Fractality and chaotic behavior of heart rate variability as hypotension predictors after spinal anesthesia: Study protocol for a randomized controlled trial

Authors

  • Hermes Melo Teixeira Batista Laboratório de Delineamento e Escrita Científica, Centro Universitário Saúde ABC – Santo André (SP), /Departmento de Anestesiologia, Hospital Universitário Júlio Bandeira (HUJB) – Cajazeiras (PB), /Faculdade de Medicina Estácio de Juazeiro do Norte (Estácio FMJ) – Juazeiro do Norte (CE)
  • Gylmara Bezerra de Menezes Silveira Hospital Regional do Cariri (ISGH) – Juazeiro do Norte (CE)
  • Marcelo Ferraz Campos Laboratório de Delineamento e Escrita Científica, Centro Universitário Saúde ABC – Santo André (SP)
  • Juliana Spat Carlesso Laboratório de Delineamento de Estudos e Escrita Científica. Centro Universitário Saúde ABC. Santo André. São Paulo
  • Vítor Engracia Valenti Departmento de Terapia da Fala e Audiologia, Faculdade de Filosofia e Ciências (UNESP) – Marília (SP)
  • Rodrigo Daminello Raimundo Laboratório de Delineamento de Estudos e Escrita Científica. Centro Universitário Saúde ABC. Santo André. São Paulo
  • Andrés Ricardo Pérez Riera Laboratório de Delineamento de Estudos e Escrita Científica. Centro Universitário Saúde ABC. Santo André. São Paulo

DOI:

https://doi.org/10.7322/jhgd.v29.9433

Keywords:

spinal anesthesia, autonomic nervous system, heart rate, recovery room post anesthetic, sympathic block, hypotension

Abstract

Introduction: All drugs and techniques that induce the anesthetic state act in some way in the Autonomic Nervous System (ANS). The administration of local anesthetics in the subarachnoid space produces motor, sensitive and sympathetic block, with latencies and variable and independent block levels. The motor block is the first to install, followed by the sympathetic and the sensitive. Sympathetic blockage affects 2 to 6 dermatomes above the sensory block. The recovery of spinal anesthesia is assessed through a scale defined in 1979 by Bromage and is based exclusively on the return of motor function and does not take into account the recovery of ANS activity. The persistence of sympathetic block may imply a higher incidence of urinary retention, bradycardia and hypotension. Objective: To assess cardiac autonomic modulation during perioperative hypotension caused by subarachnoid anesthesia. Methods: A randomised, double-blind clinical trial will be performed in a large hospital located in the southern region of Ceará, Brazil and at the HUJB in Cajazeiras, Paraíba. Sixty patients from the anaesthesia outpatient clinic were enrolled. Patients were divided into two groups: one group received Bupivacaine with clonidine, and the other group received only bupivacaine at a dose of 15 mg. The sample consisted of 60 ASA patients I to III, submitted to orthopedic surgery of lower limbs and lower abdomen under spinal anesthesia. The Heart Rate Variability will be evaluated in three moments: rest, before anesthesia; 20 min after the blockade was installed, and at the time of motor function recovery according to the Bromage criteria and prognostic indices will be evaluated in the development of perioperative hypotension in two groups. Linear methods will be used in the frequency domain and nonlinear in chaos domain, Poincaré plot, approximate entropy, Detrended Fluctuation Analysis (DFA) and Correlation Dimension. The data will be collected through a Polar V800® heart rate meter and properly submitted for analysis and filtering by Kubios 3.0® software. Discussion: In the literature we find data evaluating the installation of sympathetic block through HRV using linear methods however, there is a lack of studies using methods based on the domain of chaos. Some studies address the value of HRV as a predictor of hypotension following subarachnoid anesthesia, mainly using linear methods in the frequency domain. It is understood to be important to analyze these factors using methods already validated in the domain of chaos, complexity and fractality, more compatible with the complexity of the behavior of biological systems, in the characterization of the autonomic function during the subarachnoid anesthesia. Registry: The clinical trial was registered in the Brazilian Registry of Clinical Trials (ReBEC) under the number RBR-4Q53D6.

Downloads

Download data is not yet available.

