A Simple Clinical Prediction Tool for COVID-19 in Primary Care with Epidemiology: Temperature-Leukocytes-CT Results

Wanming Hao; Long Zhao; Xinjuan Yu; Song Wu; Weifeng Xie; Ning Wang; Weihong Lv; Akshay Sood; Shuguang Leng; Yongchun Li; Qing Sun; Jun Guan; Wei Han

doi:10.12659/MSM.931467

06 October 2021 : Database Analysis

A Simple Clinical Prediction Tool for COVID-19 in Primary Care with Epidemiology: Temperature-Leukocytes-CT Results

Wanming Hao^1CDEF, Long Zhao^2CDE, Xinjuan Yu^1CDE, Song Wu^3DE, Weifeng Xie^4BF, Ning Wang^1BF, Weihong Lv^5BF, Akshay Sood^6DF, Shuguang Leng^6DF, Yongchun Li^1DF, Qing Sun^7AEF*, Jun Guan^8EF, Wei Han^1AEF

DOI: 10.12659/MSM.931467

Med Sci Monit 2021; 27:e931467

Authors information Article notes Copyright and License information

Background Material and Methods Results Discussion Conclusions References

Related articles Order reprints Share article Share by email

View HTML version

Table 3 Logistic regression analysis of COVID-19.

Variable	β	SE	Wald χ2	OR (95% CI)	P
Epidemiological history (X)	2.657	0.905	8.612	14.248 (2.417–84.009)	0.003
Body temperature (X)	1.153	0.531	4.716	3.166 (1.119–8.960)	0.030
Leukocytes count (X)	2.125	1.070	3.945	8.376 (1.028–68.216)	0.047
Chest CT (X)	2.828	0.641	19.435	16.906 (4.809–59.430)	<0.001
Constant	−10.771	2.655	16.454	2.10E-05	<0.001
β – partial regression coefficient; SE – standard error; OR – odds ratio; CI – confidence interval.

Back to the Article

A Simple Clinical Prediction Tool for COVID-19 in Primary Care with Epidemiology: Temperature-Leukocytes-CT Results

Your Privacy