A novel risk score to predict diagnosis with coronavirus disease 2019 (COVID-19) in suspected patients: A retrospective, multicenter, and observational study

Abstract

The aim of the study was to explore a novel risk score to predict diagnosis with COVID-19 among all suspected patients at admission. This was a retrospective, multicenter, and observational study. The clinical data of all suspected patients were analyzed. Independent risk factors were identified via multivariate logistic regression analysis. Finally, 336 confirmed COVID-19 patients and 139 control patients were included. We found nine independent risk factors for diagnosis with COVID-19 at admission to hospital: epidemiological exposure histories (OR:13.32; 95%CI, 6.39-27.75), weakness/fatigue (OR:4.51, 95%CI, 1.70-11.96), heart rate less than 100 beat/minutes (OR:3.80, 95%CI, 2.00-7.22), bilateral pneumonia (OR:3.60, 95%CI, 1.83-7.10), neutrophil count less than equal to 6.3 × 109/L (OR: 6.77, 95%CI, 2.52-18.19), eosinophil count less than equal to 0.02 × 109/L (OR:3.14, 95%CI, 1.58-6.22), glucose more than equal to 6 mmol/L (OR:2.43, 95%CI, 1.04-5.66), D-dimer ≥ 0.5 mg/L (OR:3.49, 95%CI, 1.22-9.96), and C-reactive protein less than 5 mg/L (OR:3.83, 95%CI, 1.86-7.92). As for the performance of this risk score, a cut-off value of 20 (specificity: 0.866; sensitivity: 0.813) was identified to predict COVID-19 according to reciever operator characteristic curve and the area under the curve was 0.921 (95%CI: 0.896-0.945; P '.01). We designed a novel risk score which might have a promising predictive capacity for diagnosis with COVID-19 among suspected patients.