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

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

Virtual Trial And Monte Carlo Analysis Of Model-Based Glycaemic Control Protocol With Reduced Nursing Effort

Tight glycaemic management has been shown to be beneficial to the outcomes of patients receiving intensive care. However, tight glycaemic control (TGC) protocol within intensive care (ICU) comes with a high clinical demand, namely high nursing effort. Thus, there is a need for a protocol that is safe, effective, robust, yet does not require a high nursing effort. A less intensive protocol is designed to use a combination of subcutaneous long-acting insulin (glargine) with IV insulin bolus and only requires blood glucose (BG) measurements every 4 hours while maintaining measurement within 4.0-6.1 mmol/L

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

Prediction of Sepsis Progression in Critical Illness Using Artificial Neural Network

Early treatment of sepsis can reduce mortality and improve a patient condition. However, the lack of clear information and accurate methods of diagnosing sepsis at an early stage makes it become a significant challenge. The decision to start, continue or stop antimicrobial therapy is normally base on clinical judgment since blood cultures will be negative in the majority of cases of septic shock or sepsis. However, clinical guidelines are still required to provide guidance for the clinician caring for a patient with severe sepsis or septic shock. Guidelines based on patient’s unique set of clinical variables will help a clinician in the process of decision making of suitable treatment for the particular patient. Therefore, biomarkers for sepsis diagnosis with a reasonable sensitivity and specificity are a requirement in ICU settings, as a guideline for the treatment. Moreover, the biomarker should also allow availability in real-time and prediction of sepsis progression to avoid delay in treatment and worsen the patient condition

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

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

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

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)