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

Classification-based deep neural network vs mixture density network models for insulin sensitivity prediction problem

Model-based glycemic control (GC) protocols are used to treat stress-induced hyperglycaemia in intensive care units (ICUs). The STAR (Stochastic-TARgeted) glycemic control protocol - used in clinical practice in several ICUs in New Zealand, Hungary, Belgium, and Malaysia - is a model-based GC protocol using a patient-specific, model-based insulin sensitivity to describe the patient's actual state. Two neural network based methods are defined in this study to predict the patient's insulin sensitivity parameter: a classification deep neural network and a Mixture Density Network based method. Treatment data from three different patient cohorts are used to train the network models. Accuracy of neural network predictions are compared with the current model- based predictions used to guide care. The prediction accuracy was found to be the same or better than the reference. The authors suggest that these methods may be a promising alternative in model-based clinical treatment for patient state prediction. Still, more research is needed to validate these findings, including in-silico simulations and clinical validation trials.

Continuous Glucose Monitoring in Transient Neonatal Diabetes Mellitus-2 Case Reports and Literature Review

Neonatal diabetes mellitus is a rare genetic disease that affects 1 in 90,000 live births. The start of the disease is often before the baby is 6 months old, with rare cases of onset between 6 months and 1 year. It is characterized by low or absent insulin levels in the blood, leading to severe hyperglycemia in the patient, which requires temporary insulin therapy in around 50% of cases or permanent insulin therapy in other cases. Two major processes involved in diabetes mellitus are a deformed pancreas with altered insulin-secreting cell development and/or survival or faulty functioning of the existing pancreatic beta cell. We will discuss the cases of two preterm girls with neonatal diabetes mellitus in this research. In addition to reviewing the literature on the topic, we examined the different mutations, patient care, and clinical outcomes both before and after insulin treatment.

Outcomes of very preterm infants with hyperglycaemia treated with insulin: A systematic review and meta-analysis

AIM

To study the outcomes of very preterm infants with hyperglycaemia treated with Insulin.

METHODS

This is a systematic review of randomised controlled trials (RCTs) and observational studies. PubMed, Medline, EMBASE, Cochrane Library, EMCARE and MedNar databases were searched in May 2022. Data were pooled separately for adjusted and unadjusted odds ratios (ORs) using random-effects model.

MAIN OUTCOME MEASURES

Mortality and morbidities (e.g. Necrotising enterocolitis [NEC], retinopathy of prematurity [ROP]) in very preterm (<32 weeks) or very low birth weight infants (<1500 g) after treatment of hyperglycaemia with insulin.

RESULTS

Sixteen studies with data from 5482 infants were included. Meta-analysis of unadjusted ORs from cohort studies showed that insulin treatment was significantly associated with increased mortality [OR 2.98 CI (1.03 to 8.58)], severe ROP [OR 2.23 CI (1.34 to 3.72)] and NEC [OR 2.19 CI (1.11 to 4)]. However, pooling of adjusted ORs did not show significant associations for any outcomes. The only included RCT found better weight gain in the insulin group, but no effect on mortality or morbidities. Certainty of evidence was 'Low' or 'Very low'.

CONCLUSION

Very low certainty evidence suggests that Insulin therapy may not improve outcomes of very preterm infants with hyperglycaemia.

Model-based subcutaneous insulin for glycemic control of pre-term infants in the neonatal intensive care unit

