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TECHNOLOGY, COMPUTING, AND SIMULATION

An Evaluation of a Novel Software Tool for Detecting Changes in Physiological Monitoring

J. Mark Ansermino, MBBCh, MSc(Inf), FRCPC^*, Jeremy P. Daniels, BASc, EIT^*, Randy T. Hewgill, MD, FRCPC^*, Joanne Lim, MASc^*, Ping Yang, MASc, Chris J. Brouse, MASc, Guy A. Dumont, PhD, PEng, and John B. Bowering, MD, FRCPC^*

From the *Department of Anesthesiology, Pharmacology and Therapeutics, and Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada.

Address correspondence and reprint requests to J. Mark Ansermino, MBBCh, MSc(Inf), FRCPC, Department of Anesthesia, BC Children’s Hospital, 4480 Oak St., Vancouver, BC V6H 3V4, Canada. Address e-mail to anserminos{at}yahoo.ca.

BACKGROUND: We have developed a software tool (iAssist) to assist clinicians as they monitor the physiological data that guide their actions during anesthesia. The system tracks the statistical properties of multiple dynamic physiological processes and identifies new trend patterns. We report our initial evaluation of this tool (in pseudo real-time) and compare the detection of trend changes to a post hoc visual review of the full trend. We suggest a combination of criteria by which to evaluate the performance of monitoring devices that aim to enhance trend detection.

METHODS: Nineteen children and 28 adults consented to be included in the study, encompassing more than 68 h of anesthesia. In each surgical case, an anesthesiologist reported all perceived clinical changes in monitoring in real-time. A trained observer simultaneously documented the verbally reported changes and every anesthesiologist action. The same cases were subsequently evaluated offline (in pseudo real-time) by a novel software tool (iAssist). Heart rate, end-tidal carbon dioxide, exhaled minute ventilation, and respiratory rate were modeled using a dynamic linear growth model whose noise distribution was estimated by an adaptive Kalman filter based on a recursive expectation-maximization method. Changes were detected by adaptive local Cumulative Sum testing. Changes in the mean arterial noninvasive blood pressures and oxygen saturation were detected using adaptive Cumulative Sum testing on a filtered residual from an exponentially weighted moving averaging filter. In post hoc analysis, each change detected by iAssist was graded independently by two clinicians using a graphical display of the whole case. Missed changes were recorded.

RESULTS: The iAssist software tool detected 869 true positive changes (at an average of 12.76/h) with a sensitivity of 0.91 and positive predictive value of 0.87. The post hoc review identified 91 missed changes (at an average of 1.34/h), resulting in an overall ratio of true positive rates to false-negative rates of 9.55. The clinicians in real-time reported 209 changes in trend (at an average of 3.07/h).

CONCLUSION: The algorithms perform favorably compared with a visual inspection of the complete trend. Further research is needed to identify when and how to draw the clinician’s attention to these changes.

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