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

Abstract

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

<p>Assessment of Glycemic Control Protocol (STAR) Through Compliance Analysis Amongst Malaysian ICU Patients</p>

“…[22][23][24][25][26] For better, more personalised control, fixed insulin protocols have been replaced by automated patient-specific model-based glycemic control. 17,[27][28][29][30][31][32] The stochastic targeted (STAR), in particular, is focused on using patient-specific time-varying insulin sensitivity 33 to provide safe, effective control to essentially all patients with reduced workload and increased nutrition delivery. [34][35][36][37] STAR uses a well-validated physiological model 38 and a user-friendly interface designed for accuracy 39,40 to dose insulin based on risk of future changes in insulin sensitivity (S I ), 41,42 an approach proven to generalise across adult 37,43 and neonatal ICUs, [44][45][46] as well as with other technologies.…”