Normy N. Razak

A physiological Intensive Control Insulin-Nutrition-Glucose (ICING) model validated in critically ill patients

Intensive insulin therapy (IIT) and tight glycaemic control (TGC), particularly in intensive care unit (ICU), are the subjects of increasing and controversial debate in recent years. Model-based TGC has shown potential in delivering safe and tight glycaemic management, all the while limiting hypoglycaemia. A comprehensive, more physiologically relevant Intensive Control Insulin-Nutrition-Glucose (ICING) model is presented and validated using data from critically ill patients. Two existing glucose-insulin models are reviewed and formed the basis for the ICING model. Model limitations are discussed with respect to relevant physiology, pharmacodynamics and TGC practicality. Model identifiability issues are carefully considered for clinical settings. This article also contains significant reference to relevant physiology and clinical literature, as well as some references to the modeling efforts in this field. Identification of critical constant population parameters was performed in two stages, thus addressing model identifiability issues. Model predictive performance is the primary factor for optimizing population parameter values. The use of population values are necessary due to the limited clinical data available at the bedside in the clinical control scenario. Insulin sensitivity, S(I), the only dynamic, time-varying parameter, is identified hourly for each individual. All population parameters are justified physiologically and with respect to values reported in the clinical literature. A parameter sensitivity study confirms the validity of limiting time-varying parameters to S(I) only, as well as the choices for the population parameters. The ICING model achieves median fitting error of <1% over data from 173 patients (N=42,941 h in total) who received insulin while in the ICU and stayed for ≥ 72 h. Most importantly, the median per-patient 1-h ahead prediction error is a very low 2.80% [IQR 1.18, 6.41%]. It is significant that the 75th percentile prediction error is within the lower bound of typical glucometer measurement errors of 7-12%. These results confirm that the ICING model is suitable for developing model-based insulin therapies, and capable of delivering real-time model-based TGC with a very tight prediction error range. Finally, the detailed examination and discussion of issues surrounding model-based TGC and existing glucose-insulin models render this article a mini-review of the state of model-based TGC in critical care.

Tight glycemic control in critical care - The leading role of insulin sensitivity and patient variability: A review and model-based analysis

Tight glycemic control (TGC) has emerged as a major research focus in critical care due to its potential to simultaneously reduce both mortality and costs. However, repeating initial successful TGC trials that reduced mortality and other outcomes has proven difficult with more failures than successes. Hence, there has been growing debate over the necessity of TGC, its goals, the risk of severe hypoglycemia, and target cohorts. This paper provides a review of TGC via new analyses of data from several clinical trials, including SPRINT, Glucontrol and a recent NICU study. It thus provides both a review of the problem and major background factors driving it, as well as a novel model-based analysis designed to examine these dynamics from a new perspective. Using these clinical results and analysis, the goal is to develop new insights that shed greater light on the leading factors that make TGC difficult and inconsistent, as well as the requirements they thus impose on the design and implementation of TGC protocols. A model-based analysis of insulin sensitivity using data from three different critical care units, comprising over 75,000h of clinical data, is used to analyse variability in metabolic dynamics using a clinically validated model-based insulin sensitivity metric (S(I)). Variation in S(I) provides a new interpretation and explanation for the variable results seen (across cohorts and studies) in applying TGC. In particular, significant intra- and inter-patient variability in insulin resistance (1/S(I)) is seen be a major confounder that makes TGC difficult over diverse cohorts, yielding variable results over many published studies and protocols. Further factors that exacerbate this variability in glycemic outcome are found to include measurement frequency and whether a protocol is blind to carbohydrate administration.

