A rapid screening model for early predicting novel coronavirus pneumonia in Zhejiang Province of China: a multicenter study
Abstract Novel coronavirus pneumonia (NCP) has been widely spread in China and several other countries. Early finding of this pneumonia from huge numbers of suspects gives clinicians a big challenge. The aim of the study was to develop a rapid screening model for early predicting NCP in a Zhejiang p...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2021-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-83054-x |
| _version_ | 1818751995933097984 |
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| author | Yi-Ning Dai Wei Zheng Qing-Qing Wu Tian-Chen Hui Nan-Nan Sun Guo-Bo Chen Yong-Xi Tong Su-Xia Bao Wen-Hao Wu Yi-Cheng Huang Qiao-Qiao Yin Li-Juan Wu Li-Xia Yu Ji-Chan Shi Nian Fang Yue-Fei Shen Xin-Sheng Xie Chun-Lian Ma Wan-Jun Yu Wen-Hui Tu Rong Yan Ming-Shan Wang Mei-Juan Chen Jia-Jie Zhang Bin Ju Hai-Nv Gao Hai-Jun Huang Lan-Juan Li Hong-Ying Pan |
| author_facet | Yi-Ning Dai Wei Zheng Qing-Qing Wu Tian-Chen Hui Nan-Nan Sun Guo-Bo Chen Yong-Xi Tong Su-Xia Bao Wen-Hao Wu Yi-Cheng Huang Qiao-Qiao Yin Li-Juan Wu Li-Xia Yu Ji-Chan Shi Nian Fang Yue-Fei Shen Xin-Sheng Xie Chun-Lian Ma Wan-Jun Yu Wen-Hui Tu Rong Yan Ming-Shan Wang Mei-Juan Chen Jia-Jie Zhang Bin Ju Hai-Nv Gao Hai-Jun Huang Lan-Juan Li Hong-Ying Pan |
| author_sort | Yi-Ning Dai |
| collection | DOAJ |
| description | Abstract Novel coronavirus pneumonia (NCP) has been widely spread in China and several other countries. Early finding of this pneumonia from huge numbers of suspects gives clinicians a big challenge. The aim of the study was to develop a rapid screening model for early predicting NCP in a Zhejiang population, as well as its utility in other areas. A total of 880 participants who were initially suspected of NCP from January 17 to February 19 were included. Potential predictors were selected via stepwise logistic regression analysis. The model was established based on epidemiological features, clinical manifestations, white blood cell count, and pulmonary imaging changes, with the area under receiver operating characteristic (AUROC) curve of 0.920. At a cut-off value of 1.0, the model could determine NCP with a sensitivity of 85% and a specificity of 82.3%. We further developed a simplified model by combining the geographical regions and rounding the coefficients, with the AUROC of 0.909, as well as a model without epidemiological factors with the AUROC of 0.859. The study demonstrated that the screening model was a helpful and cost-effective tool for early predicting NCP and had great clinical significance given the high activity of NCP. |
| first_indexed | 2024-12-18T04:44:26Z |
| format | Article |
| id | doaj.art-1ca86e8ef78443db85a337ffe341bd30 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-18T04:44:26Z |
| publishDate | 2021-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-1ca86e8ef78443db85a337ffe341bd302022-12-21T21:20:36ZengNature PortfolioScientific Reports2045-23222021-02-0111111110.1038/s41598-021-83054-xA rapid screening model for early predicting novel coronavirus pneumonia in Zhejiang Province of China: a multicenter studyYi-Ning Dai0Wei Zheng1Qing-Qing Wu2Tian-Chen Hui3Nan-Nan Sun4Guo-Bo Chen5Yong-Xi Tong6Su-Xia Bao7Wen-Hao Wu8Yi-Cheng Huang9Qiao-Qiao Yin10Li-Juan Wu11Li-Xia Yu12Ji-Chan Shi13Nian Fang14Yue-Fei Shen15Xin-Sheng Xie16Chun-Lian Ma17Wan-Jun Yu18Wen-Hui Tu19Rong Yan20Ming-Shan Wang21Mei-Juan Chen22Jia-Jie Zhang23Bin Ju24Hai-Nv Gao25Hai-Jun Huang26Lan-Juan Li27Hong-Ying Pan28Department of Infectious Diseases, Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical CollegeDepartment of Infectious Diseases, Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical CollegeDepartment of Infectious Diseases, Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical CollegeDepartment of Infectious Diseases, Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical CollegeHangzhou Wowjoy Information Technology Co., LtdClinical Research Institute, Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical CollegeDepartment of Infectious Diseases, Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical CollegeDepartment of Infectious Diseases, Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical CollegeDepartment of Infectious Diseases, Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical CollegeDepartment of Infectious Diseases, Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical CollegeDepartment