References

1. Mordecai MM, Brull SJ. Spinal anesthesia. Curr Opin Anaesthesiol. 2005;18(5):527-33. DOI: http://doi.org/10.1097/01.aco.0000182556.09809.17

2. Cwik J. Postoperative considerations of neuraxial anesthesia. Anesthesiol Clin. 2012; 30(3):433-43. DOI: http://doi.org/10.1016/j.anclin.2012.07.005

3. Kumari A, Gupta R, Bajwa SJS, Singh A. Unanticipated cardiac arrest under spinal anesthesia: An unavoidable mystery with review of current literature. Anesth Essays Res. 2014;8(1):99-102. DOI: http://doi.org/10.4103/0259-1162.128923

4. Kopp SL, Horlocker TT, Warner ME, Hebl JR, Vachon CA, Schroeder DR, et al. Cardiac arrest during neuraxial anesthesia: frequency and predisposing factors associated with survival. Anesth Analg. 2005;100(3):855-65. DOI: http://doi.org/10.1213/01.ANE.0000144066.72932.B1

5. Wahi A, Singh AK, Syal K, Sood A, Pathania J. Comparative efficacy of intrathecal bupivacaine alone and combination of bupivacaine with clonidine in spinal anaesthesia. J Clin Diagn Res. 2016;10(4):UC06-8. DOI: http://doi.org/10.7860/JCDR/2016/16343.7565

6. Cornforth DJ, Tarvainen MP, Jelinek HF. How to calculate renyi entropy from heart rate variability, and why it matters for detecting cardiac autonomic neuropathy. Front Bioeng Biotechnol. 2014;2:34. DOI: http://doi.org/10.3389/fbioe.2014.00034

7. Lee SH, Lee DH, Ha DH, Oh YJ. Dynamics of heart rate variability in patients with type 2 diabetes mellitus during spinal anaesthesia: prospective observational study. BMC Anesthesiol. 2015;15:141. DOI: http://doi.org/10.1186/s12871-015-0125-6

8. Registro Brasileiro de Ensaios Clínicos (ReBec). Fractalidade e comportamento caótico da variabilidade da frequência cardíaca na anestesia subaracnóide. [cited 2019 Fev 28] Available from: http://ensaiosclinicos.gov.br/r g/RBR-4q53d6/.

9. Cakmakkaya OS, Kolodzie K, Apfel CC, Pace NL. Anaesthetic techniques for risk of malignant tumour recurrence. Cochrane Database Syst Rev. 2014;(11):CD008877. DOI: http://doi.org/10.1002/14651858.CD008877.pub2

10. Vanderlei FM, Vanderlei LCM, Garner DM. Heart rate dynamics by novel chaotic globals to hrv in obese youths. J Hum Growth Dev. 2015;25(1):82-8. DOI: https://doi.org/10.7322/jhgd.96772

11. Valenti VE. The recent use of heart rate variability for research. J Hum Growth Dev. 2015;25(2):137-40. DOI: https://doi.org/10.7322/jhgd.102991

12. Tulppo MP, Kiviniemi AM, Hautala AJ, Kallio M, Seppänen T, Mäkikallio TH, et al. Physiological background of the loss of fractal heart rate dynamics. Circulation. 2005; 112(3):314-9. DOI: https://doi.org/10.1161/CIRCULATIONAHA.104.523712

13. Malik M, Camm AJ, Bigger JT, Breithardt G, Cerutti S, Cohen R, et al. Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Eur Heart J. 1996;17(3):354-81.

14. Sassi R, Cerutti S, Lombardi F, Malik M, Huikuri HV, Peng CK, et al. Advances in heart rate variability signal analysis: joint position statement by the e-Cardiology ESC Working Group and the European Heart Rhythm Association co-endorsed by the Asia Pacific Heart Rhythm Society. Europace. 2015;17(9):1341-53. DOI: https://doi.org/10.1093/europace/euv015

15. Vanderlei LCM, Pastre CM, Hoshi RA, Carvalho TD, Godoy MF. Noções básicas de variabilidade da frequência cardíaca e sua aplicabilidade clínica. Rev Bras Cir Cardiovasc. 2009;24(2):205-17. DOI: http://dx.doi.org/10.1590/S0102-76382009000200018

16. Abreu LC. Variabilidade da frequência cardíaca como marcador funcional do desenvolvimento. J Hum Growth Dev. 2012;22(3):279-82. DOI: https://doi.org/10.7322/jhgd.46712

17. Fujiwara Y, Sato Y, Shibata Y, Asakura Y, Nishiwaki K, Komatsu T. A greater decrease in blood pressure after spinal anaesthesia in patients with low entropy of the RR interval. Acta Anaesthesiol Scand. 2007;51(9):1161-5. DOI: https://doi.org/10.1111/j.1399-6576.2007.01435.x

18. Tapanainen JM, Thomsen PEB, Køber L, Torp-Pedersen C, Makikallio TH, Still AM, et al. Fractal analysis of heart rate variability and mortality after an acute myocardial infarction. Am J Cardiol 2002;90(4):347-52.

