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

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 toolto helpdecision-makingin the management of the COVID-19 pandemic.

From Jul to Nov 2020, 5454 patients from Fars Province, Iran, diagnosedwith COVID-19 were enrolled.A multiple logistic regression model was trained on one dataset (training set: n=4183) and its predic-tion performance was assessed on another dataset (testing set: n=1271). This model was utilized to develop the COVID-19 risk-score in Fars (CRSF).

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 CRSFformula was obtainedusing the estimated regression coefficient values of the aforementioned factors. Thepointvalues for the risk factors varied from 2 to 19 and the total CRSFvaried 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).

This simple CRSFsystem, which has a high NPV,can be useful for predicting the risk of mortali-ty in COVID-19 patients. It can also be used as a disease severity indicator to determine triage level for hospi-talization.