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

Contact Pattern of Alveolar Consonants in the Malay Consonants of Paralysis Subject using Electropalatography

Place of articulation plays an important part to produce different sounds. Besides the place of articulation, tongue is also an active articulator during a continuous speech. During the speech, the tongue moves around creating different sounds when it is placed at different place of articulation. The movement of tongue is controlled by muscles. The lack of muscle movement will produce inactive tongue movement. Paralysis is an example of the muscle weakness in a person resulting in difficulties to move. Paralysis may occur due to several factors including stroke and spinal cord injury (SCI). One of the indirect effects of paralysis is slurred speech and difficulty in speaking. This study aims to determine the contact pattern of five paralysed subjects during speech production of alveolar consonants in the Malay Language. The subjects had paralysis due to different aetiologies and with different medical history backgrounds. All participants were required to produce five single consonants; /d/, /t/, /l/, /n/ and /s/. The data recording was done in a studio laboratory with a soundproof system. The device used for detecting the tongue and hard palate contact in this study was electropalatography (EPG). Subjects were required to wear the artificial palate consists of 62 sensors to detect the tongue and hard palate contact. The speech contact was analysed using Articulate Assistant 1.18TM. The results were then compared with the average contact pattern of Malay speaker which had been obtained in the previous study. In conclusion, the subjects who had frequent treatments produced better articulation and the subjects with positive attitudes produced better articulation during the treatment process

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

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

Levels and diagnostic value of model-based insulin sensitivity in sepsis: a preliminary study

Background and Aims: Currently, there is a lack of real time metric with high sensitivity and specificity to diagnose sepsis. Insulin sensitivity (SI) may be determined in real time using mathematical glucose insulin models; however, its effectiveness as a diagnostic test of sepsis is unknown. Our aims were to determine the levels and diagnostic value of model based SI for identification of sepsis in critically ill patients. Materials and Methods: In this retrospective, cohort study, we analysed SI levels in septic (n = 18) and nonseptic (n = 20) patients at 1 (baseline), 4, 8, 12, 16, 20, and 24 h of their Intensive Care Unit admission. Patients with diabetes mellitus Type I or Type II were excluded from the study. The SI levels were derived by fitting the blood glucose levels, insulin infusion and glucose input rates into the Intensive Control of insulin Nutrition Glucose model. Results: The median SI levels were significantly lower in the sepsis than in the nonsepsis at all follow up time points. The areas under the receiver operating characteristic curve of the model based SI at baseline for discriminating sepsis from nonsepsis was 0.814 (95% confidence interval, 0.675–0.953). The optimal cut-off point of the SI test was 1.573 × 10-4 L/mu/min. At this cut-off point, the sensitivity was 77.8%, specificity was 75%, positive predictive value was 73.7%, and negative predictive value was 78.9%. Conclusions: Model based SI ruled in and ruled out sepsis with fairly high sensitivity and specificity in our critically ill nondiabetic patients. These findings can be used as a foundation for further, prospective investigation in this area

Model-based glycemic control in a Malaysian intensive care unit: performance and safety study

Background: Stress-induced hyperglycemia is common in critically ill patients. A few forms of model-based glycemic control have been introduced to reduce this phenomena and among them is the automated STAR protocol which has been used in the Christchurch and Gyulá hospitals’ intensive care units (ICUs) since 2010. Methods: This article presents the pilot trial assessment of STAR protocol which has been implemented in the International Islamic University Malaysia Medical Centre (IIUMMC) Hospital ICU since December 2017. One hundred and forty-two patients who received STAR treatment for more than 20 hours were used in the assessment. The initial results are presented to discuss the ability to adopt and adapt the model-based control framework in a Malaysian environment by analyzing its performance and safety. Results: Overall, 60.7% of blood glucose measurements were in the target band. Only 0.78% and 0.02% of cohort measurements were below 4.0 mmol/L and 2.2 mmol/L (the limitsfor mild and severe hypoglycemia, respectively). Treatment preference-wise, the clinical staff were favorable of longer intervention options when available. However, 1 hourly treatments were still used in 73.7% of cases. Conclusion: The protocol succeeded in achieving patient-specific glycemic control while maintaining safety and was trusted by nurses to reduce workload. Its lower performance results, however, give the indication for modification in some of the control settings to better fit the Malaysian environment. © 2019 Abu-Samah et al

