Advancing Knowledge Discovery for Postoperative Pain Management

推进术后疼痛管理的知识发现

• 批准号: 10410453
• 负责人: Tina Hernandez-Boussard
• 金额: $ 63.48万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-17 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10410453
• 关键词: Absence of pain sensation; Adjuvant; Adverse event; Algorithms; Anesthesia procedures; Caring; Code; Communities; Community Hospitals; Data; Data Science; Deposition; Depressed mood; Economics; Electronic Health Record; Ensure; General Population; Generations; Geography; Goals; Grant; Health; Healthcare; Impairment; Individual; Informatics; Infrastructure; Institute of Medicine (U.S.); Knowledge Discovery; Lead; Literature; Machine Learning; Manuals; Medical; Medical center; Methods; Modeling; Morbidity - disease rate; Names; National Institute of Drug Abuse; Natural Language Processing; Obesity; Operative Surgical Procedures; Opioid; Outcome; Pain; Pain Research; Pain management; Pathway interactions; Patients; Phenotype; Population; Population Heterogeneity; Postoperative Pain; Quality of life; Records; Recovery; Reporting; Research; Risk; Risk Estimate; Risk Factors; Sampling; Site; Standardization; Structure; Techniques; Terminology; Testing; Time; United States National Institutes of Health; Validation; Variant; Veterans; Veterans Health Administration; Vulnerable Populations; Work; adverse outcome; analytical tool; base; biomedical informatics; clinical phenotype; cohort; data format; data modeling; data standards; deep learning; depressed patient; diabetic; effective therapy; electronic data; fundamental research; health care settings; health data; high risk; improved; informatics tool; innovation; learning strategy; machine learning method; model development; multimodality; novel; open source; open source library; open source tool; opiate tolerance; opioid epidemic; opioid use; pain chronification; pain outcome; pain score; population based; prescription opioid; random forest; risk sharing; risk stratification; social; structured data; support vector machine; symposium; tool; tool development; unstructured data; web site

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.

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。文学 提示在疼痛管理及其对生活质量的影响方面存在显著差异，尤其是 在弱势人群(如抑郁症、肥胖症和糖尿病患者)中。然而，缺乏风险分层。 工具，以确定这些不同的疼痛结果的高风险个人。尽管疼痛评分通常是 收集在电子健康记录(EHR)中的共享算法用于改善护理是有限的。 为了提高现有大量电子数据的高效率和有效利用，一种常见的 数据模型(CDM)是必需的：表示EHR数据的标准化结构、术语和规则。vbl.使用 用于术后疼痛研究的CMD将促进跨多个人群的及时证据生成 和环境，这可以向利益攸关方提供关键证据，并使该领域远离疼痛治疗 对于普通病人来说，对个人来说，疼痛治疗是不可能的。在这笔赠款中，我们提出了一种创新的方法来 推进跨人群术后疼痛的系统分析。我们的方法将利用 观察性医疗结果伙伴关系(OMOP)CDM开发使用标准化数据格式的工具 和命名约定；OMOP在全球拥有140多个协作站点。我们将进一步利用分析工具 由观察卫生数据科学和信息学(OHDSI)开发，以促进传播 在整个研究界。我们的方法将为以下方面开发可扩展的开源风险分层工具 不同人群中的不良疼痛后果。我们将分三个目标完成这项工作。首先，我们将 制定临床表型，以确定和提取评估术后所需的关键区分特征 使用EHR止痛。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 基于目标1中开发的表型的学习、方法和工具。最后，我们将验证我们的模型 在退伍军人事务部，并通过开放源码图书馆和公共网站传播我们的工作。这个项目 将提供来自真实世界证据的经过验证的风险分层工具，以识别高危患者 手术后的不良疼痛结果，这可能会减少处方阿片类药物在 社区--遏制阿片类药物流行的关键。