A novel epidemiological scoring system for the prediction of mortality in COVID-19 patients

Abstract

Background: Most of the reported risk score models for coronavirus disease 2019 (COVID-19) mortality are based on the levels of inflammatory markers, comorbidities or various treatment modalities, and there is a paucity of risk score models based on clinical symptoms and comorbidities. Methods: To address this need, age, clinical symptoms and comorbidities were used to develop a COVID-19 scoring system (CSS) for early prediction of mortality in severe COVID-19 patients. The CSS was developed with scores ranging from 0 to 9. A higher score indicates higher risk with good discrimination quality presented by Mann Whitney U test and area under receiver operating characteristic curve (AUROC). Results: Patient age of ≥60 y, cough, breathlessness, diabetes and any other comorbidity (with or without diabetes) are significant and independent risk factors for non-survival among COVID-19 patients. The CSS showed good sensitivity and specificity (i.e. 74.1% and 78.5% at CSS≥5, respectively), with an overall diagnostic accuracy of 82.8%, which was close to the diagnostic accuracy detected in the validation cohort (81.9%). In the validation cohort, high (8-9), medium (5-7) and low (0-4) CSS groups had 54.80%, 28.60% and 6.5% observed mortality, respectively, which was very close to the predicted mortality (62.40%, 27.60% and 5.2%, respectively, by scoring cohort). Conclusions: The CSS shows a positive relationship between a higher score and proportion of mortality and, as its validation showed, it is useful for the prediction of risk of mortality in COVID-19 patients at an early stage, so that referral for triage and admission can be predetermined even before admission to hospital.