tetano
Editor, Senior Moderator
Curr Med Res Opin
. 2020 Sep 18;1.
doi: 10.1080/03007995.2020.1825365. Online ahead of print.
Clinical Features Predicting Mortality Risk in Older Patients with COVID-19
Jing Zhou[SUP] 1 2 [/SUP], Lili Huang[SUP] 3 [/SUP], Jin Chen[SUP] 4 [/SUP], Xiaowei Yuan[SUP] 5 [/SUP], Qinhua Shen[SUP] 5 [/SUP], Su Dong[SUP] 6 [/SUP], Bei Cheng[SUP] 7 8 [/SUP], Tang-Meng Guo[SUP] 7 8 [/SUP]
Affiliations
Abstract
Background: Since December 2019, the cumulative number of coronavirus disease-2019 (COVID-19) deaths in worldwide has reached 612,876 and continues to increase as of writing. Of the deaths, more than 90% are people ages 60 and older. However, an easy-to-use clinically predictive tool for predicting the mortality risk in older individuals with COVID-19 is limited.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 January 12 to February 26, 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, NLR, lactate dehydrogenase, albumin, urea nitrogen, and D-dimer (NLAUD) show greater differences and have better regression coefficients (β) when using hierarchical comparisons in a multivariate logistic regression model. Predictors of mortality based on better regression coefficients (β) included NLR (OR =31.2, 95% CI 6.7-144.5, p < 0.0001), lactate dehydrogenase (OR =73.4, 95% CI 11.8-456.8, p < 0.0001), albumin (OR <0.1, 95% CI <0.1-0.2, p < 0.0001), urea nitrogen (OR =12.0, 95% CI 3.0-48.4, p = 0.0005), and D-dimer (OR =13.6, 95% CI 3.4-54.9, p = 0.0003). 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 < 0.001). 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 severe older patients with COVID-19.
Keywords: COVID-19; Clinical features; and specificity; mortality; older adults; sensitivity.
. 2020 Sep 18;1.
doi: 10.1080/03007995.2020.1825365. Online ahead of print.
Clinical Features Predicting Mortality Risk in Older Patients with COVID-19
Jing Zhou[SUP] 1 2 [/SUP], Lili Huang[SUP] 3 [/SUP], Jin Chen[SUP] 4 [/SUP], Xiaowei Yuan[SUP] 5 [/SUP], Qinhua Shen[SUP] 5 [/SUP], Su Dong[SUP] 6 [/SUP], Bei Cheng[SUP] 7 8 [/SUP], Tang-Meng Guo[SUP] 7 8 [/SUP]
Affiliations
- PMID: 32945707
- DOI: 10.1080/03007995.2020.1825365
Abstract
Background: Since December 2019, the cumulative number of coronavirus disease-2019 (COVID-19) deaths in worldwide has reached 612,876 and continues to increase as of writing. Of the deaths, more than 90% are people ages 60 and older. However, an easy-to-use clinically predictive tool for predicting the mortality risk in older individuals with COVID-19 is limited.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 January 12 to February 26, 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, NLR, lactate dehydrogenase, albumin, urea nitrogen, and D-dimer (NLAUD) show greater differences and have better regression coefficients (β) when using hierarchical comparisons in a multivariate logistic regression model. Predictors of mortality based on better regression coefficients (β) included NLR (OR =31.2, 95% CI 6.7-144.5, p < 0.0001), lactate dehydrogenase (OR =73.4, 95% CI 11.8-456.8, p < 0.0001), albumin (OR <0.1, 95% CI <0.1-0.2, p < 0.0001), urea nitrogen (OR =12.0, 95% CI 3.0-48.4, p = 0.0005), and D-dimer (OR =13.6, 95% CI 3.4-54.9, p = 0.0003). 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 < 0.001). 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 severe older patients with COVID-19.
Keywords: COVID-19; Clinical features; and specificity; mortality; older adults; sensitivity.