Development and Validation of a Prognostic Risk Score System for COVID-19 Inpatients: A Multi-Center Retrospective Study in China
Coronavirus disease 2019 (COVID-19) has become a worldwide pandemic. Hospitalized patients of COVID-19 suffer from a high mortality rate, motivating the development of convenient and practical methods that allow clinicians to promptly identify high-risk patients. Here, we have developed a risk score...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-01-01
|
| Series: | Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2095809920303581 |
| _version_ | 1819104117258190848 |
|---|---|
| author | Ye Yuan Chuan Sun Xiuchuan Tang Cheng Cheng Laurent Mombaerts Maolin Wang Tao Hu Chenyu Sun Yuqi Guo Xiuting Li Hui Xu Tongxin Ren Yang Xiao Yaru Xiao Hongling Zhu Honghan Wu Kezhi Li Chuming Chen Yingxia Liu Zhichao Liang Zhiguo Cao Hai-Tao Zhang Ioannis Ch. Paschaldis Quanying Liu Jorge Goncalves Qiang Zhong Li Yan |
| author_facet | Ye Yuan Chuan Sun Xiuchuan Tang Cheng Cheng Laurent Mombaerts Maolin Wang Tao Hu Chenyu Sun Yuqi Guo Xiuting Li Hui Xu Tongxin Ren Yang Xiao Yaru Xiao Hongling Zhu Honghan Wu Kezhi Li Chuming Chen Yingxia Liu Zhichao Liang Zhiguo Cao Hai-Tao Zhang Ioannis Ch. Paschaldis Quanying Liu Jorge Goncalves Qiang Zhong Li Yan |
| author_sort | Ye Yuan |
| collection | DOAJ |
| description | Coronavirus disease 2019 (COVID-19) has become a worldwide pandemic. Hospitalized patients of COVID-19 suffer from a high mortality rate, motivating the development of convenient and practical methods that allow clinicians to promptly identify high-risk patients. Here, we have developed a risk score using clinical data from 1479 inpatients admitted to Tongji Hospital, Wuhan, China (development cohort) and externally validated with data from two other centers: 141 inpatients from Jinyintan Hospital, Wuhan, China (validation cohort 1) and 432 inpatients from The Third People’s Hospital of Shenzhen, Shenzhen, China (validation cohort 2). The risk score is based on three biomarkers that are readily available in routine blood samples and can easily be translated into a probability of death. The risk score can predict the mortality of individual patients more than 12 d in advance with more than 90% accuracy across all cohorts. Moreover, the Kaplan–Meier score shows that patients can be clearly differentiated upon admission as low, intermediate, or high risk, with an area under the curve (AUC) score of 0.9551. In summary, a simple risk score has been validated to predict death in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); it has also been validated in independent cohorts. |
| first_indexed | 2024-12-22T02:01:15Z |
| format | Article |
| id | doaj.art-91ee65a830c74c58b7df5b20880da3b5 |
| institution | Directory Open Access Journal |
| issn | 2095-8099 |
| language | English |
| last_indexed | 2024-12-22T02:01:15Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Engineering |
| spelling | doaj.art-91ee65a830c74c58b7df5b20880da3b52022-12-21T18:42:39ZengElsevierEngineering2095-80992022-01-018116121Development and Validation of a Prognostic Risk Score System for COVID-19 Inpatients: A Multi-Center Retrospective Study in ChinaYe Yuan0Chuan Sun1Xiuchuan Tang2Cheng Cheng3Laurent Mombaerts4Maolin Wang5Tao Hu6Chenyu Sun7Yuqi Guo8Xiuting Li9Hui Xu10Tongxin Ren11Yang Xiao12Yaru Xiao13Hongling Zhu14Honghan Wu15Kezhi Li16Chuming Chen17Yingxia Liu18Zhichao Liang19Zhiguo Cao20Hai-Tao Zhang21Ioannis Ch. Paschaldis22Quanying Liu23Jorge Goncalves24Qiang Zhong25Li Yan26School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, ChinaLuxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval L-4367, LuxembourgSchool of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, ChinaDepartment of Emergency, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, ChinaAMITA Health Saint Joseph Hospital Chicago, Chicago, IL 60657, USASchool of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, ChinaDepartment of Anesthesiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, ChinaHuazhong University of Science and Technology-Wuxi Research Institute, Wuxi 214174, ChinaSchool of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, ChinaDepartment of Emergency, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, ChinaDivision of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, ChinaInstitute of Health Informatics, University College London, London NW1 2DA, UKInstitute of Health Informatics, University College London, London NW1 2DA, UKDepartment of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, ChinaDepartment of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, ChinaDepartment of Infectious Diseases, Shenzhen Key Laboratory of Pathogenic Microbiology and Immunology, National Clinical Research Center for Infectious Disease, The Third People’s Hospital of Shenzhen (Second Hospital Affiliated with the Southern University of Science and Technology), Shenzhen 518055, ChinaSchool of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, ChinaDepartment of Electrical and Computer Engineering & Division of Systems Engineering & Department of Biomedical Engineering, Boston University, Boston, MA 02215, USADivision of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, ChinaLuxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval L-4367, Luxembourg; Department of Plant Sciences, University of Cambridge, Cambridge CB2 1TN, UKDepartment of Emergency, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Corresponding authors.Department of Emergency, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Corresponding authors.Coronavirus disease 2019 (COVID-19) has become a worldwide pandemic. Hospitalized patients of COVID-19 suffer from a high mortality rate, motivating the development of convenient and practical methods that allow clinicians to promptly identify high-risk patients. Here, we have developed a risk score using clinical data from 1479 inpatients admitted to Tongji Hospital, Wuhan, China (development cohort) and externally validated with data from two other centers: 141 inpatients from Jinyintan Hospital, Wuhan, China (validation cohort 1) and 432 inpatients from The Third People’s Hospital of Shenzhen, Shenzhen, China (validation cohort 2). The risk score is based on three biomarkers that are readily available in routine blood samples and can easily be translated into a probability of death. The risk score can predict the mortality of individual patients more than 12 d in advance with more than 90% accuracy across all cohorts. Moreover, the Kaplan–Meier score shows that patients can be clearly differentiated upon admission as low, intermediate, or high risk, with an area under the curve (AUC) score of 0.9551. In summary, a simple risk score has been validated to predict death in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); it has also been validated in independent cohorts.http://www.sciencedirect.com/science/article/pii/S2095809920303581COVID-19Risk scoreMortality risk prediction |
| spellingShingle | Ye Yuan Chuan Sun Xiuchuan Tang Cheng Cheng Laurent Mombaerts Maolin Wang Tao Hu Chenyu Sun Yuqi Guo Xiuting Li Hui Xu Tongxin Ren Yang Xiao Yaru Xiao Hongling Zhu Honghan Wu Kezhi Li Chuming Chen Yingxia Liu Zhichao Liang Zhiguo Cao Hai-Tao Zhang Ioannis Ch. Paschaldis Quanying Liu Jorge Goncalves Qiang Zhong Li Yan Development and Validation of a Prognostic Risk Score System for COVID-19 Inpatients: A Multi-Center Retrospective Study in China Engineering COVID-19 Risk score Mortality risk prediction |
| title | Development and Validation of a Prognostic Risk Score System for COVID-19 Inpatients: A Multi-Center Retrospective Study in China |
| title_full | Development and Validation of a Prognostic Risk Score System for COVID-19 Inpatients: A Multi-Center Retrospective Study in China |
| title_fullStr | Development and Validation of a Prognostic Risk Score System for COVID-19 Inpatients: A Multi-Center Retrospective Study in China |