Impact of glucocorticoids on insulin resistance in the critically ill

Glucocorticoids (GCs) have been shown to reduce insulin sensitivity in healthy individuals. Widely used in critical care to treat a variety of inflammatory and allergic disorders, they may inadvertently exacerbate stress-hyperglycaemia. This research uses model-based methods to quantify the reduction in insulin sensitivity from GCs in critically ill patients, and thus their impact on glycaemic control. A model-based measure of insulin sensitivity (S(I)) was used to quantify changes between two matched cohorts of 40 intensive care unit (ICU) patients. Patients in one cohort received GC treatment, while patients in the control cohort did not. All patients were admitted to the Christchurch hospital ICU between 2005 and 2007 and spent at least 24h on the SPRINT glycaemic control protocol. A 31% reduction in whole-cohort median insulin sensitivity was seen between the control cohort and patients receiving glucocorticoids with a median dose equivalent to 200mg/d of hydrocortisone per patient. Comparing percentile patients as a surrogate for matched patients, reductions in median insulin sensitivity of 20%, 25%, and 21% were observed for the 25th-, 50th- and 75th-percentile patients, respectively. These cohort and percentile patient reductions are less than or equivalent to the 30-62% reductions reported in healthy subjects especially when considering the fact that the GC doses in this study are 1.3-4.0 times larger than those in studies of healthy subjects. This reduced suppression of insulin sensitivity in critically ill patients could be a result of saturation due to already increased levels of catecholamines and cortisol common in critically illness. Virtual trial simulation showed that reductions in insulin sensitivity of 20-30% associated with glucocorticoid treatment in the ICU have limited impact on glycaemic control levels within the context of the SPRINT protocol.

Development of a model-based clinical sepsis biomarker for critically ill patients

Sepsis occurs frequently in the intensive care unit (ICU) and is a leading cause of admission, mortality, and cost. Treatment guidelines recommend early intervention, however positive blood culture results may take up to 48 h. Insulin sensitivity (S(I)) is known to decrease with worsening condition and could thus be used to aid diagnosis. Some glycemic control protocols are able to accurately identify insulin sensitivity in real-time. Hourly model-based insulin sensitivity S(I) values were calculated from glycemic control data of 36 patients with sepsis. The hourly S(I) is compared to the hourly sepsis score (ss) for these patients (ss=0-4 for increasing severity). A multivariate clinical biomarker was also developed to maximize the discrimination between different ss groups. Receiver operator characteristic (ROC) curves for severe sepsis (ss ≥ 2) are created for both S(I) and the multivariate clinical biomarker. Insulin sensitivity as a sepsis biomarker for diagnosis of severe sepsis achieves a 50% sensitivity, 76% specificity, 4.8% positive predictive value (PPV), and 98.3% negative predictive value (NPV) at an S(I) cut-off value of 0.00013 L/mU/min. Multivariate clinical biomarker combining S(I), temperature, heart rate, respiratory rate, blood pressure, and their respective hourly rates of change achieves 73% sensitivity, 80% specificity, 8.4% PPV, and 99.2% NPV. Thus, the multivariate clinical biomarker provides an effective real-time negative predictive diagnostic for severe sepsis. Examination of both inter- and intra-patient statistical distribution of this biomarker and sepsis score shows potential avenues to improve the positive predictive value.

Development of a Model-Based Clinical Sepsis Biomarker for Critically Ill Patients

Abstract Sepsis occurs frequently in the intensive care unit (ICU) and is a leading cause of admission, mortality, and cost. Treatment guidelines recommend early intervention, however positive blood culture results may take up to 48 hours. Insulin sensitivity (S I ) is known to decrease with worsening condition and could thus be used to aid diagnosis. Some glycemic control protocols are able to accurately identify insulin sensitivity in real-time. Receiver operator characteristic (ROC) curves and cut-off S I values for sepsis diagnosis were calculated for real-time model-based insulin sensitivity from glycemic control data of 36 patients with sepsis. Patients were identified as having sepsis based on a clinically validated sepsis score (ss) of 2 or higher (ss = 0–4 for increasing severity). A clinical biomarker was calculated from patient clinical data to maximize the discrimination between cohorts. Insulin sensitivity as a sepsis biomarker for diagnosis of severe sepsis achieves a 50% sensitivity, 76% specificity, 4.8% PPV, and 98.3% NPV at a S I cut-off value of 0.00013 L * mU min −1 . A clinical biomarker combining S I , temperature, heart rate, respiratory rate, blood pressure, and their respective hourly rates of change achieves 73% sensitivity, 80% specificity, 8.4% PPV, and 99.2% NPV. Thus, a clinical biomarker provides an effective real-time negative predictive diagnostic for severe sepsis. Examination of both inter- and intra-patient statistical distribution of this biomarker and sepsis score show potential avenues to improve the positive predictive value.