Hyperglycaemia is a common problem in neonatal intensive care units (NICUs). Achieving good control can result in better outcomes for patients. However, good control is difficult, where poor control and resulting hypoglycaemia reduces outcomes and confounds results. Clinically validated models can provide good control, and subcutaneous insulin delivery can provide more options for insulin therapy for clinicians. However, this combination has only been significantly utilised in adult outpatient diabetes, but could hold benefit for treating NICU infants. This research combines a well-validated NICU metabolic model with subcutaneous insulin kinetics models to assess the feasibility of a model-based approach. Clinical data from 12 very/extremely pre-mature infants was collected for an average study duration of 10.1 days. Blood glucose, interstitial and plasma insulin, as well as subcutaneous and local insulin were modelled, and patient-specific insulin sensitivity profiles were identified for each patient. Modelling error was low, where the cohort median [IQR] mean percentage error was 0.8 [0.3 3.4] %. For external validation, insulin sensitivity was compared to previous NICU cohorts using the same metabolic model, where overall levels of insulin sensitivity were similar. Overall, the combined system model accurately captured observed glucose and insulin dynamics, showing the potential for a model-based approach to glycaemic control using subcutaneous insulin in this cohort. The results justify further model validation and clinical trial research to explore a model-based protocol.

Cerebral Effects of Neonatal Dysglycemia

This article summarizes the available evidence reporting the relationship between perinatal dysglycemia and long-term neurodevelopment. We review the physiology of perinatal glucose metabolism and discuss the controversies surrounding definitions of perinatal dysglycemia. We briefly review the epidemiology of hypoglycemia and hyperglycemia in fetal, preterm, and term infants. We discuss potential pathophysiologic mechanisms contributing to dysglycemia and its effect on neurodevelopment. We highlight current strategies to prevent and treat dysglycemia in the context of neurodevelopmental outcomes. Finally, we discuss areas of future research and the potential role of continuous glucose monitoring.

Assessment of Glycemic Control Protocol (STAR) Through Compliance Analysis Amongst Malaysian ICU Patients

Purpose

This paper presents an assessment of an automated and personalized stochastic targeted (STAR) glycemic control protocol compliance in Malaysian intensive care unit (ICU) patients to ensure an optimized usage.

Patients and Methods

STAR proposes 1–3 hours treatment based on individual insulin sensitivity variation and history of blood glucose, insulin, and nutrition. A total of 136 patients recorded data from STAR pilot trial in Malaysia (2017–quarter of 2019*) were used in the study to identify the gap between chosen administered insulin and nutrition intervention as recommended by STAR, and the real intervention performed.

Results

The results show the percentage of insulin compliance increased from 2017 to first quarter of 2019* and fluctuated in feed administrations. Overall compliance amounted to 98.8% and 97.7% for administered insulin and feed, respectively. There was higher average of 17 blood glucose measurements per day than in other centres that have been using STAR, but longer intervals were selected when recommended. Control safety and performance were similar for all periods showing no obvious correlation to compliance.

Conclusion

The results indicate that STAR, an automated model-based protocol is positively accepted among the Malaysian ICU clinicians to automate glycemic control and the usage can be extended to other hospitals already. Performance could be improved with several propositions.

Modelling intestinal glucose absorption in premature infants using continuous glucose monitoring data

BACKGROUND

Model-based glycaemic control protocols have shown promise in neonatal intensive care units (NICUs) for reducing both hyperglycaemia and insulin-therapy driven hypoglycaemia. However, current models for the appearance of glucose from enteral feeding are based on values from adult intensive care cohorts. This study aims to determine enteral glucose appearance model parameters more reflective of premature infant physiology.

METHODS

Peaks in CGM data associated with enteral milk feeds in preterm and term infants are used to fit a two compartment gut model. The first compartment describes glucose in the stomach, and the half life of gastric emptying is estimated as 20 min from literature. The second compartment describes glucose in the small intestine, and absorption of glucose into the blood is fit to CGM data. Two infant cohorts from two NICUs are used, and results are compared to appearances derived from data in highly controlled studies in literature.

RESULTS

The average half life across all infants for glucose absorption from the gut to the blood was 50 min. This result was slightly slower than, but of similar magnitude to, results derived from literature. No trends were found with gestational or postnatal age. Breast milk fed infants were found to have a higher absorption constant than formula fed infants, a result which may reflect known differences in gastric emptying for different feed types.

