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09 July 2021 : Database Analysis

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

Wanming Hao1CDEF, Long Zhao2CDE, Xinjuan Yu1CDE, Song Wu3DE, Weifeng Xie4BF, Ning Wang1BF, Weihong Lv5BF, Akshay Sood6DF, Shuguang Leng6DF, Yongchun Li1DF, Qing Sun7AEF, Jun Guan8EF, Wei Han1AEF

Med Sci Monit In Press  ::  DOI: 10.12659/MSM.931467  

Available online: 2021-07-09, In Press, Corrected Proof

Publication in the "In-Press" formula aims at speeding up the public availability of the pending manuscript while waiting for the final publication. The assigned DOI number is active and citable. The availability of the article in the Medline, PubMed and PMC databases as well as Web of Science will be obtained after the final publication according to the journal schedule

Abstract

BACKGROUND: Effective identification of patients with suspected COVID-19 is vital for the management. This study aimed to establish a simple clinical prediction model for COVID-19 in primary care.

MATERIAL AND METHODS: We consecutively enrolled 60 confirmed cases and 152 suspected cases with COVID-19 into the study. The training cohort consisted of 30 confirmed and 78 suspected cases, whereas the validation cohort consisted of 30 confirmed and 74 suspected cases. Four clinical variables – epidemiological history (E), body temperature (T), leukocytes count (L), and chest computed tomography (C) – were collected to construct a preliminary prediction model (model A). By integerizing coefficients of model A, a clinical prediction model (model B) was constructed. Finally, the scores of each variable in model B were summed up to build the ETLC score.

RESULTS: The preliminary prediction model A was Logit (YA)=2.657X₁+1.153X₂+2.125X₃+2.828X₄–10.771, while the model B was Logit (YB)=2.5X₁+1X₂+2X₃+3X₄–10. No significant difference was found between the area under the curve (AUC) of model A (0.920, 95% CI: 0.875-0.953) and model B (0.919, 95% CI: 0.874-0.952) (Z=0.035, P=0.972). When ETLC score was more than or equal to 9.5, the sensitivity and specificity for COVID-19 was 76.7% (46/60) and 90.1% (137/152), respectively, and the positive and negative predictive values were 75.4% (46/61) and 90.7% (137/151), respectively.

CONCLUSIONS: The ETLC score is helpful for efficiently identifying patients with suspected COVID-19.

Keywords: COVID-19; Logistic Models; ROC Curve

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