Stochastic Modelling of Insulin Sensitivity for Out of Hospital Cardiac Arrest Patients treated with Hypothermia

Abstract

Hypothermia is often used to treat out of hospital cardiac arrest (OHCA) patients who often simultaneously receive insulin for stress induced hyperglycaemia. Variations in response to insulin reflect dynamic changes in insulin sensitivity (SI), defined by the overall metabolic response to stress and therapy. Thus, tracking and forecasting this parameter is important to provide safe glycaemic control in highly dynamic patients. This study examines stochastic forecasting models of model-based SI variability in OHCA patients to assess the resulting potential impact of this therapy on glycaemic control quality and safety. A retrospective analysis of clinically validated model-based SI profiles identified using data from 240 post-cardiac arrest patients (9988 hours) treated with hypothermia, shortly after admission in the Intensive Care Unit (ICU). Data were divided into three periods: 1) cool (T ≥ 35oC); 2) idle period of 2 hours as hypothermia was removed; and 3) warm (T ≥ 37oC). The stochastic model captured 60.7% and 90.2% of SI predictions within the (25th–75th) and (5th–95th) probability forecast intervals during cool period. Equally, it is also recorded 62.8% and 92.1% of SI predictions respectively during the warm period. Maintaining the kernel density variance estimator to c = 1.0 yielded 60.7% and 90.2% for the cool period. Similarly, adjusting a variance estimator of c = 2.0 yields 60.4% and 90.1% for the warm period. A cohort-specific stochastic model of SI provided a conservative forecast for the inter-quartile range and was relatively exact for the 90% range. Adjusting the variance estimator provides a more accurate, cohort-specific stochastic model of SI dynamics for the 90% range. These latter results show clearly different levels and distribution of forecasted SI variability between the cold and warm periods.