Geoffrey M. Shaw

Therapeutic hypothermia (TH) is often used to treat out-of-hospital cardiac arrest (OHCA) patients who also often simultaneously receive insulin for stress-induced hyperglycaemia. However, the impact of TH on systemic metabolism and insulin resistance in critical illness is unknown. This study analyses the impact of TH on metabolism, including the evolution of insulin sensitivity (SI) and its variability, in patients with coma after OHCA. This study uses a clinically validated, model-based measure of SI. Insulin sensitivity was identified hourly using retrospective data from 200 post-cardiac arrest patients (8,522 hours) treated with TH, shortly after admission to the intensive care unit (ICU). Blood glucose and body temperature readings were taken every one to two hours. Data were divided into three periods: 1) cool (T 37°C). A maximum of 24 hours each for the cool and warm periods was considered. The impact of each condition on SI is analysed per cohort and per patient for both level and hour-to-hour variability, between periods and in six-hour blocks. Cohort and per-patient median SI levels increase consistently by 35% to 70% and 26% to 59% (P <0.001) respectively from cool to warm. Conversely, cohort and per-patient SI variability decreased by 11.1% to 33.6% (P <0.001) for the first 12 hours of treatment. However, SI variability increases between the 18th and 30th hours over the cool to warm transition, before continuing to decrease afterward. OCHA patients treated with TH have significantly lower and more variable SI during the cool period, compared to the later warm period. As treatment continues, SI level rises, and variability decreases consistently except for a large, significant increase during the cool to warm transition. These results demonstrate increased resistance to insulin during mild induced hypothermia. Our study might have important implications for glycaemic control during targeted temperature management.

The metabolism of critically ill patients evolves dynamically over time. Post critical insult, levels of counter-regulatory hormones are significantly elevated, but decrease rapidly over the first 12–48 hours in the intensive care unit (ICU). These hormones have a direct physiological impact on insulin sensitivity (SI). Understanding the variability of SI is important for safely managing glycaemic levels and understanding the evolution of patient condition. The objective of this study is to assess the evolution of SI over the first two days of ICU stay, and using this data, propose a separate stochastic model to reduce the impact of SI variability during glycaemic control using the STAR glycaemic control protocol. The value of SI was identified hourly for each patient using a validated physiological model. Variability of SI was then calculated as the hour-to-hour percentage change in SI. SI was examined using 6 hour blocks of SI to display trends while mitigating the effects of noise. To reduce the impact of SI variability on achieving glycaemic control a new stochastic model for the most variable period, 0–18 hours, was generated. Virtual simulations were conducted using an existing glycaemic control protocol (STAR) to investigate the clinical impact of using this separate stochastic model during this period of increased metabolic variability. For the first 18 hours, over 80% of all SI values were less than 0.5 × 10^-3 L/mU.min, compared to 65% for >18 hours. Using the new stochastic model for the first 18 hours of ICU stay reduced the number of hypoglycaemic measurements during virtual trials. For time spent below 4.4, 4.0, and 3.0 mmol/L absolute reductions of 1.1%, 0.8% and 0.1% were achieved, respectively. No severe hypoglycaemic events (BG < 2.2 mmol/L) occurred for either case. SI levels increase significantly, while variability decreases during the first 18 hours of a patients stay in ICU. Virtual trials, using a separate stochastic model for this period, demonstrated a reduction in variability and hypoglycaemia during the first 18 hours without adversely affecting the overall level of control. Thus, use of multiple models can reduce the impact of SI variability during model-based glycaemic control.