Generalisability of a Virtual Trials Method for Glycaemic Control in Intensive Care

Abstract

Abstract-Background: Elevated blood glucose (BG) concentrations (Hyperglycaemia) are a common complication in critically ill patients. Insulin therapy is commonly used to treat hyperglycaemia, but metabolic variability often results in poor BG control and low BG (hypoglycaemia). Objective: This paper presents a modelbased virtual trial method for glycaemic control protocol design, and evaluates its generalisability across different populations. Methods: Model-based insulin sensitivity (SI) was used to create virtual patients from clinical data from three different ICUs in New Zealand, Hungary, and Belgium. Glycaemic results from simulation of virtual patients under their original protocol (self-simulation) and protocols from other units (cross-simulation) were compared. Results: Differences were found between the three cohorts in median SI and inter-patient variability in SI. However, hour-to-hour intra-patient variability in SI was found to be consistent between cohorts. Self and crosssimulation results were found to have overall similarity and consistency, though results may differ in the first 24-48 hours due to different cohort starting BG and underlying SI. Conclusions and significance: Virtual patients and the virtual trial method were found to be generalisable across different ICUs. This virtual trial method is useful for in silico protocol design and testing, given an understanding of the underlying assumptions and limitations of this method.

Glycemic control in the intensive care unit: A control systems perspective

“…However, no discussion of possible models is complete due to the large number of models studied, so this review focuses on nonlinear metabolic system models used in critical care to create virtual patients, and use them to guide care, a much more limited set. Specifically: [135,136]  Glucosafe [110]  Cambridge [85]  ICING [137][138][139]  UVA/Padova [84,140] The ICU Minimal Model is based on the Bergman Minimal Model [113,121], and is integral to the LOGIC controller [141]. It is the least physiologically relevant, with a minimal number of terms aggregating all glucose appearance and disappearance routes.…”

“…The ICING model also has high physiological relevance with specific pathways modeled for the main forms of glucose appearance and uptake [137,139]. It also has a neonatal ICU (NICU) version based on the same dynamics scaled for the specific case [87,138,149].…”

A combined SHM/IDA method for assessing collapse capacity and risk in subsequent ground motions

“…While realistic, it may not hold for a given structure, impacting the benefit obtained from the resulting IDA and risk assessment when degradation is included. There is thus a need for what is effectively a "virtual structure" with predictive capacity for future events, but built off identified current values, similar to biomedical engineering "virtual patients" [41][42][43][44]. Thus, a more complex smooth hysteretic model, such as a Bouc-Wen model [45,46] could be used to capture the continuous change of plasticity in the future work.…”

Model-Based Insulin-Nutrition Administration for Glycemic Control in Malaysian Critical Care: First Pilot Trial

“…During the fitting, clinical data were used to identify hourly SI values which served as a virtual patient profile. This profile reflects the glycemic response to insulin and nutrition inputs and can be used to simulate responses to different interventions with good accuracy [15,18]. In the second phase, the profile was used to simulate glycemic response to STAR as simulated, where differences indicates non-compliance to STAR.…”

“…The Stochastic TARgeted (STAR) protocol is based on a clinically validated model [14,15] to provide specific recommendations for insulin, as well as nutrition, while controlling hyperglycemia and ensuring a maximum of 5% of the risk of light hypoglycemia [16,17]. STAR is an adaptable model-based control approach that empowers versatile, patient-specific GC.…”