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：
9313544
负责人：
MEREDITH C. B. ADAMS
金额：
$ 10.01万
依托单位：
MEDICAL COLLEGE OF WISCONSIN
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 2017-09-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9313544
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 Electronic Health Record Feedback Foundations Funding Future Genetic 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 Pharmaceutical Preparations Phenotype Population Predictive Analytics Prevention Provider Records Regimen Research Scientist Standardization 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 clinically relevant cohort computerized data processing cost disability disease phenotype effective therapy electronic data experience functional status health care service utilization health data high dimensionality improved indexing individual patient novel opioid use outcome prediction 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就诊的主要原因，影响了 100 多人每年腰痛 (LBP) 造成的损失超过 6000 亿美元，占其中的 28%。阿片类药物是医疗保健问题，也是导致残疾的主要原因。是美国最常用的处方药类别，其中大部分处方尽管阿片类药物治疗在腰痛中得到广泛应用，但患者的表型阿片类药物治疗带来的疼痛缓解效果尚未确定，因此提供者尚不清楚鉴于这种令人震惊的疾病负担和医疗保健利用，有关 LBP 的临床信息广泛存在于电子健康记录 (EHR) 中，提供了有价值的信息然而，这些信息目前除了个人患者的经历之外没有什么意义。因为来自 EHR 的大多数与疼痛相关的数据都嵌入在自由文本中。为更好地理解 LBP 提供了重要的桥梁因此，该提案的中心假设是将临床经验转化为离散且可分析的数据，特别是模拟阿片类药物反应 LBP 患者的表型将确定临床相关的表型治疗反应以测试这一点。假设，这个指导性职业发展项目将适应和应用自然语言处理 (NLP)、数据标准化、挖掘和分析工具，专门为阿片类药物反应表型建模腰痛患者的疼痛强度、功能状态以及疼痛对活动的干扰。通过综合目标，该提案将：1）支持 LBP 和阿片类药物注释语料库的注释，以及与疼痛强度、功能状态和疼痛干扰相关的临床概念的映射 2) 使用 NLP 来识别和关联 LBP 患者的相关阿片类药物反应表型在 EHR 中；3) 描述与阿片类药物剂量递增相关的 LBP 表型。统计建模来提取和转换高维临床数据，当与 PI 的领域知识创造了一个独特的机会来了解 LBP 管理、结果和最终，该基础可用于预测临床结果和反应。我们的长期目标是超越识别疾病表型的范围。创建一个系统来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，我们计划确定需要的潜在人群这项指导性职业发展补助金 (K08) 将支持临床研究。专家以系统的方法调整工具和培训，以实现程序化发展的研究对 NIH 疼痛研究议程做出了令人难以置信的响应，并且可以过渡到独立的研究 R01级资助。