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Prediction of acute postoperative pain based on intraoperative nociception level (NOL) index values: the impact of machine learning-based analysis

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Abstract

The relationship between intraoperative nociception and acute postoperative pain is still not well established. The nociception level (NOL) Index (Medasense, Ramat Gan, Israel) uses a multiparametric approach to provide a 0–100 nociception score. The objective of the ancillary analysis of the NOLGYN study was to evaluate the ability of a machine-learning aglorithm to predict moderate to severe acute postoperative pain based on intraoperative NOL values. Our study uses the data from the NOLGYN study, a randomized controlled trial that evaluated the impact of NOL-guided intraoperative administration of fentanyl on overall fentanyl consumption compared to standard of care. Seventy patients (ASA class I–III, aged 18–75 years) scheduled for gynecological laparoscopic surgery were enrolled. Variables included baseline demographics, NOL reaction to incision or intubation, median NOL during surgery, NOL time-weighted average (TWA) above or under manufacturers’ recommended thresholds (10–25), and percentage of surgical time spent with NOL > 25 or < 10. We evaluated different machine learning algorithms to predict postoperative pain. Performance was assessed using cross-validated area under the ROC curve (CV-AUC). Of the 66 patients analyzed, 42 (63.6%) experienced moderate to severe pain. NOL post-intubation (42.8 (31.8–50.6) vs. 34.8 (25.6–41.3), p = 0.05), median NOL during surgery (13 (11–15) vs. 11 (8–13), p = 0.027), percentage of surgical time spent with NOL > 25 (23% (18–18) vs. 20% (15–24), p = 0.036), NOL TWA < 10 (2.54 (2.1–3.0) vs. 2.86 (2.48–3.62), p = 0.044) and percentage of surgical time spent with NOL < 10 (41% (36–47) vs. 47% (40–55), p = 0.022) were associated with moderate to severe PACU pain. Corresponding ROC AUC for the prediction of moderate to severe PACU pain were 0.65 [0.51–0.79], 0.66 [0.52–0.81], 0.66 [0.52–0.79], 0.65 [0.51–0.79] and 0.67 [0.53–0.81]. Penalized logistic regression achieved the best performance with a 0.753 (0.718–0.788) CV-AUC. Our results, even if limited by the small number of patients, suggest that acute postoperative pain is better predicted by a multivariate machine-learning algorithm rather than individual intraoperative nociception variables. Further larger multicentric trials are highly recommended to better understand the relationship between intraoperative nociception and acute postoperative pain.

Trial registration Registered on ClinicalTrials.gov in October 2018 (NCT03776838).

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References

  1. Apfelbaum JL, Chen C, Mehta SS, Gan TJ. Postoperative pain experience: results from a national survey suggest postoperative pain continues to be undermanaged. Anesthesia Analgesia. 2003;97(2):534–40. https://doi.org/10.1213/01.Ane.0000068822.10113.9e.

    Article  Google Scholar 

  2. Gerbershagen HJ, Aduckathil S, van Wijck AJM, Peelen LM, Kalkman CJ, Meissner W. Pain intensity on the first day after surgery: a prospective cohort study comparing 179 surgical procedures. Anesthesiology. 2013;118(4):934–44. https://doi.org/10.1097/ALN.0b013e31828866b3.

    Article  Google Scholar 

  3. Gan TJ. Poorly controlled postoperative pain: prevalence, consequences, and prevention. J Pain Res. 2017;10:2287–98. https://doi.org/10.2147/JPR.S144066.

    Article  CAS  Google Scholar 

  4. Neuman MD, Bateman BT, Wunsch H. Inappropriate opioid prescription after surgery. Lancet (London, England). 2019;393(10180):1547–57. https://doi.org/10.1016/S0140-6736(19)30428-3.

    Article  Google Scholar 

  5. Perkins Frederick M, Kehlet H. Chronic pain as an outcome of surgery: a review of predictive factors. Anesthesiology. 2000;93(4):1123–33. https://doi.org/10.1097/00000542-200010000-00038.

    Article  Google Scholar 

  6. Espitalier F, Idrissi M, Fortier A, Belanger ME, Carrara L, Dakhlallah S, et al. Impact of nociception level (NOL) index intraoperative guidance of fentanyl administration on opioid consumption, postoperative pain scores and recovery in patients undergoing gynecological laparoscopic surgery. A randomized controlled trial. J Clin Anesth. 2021;75:110497. https://doi.org/10.1016/j.jclinane.2021.110497.

    Article  Google Scholar 

  7. Meijer FS, Martini CH, Broens S, Boon M, Niesters M, Aarts L, et al. Nociception-guided versus standard care during remifentanil-propofol anesthesia: a randomized controlled trial. Anesthesiology. 2019;130(5):745–55. https://doi.org/10.1097/ALN.0000000000002634.

