Performance of stochastic targeted blood glucose control protocol by virtual trials in the Malaysian intensive care unit

Background and objective: Blood glucose variability is common in healthcare and it is not related or influ- enced by diabetes mellitus. To minimise the risk of high blood glucose in critically ill patients, Stochastic Targeted Blood Glucose Control Protocol is used in intensive care unit at hospitals worldwide. Thus, this study focuses on the performance of stochastic modelling protocol in comparison to the current blood glucose management protocols in the Malaysian intensive care unit. Also, this study is to assess the ef- fectiveness of Stochastic Targeted Blood Glucose Control Protocol when it is applied to a cohort of diabetic patients. Methods: Retrospective data from 210 patients were obtained from a general hospital in Malaysia from May 2014 until June 2015, where 123 patients were having comorbid diabetes mellitus. The comparison of blood glucose control protocol performance between both protocol simulations was conducted through blood glucose fitted with physiological modelling on top of virtual trial simulations, mean calculation of simulation error and several graphical comparisons using stochastic modelling. Results: Stochastic Targeted Blood Glucose Control Protocol reduces hyperglycaemia by 16% in diabetic and 9% in nondiabetic cohorts. The protocol helps to control blood glucose level in the targeted range of 4.0–10.0 mmol/L for 71.8% in diabetic and 82.7% in nondiabetic cohorts, besides minimising the treatment hour up to 71 h for 123 diabetic patients and 39 h for 87 nondiabetic patients. Conclusion: It is concluded that Stochastic Targeted Blood Glucose Control Protocol is good in reducing hyperglycaemia as compared to the current blood glucose management protocol in the Malaysian inten- sive care unit. Hence, the current Malaysian intensive care unit protocols need to be modified to enhance their performance, especially in the integration of insulin and nutrition intervention in decreasing the hyperglycaemia incidences. Improvement in Stochastic Targeted Blood Glucose Control Protocol in terms of u en model is also a must to adapt with the diabetic cohort

Determination of favorable blood glucose target range for stochastic TARgeted (STAR) glycemic control in Malaysia

Stress-induced hyperglycemia is common in critically ill patients, but there is uncertainty about what constitutes an optimal blood glucose target range for glycemic control. Furthermore, to reduce the rate of hyperglycemic and hypoglycemic events, model-based glycemic control protocols have been introduced, such as the stochastic targeted (STAR) glycemic control protocol. This protocol has been used in the intensive care units of Christchurch and Gyulà Hospital since 2010, and in Malaysia since 2017. In this study, we analyzed the adaptability of the protocol and identified the blood glucose target range most favorable for use in the Malaysian population. Virtual simulation results are presented for two clinical cohorts: one receiving treatment by the STAR protocol itself and the other receiving intensive insulin therapy by the sliding scale method. Performance and safety were analyzed using five clinical target ranges, and best control was simulated at a target range of 6.0–10.0 mmol/L. This target range had the best balance of performance, with the lowest risk of hypoglycemia and the lowest requirement for nursing interventions. The result is encouraging as the STAR protocol is suitable to provide better and safer glycemic control while using a target range that is already widely used in Malaysian intensive care units

Study on the blood glucose management with controlled goal feed in Malaysian critically ill patients

Stress-induced hyperglycaemia is commonly occurred in the intensive care unit (ICU). It is known that the intensive insulin therapy (IIT) has successfully managed the blood glucose level within the targeted band. However, modifications on the current practice need to be considered to minimize the risk of hypoglycaemia and mortality. Thus, the aim of this study is to assess the performance of a new practice known as Stochastic Targeted (STAR) Protocol in managing blood glucose levels in Malaysia ICU setting. STAR is a tabletcomputer based protocols that provides patient-specific glucose control framework accounting for patient variability with a stochastically derived maximum 5% risk of hypoglycaemia events. A retrospective 92 non-diabetes patient’s data who underwent IIT were identified. Patient’s blood glucose levels, exogenous insulin and nutrition inputs including patient demographics were extracted from the ICU charts to create virtual patients by using physiologically mathematical model. Three trials were simulated with controlled goal feed (GF) and without GF. Only one type of nutrition is considered in this study which is Glucerna. The outcomes will be compared in terms of %BG within the targeted band of 4.4 to 10.0 mmol/L, the total number of BG measurements, and the % of severe hypoglycaemia. The results indicate that STAR virtual trial with controlled GF reduced the risk of hypoglycaemia to 3% and the clinical burden up to 1630 hours while maintaining BG within the targeted band. The total number of BG measurements also decreased to 5384 from 7038. Thus, the implementation of STAR protocol in the Malaysia ICU is beneficial and it is proven safe while aiding nurses and physicians in reducing the clinical burden and medical cost in treating stress-induce hyperglycaemia in the demanding ICU setting

The effects of insulin infusion protocol on the glycemic level of the intensive care patients

