ArticleDiabetology & metabolic syndrome2025
Exploring the impact of glycemic variability on clinical outcomes in critically ill cerebral infarction patients.
Article in Diabetology & metabolic syndrome, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predictive value of different glycemic variability indicators for prognosis in critically ill patients: a meta-analysis.Frontiers in endocrinology · 2026Pooled it
- Glycemic variability does not provide incremental prognostic value for in-hospital death in community-acquired pneumonia patients: conventional clinical variables dominate.BMC pulmonary medicine · 2026Article
- Association Between Nursing Diagnoses and Mortality in Patients with Cardiac Disease: A Retrospective Cohort Study.Clinics and practice · 2026Article
- Association of Average Glucose and Glycemic Variability With 28-Day Mortality in Patients With Cardiac Arrest: A Retrospective Study.Journal of diabetes · 2025Article
- Differential prognostic value of glycemic dysregulation markers in critically ill individuals with ischemic stroke, with and without diabetes.Diabetology & metabolic syndrome · 2025Article
- Prognostic significance and temporal patterns of glycemic variability in critically ill non-diabetic patients with ischemic stroke: a retrospective multicenter cohort study.Frontiers in neurologyArticle
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3 authors.
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Abstract
backgroundGlycemic variability (GV) is a key determinant of outcomes in critically ill patients, yet its impact on cerebral infarction patients in intensive care units (ICUs) remains underexplored. This study evaluates the association between GV and clinical outcomes, including discharge outcomes, 90-day and 1-year mortality, and ICU/hospital length of stay (LOS).
methodsThis retrospective study of 778 cerebral infarction patients from the MIMIC-IV database assessed GV, calculated as the glucose standard deviation-to-mean ratio during ICU stays. Regression models evaluated GV's impact on discharge outcomes, mortality, and ICU/hospital LOS, with adjustments for confounders. Restricted cubic spline analyses identified risk thresholds, while sensitivity and subgroup analyses validated findings. Predictive performance was assessed using AUC, NRI, and IDI, and multiple imputation methods addressed missing data.
resultsHigher GV was significantly linked to adverse outcomes. Patients in the highest GV quartile had increased risks of poor discharge outcomes (adjusted OR: 1.83; 95% CI: 1.03-3.32; P = 0.042), 90-day mortality (adjusted HR: 1.51; 95% CI: 1.03-2.22; P = 0.036), and 1-year mortality (adjusted HR: 1.53; 95% CI: 1.07-2.18; P = 0.018). RCS analysis identified critical GV thresholds (≥ 11% for 90-day and ≥ 10% for 1-year mortality). Subgroup analysis revealed stronger associations between GV and poor outcomes in non-diabetic patients (adjusted OR: 1.89; 95% CI: 1.24-2.88; P = 0.003) compared to diabetic patients (adjusted OR: 0.81; 95% CI: 0.53-1.25; P = 0.337). Sensitivity analyses confirmed the robustness of findings across imputation methods.
conclusionsGV independently predicts poor outcomes in ICU cerebral infarction patients. Integrating GV metrics into clinical workflows may improve risk stratification and guide interventions. Future research should validate these findings and explore strategies to reduce GV.
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