Transforming Monitoring and Improving Care with Variability-Derived Clinical Decision Support

  • Christophe L. Herry
  • Nathan B. Scales
  • Kimberley D. Newman
  • Andrew J. E. Seely


Monitoring of patients with existing or impending critical illness routinely involves the recording of multiple physiological waveforms. However, tracking response to interventions, gauging clinical trajectory and making informed clinical decisions still mostly rely on vital signs and laboratory tests summarized and charted over hours to days. Utilizing the currently untapped information contained in waveform data has the potential to reduce the diagnostic and prognostic uncertainty inherent in critical care, even when patients are managed by trained intensivists. This uncertainty results in delayed diagnosis, unnecessary or inappropriate therapy and increased complications, mortality and cost of care.

Heart rate variability (HRV) and respiratory rate variability (RRV) time series derived from the continuous physiological waveforms help characterize the degree and complexity of the patterns of the inter-beat and inter-breath interval time series. Decreased variability is associated with age and illness and correlates with illness severity, indicating reduced adaptability and/or increased stress. Combining waveform-based variability analysis with predictive modelling, we can enhance timely clinical decision-making at the bedside by providing probabilistic prediction of upcoming clinical events. We demonstrate this approach using data from our recent large prospective study on optimal weaning from mechanical ventilation in the ICU.

Finally, we show how these clinical decision support tools can integrate within the current processes of care to optimize individual patient care and manage resources more efficiently.


