Modeling the glucose regulatory system in extreme preterm infants
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Published on: January 4, 2018
Aaron Le Compte1, J Geoffrey Chase, Glynn Russell
1Department of Mechanical Engineering, University of Canterbury, New Zealand.
Insights
A new mathematical model accurately predicts blood glucose levels in premature infants, aiding glycemic control. This model, adapted from adult critical care, shows low prediction errors, improving care for neonates with glucose regulation issues.
Area of Science:
- Neonatal physiology
- Mathematical modeling
- Critical care medicine
Background:
- Premature infants often experience disturbed blood glucose homeostasis due to immature regulatory systems and physiological stress.
- Hypoglycemia and hyperglycemia are common in very low birth weight infants and more mature neonates.
- A model of the neonatal glucose regulatory system is needed for improved glycemic control strategies.
Purpose of the Study:
- To adapt an existing metabolic system model from adult critical care for neonatal physiology.
- To identify time-varying insulin sensitivity and glucose uptake profiles in neonates using integral-based fitting methods.
- To assess the predictive accuracy of the adapted model for clinical decision support.
Main Methods:
- Adapted a metabolic system model from adult critical care to neonatal physiology.
- Employed integral-based fitting to determine time-varying insulin sensitivity and non-insulin mediated glucose uptake.
- Evaluated model predictive ability using clinical data (1091 glucose measurements, 3567 patient hours, N=25) by comparing simulated vs. actual glucose values at 1-, 2-, and 4-hour intervals.
Main Results:
- The model achieved a median absolute percentage error of 2.4% for glucose value fitting.
- Median absolute prediction errors were 5.2% at 1 hour, 9.4% at 2 hours, and 13.6% at 4 hours.
- Low fitting and prediction errors demonstrate the model's accuracy in capturing neonatal glucose dynamics.
Conclusions:
- The developed model accurately captures and predicts neonatal metabolic dynamics for effective glycemic control decision support.
- The adaptation of adult critical care models to neonates is feasible and supported by literature data.
- Similar mathematical models can describe glucose metabolism dynamics in both premature neonates and critical care adults, indicated by low errors.
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.
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