Clinical features predicting mortality risk in older patients with COVID-19

Background Since December 2019, the cumulative number of coronavirus disease 2019 (COVID-19) deaths worldwide has reached 1,013,100 and continues to increase as of writing. Of these deaths, more than 90% are people aged 60 and older. Therefore, there is a need for an easy-to-use clinically predictive tool for predicting mortality risk in older individuals with COVID-19. Objective To explore an easy-to-use clinically predictive tool that may be utilized in predicting mortality risk in older patients with COVID-19. Methods A retrospective analysis of 118 older patients with COVID-19 admitted to the Union Dongxihu Hospital, Huazhong University of Science and Technology, Wuhan, China from 12 January to 26 February 2020. The main results of epidemiological, demographic, clinical and laboratory tests on admission were collected and compared between dying and discharged patients. Results No difference in major symptoms was observed between dying and discharged patients. Among the results of laboratory tests, neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase, albumin, urea nitrogen and D-dimer (NLAUD) show greater differences and have better regression coefficients (beta) when using hierarchical comparisons in a multivariate logistic regression model. Predictors of mortality based on better regression coefficients (beta) included NLR (OR = 31.2, 95% CI 6.7-144.5,p < .0001), lactate dehydrogenase (OR = 73.4, 95% CI 11.8-456.8,p < .0001), albumin (OR < 0.1, 95% CI <0.1-0.2,p < .0001), urea nitrogen (OR = 12.0, 95% CI 3.0-48.4,p = .0005), and D-dimer (OR = 13.6, 95% CI 3.4-54.9,p = .0003). According to the above indicators, a predictive NLAUD score was calculated on the basis of a multivariate logistic regression model to predict mortality. This model showed a sensitivity of 0.889, specificity of 0.984 and a better predictive ability than CURB-65 (AUROC = 0.955 vs. 0.703,p < .001). Bootstrap validation generated the similar sensitivity and specificity. Conclusions We designed an easy-to-use clinically predictive tool for early identification and stratified treatment of older patients with severe COVID-19.