Trial reportJournal of diabetes science and technology2009
Blood glucose controller for neonatal intensive care: virtual trials development and first clinical trials.
Trial report in Journal of diabetes science and technology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed, 56 citations in OpenAlex.
- Feasibility of automated insulin delivery guided by continuous glucose monitoring in preterm infants.Archives of disease in childhood. Fetal and neonatal edition · 2020Trial
- Computer-determined dosage of insulin in the management of neonatal hyperglycaemia (HINT2): protocol of a randomised controlled trial.BMJ open · 2017Trial
- Pilot study of a model-based approach to blood glucose control in very-low-birthweight neonates.BMC pediatrics · 2012Trial
- Data entry errors and design for model-based tight glycemic control in critical care.Journal of diabetes science and technology · 2012Trial
- Continuous Glucose Monitoring in Transient Neonatal Diabetes Mellitus-2 Case Reports and Literature Review.Diagnostics (Basel, Switzerland) · 2023Review
- The Virtual Anemia Trial: An Assessment of Model-Based In Silico Clinical Trials of Anemia Treatment Algorithms in Patients With Hemodialysis.CPT: pharmacometrics & systems pharmacology · 2018Article
- 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.Biomedical engineering online · 2018Review
- Untangling glycaemia and mortality in critical care.Critical care (London, England) · 2017Article
- Continuous glucose monitoring in neonates: a review.Maternal health, neonatology and perinatology · 2017Review
- On the problem of patient-specific endogenous glucose production in neonates on stochastic targeted glycemic control.Journal of diabetes science and technology · 2013Review
- Nasogastric aspiration as an indicator for feed absorption in model-based glycemic control in neonatal intensive care.Journal of diabetes science and technology · 2013Article
- Using stochastic modelling to identify unusual continuous glucose monitor measurements and behaviour, in newborn infants.Biomedical engineering online · 2012Article
- Variability of insulin sensitivity during the first 4 days of critical illness: implications for tight glycemic control.Annals of intensive care · 2012Article
- Stochastic targeted (STAR) glycemic control: design, safety, and performance.Journal of diabetes science and technology · 2012Article
- Interface design and human factors considerations for model-based tight glycemic control in critical care.Journal of diabetes science and technology · 2012Article
- Pilot proof of concept clinical trials of Stochastic Targeted (STAR) glycemic control.Annals of intensive care · 2011Article
- Clinical detection and monitoring of acute pulmonary embolism: proof of concept of a computer-based method.Annals of intensive care · 2011Article
- Physiological modeling, tight glycemic control, and the ICU clinician: what are models and how can they affect practice?Annals of intensive care · 2011Article
- Validation of a model-based virtual trials method for tight glycemic control in intensive care.Biomedical engineering online · 2010Article
- What makes tight glycemic control tight? The impact of variability and nutrition in two clinical studies.Journal of diabetes science and technology · 2010Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPremature 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.
methodsThis 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 Women's Hospital.
resultsTime 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.
conclusionsA 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.