Model-based optimization of pain management in surgical patients

项目摘要和摘要 手术后长期使用阿片类药物是阿片类药物流行的主要原因，阿片类药物流行造成 公共卫生方面的重大危机。在美国，每年有5100万名患者接受手术。 9%-13%的外科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%的慢性使用案例中。然而，不到一半的外科患者报告说 术后疼痛减轻，阻碍康复，增加死亡率和住院时间。最好的 结果需要患者对患者进行个性化治疗，这是 阿片类药物过量使用和疼痛失控的有害影响。然而， 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的自我报告，以及 当患者服用镇静剂或改变精神状态时，会受到阻碍。 这个项目试图定量地了解支配后继治疗效果的关系。 手术疼痛管理策略，以及如何表征不同的实时生理 可用来评估疼痛和阿片类药物需求的措施。这将会实现的 通过分析来自100,000多个外科手术的电子健康记录数据的大型数据集 在马萨诸塞州综合医院进行的手术以及术中 数千个程序的子集的脑电(EEG)记录。 该项目的目标1是模拟阿片类药物的止痛反应，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，并使用学习的动力学来计算最优处理 政策。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。这也可以通过对疼痛动力学进行建模来实现， 或通过对接受不同治疗的患者队列的结果进行统计分析 医疗模式。该项目的目标3是计算术中术后疼痛状态的相关性。 从脑电数据中。将使用信号处理方法以及深度学习来提取 脑电数据中与镇静、意识丧失和疼痛有关的特征。我们还将研究 术中干预措施与术后预后的关系。 这个项目有可能减少阿片类药物的过度使用，改善疼痛管理， 改善手术后临床结果，减少阿片类药物滥用障碍的发生率。 我们的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.