Identifying Optimal Pain Management for Elders

摘要 手术是常见的，适当的术后疼痛管理是关键，因为糟糕的管理可能会损害 并导致不良事件，包括长期使用阿片类药物和过渡到慢性疼痛。其他内容 老年手术患者的具体风险包括精神错乱和跌倒。目前的偏见指南而不是证据指南 老年人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建立阿片类药物处方的规章制度。这些政策是一刀切的。 忽视关于需要根据以下情况开出不同的阿片类药物处方的新证据的条例 临床和患者因素，特别是老年人。目前，缺乏工具来识别高危老年人 不良的疼痛后果。需要这样的工具来向利益相关者提供关于疼痛管理的关键证据 并将研究领域从“普通”老年患者的疼痛治疗转向个人的疼痛治疗。在……里面 在这笔赠款中，我们提出了一种创新的方法，以推进对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后果开发可扩展的、开源的风险分层工具。我们 将分三个目标完成这项工作。首先，我们将开发临床表型来识别和提取关键 使用EHR评估术后疼痛所需的辨别特征。下一步，我们将发展疼痛风险 使用机器学习的分层模型，包括基于表型的深度学习、方法和工具 在目标1中开发。最后，我们将在退伍军人管理局外部验证我们的模型，并通过 开源库和公共网站。该项目将提供经过验证的风险分层工具，这些工具源自 现实世界的证据，以确定手术后不良疼痛后果的高风险老年患者，这可能 有可能减少在社区中流通的处方阿片类药物--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.