CONCLUSIONS

This paper presents a methodology for estimation of glucose appearance due to enteral feeding, and model parameters suitable for a NICU model-based glycaemic control context.

Safe and effective glycaemic control in premature infants: observational clinical results from the computerised STAR-GRYPHON protocol

OBJECTIVE

Previous studies examine clinical outcomes of insulin therapy in neonatal intensive care units (NICUs), without first developing safe and effective control protocols. This research quantifies the safety and performance of a computerised model-based control algorithmSTAR-GRYPHON (Stochastic TARgeted Glucose Regulation sYstem to Prevent Hyper- and hypO-glycaemia in Neonates).

DESIGN

Retrospective observational study of glycaemic control in very/extremely low birthweight infants treated with insulin from Christchurch Women's Hospital NICU between January 2013 and June 2017. Blood glucose (BG) outcomes and control performance is compared with retrospective data (n=22) and literature.

INTERVENTIONS

Insulin infusion doses were calculated from 3 to 4 hourly BG measurements using a computerised model-based control algorithm, STAR-GRYPHON.

MAIN OUTCOME MEASURES

Mean BG, time in targeted range and incidence of hypoglycaemia.

RESULTS

STAR-GRYPHON (n=35) had lower mean BG concentration (7.0mmol/L vs 7.9 mmol/L), higher %BG within the 4.0-8.0 mmol/L target range (71.1% vs 50.9%) and lower %BG <4.0 mmol/L (0.6% vs 2.1%). There were only 2 BG <2.6 mmol/L (over n=2, 5.5% of patients, 0.03% of all BG outcomes), one of which may be attributed to clinical error. These results show better control to target and lower incidence of hypoglycaemia than most literature results from intensive insulin therapy protocols or study groups in children and infants.

CONCLUSIONS

Model-based protocols can safely and effectively control BG in very premature infants and should be used in future studies to determine the effect of insulin therapy on clinical outcomes.

Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them

Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current "one size fits all" protocolised care to adaptive, model-based "one method fits all" personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care.

Continuous glucose monitoring in neonates: a review

Continuous glucose monitoring (CGM) is well established in the management of diabetes mellitus, but its role in neonatal glycaemic control is less clear. CGM has provided important insights about neonatal glucose metabolism, and there is increasing interest in its clinical use, particularly in preterm neonates and in those in whom glucose control is difficult. Neonatal glucose instability, including hypoglycaemia and hyperglycaemia, has been associated with poorer neurodevelopment, and CGM offers the possibility of adjusting treatment in real time to account for individual metabolic requirements while reducing the number of blood tests required, potentially improving long-term outcomes. However, current devices are optimised for use at relatively high glucose concentrations, and several technical issues need to be resolved before real-time CGM can be recommended for routine neonatal care. These include: 1) limited point accuracy, especially at low or rapidly changing glucose concentrations; 2) calibration methods that are designed for higher glucose concentrations of children and adults, and not for neonates; 3) sensor drift, which is under-recognised; and 4) the need for dynamic and integrated metrics that can be related to long-term neurodevelopmental outcomes. CGM remains an important tool for retrospective investigation of neonatal glycaemia and the effect of different treatments on glucose metabolism. However, at present CGM should be limited to research studies, and should only be introduced into routine clinical care once benefit is demonstrated in randomised trials.

Model-based glycaemic control: methodology and initial results from neonatal intensive care

