A capaciflector provides continuous and accurate respiratory rate monitoring for patients at rest and during exercise

Hayward, N, Shaban, M, Badger, J, Jones, I, Wei, Y ORCID: https://orcid.org/0000-0001-6195-8595, Spencer, D, Isichei, S, Knight, M, Otto, J, Rayat, G, Levett, D, Grocott, M, Akerman, H and White, N, 2022. A capaciflector provides continuous and accurate respiratory rate monitoring for patients at rest and during exercise. Journal of Clinical Monitoring and Computing. ISSN 1387-1307

Preview

Text 1511388_Wei.pdf - Post-print Download (284kB) | Preview

Abstract

Background: Respiratory rate (RR) is a marker of critical illness, but during hospital care, RR is often inaccurately measured. The capaciflector is a novel sensor that is small, inexpensive, and flexible, thus it has the potential to provide a single-use, real-time RR monitoring device. We evaluated the accuracy of continuous RR measurements by capaciflector hardware both at rest and during exercise.

Methods: Continuous RR measurements were made with capaciflectors at four chest locations. In healthy subjects (n=20), RR was compared with strain gauge chest belt recordings during timed breathing and two different body positions at rest. In patients undertaking routine cardiopulmonary exercise testing (CPET, n=50), RR was compared with pneumotachometer recordings. Comparative RR measurement bias and limits of agreement were calculated and presented in Bland-Altman plots.

Results: The capaciflector was shown to provide continuous RR measurements with a bias less than 1 breath per minute (BPM) across four chest locations. Accuracy and continuity of monitoring were upheld even during vigorous CPET exercise, often with narrower limits of agreement than those reported for comparable technologies.

Conclusion: We provide a unique clinical demonstration of the capaciflector as an accurate breathing monitor, which may have the potential to become a simple and affordable medical device.

Item Type:	Journal article
Publication Title:	Journal of Clinical Monitoring and Computing
Creators:	Hayward, N., Shaban, M., Badger, J., Jones, I., Wei, Y., Spencer, D., Isichei, S., Knight, M., Otto, J., Rayat, G., Levett, D., Grocott, M., Akerman, H. and White, N.
Publisher:	Springer Science and Business Media LLC
Date:	18 January 2022
ISSN:	1387-1307
Identifiers:	Number Type 10.1007/s10877-021-00798-7 DOI 1511388 Other
Rights:	© 2022 Springer Nature Switzerland AG. Part of Springer Nature. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Divisions:	Schools > School of Science and Technology
Record created by:	Linda Sullivan
Date Added:	08 Feb 2022 13:53
Last Modified:	18 Jan 2023 03:00
URI:	https://irep.ntu.ac.uk/id/eprint/45559

Actions (login required)

Edit View

Statistics

Views

Downloads