Cardiovascular Computing in the Intensive Care Unit

  • Spyretta GolematiEmail author
Part of the Series in BioEngineering book series (SERBIOENG)


Clinical practice in the intensive care unit (ICU) faces a number of challenges, including accurate and early detection of pathological processes, and the related decision-making often relies on haemodynamic monitoring. In this chapter, applications are presented of computerised approaches for analysing data obtained from haemodynamic monitoring in the ICU. Haemodynamic monitoring is primarily concerned with assessing the performance of the cardiovascular system and conventionally relies on blood pressure measurements and echocardiography, for estimating cardiac output and other physiological variables. In addition to haemodynamic monitoring, less common techniques in the ICU (applanation tonometry, carotid and venous ultrasound), can also be used in cardiovascular computing applications. Such applications span a wide range of clinically relevant issues, including organisation and archiving of data into structured databases, data analytics, decision making and prediction, as well as estimation of arterial stiffness. Large, comprehensive, publicly available databases facilitate benchmarking of machine learning algorithms using real-world data. Such algorithms can in turn contribute to improving sepsis prediction in the ICU through (a) the identification of new features useful for prediction and (b) the processing of large data amounts, so as to consider combined contributions of individual features. The evidence produced so far indicates that cardiovascular data archiving and analysis using advanced computing methodologies is promising for addressing crucial issues in the ICU, and highlights the role that clinical data analysis will increasingly play in both knowledge generation and medical practice.