Very/extremely premature infants often experience glycaemic dysregulation, resulting in abnormally elevated (hyperglycaemia) or low (hypoglycaemia) blood glucose (BG) concentrations, due to prematurity, stress, and illness. STAR-GRYPHON is a computerised protocol that utilises a model-based insulin sensitivity parameter to directly tailor therapy for individual patients and their changing conditions, unlike other common insulin protocols in this cohort. From January 2013 to January 2015, 13 patients totalling 16 hyperglycaemic control episodes received insulin under STAR-GRYPHON. A significant improvement in control was achieved in comparison to a retrospective cohort, with a 26% absolute improvement in BG within the targeted range and no hypoglycaemia. This improvement was obtained predominantly due to the reduction of hyperglycaemia (%BG>10.0 mmol/l: 5.6 vs. 17.7%, p<0.001), and lowering of the median per-patient BG [6.9 (6.1-7.9) vs. 7.8 (6.6-9.1) mmol/l, p<0.001, Mann-Witney U test]. While cohort-wide control results show good control overall, there is high intra-patient variability in BG behaviour, resulting in overly conservative treatments for some patients. Patient insulin sensitivity differs between and within patients over time, with some patients having stable insulin sensitivity, while others change rapidly. These results demonstrate the trade-off between safety and performance in a highly variable and fragile cohort.

Computer-determined dosage of insulin in the management of neonatal hyperglycaemia (HINT2): protocol of a randomised controlled trial

INTRODUCTION

Neonatal hyperglycaemia is frequently treated with insulin, which may increase the risk of hypoglycaemia. Computer-determined dosage of insulin (CDD) with the STAR-GRYPHON program uses a computer model to predict an effective dose of insulin to treat hyperglycaemia while minimising the risk of hypoglycaemia. However, CDD models can require more frequent blood glucose testing than common clinical protocols. The aim of this trial is to determine if CDD using STAR-GRYPHON reduces hypoglycaemia in hyperglycaemic preterm babies treated with insulin independent of the frequency of blood glucose testing.

METHODS AND ANALYSIS

Design: Multicentre, non-blinded, randomised controlled trial.

SETTING

Neonatal intensive care units in New Zealand and Australia.

PARTICIPANTS

138 preterm babies ≤30 weeks' gestation or ≤1500 g at birth who develop hyperglycaemia (two consecutive blood glucose concentrations ≥10 mmol/L, at least 4 hours apart) will be randomised to one of three groups: (1) CDD using the STAR-GRYPHON model-based decision support system: insulin dose and frequency of blood glucose testing advised by STAR-GRYPHON, with a maximum testing interval of 4 hours; (2) bedside titration: insulin dose determined by medical staff, maximum blood glucose testing interval of 4 hours; (3) standard care: insulin dose and frequency of blood glucose testing determined by medical staff. The target range for blood glucose concentrations is 5-8 mmol/L in all groups. A subset of babies will have masked continuous glucose monitoring.

PRIMARY OUTCOME

is the number of babies with one or more episodes of hypoglycaemia (blood glucose concentration <2.6 mmol/L), during treatment with insulin.

ETHICS AND DISSEMINATION

This protocol has been approved by New Zealand's Health and Disability Ethics Committee: 14/STH/26. A data safety monitoring committee has been appointed to oversee the trial. Findings will be disseminated to participants and carers, peer-reviewed journals, guideline developers and the public.

TRIAL REGISTRATION NUMBER

12614000492651.

Insulin kinetics and the Neonatal Intensive Care Insulin-Nutrition-Glucose (NICING) model

BACKGROUND

Models of human glucose-insulin physiology have been developed for a range of uses, with similarly different levels of complexity and accuracy. STAR (Stochastic Targeted) is a model-based approach to glycaemic control. Elevated blood glucose concentrations (hyperglycaemia) are a common complication of stress and prematurity in very premature infants, and have been associated with worsened outcomes and higher mortality. This research identifies and validates the model parameters for model-based glycaemic control in neonatal intensive care.

METHODS

C-peptide, plasma insulin, and BG from a cohort of 41 extremely pre-term (median age 27.2 [26.2-28.7] weeks) and very low birth weight infants (median birth weight 839 [735-1000] g) are used alongside C-peptide kinetic models to identify model parameters associated with insulin kinetics in the NICING (Neonatal Intensive Care Insulin-Nutrition-Glucose) model. A literature analysis is used to determine models of kidney clearance and body fluid compartment volumes. The full, final NICING model is validated by fitting the model to a cohort of 160 glucose, insulin, and nutrition data records from extremely premature infants from two different NICUs (neonatal intensive care units).

