Clinical Decision Support and Closed‐Loop Control for Intensive Care Unit Sedation

Abstract

Critical care patients undergoing surgery require drug administration to regulate physiological variables such as blood pressure, cardiac output, heart rate, and degree of consciousness. The rate of infusion of each administered drug is critical, requiring constant monitoring and frequent adjustments. Patients in the intensive care unit who require mechanical ventilation due to acute respiratory failure also frequently require the administration of sedative agents. Open‐loop control (manual control) by clinical personnel can be tedious, imprecise, time‐consuming, and sometimes of poor quality, depending on the skills and judgment of the clinician. Dynamical system pharmacokinetic and pharmacodynamic modeling and closed‐loop control system design methodologies can significantly advance our understanding of the wide effects of pharmacological agents and anesthetics, as well as advance the state‐of‐the‐art in active control of drug delivery systems for clinical pharmacology. In this paper, we discuss the challenges and opportunities of clinical decision support and closed‐loop control for intensive care unit sedation.

Closed-loop control of anesthesia and mean arterial pressure using reinforcement learning

“…Surgical patients typically require deep sedation over a short duration of time. However, ICU patient sedation, especially patients assisted by mechanical ventilation to treat pulmonary insufficiency, can be more challenging [1]. Critically ill patients who are assisted by mechanical ventilators require moderate sedation for several days to ensure cooperative and safe treatment in the ICU.…”

“…Furthermore, open-loop control can be tedious, imprecise, and time-consuming. Hence, closed-loop control for anesthesia administration is imperative to improve quality of medical care and to restrain the increasing cost of health care [1].…”

“…Critically ill patients who are assisted by mechanical ventilators require moderate sedation for several days to ensure cooperative and safe treatment in the ICU. Moreover, clinical research shows that closedloop control of anesthetic drug administration can have positive outcomes in terms of patient safety, early recovery, reduced treatment cost, and effective and practical use of clinician expertise [1][2][3][4].…”

“…Due to system complexity and system uncertainty, however, fixed-gain linear controllers have proved inadequate [1,2]. Investigations using model predictive controllers (MPC) for surgical patients with system constraints have also proved deficient in terms of prolonged parameter identification time and model dependence [8].…”

Optimal Control for Pennes' Bioheat Equation

“…In 2010, Malek et al solved optimal boundary control for hyperdiffusion equation. Haddad et al discussed the challenges and opportunities of clinical decision support and closed‐loop control for intensive care unit sedation. An inversion‐based control approach to achieve a solution to the precision positioning problem associated with high‐speed operation of the Atomic Force Microscope is presented by Zou et al .…”