Corticosteroids and Insulin Resistance in the ICU

Abstract Corticosteroids reduce insulin sensitivity in healthy individuals by 30–62-percent. The aim of this research was to use model-based methods to determine whether this reduction is also true in critically ill patients and how it may affect tight glycaemic control. A clinically validated model-based measure of insulin sensitivity was used to quantify changes between two matched cohorts of 40 intensive care unit (ICU) patients from Christchurch hospital. A 9-percent reduction in median insulin sensitivity was seen between the control cohort and patients receiving corticosteroids (per patient dose equivalent to 160mg/d of hydrocortisone). On a per-patient basis 11–22-percent reductions were observed with higher percentile patients having greater suppression of insulin sensitivity. This research has shown that corticosteroids cause a much lower reduction in insulin sensitivity for critically ill patients compared to healthy controls and may thus have far less impact than suspected on glycaemic control in the ICU setting.

The effect of Glargine as basal insulin support for recovering critically ill and high dependency unit patients: An in silico study

Abstract Many critically ill patients are benefiting from extensive research done in tight glucose control (TGC) within the ICU. But moderate to high levels of hyperglycaemia are still tolerated within high dependency (HDU) and surgical units. The use and benefits of insulin protocols within these units have not yet been addressed in the literature. The management of tight glycaemic control still remains under the influence of ineffective standards characterized by tolerance for hyperglycaemia and a reluctance to use insulin intensively. A validated Glargine and intravenous insulin-glucose pharmacodynamic model are presented. Virtual trial results on 16 stable ICU patients showed that Glargine can provide effective blood glucose management for these long term recovering patients. An initial intravenous injection and higher Glargine dosing is required for the first day to quickly lower elevated blood glucose levels. However, once patients blood glucose levels are within a desirable range, Glargine alone can provide effective glycaemic management, thus reducing nursing effort. Median blood glucose for the entire cohort when simulated with the combination of Glargine and an intravenous insulin injection is 6.5 with interquartile range of [5.6, 7.5]. The 90% confidence interval is [4.6, 9.7] with no occurrence of hypoglycaemia. This in silico study provides a first virtual trial analysis of the in-hospital transition between intravenous and subcutaneous insulin for TGC.

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 tar gated 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.

Tight Glycemic Control - The leading role of insulin sensitivity in determining efficacy and thus outcome

Abstract Tight glycaemic control (TGC) has emerged as a major focus in critical care. However, repeating the initial successful reductions in reducing mortality and other outcomes via TGC has proven very difficult. Hence, there has been growing debate over the necessity of TGC, its goals, safety from hypoglycemia, and target cohorts. This article reviews existing knowledge and results to provide a new interpretation and explanation for the variable results in applying TGC. It then uses a validated metabolic system model to show how the root cause is the intra- and inter- patient variability, which makes TGC difficult over diverse cohorts and thus yields such variable results over many protocols.

A model-based control protocol for transition from ICU to HDU: Robustness analysis

The robustness of a model-based control protocol as a less intensive TGC protocol using insulin Glargine for provision of basal insulin is simulated in this study. To quantify the performance and robustness of the protocol to errors, namely physiological variability and sensor errors, an in-silico Monte Carlo analysis is performed. Actual patient data from Christchurch Hospital, New Zealand were used as virtual trial patients.