Skip to main content
SpringerLink
Log in

Monitoring the nociception level: a multi-parameter approach

  • Original Paper
  • Published:
Journal of Clinical Monitoring and Computing Aims and scope Submit manuscript

Abstract

The aim of the present study was to develop and validate an objective index for nociception level (NoL) of patients under general anesthesia, based on a combination of multiple physiological parameters. Twenty-five patients scheduled for elective surgery were enrolled. For clinical reference of NoL, the combined index of stimulus and analgesia was defined as a composite of the surgical stimulus level and a scaled effect-site concentration of opioid. The physiological parameters heart rate, heart rate variability (0.15–0.4 Hz band power), plethysmograph wave amplitude, skin conductance level, number of skin conductance fluctuations, and their time derivatives, were extracted. Two techniques to incorporate these parameters into a single index representing the NoL have been proposed: NoLlinear, based on an ordinary linear regression, and NoLnon-linear, based on a non-linear Random Forest regression. NoLlinear and NoLnon-linear significantly increased after moderate to severe noxious stimuli (Wilcoxon rank test, p < 0.01), while the individual parameters only partially responded. Receiver operating curve analysis showed that NoL index based on both techniques better discriminated noxious and non-noxious surgical events [area under curve (AUC) = 0.97] compared with individual parameters (AUC = 0.56–0.74). NoLnon-linear better ranked the level of nociception compared with NoLlinear (R = 0.88 vs. 0.77, p < 0.01). These results demonstrate the superiority of multi-parametric approach over any individual parameter in the evaluation of nociceptive response. In addition, advanced non-linear technique may have an advantage over ordinary linear regression for computing NoL index. Further research will define the usability of the NoL index as a clinical tool to assess the level of nociception during general anesthesia.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Holte K, Kehlet H. Epidural anaesthesia and analgesia–effects on surgical stress responses and implications for postoperative nutrition. Clin Nutr. 2002;21:199–206.

    Article  PubMed  CAS  Google Scholar 

  2. Kehlet H, Jensen T, Woolf C. Persistent postsurgical pain: risk factors and prevention. Lancet. 2006;367:1618–25.

    Article  PubMed  Google Scholar 

  3. Brook A, Ahrens T, Schaiff R, Prentice D, Sherman G, Shannon W, Kollef M. Effect of a nursing-implemented sedation protocol on the duration of mechanical ventilation. Crit Care Med. 1999;27:2609–15.

    Article  PubMed  CAS  Google Scholar 

  4. Kress J, Pohlman A, O’Connor M, Hall J. Daily interruption of sedative infusions in critically ill patients undergoing mechanical ventilation. N Engl J Med. 2000;342:1471–7.

    Article  PubMed  CAS  Google Scholar 

  5. Nevius K, Yvonne D. Proper pain management. OR Nurse. 2008;2:39–40.

    Article  Google Scholar 

  6. Mascia M, Koch M, Medicis J. Pharmacoeconomic impact of rational use guidelines on the provision of analgesia, sedation, and neuromuscular blockade in critical care. Crit Care Med. 2000;28:2300–6.

    Article  PubMed  CAS  Google Scholar 

  7. Jänig W. Autonomic reactions in pain. Pain. 2012;153:733–936.

    Article  PubMed  Google Scholar 

  8. Benarroch E. Pain-autonomic interactions. Neurol Sci. 2006;27:130–3.

    Article  Google Scholar 

  9. Schlereth T, Birklein F. The sympathetic nervous system and pain. Neuromol Med. 2008;10:141–7.

    Article  CAS  Google Scholar 

  10. Loeser JD, Treede RD. The Kyoto protocol of IASP basic pain terminology. Pain. 2008;137:473–7.

    Article  PubMed  Google Scholar 

  11. Dowling J. Autonomic measures and behavioral indices of pain sensitivity. Pain. 1983;16:193–200.

    Article  PubMed  CAS  Google Scholar 

  12. Evans J. Clinical signs and autonomic responses. In: Rosen M, Lunn JN, editors. Consciousness, awareness and pain in general anaesthesia. London: Butterworth; 1987. p. 18–34.

    Google Scholar 

  13. Latson T, O’flaherty O. Effects of surgical stimulation on autonomic reflex function: assessment by changes in heart rate variability. Br J Anaesth. 1993;70:301–5.

    Article  PubMed  CAS  Google Scholar 

  14. Logier R, Jeanne M, Tavernier B, De jonckheere J (2006) Pain/analgesia evaluation using heart rate variability analysis. Engineering in Medicine and Biology Society. EMBS’06, 28th annual international conference of the IEEE.

  15. Jeanne M, Clément C, De Jonckheere J, Logier R, Tavernier B. Variations of the analgesia nociception index during general anaesthesia for laparoscopic abdominal surgery. J Clin Monit Comput. 2012;26:289–94.