RESULTS

Six model parameters related to insulin kinetics were identified. The resulting NICING model is more physiologically descriptive than prior model iterations, including clearance pathways of insulin via the liver and kidney, rather than a lumped parameter. In addition, insulin diffusion between plasma and interstitial spaces is evaluated, with differences in distribution volume taken into consideration for each of these spaces. The NICING model was shown to fit clinical data well, with a low model fit error similar to that of previous model iterations.

CONCLUSIONS

Insulin kinetic parameters have been identified, and the NICING model is presented for glycaemic control neonatal intensive care. The resulting NICING model is more complex and physiologically relevant, with no loss in bedside-identifiability or ability to capture and predict metabolic dynamics.

[Evaluation of the measurement of capillary glucose concentration versus plasma glucose in the newborn]

BACKGROUND

The reliability of blood glucose monitoring in neonatology is not always confirmed. The aim of this study was to evaluate the reliability of blood glucose measurements made with three different devices in newborns.

PATIENTS AND METHODS

The study was prospective, conducted in a medical and neonatal intensive care department over a period of 4 months. Capillary glucose level was measured with three different glucometers and compared with venous glucose level determined using the hexokinase method. An ANOVA and Scheffe test were used for the correlation analysis.

RESULTS

Three hundred and nine infants were included, with a mean age of 55h and a mean term of 39 weeks of gestation. Mean blood glucose in the laboratory was 0.62±0.15g/L, 0.71±0.17g/L for Accu-Chek(®) Active, 0.80±0.17g/L for Accu-Chek(®) Performa, and 0.83±0.12g/L for Bionime. An ANOVA showed statistically significant differences between the measurements made by glucometers compared to the reference blood glucose levels, and the Scheffé method showed that glucometers overestimated the real plasma glucose levels.

CONCLUSION

None of the devices used in this study was satisfactory. However, an estimation of blood glucose taking into consideration this numerical overestimation would allow early detection of hypoglycemia.

The potential risks and benefits of insulin treatment in hyperglycaemic preterm neonates

Preterm hyperglycaemia in the first 2 weeks of life is common under 29 weeks gestation and is associated with increased mortality and morbidity. While the definition of hyperglycaemia is reasonably consistent (>8 mmol/L) the treatment threshold varies widely in clinical practice. Insulin therapy is the most common approach despite international guidance urging caution because of hypoglycaemia. Significant hypoglycaemia is unusual outside studies targeting normoglycaemia. Insulin treatment also forms part of a nutritional strategy aiming to optimise early protein and energy intake so minimising the risk of preterm postnatal growth failure. Early parenteral amino acids also improve blood glucose control. There is some evidence of improved postnatal head growth with this approach but longer term neurodevelopmental studies are required. Glucose reduction is the alternative approach. This compromises early nutritional intake but avoids the potential for long-term cardiovascular and metabolic complications linked with high postnatal nutritional intakes and theoretically, insulin treatment.

Low phosphatemia in extremely low birth weight neonates: A risk factor for hyperglycemia?

BACKGROUND & AIMS

Hyperglycemia occurs in more than half of the extremely low birth weight (ELBW) neonates during the first weeks of life, and is correlated with an increased risk of morbi-mortality. Hypophosphatemia is another frequent metabolic disorder in this population. Data from animal, adult studies and clinical observation suggest that hypophosphatemia could induce glucose intolerance. Our aim was to determine whether a low phosphatemia is associated with hyperglycemia in ELBW neonates.

METHODS

This observational study included ELBW infants admitted in a tertiary neonatal care center (2010-2011). According to the center's policy, they received parenteral nutrition from birth and human milk from day 1. Phosphatemia and glycemia were measured routinely during parenteral nutrition. Hyperglycemia was defined by two consecutives values >8.3 mmol/L (150 mg/dL). Statistical analysis used a joint model combining a mixed-effects and a survival submodels to measure the association between phosphate and hyperglycemia.

