Changes in nonlinear dynamic complexity measures of blood pressure during anesthesia for cardiac surgeries using cardio pulmonary bypass

  • Valluvan Rangasamy
  • Teresa S. Henriques
  • Pooja A. Mathur
  • Roger B. Davis
  • Murray A. Mittleman
  • Balachundhar SubramaniamEmail author
Original Research


Nonlinear complexity measures computed from beat-to-beat arterial BP dynamics have shown associations with standard cardiac surgical risk indices. They reflect the physiological adaptability of a system and has been proposed as dynamical biomarkers of overall health status. We sought to determine the impact of anesthetic induction and cardiopulmonary bypass (CPB) upon the complexity measures computed from perioperative BP time series. In this prospective, observational study, 300 adult patients undergoing cardiac surgery were included. Perioperative period was divided as: (1) Preoperative (PreOp); (2) ORIS—induction to sternotomy; (3) ORSB- sternotomy to CPB; (4) ORposB—post CPB and within 30 min before leaving OR and (5) postoperative phase (PostOp)—initial 30 min in the cardiac surgical intensive care unit. BP waveforms for systolic (SAP), diastolic (DAP), mean arterial pressure (MAP) and pulse pressure (PP) were recorded, and their corresponding complexity index (MSE) was calculated. Significant decrease in MSE from Preop to PostOp phases was observed for all BP time series. Maximum fall was seen during post anesthetic induction (ORIS) phase. Mild recovery during the subsequent phases was observed but they never reached the baseline values. In an exploratory analysis, preoperative MSE showed a significant correlation with postoperative length of ICU stay. Blood pressure complexity varies at different time points and is not fixed for a given individual. Preoperative BP Complexity decreased significantly following anesthetic induction and did not recover to baseline until 30 min after surgery. Prevention of this significant fall may offer restoration of MSE throughout surgery. Furthermore, preoperative BP complexity needs to be explored as a predictor of major postoperative adverse events by itself or in addition with the current risk indices.


Cardiopulmonary bypass Blood pressure variability Complexity Multiscale entropy Non-linear analysis 


Author contributions

BS, TH, VR, PM, RD, MM: Conception or design of the work; acquisition, analysis and interpretation of data. VR, TH, BS, PM, RD, MM: Drafting and revising work for important intellectual content. VR, BS, TH, PM, RD, MM: Final approval of the version to be published. BS, VR, TH, PM, RD, MM: Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.


BS is supported by the National Institute of Health, Research Project Grant GM 098406.

Compliance with ethical standards

Conflicts of interest

The authors declare no competing interests or conflicts of interests.

Supplementary material

10877_2019_370_MOESM1_ESM.docx (34 kb)
Supplementary material 1 (DOCX 34 kb)


