Athirah, Abdul Razak and Asma, Abu-Samah and Normy Norfiza, Abdul Razak and Ummu Kulthum, Jamaludin and Fatanah, Suhaimi and Azrina, Ralib and Mohd Basri, Mat Nor and Pretty, Christopher G. and Knopp, Jennifer Launa and Chase, J. Geoffrey (2020) Assessment of glycemic control protocol (STAR) through compliance analysis amongst Malaysian ICU patients. Medical Devices: Evidence and Research, 13. pp. 139-149. ISSN 1179-1470. (Published)
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Abstract
Purpose: This paper presents an assessment of an automated and personalized stochastic targeted (STAR) glycemic control protocol compliance in Malaysian intensive care unit (ICU) patients to ensure an optimized usage. Patients and Methods: STAR proposes 1– 3 hours treatment based on individual insulin sensitivity variation and history of blood glucose, insulin, and nutrition. A total of 136 patients recorded data from STAR pilot trial in Malaysia (2017–quarter of 2019*) were used in the study to identify the gap between chosen administered insulin and nutrition intervention as recommended by STAR, and the real intervention performed. Results: The results show the percentage of insulin compliance increased from 2017 to first quarter of 2019* and fluctuated in feed administrations. Overall compliance amounted to 98.8% and 97.7% for administered insulin and feed, respectively. There was higher average of 17 blood glucose measurements per day than in other centres that have been using STAR, but longer intervals were selected when recommended. Control safety and performance were similar for all periods showing no obvious correlation to compliance. Conclusion: The results indicate that STAR, an automated model-based protocol is positively accepted among the Malaysian ICU clinicians to automate glycemic control and the usage can be extended to other hospitals already. Performance could be improved with several propositions.
| Item Type: | Article |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Compliance; Glycemic control; Diabetes; Stochastic targeted prediction; Modelbased control |
| Subjects: | R Medicine > RC Internal medicine T Technology > TJ Mechanical engineering and machinery T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Faculty of Mechanical Engineering |
| Depositing User: | Mrs Norsaini Abdul Samat |
| Date Deposited: | 18 Aug 2022 06:59 |
| Last Modified: | 18 Aug 2022 06:59 |
| URI: | http://umpir.ump.edu.my/id/eprint/30100 |
| Download Statistic: | View Download Statistics |
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