1. INTRODUCTION Stress-induced hyperglycaemia is common in critical care 1 Hyperglycaemia worsens patient outcomes, increasing risk of infection 2, myocardial infarction 1, polyneuropathy and multi- organ failure 3 Published protocols require significant added clinical effort 4 Very high effective insulin resistance challenges insulin-only protocols 4,5 Model-based protocols that modulate both insulin and nutrition have shown promising results, however computational resources are not typically available in critical care SPRINT is an easy-to-use alternative that provides control equivalent to model-based methods Developed from model-based methods using virtual cohorts 6 Nurse-driven protocol requires no external clinical intervention Hourly blood glucose measurements to gain control. Two-hourly measurements once stable to reduce clinical effort Insulin administered in bolus form for patient safety “Goal feed” computed based on age, size and gender, effectively customising the protocol for each patient 5 Nutrition optimised to improve critical care outcome 7 Easy-to-implement protocol gained high level of support from clinical and nursing staff and minimum non-compliance (<0.1%) JG Chase, G. Shaw, A. Le Compte, D. Lee, T. Lonergan, M. Willacy, J. Wong, J. Lin, T. Lotz, C. Hann Tight Glycaemic Control in Critical Care Using Insulin and Nutrition: The SPRINT Protocol METHOD: SPRINT (Specialised Relative Insulin-Nutrition Tables) RESULTS & CONCLUSIONS REFERENCES [1] S. E. Capes, et al., "Stress hyperglycaemia and increased risk of death after myocardial infarction in patients with and without diabetes: a systematic overview," Lancet, vol. 355, pp. 773-778, 2000. [2] B. R. Bistrian, "Hyperglycemia and Infection: Which is the Chicken and Which is the Egg?," JPEN J Parenter Enteral Nutr, vol. 25, pp. 180-181, 2001. [3] G. Van den Berghe, et al., "Intensive insulin therapy in the critically ill patients," N Engl J Med, vol. 345, pp. 1359-1367, 2001. [4] S. Meijering, et al., "Towards a feasible algorithm for tight glycaemic control in critically ill patients: a systematic review of the literature," Crit Care, vol. 10, pp. R19, 2006. [5] G. M. Shaw, et al., "Rethinking glycaemic control in critical illness - from concept to clinical practice change," Crit Care Resusc, vol. 8, pp. 90-9, 2006. [6] T. Lonergan, et al., "A Simple Insulin-Nutrition Protocol for Tight Glycemic Control in Critical Illness: Development and Protocol Comparison," Diabetes Technol Ther, vol. 8, pp. 191-206, 2006. [7] J. A. Krishnan, et al., "Caloric intake in medical ICU patients: consistency of care with guidelines and relationship to clinical outcomes," Chest, vol. 124, pp. 297-305, 2003. Over 23,000 hours of control for 165 severely ill patients Tight control to the 4-6 mmol/L and 4-7.75 mmol/L bands No clinically significant hypoglycaemia Statistically significant reductions in mortality compared to similar hyperglycaemic retrospective cohort ( APACHE II =21, Risk of Death = 33%) Time (days) 3025201510 50 100 90 80 70 60 50 40 SPRINT - ICU Mortality Retrospective - ICU Mortality Percent Time (days) 302520151050 100 90 80 70 60 50 40 30 SPRINT - Hospital Mortality Retrospective - Hospital Mortality Percent Improved ICU and hospital survival. 051015202530354045 0 2 4 6 8 10 12 14 Time since initiation of SPRINT [hours] Average BG [mmol/L] Time to band average of 6 hours and control maintained throughout patient stay Significance in mortality reductions improves with increasing stay 510152025 0.05 0.1 0.15 0.2 0.25 0.3 0.35 Blood glucose [mmol/L] Density Clinical ICU data - SPRINT Simulation - van den Berghe Simulation - Krinsley Simulation - Insulin sliding scale Simulation - SPRINT Simulation predictions match clinical results.