Development of a novel risk score to predict mortality in patients admitted to hospital with COVID-19

Abstract

Patients hospitalised with COVID-19 have a high mortality. Identification of patients at increased risk of adverse outcome would be important, to allow closer observation and earlier medical intervention for those at risk, and to objectively guide prognosis for friends and family of affected individuals. We conducted a single-centre retrospective cohort study in all-comers with COVID-19 admitted to a large general hospital in the United Kingdom. Clinical characteristics and features on admission, including observations, haematological and biochemical characteristics, were used to develop a score to predict 30-day mortality, using multivariable logistic regression. We identified 316 patients, of whom 46% died within 30-days. We developed a mortality score incorporating age, sex, platelet count, international normalised ratio, and observations on admission including the Glasgow Coma Scale, respiratory rate and blood pressure. The score was highly predictive of 30-day mortality with an area under the receiver operating curve of 0.7933 (95% CI 0.745–0.841). The optimal cut-point was a score ≥ 4, which had a sensitivity of 78.36% and a specificity of 67.59%. Patients with a score ≥ 4 had an odds ratio of 7.6 for 30-day mortality compared to those with a score < 4 (95% CI 4.56–12.49, p < 0.001). This simple, easy-to-use risk score calculator for patients admitted to hospital with COVID-19 is a strong predictor of 30-day mortality. Whilst requiring further external validation, it has the potential to guide prognosis for family and friends, and to identify patients at increased risk, who may require closer observation and more intensive early intervention.