Model-based optimization of pain management in surgical patients

基于模型的手术患者疼痛管理优化

• 批准号: 10536063
• 负责人: Ran Liu
• 金额: $ 6.68万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10536063
• 关键词: Absence of pain sensation; Accounting; Acute; Acute Pain; Adverse effects; American; Analgesics; Biological Markers; Chronic; Clinical; Data; Differential Equation; Disease; Electroencephalogram; Electronic Health Record; Equilibrium; Evolution; Frequencies; Future; General Hospitals; Goals; Group Identifications; Guidelines; Healthcare; Incidence; Individual; Intervention; Length of Stay; Machine Learning; Massachusetts; Measures; Methods; Modality; Modeling; Operative Surgical Procedures; Opioid; Outcome; Pain; Pain Measurement; Pain management; Patient Self-Report; Patient-Focused Outcomes; Patients; Phase; Physiological; Policies; Postoperative Pain; Postoperative Period; Procedures; Public Health; Recommendation; Recovery; Reporting; Research; Risk; Sedation procedure; Societies; Statistical Data Interpretation; Time; Unconscious State; United States; analytical method; base; clinical decision support; cohort; deep learning; dosage; experience; improved; large datasets; mental state; mortality; non-opioid analgesic; opioid abuse; opioid epidemic; opioid sparing; opioid use; opioid use disorder; optimal treatments; pain model; pain relief; pain score; personalized medicine; predicting response; relating to nervous system; response; signal processing; time use; treatment response

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.

项目概要和摘要 手术后长期使用阿片类药物是阿片类药物流行的一个主要原因，这构成了 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 8-12% 的长期使用病例。然而，不到一半的手术患者表示足够 术后疼痛缓解，这会阻碍康复，增加死亡率和住院时间。最好的 结果需要针对患者进行个性化治疗，考虑到 过量使用阿片类药物和不受控制的疼痛都会产生有害影响。然而， 目前的疼痛管理临床指南并未就如何最好地提供明确的指导 调整疗程。此外，疼痛的评估依赖于患者的自我报告，并且 当患者服用镇静剂或精神状态改变时，该功能就会受到阻碍。 该项目旨在定量地了解控制后效果的关系。 手术疼痛管理策略，并描述不同的实时生理情况 措施可用于评估疼痛和阿片类药物的需求。这将实现 通过对来自超过 100,000 名手术患者的电子健康记录数据的大型数据集进行分析 在马萨诸塞州总医院进行的手术以及术中 数千个此类程序的子集的脑电图 (EEG) 记录。 该项目的目标 1 是模拟阿片类药物的镇痛反应，识别过量的案例 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佳治疗 政策。该项目的目标 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。该项目的目标 3 是计算术后疼痛状态的术中相关性 来自脑电图数据。信号处理方法以及深度学习将用于提取 脑电图数据中与镇静、意识丧失和疼痛相关的特征。我们也会学习 术中干预与术后结果之间的关系。 该项目有潜力减少阿片类药物的过量使用并改善疼痛管理， 改善术后临床结果并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。