Development and optimisation of stochastic targeted (STAR) glycaemic control for neonatal intensive care

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 and relying heavily on clinical judgment. 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 hours) are used to develop and optimise a STAR protocol that improves on current STAR-NICU performance and reduce hypoglycaemia. Five approaches are used to maximize the stochastic range of BG outcomes within 4.0-8.0mmol/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 4.0mmol/L band. The optimised protocol increased %BG in the 4.0-8.0mmol/L band by 7% and the incidence of BG<2.6mmol/L by 1 patient (50%). Significant intra- and inter- patient variability reduced possible performance gains, indicating a need for patient-specific or sub-cohort specific approaches to manage variability.