Identifying opioid response phenotypes in low back pain electronic health data

识别腰痛电子健康数据中的阿片类药物反应表型

基本信息

批准号：
9897632
负责人：
MEREDITH C. B. ADAMS
金额：
$ 18.44万
依托单位：
WAKE FOREST UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-04-01 至 2022-09-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9897632
关键词：
Accident and Emergency department Adult Advisory Committees American Anatomy Biomedical Research Clinical Clinical Data Clinical Trials Communities Data Data Analyses Data Set Data Sources Development Dimensions Documentation Dose Drug Prescriptions Electronic Health Record Feedback Foundations Funding Future Genetic Genomic medicine Genomics Goals Gold Grant Growth Health Health Personnel Healthcare Individual Knowledge Lead Left Low Back Pain Maps Medical Medicine Mentors Methods Mining Modeling Natural Language Processing Ontology Opioid Outcome Outpatients Output Pain Pain Research Pain intensity Pain interference Pain management Pathway interactions Patient Care Patient-Focused Outcomes Patients Pattern Phenotype Population Predictive Analytics Prevention Provider Records Regimen Research Scientist Statistical Models Structure System Testing Text Therapeutic Intervention Training Translating Treatment Efficacy United States United States National Institutes of Health Validation Visit Vocabulary base burden of illness career development clinical care clinical pain clinically relevant cohort computerized data processing cost data standards disability disease phenotype effective therapy electronic data experience functional status health care service utilization health data high dimensionality improved indexing individual patient novel opioid therapy opioid use outcome prediction pain patient pain relief patient stratification personalized medicine predict clinical outcome predictive modeling response search engine symposium tool treatment response

项目摘要

Pain is the leading reason for adult outpatient and emergency department medical visits, impacting over 100 million Americans at a cost of over $600 billion dollars annually. Low back pain (LBP) represents 28% of this health-care problem and is the leading cause of disability, both in the United States and worldwide. Opioids are the most commonly prescribed drug class in the United States, and the majority of these prescriptions are for LBP. Despite the broad application of opioid therapy in LBP, the phenotypes of individuals who experience pain relief from opioid treatment have not been identified, leaving providers without clear guidance for safe and effective therapy. Given this staggering burden of disease and health-care utilization, clinical information regarding LBP widely populates the electronic health record (EHR), providing a valuable data source. However, this information presently has little meaning beyond the individual patient experience because the majority of pain-related data from the EHR is embedded in free text. Using EHR data may provide the crucial bridge to a better understanding of LBP. Thus, the central hypothesis of this proposal is that translating clinical experiences into discrete and analyzable data, specifically modeling opioid response phenotypes for patients with LBP, will identify clinically relevant phenotypic treatment responses. To test this hypothesis, this mentored career development project will adapt and apply natural language processing (NLP), data standardization, mining, and analysis tools to specifically model opioid response phenotypes for patients with LBP to characterize pain intensity, functional status, and pain interference with activity. Through integrated aims, this proposal will, 1) support the annotation of LBP and opioid note corpus, and the mapping of clinical concepts related to pain intensity, functional status, and pain interference with activities; 2) use NLP to identify and relate relevant opioid response phenotypes in patients with LBP in the EHR; and 3) characterize LBP phenotypes associated with opioid dose escalation. Clinical NLP uses statistical modeling to extract and transform high dimensional clinical data, which, when developed with the PI’s domain knowledge, creates a unique opportunity to understand LBP management, outcomes, and therapeutic efficacy. Ultimately this foundation may be used to predict clinical outcomes and responses to therapeutic interventions. Our long-term goal is to move beyond identifying disease phenotype profiles to create a system to identify treatment response phenotypes. Stratifying patients based on pain intensity, functional status, pain interference and other factors, we plan to identify potential cohorts that warrant further study from a genetic focus. This mentored career development grant (K08) will support a clinical expert’s adaptation of tools and training in a systematic method to allow growth toward a programmatic line of research that is incredibly responsive to the NIH pain research agenda and can transition to independent R01-level funding.

疼痛是成人门诊和急诊科医疗就诊的主要原因，影响了100多个百万美国人每年以超过6000亿美元的价格成本。腰痛（LBP）代表其中的28％医疗保健问题，是美国和全球范围内残疾的主要原因。阿片类药物是美国最常见的药物类别，大多数这些处方用于LBP。尽管阿片类药物疗法在LBP中广泛应用，但尚未确定阿片类药物治疗的经验缓解疼痛安全有效治疗指南。鉴于这种疾病和医疗保健利用的惊人燃烧，有关LBP广泛填充电子健康记录（EHR）的临床信息，提供了宝贵数据源。但是，此信息目前几乎没有任何含义因为来自EHR的大多数与疼痛相关的数据都嵌入了自由文本中。使用EHR数据可能提供至关重要的桥梁，以更好地了解LBP。那，该提议的核心假设是将临床经验转化为离散和可分析的数据，特别是对阿片类药物反应进行建模 LBP患者的表型将确定临床相关的表型治疗反应。测试这个假设，这个修改的职业发展项目将适应并应用自然语言处理（NLP），数据标准化，采矿和分析工具，专门为阿片类药物反应表型建模 LBP患者以表征疼痛强度，功能状态和对活动的疼痛干扰。通过综合目的，该提案将：1）支持LBP和阿片类药物的注释，以及与疼痛强度，功能状态和疼痛干扰有关的临床概念的映射活动; 2）使用NLP识别LBP患者的和相关的相关阿片类药物反应表型在EHR中； 3）表征与阿片剂量升级相关的LBP表型。临床NLP使用统计建模以提取和转换高维临床数据，并在使用 Pi的领域知识，创造了一个独特的机会来了解LBP管理，成果和治疗效率。最终，该基础可用于预测临床结果和对治疗干预措施。我们的长期目标是超越识别疾病表型概况到创建一个系统来识别治疗反应表型。根据疼痛强度对患者进行分层功能状态，疼痛干扰和其他因素，我们计划确定有必要的潜在队列从遗传重点进行进一步研究。这项修订的职业发展赠款（K08）将支持临床专家以系统的方法适应工具和培训对NIH疼痛研究议程的反应非常敏感的研究，可以过渡到独立 R01级资金。

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Consensus practice guidelines on interventions for cervical spine (facet) joint pain from a multispecialty international working group.

DOI：
10.1136/rapm-2021-103031
发表时间：
2022-01
期刊：
Regional anesthesia and pain medicine
影响因子：
5.1
作者：
Hurley RW;Adams MCB;Barad M;Bhaskar A;Bhatia A;Chadwick A;Deer TR;Hah J;Hooten WM;Kissoon NR;Lee DW;Mccormick Z;Moon JY;Narouze S;Provenzano DA;Schneider BJ;van Eerd M;Van Zundert J;Wallace MS;Wilson SM;Zhao Z;Cohen SP
通讯作者：
Cohen SP

The world needs our science: broadening the research pipeline in anesthesiology.