A New Rapid Approach for Predicting Death in Coronavirus Patients: the Development and Validation of the COVID-19 Risk-Score in Fars Province (CRSF)

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.