{"title":"Prognostic value of the Glucose-to-Albumin ratio in sepsis-related mortality: A retrospective ICU study","authors":"Yuanshuo Ge , Zhe Wang , Youran Ma , Cheng Zhang","doi":"10.1016/j.diabres.2025.112217","DOIUrl":null,"url":null,"abstract":"<div><h3>Aims</h3><div>To investigate the prognostic value of the glucose-to-albumin ratio (GAR) in predicting 30-day and 90-day mortality in septic ICU patients.</div></div><div><h3>Methods</h3><div>Kaplan-Meier analysis with log-rank tests assessed survival by GAR quartiles. Multivariable Cox regression and restricted cubic splines (RCS) explored GAR’s relationship with mortality. ROC curves evaluated predictive performance, and Boruta identified key variables. Machine learning models assessed GAR’s predictive ability, with indirect effects analyzed through anion gap and BUN.</div></div><div><h3>Results</h3><div>Quartile 4 exhibited the lowest survival probability (log-rank p < 0.0001). GAR demonstrated the highest AUC for 30-day (0.66) and 90-day (0.65) mortality among individual predictors, while the stacked model achieved an AUC of 0.826. Cox regression showed GAR was independently associated with both 30-day (HR: 1.071 (95 % CI: 1.063–1.078, p < 0.001).) and 90-day mortality (HR: 1.071 (95 % CI: 1.064–1.078, p < 0.001). RCS analysis revealed an L-shaped relationship between GAR and mortality.</div></div><div><h3>Conclusions</h3><div>GAR is a strong predictor of 30-day and 90-day mortality in septic ICU patients. Incorporating GAR into clinical risk models could improve decision-making and sepsis management.</div></div>","PeriodicalId":11249,"journal":{"name":"Diabetes research and clinical practice","volume":"224 ","pages":"Article 112217"},"PeriodicalIF":6.1000,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Diabetes research and clinical practice","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0168822725002311","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
引用次数: 0
Abstract
Aims
To investigate the prognostic value of the glucose-to-albumin ratio (GAR) in predicting 30-day and 90-day mortality in septic ICU patients.
Methods
Kaplan-Meier analysis with log-rank tests assessed survival by GAR quartiles. Multivariable Cox regression and restricted cubic splines (RCS) explored GAR’s relationship with mortality. ROC curves evaluated predictive performance, and Boruta identified key variables. Machine learning models assessed GAR’s predictive ability, with indirect effects analyzed through anion gap and BUN.
Results
Quartile 4 exhibited the lowest survival probability (log-rank p < 0.0001). GAR demonstrated the highest AUC for 30-day (0.66) and 90-day (0.65) mortality among individual predictors, while the stacked model achieved an AUC of 0.826. Cox regression showed GAR was independently associated with both 30-day (HR: 1.071 (95 % CI: 1.063–1.078, p < 0.001).) and 90-day mortality (HR: 1.071 (95 % CI: 1.064–1.078, p < 0.001). RCS analysis revealed an L-shaped relationship between GAR and mortality.
Conclusions
GAR is a strong predictor of 30-day and 90-day mortality in septic ICU patients. Incorporating GAR into clinical risk models could improve decision-making and sepsis management.
期刊介绍:
Diabetes Research and Clinical Practice is an international journal for health-care providers and clinically oriented researchers that publishes high-quality original research articles and expert reviews in diabetes and related areas. The role of the journal is to provide a venue for dissemination of knowledge and discussion of topics related to diabetes clinical research and patient care. Topics of focus include translational science, genetics, immunology, nutrition, psychosocial research, epidemiology, prevention, socio-economic research, complications, new treatments, technologies and therapy.