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

Performance of STAR virtual trials for diabetic and non-diabetic in HTAA Intensive Care Unit

Critically ill patients are commonly linked to stress-induced hyperglycaemia which relates to insulin resistance and the risk of per-diagnosed with diabetes and other metabolic illnesses. Thus, it is essential to choose the best practice of blood glucose management in order to reduce morbidity and mortality rates in intensive care unit. This study is focusing on clinical data of 210 critically ill patients in Hospital Tengku Ampuan Afzan (HTAA), Kuantan who underwent Intensive Insulin Therapy which utilized a sliding scale method. Patients were identified in two main groups of diabetic (123) and non-diabetic (87) where stochastic model is generated to observe 90% confidence interval of insulin sensitivity. Blood glucose levels comparison between these two cohorts is conducted to observe the percentage of blood glucose levels within targeted band of 4.4 – 10.0 mmol/L. It is found that 82% of BG levels are within targated band for non-diabetes cohort under stochastic targeted (STAR) glycaemic control protocol. However, only 59.6% and 70.6% BG levels are within targeted band for diabetes cohort for insulin infusion therapy used in HTAA and STAR protocols. Thus, further investigation on blood glucose control protocol for diabetes patients is required to increase the reliability and efficacy of current practice despite of patient safety

A study on controllable aluminium doped zinc oxide patterning by chemical etching for MEMS application

This present work reports on the study of controllable aluminium doped zinc oxide (AZO) patterning by chemical etching for MEMS application. The AZO thin film was prepared by RF magnetron sputtering as it is capable of producing uniform thin film at high deposition rates. X-Ray diffraction (XRD) and atomic force microscopy (AFM) characterization were done to characterize AZO thin film. The sputtered AZO thin film shows c-axis (002) orientation, low surface roughness and high crystalline quality. To pattern AZO thin film for MEMS application, wet etching was chosen due to its ease of processing with few controlling parameters. Four etching solutions were used namely: 10 % Nitric acid, 10 % Phosphoric acid, 10 % Acetic acid and Molybdenum etch solutions. For the first time, chemical etching using Molybdenum etch that consist of a mixture of CH3COOH, HNO3 and H3PO4 was characterized and reported. The effect of these acidic solutions on the undercut etching, vertical and lateral etch rate were studied. The etched AZO were characterized by scanning electron microscopy (SEM) and stylus profilometer. The investigations showed that the Molybdenum etch has the lowest undercut etching of 7.11 µm, and is highly effective in terms of lateral and vertical etching with an etch ratio of 1.30. Successful fine patterning of AZO thin films was demonstrated at device level on a surface acoustic wave resonator fabricated in 0.35 μm CMOS technology. The AZO thin film acts as the piezoelectric thin film for acoustic wave generation. Patterning of the AZO thin film is necessary for access to measurement probe pads. The working acoustic resonator showed resonance peak at 1.044 GHz at 45.28 dB insertion loss indicating that the proposed Molybdenum etch method does not adversely affect the device’s operating characteristic

Estimation of Plasma Insulin and Endogenous Insulin Secretion in Critically Ill Patients Using ICING Model

The objective of this study is to estimate total plasma insulin level and endogenous insulin secretion by using Intensive Control Insulin-Nutrition-Glucose (ICING) model and 90 critically ill patients’ data from Hospital Tengku Ampuan Afzan, Kuantan. Integral-based method was applied to solve mathematical equations defined in ICING model to find critical parameters of insulin sensitivity (SI) and results of total endogenous insulin secretion and total plasma insulin level were presented in median and 95% confidence interval (CI). It is reported that the total median plasma insulin is 1.35 x 106 mU while (6.59 x 105, 2.79 x 106) mU is in 95% CI, and the total median endogenous insulin secretion is 12.9% from the total median plasma insulin. The results elucidated the effectiveness of current practice via Intensive Insulin Infusion Therapy (IIT) and also suggest a further study on investigating the incretin mechanism which is strongly believed to contribute to the total plasma insulin level and help to simulate endogenous insulin secretion