    Article  PubMed  CAS  Google Scholar 

  16. Jeanne M, Logier R, De Jonckheere J, Tavernier B. Heart rate variability during total intravenous anesthesia: effects of nociception and analgesia. Auton Neurosci. 2009;147:91–6.

    Article  PubMed  CAS  Google Scholar 

  17. Korhonen I, Yli-Hankala A. Photoplethysmography and nociception. Acta Anaesth Scand. 2009;53:975–85.

    Article  PubMed  CAS  Google Scholar 

  18. Luginbühl M, Reichlin F, Sigurdsson G, Zbinden A, Peterson-Felix S. Prediction of the haemodynamic response to tracheal intubation: comparison of laser-Doppler skin vasomotor reflex and pulse wave reflex. Br J Anaesth. 2002;89:389–97.

    PubMed  Google Scholar 

  19. Murray W, Foster P. The peripheral pulse wave: information overlooked. J Clin Monit Comput. 1996;12:365–77.

    CAS  Google Scholar 

  20. Storm H, Shafiei M, Myre K, Raeder J. Palmar skin conductance compared to a developed stress score and to noxious and awakening stimuli on patients in anaesthesia. Acta Anaesth Scand. 2005;49:798–803.

    Article  PubMed  CAS  Google Scholar 

  21. Ledowski T, Bromilow J, Paech M, Storm H, Hacking R, Schug S. Monitoring of skin conductance to assess postoperative pain intensity. Br J Anaesth. 2006;97:862–5.

    Article  PubMed  CAS  Google Scholar 

  22. Treister R, Kliger M, Zuckerman G, Aryeh I, Eisenberg E. Differentiating between heat pain intensities: the combined effect of multiple autonomic parameters. Pain. 2012;153:1807–14.

    Article  PubMed  Google Scholar 

  23. Rantanen M, Yli-Hankala A, Van Gils M, Yppärilä-Wolters H, Takala P, Huiku M, Kymäläinen M, Seitsonen E, Korhonen I. Novel multiparameter approach for measurement of nociception at skin incision during general anaesthesia. Br J Anaesth. 2006;96:367–76.

    Article  PubMed  CAS  Google Scholar 

  24. Huiku M, Uutela K, Van Gils M, Korhonen I, Kymäläinen M, Meriläinen P, Paloheimo M, Rantanen M, Takala P, Viertiö-Oja H, Yli-Hankala A. Assessment of surgical stress during general anaesthesia. Br J Anaesth. 2007;98:447–55.

    Article  PubMed  CAS  Google Scholar 

  25. Mathews D, Clark L, Johansen J, Matute E, Seshagiri C. Lower composite variability index (CVI) was associated with better clinical global impression scores. Eur J Anaesth. 2009;26(Suppl 45):30–1.

    Google Scholar 

  26. Cannesson M, Rinehart J. Innovative technologies applied to anesthesia: how will they impact the way clinicians practice? J Cardiothor Vasc Anesth. 2012;26:711–20.

    Article  Google Scholar 

  27. Breiman L. Random forests. Mach Learn. 2001;45:5–32.

    Article  Google Scholar 

  28. Laguna P, Moody G, Mark R. Power spectral density of unevenly sampled data by least-square analysis: performance and application to heart rate signals. IEEE Trans Biomed Eng. 1998;45:698–715.

    Article  PubMed  CAS  Google Scholar 

  29. Minto C, Schnider T, Egan T, Youngs E, Lemmens H, Gambus P, Billard V, Hoke J, Moore K, Hermann DJ, Muir KT, Mandema JW, Shafer SL. Influence of age and gender on the pharmacokinetics and pharmacodynamics of remifentanil: I. Model development. Anesthesiology. 1997;86:10–23.

    Article  PubMed  CAS  Google Scholar 

  30. Shafer S, Varvel J, Aziz N, Scott JC. Pharmacokinetics of fentanyl administered by computer-controlled infusion pump. Anesthesiology. 1990;73:1091–102.

    Article  PubMed  CAS  Google Scholar 

  31. Egan T, Muir K, Hermann D, Stanski D, Shafer S. The electroencephalogram (EEG) and clinical measures of opioid potency: defining the EEG-clinical potency relationship (‘fingerprint’) with application to remifentanil. Int J Pharm Med. 2001;15:11–9.

    Article  Google Scholar 

  32. Lemmens H, Dyck J, Shafer S, Stanski D. Pharmacokinetic-pharmacodynamic modeling in drug development: application to the investigational opioid trefentanil. Clin Pharmacol Ther. 1994;56:261–71.

    Article  PubMed  CAS  Google Scholar 

  33. Egan T, Minto C, Hermann D, Barr J, Muir K, Shafer S. Remifentanil versus alfentanil: comparative pharmacokinetics and pharmacodynamics in healthy adult male volunteers. Anesthesiology. 1996;84:821–33.