  1. 1.
    Angus DC (2007) Caring for the critically ill patient: challenges and opportunities. JAMA 298:456–458CrossRefGoogle Scholar
  2. 2.
    Vincent JL (2006) Is the current management of severe sepsis and septic shock really evidence based? PLoS Med 3(9):e346CrossRefGoogle Scholar
  3. 3.
    Laher AE, Watermeyer MJ, Buchanan SK, Dippenaar N, Simo NCT, Motara F, Moolla M (2017) A review of hemodynamic monitoring techniques, methods and devices for the emergency physician. Am J Emerg Med 35:1335–1347CrossRefGoogle Scholar
  4. 4.
    Huygh J, Peeters Y, Bernards J, Malbrain MLNG (2016) Hemodynamic monitoring in the critically ill: an overview of current cardiac output monitoring methods. F1000Res 5: F1000Faculty Rev-2855Google Scholar
  5. 5.
    Augusto JF, Teboul JL, Radermacher P, Asfar P (2011) Interpretation of blood pressure signal: physiological bases, clinical relevance, and objectives during shock states. Intensiv Care Med 37(3):411–419CrossRefGoogle Scholar
  6. 6.
    Au SM, Vieillard-Baron A (2012) Bedside echocardiography in critically ill patients: a true hemodynamic monitoring tool. J Clin Monit Comput 26(5):355–360CrossRefGoogle Scholar
  7. 7.
    Pinsky MR, Payen D (2005) Functional hemodynamic monitoring. Crit Care 9(6):566–572CrossRefGoogle Scholar
  8. 8.
    Meidert AS, Huber W, Müller JN et al (2014) Radial artery applanation tonometry for continuous non-invasive arterial pressure monitoring in intensive care unit patients: comparison with invasively assessed radial arterial pressure. Br J Anaesth 112(3):521–528CrossRefGoogle Scholar
  9. 9.
    Gassner M, Killu K, Bauman Z, Coba V, Rosso K, Blyden D (2015) Feasibility of common carotid artery point of care ultrasound in cardiac output measurements compared to invasive methods. J Ultrasound 18(2):127–133CrossRefGoogle Scholar
  10. 10.
    Nakamura K, Qian K, Ando T, Inokuchi R, Doi K, Kobayashi E, Sakuma I, Nakajima S, Yahagi N (2016) Cardiac variation of internal jugular vein for the evaluation of hemodynamics. Ultrasound Med Biol 42(8):1764–1770CrossRefGoogle Scholar
  11. 11.
    Celi LA, Mark RG, Stone DJ, Montgomery RA (2013) “Big data” in the intensive care unit. closing the data loop. Am J Respir Crit Care 187(11):1157–1160CrossRefGoogle Scholar
  12. 12.
    McShea M, Holl R, Badawi O, Riker RR, Silfen E (2010) The eICU research institute - a collaboration between industry, health-care providers, and academia. IEEE Eng Med Biol Mag 29:18–25CrossRefGoogle Scholar
  13. 13.
    Johnson AEW, Pollard TJ, Shen L, Lehman LH, Feng M, Ghassemi M, Moody B, Szolovits P, Celi LA, Mark RG (2016) MIMIC-III: a freely accessible critical care database. Sci Data 3:160035CrossRefGoogle Scholar
  14. 14.
    Pinsky MR, Dubrawski A (2014) Gleaning knowledge from data in the intensive care unit. Am J Respir Crit Care Med 190(6):606–610CrossRefGoogle Scholar
  15. 15.
    Mayaud L, Lai PS, Clifford GD, Tarassenko L, Celi LA, Annane D (2013) Dynamic data during hypotensive episode improves mortality predictions among patients with sepsis and hypotension. Crit Care Med 41(4):954–962CrossRefGoogle Scholar
  16. 16.
    Vieira SM, Mendona LF, Farinha GJ, Sousa JM (2013) Modified binary PSO for feature selection using SVM applied to mortality prediction of septic patients. Appl Soft Comput 13(8):3494–3504CrossRefGoogle Scholar
  17. 17.
    Carrara M, Baselli G, Ferrario M (2015) Mortality prediction model of septic shock patients based on routinely recorded data. Comput Math Methods Med 2015:761435CrossRefGoogle Scholar
  18. 18.
    Ghosh S, Li J, Cao L, Ramamohanarao K (2017) Septic shock prediction for ICU patients via coupled HMM walking on sequential contrast patterns. J Biomed Inform 66:19–31CrossRefGoogle Scholar
  19. 19.
    Sandfort V, Johnson AEW, Kunz LW, Vargas JD, Rosing DR (2018) Prolonged elevated heart rate and 90-day survival in acutely ill patients: data from the MIMIC-III database. J Intensiv Care Med (in press)Google Scholar
  20. 20.
    Lee J, Kothari R, Ladapo JA, Scott DJ, Celi LA (2012) Interrogating a clinical database to study treatment of hypotension in the critically ill. BMJ Open 2:e000916CrossRefGoogle Scholar
  21. 21.
    Oh J, Cho D, Park J et al (2018) Prediction and early detection of delirium in the intensive care unit by using heart rate variability and machine learning. Physiol Meas 39: 035004, 14CrossRefGoogle Scholar
  22. 22.
    Heerman JR, Segers P, Roosens CD, Gasthuys F, Verdonck PR, Poelaert JI (2005) Echocardiographic assessment of aortic elastic properties with automated border detection in an ICU: in vivo application of the arctangent Langewouters model. Am J Physiol Heart Circ Physiol 288:H2504–H2511CrossRefGoogle Scholar
  23. 23.
    Lamia B, Teboul JL, Monnet X, Osman D, Maizel J, Richard C, Chemla D (2007) Contribution of arterial stiffness and stroke volume to peripheral pulse pressure in ICU patients: an arterial tonometry study. Intensiv Care Med 33:1931–1937CrossRefGoogle Scholar
  24. 24.
    Wittrock M, Scholze A, Compton F, Shaefer JH, Zidek W, Tepel M (2009) Noninvasive pulse wave analysis for the determination of central artery stiffness. Microvasc Res 77:109–112CrossRefGoogle Scholar
  25. 25.
    Monge García MI, Cano AG, Romero MG (2011) Dynamic arterial elastance to predict arterial pressure response to volume loading in preload-dependent patients. Critic Care 15(R15):9CrossRefGoogle Scholar
  26. 26.
    Mackenzie IS, Wilkinson IB, Cockroft JR (2002) Assessment of arterial stiffness in clinical practice. Q J Med 95:67–74CrossRefGoogle Scholar
  27. 27.
    Golemati S, Cokkinos DD, Zakynthinos S (2018) Shear strain in the carotid artery of young and elderly subjects using B-mode ultrasound: a pilot study. In: World congress on biomechanics, Dublin, IrelandGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.First Intensive Care Unit, Medical SchoolNational Kapodistrian University of AthensAthensGreece

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