Evidence map›Paper›PMID 40128879›Full record

ArticleDiabetology & metabolic syndrome2025

Exploring the impact of glycemic variability on clinical outcomes in critically ill cerebral infarction patients.

Hui Yang, Hongcai Wang, Yan Jiang

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Hui YangDepartment of Neurology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, 150001, China. ethanparker123@163.com.
Hongcai WangDepartment of Neurology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, 150001, China.
Yan JiangDepartment of Neurology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, 150001, China.

Funding

Heilongjiang Natural Science Foundation Joint Guidance Project LH2020H065
6 · The paper itself

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.

Indexed as

Cerebral infarctionClinical outcomesGlycemic variabilityInsulin resistance

Identifiers

PMID40128879
PMCPMC11934728

What Socratic holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.