Interface Design and Human Factors Consideration for Model-Based Tight Glycemic Control in Critical Care

in Journal of Diabetes Science and Technology (2012)

Data Entry Errors and Design for Model-Based Tight Glycemic Control in Critical Care

in Journal of Diabetes Science and Technology (2012)

Tight Glycemic Control in Critically Ill Patients: the STAR Framework

Poster (2011, October)

Enhanced insulin sensitivity variability in the first 3 days of ICU stay: Implications for TGC

in Critical Care (2011, March)

Validation of a Virtual Patient and Virtual Trials Method for Accurate Prediction of TGC Protocol Performance

Poster (2011, March)

Insulin Sensitivity, Its Variability and Glycemic Outcome: A model-based analysis of the difficulty in achieving tight glycemic control in critical care

in 18th World Congress of the International Federation of Automatic Control (IFAC) (2011)

Effective tight glycemic control (TGC) can improve outcomes in intensive care unit (ICU) <br />patients, but is difficult to achieve consistently. Glycemic level and variability, particularly early in a ... [more ▼]

Effective tight glycemic control (TGC) can improve outcomes in intensive care unit (ICU) <br />patients, but is difficult to achieve consistently. Glycemic level and variability, particularly early in a <br />patient’s stay, are a function of variability in insulin sensitivity/resistance resulting from the level and <br />evolution of stress response, and are independently associated with mortality. This study examines the <br />daily evolution of variability of insulin sensitivity in ICU patients using patient data (N = 394 patients, <br />54019 hours) from the SPRINT TGC study. Model-based insulin sensitivity (SI) was identified each hour <br />and hour-to-hour percent changes in SI were assessed for Days 1-3 individually and Day 4 Onward, as <br />well as over all days. Cumulative distribution functions (CDFs), median values, and inter-quartile points <br />(25th and 75th percentiles) are used to assess differences between groups and their evolution over time. <br />Compared to the overall (all days) distributions, ICU patients are more variable on Days 1 and 2 (p < <br />0.0001), and less variable on Days 4 Onward (p < 0.0001). Day 3 is similar to the overall cohort (p = 0.74). <br />Absolute values of SI start lower and rise for Days 1 and 2, compared to the overall cohort (all days), (p < <br />0.0001), are similar on Day 3 (p = .72) and are higher on Days 4 Onward (p < 0.0001). ICU patients have <br />lower insulin sensitivity (greater insulin resistance) and it is more variable on Days 1 and 2, compared to <br />an overall cohort on all days. This is the first such model-based analysis of its kind. Greater variability <br />with lower SI early in a patient’s stay greatly increases the difficulty in achieving and safely maintaining <br />glycemic control, reducing potential positive outcomes. Clinically, the results imply that TGC patients will <br />require greater measurement frequency, reduced reliance on insulin, and more explicit specification of <br />carbohydrate nutrition in Days 1-3 to safely minimise glycemic variability for best outcome. [less ▲]

Pilot Trials of STAR Target to Range Glycemic Control

in Intensive Care Medicine (2011), 37 (Suppl 1)

Variability of insulin sensitivity for diabetics and non-diabetics during the first 3 days of ICU stay

Poster (2011)

Safety and Performance of Stochastic Targeted (STAR) Glycemic Control of Insulin and Nutrition – First Pilot Results

in Intensive Care Medicine (2011)

Glycemic Variability, Hypoglycemia and Organ Failure in the Glucontrol Study

in 10th Belgian Day on Biomedical Engineering (2011)