection. Receiver operating characteristic analysis was performed and the area under the curve (AUC) was calculated. The optimal cutoff value was determined by maximizing the Youden index in each subcohort. A radiology score and integrated models were then constructed by combining the qualitative CT features and clinical features, respectively, using multivariate logistic regression with stepwise elimination | ||
| 520 | |a Results: A total of 962 patients (aged, 61.7 ± 19.6 years; 490 men) were included; 179 (18.6%) were classified as severe/critical COVID-19, while 344 (35.8%) had a typical Radiological Society of North America (RSNA) COVID-19 appearance. The AUCs of the CT score models reached 0.91 (95% confidence interval (CI) 0.88-0.94), 0.82 (95% CI 0.76-0.87), and 0.83 (95% CI 0.77-0.89) during the early, intermediate, and late phases, respectively. The best cutoff values of the CT scores during each phase were 1.5, 4.5, and 5.5. The predictive accuracies associated with the time-dependent cutoff values reached 88% (vs.78%), 73% (vs. 63%), and 87% (vs. 57%), which were greater than those associated with universal cutoff value (all P < 0.001). The radiology score models reached AUCs of 0.96 (95% CI 0.94-0.98), 0.90 (95% CI 0.87-0.94), and 0.89 (95% CI 0.84-0.94) during the early, intermediate, and late phases, respectively. The integrated models including demographic and clinical risk factors greatly enhanced the AUC during the intermediate and late phases compared with the values obtained with the radiology score models; however, an improvement in accuracy was not observed | ||
| 520 | |a Conclusion: The time interval between symptom onset and CT examination should be tracked to determine the cutoff value for the CT score for identifying severe/critical COVID-19. The radiology score combining qualitative CT features and the CT score complements clinical factors for identifying severe/critical COVID-19 patients and facilitates timely hierarchical diagnoses and treatment | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a CT score | |
| 650 | 4 | |a Computed tomography | |
| 650 | 4 | |a Fever clinic | |
| 650 | 4 | |a SARS-CoV-2 | |
| 700 | 1 | |a Hu, Ge |e verfasserin |4 aut | |
| 700 | 1 | |a Ying, Zhoumeng |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Jinhua |e verfasserin |4 aut | |
| 700 | 1 | |a Han, Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Pan, Zhengsong |e verfasserin |4 aut | |
| 700 | 1 | |a Tian, Xinlun |e verfasserin |4 aut | |
| 700 | 1 | |a Song, Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Sui, Xin |e verfasserin |4 aut | |
| 700 | 1 | |a Song, Lan |e verfasserin |4 aut | |
| 700 | 1 | |a Jin, Zhengyu |e verfasserin |4 aut | |
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