Prediction of postoperative opioid analgesia using clinical-experimental parameters and electroencephalography
Prediction of postoperative opioid analgesia using clinical-experimental parameters and electroencephalography
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DOI：
10.1002/ejp.921
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发表时间：
2017-02-01
影响因子：
3.6
通讯作者：
Drewes, A. M.
中科院分区：
文献类型：
--
作者：
Gram, M.;Erlenwein, J.;Drewes, A. M.
背景阿片类药物通常用于疼痛治疗，但反应通常不足并且取决于例如。疼痛状况、遗传因素和药物类别。因此，迫切需要识别生物标志物，以便为个体患者选择合适的药物，这一概念被称为个性化医疗。定量感官测试（QST）和临床参数可以为反应提供一些指导，但迫切需要更好、更客观的生物标志物。脑电图（EEG）可能是合适的，因为它可以评估阿片类药物介导其作用的中枢神经系统。方法在全髋关节置换手术前一天记录患者的临床参数、QST 和脑电图（休息和强直性疼痛期间）。使用羟考酮和吡曲胺作为患者自控镇痛剂进行术后疼痛治疗。根据术后 24 小时的疼痛评级，将患者分为有反应者和无反应者。使用传统的分组统计方法分析参数。此外，通过机器学习分析脑电图以预测个体反应。结果纳入 81 名患者，其中 51 名对术后阿片类药物治疗有反应（30 名无反应）。传统统计数据显示，对阿片类药物治疗无反应的患者中普遍存在更严重的先前存在的慢性疼痛（p=0.04）。术前 EEG 分析能够预测反应者，准确度为 65% (p=0.009)，但仅限于强直性疼痛期间。结论 术前慢性疼痛分级与术后疼痛治疗的结果相关。此外，脑电图显示出作为客观生物标志物的潜力，并可用于预测术后阿片类镇痛。意义当前的临床研究证明了脑电图作为生物标志物的可行性，并且结果与之前的实验结果一致。机器学习和脑电图的结合方法为个性化疼痛治疗的未来发展提供了有希望的结果。
BackgroundOpioids are often used for pain treatment, but the response is often insufficient and dependent on e.g. the pain condition, genetic factors and drug class. Thus, there is an urgent need to identify biomarkers to enable selection of the appropriate drug for the individual patient, a concept known as personalized medicine. Quantitative sensory testing (QST) and clinical parameters can provide some guidance for response, but better and more objective biomarkers are urgently warranted. Electroencephalography (EEG) may be suitable since it assesses the central nervous system where opioids mediate their effects.MethodsClinical parameters, QST and EEG (during rest and tonic pain) was recorded from patients the day prior to total hip replacement surgery. Postoperative pain treatment was performed using oxycodone and piritramide as patient-controlled analgesia. Patients were stratified into responders and non-responders based on pain ratings 24h post-surgery. Parameters were analysed using conventional group-wise statistical methods. Furthermore, EEG was analysed by machine learning to predict individual response.ResultsEighty-one patients were included, of which 51 responded to postoperative opioid treatment (30 non-responders). Conventional statistics showed that more severe pre-existing chronic pain was prevalent among non-responders to opioid treatment (p=0.04). Preoperative EEG analysis was able to predict responders with an accuracy of 65% (p=0.009), but only during tonic pain.ConclusionsChronic pain grade before surgery is associated with the outcome of postoperative pain treatment. Furthermore, EEG shows potential as an objective biomarker and might be used to predict postoperative opioid analgesia.SignificanceThe current clinical study demonstrates the viability of EEG as a biomarker and with results consistent with previous experimental results. The combined method of machine learning and electroencephalography offers promising results for future developments of personalized pain treatment.