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Applying Computer Models to Realize Closed-Loop Neonatal Oxygen Therapy

Morozoff, Edmund MASc; Smyth, John A. LRCPSI, FRCPC; Saif, Mehrdad PhD

doi: 10.1213/ANE.0000000000001367
Technology, Computing, and Simulation: Original Clinical Research Report

BACKGROUND: Within the context of automating neonatal oxygen therapy, this article describes the transformation of an idea verified by a computer model into a device actuated by a computer model. Computer modeling of an entire neonatal oxygen therapy system can facilitate the development of closed-loop control algorithms by providing a verification platform and speeding up algorithm development.

METHODS: In this article, we present a method of mathematically modeling the system’s components: the oxygen transport within the patient, the oxygen blender, the controller, and the pulse oximeter. Furthermore, within the constraints of engineering a product, an idealized model of the neonatal oxygen transport component may be integrated effectively into the control algorithm of a device, referred to as the adaptive model. Manual and closed-loop oxygen therapy performance were defined in this article by 3 criteria in the following order of importance: percent duration of SpO2 spent in normoxemia (target SpO2 ± 2.5%), hypoxemia (less than normoxemia), and hyperoxemia (more than normoxemia); number of 60-second periods <85% SpO2 and >95% SpO2; and number of manual adjustments.

RESULTS: Results from a clinical evaluation that compared the performance of 3 closed-loop control algorithms (state machine, proportional-integral-differential, and adaptive model) with manual oxygen therapy on 7 low-birth-weight ventilated preterm babies, are presented. Compared with manual therapy, all closed-loop control algorithms significantly increased the patients’ duration in normoxemia and reduced hyperoxemia (P < 0.05). The number of manual adjustments was also significantly reduced by all of the closed-loop control algorithms (P < 0.05).

CONCLUSIONS: Although the performance of the 3 control algorithms was equivalent, it is suggested that the adaptive model, with its ease of use, may have the best utility.

Published ahead of print August 2, 2016.

From the *British Columbia’s Women’s Hospital and Health Center, Vancouver, British Columbia, Canada; School of Engineering, Simon Fraser University, Burnaby, British Columbia, Canada; and Faculty of Engineering, University of Windsor, Windsor, Ontario, Canada.

Published ahead of print August 2, 2016.

Edmund Morozoff, MASc, is currently affiliated with the New Product Development, Philips Respironics, Kennesaw, Georgia.

Accepted for publication March 24, 2016.

Funding: Clinical evaluation funding was provided by the Vancouver Foundation.

The authors declare no conflicts of interest.

This report was previously presented, in part, at the Innovations and Applications of Monitoring Perfusion, Oxygenation and Ventilation (IAMPOV) Symposium 2015. This is the second submission. It has been revised and submitted as a research report.

Reprints will not be available from the authors.

Address correspondence to Edmund Morozoff, MASc, New Product Development, Philips Respironics, 175 Chastain Meadows Court, Kennesaw, GA 30144, Address e-mail to paul.morozoff@philips.com.

© 2017 International Anesthesia Research Society
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