Evidence map›Paper›PMID 43004379›Full record

ArticleDiabetology & metabolic syndrome2026

Association between glycemic variability and delirium in critically ill older adults patients: a retrospective cohort study.

Yifan Wu, Lin Shi, Huan Ye

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Article in Diabetology & metabolic syndrome, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Yifan Wu *Department of Emergency, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, 215200, China. wuyifan20210523@163.com.
Lin ShiDepartment of Gastroenterology, Xuzhou Central Hospital, xuzhou, China.
Huan Ye *Department of Neurology, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, 215200, China. yehuan2014@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDelirium affects 30-70% of critically ill older adults patients and is associated with poor outcomes. While glycemic control is essential in intensive care, the impact of glycemic variability (GV) on delirium remains understudied, particularly in older adults patients. This study investigated the association between GV and delirium in critically ill older adults patients.

methodsWe analyzed 16,335 patients aged ≥ 65 years from the MIMIC-IV database. GV was calculated as the coefficient of variation of blood glucose measurements and categorized into quartiles. Delirium was assessed using Confusion Assessment Method for the Intensive Care Unit (CAM-ICU). Multivariate logistic regression evaluated the association between GV and delirium, adjusting for demographics, clinical parameters, and comorbidities. We assessed the added predictive value of GV using area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Random forest and Boruta algorithm determined variable importance.

resultsDelirium incidence increased across GV quartiles (28.7% in Q1 vs. 41.8% in Q4, p < 0.001). After full adjustment, the highest GV quartile showed 42% higher delirium risk compared to the lowest (OR 1.42, 95% CI 1.18-1.71, p = 0.003), with a significant dose-response relationship. Random forest and Boruta algorithm identified GV as the most important predictor of delirium. Adding GV to prediction models significantly improved risk reclassification, with significant NRI in the fully adjusted model (p = 0.01) and significant IDIs across all models (all p < 0.05). The association remained consistent across all subgroups regardless of diabetes status.

conclusionsHigher GV independently predicts increased delirium risk in critically ill older adults patients. Monitoring and managing GV may represent a modifiable target for delirium prevention in this vulnerable population.

Indexed as

AgingCritical careDeliriumGlycemic variabilityPredicative performance

Identifiers

PMID43004379
PMCPMC13746748

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