A tri-light warning system for hospitalized COVID-19 patients: Credibility-based risk stratification for future pandemic preparedness
Purpose: The novel coronavirus pneumonia (COVID-19) has continually spread and mutated, requiring a patient risk stratification system to optimize medical resources and improve pandemic response. We aimed to develop a conformal prediction-based tri-light warning system for stratifying COVID-19 patie...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | European Journal of Radiology Open |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352047724000583 |
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| author | Chuanjun Xu Qinmei Xu Li Liu Mu Zhou Zijian Xing Zhen Zhou Danyang Ren Changsheng Zhou Longjiang Zhang Xiao Li Xianghao Zhan Olivier Gevaert Guangming Lu |
| author_facet | Chuanjun Xu Qinmei Xu Li Liu Mu Zhou Zijian Xing Zhen Zhou Danyang Ren Changsheng Zhou Longjiang Zhang Xiao Li Xianghao Zhan Olivier Gevaert Guangming Lu |
| author_sort | Chuanjun Xu |
| collection | DOAJ |
| description | Purpose: The novel coronavirus pneumonia (COVID-19) has continually spread and mutated, requiring a patient risk stratification system to optimize medical resources and improve pandemic response. We aimed to develop a conformal prediction-based tri-light warning system for stratifying COVID-19 patients, applicable to both original and emerging variants. Methods: We retrospectively collected data from 3646 patients across multiple centers in China. The dataset was divided into a training set (n = 1451), a validation set (n = 662), an external test set from Huoshenshan Field Hospital (n = 1263), and a specific test set for Delta and Omicron variants (n = 544). The tri-light warning system extracts radiomic features from CT (computed tomography) and integrates clinical records to classify patients into high-risk (red), uncertain-risk (yellow), and low-risk (green) categories. Models were built to predict ICU (intensive care unit) admissions (adverse cases in training/validation/Huoshenshan/variant test sets: n = 39/21/262/11) and were evaluated using AUROC ((area under the receiver operating characteristic curve)) and AUPRC ((area under the precision-recall curve)) metrics. Results: The dataset included 1830 men (50.2 %) and 1816 women (50.8 %), with a median age of 53.7 years (IQR [interquartile range]: 42–65 years). The system demonstrated strong performance under data distribution shifts, with AUROC of 0.89 and AUPRC of 0.42 for original strains, and AUROC of 0.77–0.85 and AUPRC of 0.51–0.60 for variants. Conclusion: The tri-light warning system can enhance pandemic responses by effectively stratifying COVID-19 patients under varying conditions and data shifts. |
| format | Article |
| id | doaj-art-c18272aff51b4df1a687f4c510bb6b87 |
| institution | Kabale University |
| issn | 2352-0477 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | European Journal of Radiology Open |
| spelling | doaj-art-c18272aff51b4df1a687f4c510bb6b872024-12-15T06:15:48ZengElsevierEuropean Journal of Radiology Open2352-04772024-12-0113100603A tri-light warning system for hospitalized COVID-19 patients: Credibility-based risk stratification for future pandemic preparednessChuanjun Xu0Qinmei Xu1Li Liu2Mu Zhou3Zijian Xing4Zhen Zhou5Danyang Ren6Changsheng Zhou7Longjiang Zhang8Xiao Li9Xianghao Zhan10Olivier Gevaert11Guangming Lu12Department of Radiology, the Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing 210003, ChinaDepartment of Biomedical Data Science (BMIR), Department of Medicine, Stanford University, Stanford, CA 94304, USADepartment of Computer Science, University of California Santa Cruz, Santa Cruze, CA 95064, USADepartment of Computer Science, Rutgers University, 110 Frelinghuysen Road, Piscataway, NJ 08854, USADepartment of Deepwise AI Lab, Deepwise Inc., Beijing, ChinaDepartment of Deepwise AI Lab, Deepwise Inc., Beijing, ChinaDepartment of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USADepartment of Medical Imaging, Jinling Hospital, Nanjing, Jiangsu, ChinaDepartment of Medical Imaging, Jinling Hospital, Nanjing, Jiangsu, ChinaDepartment of Medical Imaging, Jinling Hospital, Nanjing, Jiangsu, China; Corresponding authors.Department of Bioengineering, Stanford University, Stanford 94305, USA; Corresponding authors.Department of Biomedical Data Science (BMIR), Department of Medicine, Stanford University, Stanford, CA 94304, USA; Corresponding authors.Department of Medical Imaging, Jinling Hospital, Nanjing, Jiangsu, China; Corresponding authors.Purpose: The novel coronavirus pneumonia (COVID-19) has continually spread and mutated, requiring a patient risk stratification system to optimize medical resources and improve pandemic response. We aimed to develop a conformal prediction-based tri-light warning system for stratifying COVID-19 patients, applicable to both original and emerging variants. Methods: We retrospectively collected data from 3646 patients across multiple centers in China. The dataset was divided into a training set (n = 1451), a validation set (n = 662), an external test set from Huoshenshan Field Hospital (n = 1263), and a specific test set for Delta and Omicron variants (n = 544). The tri-light warning system extracts radiomic features from CT (computed tomography) and integrates clinical records to classify patients into high-risk (red), uncertain-risk (yellow), and low-risk (green) categories. Models were built to predict ICU (intensive care unit) admissions (adverse cases in training/validation/Huoshenshan/variant test sets: n = 39/21/262/11) and were evaluated using AUROC ((area under the receiver operating characteristic curve)) and AUPRC ((area under the precision-recall curve)) metrics. Results: The dataset included 1830 men (50.2 %) and 1816 women (50.8 %), with a median age of 53.7 years (IQR [interquartile range]: 42–65 years). The system demonstrated strong performance under data distribution shifts, with AUROC of 0.89 and AUPRC of 0.42 for original strains, and AUROC of 0.77–0.85 and AUPRC of 0.51–0.60 for variants. Conclusion: The tri-light warning system can enhance pandemic responses by effectively stratifying COVID-19 patients under varying conditions and data shifts.http://www.sciencedirect.com/science/article/pii/S2352047724000583COVID-19 pandemicMulti-modal artificial intelligenceRisk stratificationConformal predictionMulti-center study |
| spellingShingle | Chuanjun Xu Qinmei Xu Li Liu Mu Zhou Zijian Xing Zhen Zhou Danyang Ren Changsheng Zhou Longjiang Zhang Xiao Li Xianghao Zhan Olivier Gevaert Guangming Lu A tri-light warning system for hospitalized COVID-19 patients: Credibility-based risk stratification for future pandemic preparedness European Journal of Radiology Open COVID-19 pandemic Multi-modal artificial intelligence Risk stratification Conformal prediction Multi-center study |
| title | A tri-light warning system for hospitalized COVID-19 patients: Credibility-based risk stratification for future pandemic preparedness |
| title_full | A tri-light warning system for hospitalized COVID-19 patients: Credibility-based risk stratification for future pandemic preparedness |
| title_fullStr | A tri-light warning system for hospitalized COVID-19 patients: Credibility-based risk stratification for future pandemic preparedness |
| title_full_unstemmed | A tri-light warning system for hospitalized COVID-19 patients: Credibility-based risk stratification for future pandemic preparedness |
| title_short | A tri-light warning system for hospitalized COVID-19 patients: Credibility-based risk stratification for future pandemic preparedness |
| title_sort | tri light warning system for hospitalized covid 19 patients credibility based risk stratification for future pandemic preparedness |
| topic | COVID-19 pandemic Multi-modal artificial intelligence Risk stratification Conformal prediction Multi-center study |
| url | http://www.sciencedirect.com/science/article/pii/S2352047724000583 |
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