RESULTS

The study included 148 patients. Mean gestational (Standard Deviation) age was 27.3 (1.6) weeks; mean birth weight was 803 (124) grams; 57% presented hyperglycemia. The multivariate joint model showed that the hazard of hyperglycemia at a given time was multiplied by 3 for each 0.41 mmol/L decrease of phosphate level at this time (p = 0.002) and by 3.85 for the same decreased of phosphate the day before (p = 0.0015).

CONCLUSION

To our knowledge, this is the first study suggesting that low phosphatemia can be associated with hyperglycemia in ELBW neonates. Further studies will have to demonstrate whether better control of phosphatemia could help in preventing hyperglycemia.

A C-Peptide-Based Model of Pancreatic Insulin Secretion in Extremely Preterm Neonates in Intensive Care

BACKGROUND

Model-based glycemic control relies on sufficiency of underlying models to describe underlying patient physiology. In particular, very preterm infant glucose-insulin metabolism can differ significantly from adults, and is relatively unstudied. In this study, C-peptide concentrations are used to develop insulin-secretion models for the purposes of glycemic control in neonatal intensive care.

METHODS

Plasma C-peptide, insulin, and blood glucose concentrations (BGC) were retrospectively analyzed from a cohort of 41 hyperglycemic very preterm (median age 27.2 [26.2-28.7] weeks) and very low birth-weight infants (median birth weight 839 [735-1000] g). A 2-compartment model of C-peptide kinetics was used to estimate insulin secretion. Insulin secretion was examined with respect to nutritional intake, exogenous and plasma insulin concentration, and BGC.

RESULTS

Insulin secretion was found to be highly variable between patients and over time, and could not be modeled with respect to age, weight, or protein or dextrose intake. In 13 of 54 samples exogenous insulin was being administered, and insulin secretion was lower. However, low data numbers make this result inconclusive. Insulin secretion was found to increase with BG, with a stronger association in female infants than males (R(2) = .51 vs R(2) = .13, and R(2) = .26 for the combined cohort).

CONCLUSIONS

A sex-based insulin secretion model was created and incorporated into a model-based glycemic control framework. Nutritional intake did not predict insulin secretion, indicating that insulin secretion is a complex function of a number of metabolic factors.

Brain mass estimation by head circumference and body mass methods in neonatal glycaemic modelling and control

INTRODUCTION

Hyperglycaemia is a common complication of stress and prematurity in extremely low-birth-weight infants. Model-based insulin therapy protocols have the ability to safely improve glycaemic control for this group. Estimating non-insulin-mediated brain glucose uptake by the central nervous system in these models is typically done using population-based body weight models, which may not be ideal.

METHOD

A head circumference-based model that separately treats small-for-gestational-age (SGA) and appropriate-for-gestational-age (AGA) infants is compared to a body weight model in a retrospective analysis of 48 patients with a median birth weight of 750g and median gestational age of 25 weeks. Estimated brain mass, model-based insulin sensitivity (SI) profiles, and projected glycaemic control outcomes are investigated. SGA infants (5) are also analyzed as a separate cohort.

RESULTS

Across the entire cohort, estimated brain mass deviated by a median 10% between models, with a per-patient median difference in SI of 3.5%. For the SGA group, brain mass deviation was 42%, and per-patient SI deviation 13.7%. In virtual trials, 87-93% of recommended insulin rates were equal or slightly reduced (Δ<0.16mU/h) under the head circumference method, while glycaemic control outcomes showed little change.

CONCLUSION

The results suggest that body weight methods are not as accurate as head circumference methods. Head circumference-based estimates may offer improved modelling accuracy and a small reduction in insulin administration, particularly for SGA infants.

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) &lt;32 weeks and a birth weight (BW) &lt;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.