Abstract :
[en] Tight glycemic control (TGC) is prevalent in critical care. Providing safe, effective TGC has proven very difficult to achieve with clinically derived protocols. The prob-lem is exacerbated by extreme patient variability and the need to minimize clinical effort and burden. These ingredients make an ideal scenario for model-based methods to provide opti-mised solutions. This paper presents the development, clinical-ly validated virtual trials optimisation, and initial clinical implementation of a stochastic targeted (STAR) TGC method and framework. It is compared to a prior successful, model-derived, less flexible and dynamic TGC protocol (SPRINT). The use of stochastic models to safely forecast a range of glu-cose outcomes over 1-3 hours ensures better performance, more dynamic use of the range of insulin and nutrition inputs and thus better glycemic performance and safety from hypo-glycemia, the latter of which was reduced by 3.0x times. Hence, the paper presents an overall engineering approach to TGC from engineering models to clinical implementation and ongo-ing clinical practice change.
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