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Detection of impending reflex syncope by means of an integrated multisensor patch-type recorder

Journal of Human Hypertension volume 37, pages 1098–1104 (2023)Cite this article

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Abstract

We assessed the capability of an integrated multisensory patch-type monitor (RootiRx®) in detecting episodes of reflex (pre)syncope induced by tilt table test (TTT). Firstly, we performed an intrapatient comparison of cuffless systolic blood pressure (SBP), R–R interval (RRI) and variability (power spectrum analysis) obtained by means of the RootiRx® with those obtained with conventional methods (CONV) with validated finger pressure devices at baseline in supine position and repeatedly during TTT in 32 patients affected by likely reflex syncope. Secondly, the LF/HF values obtained with RootiRx® during TTT were analyzed in 50 syncope patients. Compared with baseline supine recordings, during TTT a decrement of median SBP was observed with CONV (−53.5 mmHg) but not with RootiRx® ®(−1 mmHg). Conversely, RRI reduction (CONV: 102 ms; RootiRx®: 127 ms) and RRI Low Frequency/High Frequency powers ratio (LF/HF) increase (CONV: 1.6; RootiRx®: 2.5) were similar. The concordance was good for RRI (0.97 [95% CI 0.96–0.98]) and fair for LF/HF ratio (0.69 [95% CI 0.46-0.83]). During the first 5 min of TTT the LF/HF ratio was higher in patients who later developed syncope than in no-syncope patients. This ratio was significantly different among patients with syncope, presyncope or without symptoms at the time of syncope (p value = 0.02). In conclusion, cuffless RootiRx® was unable to detect rapid drops of SBP occurring during impending reflex syncope and thus cannot be used as a diagnostic tool for hypotensive syncope. On the other hand, RRI mean values and LF/HF power ratios obtained with RootiRx® were consistent with those simultaneously obtained using conventional methods.

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Fig. 1: The RootiRx.
Fig. 2: Intrapatient comparison of systolic blood pressure (SBP), R-R interval (RRI) LF/HF spectral power between conventional and RootiRx methods.
Fig. 3: Spaghetti plot of differences in LF/HF ratio between RootiRx® and CONV.
Fig. 4: LF/HF patterns obtained with RootiRx® according to TTT results in 50 syncope patients.

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Data availability

The datasets generated during and/or analyzed during the current study are available upon reasonable request https://zenodo.org/badge/DOI/10.5281/zenodo.7793208.svg

References

  1. Brignole M, Rivasi G. New insights in diagnostics and therapies in syncope: a novel approach to non-cardiac syncope. Heart. 2021;107:864–73.

    Article  CAS  PubMed  Google Scholar 

  2. Parati G, Saul JP, Di Rienzo M, Mancia G. Spectral analysis of blood pressure and heart rate variability in evaluating cardiovascular regulation. A critical appraisal. Hypertension. 1995;25:1276–786.

    Article  CAS  PubMed  Google Scholar 

  3. Brignole M, Moya A, de Lange FJ, Deharo JC, Elliott PM, Fanciulli A, et al. ESC Scientific Document Group. 2018 ESC guidelines for the diagnosis and management of syncope. Eur Heart J. 2018;39:1883–948.

    Article  PubMed  Google Scholar 

  4. Bartoletti A, Alboni P, Ammirati F, Brignole M, Del Rosso A, Foglia Manzillo G, et al. ‘The Italian Protocol’: a simplified head-up tilt testing potentiated with oral nitroglycerin to assess patients with unexplained syncope. Europace. 2000;2:339–42.

    Article  CAS  PubMed  Google Scholar 

  5. Scalise F, Margonato D, Sole A, Sorropago A, Sorropago G, Mancia G. Ambulatory blood pressure monitoring by a novel cuffless device: a pilot study. Blood Press. 2020;29:375–81.

    Article  PubMed  Google Scholar 

  6. Lomb NR. Least-squares frequency analysis of unequally spaced data. Astrophys Space Sci. 1976;39:447–62. https://doi.org/10.1007/BF00648343

    Article  Google Scholar 

  7. Scargle J. Studies in astronomical time series analysis. II – statistical aspects of spectral analysis of unevenly spaced data. Astrophys Space J. 1983;263. https://doi.org/10.1086/160554.

  8. Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology. Heart rate variability: standards of measurement, physiological interpretation and clinical use. Circulation 1996;93:1043–65.

