Access Type

Open Access Dissertation

Date of Award

January 2015

Degree Type

Dissertation

Degree Name

Ph.D.

Department

Biomedical Engineering

First Advisor

John M. Cavanaugh

Second Advisor

George A. Mashour

Abstract

Background: Intraoperative awareness with explicit recall (AWR) is a feared complication of surgery that can lead to significant psychological distress. Several large prospective trials have been completed comparing two methods of monitoring anesthetic depth [minimum alveolar concentration (MAC) or electroencephalography (EEG) monitoring using the bispectral index (BIS)] for the prevention of AWR. However, these trials were conducted in high risk populations, limiting generalizability.

Research Hypothesis: Real-time decision support with Anesthesia Information Management System alerts based on a novel anesthetic concentration algorithm (incorporating the use of intravenous anesthetics) or an EEG-guided algorithm will reduce the known incidence of AWR.

Methods: First, a MAC-equivalent alerting algorithm that incorporates the use of intravenous anesthetics was developed and retrospectively applied to previously collected data. A threshold was calculated that demonstrated optimal sensitivity and specificity for detecting AWR. Next, a large prospective randomized controlled trial was performed in an unselected surgical population to compare the MAC-equivalent or a BIS alerting algorithm for the prevention of AWR. Finally, discrete intraoperative data collected during that trial were analyzed to determine which specific threshold for MAC or BIS demonstrated optimal sensitivity and specificity in the eradication of AWR.

Results: Retrospective analysis revealed that a MAC-equivalent of <0.5 was associated with the highest positive likelihood ratio; this was used as the threshold in the prospective trial. No difference was detected between BIS or MAC-equivalent alerting algorithms in the reduction of AWR. Post hoc analysis revealed that BIS, when compared to routine clinical care without alerts, demonstrated a 4.7 fold reduction in definite or possible AWR. By secondary analysis, neither MAC nor BIS demonstrated a discrete population-based threshold with optimal sensitivity and specificity in the prevention of AWR.

Conclusion: No difference was detected in the reduction of AWR between BIS or MAC alerting. However, BIS alerting when compared to standard of care reduced the incidence of AWR. There were no discriminating thresholds of MAC or BIS values at the population level associated with the eradication of AWR. In conclusion, real-time decision support reduces the incidence of AWR but individualized patient-based alerting algorithms will be required for its eradication.