Model-based optimization of pain management in surgical patients

项目总结和摘要 手术后长期使用阿片类药物是阿片类药物流行病的主要原因， 公共卫生的重大危机。美国每年有5100万患者接受手术。 9-13%的手术患者继续长期使用阿片类药物，导致阿片类药物使用障碍 在8-12%的慢性使用病例中。然而，不到一半的手术患者报告 术后疼痛缓解，这阻碍了恢复，增加了死亡率和住院时间。最好 结果需要个性化的治疗，从病人到病人，占 过量阿片类药物给药和不受控制的疼痛的有害影响。然而，在这方面， 目前关于疼痛管理的临床指南没有提供关于如何最好地 调整疗程。此外，疼痛评估依赖于患者自我报告， 当病人服用镇静剂或精神状态改变时， 该项目旨在定量地了解管理后的功效的关系， 手术疼痛管理策略，并表征如何不同的实时生理 可以使用测量来评估疼痛和阿片样物质需求。这将是完成 通过分析来自10万多名外科医生的电子健康记录数据的大型数据集， 在马萨诸塞州总医院进行的手术，以及术中 脑电图（EEG）记录的一个子集的几千个这些程序。 该项目的目的1是模拟阿片类药物的镇痛反应，识别过量as的病例 以及阿片类药物使用不足。我们建议使用神经网络来模拟疼痛随时间的演变， 常微分方程模型，并使用学习动态计算最佳治疗 施政纲要而本项目的目标2是确定可以通过使用非 阿片类药物治疗方式。这也可以通过疼痛动力学的建模来实现， 或通过对接受不同治疗的患者队列的结果进行统计分析 方式。本项目的目的3是计算术后疼痛状态的术中相关性 脑电图数据。信号处理方法以及深度学习将用于提取 EEG数据中与镇静、意识丧失和疼痛相关的特征。我们还将研究 术中干预与术后结局之间的关系。 该项目有可能减少阿片类药物的过量使用并改善疼痛管理， 改善手术后临床结果并降低阿片类药物滥用障碍的发生率。 我们的研究结果还将提供客观评估疼痛和治疗要求的能力。

PROJECT SUMMARY AND ABSTRACT Chronic opioid usage after surgery is a major contributor to the opioid epidemic, which poses a major crisis in public health. 51 million patients undergo surgery each year in the United States. Between 9-13% of surgical patients continue chronic use of opioids, leading to opioid use disorder in 8-12% of cases of chronic use. However, under half of surgical patients report adequate postoperative pain relief, which hinders recovery, increasing mortality, and length of stay. Best outcomes require personalization of treatment from patient to patient, accounting for the detrimental effects of both excessive opioid administration and uncontrolled pain. However, current clinical guidelines on pain management do not provide clear guidance on how best to adjust courses of treatment. Moreover, assessment of pain is reliant upon patient self-report, and is hindered when patients are sedated or have altered mental status. This project seeks to quantitatively understand the relationships which govern the efficacy of post- operative pain management strategies, and to characterize how different real-time physiological measures may be used to assess pain and opioid requirements. This will be accomplished through the analysis of a large dataset of electronic health record data from over 100,000 surgical procedures performed at Massachusetts General Hospital, as well as intraoperative electroencephalogram (EEG) recordings for a subset of several thousand of these procedures. Aim 1 of this project is to model analgesic response to opioids, identifying cases of excessive as well as inadequate opioid usage. We propose to model pain evolution over time using neural ordinary differential equation models, and to use learned dynamics to compute optimal treatment policies. Aim 2 of this project is to identify cases where can be improved through usage of non- opioid treatment modalities. This can also be accomplished through modeling of pain dynamics, or through statistical analyses of the outcomes of cohorts of patients receiving different treatment modalities. Aim 3 of this project is to compute intraoperative correlates of postoperative pain state from EEG data. Signal processing methods as well as deep learning will be used to extract features from EEG data related to sedation, loss of consciousness, and pain. We will also study the relationship between intraoperative interventions and postoperative outcomes. This project has the potential to reduce excess opioid usage and improve pain management, improving post-surgical clinical outcomes and reducing the incidence of opioid abuse disorder. Our results will also provide the ability to objectively assess pain and treatment requirements.