Development and Validation of a Simple Risk Scoring System for a COVID-19 Diagnostic Prediction Model

Introduction: In a resource-constrained situation, a clinical risk stratification system can assist in identifying individuals who are at higher risk and should be tested for COVID-19. This study aims to find a predictive scoring model to estimate the COVID-19 diagnosis. Materials and Methods: Patients who applied to the emergency pandemic clinic between April 2020 and March 2021 were enrolled in this retrospective study. At admission, demographic characteristics, symptoms, comorbid diseases, chest computed tomography (CT), and laboratory findings were all recorded. Development and validation datasets were created. The scoring system was performed using the coefficients of the odds ratios obtained from the multivariable logistic regression analysis. Results: Among 1187 patients admitted to the hospital, the median age was 58 years old (22-96), and 52.7% were male. In a multivariable analysis, typical radiological findings (OR= 8.47, CI= 5.48-13.10, p< 0.001) and dyspnea (OR= 2.85, CI= 1.71-4.74, p< 0.001) were found to be the two important risk factors for COVID-19 diagnosis, followed by myalgia (OR= 1.80, CI= 1.082.99, p= 0.023), cough (OR= 1.65, CI= 1.16-2.26, p= 0.006) and fatigue symptoms (OR= 1.57, CI= 1.06-2.30, p= 0.023). In our scoring system, dyspnea was scored as 2 points, cough as 1 point, fatigue as 1 point, myalgia as 1 point, and typical radiological findings were scored as 5 points. This scoring system had a sensitivity of 71% and a specificity of 76.3% for a cut-off value of >2, with a total score of 10 (p< 0.001). Conclusion: The predictive scoring system could accurately predict the diagnosis of COVID-19 infection, which gave clinicians a theoretical basis for devising immediate treatment options. An evaluation of the predictive