19. Sassi R, Signorini MG, Cerutti S. Multifractality and heart rate variability. Chaos. 2009;19(2):028507. DOI: https://doi.org/10.1063/1.3152223

20. Silva MAC, Nicácio MB, Pimentel IDO, Lopes PFF, Rebouças GM, Medeiros HJ. Métodos não lineares para a mensuração da modulação autonômica. Neurociências. 2014; 10(2):94-103.

21. Roy B, Ghatak S. Métodos não-lineares para avaliar mudanças na variabilidade da frequência cardíaca em pacientes com diabetes tipo 2. Arq Bras Cardiol. 2013;101(4):317-27. DOI: http://dx.doi.org/10.5935/abc.20130181

22. Introna R, Yodlowski E, Pruett J, Montano N, Porta A, Crumrine R. Sympathovagal effects of spinal anesthesia assessed by heart rate variability analysis. Anesth Analg. 1995; 80(2):315-21.

23. Pincus SM. Approximate entropy as a measure of system complexity. Proc Natl Acad Sci USA. 1991;88(6):2297-301.

24. Wagner CD, Persson PB. Chaos in the cardiovascular system: na update. Cardiovasc Res. 1998;40(2):257-64.

25. Lima DGS, Saraiva AO, Santos CY, Oliveira SMR, Pereira RP, Sombra WG, et al. Characterization of heart rate variability during total venous anesthesia: a case report. Amadeus Int Multidisc J. 2018;2(4):57-70 . DOI: https://doi.org/10.14295/aimj.v2i4.31

26. Peng CK, Havlin S, Stanley HE, Goldberger AL. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos. 1995;5(1):82-7. DOI: https://doi.org/10.1063/1.166141

27. Tapanainen JM, Thomsen PEB, Køber L, Torp-Pedersen C, Ma¨kikallio TH, Still AM, et al. Fractal analysis of heart rate variability and mortality after an acute myocardial infarction. Am J Cardiol. 2002;90(4):347-52.

28. Krstacic G, Krstacic A, Smalcelj A, Milicic D, Jembrek-Gostovic M. The “Chaos Theory” and nonlinear dynamics in heart rate variability analysis: does it work in short-time series in patients with coronary heart disease? Ann Noninvasive Electrocardiol. 2007;12(2):130-6. DOI: https://doi.org/10.1111/j.1542-474X.2007.00151.x

29. Ferreira MT, Messias M, Vanderlei LCM, Pastre CM. Caracterização do comportamento caótico da variabilidade da frequência cardíaca (VFC) em jovens saudáveis. Tend Mat Apl Comput. 2010; 11(2):141-150.

30. Wagner CD, Persson PB. Chaos in the cardiovascular system: an update. Cardiovasc Res. 1998;40(2):257-64. DOI: https://doi.org/10.1016/S0008-6363(98)00251-X

31. Edry R, Recea V, Dikust Y, Sessler DI. Preliminary intraopera-tive validation of the nociception level index: A noninvasivenociception monitor. Anesthesiology. 2016;125(1):193-203. DOI: https://doi.org/10.1097/ALN.0000000000001130

32. Prashanth A, Chakravarthy M, George A, Mayur R, Hosur R, Pargaonkar S. Sympatho-vagal balance, as quantified by ANSindex, predicts post spinal hypotension and vasopressor requirement in parturientsundergoing lower segmental cesarean section: a single blinded prospective observational study. J Clin Monit Comput. 2017;31(4):805-11. DOI: https://doi.org/10.1007/s10877-016-9906-9

Downloads

Published

2019-11-05

Issue

Vol. 29 No. 2 (2019)

Section

ORIGINAL ARTICLES