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：
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
项目类别：
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

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.

抽象的手术是常见的，适当的后疼痛管理至关重要，因为管理不善会损害恢复并导致不良事件，包括长时间的阿片类药物使用和过渡到慢性疼痛。文学建议在疼痛管理及其生活质量的影响方面存在重大分布，特别是在弱势群体中（例如沮丧，肥胖和糖尿病患者）。但是，存在风险分层虽然疼痛评分通常是在电子健康记录（EHRS）中收集的，共享的算法将其用于护理改进是有限的。为了提高现已可用的大量电子数据的有效和有效利用，这是一个常见数据模型（CDM）是必要的：标准化结构，术语和代表EHR数据的规则。使用用于术后疼痛研究的CMD将有助于及时在多个人群中产生证据和设置，可以为利益相关者提供关键证据，并使领域远离痛苦治疗对于“普通”患者进行个人的疼痛治疗。在这笔赠款中，我们提出了一种创新的方法推进对种群术后疼痛的系统分析。我们的方法将利用观察医学结果伙伴关系（OMOP）CDM开发使用标准化数据格式的工具和命名惯例； OMOP在GloAbl上拥有140多个合作网站。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）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潜水员种群的不良疼痛结果。我们将以三个目标完成这项工作。首先，我们会的开发临床表型，以识别和提取评估术后所需的关键区分特征使用EHR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弗吉尼亚州外部，通过开源库和公共网站传播我们的工作。这个项目将提供从现实世界证据得出的经过验证的风险分层工具，以识别具有高风险的患者手术后的不良疼痛结局，这可能会减少在循环中循环的处方阿片类药物社区 - 遏制Ooid流行病的关键。

项目成果

期刊论文数量（12）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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
影响因子：
0
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

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
作者：
通讯作者：

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)
影响因子：
0
作者：
Hernandez-Boussard,Tina;Siddique,ShaziaMehmood;Bierman,ArleneS;Hightower,Maia;Burstin,Helen
通讯作者：
Burstin,Helen

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
影响因子：
0
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