  1. 1.
    Detsky AS, Stricker SC, Mulley AG, Thibault GE. Prognosis, survival, and the expenditure of hospital resources for patients in an intensive-care unit. N Engl J Med. 1981;305(12):667–72.Google Scholar
  2. 2.
    Luce JM, Rubenfeld GD. Can health care costs be reduced by limiting intensive care at the end of life? Am J Respir Crit Care Med. 2002;165(6):750–4.Google Scholar
  3. 3.
    Burchardi H, Schneider H. Economic aspects of severe sepsis: a review of intensive care unit costs, cost of illness and cost effectiveness of therapy. Pharmacoeconomics 2004;22(12): 793–813.Google Scholar
  4. 4.
    Epstein SK. Extubation failure: an outcome to be avoided. Crit Care 2004;8(5):310–2.Google Scholar
  5. 5.
    Needham DM, Bronskill SE, Calinawan JR, Sibbald WJ, Pronovost PJ, Laupacis A. Projected incidence of mechanical ventilation in Ontario to 2026: preparing for the aging baby boomers. Crit Care Med. 2005;33(3):574–9.Google Scholar
  6. 6.
    Wunsch H, Linde-Zwirble WT, Angus DC, Hartman ME, Milbrandt EB, Kahn JM. The epidemiology of mechanical ventilation use in the United States. Crit Care Med. 2010;38(10):1947–53.Google Scholar
  7. 7.
    Barnaby D, Ferrick K, Kaplan DT, Shah S, Bijur P, Gallagher EJ. Heart rate variability in emergency department patients with sepsis. Acad Emerg Med Off J Soc Acad Emerg Med. 2002;9(7):661–70.Google Scholar
  8. 8.
    Garrard CS, Kontoyannis DA, Piepoli M. Spectral analysis of heart rate variability in the sepsis syndrome. Clin Auton Res Off J Clin Auton Res Soc. 1993;3(1):5–13.Google Scholar
  9. 9.
    Korach M, Sharsha T, Jarrin I, Fouillot JP, Raphaël JC, Gajdos P. Cardiac variability in critically ill adults: influence of sepsis. Crit Care Med. 2001;29(7):1380–5.Google Scholar
  10. 10.
    Ahmad S, Ramsay T, Huebsch L, Flanagan S, McDiarmid S, Batkin I, McIntyre L, Sundaresan SR, Maziak DE, Shamji FM, Hebert P, Fergusson D, Tinmouth A, Seely AJ. Continuous multi-parameter heart rate variability analysis heralds onset of sepsis in adults. PloS One 2009;4(8):e6642.Google Scholar
  11. 11.
    Bravi A, Green G, Longtin A, Seely AJE. Monitoring and identification of sepsis development through a composite measure of heart rate variability. PloS One 2012;7(9):e45666.Google Scholar
  12. 12.
    Green GC, Bradley B, Bravi A, Seely AJE. Continuous multiorgan variability analysis to track severity of organ failure in critically ill patients. J Crit Care 2013;28(5):879.e1–11.Google Scholar
  13. 13.
    Brack T, Jubran A, Tobin MJ. Dyspnea and decreased variability of breathing in patients with restrictive lung disease. Am J Respir Crit Care Med. 2002;165(9):1260–4.Google Scholar
  14. 14.
    Papaioannou VE, Chouvarda I, Maglaveras N, Dragoumanis C, Pneumatikos I. Changes of heart and respiratory rate dynamics during weaning from mechanical ventilation: a study of physiologic complexity in surgical critically ill patients. J Crit Care 2011;26(3);262–72.Google Scholar
  15. 15.
    Seely AJE, Bravi A, Herry C, Green G, Longtin A, Ramsay T, Fergusson D, McIntyre L, Kubelik D, Maziak DE, Ferguson N, Brown SM, Mehta S, Martin C, Rubenfeld G, Jacono FJ, Clifford G, Fazekas A, Marshall J, Canadian Critical Care Trials Group (CCCTG). Do heart and respiratory rate variability improve prediction of extubation outcomes in critically ill patients? Crit Care Lond Engl. 2014;18(2):R65.Google Scholar
  16. 16.
    Segal LN, Oei E, Oppenheimer BW, Goldring RM, Bustami RT, Ruggiero S, Berger KI, Fiel SB. Evolution of pattern of breathing during a spontaneous breathing trial predicts successful extubation. Intensive Care Med. 2010;36(3):487–95.Google Scholar
  17. 17.
    Bravi A, et al. Towards the identification of independent measures of heart rate variability. J Crit Care 2013;28(1):e16.Google Scholar
  18. 18.
    Bravi A, Longtin A, Seely AJE. Review and classification of variability analysis techniques with clinical applications. Biomed Eng Online 2011;10:90.Google Scholar
  19. 19.
    Goldberger AL, Peng C-K, Lipsitz LA. What is physiologic complexity and how does it change with aging and disease? Neurobiol Aging 2002;23(1):23–6.Google Scholar
  20. 20.
    Boles J-M, et al. Weaning from mechanical ventilation. Eur Respir J. 2007;29(5):1033–56.Google Scholar
  21. 21.
    Seymour CW, Martinez A, Christie JD, Fuchs BD. The outcome of extubation failure in a community hospital intensive care unit: a cohort study. Crit Care Lond Engl. 2004;8(5): R322–7.Google Scholar
  22. 22.
    Rello J, Ollendorf DA, Oster G, Vera-Llonch M, Bellm L, Redman R, Kollef MH; VAP Outcomes Scientific Advisory Group. Epidemiology and outcomes of ventilator-associated pneumonia in a large US database. Chest 2002;122(6):2115–21.Google Scholar
  23. 23.
    Yang KL, Tobin MJ. A prospective study of indexes predicting the outcome of trials of weaning from mechanical ventilation. N Engl J Med. 1991;324(21):1445–50.Google Scholar
  24. 24.
    Meade M, Guyatt G, Cook D, Griffith L, Sinuff T, Kergl C, Mancebo J, Esteban A, Epstein S. Predicting success in weaning from mechanical ventilation. Chest 2001;120 Suppl 6:400S-24S.Google Scholar
  25. 25.
    Esteban A, Alía I, Tobin MJ, Gil A, Gordo F, Vallverdú I, Blanch L, Bonet A, Vázquez A, de Pablo R, Torres A, de La Cal MA, Macías S. Effect of spontaneous breathing trial duration on outcome of attempts to discontinue mechanical ventilation. Am J Respir Crit Care Med. 1999;159(2):512–8.Google Scholar
  26. 26.
    Esteban A, Frutos F, Tobin MJ, Alía I, Solsona JF, Valverdu V, Fernández R, de la Cal MA, Benito S, Tomás R, Carriedo D, Macías S, Blanco J, for the Spanish Lung Failure Collaborative Group. A comparison of four methods of weaning patients from mechanical ventilation. N Engl J Med. 1995;332(6):345–50.Google Scholar
  27. 27.
    Shen H-N, Lin LY, Chen KY, Kuo PH, Yu CJ, Wu HD, Yang PC. Changes of heart rate variability during ventilator weaning. Chest 2003;123(4):1222–8.Google Scholar
  28. 28.
    Wysocki M, Cracco C, Teixeira A, Mercat A, Diehl JL, Lefort Y, Derenne JP, Similowski T. Reduced breathing variability as a predictor of unsuccessful patient separation from mechanical ventilation. Crit Care Med. 2006;34(8):2076–83.Google Scholar
  29. 29.
    Engoren M, Blum JM. A comparison of the rapid shallow breathing index and complexity measures during spontaneous breathing trials after cardiac surgery. J Crit Care 2013;28(1):69–76.Google Scholar
  30. 30.
    Huang C-T, Tsai Y-J, Lin J-W, Ruan S-Y, Wu H-D, Yu C-J. Application of heart-rate variability in patients undergoing weaning from mechanical ventilation. Crit. Care Lond Engl. 2014;18(1):R21.Google Scholar
  31. 31.
    Glass L, Mackey MC. From clocks to chaos: the rhythms of life. Princeton: Princeton University Press; 1988.Google Scholar
  32. 32.
    Goldberger AL, West BJ. Chaos in physiology: health or disease? In: Chaos in biological systems. New York: Plenum Press; 1987.Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Christophe L. Herry
    • 1
  • Nathan B. Scales
    • 1
  • Kimberley D. Newman
    • 1
  • Andrew J. E. Seely
    • 1
  1. 1.The Ottawa Hospital Research Institute, Clinical Epidemiology ProgramOttawaCanada

Personalised recommendations