of Infectious Diseases, Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical CollegeDepartment of Infectious Diseases, First People’s Hospital of TongxiangDepartment of Infectious Diseases, People’s Hospital of ShaoxingDepartment of Infectious Diseases, Wenzhou Central HospitalDepartment of Infectious Diseases, First Hospital of TaizhouDepartment of Infectious Diseases, First People’s Hospital of XiaoshanDepartment of Infectious Diseases, First Hospital of JiaxingDepartment of Infectious Diseases, First People’s Hospital of WenlingDepartment of Respiratory and Critical Care Medicine, Yinzhou People’s Hospital, Affiliated Yinzhou Hospital, College of Medicine, Ningbo UniversityDepartment of Infectious Diseases, Taizhou Municipal HospitalDepartment of Infectious Diseases, Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical CollegeDepartment of Infectious Diseases, Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical CollegeDepartment of Infectious Diseases, Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical CollegeDepartment of Infectious Diseases, Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical CollegeHangzhou Wowjoy Information Technology Co., LtdDepartment of Infectious Diseases, ShuLan (Hangzhou) Hospital Affiliated to Zhejiang Shuren University Shulan International Medical CollegeDepartment of Infectious Diseases, Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical CollegeState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Centre for Infectious Diseases, Collaborative Innovation Centre for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, College of Medicine, Zhejiang UniversityDepartment of Infectious Diseases, Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical CollegeAbstract Novel coronavirus pneumonia (NCP) has been widely spread in China and several other countries. Early finding of this pneumonia from huge numbers of suspects gives clinicians a big challenge. The aim of the study was to develop a rapid screening model for early predicting NCP in a Zhejiang population, as well as its utility in other areas. A total of 880 participants who were initially suspected of NCP from January 17 to February 19 were included. Potential predictors were selected via stepwise logistic regression analysis. The model was established based on epidemiological features, clinical manifestations, white blood cell count, and pulmonary imaging changes, with the area under receiver operating characteristic (AUROC) curve of 0.920. At a cut-off value of 1.0, the model could determine NCP with a sensitivity of 85% and a specificity of 82.3%. We further developed a simplified model by combining the geographical regions and rounding the coefficients, with the AUROC of 0.909, as well as a model without epidemiological factors with the AUROC of 0.859. The study demonstrated that the screening model was a helpful and cost-effective tool for early predicting NCP and had great clinical significance given the high activity of NCP.https://doi.org/10.1038/s41598-021-83054-x |
| spellingShingle | Yi-Ning Dai Wei Zheng Qing-Qing Wu Tian-Chen Hui Nan-Nan Sun Guo-Bo Chen Yong-Xi Tong Su-Xia Bao Wen-Hao Wu Yi-Cheng Huang Qiao-Qiao Yin Li-Juan Wu Li-Xia Yu Ji-Chan Shi Nian Fang Yue-Fei Shen Xin-Sheng Xie Chun-Lian Ma Wan-Jun Yu Wen-Hui Tu Rong Yan Ming-Shan Wang Mei-Juan Chen Jia-Jie Zhang Bin Ju Hai-Nv Gao Hai-Jun Huang Lan-Juan Li Hong-Ying Pan A rapid screening model for early predicting novel coronavirus pneumonia in Zhejiang Province of China: a multicenter study Scientific Reports |
| title | A rapid screening model for early predicting novel coronavirus pneumonia in Zhejiang Province of China: a multicenter study |
| title_full | A rapid screening model for early predicting novel coronavirus pneumonia in Zhejiang Province of China: a multicenter study |
| title_fullStr | A rapid screening model for early predicting novel coronavirus pneumonia in Zhejiang Province of China: a multicenter study |
| title_full_unstemmed | A rapid screening model for early predicting novel coronavirus pneumonia in Zhejiang Province of China: a multicenter study |
| title_short | A rapid screening model for early predicting novel coronavirus pneumonia in Zhejiang Province of China: a multicenter study |
| title_sort | rapid screening model for early predicting novel coronavirus pneumonia in zhejiang province of china a multicenter study |
| url | https://doi.org/10.1038/s41598-021-83054-x |
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