025 Estimates of glomerular filtration rate in the critically ill with sepsis

Accurate assessment of glomerular filtration rate (GFR) in ICU patients is very important for institution of supportive therapy, preventive therapy, early renal support, drug dosing modification or avoidance of nephrotoxic drugs. Kinetic estimate of GFR (keGFR) takes into account the changes of creatinine over time, creatinine production rate, and the volume of distribution, hence postulated to be a more accurate estimate of GFR in the acute setting, where there are rapidly changing kidney functions as in the critically ill. We evaluated the association of the keGFR with estimated GFR (eGFR) by conventional method

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 sensitivity. 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 the Malaysian intensive care unit

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 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

Estimation of plasma insulin and endogenous insulin secretion in critically ill patients using intensive control insulin-nutrition-glucose 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 × 106 mU while (6.59 × 105, 2.79 × 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

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. © 2018 Authors

Efficacy and safety of SPRINT and STAR protocol on Malaysian critically-ill patients

Abstract—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 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

Insulin sensitivity and blood glucose level of sepsis patients in the intensive care unit

Sepsis and hyperglycemia are highly associated with increases in mortality rates, particularly in the critically ill patients. Sepsis diagnosis has been proven challenging due to delay in getting the blood culture results. Thus, often clinical experiences overrule the protocol to prevent the worsening outcome of the patients. In some cases, the erroneous clinical judgement cause antibiotic resistance and even adverse clinical outcomes. This paper investigates the correlation between two parameters; insulin sensitivity and blood glucose level among sepsis patients. The blood glucose level is measured at the bedside during the patient's stay, whereas insulin sensitivity is obtained using the validated glucose-insulin model. Thus, the insulin sensitivity is a specific parameter of the patient, unregimented of the protocol given to the patient. The same parameters, blood glucose and insulin sensitivity, are also compared to the non-sepsis patients to establish a relationship that can be used for sepsis diagnosis. Given the availability of these two parameters that can be captured rapidly and instantly, a significant relationship can, therefore, help clinicians to identify sepsis at an early stage without second-guessing

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