Tehran University of Medical Sciences Iranian Journal of Public Health 2251-6085 51 1 2022 01 04 178 187 EN Mehrdad Sharifi Department of Emergency Medicine, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran AND Emergency Medicine Research Center, Shiraz University of Medical Sciences, Shiraz, Iran Mohammad Hossein Khademian Department of Medical Surgical Nursing, School of Nursing and Midwifery, Shiraz University of Medical Sciences, Shiraz, Iran Razieh Sadat Mousavi-Roknabadi 1. Department of Emergency Medicine, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran 2. Emergency Medicine Research Center, Shiraz University of Medical Sciences, Shiraz, Iran Vahid Ebrahimi Department of Biostatistics, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran Robab Sadegh Department of Emergency Medicine, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran 2021 02 05 2021 04 26 Background:Patients who are identified to be at a higher risk of mortality from COVID-19 should receive better treatment and monitoring. This study aimed to propose a simple yet accurate risk assessment tool to help decision-making in the management of the COVID-19 pandemic. Methods: From Jul to Nov 2020, 5454 patients from Fars Province, Iran, diagnosed with COVID-19 were enrolled. A multiple logistic regression model was trained on one dataset (training set: n=4183) and its prediction performance was assessed on another dataset (testing set: n=1271). This model was utilized to develop the COVID-19 risk-score in Fars (CRSF). Results: Five final independent risk factors including gender (male: OR=1.37), age (60-80: OR=2.67 and >80: OR=3.91), SpO2 (≤85%: OR=7.02), underlying diseases (yes: OR=1.25), and pulse rate (<60: OR=2.01 and >120: OR=1.60) were significantly associated with in-hospital mortality. The CRSF formula was obtained using the estimated regression coefficient values of the aforementioned factors. The point values for the risk factors varied from 2 to 19 and the total CRSF varied from 0 to 45. The ROC analysis showed that the CRSF values of ≥15 (high-risk patients) had a specificity of 73.5%, sensitivity of 76.5%, positive predictive value of 23.2%, and negative predictive value (NPV) of 96.8% for the prediction of death (AUC=0.824, P<0.0001). Conclusion:This simple CRSF system, which has a high NPV,can be useful for predicting the risk of mortality in COVID-19 patients. It can also be used as a disease severity indicator to determine triage level for hospitalization. https://ijph.tums.ac.ir/index.php/ijph/article/view/23768 https://ijph.tums.ac.ir/index.php/ijph/article/download/23768/7479