Advancing Knowledge Discovery for Postoperative Pain Management

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。文学 表明在疼痛管理及其对生活质量的影响方面存在显着差异，特别是 弱势群体（例如抑郁症、肥胖症和糖尿病患者）。然而，缺乏风险分层 工具来识别这些不同疼痛结果的高风险个体。虽然疼痛评分通常是 由于电子健康记录（EHR）中收集的数据不多，因此利用它们来改善护理的共享算法是有限的。 为促进现有大量电子数据的有效使用， 数据模型（CDM）是必要的：标准化的结构，术语和规则来表示EHR数据。使用 术后疼痛研究的CMD将有助于在多个人群中及时生成证据 和设置，这可以为利益相关者提供关键证据，并将该领域从疼痛治疗中转移出来 从“普通”病人到个人的疼痛治疗。在这项拨款中，我们提出了一种创新的方法， 推进人群间术后疼痛的系统分析。我们的方法将利用 观察性医学成果伙伴关系（OMOP）CDM，开发使用标准化数据格式的工具 和命名约定; OMOP在全球有超过140个合作站点。我们将进一步利用分析工具 由观察健康数据科学和信息学（OHDSI）在此CDM上开发，以促进传播 在整个研究界。我们的方法将开发可扩展的开源风险分层工具， 不同人群的不良疼痛结局。我们将从三个方面来完成这项工作。一是 开发临床表型，以识别和提取评估术后所需的关键鉴别特征 使用EHR的疼痛。接下来，我们将使用机器学习开发疼痛风险分层模型，包括深度 学习，方法和工具的基础上的表型开发的目标1。最后，我们将验证我们的模型 在VA的外部，并通过开源图书馆和公共网站传播我们的工作。这个项目 将提供来自真实的世界证据的经验证的风险分层工具，以识别高风险患者， 手术后的不良疼痛结局，这可能会减少处方阿片类药物在患者体内的循环。 社区-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. Literature suggests significant disparities exist with regard to pain management and its quality-of-life impacts, particularly among vulnerable populations (e.g. depressed, obese and diabetics). However, there lacks risk stratification tools to identify individuals at high risk for these disparate pain outcomes. Although pain scores are routinely collected in electronic health records (EHRs), shared algorithms to utilize them for care improvement are limited. To advance the efficient and effective use of the abundant amount of electronic data now available, a common data model (CDM) is necessary: standardized structures, terminologies, and rules to represent EHR data. Using a CMD for postoperative pain research would facilitate timely evidence generation across multiple populations and settings, which can provide critical evidence to stakeholders and move the field away from pain treatment for the ‘average’ patient to pain treatment for an individual. In this grant, we propose an innovative approach to advance the systematic analysis of postoperative pain across populations. Our approach will leverage the Observational Medical Outcomes Partnership (OMOP) CDM to develop tools that use standardize data formats and naming conventions; OMOP has over 140 collaborating sites gloablly. We will further utilize analytical tools developed by Observational Health Data Sciences and Informatics (OHDSI) on this CDM to facilitate disseminate across the research community. Our approach will develop scalable, open source risk stratification tools for adverse pain outcomes across diverse populations. 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 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.