Patch validation: an observational study protocol for the evaluation of a multisignal wearable sensor in patients during anaesthesia and in the postanaesthesia care unit

Morgan Le Guen, Pierre Squara, Sabrina Ma, Shérifa Adjavon, Bernard Trillat, Messaouda Merzoug, Philippe Aegerter, Marc Fischler, Morgan Le Guen, Pierre Squara, Sabrina Ma, Shérifa Adjavon, Bernard Trillat, Messaouda Merzoug, Philippe Aegerter, Marc Fischler

Abstract

Introduction: Except for operating rooms, postanaesthesia care units and intensive care units, where the monitoring of vital signs is continuous, intermittent care is standard practice. However, at a time when only the patients with the most serious conditions are hospitalised and only a fraction of these patients are in intensive care units, this type of monitoring is no longer sufficient. Wireless monitoring has been proposed, but it requires rigorous validation. The aim of this observational study is to compare vital signs obtained from a precordial patch sensor to those obtained with conventional monitoring.

Methods and analysis: This patch validation trial will be an observational, prospective, single-centre open study of 115 anaesthetised adult patients monitored with both a wireless sensor (myAngel VitalSigns, Devinnova, Montpellier, France) and a standard bedside monitor (Carescape Monitor B850, GE Healthcare, Chicago, Illinois). Both sensors will be used to record peripheral oxygen saturation, respiratory rate, heart rate, body temperature and blood pressure (systolic and diastolic). The main objective will be to assess the degree of agreement between the two systems during the patients' stay in the postanaesthesia care unit, both at the raw signal level and at the clinical parameter level. The secondary objectives will be to assess the same performance under anaesthesia, the frequency of missing data or artefacts, the diagnostic performance of the systems, the influence of patients' characteristics on agreement between the two systems, the adverse events and the acceptability of the patch to patients. Bland-Altman plots will be used in the main analysis to detect discrepancies and estimate the limits of agreement.

Ethics and dissemination: Ethics approval was obtained from the Ethical Committee (Toulouse, France) on 10 April 2020. We are not yet recruiting subjects for this study. The results will be submitted for publication in peer-reviewed journals.

Trial registration number: NCT04344093.

Keywords: adult anaesthesia; adult intensive & critical care; surgery; telemedicine.

Conflict of interest statement

Competing interests: None declared.

© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Figures

Figure 1
Figure 1
Placement of the patch sensor.

