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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