Fatanah M. Suhaimi

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.

Validation of a Model-based Virtual Trials Method for Tight Glycemic Control in Intensive Care

BackgroundIn-silico virtual patients and trials offer significant advantages in cost, time and safety for designing effective tight glycemic control (TGC) protocols. However, no such method has fully validated the independence of virtual patients (or resulting clinical trial predictions) from the data used to create them. This study uses matched cohorts from a TGC clinical trial to validate virtual patients and in-silico virtual trial models and methods.MethodsData from a 211 patient subset of the Glucontrol trial in Liege, Belgium. Glucontrol-A (N = 142) targeted 4.4-6.1 mmol/L and Glucontrol-B (N = 69) targeted 7.8-10.0 mmol/L. Cohorts were matched by APACHE II score, initial BG, age, weight, BMI and sex (p > 0.25). Virtual patients are created by fitting a clinically validated model to clinical data, yielding time varying insulin sensitivity profiles (SI(t)) that drives in-silico patients.Model fit and intra-patient (forward) prediction errors are used to validate individual in-silico virtual patients. Self-validation (tests A protocol on Group-A virtual patients; and B protocol on B virtual patients) and cross-validation (tests A protocol on Group-B virtual patients; and B protocol on A virtual patients) are used in comparison to clinical data to assess ability to predict clinical trial results.ResultsModel fit errors were small (<0.25%) for all patients, indicating model fitness. Median forward prediction errors were: 4.3, 2.8 and 3.5% for Group-A, Group-B and Overall (A+B), indicating individual virtual patients were accurate representations of real patients. SI and its variability were similar between cohorts indicating they were metabolically similar.Self and cross validation results were within 1-10% of the clinical data for both Group-A and Group-B. Self-validation indicated clinically insignificant errors due to model and/or clinical compliance. Cross-validation clearly showed that virtual patients enabled by identified patient-specific SI(t) profiles can accurately predict the performance of independent and different TGC protocols.ConclusionsThis study fully validates these virtual patients and in silico virtual trial methods, and clearly shows they can accurately simulate, in advance, the clinical results of a TGC protocol, enabling rapid in silico protocol design and optimization. These outcomes provide the first rigorous validation of a virtual in-silico patient and virtual trials methodology.

Pilot proof of concept clinical trials of Stochastic Targeted (STAR) glycemic control.

IntroductionTight glycemic control (TGC) has shown benefits but has been difficult to achieve consistently. STAR (Stochastic TARgeted) is a flexible, model-based TGC approach directly accounting for intra- and inter- patient variability with a stochastically derived maximum 5% risk of blood glucose (BG) < 4.0 mmol/L. This research assesses the safety, efficacy, and clinical burden of a STAR TGC controller modulating both insulin and nutrition inputs in pilot trials.MethodsSeven patients covering 660 hours. Insulin and nutrition interventions are given 1-3 hourly as chosen by the nurse to allow them to manage workload. Interventions are calculated by using clinically validated computer models of human metabolism and its variability in critical illness to maximize the overlap of the model-predicted (5-95th percentile) range of BG outcomes with the 4.0-6.5 mmol/L band while ensuring a maximum 5% risk of BG < 4.0 mmol/L. Carbohydrate intake (all sources) was selected to maximize intake up to 100% of SCCM/ACCP goal (25 kg/kcal/h). Maximum insulin doses and dose changes were limited for safety. Measurements were made with glucometers. Results are compared to those for the SPRINT study, which reduced mortality 25-40% for length of stay ≥3 days. Written informed consent was obtained for all patients, and approval was granted by the NZ Upper South A Regional Ethics Committee.ResultsA total of 402 measurements were taken over 660 hours (~14/day), because nurses showed a preference for 2-hourly measurements. Median [interquartile range, (IQR)] cohort BG was 5.9 mmol/L [5.2-6.8]. Overall, 63.2%, 75.9%, and 89.8% of measurements were in the 4.0-6.5, 4.0-7.0, and 4.0-8.0 mmol/L bands. There were no hypoglycemic events (BG < 2.2 mmol/L), and the minimum BG was 3.5 mmol/L with 4.5% < 4.4 mmol/L. Per patient, the median [IQR] hours of TGC was 92 h [29-113] using 53 [19-62] measurements (median, ~13/day). Median [IQR] results: BG, 5.9 mmol/L [5.8-6.3]; carbohydrate nutrition, 6.8 g/h [5.5-8.7] (~70% goal feed median); insulin, 2.5 U/h [0.1-5.1]. All patients achieved BG < 6.1 mmol/L. These results match or exceed SPRINT and clinical workload is reduced more than 20%.ConclusionsSTAR TGC modulating insulin and nutrition inputs provided very tight control with minimal variability by managing intra- and inter- patient variability. Performance and safety exceed that of SPRINT, which reduced mortality and cost in the Christchurch ICU. The use of glucometers did not appear to impact the quality of TGC. Finally, clinical workload was self-managed and reduced 20% compared with SPRINT.