    Article  Google Scholar 

  9. Castiglioni P, Di Rienzo M, On the evaluation of heart rate spectra: the Lomb periodogram. In: Computers in cardiology; 1996. p. 505–8. https://doi.org/10.1109/CIC.1996.542584

  10. Castiglioni P. “Lomb periodogram” In: Encyclopedia of biostatistics, 2nd ed. Vol 4. Armitage P, Colton T, editors. John Wiley & Sons; 2005. p. 2905–6. isbn:0-470-84907-X

  11. Sim I. Mobile devices and health. N Engl J Med. 2019;381:956–68.

    Article  PubMed  Google Scholar 

  12. Karaoğuz MR, Yurtseven E, Aslan G, Deliormanlı BG, Adıgüzel Ö, Gönen M, et al. The quality of ECG data acquisition, and diagnostic performance of a novel adhesive patch for ambulatory cardiac rhythm monitoring in arrhythmia detection. J Electrocardiol. 2019;54:28–35.

    Article  PubMed  Google Scholar 

  13. Imholz BPM, Van Montfrans GA, Settels JS, Van der Hoeven GMA, Karemaker JM, Wieling W. Continuous non-invasive blood pressure monitoring: reliability of Finapres device during the Valsalva manoeuvre. Cardiovasc Res. 1988;22:390–7.

    Article  CAS  PubMed  Google Scholar 

  14. Parati G, Ongaro G, Bilo G, Glavina F, Castiglioni P, Di Rienzo M, et al. Non-invasive beat-to-beat blood pressure monitoring: new developments. Blood Press Monit. 2003;8:31–6.

    Article  PubMed  Google Scholar 

  15. Thijs RD, Brignole M, Falup-Pecurariu C, Fanciulli A, Freeman R, Guaraldi P, et al. Recommendations for tilt table testing and other provocative cardiovascular autonomic tests in conditions that may cause transient loss of consciousness. Clin Auton Res. 2021;31:369–84.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Parati G, Casadei R, Groppelli A, Di Rienzo M, Mancia G. Comparison of finger and intra-arterial blood pressure monitoring at rest and during laboratory testing. Hypertension 1989;13:647–55. https://doi.org/10.1161/01.hyp.13.6.647.

    Article  CAS  PubMed  Google Scholar 

  17. Maggi R, Viscardi V, Furukawa T, Brignole M. Non-invasive continuous blood pressure monitoring of tachycardic episodes during interventional electrophysiology. Europace. 2010;12:1616–22.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Ilies C, Grudev G, Hedderich J, Renner J, Steinfath M, Bein B, et al. Comparison of a continuous noninvasive arterial pressure device with invasive measurements in cardiovascular postsurgical intensive care patients: A prospective observational study. Eur J Anaesthesiol. 2015;32:20–8.

    Article  PubMed  Google Scholar 

  19. Fortin J, Marte W, Grüllenberger R, Hacker A, Habenbacher W, Heller A, et al. Continuous non-invasive blood pressure monitoring using concentrically interlocking control loops. Comput Biol Med. 2006;36:941–57.

    Article  CAS  PubMed  Google Scholar 

  20. Pagani M, Lombardi F, Guzzetti S, Rimoldi O, Furlan R, Pizzinelli P, et al. Power spectral analysis of heart rate and arterial pressure variabilities as a marker of sympatho-vagal interaction in man and conscious dog. Circ Res. 1986;59:178–93.

    Article  CAS  PubMed  Google Scholar 

  21. Parati G, Di Rienzo M, Bertinieri G, Pomidossi G, Casadei R, Groppelli A, et al. Evaluation of the baroreceptor-heart rate reflex by 24-hour intra-arterial blood pressure monitoring in humans. Hypertension 1988;12:214–22.

    Article  CAS  PubMed  Google Scholar 

  22. Omboni S, Parati G, Frattola A, Mutti E, Di Rienzo M, Castiglioni P, et al. Spectral and sequence analysis of finger blood pressure variability. Comparison with analysis of intra-arterial recordings. Hypertension. 1993;22:26–33.

    Article  CAS  PubMed  Google Scholar 

  23. Furlan R, Porta A, Costa F, Tank J, Baker L, Schiavi R, et al. Oscillatory patterns in sympathetic neural discharge and cardiovascular variables during orthostatic stimulus. Circulation. 2000;101:886–92.

    Article  CAS  PubMed  Google Scholar 

  24. Carrasco JL, Phillips BR, Puig-Martinez J, King TS, Chinchilli VM. Estimation of the concordance correlation coefficient for repeated measures using SAS and R. Comput Methods Prog Biomed. 2013;109:293–304.

    Article  Google Scholar 

  25. Parati GF, Torlasco C, Omboni S, Pellegrini D. Smartphone applications for hypertension management: a potential game-changer that needs more control. Curr Hypertens Rep. 2017;19:48.

