ArticleMedical science monitor : international medical journal of experimental and clinical research2021
A Simple Clinical Prediction Tool for COVID-19 in Primary Care with Epidemiology: Temperature-Leukocytes-CT Results.
Article in Medical science monitor : international medical journal of experimental and clinical research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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