Adrienne Lynn

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.

Blood Glucose Controller for Neonatal Intensive Care: Virtual Trials Development and First Clinical Trials

Background: Premature neonates often experience hyperglycemia, which has been linked to worsened outcomes. Insulin therapy can assist in controlling blood glucose (BG) levels. However, a reliable, robust control protocol is required to avoid hypoglycemia and to ensure that clinically important nutrition goals are met. Methods: This study presents an adaptive, model-based predictive controller designed to incorporate the unique metabolic state of the neonate. Controller performance was tested and refined in virtual trials on a 25-patient retrospective cohort. The effects of measurement frequency and BG sensor error were evaluated. A stochastic model of insulin sensitivity was used in control to provide a guaranteed maximum 4% risk of BG < 72 mg/dl to protect against hypoglycemia as well as account for patient variability over 1–3 h intervals when determining the intervention. The resulting controller is demonstrated in two 24 h clinical neonatal pilot trials at Christchurch Womens Hospital. Results: Time in the 72–126 mg/dl BG band was increased by 103–161% compared to retrospective clinical control for virtual trials of the controller, with fewer hypoglycemic measurements. Controllers were robust to BG sensor errors. The model-based controller maintained glycemia to a tight target control range and accounted for interpatient variability in patient glycemic response despite using more insulin than the retrospective case, illustrating a further measure of controller robustness. Pilot clinical trials demonstrated initial safety and efficacy of the control method. Conclusions: A controller was developed that made optimum use of the very limited available BG measurements in the neonatal intensive care unit and provided robustness against BG sensor error and longer BG measurement intervals. It used more insulin than typical sliding scale approaches or retrospective hospital control. The potential advantages of a model-based approach demonstrated in simulation were applied to initial clinical trials.

Blood Glucose Prediction Using Stochastic Modeling in Neonatal Intensive Care

Hyperglycemia is a common metabolic problem in premature, low-birth-weight infants. Blood glucose homeostasis in this group is often disturbed by immaturity of endogenous regulatory systems and the stress of their condition in intensive care. A dynamic model capturing the fundamental dynamics of the glucose regulatory system provides a measure of insulin sensitivity (SI). Forecasting the most probable future SI can significantly enhance real-time glucose control by providing a clinically validated/proven level of confidence on the outcome of an intervention, and thus, increased safety against hypoglycemia. A 2-D kernel model of SI is fitted to 3567 h of identified, time-varying SI from retrospective clinical data of 25 neonatal patients with birth gestational age 23 to 28.9 weeks. Conditional probability estimates are used to determine SI probability intervals. A lag-2 stochastic model and adjustments of the variance estimator are used to explore the bias-variance tradeoff in the hour-to-hour variation of SI. The model captured 62.6% and 93.4% of in-sample SI predictions within the (25th-75th) and (5th-95th) probability forecast intervals. This overconservative result is also present on the cross-validation cohorts and in the lag-2 model. Adjustments to the variance estimator found a reduction to 10%-50% of the original value provided optimal coverage with 54.7% and 90.9% in the (25th-75th) and (5th-95th) intervals. A stochastic model of SI provided conservative forecasts, which can add a layer of safety to real-time control. Adjusting the variance estimator provides a more accurate, cohort-specific stochastic model of SI dynamics in the neonate.

Pilot study of a model-based approach to blood glucose control in very-low-birthweight neonates

BackgroundHyperglycemia often occurs in premature, very low birthweight infants (VLBW) due to immaturity of endogenous regulatory systems and the stress of their condition. Hyperglycemia in neonates has been linked to increased morbidities and mortality and occurs at increasing rates with decreasing birthweight. In this cohort, the emerging use of insulin to manage hyperglycemia has carried a significant risk of hypoglycemia. The efficacy of blood glucose control using a computer metabolic system model to determine insulin infusion rates was assessed in very-low-birth-weight infants.MethodsInitial short-term 24-hour trials were performed on 8 VLBW infants with hyperglycemia followed by long-term trials of several days performed on 22 infants. Median birthweight was 745 g and 760 g for short-term and long-term trial infants, and median gestational age at birth was 25.6 and 25.4 weeks respectively. Blood glucose control is compared to 21 retrospective patients from the same unit who received insulin infusions determined by sliding scales and clinician intuition. This study was approved by the Upper South A Regional Ethics Committee, New Zealand (ClinicalTrials.gov registration NCT01419873).ResultsReduction in hyperglycemia towards the target glucose band was achieved safely in all cases during the short-term trials with no hypoglycemic episodes. Lower median blood glucose concentration was achieved during clinical implementation at 6.6 mmol/L (IQR: 5.5 – 8.2 mmol/L, 1,003 measurements), compared to 8.0 mmol/L achieved in similar infants previously (p < 0.01). No significant difference in incidence of hypoglycemia during long-term trials was observed (0.25% vs 0.25%, p = 0.51). Percentage of blood glucose within the 4.0 – 8.0 mmol/L range was increased by 41% compared to the retrospective cohort (68.4% vs 48.4%, p < 0.01).ConclusionsA computer model that accurately captures the dynamics of neonatal metabolism can provide safe and effective blood glucose control without increasing hypoglycemia.Trial RegistrationClinicalTrials.gov registration NCT01419873

