Diagnostic Model of COVID-19 Infection Based on the Combination of Clinical Symptoms, Chest Radiography and Laboratory Test

Abstract

Background: COVID-19 is an infection caused by SARS-COV 2.For screening the patient, Rapid antigen for COVID-19 is used with a high diagnostic value. However, there are still some cases of false-negative even with clinical symptoms suggesting COVID-19. Undetected COVID-19 patients certainly will increase transmission. A simple and practical diagnostic model, using determining factors, is required to guide physicians through a quicker decision making process, especially when deciding the need for the isolation rooms for patients with COVID-like symptoms. Methods: This study is a cross-sectional study. The study was conducted at CiptoMangunkusumo Hospital, Jakarta.History of contact with COVID-19, clinical symptoms, laboratory examination, and chest radiograph data were taken from medical records. Bivariate and multivariate analyses were conducted to assess the effect sizes of patient factors on the diagnostic results.ROCcurve and Hosmer-Lemeshow calibration was used to make the scoring. Results: There were 187 patients with the majority of subjects in the age group 20 breaths/minute, leucocyte ≤ 10.000 cells/mLand typical chest radiography. The area under the curve for this model was 0,777 (CI95% (0,706-0,847), P<0,001). The probability was 82% with a cut-off point ≥ 4. Conclusion: Determinant models based on the combination of contact history, presence or history of fever, dyspnea, leucocyte count ≤ 10.000 cells/mL and typical chest radiography provides good accuracy to aid physicians in managing isolation room needs for patients with suspected COVID-19.

Keywords

• covid-19
• sars-cov 2 diagnostic model
• scoring