Advancing Knowledge Discovery for Postoperative Pain Management

推进术后疼痛管理的知识发现

• 批准号: 10646490
• 负责人: Tina Hernandez-Boussard
• 金额: $ 62.05万
• 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 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10646490
• 关键词: Absence of pain sensation; Adjuvant; Adverse event; Algorithms; Anesthesia procedures; Caring; Code; Collaborations; Communities; Community Hospitals; Data; Data Science; Deposition; Depressed mood; Disparate; Disparity; 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; biomedical informatics; clinical phenotype; cohort; data format; data modeling; data standards; deep learning; depressed patient; diabetic; effective therapy; electronic data; fundamental research; health assessment; health care settings; health data; high risk; improved; informatics tool; innovation; 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.

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。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（例如抑郁症患者、肥胖症患者和糖尿病患者）。但缺乏风险分层 识别这些不同疼痛结果高风险人群的工具。尽管疼痛评分通常是 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。使用 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 为“普通”患者提供针对个人的疼痛治疗。在这笔赠款中，我们提出了一种创新方法 推进对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 在全球拥有超过 140 个合作网站。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 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果。我们将通过三个目标来完成这项工作。首先，我们将 开发临床表型来识别和提取评估术后所需的关键区分特征 使用 EHR 来缓解疼痛。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 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。这个项目 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。

项目成果

Changes in postoperative opioid prescribing across three diverse healthcare systems, 2010-2020.

• DOI: 10.3389/fdgth.2022.995497
• 发表时间: 2022
• 期刊: FRONTIERS IN DIGITAL HEALTH
• 作者: Coquet, Jean;Zammit, Alban;El Hajouji, Oualid;Humphreys, Keith;Asch, Steven M.;Osborne, Thomas F.;Curtin, Catherine M.;Hernandez-Boussard, Tina
• 通讯作者: Hernandez-Boussard, Tina

Opioid2MME: Standardizing opioid prescriptions to morphine milligram equivalents from electronic health records.

• DOI: 10.1016/j.ijmedinf.2022.104739
• 发表时间: 2022-03-16
• 期刊: INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
• 影响因子: 4.9
• 作者: Lossio-Ventura, Juan Antonio;Song, Wenyu;Sainlaire, Michael;Dykes, Patricia C.;Hernandez-Boussard, Tina
• 通讯作者: Hernandez-Boussard, Tina

Promoting Equity In Clinical Decision Making: Dismantling Race-Based Medicine.

促进临床决策的公平：废除基于种族的医学。

• DOI: 10.1377/hlthaff.2023.00545
• 发表时间: 2023
• 期刊: Health affairs (Project Hope)
• 作者: Hernandez-Boussard,Tina;Siddique,ShaziaMehmood;Bierman,ArleneS;Hightower,Maia;Burstin,Helen
• 通讯作者: Burstin,Helen

Postoperative opioid prescribing patients with diabetes: Opportunities for personalized pain management.

• DOI: 10.1371/journal.pone.0287697
• 发表时间: 2023
• 期刊: PloS one
• 影响因子: 3.7

Prescription quantity and duration predict progression from acute to chronic opioid use in opioid-naïve Medicaid patients.

处方数量和持续时间可以预测从未使用过阿片类药物的医疗补助患者从急性阿片类药物使用到慢性阿片类药物使用的进展。

• DOI: 10.1371/journal.pdig.0000075
• 发表时间: 2022
• 期刊: PLOS digital health
• 作者: Johnson,DrakeG;Ho,VyThuy;Hah,JenniferM;Humphreys,Keith;Carroll,Ian;Curtin,Catherine;Asch,StevenM;Hernandez-Boussard,Tina
• 通讯作者: Hernandez-Boussard,Tina