    Article  PubMed  Google Scholar 

  26. Simjanoska M, Gjoreski M, Gams M, Madevska Bogdanova A. Non-Invasive blood pressure estimation from ecg using machine learning techniques. Sensors. 2018;18:1160 https://doi.org/10.3390/s18041160

    Article  PubMed  PubMed Central  Google Scholar 

  27. Association for the Advancement of Medical Instrumentation. American National Standard: non-invasive sphygmomanometers – part 2: clinical validation of automated measurement type; ANSI/AAMI/ISO. 2013; 81060-2. http://my.aami.org/store/detail.aspx?xml:id=8106002

  28. Rivasi G, Groppelli A, Brignole M, Soranna D, Zambon A, Bilo G, et al. Association between hypotension during 24 h ambulatory blood pressure monitoring and reflex syncope: the SynABPM 1 study. Eur Heart J. 2022;43:3765–76.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Lung SC, Chen N, Hwang JS, Hu SC, Wang WV, Wen TJ, et al. Panel study using novel sensing devices to assess associations of PM2.5 with heart rate variability and exposure sources. J Expo Sci Environ Epidemiol. 2020;30:937–48.

    Article  CAS  PubMed  Google Scholar 

  30. Kochiadakis GE, Orfanakis A, Chryssostomakis SI, Manios EG, Kounali DK, Vardas PE. Autonomic nervous system activity during tilt testing in syncopal patients, estimated by power spectral analysis of heart rate variability. Pacing and clinical electrophysiology. PACE. 1997;20:1332–41.

    Article  CAS  PubMed  Google Scholar 

  31. Kochiadakis GE, Kanoupakis EM, Igoumenidis NE, Marketou ME, Solomou MC, Vardas PE. Spectral analysis of heart rate variability during tilt-table testing in patients with vasovagal syncope. Int J Cardiol. 1998;64:185–94.

    Article  CAS  PubMed  Google Scholar 

  32. Boulos M, Barron S, Nicolski E, Markiewicz W. Power spectral analysis of heart rate variability during upright tilt test: a comparison of patients with syncope and normal subjects. Cardiology. 1996;87:28–32.

    Article  CAS  PubMed  Google Scholar 

  33. Kouakam C, Lacroix D, Zghal N, Logier R, Klug D, Le Franc P, et al. Inadequate sympathovagal balance in response to orthostatism in patients with unexplained syncope and a positive head up tilt test. Heart (Br Card Soc). 1999;82:312–8.

    CAS  Google Scholar 

  34. Lipsitz LA, Mietus J, Moody G, Goldberger A. Spectral characteristics of heart rate variability before and during postural tilt relations to aging and risk of syncope. Circulation. 1990;81:1803–10.

    Article  CAS  PubMed  Google Scholar 

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Funding

Funding

Research partially funded by the Italian Ministry of Health.

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Author notes

  1. These authors contributed equally: Antonella Groppelli, Martina Rafanelli

Authors and Affiliations

  1. IRCCS Istituto Auxologico Italiano, Faint & Fall programme, Cardiology Unit and Department of Cardiology, San Luca Hospital, Milan, Italy

    Antonella Groppelli, Michele Brignole & Gianfranco Parati

  2. Syncope Unit, Division of Geriatric and Intensive Care Medicine, University of Florence and Azienda Ospedaliero Universitaria Careggi, Florence, Italy

    Martina Rafanelli, Giuseppe Dario Testa, Samuele Agusto, Giulia Rivasi & Andrea Ungar

  3. Biostatistics Unit, IRCCS Istituto Auxologico Italiano, Milan, Italy

    Erika Carbone, Davide Soranna & Antonella Zambon

  4. Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy

    Antonella Zambon

  5. IRCCS Fondazione Don C. Gnocchi ONLUS, Milan, Italy

    Paolo Castiglioni

  6. Department of Biotechnology and Life sciences (DBSV), University of Insubria, Varese, Italy

    Paolo Castiglioni

  7. Department of Medicine and Surgery, University of Milano Bicocca, Milan, Italy

    Gianfranco Parati

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Contributions

MB, GFP and AU designed the study and wrote the text. AG and MR designed the CRF and the database, recruited the patients, and acquired the data. GDT, SA, and GR recruited the patients, and acquired the data. EC, DS, AZ, and PC performed statistical analysis and methodology of the study. All authors contributed to the discussion of results and to the revision of the text.

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Correspondence to Michele Brignole.

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Groppelli, A., Rafanelli, M., Testa, G.D. et al. Detection of impending reflex syncope by means of an integrated multisensor patch-type recorder. J Hum Hypertens 37, 1098–1104 (2023). https://doi.org/10.1038/s41371-023-00840-y

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