Identifying Optimal Pain Management for Elders

摘要 手术是常见的，适当的术后疼痛管理至关重要，因为管理不善可能会损害 恢复并导致不良事件，包括长期使用阿片类药物和转变为慢性疼痛。额外 老年手术患者的特殊风险包括谵妄和福尔斯。目前的偏见而不是证据指南 老年围手术期疼痛管理的复杂问题。鉴于美国阿片类药物流行的严重性， 政策制定者正在迅速制定阿片类药物处方的规则和条例。这些政策是全面的 法规忽视了新出现的证据，这些证据表明需要根据以下因素制定不同的阿片类药物处方： 临床和患者因素，尤其是老年人。目前，缺乏工具来识别高风险的老年人， 不良疼痛结局。需要这些工具为利益相关者提供疼痛管理的关键证据 并将领域从针对“普通”老年患者的疼痛治疗转移到针对个人的疼痛治疗。在 在这项资助下，我们提出了一种创新的方法来推进老年人术后疼痛的系统分析。 我们的方法将为老年人的不良疼痛结局开发可扩展的开源风险分层工具。我们 将在三个目标中完成这项工作。首先，我们将开发临床表型，以识别和提取关键 使用EHR评估术后疼痛所需的鉴别特征。接下来，我们将开发疼痛风险 使用机器学习的分层模型，包括深度学习，基于表型的方法和工具 在Aim 1中开发。最后，我们将在VA外部验证我们的模型，并通过 开放源代码库和公共网站。该项目将提供经验证的风险分层工具， 真实的世界证据，以确定老年患者术后不良疼痛结局的高风险， 潜在地减少处方阿片类药物在社区中的流通-这是遏制阿片类药物流行的关键。

ABSTRACT Surgery is common and appropriate postoperative pain management is critical as poor management can impair recovery and lead to adverse events, including prolonged opioid use and transition to chronic pain. Additional specific risks in elder surgical patients include delirium and falls. Currently prejudice rather than evidence guides the complex problem of elder perioperative pain management. Given the gravity of the US opioid epidemic, policy makers are quickly establishing rules and regulations for opioid prescribing. These policies are blanket regulations that neglect emerging evidence regarding the need for differential opioid prescriptions based on clinical and patient factors, particularly in elders. Currently, there lacks tools to identify elders at high risk for adverse pain outcomes. Such tools are needed to provide critical evidence on pain management to stakeholders and move the field away from pain treatment for the ‘average’ elder patient to pain treatment for an individual. In this grant, we propose an innovative approach to advance the systematic analysis of postoperative pain in elders. Our approach will develop scalable, open source risk stratification tools for adverse pain outcomes in elders. We will accomplish this work in three aims. First, we will develop clinical phenotypes to identify and extract key discriminating features necessary to assess postoperative pain using EHRs. Next, we will develop pain risk stratification models using machine learning, including deep learning, methods and tools based on phenotypes developed in Aim 1. Finally, we will validate our models externally at the VA and disseminate our work through open source libraries and public websites. This project will deliver validated risk-stratification tools derived from real world evidence to identify elder patients at high risk for adverse pain outcomes following surgery, which can potentially reduce prescribed opioids circulating in the community– a key to curbing the opioid epidemic.