Skip to main content
Log in

Evaluation of a new smartphone optical blood pressure application (OptiBP™) in the post-anesthesia care unit: a method comparison study against the non-invasive automatic oscillometric brachial cuff as the reference method

  • Original Research
  • Published:
Journal of Clinical Monitoring and Computing Aims and scope Submit manuscript

Abstract

We compared blood pressure (BP) values obtained with a new optical smartphone application (OptiBP™) with BP values obtained using a non-invasive automatic oscillometric brachial cuff (reference method) during the first 2 h of surveillance in a post-anesthesia care unit in patients after non-cardiac surgery. Three simultaneous BP measurements of both methods were recorded every 30 min over a 2-h period. The agreement between measurements was investigated using Bland–Altman and error grid analyses. We also evaluated the performance of the OptiBP™ using ISO81060–2:2018 standards which requires the mean of the differences ± standard deviation (SD) between both methods to be less than 5 mmHg ± 8 mmHg. Of 120 patients enrolled, 101 patients were included in the statistical analysis. The Bland–Altman analysis demonstrated a mean of the differences ± SD between the test and reference methods of + 1 mmHg ± 7 mmHg for mean arterial pressure (MAP), + 2 mmHg ± 11 mmHg for systolic arterial pressure (SAP), and + 1 mmHg ± 8 mmHg for diastolic arterial pressure (DAP). Error grid analysis showed that the proportions of measurement pairs in risk zones A to E were 90.3% (no risk), 9.7% (low risk), 0% (moderate risk), 0% (significant risk), 0% (dangerous risk) for MAP and 89.9%, 9.1%, 1%, 0%, 0% for SAP. We observed a good agreement between BP values obtained by the OptiBP™ system and BP values obtained with the reference method. The OptiBP™ system fulfilled the AAMI validation requirements for MAP and DAP and error grid analysis indicated that the vast majority of measurement pairs (≥ 99%) were in risk zones A and B.

Trial Registration ClinicalTrials.gov Identifier: NCT04262323.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Mills KT, Stefanescu A, He J. The global epidemiology of hypertension. Nat Rev Nephrol. 2020;16:223–37.

    Article  CAS  Google Scholar 

  2. Floras JS, Jones JV, Hassan MO, Osikowska B, Sever PS, Sleight P. Cuff and ambulatory blood pressure in subjects with essential hypertension. Lancet. 1981;2:107–9.

    Article  CAS  Google Scholar 

  3. Patel AA. Developing and evaluating mHealth solutions for chronic disease prevention in primary care. Circulation. 2019;139:392–4.

    Article  Google Scholar 

  4. Michard F. Smartphones and e-tablets in perioperative medicine. Korean J Anesthesiol. 2017;70:493–9.

    Article  Google Scholar 

  5. Michard F, Barrachina B, Schoettker P. Is your smartphone the future of physiologic monitoring? Intensive Care Med. 2019;45:869–71.

    Article  Google Scholar 

  6. Michard F. Toward smart monitoring with phones, watches, and wearable sensors. Anesthesiol Clin. 2021;39:555–64.

    Article  Google Scholar 

  7. Hoppe P, Gleibs F, Briesenick L, Joosten A, Saugel B. Estimation of pulse pressure variation and cardiac output in patients having major abdominal surgery: a comparison between a mobile application for snapshot pulse wave analysis and invasive pulse wave analysis. J Clin Monit Comput 2021;35(5):1203–9

  8. Joosten A, Boudart C, Vincent JL, et al. Ability of a new smartphone pulse pressure variation and cardiac output application to predict fluid responsiveness in patients undergoing cardiac surgery. Anesth Analg. 2019;128:1145–51.

    Article  Google Scholar 

  9. Desebbe O, Vincent JL, Saugel B, Rinehart J, Joosten A. Pulse pressure variation using a novel smartphone application (Capstesia) versus invasive pulse contour analysis in patients undergoing cardiac surgery: a secondary analysis focusing on clinical decision making. J Clin Monit Comput. 2020;34:379–80.

    Article  Google Scholar 

  10. Joosten A, Jacobs A, Desebbe O, et al. Monitoring of pulse pressure variation using a new smartphone application (Capstesia) versus stroke volume variation using an uncalibrated pulse wave analysis monitor: a clinical decision making study during major abdominal surgery. J Clin Monit Comput. 2019;33:787–93.

    Article  Google Scholar 

  11. Desebbe O, Joosten A, Suehiro K, et al. A novel mobile phone application for pulse pressure variation monitoring based on feature extraction technology: a method comparison study in a simulated environment. Anesth Analg. 2016;123:105–13.

    Article  Google Scholar 

  12. Ghamri Y, Proença M, Hofmann G, et al. Automated pulse oximeter waveform analysis to track changes in blood pressure during anesthesia induction: a proof-of-concept study. Anesth Analg. 2020;130:1222–33.

    Article  Google Scholar 

  13. Jorge J, Proenca M, Aguet C, et al. Machine learning approaches for improved continuous, non-occlusive arterial pressure monitoring using photoplethysmography. Annu Int Conf IEEE Eng Med Biol Soc. 2020;2020:910–3.