Feasibility Of An Intensive Control Insulin-Nutrition Glucose Model ‘Icing’ With Malaysian Critically-Ill Patient

A clinically verified patient-specific glucose-insulin metabolic model known as ICING is used to account for time-varying insulin sesnsitivity. ICING was developed and validated from critically-ill patients with various medical conditions in the intensive care unit in Christchurch Hospital, New Zealand. Hence, it is interesting and vital to analyse the compatibility of the model once fitted to Malaysian critically-ill data. Results were assessed in terms of percentage of model-fit error, both by cohort and per-patient analysis. The ICING model accomplished median fitting error of <1% over data from 63 patients. Most importantly, the median per-patients is at a low fitting error of 0.34% and per cohort is 0.35%. These results provide a promising avenue for near future simulations of developing tight glycaemic control protocol in Malaysian intensive care unit

Mathematical Modelling of Glucose-Insulin System Behaviour in Hospital Tengku Ampuan Afzan Intensive Care Unit Patients

Mathematical modelling of glucose-insulin system is significantly important to understand the body regulation control, to analyze experimental data based on clinical trials, to identify and quantify relevant physiological parameters, to design proper clinical trials and to assess diabetes therapies. In general, critically ill patients with blood glucose concentrations between 10.0 to 12.2 mmol/l is identified to develop an acute hyperglycaemia or high blood glucose (BG). Thus, to monitor hyperglycaemia among critically ill patients, this study is focused on observing the glucose-insulin system behaviour based on 40 patients’ clinical data collected in Hospital Tengku Ampuan Afzan, Kuantan, Pahang with clinically validated mathematical glucose-insulin model. By using this model, a critical model-based parameter known as insulin sensitivity (SI) that illustrates patient’s severity were identified hourly for all patients whose on insulin infusion therapy protocol for average four to six days. The results show that a BG normal distribution is attained with median kurtosis of 2.72. While, the 40 patient-specific SI indicate that an outliers-prone distribution occurred as kurtosis 3.96. Thus, abrupt changes in SI is basically due to chaotic interaction between blood glucose and insulin concentrations in bloodstreams. Also, the glucose-insulin behaviour pattern among these 40 critically ill patients might be varied due to their main diagnotics illness such as acute kidney failure, cardiovascular disease, etc. Overall, these results might assist clinicians and researchers to understand the glucose-insulin behaviour based on patient’s severity illness and helps to inform glycaemic control protocol development in a larger group of critically ill patients

Performance of Glycemic Control Protocol and Virtual Trial

Model-based glycemic control offers direct management of patient-specific variability and better adaptive control. Implementation of the model-based glycemic control has the potential to reduce hyperglycemia episodes, mortality and morbidity as seen in some successful TGC. The design of any TGC must consider not only the glycemic target range but also safety and efficacy of the insulin therapy. This paper presents the evaluation of glycemic control protocol adapted in the ICU of Tengku Ampuan Afzan Hospital. Virtual trials method is used to simulate the controller algorithm on a virtual patient with feed variation factor. Data from actual clinical and the virtual trial are compared to analyze the protocol performance concerning blood glucose outcome and insulin efficacy. A stochastic model is also used to indicate metabolic response and metabolic variation of the cohort

Probabilistic glycemic control decision support in ICU : proof of concept using bayesian network

Glycemic control in intensive care 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 personalized automated recommendations. One of the most promising solutions for this is the STAR protocol, which is based on a clinically validated insulin-nutrition-glucose physiological model. However, this approach does not consider demographical background such as age, weight, height, and ethnicity. This article presents the extension to intensive care personalized solution by integrating per-patient demographical, and upon admission information to intensive care conditions to automate decision support for clinical staff. In this context, a virtual study was conducted on 210 retrospectives intensive care 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 performance of the STAR control. The proof of concept shows 71.43% and 73.90% overall inference precision, and reliability, respectively, on the test dataset. With more data, improved Bayesian Network is believed to be reproduced. These results, nevertheless, points at the feasibility of the network to act as an effective classifier using intensive care units data, and glycemic control performance to be the basis of a probabilistic, personalized, and automated decision support in the intensive care unit