Association between blood urea nitrogen to albumin ratio and 28-day mortality in ICU patients with acute respiratory failure: a retrospective analysis of MIMIC-IV database.
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引用次数: 0
Abstract
Purpose: The effect of the blood urea nitrogen-to-albumin ratio (BAR) on 28-day mortality in intensive care unit (ICU) patients with acute respiratory failure (ARF) is unknown.
Methods: Patients diagnosed with ARF were screened and randomly divided into training and validation sets (7:3) on the basis of the ICD-9 and ICD-10 diagnosis codes in the Medical Information Mart for Intensive Care IV (v.2.2) database. The primary outcome was the 28-day mortality after ICU admission. The training set was categorized into the low- and high-BAR groups on the basis of the optimal BAR cutoff values for 28-day mortality determined via receiver operating characteristic analysis. The clinical significance of the BAR was evaluated by the areas under the curve (AUCs), decision curve analysis (DCA), Kaplan-Meier (K-M) survival curve, logistic regression analyses and subgroup analysis.
Results: In total, 2,766 patients were included. The 28-day mortality rate was 30.2%. The AUCs and 95% confidence interval (CI) for the BAR were AUC 0.644 (95%CI, 0.618 to 0.671) in training set. Multivariate logistic regression revealed that the BAR was an independent factor affecting the prognosis of ARF in both training and validation sets. K-M curves revealed a significant difference in 28-day mortality between the low- and high-BAR groups (p < 0.001). DCA showed moderate performance. No obvious interaction was found by subgroup analysis in most subgroups.
Conclusion: The present work revealed that elevated BAR was significantly associated with worse 28-day mortality in patients with any cause of ARF. It remains to be shown whether retrospective analysis of an independent cohort can confirm the high predictive value of BAR.
期刊介绍:
BMC Medical Informatics and Decision Making is an open access journal publishing original peer-reviewed research articles in relation to the design, development, implementation, use, and evaluation of health information technologies and decision-making for human health.