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Mobile Devices for Hemodynamic Monitoring

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Annual Update in Intensive Care and Emergency Medicine 2020

Part of the book series: Annual Update in Intensive Care and Emergency Medicine ((AUICEM))

Abstract

Smartphones and mobile devices using wireless internet are used ubiquitously today. The “internet of things” interconnects sensors in everyday objects with mobile and other computing devices via the internet, giving access to a multitude of new data sources. This provides the basis for monitoring and recording physiologic information on mobile devices and has led to the development of new tools and applications that may improve patient safety and guide therapy. Hemodynamic monitoring and management are essential to patient care in perioperative and intensive care medicine. However, conventional hemodynamic monitoring devices and their sensors today are often invasive and bulky, not wireless or integrated, expensive and uncomfortable for awake patients. Therefore, continuous hemodynamic monitoring is currently limited to the operating room and the intensive care unit. Monitoring of patients on medical and surgical normal wards remains basic and intermittent. In the future, mobile devices could offer small, wireless, non-invasive, and integrated hemodynamic monitoring options creating an accessible, integrated, and interconnected network of data sources. This network of data sources has the potential to optimize patient care, leveraging the use of real-time hemodynamic monitoring to a broader patient population and even facilitating ward and home monitoring.

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References

  1. Oxford English Dictionary. 3 ed. Internet: Oxford University Press. “smartphone, n.”

    Google Scholar 

  2. Eapen ZJ, Peterson ED. Can mobile health applications facilitate meaningful behavior change? Time for answers. JAMA. 2015;314:1236–7.

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  Google Scholar 

  4. Sessler DI, Bloomstone JA, Aronson S, et al. Perioperative quality initiative consensus statement on intraoperative blood pressure, risk and outcomes for elective surgery. Br J Anaesth. 2019;122:563–74.

    Article  PubMed  Google Scholar 

  5. Vincent JL, Pelosi P, Pearse R, et al. Perioperative cardiovascular monitoring of high-risk patients: a consensus of 12. Crit Care. 2015;19:224.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Vincent JL, Rhodes A, Perel A, et al. Clinical review: update on hemodynamic monitoring—a consensus of 16. Crit Care. 2011;15:229.

    Article  PubMed  PubMed Central  Google Scholar 

  7. McGillion MH, Duceppe E, Allan K, et al. Postoperative remote automated monitoring: need for and state of the science. Can J Cardiol. 2018;34:850–62.

    Article  PubMed  Google Scholar 

  8. Vincent JL, Einav S, Pearse R, et al. Improving detection of patient deterioration in the general hospital ward environment. Eur J Anaesthesiol. 2018;35:325–33.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Sun Z, Sessler DI, Dalton JE, et al. Postoperative hypoxemia is common and persistent: a prospective blinded observational study. Anesth Analg. 2015;121:709–15.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Jones D, Mitchell I, Hillman K, Story D. Defining clinical deterioration. Resuscitation. 2013;84:1029–34.

    Article  PubMed  Google Scholar 

  11. Sessler DI, Saugel B. Beyond ‘failure to rescue’: the time has come for continuous ward monitoring. Br J Anaesth. 2019;122:304–6.

    Article  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  Google Scholar 

  14. Michard F, Badheka A. Toward the ‘Shazam-Like’ identification of valve diseases with digital auscultation? Am J Med. 2019;132:e595–e6.

    Article  PubMed  Google Scholar 

  15. Michard F. A sneak peek into digital innovations and wearable sensors for cardiac monitoring. J Clin Monit Comput. 2017;31:253–9.

    Article  PubMed  Google Scholar 

  16. Lip GYH, Tse HF, Lane DA. Atrial fibrillation. Lancet. 2012;379:648–61.

    Article  PubMed  Google Scholar 

  17. Sposato LA, Cipriano LE, Saposnik G, Vargas ER, Riccio PM, Hachinski V. Diagnosis of atrial fibrillation after stroke and transient ischaemic attack: a systematic review and meta-analysis. Lancet Neurol. 2015;14:377–87.

