J. Geoffrey Chase

Modelling and control of the agitation-sedation cycle

Abstract Agitation-sedation cycling in critically ill patients, characterised by oscillations between states of agitation and over-sedation, is damaging to patient health and increases length of stay and healthcare cost. The mathematical model presented captures the essential dynamics of the agitation-sedation system for the first time, and is validated by accurately simulating known patient response. Simulations using heavy derivative control highlight the potential of automated systems to reduce the magnitude, duration, and severity of agitation-sedation cycling, without significant increases in required drug dose.

Wavelet Signatures and Diagnostics for the Assessment of ICU Agitation-Sedation Protocols

The use of quantitative modelling to enhance understanding of the agitation-sedation (A-S) system and the provision of an A-S simulation platform are key tools in this area of patient critical care. A suite of wavelet techniques and metrics based on the discrete wavelet transform (DWT) are developed in this chapter which are shown to successfully establish the validity of deterministic agitation-sedation (A-S) models against empirical (recorded) dynamic A-S infusion profiles. The DWT approach is shown to provide robust performance metrics of A-S control and also yield excellent visual assessment tools. This approach is generalisable to any study which investigates the similarity or closeness of bivariate time series of, say, a large number of units (patients, households etc) and of disparate lengths and of possibly extremely long length. This work demonstrates the value of the DWT for assessing ICU agitation-sedation deterministic models, and suggests new wavelet based diagnostics by which to assess the A-S models. Typically agitation-sedation cycling in critically ill patients involves oscillations between states of agitation and over-sedation, which is detrimental to patient health, and increases hospital length of stay (Rudge et al., 2006a; 2006b; Chase et al., 2004; Rudge et al 2005). Agitation management via effective sedation management is an important and fundamental activity in the intensive care unit (ICU), where in the hospitalized adult agitation is defined as excessive verbal behaviour that interferes with patient care, and the patient’s medical therapies (Chase et al., 2004). The main goal of sedation is to control agitation, while also preventing over-sedation and over-use of drugs. In clinical practice, however, a lack of understanding of the underlying dynamics of A-S, combined with a lack of subjective assessment tools, makes effective and consistent clinical agitation management difficult (Chase et al., 2004; Rudge et al., 2005, 2006b). Early agitation management methods traditionally relied on subjective agitation assessment, and sedation assessment scales, combined with medical staff experience and intuition, to deliver appropriate sedation; and an appropriate sedation input response, from recorded at bedside agitation scales (Fraser &

A minimal cardiovascular system haemodynamic model for rapid diagnostic assistance

Abstract Characterising circulatory dysfunction and choosing a suitable treatment is often difficult, and time consuming. This paper outlines a numerically stable minimal model of the human cardiovascular system (CVS) specifically aimed for rapid, on site modelling to assist in diagnosis and treatment. A minimal number of governing equations and a realistic valve law are used to accurately capture trends in CVS dynamics. The model is shown to have long-term stability and consistency with non-specific initial conditions. Results show that the model adequately provides appropriate magnitudes and trends for a variety of physiologically verified test cases.

Density Estimation and Wavelet Thresholding via Bayesian Methods: A Wavelet Probability Band and Related Metrics Approach to Assess Agitation and Sedation in ICU Patients

A wave is usually defined as an oscillating function that is localized in both time and frequency. A wavelet is a small wave, which has its energy concentrated in time providing a tool for the analysis of transient, non-stationary, or time-varying phenomena. Wavelets have the ability to allow simultaneous time and frequency analysis via a flexible mathematical foundation. Wavelets are well suited to the analysis of transient signals in particular. The localizing property of wavelets allows a wavelet expansion of a transient component on an orthogonal basis to be modelled using a small number of wavelet coefficients using a low pass filter. This wavelet paradigm has been applied in a wide range of fields, such as signal processing, data compression and image analysis.