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ABSTRACT: Background
Nowadays, there is still no effective treatment developed for COVID-19, and early identification and supportive therapies are essential in reducing the morbidity and mortality of COVID-19. This is the first study to evaluate D-dimer to lymphocyte ratio (DLR) as a prognostic utility in patients with COVID-19.Methods
We retrospectively analyzed 611 patients and separated them into groups of survivors and non-survivors. The area under the curve (AUC) of various predictors integrated into the prognosis of COVID-19 was compared using the receiver operating characteristic (ROC) curve. In order to ascertain the interaction between DLR and survival in COVID-19 patients, the Kaplan-Meier (KM) curve was chosen.Results
Age (OR = 1.053; 95% CI, 1.022-1.086; P = 0.001), NLR (OR = 1.045; 95% CI, 1.001-1.091; P = 0.046), CRP (OR = 1.010; 95% CI, 1.005-1.016; P < 0.001), PT (OR = 1.184; 95% CI, 1.018-1.377; P = 0.029), and DLR (OR = 1.048; 95% CI, 1.018-1.078; P = 0.001) were the independent risk factors related with the mortality of COVID-19. DLR had the highest predictive value for COVID-19 mortality with the AUC of 0.924. Patients' survival was lower when compared to those with lower DLR (Log Rank P <0.001).Conclusion
DLR might indicate a risk factor in the mortality of patients with COVID-19.
SUBMITTER: Peng F
PROVIDER: S-EPMC9797859 | biostudies-literature | 2022
REPOSITORIES: biostudies-literature
Peng Fei F Yi Qiong Q Zhang Quan Q Deng Jiayi J Li Cheng C Xu Min M Wu Chenfang C Zhong Yanjun Y Wu Shangjie S
Frontiers in cellular and infection microbiology 20221215
<h4>Background</h4>Nowadays, there is still no effective treatment developed for COVID-19, and early identification and supportive therapies are essential in reducing the morbidity and mortality of COVID-19. This is the first study to evaluate D-dimer to lymphocyte ratio (DLR) as a prognostic utility in patients with COVID-19.<h4>Methods</h4>We retrospectively analyzed 611 patients and separated them into groups of survivors and non-survivors. The area under the curve (AUC) of various predictors ...[more]