Stochastic targeted (STAR) glycemic control: design, safety, and performance.

Introduction: Tight glycemic control (TGC) has shown benefits but has been difficult to achieve consistently. STAR (Stochastic TARgeted) is a flexible, model-based TGC approach that directly accounts for intra- and interpatient variability with a stochastically derived maximum 5% risk of blood glucose (BG) below 72 mg/dl. This research assesses the safety, efficacy, and clinical burden of a STAR TGC controller modulating both insulin and nutrition inputs in virtual and clinical pilot trials. Methods: Clinically validated virtual trials using data from 370 patients in the SPRINT (Specialized Relative Insulin and Nutrition Titration) study were used to design the STAR protocol and test its safety, performance, and required clinical effort prior to clinical pilot trials. Insulin and nutrition interventions were given every 1–3 h as chosen by the nurse to allow them to manage workload. Interventions were designed to maximize the overlap of the model-predicted (5–95th percentile) range of BG outcomes with the 72–117 mg/dl band and thus provide a maximum 5% risk of BG <72 mg/dl. Interventions were calculated using clinically validated computer models of human metabolism and its variability in critical illness. Carbohydrate intake (all sources) was selected to maximize intake up to 100% of the American College of Chest Physicians/Society of Critical Care Medicine (ACCP/SCCM) goal (25 kg/kcal/h). Insulin doses were limited (8 U/h maximum), with limited increases based on current rate (0.5–2.0 U/h). Initial clinical pilot trials involved 3 patients covering ~450 h. Approval was granted by the Upper South A Regional Ethics Committee. Results: Virtual trials indicate that STAR provides similar glycemic control performance to SPRINT with 2–3 h (maximum) measurement intervals. Time in the 72–126 mg/dl and 72–145 mg/dl bands was equivalent for all controllers, indicating that glycemic outcome differences between protocols were only shifted in this range. Safety from hypoglycemia was improved. Importantly, STAR using 2–3 h (maximum) intervention intervals reduced clinical burden up to 30%, which is clinically very significant. Initial clinical trials showed glycemic performance, safety, and management of inter- and intrapatient variability that matched or exceeded the virtual trial results. Conclusions: In virtual trials, STAR TGC provided tight control that maximized the likelihood of BG in a clinically specified glycemic band and reduced hypoglycemia with a maximum 5% (or lower) expected risk of light hypoglycemia (BG <72 mg/dl) via model-based management of intra- and interpatient variability. Clinical workload was self-managed and reduced up to 30% compared with SPRINT. Initial pilot clinical trials matched or exceeded these virtual results.

What makes tight glycemic control tight? The impact of variability and nutrition in two clinical studies.

Introduction: Tight glycemic control (TGC) remains controversial while successful, consistent, and effective protocols remain elusive. This research analyzes data from two TGC trials for root causes of the differences achieved in control and thus potentially in glycemic and other outcomes. The goal is to uncover aspects of successful TGC and delineate the impact of differences in cohorts. Methods: A retrospective analysis was conducted using records from a 211-patient subset of the GluControl trial taken in Liege, Belgium, and 393 patients from Specialized Relative Insulin Nutrition Titration (SPRINT) in New Zealand. Specialized Relative Insulin Nutrition Titration targeted 4.0–6.0 mmol/liter, similar to the GluControl A (N = 142) target of 4.4–6.1 mmol/liter. The GluControl B (N = 69) target was 7.8–10.0 mmol/liter. Cohorts were matched by Acute Physiology and Chronic Health Evaluation II score and percentage males (p > .35); however, the GluControl cohort was slightly older (p = .011). Overall cohort and per-patient comparisons (median, interquartile range) are shown for (a) glycemic levels achieved, (b) nutrition from carbohydrate (all sources), and (c) insulin dosing for this analysis. Intra- and interpatient variability were examined using clinically validated model-based insulin sensitivity metric and its hour-to-hour variation. Results: Cohort blood glucose were as follows: SPRINT, 5.7 (5.0–6.6) mmol/liter; GluControl A, 6.3 (5.3–7.6) mmol/liter; and GluControl B, 8.2 (6.9–9.4) mmol/liter. Insulin dosing was 3.0 (1.0–3.0), 1.5 (0.5–3), and 0.7 (0.0–1.7) U/h, respectively. Nutrition from carbohydrate (all sources) was 435.5 (259.2–539.1), 311.0 (0.0–933.1), and 622.1 (103.7–1036.8) kcal/day, respectively. Median per-patient results for blood glucose were 5.8 (5.3–6.4), 6.4 (5.9–6.9), and 8.3 (7.6–8.8) mmol/liter. Insulin doses were 3.0 (2.0–3.0), 1.5 (0.8–2.0), and 0.5 (0.0–1.0) U/h. Carbohydrate administration was 383.6 (207.4–497.7), 103.7 (0.0–829.4), and 207.4 (0.0–725.8) kcal/day. Overall, SPRINT gave ∼2x more insulin with a 3–4x narrower, but generally non-zero, range of nutritional input to achieve equally TGC with less hypoglycemia. Specialized Relative Insulin Nutrition Titration had much less hypoglycemia (<2.2 mmol/liter), with 2% of patients, compared to GluControl A (7.7%) and GluControl B (2.9%), indicating much lower variability, with similar results for glucose levels <3.0 mmol/liter. Specialized Relative Insulin Nutrition Titration also had less hyperglycemia (>8.0 mmol/liter) than groups A and B. GluControl patients (A+B) had a ∼2x wider range of insulin sensitivity than SPRINT. Hour-to-hour variation was similar. Hence GluControl had greater interpatient variability but similar intrapatient variability. Conclusion: Protocols that dose insulin blind to carbohydrate administration can suffer greater outcome glycemic variability, even if average cohort glycemic targets are met. While the cohorts varied significantly in model-assessed insulin resistance, their variability was similar. Such significant intra- and interpatient variability is a further significant cause and marker of glycemic variability in TGC. The results strongly recommended that TGC protocols be explicitly designed to account for significant intra- and interpatient variability in insulin resistance, as well as specifying or having knowledge of carbohydrate administration to minimize variability in glycemic outcomes across diverse cohorts and/or centers.