References

    1. Churpek MM, Edelson DP, Lee JY, et al. . Association between survival and time of day for rapid response team calls in a national registry. Crit Care Med 2017;45:1677–82. 10.1097/CCM.0000000000002620
    1. International Surgical Outcomes Study group Global patient outcomes after elective surgery: prospective cohort study in 27 low-, middle- and high-income countries. Br J Anaesth 2016;117:601–9. 10.1093/bja/aew316
    1. Michard F, Mountford WK, Krukas MR, et al. . Potential return on investment for implementation of perioperative goal-directed fluid therapy in major surgery: a nationwide database study. Perioper Med 2015;4:11. 10.1186/s13741-015-0021-0
    1. Ghaferi AA, Birkmeyer JD, Dimick JB. Variation in hospital mortality associated with inpatient surgery. N Engl J Med 2009;361:1368–75. 10.1056/NEJMsa0903048
    1. Morgan RJM, Williams F, Wright M. An early warning scoring system for detecting developing critical illness. Clin Intensive Care 1997;8:100.
    1. Royal College of Physicians Resources to support the adoption of the National early warning score. Available:
    1. NHS Available:
    1. McGaughey J, Alderdice F, Fowler R, et al. . Outreach and early warning systems (EWS) for the prevention of intensive care admission and death of critically ill adult patients on General Hospital wards. Cochrane Database Syst Rev 2007:CD005529.. 10.1002/14651858.CD005529.pub2
    1. Alam N, Hobbelink EL, van Tienhoven AJ, et al. . The impact of the use of the early warning score (EWS) on patient outcomes: a systematic review. Resuscitation 2014;85:587–94. 10.1016/j.resuscitation.2014.01.013
    1. Shamout F, Zhu T, Clifton L, et al. . Early warning score adjusted for age to predict the composite outcome of mortality, cardiac arrest or unplanned intensive care unit admission using observational vital-sign data: a multicentre development and validation. BMJ Open 2019;9:e033301. 10.1136/bmjopen-2019-033301
    1. Mestrom E, De Bie A, Steeg Mvande, et al. . Implementation of an automated early warning scoring system in a surgical ward: practical use and effects on patient outcomes. PLoS One 2019;14:e0213402. 10.1371/journal.pone.0213402
    1. Weenk M, Koeneman M, van de Belt TH, et al. . Wireless and continuous monitoring of vital signs in patients at the general ward. Resuscitation 2019;136:47–53. 10.1016/j.resuscitation.2019.01.017
    1. Michard F, Gan TJ, Kehlet H. Digital innovations and emerging technologies for enhanced recovery programmes. Br J Anaesth 2017;119:31–9. 10.1093/bja/aex140
    1. Beg MS, Gupta A, Stewart T, et al. . Promise of wearable physical activity monitors in oncology practice. J Oncol Pract 2017;13:82–9. 10.1200/JOP.2016.016857
    1. Leth S, Hansen J, Nielsen OW, et al. . Evaluation of commercial self-monitoring devices for clinical purposes: results from the future patient trial, phase I. Sensors 2017;17. 10.3390/s17010211. [Epub ahead of print: 22 Jan 2017].
    1. Gillinov S, Etiwy M, Wang R, et al. . Variable accuracy of wearable heart rate monitors during aerobic exercise. Med Sci Sports Exerc 2017;49:1697–703. 10.1249/MSS.0000000000001284
    1. van Lier HG, Pieterse ME, Garde A, et al. . A standardized validity assessment protocol for physiological signals from wearable technology: methodological underpinnings and an application to the E4 biosensor. Behav Res Methods 2020;52:607–29. 10.3758/s13428-019-01263-9
    1. Breteler MJM, KleinJan EJ, Dohmen DAJ, et al. . Vital signs monitoring with wearable sensors in high-risk surgical patients: a clinical validation study. Anesthesiology 2020;132:424–39. 10.1097/ALN.0000000000003029
    1. Leenen JPL, Leerentveld C, van Dijk JD, et al. . Current evidence for continuous vital signs monitoring by wearable wireless devices in hospitalized adults: systematic review. J Med Internet Res 2020;22:e18636. 10.2196/18636
    1. Wang R, Jia W, Mao Z. Cuff-free blood pressure estimation using pulse transit time and heart rate. 2014 12th International Conference on signal processing (ICSP), 2014:115–8.
    1. Gholamhosseini H, Meintjes A, Baig M, et al. . Smartphone-Based continuous blood pressure measurement using pulse transit time. Stud Health Technol Inform 2016;224:84–9.
    1. Zhang Q, Zhou D, Zeng X. Highly wearable cuff-less blood pressure and heart rate monitoring with single-arm electrocardiogram and photoplethysmogram signals. Biomed Eng Online 2017;16:23. 10.1186/s12938-017-0317-z
    1. Li G, Yu Y, Zhang C, et al. . An efficient optimization method to improve the measuring accuracy of oxygen saturation by using triangular wave optical signal. Rev Sci Instrum 2017;88:093103. 10.1063/1.5000952
    1. Association for the Advancement of Medical Instrumentation Cardiac monitors hrm, and alarms. Arlington, VA: American National Standard (ANSI/AAMI EC13: 2002), 2002: 1–87.
    1. Smolle K-H, Schmid M, Prettenthaler H, et al. . The accuracy of the CNAP® device compared with invasive radial artery measurements for providing continuous noninvasive arterial blood pressure readings at a medical intensive care unit: a Method-Comparison study. Anesth Analg 2015;121:1508–16. 10.1213/ANE.0000000000000965
    1. Breteler MJM, Huizinga E, van Loon K, et al. . Reliability of wireless monitoring using a wearable patch sensor in high-risk surgical patients at a step-down unit in the Netherlands: a clinical validation study. BMJ Open 2018;8:e020162. 10.1136/bmjopen-2017-020162
    1. Lu M-J, Zhong W-H, Liu Y-X, et al. . Sample size for assessing agreement between two methods of measurement by Bland-Altman method. Int J Biostat 2016;12. 10.1515/ijb-2015-0039. [Epub ahead of print: 01 Nov 2016].
    1. Morillo DS, Gross N. Probabilistic neural network approach for the detection of SAHS from overnight pulse oximetry. Med Biol Eng Comput 2013;51:305–15. 10.1007/s11517-012-0995-4
    1. Cunningham S, Symon AG, McIntosh N. The practical management of artifact in computerised physiological data. Int J Clin Monit Comput 1994;11:211–6. 10.1007/BF01139872
    1. Kool NP, van Waes JAR, Bijker JB, et al. . Artifacts in research data obtained from an anesthesia information and management system. Can J Anaesth 2012;59:833–41. 10.1007/s12630-012-9754-0
    1. Bland JM, Altman DG. Agreement between methods of measurement with multiple observations per individual. J Biopharm Stat 2007;17:571–82. 10.1080/10543400701329422
    1. Myles PS, Cui J. Using the Bland-Altman method to measure agreement with repeated measures. Br J Anaesth 2007;99:309–11. 10.1093/bja/aem214
    1. Clarke WL, Cox D, Gonder-Frederick LA, et al. . Evaluating clinical accuracy of systems for self-monitoring of blood glucose. Diabetes Care 1987;10:622–8. 10.2337/diacare.10.5.622
    1. Jakob S, Korhonen I, Ruokonen E, et al. . Detection of artifacts in monitored trends in intensive care. Comput Methods Programs Biomed 2000;63:203–9. 10.1016/S0169-2607(00)00110-3

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