Blood Glucose Prediction Using Stochastic Modeling in Neonatal Intensive Care (2010)
AuthorsLe Compte, A.J., Lee, D.S., Chase, J.G., Lin, J., Lynn, A., Shaw, G.M.show all
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 dynamicmodel 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 biasvariance 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, cohortspecific stochastic model of SI dynamics in the neonate.
CitationLe Compte, A.J., Lee, D.S., Chase, J.G., Lin, J., Lynn, A., Shaw, G.M. (2010) Blood Glucose Prediction Using Stochastic Modeling in Neonatal Intensive Care. IEEE Transactions on Biomedical Engineering, 57(3), pp. 509-518.
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Keywordsforecasting; human factors; stochastic
ANZSRC Fields of Research11 - Medical and Health Sciences::1102 - Cardiovascular Medicine and Haematology::110202 - Haematology
11 - Medical and Health Sciences::1103 - Clinical Sciences::110310 - Intensive Care