Physiological modelling of agitation-sedation dynamics including endogenous agitation reduction.

Sedation administration and agitation management are fundamental activities in any intensive care unit. A lack of objective measures of agitation and sedation, as well as poor understanding of the underlying dynamics, contribute to inefficient outcomes and expensive healthcare. Recent models of agitation-sedation pharmacodynamics have enhanced understanding of the underlying dynamics and enable development of advanced protocols for semi-automated sedation administration. However, these initial models do not capture all observed dynamics, particularly periods of low sedative infusion. A physiologically representative model that incorporates endogenous agitation reduction (EAR) dynamics is presented and validated using data from 37 critical care patients. High median relative average normalised density (RAND) values of 0.77 and 0.78 support and minimum RAND values of 0.51 and 0.55 for models without and with EAR dynamics respectively show that both models are valid representations of the fundamental agitation-sedation dynamics present in a broad spectrum of intensive care unit (ICU) patients. While the addition of the EAR dynamic increases the ability of the model to capture the observed dynamics of the agitation-sedation system, the improvement is relatively small and the sensitivity of the model to the EAR dynamic is low. Although this may represent a limitation of the model, the inclusion of EAR is shown to be important for accurately capturing periods of low, or no, sedative infusion, such as during weaning prior to extubation.

Physiological modelling of agitation-sedation dynamics.

Agitation-sedation cycling in critically ill patients, characterized by oscillations between states of agitation and over-sedation, damages patient health and increases length of stay and cost. A model that captures the essential dynamics of the agitation-sedation system and is physiologically representative is developed, and validated using data from 37 critical care patients. It is more physiologically representative than a previously published agitation-sedation model, and captures more realistic and complex dynamics. The median time in the 90% probability band is 90%, and the total drug dose, relative to recorded drug dose data, is a near ideal 101%. These statistical model validation metrics are 5-13% better than a previously validated model. Hence, this research provides a platform to develop and test semi-automated sedation management controllers that offer the significant clinical potential of improved agitation management and reduced length of stay in critical care.

Automated agitation management accounting for saturation dynamics.

Agitation-sedation cycling in critically ill is damaging to patient health and increases length of and cost. A physiologically representative model of the agitation-sedation system is used as a platform to evaluate feedback controllers offering improved agitation management. A heavy-derivative controller with upper and infusion rate bounds maintains minimum plasma concentrations through a low constant infusion, and minimizes outbursts of agitation through strong, timely boluses. controller provides improved agitation management using from 37 critically ill patients, given the saturation of effect at high concentration. Approval was obtained the Canterbury Ethics Board for this research.

Physiologically-based minimal model of agitation-sedation dynamics.

Agitation-sedation cycling in critically ill patients, characterized by oscillations between states of agitation and over-sedation, damages patient health and increases length of stay and cost. The model presented captures the essential dynamics of the agitation-sedation system, is physiologically representative, and is validated by accurately simulating patient response for 37 critical care patients. The model provides a platform to develop and test controllers that offer the potential of improved agitation management.

Rethinking sedation and agitation management in critical illness.

OBJECTIVE: To examine difficulties in sedation management in the critically ill patient and explore how a semi automated sedation controller can improve agitation control. To present recent work on measurements of agitation, dynamic systems modelling and control of patient agitation response. DATA SOURCES: Articles and peer-reviewed studies identified through a PUBMED search and selected original works from the biomedical engineering literature of relevance to agitation control and management. SUMMARY OF REVIEW: Over-sedation has an adverse impact on intensive care resources. Interventions to constrain sedation delivery through development of protocols or regular cessation of infusions result in reduction in resource utilisation, but have not significantly addressed existing difficulties in agitation control. We develop a paradigm in which control of agitation in critically ill patients becomes the primary objective of sedation management. This principle is central to the function of a nurse-managed semi-automated sedation delivery device. The clinical application of this device using subjective assessments of agitation is presented. A framework for the development of improved automated sedation delivery systems using objective measurements of agitation and control, based on agitation feedback, is described. Using dynamic systems modelling and a simulated nurse, a bolus-driven approach significantly reduced agitation and minimised drug utilisation. This result challenges the current practice of sedating patients using continuous infusions. CONCLUSIONS: A simple computerised interface with an algorithm that continually reduces the infusion rate in the absence of agitation has successfully been introduced into clinical practice. Nursing staff reported high levels of satisfaction with this device and it has enabled detailed data on patterns of sedation administration to be extracted for analysis. This data has been used to validate a model of the fundamental agitation-sedation dynamics.