Applying Computer Models to Realize Closed-Loop Neonatal Oxygen Therapy

Document Type

Article

Publication Date

1-1-2017

Publication Title

Anesthesia and Analgesia

Volume

124

Issue

1

First Page

95

Last Page

103

Abstract

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.

DOI

10.1213/ANE.0000000000001367

ISSN

00032999

E-ISSN

15267598

PubMed ID

27992386

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