    PubMed  Google Scholar 

  14. Schoettker P, Degott J, Hofmann G, et al. Blood pressure measurements with the OptiBP smartphone app validated against reference auscultatory measurements. Sci Rep. 2020;10:17827.

    Article  CAS  Google Scholar 

  15. Degott J, Ghajarzadeh-Wurzner A, Hofmann G, et al. Smartphone based blood pressure measurement: accuracy of the OptiBP mobile application according to the AAMI/ESH/ISO universal validation protocol. Blood Press Monit 2021

  16. Desebbe O, Tighenifi A, Jacobs A, et al. Evaluation of a novel mobile phone application for blood pressure monitoring: a proof of concept study. J Clin Monit Comput 2021

  17. Ramsey M 3rd. Blood pressure monitoring: automated oscillometric devices. J Clin Monit. 1991;7:56–67.

    Article  Google Scholar 

  18. Bland JM, Altman DG. Agreement between methods of measurement with multiple observations per individual. J Biopharm Stat. 2007;17:571–82.

    Article  Google Scholar 

  19. Stergiou GS, Palatini P, Asmar R, et al. Recommendations and Practical Guidance for performing and reporting validation studies according to the Universal Standard for the validation of blood pressure measuring devices by the Association for the Advancement of Medical Instrumentation/European Society of Hypertension/International Organization for Standardization (AAMI/ESH/ISO). J Hypertens. 2019;37:459–66.

    Article  CAS  Google Scholar 

  20. Grothe O, Kaplan A, Kouz K, Saugel B. Computer program for error grid analysis in arterial blood pressure method comparison studies. Anesth Analg. 2020;130:e71–4.

    Article  Google Scholar 

  21. Saugel B, Grothe O, Nicklas JY. Error grid analysis for arterial pressure method comparison studies. Anesth Analg. 2018;126:1177–85.

    Article  Google Scholar 

  22. Kottner J, Audigé L, Brorson S, et al. Guidelines for Reporting Reliability and Agreement Studies (GRRAS) were proposed. J Clin Epidemiol. 2011;64:96–106.

    Article  Google Scholar 

  23. Cecconi M, Dawson D, Grounds RM, Rhodes A. Lithium dilution cardiac output measurement in the critically ill patient: determination of precision of the technique. Intensive Care Med. 2009;35:498–504.

    Article  CAS  Google Scholar 

  24. Williams B, Mancia G, Spiering W, et al. 2018 ESC/ESH Guidelines for the management of arterial hypertension: The Task Force for the management of arterial hypertension of the European Society of Cardiology and the European Society of Hypertension: the Task Force for the management of arterial hypertension of the European Society of Cardiology and the European Society of Hypertension. J Hypertens. 2018;36:1953–2041.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

All the clinicians and nurses from the emergency department.

Funding

This work was supported by the Department of Anesthesiology, Erasme Hospital, Brussels.

Author information

Authors and Affiliations

Authors

Contributions

All authors read and approved the final manuscript. OD: designed the study, analyzed the data and edited the final manuscript. MEH: collected and analyzed the data and edited the final manuscript. KK: collected the data and edited the final manuscript. BA: analyzed the data and edited the final manuscript. LK: analyzed the data and edited the final manuscript. DC: analyzed the data and edited the final manuscript. JFK: analyzed the data and edited the final manuscript. JD: analyzed the data and edited the final manuscript. PS: analyzed the data and edited the final manuscript. FM: analyzed the data and edited the final manuscript. BS: analyzed the data and edited the final manuscript. JLV: analyzed the data and edited the final manuscript. AJ: designed the study, analyzed the data and drafted the manuscript.

Corresponding author

Correspondence to Alexandre Joosten.

Ethics declarations

Conflict of interest

OD is consultant for Medtronic (Trévoux, FRANCE) and and Livanova (Châtillon, France). JFK is working for Biospectal SA, Lausanne, Switzerland. PS is an advisor of Biospectal SA, Lausanne, Switzerland. AJ is a consultant for Edwards Lifesciences (Irvine, California, USA). The other authors have no conflicts of interest to declare.

Ethical approval

The present study was approved by the ethics committee of Erasme Hospital on October 20th, 2020 under the reference A2020/199 and registered in Clinical Trial.gov on February 10th, 2020 under the reference NCT04262323 (Principal Investigator: Alexandre Joosten).

Consent to participate

Written informed consent was obtained from all individual participants included in the study.

Consent for publication

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix 1: Boxplots of mean of the differences at each time point

Appendix 1: Boxplots of mean of the differences at each time point

figure a

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Desebbe, O., El Hilali, M., Kouz, K. et al. Evaluation of a new smartphone optical blood pressure application (OptiBP™) in the post-anesthesia care unit: a method comparison study against the non-invasive automatic oscillometric brachial cuff as the reference method. J Clin Monit Comput 36, 1525–1533 (2022). https://doi.org/10.1007/s10877-021-00795-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10877-021-00795-w

Keywords

Navigation