# Identifying opioid response phenotypes in low back pain electronic health data

> **NIH NIH K08** · WAKE FOREST UNIVERSITY HEALTH SCIENCES · 2020 · $184,373

## Abstract

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...

## Key facts

- **NIH application ID:** 9897632
- **Project number:** 5K08EB022631-05
- **Recipient organization:** WAKE FOREST UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** MEREDITH C. B. ADAMS
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $184,373
- **Award type:** 5
- **Project period:** 2017-04-01 → 2022-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9897632

## Citation

> US National Institutes of Health, RePORTER application 9897632, Identifying opioid response phenotypes in low back pain electronic health data (5K08EB022631-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9897632. Licensed CC0.

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