Andrew D. Rudge

Quantifying agitation in sedated ICU patients using digital imaging

Agitation is a significant problem in the Intensive Care Unit (ICU), affecting 71% of sedated adult patients during 58% of ICU patient-days. Subjective scale based assessment-methods focused primarily on assessing excessive patient motion are currently used to assess the level of patient agitation, but are limited in their accuracy and resolution. This research quantifies this approach by developing an objective agitation measurement from patient motion that is sensed using digital video image processing. A fuzzy inference system (FIS) is developed to classify levels of motion that correlate with observed patient agitation, while accounting for motion due to medical staff working on the patient. Clinical tests for five ICU patients have been performed to verify the validity of this approach in comparison to agitation graded by nursing staff using the Riker Sedation-Agitation Scale (SAS). All trials were performed in the Christchurch Hospital Department of Intensive Care, with ethics approval from the Canterbury Ethics Committee. Results show good correlation with medical staff assessment with no false positive results during calm periods. Clinically, this initial agitation measurement method promises the ability to consistently and objectively quantify patient agitation to enable better management of sedation and agitation through optimised drug delivery leading to reduced length of stay and improved outcome.

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.

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.

Micro-electro-mechanical-systems direct fluid shear stress sensor arrays for flow control

The design and analysis of non-intrusive micro-electro-mechanical-systems sensors to measure fluid shear stress on the wing surface of a commercial jetliner is presented. A design specification is derived from analysis of flight loading data using computational fluid dynamics. The specification accounts for different flight conditions, aerodynamic smoothness and sensor bandwidth. Capacitive and PZT-based direct fluid shear sensor designs based upon the force-displacement relationship of a tethered plate are created. The sensors have overall dimensions <0.7 mm, can be collated into large arrays and provide scaled digital outputs, and their design methodology is easily generalized to similar applications.

A new model validation tool using kernel regression and density estimation

In physiological system modelling for control or decision support, model validation is a critical element. A nonparametric approach for assessing the validity of deterministic dynamic models against empirical data is developed, based on kernel regression and kernel density estimation, yielding visual graphical assessment tools as well as numerical metrics of compatibility between the model and the data. Nonparametric regression has been suggested for assessing a parametric statistical model by constructing a confidence band for the proposed model and then checking whether the nonparametric regression curve lies within the band. However, for deterministic models, there is no confidence band that can be constructed. A reversal of roles is therefore suggested--construct a probability band for the nonparametric regression curve and check whether the proposed model lies within the band. This approach extends the utility of nonparametric regression for model assessment to deterministic models. Weighted kernel density estimation is incorporated to derive a density profile for the regression curve, creating a local graphical validation tool. In addition, the density profile is used to define and compute two numerical measures--average normalized density (AND) and relative average normalized density (RAND), representing global statistical validity measures. These tools are demonstrated using a biomedical system model for agitation-sedation and sedation management control.

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 &

Parameter identification and sedative sensitivity analysis of an agitation–sedation model

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 dynamics have enhanced understanding of the underlying dynamics and enable development of advanced protocols for semi-automated sedation administration. In this research, the agitation-sedation model parameters are identified using an integral-based fitting method developed in this work. Parameter variance is then analysed over 37 intensive care unit patients. The parameter identification method is shown to be effective and computationally inexpensive, making it suited to real-time clinical control applications. Sedative sensitivity, an important model parameter, is found to be both patient-specific and time-varying. However, while the variation between patients is observed to be as large as a factor 10, the observed variation in time is smaller, and varies slowly over a period of days rather than hours. The high fitted model performance across all patients show that the agitation-sedation model presented captures the fundamental dynamics of the agitation-sedation system. Overall, these results provide additional insight into the system and clinical dynamics of sedation management.

H∞ control analysis of patient agitation management in the critically ill

Patient agitation in the critically ill damages patient health, increases length of stay, and contributes to rising healthcare costs. Recently developed quantifiable measures of patient agitation create the potential for automated sedation management. A physiologically verified non-linear model of agitation-sedation dynamics is used to design and evaluate sedative infusion controllers. A simplified linear model is used for H∞ analysis and control design to minimise the transfer function between unknown stimulus and resulting patient agitation. Regions of stability and instability are identified using Hi¾∞ Lyapunov methods, and bolus-based heavy derivative feedback control is shown to best minimise the Hi¾∞ norm. Hi¾∞ controllers yield an approximately 25% improvement over prior choices, and a nearly 50% improvement over current clinical practice. Importantly, these results show that general trends in the linear system are still reflected.

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.

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.