    Article  CAS  Google Scholar 

  8. Meijer F, Honing M, Roor T, Toet S, Calis P, Olofsen E, et al. Reduced postoperative pain using nociception level-guided fentanyl dosing during sevoflurane anaesthesia: a randomised controlled trial. Br J Anaesth. 2020;125(6):1070–8. https://doi.org/10.1016/j.bja.2020.07.057.

    Article  CAS  Google Scholar 

  9. Ben-Israel N, Kliger M, Zuckerman G, Katz Y, Edry R. Monitoring the nociception level: a multi-parameter approach. J Clin Monit Comput. 2013;27(6):659–68. https://doi.org/10.1007/s10877-013-9487-9.

    Article  Google Scholar 

  10. Martini CH, Boon M, Broens SJ, Hekkelman EF, Oudhoff LA, Buddeke AW, et al. Ability of the nociception level, a multiparameter composite of autonomic signals, to detect noxious stimuli during propofol-remifentanil anesthesia. Anesthesiology. 2015;123(3):524–34. https://doi.org/10.1097/ALN.0000000000000757.

    Article  CAS  Google Scholar 

  11. Edry R, Recea V, Dikust Y, Sessler DI. Preliminary intraoperative validation of the nociception level index: a noninvasive nociception monitor. Anesthesiology. 2016;125(1):193–203. https://doi.org/10.1097/ALN.0000000000001130.

    Article  Google Scholar 

  12. Stockle PA, Julien M, Issa R, Decary E, Brulotte V, Drolet P, et al. Validation of the PMD100 and its NOL Index to detect nociception at different infusion regimen of remifentanil in patients under general anesthesia. Minerva Anestesiol. 2018;84(10):1160–8. https://doi.org/10.23736/S0375-9393.18.12720-9.

    Article  Google Scholar 

  13. Renaud-Roy E, Stockle PA, Maximos S, Brulotte V, Sideris L, Dube P, et al. Correlation between incremental remifentanil doses and the nociception level (NOL) index response after intraoperative noxious stimuli. Can J Anaesth. 2019;66(9):1049–61. https://doi.org/10.1007/s12630-019-01372-1.

    Article  Google Scholar 

  14. Ledowski T, Schlueter P, Hall N. Nociception level index: do intra-operative values allow the prediction of acute postoperative pain? J Clin Monit Comput. 2021. https://doi.org/10.1007/s10877-021-00654-8.

    Article  Google Scholar 

  15. Guyon I, Weston J, Barnhill S, Vapnik V. Gene selection for cancer classification using support vector machines. Machine Learning. 2002;46(1):389–422. https://doi.org/10.1023/A:1012487302797.

    Article  Google Scholar 

  16. LeDell E, Petersen M, van der Laan M. Computationally efficient confidence intervals for cross-validated area under the ROC curve estimates. Electron J Stat. 2015;9(1):1583–607. https://doi.org/10.1214/15-EJS1035.

    Article  Google Scholar 

  17. Lundberg SM, Lee S-I (2017) A unified approach to interpreting model predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems. Curran Associates Inc, Long Beach

  18. Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMC Med. 2015;13(1):1. https://doi.org/10.1186/s12916-014-0241-z.

    Article  Google Scholar 

  19. Boselli E, Bouvet L, Begou G, Dabouz R, Davidson J, Deloste JY, et al. Prediction of immediate postoperative pain using the analgesia/nociception index: a prospective observational study. Br J Anaesth. 2014;112(4):715–21. https://doi.org/10.1093/bja/aet407.

    Article  CAS  Google Scholar 

  20. Ledowski T, Burke J, Hruby J. Surgical pleth index: prediction of postoperative pain and influence of arousal. Br J Anaesth. 2016;117(3):371–4. https://doi.org/10.1093/bja/aew226.

    Article  CAS  Google Scholar 

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Acknowledgments

Thanks to Mr. Kyle Vaughn Roerick, M.A., for his English language editorial work.

Funding

Financial support for the original NOLGYN study was provided by Medasense Bio- metrics Ltd. (4 Hachilazon St., Ramat-Gan, Israel) under an Independent Investigator Initiated Trial grant (IIIT) and by the department of anesthesiology and pain medicine of Maisonneuve-Rosemont hospital / CEMTL.

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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by LM, PR and PLL. The first draft of the manuscript was written by LM and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Louis Morisson.

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Competing interest

The authors declare no competing interests, except for Dr. Philippe Richebé who received honoraria for educational lectures given for Medasense Biometrics Ltd.

Ethical approval

The NOLGYN study was approved by the CIUSSS de l’Est de l’Ile de Montréal Research Scientific and Ethics Committee, Maisonneuve-Rosemont Hospital, Montréal, Québec, Canada on 25 May 2018 and registered in ClinicalTrials.gov under the number NCT03776838 in October 2018.

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Informed consent was obtained from all included patients.

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Morisson, L., Nadeau-Vallée, M., Espitalier, F. et al. Prediction of acute postoperative pain based on intraoperative nociception level (NOL) index values: the impact of machine learning-based analysis. J Clin Monit Comput 37, 337–344 (2023). https://doi.org/10.1007/s10877-022-00897-z

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