Analysis of Insulin Sensitivity Stochastic Models Between STAR Original and Malaysian Cohorts. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Wai Lee, Jay Wing; Chiew, Yeong Shiong; Tan, Chee Pin; Razak, Athirah Abdul; Abdul Razak, Normy Norfiza
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 16143
• Page End: 16148
• Publication Date: 2020
• Subjects: Decision support -- Control of physiological -- clinical variables -- Stochastic control; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.436
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Maintaining healthy blood glucose (BG) levels is vital in ensuring the health of intensive care unit patients. In present work, there exists model-based glycemic control protocols that capture insulin-glucose dynamics that can provide patient-specific treatments. The Stochastic Targeted Glycemic Control (STAR) protocol is a model-based glycemic control protocol that utilizes stochastic modelling together with the Intensive Control Insulin Glycemic Control (ICING) model. STAR has shown its effectiveness in Christchurch and Hungary. However, it is currently less effective in Malaysia. A study is conducted to compare the stochastic model between the STAR original and Malaysian cohort to identify if the difference in effectiveness is due to a difference in stochastic insulin sensitivity (SI ) models between cohorts. Results from this study show that there could be a difference of up to 49.4% in predictive ability of the stochastic models from the two cohorts, suggesting that it could play a role in being the cause for its lack in effectiveness. With further patient data collection, this hypothesis could be proven or otherwise eliminated from the possible causes for the lack of effectiveness of the STAR protocol in Malaysia.