Evaluation of a novel optical smartphone blood pressure application: a method comparison study against invasive arterial blood pressure monitoring in intensive care unit patients

Olivier Desebbe, Chbabou Anas, Brenton Alexander, Karim Kouz, Jean-Francois Knebel, Patrick Schoettker, Jacques Creteur, Jean-Louis Vincent, Alexandre Joosten, Olivier Desebbe, Chbabou Anas, Brenton Alexander, Karim Kouz, Jean-Francois Knebel, Patrick Schoettker, Jacques Creteur, Jean-Louis Vincent, Alexandre Joosten

Abstract

Background: Arterial hypertension is a worldwide public health problem. While it is currently diagnosed and monitored non-invasively using the oscillometric method, having the ability to measure blood pressure (BP) using a smartphone application could provide more widespread access to hypertension screening and monitoring. In this observational study in intensive care unit patients, we compared blood pressure values obtained using a new optical smartphone application (OptiBP™; test method) with arterial BP values obtained using a radial artery catheter (reference method) in order to help validate the technology.

Methods: We simultaneously measured three BP values every hour for five consecutive hours on two consecutive days using both the smartphone and arterial methods. Bland-Altman and error grid analyses were used for agreement analysis between both approaches. The performance of the smartphone application was investigated using the Association for the Advancement of Medical Instrumentation (AAMI) and the International Organization for Standardization (ISO) definitions, which require the bias ± SD between two technologies to be below 5 ± 8 mmHg.

Results: Among the 30 recruited patients, 22 patients had adequate OptiBP™ values and were thus analyzed. In the other 8 patients, no BP could be measured due to inadequate signals. The Bland-Altman analysis revealed a mean of the differences ± SD between both methods of 0.9 ± 7 mmHg for mean arterial pressure (MAP), 0.2 ± 14 mmHg for systolic arterial pressure (SAP), and 1.1 ± 6 mmHg for diastolic arterial pressure (DAP). Error grid analysis demonstrated that the proportions of measurement pairs in risk zones A to E were 88.8% (no risk), 10% (low risk), 1% (moderate risk), 0% (significant risk), and 0% (dangerous risk) for MAP and 88.4%, 8.6%, 3%, 0%, 0%, respectively, for SAP.

Conclusions: This method comparison study revealed good agreement between BP values obtained using the OptiBP™ and those done invasively. The OptiBP™ fulfills the AAMI/ISO universal standards for MAP and DAP (but not SAP). Error grid showed that the most measurements (≥ 97%) were in risk zones A and B.

Trial registration: ClinicalTrials.gov registration: NCT04728477.

Keywords: Arterial hypertension, mobile phone; Hemodynamic; Hemodynamic monitoring; International standards; Mobile health; Optical signal.

Conflict of interest statement

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.

© 2022. The Author(s).

Figures

Fig. 1
Fig. 1
Description of the smartphone application: Fingertip on the smartphone’s camera OptiBP™ app uses image data generated from volumetric blood flow changes via light passing through the fingertip, reflecting off blood flowing through the vessels, and then passing to the phone camera's image sensor
Fig. 2
Fig. 2
Bland–Altman plots showing the agreement between the smartphone application for mean arterial pressure (MAP) measurements and the reference method (upper arm oscillometry)
Fig. 3
Fig. 3
Bland–Altman plots showing the agreement between the smartphone application for systolic blood pressure (SAP) measurements and the reference method (upper arm oscillometry)
Fig. 4
Fig. 4
Bland–Altman plots showing the agreement between the smartphone application for diastolic blood pressure (DAP) measurements and the reference method (upper arm oscillometry)
Fig. 5
Fig. 5
Error grid analysis comparing systolic (left panel) and mean (right panel) arterial blood pressure measurements from the smartphone application with those from the radial artery catheter (reference method). The background colors correspond to the continuous risk level for each pair of measurements. The continuous risk level ranges from 0 to 100% as shown at the bottom of the figure

