Investigation on Different Types of Blood Glucose Control System / Siva Gopalakrishnan and Ummu Kulthum Jamaludin

Basically it is a case study on proposed blood glucose protocols. Among the protocols been implemented in Intensive Care Unit or critically ill patients were been studied and the outcome of this project is to propose the suitable blood glucose protocol for critically ill patients in Malaysia. Two main protocols known as HTAA and SPRINT been implement in Hospital Tengku Ampuan Afzan Malaysia and Christchurch Hospital, New Zealand respectively. Besides of studying whether both protocols are capable to reduce the mortality rate, these two are compared in terms of patients suitability whether they can adapt to the current protocols or not. Next to find out whether both protocols are same in their goal and the significant level of the patient’s data obtained from both hospitals (HTAA and Christchurch). In order to find out the patients data is significant or not and the goal of the protocols whether similar, some statistical analysis been carried out solidify the hypothesis statement. This is to ensure the outcome statement of proposing the best protocol between HTAA and SPRINT to critically ill patients in Malaysia

Brief review on polymeric materials concerning degradable polymers

The demand for cutting-edge functional materials has been increasing since the decade. Polymeric materials usage in the past decade contributes to its commercial accomplishment, thus encouraging more groundbreaking research-based activities. Although this news is promising for polymer-related industries, the fast consumption rate of these materials throughout the world will seriously harm the environment through the accumulation of waste materials sourced primarily from by-products, faulty products or municipal from various agricultural farms and industries with disposal difficulties. Wide usage of polymeric materials is due to their ease of processing, light weight and relatively low manufacturing cost. Various advancements were made over the years in developing polymeric materials of high performance. Structure and ionic bonds of polymeric and biomaterials are the reason behind their physical and chemical properties. However, their usage is limited due to expensive manufacturing cost and difficulty in shaping and processing them

Prediction of blood glucose level based on lipid profile and blood pressure using multiple linear regression model

Diabetes mellitus refers to a metabolic disorder that occurs due to insulin resistance and/or inability to produce enough insulin from islet β–cells in pancreas leads to increasing levels of blood glucose. Due to perturbation towards current diabetes screening and diagnosis procedures that require fasting, oral glucose consumption and involve invasive and finger-pricks, numbers of undiagnosed diabetes mellitus kept increasing due to hesitation of these people to take screening tests as their routine check-up. Since diabetes mellitus is closely related to blood glucose level, a multiple linear regression model for predicting the blood glucose level gives the impression as one of the alternatives. Thus, this study proposed a multiple linear regression equation for predicting the fasting blood glucose level based on independent parameters of lipid profile and blood pressure as high blood cholesterol and high blood pressure are known as risk factors for diabetes. There are 302 data collected from UMP’s retrospective data via data directory from University Health Centre in 2017 to 2018. This study shows that the adjusted R2 of 46.8% for multiple linear regression model of fasting blood glucose level was obtained to predict the possibility of pre-screening diabetes without fasting procedures. This model equation was solely based on high density lipoprotein cholesterol, triglyceride and systolic blood pressure levels with the prediction made by the model are acceptable with moderate accuracy (MAPE = 9.46%). In order to increase the accuracy of the model, future research should consider a bigger and wider cohort from different comorbidities background which can be an alternative method in screening diabetes mellitus

Archimedes screw pump efficiency based on three design parameters using computational fluid dynamics software – Ansys cfx

Utilization of Archimedes screw pumps as water lifting pumps has become widespread in past decade due to frequent occurrences of floods in Malaysia. The problem of insufficient drainage in various urban areas exacerbates the impact of heavy rainfall, prompting efforts to mitigate this issue with minimal maintenance cost and low impact to the environment. Thus, this study is aiming to study the design parameters of screw pump to obtain the optimal efficiency of the Archimedes screw pump specifically for flood mitigation in Malaysia. The main design parameters affecting pump's efficiency are rotor profile, pitch length, length of the pump, rotational speed, inclination angle, and material selection. However, only three design parameters were considered in the study, that are the angle of inclination, the number of blades, and the angular velocity of the rotating pump. These three design parameters are selected as many previous findings focusing on varying angle of inclination with number of blades with constant rotational speed. Thus, this study will find the highest efficiency when these three design parameters are integrated with variation of rotational speeds at 25, 30 and 40 RPM. Basically, screw pump is designed using SOLIDWORKS and simulations with specific boundary conditions are conducted using the ANSYS-CFX software, which utilizes computational fluid dynamics (CFD) techniques. These boundary conditions are based on previous study by Rosly et al in 2016. The inlet flow rate of 0.002 m3/s and diameter of the screw pump are constant while the other three main parameters are varying within the acceptable ranges which are reported from prior studies. The outcomes found that the highest torque is generated by a single rotating blade at 5.65 Nm which rotates at 30 RPM at 30° angle of inclination. Meanwhile, the highest efficiency of 24.04% is obtained with a single rotating blade at 40 RPM with 20° angle of inclination. Based on the findings, it is concluded that these three main design parameters of screw pump may not be sufficient to obtain the optimal efficiency for the specific boundary conditions used in the simulation study. Thus, several combinations of design parameters should be considered in the future to increase the screw pump's efficiency

