ArticleJournal of diabetes science and technology2010
What makes tight glycemic control tight? The impact of variability and nutrition in two clinical studies.
Article in Journal of diabetes science and technology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 67 citations in OpenAlex.
- Pilot study of the SPRINT glycemic control protocol in a Hungarian medical intensive care unit.Journal of diabetes science and technology · 2012Trial
- Insulin sensitivity in critically ill patients: are women more insulin resistant?Annals of intensive care · 2021Article
- Risk and reward: extending stochastic glycaemic control intervals to reduce workload.Biomedical engineering online · 2020Article
- 3D kernel-density stochastic model for more personalized glycaemic control: development and in-silico validation.Biomedical engineering online · 2019Article
- Nutrition delivery of a model-based ICU glycaemic control system.Annals of intensive care · 2018Article
- Autoregressive Modeling of Drift and Random Error to Characterize a Continuous Intravascular Glucose Monitoring Sensor.Journal of diabetes science and technology · 2018Article
- Continuous Glucose Monitoring Measures Can Be Used for Glycemic Control in the ICU: An In-Silico Study.Journal of diabetes science and technology · 2018Article
- Untangling glycaemia and mortality in critical care.Critical care (London, England) · 2017Article
- Simulation and qualitative analysis of glucose variability, mean glucose, and hypoglycemia after subcutaneous insulin therapy for stress hyperglycemia.Theoretical biology & medical modelling · 2016Article
- The benefits of tight glycemic control in critical illness: Sweeter than assumed?Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2014Review
- Daily evolution of insulin sensitivity variability with respect to diagnosis in the critically ill.PloS one · 2013Article
- Variability of insulin sensitivity during the first 4 days of critical illness: implications for tight glycemic control.Annals of intensive care · 2012Article
- Stochastic targeted (STAR) glycemic control: design, safety, and performance.Journal of diabetes science and technology · 2012Article
- Pilot proof of concept clinical trials of Stochastic Targeted (STAR) glycemic control.Annals of intensive care · 2011Article
- Physiological modeling, tight glycemic control, and the ICU clinician: what are models and how can they affect practice?Annals of intensive care · 2011Article
- Validation of a model-based virtual trials method for tight glycemic control in intensive care.Biomedical engineering online · 2010Article
- Organ failure and tight glycemic control in the SPRINT study.Critical care (London, England) · 2010Article
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Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionTight glycemic control (TGC) remains controversial while successful, consistent, and effective protocols remain elusive. This research analyzes data from two TGC trials for root causes of the differences achieved in control and thus potentially in glycemic and other outcomes. The goal is to uncover aspects of successful TGC and delineate the impact of differences in cohorts.
methodsA retrospective analysis was conducted using records from a 211-patient subset of the GluControl trial taken in Liege, Belgium, and 393 patients from Specialized Relative Insulin Nutrition Titration (SPRINT) in New Zealand. Specialized Relative Insulin Nutrition Titration targeted 4.0-6.0 mmol/liter, similar to the GluControl A (N = 142) target of 4.4-6.1 mmol/liter. The GluControl B (N = 69) target was 7.8-10.0 mmol/liter. Cohorts were matched by Acute Physiology and Chronic Health Evaluation II score and percentage males (p > .35); however, the GluControl cohort was slightly older (p = .011). Overall cohort and per-patient comparisons (median, interquartile range) are shown for (a) glycemic levels achieved, (b) nutrition from carbohydrate (all sources), and (c) insulin dosing for this analysis. Intra- and interpatient variability were examined using clinically validated model-based insulin sensitivity metric and its hour-to-hour variation.
resultsCohort blood glucose were as follows: SPRINT, 5.7 (5.0-6.6) mmol/liter; GluControl A, 6.3 (5.3-7.6) mmol/liter; and GluControl B, 8.2 (6.9-9.4) mmol/liter. Insulin dosing was 3.0 (1.0-3.0), 1.5 (0.5-3), and 0.7 (0.0-1.7) U/h, respectively. Nutrition from carbohydrate (all sources) was 435.5 (259.2-539.1), 311.0 (0.0-933.1), and 622.1 (103.7-1036.8) kcal/day, respectively. Median per-patient results for blood glucose were 5.8 (5.3-6.4), 6.4 (5.9-6.9), and 8.3 (7.6-8.8) mmol/liter. Insulin doses were 3.0 (2.0-3.0), 1.5 (0.8-2.0), and 0.5 (0.0-1.0) U/h. Carbohydrate administration was 383.6 (207.4-497.7), 103.7 (0.0-829.4), and 207.4 (0.0-725.8) kcal/day. Overall, SPRINT gave approximately 2x more insulin with a 3-4x narrower, but generally non-zero, range of nutritional input to achieve equally TGC with less hypoglycemia. Specialized Relative Insulin Nutrition Titration had much less hypoglycemia (<2.2 mmol/liter), with 2% of patients, compared to GluControl A (7.7%) and GluControl B (2.9%), indicating much lower variability, with similar results for glucose levels <3.0 mmol/liter. Specialized Relative Insulin Nutrition Titration also had less hyperglycemia (>8.0 mmol/liter) than groups A and B. GluControl patients (A+B) had a approximately 2x wider range of insulin sensitivity than SPRINT. Hour-to-hour variation was similar. Hence GluControl had greater interpatient variability but similar intrapatient variability.
conclusionProtocols that dose insulin blind to carbohydrate administration can suffer greater outcome glycemic variability, even if average cohort glycemic targets are met. While the cohorts varied significantly in model-assessed insulin resistance, their variability was similar. Such significant intra- and interpatient variability is a further significant cause and marker of glycemic variability in TGC. The results strongly recommended that TGC protocols be explicitly designed to account for significant intra- and interpatient variability in insulin resistance, as well as specifying or having knowledge of carbohydrate administration to minimize variability in glycemic outcomes across diverse cohorts and/or centers.
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