Insulin infusion protocol is the standard protocol that has been practiced in Malaysia's intensive care unit (ICU) for controlling the hyperglycemia. Multiple sliding scale method of the insulin infusion protocol may drive conflict in selecting an appropriate scale to be applied to the patient. The objective of this paper is to analyse the blood glucose outcome of eight sliding scales insulin infusion protocol adopted in the Universiti Sains Malaysia Hospital (HUSM). A retrospective data of 78 ICU patients of HUSM were fitted using a validated glucose-insulin system to identify insulin sensitivity profiles of the patients. Then, these SI profiles were simulated on various scale protocols. The results obtained from this study showed that among eight scales, Scale 4 had the highest percentage of BG within the HUSM's target of 6.0-10.0 mmol/L. Scale 1 had the highest percentage of BG for the BG measurement more than 10.0 mmol/L while Scale 8 had the highest percentage of BG measurement of less than 6.0 mmol/L. However, none of the scale shown better performance than the current clinical practice. Furthermore, all of the eight scales had a more substantial number of BG measurement compared to the clinical. This study shows that Scale 2 and Scale 3 result in a similar outcome. Similarly, Scale 5 is almost the same as Scale 6. Thus, at least two sets of scale can be combined to reduce the number of scales. The reduction of scales consequently avoid confusion and helps the clinician in selecting the appropriate scale to be applied to the patients. From this study, it can be concluded that the HUSM protocol is a combination of scales. The scales may be shifted from one to another scale depending on patient condition and clinician judgement. A proper guideline for the scale shifting seems necessary to allow optimum glycemic management in the ICU

Assessment of glycemic control protocol (STAR) through compliance analysis amongst Malaysian ICU patients

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

Model-based insulin-nutrition administration for glycemic control in Malaysian critical care: First pilot trial

© 2018, Springer Science+Business Media Singapore. Stress-induced hyperglycemia is prevalent in critical care, even in patients with no history of diabetes. Control of blood glucose level with tight insulin therapy has been shown to reduce incidences of hyperglycemia leading to reduced mortality and improved clinical outcomes. STAR is a tablet-based glucose control protocol with a specialized user interface into which insulin and nutrition information can be entered and predicted. This research describes the first clinical pilot trial of STAR approach in International Islamic University Hospital, Kuantan, Malaysia. The clinically specified target for blood glucose level is between 4.4 and 8.0 mmol/L. Seven episodes (of 359 h) were recruited based on the need for glucose control. Overall, 43.93% of measurement are in the range of 4.4–8.0 mmol/L band. The blood glucose median is 8.30 [6.32–10.00] mmol/L with only 1 patient having below than 2.22 mmol/L which is the guaranteed minimum risk level. This pilot study shows that STAR protocol is a patient specific approach that provides a good glycemic control in critically ill patients. Nevertheless, its implementation in Malaysian intensive care environments requires modifications and improvements in certain areas

Efficacy and Safety of SPRINT and STAR Protocol on Malaysian Critically-ill Patients

Intensive care unit patients may have a better glycaemic management with the right control protocol. Results of virtual trial performance on Malaysian critically-ill patients adopting a model-derived and model-based control protocol known as SPRINT and STAR are presented in this paper. These ICU patients have been treated by intensive sliding-scale insulin infusion. The effectiveness and safety of glycaemic control are then analysed. Results showed that patient safety improved by 83% with SPRINT and STAR protocol as the number of hypoglycaemic patients significantly reduced (BG<;2.2 mmol/L). Percentage of time within desired bands and median BG improves in both SPRINT and STAR. However, the improvements are associated with higher number of BG measurements (workload)

Virtual trial of glycaemic control performance and nursing workload assessment in diabetic critically ill patients

Tight glycaemic control in critically ill patients is used to reduce mortality in intensive care units. However, its usage is debatable in reducing hypoglycaemia or accurately maintain normoglycaemia level. This paper presents the assessment for two ‘wider’ Stochastic TARgeted (STAR) glycemic controllers, namely Controller A (blood glucose (BG) target 4.4-8.0 mmol/L) and Controller B (BG target 4.4-10.0 mmol/L) with 1 to 3 hour nursing interventions. These controllers were assessed to determine the better control on diabetic and non-diabetic patients. 66 diabetic and 66 non-diabetic critically ill patient’s data from Hospital Tunku Ampuan Afzan (HTAA) were employed for virtual trial simulations with a clinically validated physiological model. Performance metrics were assessed within the percentage time in band (TIB) of 4.4 to 8.0 mmol/L, 4.4 to 10.0 mmol/L, and 6.0 to 10.0 mmol/L. Controller A shows better performance in normoglycaemic TIB of 4.4 to 10.0 mmol/L where non-diabetic and diabetic patients achieved 92.5% and 83.8% respectively. In conclusion, Controller A is higher in efficiency and safer to be used for both patients cohorts. However, higher clinical interventions in diabetic patients within this control raise the alarm to reduce nursing workload. This is believed to improve clinical interventions burnout and ensure patient’s comfortability