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

Insulin Sensitivity and Sepsis Score: A Correlation between Model-based Metric and Sepsis Scoring System in Critically Ill Patients

Sepsis is highly correlated with mortality and morbidity. Sepsis is a clinical condition demarcated as the existence of infection and systemic inflammatory response syndrome, SIRS. Confirmation of infection requires a blood culture test, which requires incubation, and thus results take at least 48 h for a syndrome that requires early direct treatment. Since sepsis has a strong inflammatory component, it is hypothesized that metabolic markers affected by inflammation, such as insulin sensitivity, might provide a metric for more rapid, real-time diagnosis. This study uses clinical data from 30 sepsis patients (7624 h in ICU) of whom 60% are male. Median age and median Apache II score are 63 years and 19, respectively. Model-identified insulin sensitivity (SI) profiles were obtained for each patient, and insulin sensitivity and its hourly changes were correlated with modified hourly sepsis scores (SSH1). SI profiles and values were similar across the cohort. The sepsis score is highly variable and changes rapidly. The modified hourly sepsis score, SSH1, shows a better relation with insulin sensitivity due to less fluctuation in the SIRS element. Median SI and median SI of the cohort is 0.4193e-3 and 0.004253e-3 L/mU.min, respectively. Additionally, median SI are 4.392 × 10−4 L/mU min (SSH1 = 0), 4.153 × 10−4 L/mU min (SSH1 = 1), 3.752 × 10−4 L/mU min (SSH1 = 2) and 2.353 × 10−4 L/mU min (SSH1 = 3). Significant relationship between insulin sensitivity across different SSH1 groups was observed (p < 0.05) even when corrected for multiple comparisons. CDF of SI indicates that insulin sensitivity is more significant when comparing an hourly sepsis score at a very distinguished level

Model-based insulin sensitivity as a new biomarker of sepsis diagnosis in the intensive care unit

Introduction: Currently, there is a lack of real-time biomarker to diagnose sepsis. Insulin sensitivity (SI) may be determined in real-time using mathematical glucose-insulin models, but its effectiveness as a diagnostic test of sepsis remains unexplored. We aimed to explore the diagnostic value of model-based SI as a new biomarker of sepsis in a mixed cohort of diabetic and non-diabetic patients newly admitted to the intensive care unit (ICU). Materials and methods: In this cross-sectional study, we analysed SI levels derived from the Intensive[1]Control-of-Insulin-Nutrition-Glucose model in septic (n=45) and non-septic (n = 41) patients upon their ICU admission. The diagnostic value of model-based SI for sepsis was determined through analysis of the area under the curve (AUC) of the receiver operating characteristic curve. Results: Baseline SI levels were significantly lower in patients with sepsis than those without sepsis (0.560 (SD=0.676) vs. 1.097 (SD=1.473) x 10-4 L/mU/min, P = 0.037). However, the AUC of 0.588 revealed that model-based SI was a poor diagnostic test of sepsis in the mixed cohort of diabetics and non-diabetics. In a separate analysis among the non-diabetics (n=19), model-based SI predicted sepsis with clinically valid performance (AUC 0.911). Conclusion: Presence of sepsis significantly reduced SI in the critically ill patients but a low SI could predict sepsis only in the non-diabetic cohort

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)

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

Model comparison of estimated glomerular filtration rate for acute kidney injury in intensive care unit

Acute kidney injury (AKI) is common in critically ill patients and often associated with higher mortality. It is commonly diagnosed using plasma creatinine, a fluid excreted by glomerular filtration. In this study, we analysed the highly nonlinear and complex behaviour within human systems of estimating glomerular filtration rate in critically ill patients to estimate AKI outcome by developing an application program that describes various numerical mathematical models estimated glomerular filtration rate (eGFR). The program was developed to collect numerous clinical patient data and perform computations of the governing equations that may provide clinicians with rapid estimation of GFR which allow prediction of recovery or worsening of renal function at bedside. Data collection using the application program was conducted at the intensive care unit in Kuantan, Malaysia from January to August 2016. It was found that the eGFR by CKD-EPI shows the best correlation with kinetic GFR (keGFR) equations compared to other mathematical methods for Malaysian ICU patients. It is endeavoured that more patient data would be collected using this mobile application, to develop a more accurate GFR estimation model suitable for Asian populations since creatinine based equations are often derived based on white populations