Impact of variation in patient response on model-based control of glycaemia in critically ill patients

Critically ill patients commonly experience stress-induced hyperglycaemia, and several studies have shown tight glycaemic control (TGC) can reduce patient mortality. However, tight control is often difficult to achieve due to conflicting drug therapies and evolving patient condition. Thus, a number of studies have failed to achieve consistently safe and effective TGC possibly due to the use of fixed insulin dosing protocols over adaptive patient-specific methods. Model-based targeted glucose control can adapt insulin and dextrose interventions to match identified patient insulin sensitivity. This study explores the impact on glycaemic control of assuming patient response to insulin is constant, as many protocols do, versus time-varying. Validated virtual trial simulations of glucose control were performed on adult and neonatal virtual patient cohorts. Results indicate assumptions of constant insulin sensitivity can lead to six-fold increases in incidence of hypoglycaemia, similar to literature reports and a commonly cited issue preventing increased adoption of TGC in critical care. It is clear that adaptive, patient-specific, approaches are better able to manage inter- and intra-patient variability than typical, fixed protocols.

Modeling the glucose regulatory system in extreme preterm infants

BACKGROUND Premature infants represent a significant proportion of the neonatal intensive care population. Blood glucose homeostasis in this group is often disturbed by immaturity of endogenous regulatory systems and the stress of their condition. Hypo- and hyperglycemia are frequently reported in very low birth weight infants, and more mature infants often experience low levels of glycemia. A model capturing the unique fundamental dynamics of the neonatal glucose regulatory system could be used to develop better blood glucose control methods. METHODS A metabolic system model is adapted from adult critical care to the unique physiological case of the neonate. Integral-based fitting methods were used to identify time-varying insulin sensitivity and non-insulin mediated glucose uptake profiles. The clinically important predictive ability of the model was assessed by assuming insulin sensitivity was constant over prediction intervals of 1, 2 and 4h forward and comparing model-simulated versus actual clinical glucose values for all recorded interventions. The clinical data included 1091 glucose measurements over 3567 total patient hours, along with all associated insulin and nutritional infusion data, for N=25 total cases. Ethics approval was obtained from the Upper South A Regional Ethics Committee for this study. RESULTS The identified model had a median absolute percentage error of 2.4% [IQR: 0.9-4.8%] between model-fitted and clinical glucose values. Median absolute prediction errors at 1-, 2- and 4-h intervals were 5.2% [IQR: 2.5-10.3%], 9.4% [IQR: 4.5-18.4%] and 13.6% [IQR: 6.3-27.6%] respectively. CONCLUSIONS The model accurately captures and predicts the fundamental dynamic behaviors of the neonatal metabolism well enough for effective clinical decision support in glycemic control. The adaptation from adult to a neonatal case is based on the data from the literature. Low prediction errors and very low fitting errors indicate that the fundamental dynamics of glucose metabolism in both premature neonates and critical care adults can be described by similar mathematical models.

Electrolytes in sick neonates - which sodium is the right answer?

Introduction Hypoproteinaemia leads to spuriously high-sodium values when measured by indirect ion-selective electrodes (ISE) as used in main laboratory analysers compared with direct ISE employed in point-of-care analysers (POCT). The authors, therefore, investigated the occurrence of hypoalbuminaemia and its effect on measured sodium from POCT and the main laboratory analyser of neonatal intensive-care samples. Method Sodium, in paired retrospective samples, measured by the main laboratory and neonatal unit blood-gas (POCT) analysers were compared. Results Hypoalbuminaemia (<30 g/l) was present in 1400/2420 paired results. Sodium was higher when measured by laboratory analyser, the difference increased with decreasing albumin; sodium (laboratory – POCT)=7.6 (±1.1)–0.22 (±0.04)×albumin. A difference >3 mmol/l was present in 31% and consequently underestimated (9.4%) hyponatraemia and overestimated (3.8%) hypernatraemia. Conclusion Hypoalbuminaemia is common in sick neonates and monitoring electrolytes using POCT and laboratory analysers frequently yield significantly different results with consequent misclassification. In these patients, measurement of electrolytes by direct ISE (blood-gas analyser) may be more accurate.

