| Autor: |
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 |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Scientific Reports, Vol 11, Iss 1, Pp 1-11 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2045-2322 |
| DOI: |
10.1038/s41598-021-83054-x |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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