Advancing Knowledge Discovery for Postoperative Pain Management

推进术后疼痛管理的知识发现

基本信息

批准号：
10165821
负责人：
Tina Hernandez-Boussard
金额：
$ 64.4万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
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/10165821
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 Libraries 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 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

项目摘要

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.

抽象的手术很常见，适当的术后疼痛管理至关重要，因为管理不善可能会损害患者的健康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。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，特别是然而，缺乏风险分层。尽管疼痛评分是常规的，但仍可以使用工具来识别这些不同疼痛结果的高风险人群。收集在电子健康记录（EHR）中的数据，利用它们来改善护理的共享算法是有限的。为了促进现有大量电子数据的高效和有效利用，一个共同的数据模型（CDM）是必要的：表示 EHR 数据的标准化结构、术语和规则。用于术后疼痛研究的 CMD 将有助于在多个人群中及时生成证据和环境，可以为利益相关者提供关键证据，并使该领域远离疼痛治疗在这项资助中，我们提出了一种创新方法来帮助“普通”患者接受个体疼痛治疗。我们的方法将利用对人群术后疼痛进展的系统分析。观察性医疗结果合作伙伴 (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，以促进传播我们的方法将为整个研究界开发可扩展的开源风险分层工具。我们将通过三个目标来完成这项工作。开发临床表型以识别和提取评估术后所需的关键区分特征接下来，我们将使用机器学习（包括深度学习）开发疼痛风险分层模型。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 VA 外部，并通过开源库和公共网站传播我们的工作。将提供源自现实世界证据的经过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果可能会减少处方阿片类药物在体内的循环社区——遏制阿片类药物流行的关键。