On the Problem of Patient-Specific Endogenous Glucose Production in Neonates on Stochastic Targeted Glycemic Control

Background: Both stress and prematurity can induce hyperglycemia in the neonatal intensive care unit, which, in turn, is associated with worsened outcomes. Endogenous glucose production (EGP) is the formation of glucose by the body from substrates and contributes to blood glucose (BG) levels. Due to the inherent fragility of the extremely low birth weight (ELBW) neonates, true fasting EGP cannot be explicitly determined, introducing uncertainty into glycemic models that rely on quantifying glucose sources. Stochastic targeting, or STAR, is one such glycemic control framework. Methods: A literature review was carried out to gather metabolic and EGP values on preterm infants with a gestational age (GA) <32 weeks and a birth weight (BW) <2 kg. The data were analyzed for EGP trends with BW, GA, BG, plasma insulin, and glucose infusion (GI) rates. Trends were modeled and compared with a literature-derived range of population constant EGP models using clinically validated virtual trials on retrospective clinical data. Results: No clear relationship was found for EGP and BW, GA, or plasma insulin. Some evidence of suppression of EGP with increasing GI or BG was seen. Virtual trial results showed that population-constant EGP models fit clinical data best and gave tighter control performance to a target band in virtual trials. Conclusions: Variation in EGP cannot easily be quantified, and EGP is sufficiently modeled as a population constant in the neonatal intensive care insulin-nutrition-glucose model. Analysis of the clinical data and fitting error suggests that ELBW hyperglycemic preterm neonates have unsuppressed EGP in the higher range than that seen in literature.

Development of blood glucose control for extremely premature infants

Extremely premature neonates often experience hyperglycaemia, which has been linked to increased mortality and worsened outcomes. Insulin therapy can assist in controlling blood glucose levels and promoting needed growth. This study presents the development of a model-based stochastic targeted controller designed to adapt insulin infusion rates to match the unique and changing metabolic state and control parameters of the neonate. Long-term usage of targeted BG control requires successfully forecasting variations in neonatal metabolic state, accounting for differences in clinical practices between units, and demonstrating robustness to errors that can occur in everyday clinical usage. Simulation studies were used to evaluate controller ability to target several common BG ranges and evaluate controller sensitivity to missed BG measurements and delays in control interventions on a virtual patient cohort of 25 infants developed from retrospective data. Initial clinical pilot trials indicated model performance matched expected performance from simulations. Stochastic targeted glucose control developed using validated patient-specific virtual trials can yield effective protocols for this cohort. Long-term trials show fundamental success, however clinical interface design appears as a critical factor to ensuring good compliance and thus good control.

External validation and sub-cohort analysis of stochastic forecasting models in NICU cohorts

Abstract Hyperglycaemia is a prevalent complication in the neonatal intensive care unit (NICU) and is associated with worsened outcomes. It occurs as a result of prematurity, under-developed endogenous glucose regulatory systems, and clinical stress. The stochastic targeting (STAR) framework provides patient-specific, model-based glycaemic control with a clinically proven level of confidence on the outcome of treatment interventions, thus directly managing the risk of hypo- and hyper-glycaemia. However, stochastic models that are over conservative can limit control performance. Retrospective clinical data from 61 episodes (25 retrospective to STAR, and 36 from a prospective-STAR blood glucose control study) of insulin therapy in very-low birth weight (VLBW) and extremely-low birth weight (ELBW) neonates are used to create a new stochastic model of model-based insulin sensitivity ( S I [L/mU/min]). Sub-cohort models based on gestational age (GA) and birth weight (BW) are also created. Performance is assessed by the percentage of patients who have 90% of actual intra-patient variability in S I captured by the 90% confidence bands of the cohort based (inter-patient) stochastic variability model created. This assessment measures per-patient accuracy for any given cohort model. Per-patient coverage trends were very similar between prospective and retrospective cohorts, providing a measure of external validation of cohort similarity. Per-patient coverage was improved through the use of BW and GA dependent stochastic models, which ensures that the stochastic models more accurately capture both inter- and intra-patient variability. Stochastic models based on insulin sensitivities during insulin treatment periods are tighter, and give better and safer glycaemic control. Overall it seems that inter-patient variation is more significant than intra-patient variation as a limiting factor in this stochastic forecasting model, and a small number of patients are essentially different in behaviour. More patient specific methods, particularly in the modelling of endogenous insulin and glucose production, will be required to further improve forecasting and glycaemic control.