A Simple Clinical Prediction Tool for COVID-19 in Primary Care with Epidemiology: Temperature-Leukocytes-CT Results.
Med Sci Monit
; 27: e931467, 2021 Oct 06.
Article
in English
| MEDLINE | ID: covidwho-1344552
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Primary Health Care
/
Diagnosis, Computer-Assisted
/
COVID-19
Type of study:
Cohort study
/
Diagnostic study
/
Observational study
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
Med Sci Monit
Journal subject:
Medicine
Year:
2021
Document Type:
Article
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