Morisson Louis, Nadeau-Vallée Mathieu, Espitalier Fabien, Laferrière-Langlois Pascal, Idrissi Moulay, Lahrichi Nadia, Gélinas Céline, Verdonck Olivier, Richebé Philippe
Department of Anesthesiology and Pain Medicine, CIUSSS de l'Est de l'Ile de Montréal, Maisonneuve-Rosemont Hospital, Montréal, Québec, Canada.
Department of Anesthesiology and Pain Medicine, University of Montréal, Montréal, Québec, Canada.
J Clin Monit Comput. 2023 Feb;37(1):337-344. doi: 10.1007/s10877-022-00897-z. Epub 2022 Aug 4.
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).
术中伤害感受与术后急性疼痛之间的关系仍未完全明确。伤害感受水平（NOL）指数（Medasense，以色列拉马特甘）采用多参数方法提供0至100的伤害感受评分。NOLGYN研究辅助分析的目的是评估一种机器学习算法基于术中NOL值预测中度至重度术后急性疼痛的能力。我们的研究使用了NOLGYN研究的数据，这是一项随机对照试验，与标准治疗相比，评估了NOL引导下术中给予芬太尼对芬太尼总消耗量的影响。纳入了70例计划进行妇科腹腔镜手术的患者（ASA分级I-III，年龄18至75岁）。变量包括基线人口统计学数据、对切口或插管的NOL反应、手术期间的NOL中位数、高于或低于制造商推荐阈值（10 - 25）的NOL时间加权平均值（TWA），以及NOL > 25或< 10时的手术时间百分比。我们评估了不同的机器学习算法来预测术后疼痛。使用ROC曲线下的交叉验证面积（CV-AUC）评估性能。在分析的66例患者中，42例（63.6%）经历了中度至重度疼痛。插管后NOL（42.8（31.8 - 50.6）对34.8（25.6 - 41.3），p = 0.05）、手术期间的NOL中位数（13（11 - 15）对11（8 - 13），p = 0.027）、NOL > 25时的手术时间百分比（23%（18 - 18）对20%（15 - 24），p = 0.036）、NOL TWA < 10（2.54（2.1 - 3.0）对2.86（2.48 - 3.62），p = 0.044）以及NOL < 10时的手术时间百分比（41%（36 - 47）对47%（40 - 55），p = 0.022）与中度至重度PACU疼痛相关。预测中度至重度PACU疼痛的相应ROC AUC分别为0.65 [0.51 - 0.79]、0.66 [0.52 - 0.81]、0.66 [0.52 - 0.79]、0.65 [0.51 - 0.79]和0.67 [0.53 - 0.81]。惩罚逻辑回归的表现最佳，CV-AUC为0.753（0.718 - 0.788）。我们的结果即使受到患者数量较少的限制，也表明多变量机器学习算法比个体术中伤害感受变量能更好地预测术后急性疼痛。强烈建议进一步开展更大规模的多中心试验，以更好地理解术中伤害感受与术后急性疼痛之间的关系。试验注册于2018年10月在ClinicalTrials.gov上注册（NCT03776838）。
