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

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/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 (Y(A))=2.657X(1)+1.153X(2)+2.125X(3)+2.828X(4)–10.771, while the model B was Logit (Y(B))=2.5X(1)+1X(2)+2X(3)+3X(4)–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.