ArticleCritical care (London, England)2017
Untangling glycaemia and mortality in critical care.
Article in Critical care (London, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Association between trajectory of triglyceride-glucose index and all-cause mortality in critically ill patients with atrial fibrillation: a retrospective cohort study.Cardiovascular diabetology · 2025Observational
- The Value of Glycemic Gap for Predicting Mortality in ICU in Patients With and Without Diabetes.Journal of diabetes research · 2025Article
- Variation in monitoring: Glucose measurement in the ICU as a case study to preempt spurious correlations.Journal of biomedical informatics · 2024Observational
- Hyperglycemia and glucose variability are associated with worse survival in mechanically ventilated COVID-19 patients: the prospective Maastricht Intensive Care Covid Cohort.Diabetology & metabolic syndrome · 2023Article
- Toward nutrition improving outcome of critically ill patients: How to interpret recent feeding RCTs?Critical care (London, England) · 2023Review
- Estimating Enhanced Endogenous Glucose Production in Intensive Care Unit Patients with Severe Insulin Resistance.Journal of diabetes science and technology · 2022Article
- Impact of prolonged requirement for insulin on 90-day mortality in critically ill patients without previous diabetic treatments: a post hoc analysis of the CONTROLING randomized control trial.Critical care (London, England) · 2022Article
- Association between dysglycemia and mortality by diabetes status and risk factors of dysglycemia in critically ill patients: a retrospective study.Acta diabetologica · 2022Article
- Effect of glycemic gap upon mortality in critically ill patients with diabetes.Journal of diabetes investigation · 2021Observational
- Incorporating real-world evidence into the development of patient blood glucose prediction algorithms for the ICU.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Estimating Increased EGP During Stress Response in Critically Ill Patients.Journal of diabetes science and technology · 2021Article
- Insulin sensitivity in critically ill patients: are women more insulin resistant?Annals of intensive care · 2021Article
- Increased insulin resistance in intensive care: longitudinal retrospective analysis of glycaemic control patients in a New Zealand ICU.Therapeutic advances in endocrinology and metabolism · 2021Article
- Risk and reward: extending stochastic glycaemic control intervals to reduce workload.Biomedical engineering online · 2020Article
- Higher glycemic variability within the first day of ICU admission is associated with increased 30-day mortality in ICU patients with sepsis.Annals of intensive care · 2020Article
- Assessment of Glycemic Control Protocol (STAR) Through Compliance Analysis Amongst Malaysian ICU Patients.Medical devices (Auckland, N.Z.) · 2020Article
- Updates in Glycemic Management in the Hospital.Current diabetes reports · 2019Review
- 3D kernel-density stochastic model for more personalized glycaemic control: development and in-silico validation.Biomedical engineering online · 2019Article
- Model-based glycemic control in a Malaysian intensive care unit: performance and safety study.Medical devices (Auckland, N.Z.) · 2019Article
- Improving glycemic control in critically ill patients: personalized care to mimic the endocrine pancreas.Critical care (London, England) · 2018Review
Corrections and comments
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Authors and funding
5 authors.
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Abstract
backgroundHyperglycaemia is associated with adverse outcomes in the intensive care unit, and initial studies suggested outcome benefits of glycaemic control (GC). However, subsequent studies often failed to replicate these results, and they were often unable to achieve consistent, safe control, raising questions about the benefit or harm of GC as well as the nature of the association of glycaemia with mortality and clinical outcomes. In this study, we evaluated if non-survivors are harder to control than survivors and determined if glycaemic outcome is a function of patient condition and eventual outcome or of the glycaemic control provided.
methodsClinically validated, model-based, hour-to-hour insulin sensitivity (SI) and its hour-to-hour variability (%ΔSI) were identified over the first 72 h of therapy in 145 patients (119 survivors, 26 non-survivors). In hypothesis testing, we compared distributions of SI and %ΔSI in 6-hourly blocks for survivors and non-survivors. In equivalence testing, we assessed if differences in these distributions, based on blood glucose measurement error, were clinically significant.
resultsSI level was never equivalent between survivors and non-survivors (95% CI of percentage difference in medians outside ±12%). Non-survivors had higher SI, ranging from 9% to 47% higher overall in 6-h blocks, and this difference became statistically significant as glycaemic control progressed. %ΔSI was equivalent between survivors and non-survivors for all 6-hourly blocks (95% CI of difference in medians within ±12%) and decreased in general over time as glycaemic control progressed.
conclusionsWhereas non-survivors had higher SI levels, variability was equivalent to that of survivors over the first 72 h. These results indicate survivors and non-survivors are equally controllable, given an effective glycaemic control protocol, suggesting that glycaemia level and variability, and thus the association between glycaemia and outcome, are essentially determined by the control provided rather than by underlying patient or metabolic condition.
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