  1. 1.
    Alexander JH, Smith PK. Coronary-artery bypass grafting. N Engl J Med. 2016;374:1954–64.CrossRefPubMedGoogle Scholar
  2. 2.
    Vetta F, Locorotondo G, Vetta G, Mignano M, Bracchitta S. Prognostic impact of frailty in elderly cardiac surgery patients. Monaldi Arch Chest Dis. 2017;87:855.CrossRefPubMedGoogle Scholar
  3. 3.
    Maslow A, Casey P, Poppas A, Schwartz C, Singh A. Aortic valve replacement with or without coronary artery bypass graft surgery: the risk of surgery in patients > or = 80 years old. J Cardiothorac Vasc Anesth. 2010;24:18–24.CrossRefPubMedGoogle Scholar
  4. 4.
    Pinna-Pintor P, Bobbio M, Colangelo S, Veglia F, Giammaria M, Cuni D, et al. Inaccuracy of four coronary surgery risk-adjusted models to predict mortality in individual patients. Eur J Cardiothorac Surg. 2002;21:199–204.CrossRefPubMedGoogle Scholar
  5. 5.
    Zhang R, Iwasaki K, Zuckerman JH, Behbehani K, Crandall CG, Levine BD. Mechanism of blood pressure and R-R variability: insights from ganglion blockade in humans. J Physiol (Lond). 2002;543:337–48.CrossRefPubMedCentralGoogle Scholar
  6. 6.
    Monk TG, Bronsert MR, Henderson WG, Mangione MP, Sum-Ping STJ, Bentt DR, et al. Association between intraoperative hypotension and hypertension and 30-day postoperative mortality in noncardiac surgery. Anesthesiology. 2015;123:307–19.CrossRefPubMedGoogle Scholar
  7. 7.
    Aronson S, Stafford-Smith M, Phillips-Bute B, Shaw A, Gaca J, Newman M, et al. Intraoperative systolic blood pressure variability predicts 30-day mortality in aortocoronary bypass surgery patients. Anesthesiology. 2010;113:305–12.CrossRefPubMedGoogle Scholar
  8. 8.
    Mascha EJ, Yang D, Weiss S, Sessler DI. Intraoperative mean arterial pressure variability and 30-day mortality in patients having noncardiac surgery. Anesthesiology. 2015;123:79–91.CrossRefPubMedGoogle Scholar
  9. 9.
    Kirkness CJ, Burr RL, Mitchell PH. Intracranial and blood pressure variability and long-term outcome after aneurysmal sub-arachnoid hemorrhage. Am J Crit Care. 2009;18:241–51.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Subramaniam B, Khabbaz KR, Heldt T, Lerner AB, Mittleman MA, Davis RB, et al. Blood pressure variability: can nonlinear dynamics enhance risk assessment during cardiovascular surgery? J Cardiothorac Vasc Anesth. 2014;28:392–7.CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Costa M, Goldberger AL, Peng CK. Multiscale entropy analysis of biological signals. Phys Rev E. 2005;71:021906.CrossRefGoogle Scholar
  12. 12.
    Costa M, Goldberger AL, Peng CK. Multiscale entropy analysis of complex physiologic time series. Phys Rev Lett. 2002;89:068102.CrossRefPubMedGoogle Scholar
  13. 13.
    Lakusic N, Mahovic D, Sonicki Z, Slivnjak V, Baborski F. Outcome of patients with normal and decreased heart rate variability after coronary artery bypass grafting surgery. Int J Cardiol. 2013;166:516–8.CrossRefPubMedGoogle Scholar
  14. 14.
    Padley JR, Ben-Menachem E. Low pre-operative heart rate variability and complexity are associated with hypotension after anesthesia induction in major abdominal surgery. J Clin Monit Comput. 2018;32:245–52.CrossRefPubMedGoogle Scholar
  15. 15.
    Hu J, Gao J, Tung W, Cao Y. Multiscale analysis of heart rate variability: a comparison of different complexity measures. Ann Biomed Eng. 2010;38:854–64.CrossRefPubMedGoogle Scholar
  16. 16.
    Molon G, Solimene F, Melissano D, Curnis A, Belotti G, Marrazzo N, et al. Baseline heart rate variability predicts clinical events in heart failure patients implanted with cardiac resynchronization therapy: validation by means of related complexity index. Ann Noninvasive Electrocardiol. 2010;15:301–7.CrossRefPubMedGoogle Scholar
  17. 17.
    Henriques TS, Costa MD, Mathur P, Mathur P, Davis RB, Mittleman MA, et al. Complexity of preoperative blood pressure dynamics: possible utility in cardiac surgical risk assessment. J Clin Monit Comput. 2018;33:31–8.CrossRefPubMedGoogle Scholar
