Article

Early insulin therapy in very-low-birth-weight infants

University of Cambridge, Cambridge, United Kingdom.
New England Journal of Medicine (Impact Factor: 54.42). 11/2008; 359(18):1873-84. DOI: 10.1056/NEJMoa0803725
Source: PubMed

ABSTRACT Studies involving adults and children being treated in intensive care units indicate that insulin therapy and glucose control may influence survival. Hyperglycemia in very-low-birth-weight infants is also associated with morbidity and mortality. This international randomized, controlled trial aimed to determine whether early insulin replacement reduced hyperglycemia and affected outcomes in such neonates.
In this multicenter trial, we assigned 195 infants to continuous infusion of insulin at a dose of 0.05 U per kilogram of body weight per hour with 20% dextrose support and 194 to standard neonatal care on days 1 to 7. The efficacy of glucose control was assessed by continuous glucose monitoring. The primary outcome was mortality at the expected date of delivery. The study was discontinued early because of concerns about futility with regard to the primary outcome and potential harm.
As compared with infants in the control group, infants in the early-insulin group had lower mean (+/-SD) glucose levels (6.2+/-1.4 vs. 6.7+/-2.2 mmol per liter [112+/-25 vs. 121+/-40 mg per deciliter], P=0.007). Fewer infants in the early-insulin group had hyperglycemia for more than 10% of the first week of life (21% vs. 33%, P=0.008). The early-insulin group had significantly more carbohydrate infused (51+/-13 vs. 43+/-10 kcal per kilogram per day, P<0.001) and less weight loss in the first week (standard-deviation score for change in weight, -0.55+/-0.52 vs. -0.70+/-0.47; P=0.006). More infants in the early-insulin group had episodes of hypoglycemia (defined as a blood glucose level of <2.6 mmol per liter [47 mg per deciliter] for >1 hour) (29% in the early-insulin group vs. 17% in the control group, P=0.005), and the increase in hypoglycemia was significant in infants with birth weights of more than 1 kg. There were no differences in the intention-to-treat analyses for the primary outcome (mortality at the expected date of delivery) and the secondary outcome (morbidity). In the intention-to-treat analysis, mortality at 28 days was higher in the early-insulin group than in the control group (P=0.04).
Early insulin therapy offers little clinical benefit in very-low-birth-weight infants. It reduces hyperglycemia but may increase hypoglycemia (Current Controlled Trials number, ISRCTN78428828.)

Download full-text

Full-text

Available from: Claire Theyskens, Jun 20, 2015
1 Follower
 · 
264 Views
  • Source
    [Show abstract] [Hide abstract]
    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 (SI [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 SI 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.
    Biomedical Signal Processing and Control 07/2013; 8(4):1746-8094. DOI:10.1016/j.bspc.2013.01.006 · 1.53 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Hyperglycaemia is a common complication of prematurity and stress in neonatal intensive care units (NICUs). It has been linked to worsened outcomes and mortality. There is currently no universally accepted best practice glycaemic control method, with many protocols lacking patient specificity or relying heavily on ad hoc clinical judgment from clinical staff who may be caring or overseeing care for several patients at once. The result is persistent hypoglycaemia and poor control. This research presents the virtual trial design and optimisation of a stochastic targeted (STAR) approach to improve performance and reduce hypoglycaemia. Clinically validated virtual trials based on NICU patient data (N = 61 patients, 7006 h) are used to develop and optimise a STAR protocol that improves on current STAR-NICU performance and reduce hypoglycaemia. Five approaches are used to maximise the stochastic range of BG outcomes within 4.0–8.0 mmol/L, and are designed based on an overall cohort risk to provide clinically specified risk (5%) of BG above or below a clinically specified level. The best protocol placed the 5th percentile BG outcome for an intervention on 4.0 mmol/L band. The optimised protocol increased %BG in the 4.0–8.0 mmol/L band by 3.5% and the incidence of BG < 2.6 mmol/L by 1 patient (50%). Significant intra- and inter-patient variability limited possible performance gains so that they are unlikely to be clinically substantial, indicating a need for a further increase patient-specific or sub-cohort specific approaches to manage variability. This result sets the direction for future research.
    Biomedical Signal Processing and Control 03/2013; 8(2):215-221. DOI:10.1016/j.bspc.2012.08.002 · 1.53 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: 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. 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. 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. 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.
    Computer methods and programs in biomedicine 06/2011; 102(3):253-66. DOI:10.1016/j.cmpb.2010.05.006 · 1.09 Impact Factor