Association between Diabetes Mellitus and Sepsis for the Glycemic Control Outcome of Two Intensive Care Units in Malaysia

Close monitoring and tight glycemic control are required among critically ill patients as they have dynamic metabolism which may precipitate stress-induced hyperglycemia. Clinically, diabetes mellitus (DM) patient with sepsis indicated a high mortality rate. This study investigates the association between DM and non-DM related to sepsis and non-sepsis patients from different insulin infusion therapy management. This study used 128 retrospective data from Hospital A, and 37 retrospective data from Hospital B. ICU patients who received insulin infusion therapy during their stay in the ICU were selected. Both centres implement the sliding scale-based insulin infusion therapy with the target range for blood glucose (BG) level within 6.0 – 10.0 mmol/L. The retrospective clinical data were compared among cohorts for DM and non-DM associated with sepsis and non-sepsis conditions. Findings showed that the DM group had higher insulin sensitivity than non-DM for both cohorts. Meanwhile, cohort B had higher insulin sensitivity than cohort A for all classes. Cohort A (DM+Sepsis) had low insulin sensitivity (66.7 L/(mU.min) and worst condition with sepsis&nbsp;which resulted from the lowest percentage (30.81%) of BG measurement within the target range. The (nonDM+nonSepsis) class had the tightest glycemic control for cohort A (3.4 mmol/L) and cohort B (2.2 mmol/L), as observed by the BG interquartile range. Furthermore, cohort A (nonDM+nonSepsis) had a 41.55% of severe hyperglycemia and 0.12% for severe hypoglycemia. Contrary, cohort B (nonDM+nonSepsis) had the highest percentage within the target range (74.31%) and the lowest percentage of hyperglycemia (18.78%). There was significantly different (p-values &lt;0.05) between cohort A and cohort B in BG level and glucose intake, likewise between sepsis and non-sepsis of non-DM for both cohorts. The findings indicate that a successful glycemic control protocol is much influenced by insulin sensitivity, patient variability, diabetes condition, and patient sepsis status

Probabilistic glycemic control decision support in icu: proof of concept using Bayesian network

Glycemic control in critically ill patients is complex in terms of patients’ response to care and treatment. The variability and the search for improved insulin therapy outcomes have led to the use of human physiology model based on per-patient metabolic condition to provide automated recommendations. One of the most promising solution for this is the STAR protocol which is based on a clinically validated ICING insulin and nutrition physiological model, however this approach does not consider demographical background such as age, weight, height and ethnicity. This article presents the extension to their personalized care solution by integrating per-patient demographical and upon admission to intensive care conditions to automate decision support for clinical staffs. In this context, a virtual study was conducted on 210 retrospectives critically ill patients’ data. To provide a ground, the integration concept is presented roughly, but the details are given in terms of a proof of concept using Bayesian Network, linking the admission background and the STAR control’s performance. The proof of concept study shows the feasibility and the clinical potential to employ the probabilistic method as a decision support towards a more personalized care. ************************************************************************************* Kawalan glisemik dalam pesakit kritikal di unit rawatan rapi adalah rumit dari segi tindak balas pesakit terhadap penjagaan dan rawatan. Sifat keberubahan individu dan pencarian hasil terapi insulin yang lebih baik telah membawa kepada penggunaan model matematik fisiologi manusia berdasarkan keadaan metabolik pesakit untuk memberikan cadangan rawatan secara individu. Salah satu penyelesaian yang paling menjanjikan harapan adalah protokol STAR yang berdasarkan kepada model fisiologi insulin-nutrisi-glukosa yang telah disahkan secara klinikal. Namun pendekatan ini tidak mengambil kira latar belakang demografi seperti umur, berat, ketinggian dan etnik. Artikel ini membentangkan lanjutan kepada penyelesaian rawatan secara peribadi mereka dengan mengintegrasikan informasi demografi pesakit dan keadaan mereka semasa dimasukkan ke dalam unit rawatan rapi untuk mengautomasikan sokongan keputusan untuk kakitangan unit. Dalam konteks ini, satu kajian ‘virtual’ dilakukan pada data 210 pesaki. Sebagai kajian kes, konsep integrasi dibentangkan secara kasar, tetapi butiran diberikan dari segi bukti konsep yang menggunakan Rangkaian Bayesian, menghubungkan latar belakang kemasukan ke unit dan prestasi kawalan STAR. Bukti kajian kes menunjukkan 71.43% dan 73.90% ketepatan dan kebolehlaksanaan unjuran masing-masing dengan dataset ujian. Dengan lebih banyak data, rangkaian Bayesian yang lebih baik dipercayai boleh dihasilkan. Walaubagaimanapun, keputusan ini menunjukkan kemungkinan rangkaian ini bertindak sebagai pengelas yang berkesan dengan menggunakan data dari unit rawatan rapi dan prestasi kawalan glisemik untuk menjadi asas sokongan keputusan bersifat probabilistik, peribadi dan automatic dalam unit rawatan rapi