    Article  PubMed  Google Scholar 

  18. Scully CG, Lee J, Meyer J, et al. Physiological parameter monitoring from optical recordings with a mobile phone. IEEE Trans Biomed Eng. 2012;59:303–6.

    Article  PubMed  Google Scholar 

  19. Chan PH, Wong CK, Poh YC, et al. Diagnostic performance of a smartphone-based photoplethysmographic application for atrial fibrillation screening in a primary care setting. J Am Heart Assoc. 2016;5:e003428.

    PubMed  PubMed Central  Google Scholar 

  20. Halcox JPJ, Wareham K, Cardew A, et al. Assessment of remote heart rhythm sampling using the AliveCor heart monitor to screen for atrial fibrillation: the REHEARSE-AF study. Circulation. 2017;136:1784–94.

    Article  PubMed  Google Scholar 

  21. Rosenfeld LE, Amin AN, Hsu JC, Oxner A, Hills MT, Frankel DS. The Heart Rhythm Society/American College of Physicians Atrial Fibrillation Screening and Education Initiative. Heart Rhythm. 2019;16:e59–65.

    Article  PubMed  Google Scholar 

  22. Muhlestein JB, Le V, Albert D, et al. Smartphone ECG for evaluation of STEMI: results of the ST LEUIS pilot study. J Electrocardiol. 2015;48:249–59.

    Article  PubMed  Google Scholar 

  23. Barbagelata A, Bethea CF, Severance HW, et al. Smartphone ECG for evaluation of ST-segment elevation myocardial infarction (STEMI): design of the ST LEUIS international multicenter study. J Electrocardiol. 2018;51:260–4.

    Article  PubMed  Google Scholar 

  24. Maheshwari K, Nathanson BH, Munson SH, et al. The relationship between ICU hypotension and in-hospital mortality and morbidity in septic patients. Intensive Care Med. 2018;44:857–67.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Sudfeld S, Brechnitz S, Wagner JY, et al. Post-induction hypotension and early intraoperative hypotension associated with general anaesthesia. Br J Anaesth. 2017;119:57–64.

    Article  PubMed  CAS  Google Scholar 

  26. Lewington S, Clarke R, Qizilbash N, et al. Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies. Lancet. 2002;360:1903–13.

    Article  PubMed  Google Scholar 

  27. Muntner P, Shimbo D, Carey RM, et al. Measurement of blood pressure in humans: a scientific statement from the American Heart Association. Hypertension. 2019;73:e35–66.

    CAS  PubMed  Google Scholar 

  28. Baruch MC, Warburton DE, Bredin SS, Cote A, Gerdt DW, Adkins CM. Pulse decomposition analysis of the digital arterial pulse during hemorrhage simulation. Nonlinear Biomed Phys. 2011;5:1.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Gratz I, Deal E, Spitz F, et al. Continuous non-invasive finger cuff CareTaker® comparable to invasive intra-arterial pressure in patients undergoing major intra-abdominal surgery. BMC Anesthesiol. 2017;17:1–11.

    Article  Google Scholar 

  30. Chandrasekhar A, Natarajan K, Yavarimanesh M, Mukkamala R. An iPhone application for blood pressure monitoring via the oscillometric finger pressing method. Sci Rep. 2018;8:13136.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Luo H, Yang D, Barszczyk A, et al. Smartphone-based blood pressure measurement using transdermal optical imaging technology. Circ Cardiovasc Imaging. 2019;12:e008857.

    Article  PubMed  Google Scholar 

  32. Plante TB, Urrea B, MacFarlane ZT, et al. Validation of the instant blood pressure smartphone app. JAMA Intern Med. 2016;176:700–2.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Monnet X, Marik PE, Teboul JL. Prediction of fluid responsiveness: an update. Ann Intensive Care. 2016;6:1–11.

    Article  CAS  Google Scholar 

  34. Bellamy MC. Wet, dry or something else? Br J Anaesth. 2006;97:755–7.

    Article  PubMed  CAS  Google Scholar 

  35. Rinehart J, Islam T, Boud R, et al. Visual estimation of pulse pressure variation is not reliable: a randomized simulation study. J Clin Monit Comput. 2012;26:191–6.

