A novel risk score to predict diagnosis with coronavirus disease 2019 (COVID‐19) in suspected patients: A retrospective, multicenter, and observational study
Dong Huang, Ting Wang, Zhu Chen, Huan Yang, Rong Yao, Zongan Liang
Abstract
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 × 10 9 /L (OR: 6.77, 95%CI, 2.52‐18.19), eosinophil count less than equal to 0.02 × 10 9 /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.