    Article  PubMed  CAS  Google Scholar 

  34. Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference and prediction. 2nd ed. New York: Springer; 2009.

    Book  Google Scholar 

  35. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143:29–36.

    PubMed  CAS  Google Scholar 

  36. Johnson K, Syroid N, Gupta D, Manyam S, Egan T, Huntington J, White J, Tyler D, Westenskow D. An evaluation of remifentanil propofol response surfaces for loss of responsiveness, loss of response to surrogates of painful stimuli and laryngoscopy in patients undergoing surgery. Anesth Analg. 2008;106:471–9.

    Article  PubMed  CAS  Google Scholar 

  37. Lang E, Kapila A, Shlugman D, Hoke J, Sebel P, Glass P. Reduction of isoflurane minimal alveolar concentration by remifentanil. Anesthesiology. 1996;85:721–8.

    Article  PubMed  CAS  Google Scholar 

  38. McEwan A, Smith C, Dyar O, Goodman D, Smith L, Glass P. Isoflurane minimum alveolar concentration reduction by fentanyl. Anesthesiology. 1993;78:864–9.

    Article  PubMed  CAS  Google Scholar 

  39. Sinha P, Koshy T. Monitoring devices for measuring the depth of anaesthesia—an overview. Indian J Anaesth. 2007;51:365–81.

    Google Scholar 

  40. Lu S, Zhao H, Ju K, Shin K, Lee M, Shelley K, Chon KH. Can photoplethysmography variability serve as an alternative approach to obtain heart rate variability information? J Clin Monit Comput. 2008;22:23–9.

    Article  PubMed  Google Scholar 

  41. Gil E, Orini M, Bailón R, Vergara JM, Mainardi L, Laguna P. Photoplethysmography pulse rate variability as a surrogate measurement of heart rate variability during non-stationary conditions. Physiol Meas. 2010;31:1271–90.

    Article  PubMed  CAS  Google Scholar 

  42. Matsukawa T, Kurz A, Sessler DI, et al. Propofol linearly reduces the vasoconstriction and shivering thresholds. Anesthesiology. 1995;82:1169–80.

    Article  PubMed  CAS  Google Scholar 

  43. Xiong J, Kurz A, Sessler DI, et al. Isoflurane produces marked and nonlinear decreases in the vasoconstriction and shivering thresholds. Anesthesiology. 1996;85:240–5.

    Article  PubMed  CAS  Google Scholar 

  44. Heyse B, Proost JH, Schumacher PM, Bouillon TW, Vereecke HE, Eleveld DJ, Luginbühl M, Struys MM. Sevoflurane remifentanil interaction: comparison of different response surface models. Anesthesiology. 2012;116:311–23.

    Article  PubMed  CAS  Google Scholar 

  45. Bouillon TW, Bruhn J, Radulescu L, Andresen C, Shafer TJ, Cohane C, Shafer SL. Pharmacodynamic interaction between propofol and remifentanil regarding hypnosis, tolerance of laryngoscopy, bispectral index, and electroencephalographic approximate entropy. Anesthesiology. 2004;100:1072–353.

    Article  Google Scholar 

Download references

Declaration of interest

The study was funded by Medasense Biometrics Ltd. N.B., M.K., and G.Z. are employees of Medasense Biometrics. Y.K. serves on the advisory board of Medasense Biometrics Ltd.

Author contributions

All authors jointly designed the study; R.E. and Y.K. recruited the patients, coordinated and supervised administration of anesthesia during operations; R.E., N.B., and G.Z. collected and annotated the data; N.B., M.K., and G.Z. developed NoL index and CISA score and wrote MATLAB code; N.B. and M.K. designed and performed the statistical analysis; N.B., M.K., and R.E. wrote the manuscript; G.Z. and Y.K. edited the manuscript. All authors read and approved the final manuscript.

Author information

Authors and Affiliations

  1. Medasense Biometrics Ltd., Ramat Yishai, Israel

    Nir Ben-Israel, Mark Kliger & Galit Zuckerman

  2. Department of Anesthesiology, Technion, Israel Institute of Technology, Rambam Healthcare Campus, Haifa, Israel

    Yeshayahu Katz & Ruth Edry

Authors
  1. Nir Ben-Israel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mark Kliger
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Galit Zuckerman
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yeshayahu Katz
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ruth Edry
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ruth Edry.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ben-Israel, N., Kliger, M., Zuckerman, G. et al. Monitoring the nociception level: a multi-parameter approach. J Clin Monit Comput 27, 659–668 (2013). https://doi.org/10.1007/s10877-013-9487-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10877-013-9487-9

Keywords

Search

Navigation