    Article  PubMed  Google Scholar 

  36. 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  PubMed  Google Scholar 

  37. 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(6):1145–51.

    Article  PubMed  Google Scholar 

  38. Shah SB, Bhargava AK, Hariharan U, Vishvakarma G, Jain CR, Kansal A. Cardiac output monitoring: a comparative prospective observational study of the conventional cardiac output monitor Vigileo and the new smartphone-based application Capstesia. Indian J Anaesth. 2018;62:584–91.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Scolletta S, Bodson L, Donadello K, et al. Assessment of left ventricular function by pulse wave analysis in critically ill patients. Intensive Care Med. 2013;39:1025–33.

    Article  PubMed  Google Scholar 

  40. Pahlevan NM, Rinderknecht DG, Tavallali P, et al. Noninvasive iPhone measurement of left ventricular ejection fraction using intrinsic frequency methodology. Crit Care Med. 2017;45:1115–20.

    Article  PubMed  Google Scholar 

  41. Michard F, Range G, Biais M. Smartphones to assess cardiac function: Novelty blindness or fresh perspectives? Crit Care Med. 2017;45:e1199–e201.

    Article  PubMed  Google Scholar 

  42. Mueller MM, Van Remoortel H, Meybohm P, et al. Patient blood management: recommendations from the 2018 Frankfurt Consensus Conference. JAMA. 2019;321:983–97.

    Article  PubMed  Google Scholar 

  43. Holmes AA, Konig G, Ting V, et al. Clinical evaluation of a novel system for monitoring surgical hemoglobin loss. Anesth Analg. 2014;119:588–94.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Saoud F, Stone A, Rahman M, et al. 163: Quantification of blood loss during cesarean delivery using an iPad based application (Triton). Am J Obstet Gynecol. 2019;220:S122–3; (abst)

    Article  Google Scholar 

  45. Platz E, Solomon SD. Point-of-care echocardiography in the accountable care organization era. Circ Cardiovasc Imaging. 2012;5:676–82.

    Article  PubMed  Google Scholar 

  46. Scali MC, de Azevedo Bellagamba CC, Ciampi Q, et al. Stress echocardiography with smartphone: real-time remote reading for regional wall motion. Int J Card Imaging. 2017;33:1731–6.

    Article  Google Scholar 

  47. Boland CS, Khan U, Ryan G, et al. Sensitive electromechanical sensors using viscoelastic graphene-polymer nanocomposites. Science. 2016;354:1257–60.

    Article  PubMed  CAS  Google Scholar 

  48. Michard F. Hemodynamic monitoring in the era of digital health. Ann Intensive Care. 2016;6:15.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Amir O, Rappaport D, Zafrir B, Abraham WT. A novel approach to monitoring pulmonary congestion in heart failure: initial animal and clinical experiences using remote dielectric sensing technology. Congest Heart Fail. 2013;19:149–55.

    Article  PubMed  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Anesthesiology, Center of Anesthesiology and Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

    L. Briesenick & B. Saugel

  2. MiCo, Switzerland

    F. Michard

Authors
  1. L. Briesenick
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  2. F. Michard
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  3. B. Saugel
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Corresponding author

Correspondence to B. Saugel .

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Editors and Affiliations

  1. Department of Intensive Care, Erasme University Hospital, Université libre de Bruxelles, Brussels, Belgium

    Jean-Louis Vincent

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Briesenick, L., Michard, F., Saugel, B. (2020). Mobile Devices for Hemodynamic Monitoring. In: Vincent, JL. (eds) Annual Update in Intensive Care and Emergency Medicine 2020. Annual Update in Intensive Care and Emergency Medicine. Springer, Cham. https://doi.org/10.1007/978-3-030-37323-8_50

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  • DOI: https://doi.org/10.1007/978-3-030-37323-8_50

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37322-1

  • Online ISBN: 978-3-030-37323-8

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