  18. 18.
    Murphy GS, Hessel EA, Groom RC. Optimal perfusion during cardiopulmonary bypass: an evidence-based approach. Anesth Analg. 2009;108:1394–417.CrossRefPubMedGoogle Scholar
  19. 19.
    Mets B. The pharmacokinetics of anesthetic drugs and adjuvants during cardiopulmonary bypass. Acta Anaesthesiol Scand. 2000;44:261–73.CrossRefPubMedGoogle Scholar
  20. 20.
    von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP, et al. The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. Ann Intern Med. 2007;147:573–7.CrossRefGoogle Scholar
  21. 21.
    Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation. 2000;101:E215–20.PubMedGoogle Scholar
  22. 22.
    Zong W, Heldt T, Moody GB, Mark RG. An open-source algorithm to detect onset of arterial blood pressure pulses. Comput Cardiol. 2003;2003:259–62.Google Scholar
  23. 23.
    Saeed M, Villarroel M, Reisner AT, Clifford G, Lehman L-W, Moody G, et al. Multiparameter intelligent monitoring in intensive care II: a public-access intensive care unit database. Crit Care Med. 2011;39:952–60.CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Parati G, Ochoa JE, Lombardi C, Bilo G. Assessment and management of blood-pressure variability. Nat Rev Cardiol. 2013;10:143–55.CrossRefPubMedGoogle Scholar
  25. 25.
    Richman JS, Moorman JR. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol. 2000;278:H2039–49.CrossRefPubMedGoogle Scholar
  26. 26.
    [R] Citation in the literature [Internet]. [cited 2018 Oct 19]. Available from:
  27. 27.
    Souza Neto EP, Loufouat J, Saroul C, Paultre C, Chiari P, Lehot J-J, et al. Blood pressure and heart rate variability changes during cardiac surgery with cardiopulmonary bypass. Fundam Clin Pharmacol. 2004;18:387–96.CrossRefPubMedGoogle Scholar
  28. 28.
    Yang MW, Kuo TB, Lin SM, Chan KH, Chan SH. Continuous, on-line, real-time spectral analysis of SAP signals during cardiopulmonary bypass. Am J Physiol. 1995;268:H2329–35.PubMedGoogle Scholar
  29. 29.
    Marty J, Gauzit R, Lefevre P, Couderc E, Farinotti R, Henzel C, et al. Effects of diazepam and midazolam on baroreflex control of heart rate and on sympathetic activity in humans. Anesth Analg. 1986;65:113–9.CrossRefPubMedGoogle Scholar
  30. 30.
    Reich DL, Hossain S, Krol M, Baez B, Patel P, Bernstein A, et al. Predictors of hypotension after induction of general anesthesia. Anesth Analg. 2005;101:622–8.CrossRefPubMedGoogle Scholar
  31. 31.
    Ebert TJ, Muzi M, Berens R, Goff D, Kampine JP. Sympathetic responses to induction of anesthesia in humans with propofol or etomidate. Anesthesiology. 1992;76:725–33.CrossRefPubMedGoogle Scholar
  32. 32.
    Kato M, Komatsu T, Kimura T, Sugiyama F, Nakashima K, Shimada Y. Spectral analysis of heart rate variability during isoflurane anesthesia. Anesthesiology. 1992;77:669–74.CrossRefPubMedGoogle Scholar
  33. 33.
    Huang HH, Chan HL, Lin PL, Wu CP, Huang CH. Time-frequency spectral analysis of heart rate variability during induction of general anaesthesia. Br J Anaesth. 1997;79:754–8.CrossRefPubMedGoogle Scholar
  34. 34.
    Pinsky MR. Complexity modeling: identify instability early. Crit Care Med. 2010;38:S649–55.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of AnesthesiaCritical Care, and Pain Medicine, Harvard Medical School, Beth Israel Deaconess Medical CenterBostonUSA
  2. 2.Institute for Systems and Computer Engineering, Technology and Science (INESC-TEC)PortoPortugal
  3. 3.Division of General Medicine and Primary Care, Department of MedicineBeth Israel Deaconess Medical CenterBostonUSA
  4. 4.Department of EpidemiologyHarvard School of Public HealthBostonUSA
  5. 5.Department of Anesthesia, Harvard Medical SchoolCentre for Anesthesia Research Excellence (CARE), Beth Israel Deaconess Medical CenterBostonUSA

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