Closed-loop control for cardiopulmonary management and intensive care unit sedation using expert systems

Abstract

Patients in the intensive care unit who require mechanical ventilation due to acute respiratory failure also frequently require the administration of sedative agents. The need for sedation arises both from patient anxiety due to the loss of personal control and the unfamiliar and intrusive environment of the intensive care unit, and also due to pain or other variants of noxious stimuli. While physicians select the agent(s) used for sedation and cardiovascular function, the actual administration of these agents is the responsibility of the nursing staff. If clinical decision support systems and closedloop control systems could be developed for critical care monitoring and lifesaving interventions as well as the administration of sedation and cardiopulmonary management, the intensive care unit nurse could be released from the intense monitoring of sedation, allowing her/him to focus on other critical tasks. One particularly attractive strategy is to utilize the knowledge and experience of skilled clinicians, capturing explicitly the rules expert clinicians use to decide on how to titrate drug doses depending on the level of sedation. In this paper, we develop a rule-based expert system for cardiopulmonary management and intensive care unit sedation. Furthermore, we use probability theory to quantify uncertainty and to extend the proposed rulebased expert system to deal with more realistic situations.

Clinical Decision Support and Closed-Loop Control for Cardiopulmonary Management and Intensive Care Unit Sedation Using Expert Systems

“…In [1], a rule-based expert system is introduced for cardiopulmonary management and ICU sedation. One of the limitations of the rule-based expert system proposed in [1] is its inability to deal with uncertainty.…”

“…One of the limitations of the rule-based expert system proposed in [1] is its inability to deal with uncertainty. More specifically, it assumes perfect accuracy in the measurement of present and previous MAAS scores, blood pressure, and heart rate.…”

“…A more general approach would allow for rules with multiple conclusions, where a different level of uncertainty is associated with each conclusion. In this section, we use probability theory to quantify uncertainty to extend the rule-based expert system given in [1] to deal with more realistic situations.…”

“…Let $\mathcal{B}=(\mathrm{scriptG},ϴ)$ represent the Bayesian network and let the patient's current MAAS score, previous MAAS score, blood pressure, heart rate, and required drug dose for sedation be given by the random variables M , M ’, B, H , and D , respectively, where range( M ) = range( M ’) = {0, 1,..., 6}, range( D ) = {1, 2,..., 12}, $\text{range}\left(B\right)=\text{range}\left(H\right)={\mathrm{double-struckR}}_{+}$, where ${\mathrm{double-struckR}}_{+}$ denotes the set of positive scalars, and, for a given function $f:\mathcal{X}\to \mathcal{Y}$, $\text{range}\left(f\right)\triangleq \mathcal{Y}$. Note that there are 12 distinct actions (primary action) given in the first part of the conclusion of each rule in Table I in [1], and hence, we have assigned a unique number to each distinct action. The graph $\mathrm{scriptG}$ for this Bayesian network is given in Fig.…”

“…Once such a parameter is discovered and validated, it then becomes necessary to use the measure of sedation for the titration of drug dose. One particularly attractive strategy is to utilize the knowledge and experience of skilled clinicians, capturing explicitly the rules expert clinicians use to decide on how to titrate drug doses depending on the level of sedation [1]. In this paper, we extend the rule-based expert system approach for cardiopulmonary management and ICU sedation presented in [1] to a stochastic setting by using probability theory to quantify system uncertainty and hence deal with more realistic clinical situations.…”