References

    1. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global burden of disease study 2017. Lancet. 2018;392(10159):1923–94.
    1. Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H, Amann M, Anderson HR, Andrews KG, Aryee M, et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the global burden of disease study 2010. Lancet. 2012;380(9859):2224–2260. doi: 10.1016/S0140-6736(12)61766-8.
    1. Mills KT, Stefanescu A, He J. The global epidemiology of hypertension. Nat Rev Nephrol. 2020;16(4):223–237. doi: 10.1038/s41581-019-0244-2.
    1. Mills KT, Bundy JD, Kelly TN, Reed JE, Kearney PM, Reynolds K, Chen J, He J. Global disparities of hypertension prevalence and control: A systematic analysis of population-based studies from 90 countries. Circulation. 2016;134(6):441–450. doi: 10.1161/CIRCULATIONAHA.115.018912.
    1. McManus RJ, Mant J, Franssen M, Nickless A, Schwartz C, Hodgkinson J, Bradburn P, Farmer A, Grant S, Greenfield SM, et al. Efficacy of self-monitored blood pressure, with or without telemonitoring, for titration of antihypertensive medication (TASMINH4): an unmasked randomised controlled trial. Lancet. 2018;391(10124):949–959. doi: 10.1016/S0140-6736(18)30309-X.
    1. Flint AC, Conell C, Bhatt DL. Systolic and diastolic blood pressure and cardiovascular outcomes. Reply N Engl J Med. 2019;381(17):1692–1693.
    1. Williams B, Mancia G, Spiering W, Agabiti Rosei E, Azizi M, Burnier M, Clement D, Coca A, De Simone G, Dominiczak A, et al. 2018 Practice Guidelines for the management of arterial hypertension of the European society of hypertension and the European society of cardiology: ESH/ESC task force for the management of arterial hypertension. J Hypertens. 2018;36(12):2284–2309. doi: 10.1097/HJH.0000000000001961.
    1. Patel AA. Developing and evaluating mhealth solutions for chronic disease prevention in primary care. Circulation. 2019;139(3):392–394. doi: 10.1161/CIRCULATIONAHA.118.038389.
    1. Burke LE, Ma J, Azar KM, Bennett GG, Peterson ED, Zheng Y, Riley W, Stephens J, Shah SH, Suffoletto B, et al. Current science on consumer use of mobile health for cardiovascular disease prevention: a scientific statement from the american heart association. Circulation. 2015;132(12):1157–1213. doi: 10.1161/CIR.0000000000000232.
    1. Michard F. Toward smart monitoring with phones, watches, and wearable sensors. Anesthesiol Clin. 2021;39(3):555–564. doi: 10.1016/j.anclin.2021.04.005.
    1. Michard F. Smartphones and e-tablets in perioperative medicine. Korean J Anesthesiol. 2017;70(5):493–499. doi: 10.4097/kjae.2017.70.5.493.
    1. 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):1203–1209. doi: 10.1007/s10877-020-00572-1.
    1. Desebbe O, Joosten A, Suehiro K, Lahham S, Essiet M, Rinehart J, Cannesson M. 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(1):105–113. doi: 10.1213/ANE.0000000000001282.
    1. Joosten A, Boudart C, Vincent JL, Vanden Eynden F, Barvais L, Van Obbergh L, Rinehart J, Desebbe O. 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(6):1145–1151. doi: 10.1213/ANE.0000000000003652.
    1. Joosten A, Jacobs A, Desebbe O, Vincent JL, Sarah S, Rinehart J, Van Obbergh L, Hapfelmeier A, Saugel B. 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(5):787–793. doi: 10.1007/s10877-018-00241-4.
    1. Degott J, Ghajarzadeh-Wurzner A, Hofmann G, Proença M, Bonnier G, Lemkaddem A, Lemay M, Christen U, Knebel JF, Durgnat V, 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;26(6):441–448. doi: 10.1097/MBP.0000000000000556.
    1. Schoettker P, Degott J, Hofmann G, Proença M, Bonnier G, Lemkaddem A, Lemay M, Schorer R, Christen U, Knebel JF, et al. Blood pressure measurements with the OptiBP smartphone app validated against reference auscultatory measurements. Sci Rep. 2020;10(1):17827. doi: 10.1038/s41598-020-74955-4.
    1. Desebbe O, Tighenifi A, Jacobs A, Toubal L, Zekhini Y, Chirnoaga D, Collange V, Alexander B, Knebel JF, Schoettker P, et al. Evaluation of a novel mobile phone application for blood pressure monitoring: a proof of concept study. J Clin Monit Comput. 2021;36(4):1147–1153. doi: 10.1007/s10877-021-00749-2.
    1. Desebbe O, El Hilali M, Kouz K, Alexander B, Karam L, Chirnoaga D, Knebel JF, Degott J, Schoettker P, Michard F 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 2022. Jan 3. ahead of print.
    1. Ghamri Y, Proença M, Hofmann G, Renevey P, Bonnier G, Braun F, Axis A, Lemay M, Schoettker P. Automated pulse oximeter waveform analysis to track changes in blood pressure during anesthesia induction: A proof-of-concept study. Anesth Analg. 2020;130(5):1222–1233. doi: 10.1213/ANE.0000000000004678.
    1. Stergiou GS, Palatini P, Asmar R, Ioannidis JP, Kollias A, Lacy P, McManus RJ, Myers MG, Parati G, Shennan A, 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(3):459–466. doi: 10.1097/HJH.0000000000002039.
    1. Saugel B, Grothe O, Nicklas JY. Error grid analysis for arterial pressure method comparison studies. Anesth Analg. 2018;126(4):1177–1185. doi: 10.1213/ANE.0000000000002585.

Source: PubMed

3
Subscribe