The Impact of Insulin and Insulin Therapy on Physiology in Critical Illness

Hyperglycemia is prevalent in critical care, as patients experience stress-induced hyperglycemia, even with no history of diabetes. Hyperglycemia has a significant impact on patient mortality and other negative clinical outcomes such as severe infection, sepsis and septic shock. Tight glycemic control can significantly reduce these negative outcomes by reducing hyperglycemic episode, but achieving it remains clinically elusive, particularly with regard to what constitutes tight control and what protocols are optimal in terms of results and clinical effort. The model used in this thesis is validated using an independent data and readily be used for different clinical interventions. Moreover, this model also able to accurately predict clinical intervention outcomes given that the model prediction error is very small, which is better than any other reported model. In particular, model-based glycemic control methods is used to capture patient-specific physiological dynamics, such as insulin sensitivity, SI. To date, sepsis diagnosis has been a great challenge despite advancement in technologies and medical research. Critically, septic patients are often classified by practitioners according to their experience before standard test results can be assessed, as to avoid delay in treatment. Moreover, several scoring systems have also been widely used to represent sepsis condition and better standardization of sepsis definition across different centers. In this thesis, insulin sensitivity, SI, a model-based metric is used to identify sepsis condition based on the finding that SI represents metabolic condition of a patient. Additionally, several clinical and physiological variables obtained during patient’s stay in critical care are also investigated using mathematical computation and statistical analysis to identify relevant metric which can be accurately use for sepsis interventions. Even though information on SI, clinical and physiological variables of a patient are insufficient to determine the sepsis status, these informations have brought to a different perspective of diagnosing sepsis. Microcirculation dysfunction is very common in sepsis. Tracking of microcirculation state among septic patient enable better tracking of patient state particularly sepsis status. The tracking can potentially be done by using a pulse oximeter that can extract additional information related to oxygen extraction level. The processed signals are therefore represent relative absorption of oxyhemoglobin and reduced hemoglobin that can be used to assess microcirculation status. In addition, this thesis focus on the real challenge of early treatment of sepsis and sepsis diagnosis where several potential metabolic markers are investigated. Microcirculation conditions are assessed using a non-invasive method that is generally used in typical ICU settings. In particular, the concept and method used to assess microcirculation and metabolic conditions are developed in this thesis. Finally, the work presented in this thesis can act as a starting point for many other glycemic control problems in other environments. These areas include cardiac critical care and neonatal critical care that share most similarities to the environment studied in this thesis, to general diabetes where the population is growing exponentially world wide. Eventually, this added knowledge can lead clinical developments from protocol simulations to better clinical decision making

A simplified low‐cost phantom for image quality assessment of dental cone beam computed tomography unit

Abstract Introduction A standardised testing protocol for evaluation of a wide range of dental cone beam computed tomography (CBCT) performance and image quality (IQ) parameters is still limited and commercially available testing tool is unaffordable by some centres. This study aims to assess the performance of a low‐cost fabricated phantom for image quality assessment (IQA) of digital CBCT unit. Methods A customised polymethyl methacrylate (PMMA) cylindrical phantom was developed for performance evaluation of Planmeca ProMax 3D Mid digital dental CBCT unit. The fabricated phantom consists of four different layers for testing specific IQ parameters such as CT number accuracy and uniformity, noise and CT number linearity. The phantom was scanned using common scanning protocols in clinical routine (90.0 kV, 8.0 mA and 13.6 s). In region‐of‐interest (ROI) analysis, the mean CT numbers (in Hounsfield unit, HU) and noise for water and air were determined and compared with the reference values (0 HU for water and −1000 HU for air). For linearity test, the correlation between the measured HU of different inserts with their density was studied. Results The average CT number were −994.1 HU and −2.4 HU, for air and water, respectively and the differences were within the recommended acceptable limit. The linearity test showed a strong positive correlation (R2 = 0.9693) between the measured HU and their densities. Conclusion The fabricated IQ phantom serves as a simple and affordable testing tool for digital dental CBCT imaging