Association Between D-dimer-to-albumin Ratio and 28-days All-cause Mortality in Patients with Sepsis

Overview

Journal Sci Rep

Specialty Science

Date 2024 Nov 16

PMID 39550487

Authors

Jing Lu

Weizhi Fang

Yu Lei

Jie Yang

Affiliations

Soon will be listed here.

Abstract

We aimed to investigate the correlation between the serum D-dimer (D-D) to albumin (ALB) ratio (DAR) and 28-day all-cause mortality in patients with sepsis. Data from sepsis patients admitted to the intensive care unit (ICU) of Wuhan Fourth Hospital from October 2021 to January 2024 were collected. Univariate cox analysis was performed for mortality factors in sepsis patients, and multiple cox regression models were used to analyze independent mortality risk factors. The receiver operating characteristics (ROC) curve was used to analyze the value of DAR in predicting sepsis mortality, and the Kaplan-Meier method was used to plot the survival curve. A total of 833 patients with sepsis in the ICU of our hospital were selected and divided into alive group (n = 574) and death group (n = 171) according to their 28-day survival. In the death group, D-D and DAR levels were higher, while ALB levels was lower than in the alive group. Spearman analysis found that DAR level were positively correlated with APACHE II and SOFA scores. Multivariate cox regression analysis showed that DAR was an independent predictor of all-cause mortality within 28 days of admission for sepsis patients (HR = 17.956, 95% CI 3.435-93.851, p < 0.001). The ROC curve results showed that the cut-off value of DAR was 0.068, with a sensitivity of 78.4% and a Youden index of 0.375, predicting mortality in sepsis patients, with an area under curve (AUC) of 0.767 (95% CI 0.744-0.790, P < 0.001). Further analysis divided patients into low DAR (DAR < 0.068) and high DAR (DA ≥ 0.068) groups based on the optimal cut-off value. Kaplan-Meier analysis found higher mortality in the high DAR group. DAR is an independent predictor of all-cause mortality within 28 days of admission in sepsis patients. Combining these two indicators can improve clinical treatment guidance and reduce the risk of death.

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