Second pilot trials of the STAR-Liege protocol for tight glycemic control in critically ill patients

BackgroundCritically ill patients often present increased insulin resistance and stress-induced hyperglycemia. Tight glycemic control aims to reduce blood glucose (BG) levels and variability while ensuring safety from hypoglycemia. This paper presents the results of the second Belgian clinical trial using the customizable STAR framework in a target-to-range control approach. The main objective is reducing measurement frequency while maintaining performance and safety of the glycemic control.MethodsThe STAR-Liege 2 (SL2) protocol targeted the 100–140 mg/dL glycemic band and offered 2-hourly and 3-hourly interventions. Only insulin rates were adjusted, and nutrition inputs were left to the attending clinicians. This protocol restricted the forecasted risk of BG < 90 mg/dL to a 5% level using a stochastic model of insulin sensitivity to assess patient-specific responses to insulin and its future likely variability to optimize insulin interventions. The clinical trial was performed at the Centre Hospitalier Universitaire de Liege and included 9 patients. Results are compared to 24-hour pre-trial and 24-hour post-trial, but also to the results of the first pilot trial performed in Liege, STAR-Liege 1 (SL1). This trial was approved by the Ethics Committee of the Medical Faculty of the University of Liege (Liege, Belgium).ResultsDuring the SL2 trial, 91 measurements were taken over 194 hours. BG levels were tightly distributed: 54.9% of BG within 100–140 mg/dL, 40.7% were ≥ 140 mg/dL and 4.4% were < 100 mg/dL with no BG < 70 mg/dL. Comparing these results with 24-hour pre-trial and post-trial shows that SL2 reduced high and low BG levels and reduced glycemic variability. Nurses selected 3-hourly measurement only 5 of 16 times and overrode 12% of 91 recommended interventions (35% increased insulin rates and 65% decreased insulin rates). SL1 and SL2 present similar BG levels distribution (p > 0.05) with significantly reduced measurement frequency for SL2 (p < 0.05).ConclusionsThe SL2 protocol succeeded in reducing clinical workload while maintaining safety and effectiveness of the glycemic control. SL2 was also shown to be safer and tighter than hospital control. Overall results validate the efficacy of significantly customizing the STAR framework.

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.

Analysis of consonant /s/ and syllables in Malay language using electropalatography

During articulation and speech, tongue is in contact with hard palate. This contact can be measured using an Electropalatography (EPG). EPG is widely used for speech therapy among subjects having cleft palate, congenital aglossia, and phonemic paraphasic disorder, and also for language analysis and therapy. This study aims to analyse the production of /s/ and syllables by a Malay adult speaker using the Reading EPG palate with 62 electrodes that are built to match the tongue-palate contact. The vowel used in this study are /a/, /i/ and /u/. Data was analysed using Articulate Assist 1.18 software. There are four parts of palate zones, namely alveolar, post-alveolar, palatal and velar. In this study, there are three normal Malay-speaking adults with age ranging from 30 to 35 years old (mean age of 32 years). In the production of /s/, the percentage contact for subject 1 is 16% at the left side and 32% at the right side. For subject 2, there is 39% in the left side and 32% in the right side. Subject 3 has 13...