Pilot study: Efficacy of enhanced model predictive control (eMPC) in insulin therapy in the critically ill / Cheng Yee Shin - Cheng , Yee Shin (2016)
Background: Hyperglycaemia, in patients with diabetes or stress-induced, is known to be associated with poor outcome. The main cause of hyperglycaemia in the critically ills is due to the release of counter-regulatory stress hormones, and pro-inflammatory cytokines. Insulin therapy has been shown to improve patient outcome. However, blood sugar management is a challenging. The eMPC(Enhanced Model Predictive Control) algorithm is a computer-based decision support system to help with blood glucose management. The eMPC algorithm has been successfully tested in several clinical trials involving more than 200 patients. We thus aim to undertake a prospective, randomized, open-label, single centre study to investigate the effectiveness of the algorithm in local adult intensive care patients with sepsis. This is a pilot study to determine the appropriate design of the study. Methods: Patients are to be randomized into 2 groups. One group of patients receiving the convention insulin therapy via Insulin Sliding Scale, another group receives insulin therapy delivered via the eMPC algorithm based machine. The study endpoint is to compare the effectiveness of each intervention, with glucose within targeted range of 5.5mmol/l to 8.9mmol/1. Results: A total of 14 patients have been recruited. We see a higher mean percentage of blood glucose within targeted range in eMPC group, 54.2%, compared to the insulin sliding scale group, 37.5% (p<0.05). There was no significant difference in the number of times blood glucose testing is done, and insulin dosage for both groups (p>0.05). Conclusion: The eMPC model is effective in maintaining blood glucose level in critically ill patients in intensive care patients.