| title_full_unstemmed | Development and Validation of a Prognostic Risk Score System for COVID-19 Inpatients: A Multi-Center Retrospective Study in China |
| title_short | Development and Validation of a Prognostic Risk Score System for COVID-19 Inpatients: A Multi-Center Retrospective Study in China |
| title_sort | development and validation of a prognostic risk score system for covid 19 inpatients a multi center retrospective study in china |
| topic | COVID-19 Risk score Mortality risk prediction |
| url | http://www.sciencedirect.com/science/article/pii/S2095809920303581 |
| work_keys_str_mv | AT yeyuan developmentandvalidationofaprognosticriskscoresystemforcovid19inpatientsamulticenterretrospectivestudyinchina AT chuansun developmentandvalidationofaprognosticriskscoresystemforcovid19inpatientsamulticenterretrospectivestudyinchina AT xiuchuantang developmentandvalidationofaprognosticriskscoresystemforcovid19inpatientsamulticenterretrospectivestudyinchina AT chengcheng developmentandvalidationofaprognosticriskscoresystemforcovid19inpatientsamulticenterretrospectivestudyinchina AT laurentmombaerts developmentandvalidationofaprognosticriskscoresystemforcovid19inpatientsamulticenterretrospectivestudyinchina AT maolinwang developmentandvalidationofaprognosticriskscoresystemforcovid19inpatientsamulticenterretrospectivestudyinchina AT taohu developmentandvalidationofaprognosticriskscoresystemforcovid19inpatientsamulticenterretrospectivestudyinchina AT chenyusun developmentandvalidationofaprognosticriskscoresystemforcovid19inpatientsamulticenterretrospectivestudyinchina AT yuqiguo developmentandvalidationofaprognosticriskscoresystemforcovid19inpatientsamulticenterretrospectivestudyinchina AT xiutingli developmentandvalidationofaprognosticriskscoresystemforcovid19inpatientsamulticenterretrospectivestudyinchina AT huixu developmentandvalidationofaprognosticriskscoresystemforcovid19inpatientsamulticenterretrospectivestudyinchina AT tongxinren developmentandvalidationofaprognosticriskscoresystemforcovid19inpatientsamulticenterretrospectivestudyinchina AT yangxiao developmentandvalidationofaprognosticriskscoresystemforcovid19inpatientsamulticenterretrospectivestudyinchina AT yaruxiao developmentandvalidationofaprognosticriskscoresystemforcovid19inpatientsamulticenterretrospectivestudyinchina AT honglingzhu developmentandvalidationofaprognosticriskscoresystemforcovid19inpatientsamulticenterretrospectivestudyinchina AT honghanwu developmentandvalidationofaprognosticriskscoresystemforcovid19inpatientsamulticenterretrospectivestudyinchina AT kezhili developmentandvalidationofaprognosticriskscoresystemforcovid19inpatientsamulticenterretrospectivestudyinchina AT chumingchen developmentandvalidationofaprognosticriskscoresystemforcovid19inpatientsamulticenterretrospectivestudyinchina AT yingxialiu developmentandvalidationofaprognosticriskscoresystemforcovid19inpatientsamulticenterretrospectivestudyinchina AT zhichaoliang developmentandvalidationofaprognosticriskscoresystemforcovid19inpatientsamulticenterretrospectivestudyinchina AT zhiguocao developmentandvalidationofaprognosticriskscoresystemforcovid19inpatientsamulticenterretrospectivestudyinchina AT haitaozhang developmentandvalidationofaprognosticriskscoresystemforcovid19inpatientsamulticenterretrospectivestudyinchina AT ioannischpaschaldis developmentandvalidationofaprognosticriskscoresystemforcovid19inpatientsamulticenterretrospectivestudyinchina AT quanyingliu developmentandvalidationofaprognosticriskscoresystemforcovid19inpatientsamulticenterretrospectivestudyinchina AT jorgegoncalves developmentandvalidationofaprognosticriskscoresystemforcovid19inpatientsamulticenterretrospectivestudyinchina AT qiangzhong developmentandvalidationofaprognosticriskscoresystemforcovid19inpatientsamulticenterretrospectivestudyinchina AT liyan developmentandvalidationofaprognosticriskscoresystemforcovid19inpatientsamulticenterretrospectivestudyinchina |