| Autor: |
Zhuoyu Sun, Yi’an Guo, Wei He, Shiyue Chen, Changqing Sun, Hong Zhu, Jing Li, Yongjie Chen, Yue Du, Guangshun Wang, Xilin Yang, Hongjun Su |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
International Journal of Public Health, Vol 67 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
1661-8564 |
| DOI: |
10.3389/ijph.2022.1604794 |
| Popis: |
Objectives: To develop and internally validate two clinical risk scores to detect coronavirus disease 2019 (COVID-19) during local outbreaks.Methods: Medical records were extracted for a retrospective cohort of 336 suspected patients admitted to Baodi hospital between 27 January to 20 February 2020. Multivariate logistic regression was applied to develop the risk-scoring models, which were internally validated using a 5-fold cross-validation method and Hosmer-Lemeshow (H-L) tests.Results: Fifty-six cases were diagnosed from the cohort. The first model was developed based on seven significant predictors, including age, close contact with confirmed/suspected cases, same location of exposure, temperature, leukocyte counts, radiological findings of pneumonia and bilateral involvement (the mean area under the receiver operating characteristic curve [AUC]:0.88, 95% CI: 0.84–0.93). The second model had the same predictors except leukocyte and radiological findings (AUC: 0.84, 95% CI: 0.78–0.89, Z = 2.56, p = 0.01). Both were internally validated using H-L tests and showed good calibration (both p > 0.10).Conclusion: Two clinical risk scores to detect COVID-19 in local outbreaks were developed with excellent predictive performances, using commonly measured clinical variables. Further external validations in new outbreaks are warranted. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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