BACKGROUND: The aim of the study was to explore a novel risk score to predict diagnosis with COVID‐19 among all suspected patients at admission. METHODS: This was a retrospective, multi‐center, observational study. The clinical data of all suspected patients were analyzed. Independent risk factors were identified via multivariate logistic regression analysis. RESULTS: 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 <100 beat/min (OR:3.80, 95%CI 2.00‐7.22), bilateral pneumonia (OR:3.60, 95%CI 1.83‐7.10), neutrophil count ≤6.3×10(9)/L (OR: 6.77, 95%CI 2.52‐18.19), eosinophil count ≤0.02×10(9)/L (OR:3.14, 95%CI 1.58‐6.22), glucose ≥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 <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 ROC curve and the area under the curve (AUC) was 0.921 (95%CI: 0.896‐0.945, p<0.01). CONCLUSIONS: We designed a novel risk score which might have a promising predictive capacity for diagnosis with COVID‐19 among suspected patients. This article is protected by copyright. All rights reserved.
A novel risk score to predict diagnosis with Coronavirus Disease 2019 (COVID‐19) in suspected patients: A retrospective, multi‐center, observational study