Assessment of Probable Opioid Use Disorder Using Electronic Health Record Documentation

Palumbo, Sarah A.; Adamson, Kayleigh M.; Krishnamurthy, Sarathbabu; Manoharan, Shivani; Beiler, Donielle; Seiwell, Anthony; Young, Colt; Metpally, Raghu; Crist, Richard C.; Doyle, Glenn A.; Ferraro, Thomas N.; Li, Mingyao; Berrettini, Wade H.; Robishaw, Janet D.; Troiani, Vanessa

doi:10.1001/jamanetworkopen.2020.15909

Cited by 50 publications

(52 citation statements)

References 41 publications

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“…We reviewed records using a keyword template developed from keywords in the Diagnostic and Statistical Manual of Mental Disorders, 5 th Ed. (DSM V) criteria for OUD [34], the Addiction Behaviors Checklist [35], and previous studies describing problematic opioid use detection in EHRs [15,19,20]. Periodic interim analyses assessed word performance, and we trimmed duplicate words (i.e.…”

Section: Methodsmentioning

confidence: 99%

“…Several existing methods have utilized EHR data for OUD prediction [14][15][16][17][18][19][20]. Some models identify OUD cohorts using ICD codes [16][17], but this approach likely underrepresents problematic opioid use [13].…”

Section: Introductionmentioning

confidence: 99%

“…For example, one study used machine learning methods to produce a continuous measure of OUD risk in Medicare data purchased through the Centers for Medicare and Medicaid (CMS) [22]. These studies provide indispensable insight into the continuum of problematic opioid use, but come at the expense of poor reproducibility in most EHR systems [13,[20][21][22].…”

Section: Introductionmentioning

confidence: 99%

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Two Data-Driven Approaches to Identifying the Spectrum of Problematic Opioid Use: A Pilot Study within a Chronic Pain Cohort

Schirle¹,

Jeffery²,

Yaqoob

et al. 2021

Preprint

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Background: Although electronic health records (EHR) have significant potential for the study of opioid use disorders (OUD), detecting OUD in clinical data is challenging. Models using EHR data to predict OUD often rely on case/control classifications focused on extreme opioid use. There is a need to expand this work to characterize the spectrum of problematic opioid use. Methods: Using a large academic medical center database, we developed 2 data-driven methods of OUD detection: (1) a Comorbidity Score developed from a Phenome-Wide Association Study of phenotypes associated with OUD and (2) a Text-based Score using natural language processing to identify OUD-related concepts in clinical notes. We evaluated the performance of both scores against a manual review with correlation coefficients, Wilcoxon rank sum tests, and area-under the receiver operating characteristic curves. Records with the highest Comorbidity and Text-based scores were re-evaluated by manual review to explore discrepancies. Results: Both the Comorbidity and Text-based OUD risk scores were significantly elevated in the patients judged as High Evidence for OUD in the manual review compared to those with No Evidence (p = 1.3E-5 and 1.3E-6, respectively). The risk scores were positively correlated with each other (rho = 0.52, p < 0.001). AUCs for the Comorbidity and Text-based scores were high (0.79 and 0.76, respectively). Follow-up manual review of discrepant findings revealed strengths of data-driven methods over manual review, and opportunities for improvement in risk assessment. Conclusion: Risk scores comprising comorbidities and text offer differing but synergistic insights into characterizing problematic opioid use. This pilot project establishes a foundation for more robust work in the future.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Two Data-Driven Approaches to Identifying the Spectrum of Problematic Opioid Use: A Pilot Study within a Chronic Pain Cohort

Schirle¹,

Jeffery²,

Yaqoob

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…As part of the consent process, patients give permission to access their entire health record and give permission to link non-health record data (such as insurance claims, external prescription databases, etc.). Following enrollment, the study plan comprises several elements, including estimating an OUD risk score based on chart review criteria [ 11 ], extracting brain imaging using the clinical imaging database pipeline, genomic analyses, and assessment of phenotypic stability based on EHR review. More detailed analytic plans are described in the Data Analysis section, below.…”

Section: Methodsmentioning

confidence: 99%

“…The same study reports that depression, anxiety, illicit drug use, post-traumatic stress disorder, alcohol dependence, being under 65 years old, and patient-reported assessments of poor health are all associated with increased risk for OUD. In subsequent analyses of the Geisinger population, we find that patients treated with opioids for chronic non-progressive pain, who are enrolled in a contract-based medication management program, are much more likely to have characteristics of OUD determined via chart review, as well as comorbid conditions, such as depression and anxiety [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Genetics and prescription opioid use (GaPO): study design for consenting a cohort from an existing biobank to identify clinical and genetic factors influencing prescription opioid use and abuse

et al. 2021

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Background Prescription opioids (POs) are commonly used to treat moderate to severe chronic pain in the health system setting. Although they improve quality of life for many patients, more work is needed to identify both the clinical and genetic factors that put certain individuals at high risk for developing opioid use disorder (OUD) following use of POs for pain relief. With a greater understanding of important risk factors, physicians will be better able to identify patients at highest risk for developing OUD for whom non-opioid alternative therapies and treatments should be considered. Methods We are conducting a prospective observational study that aims to identify the clinical and genetic factors most stongly associated with OUD. The study design leverages an existing biobank that includes whole exome sequencing and array genotyping. The biobank is maintained within an integrated health system, allowing for the large-scale capture and integration of genetic and non-genetic data. Participants are enrolled into the health system biobank via informed consent and then into a second study that focuses on opioid medication use. Data capture includes validated self-report surveys measuring addiction severity, depression, anxiety, and nicotine use, as well as additional clinical, prescription, and brain imaging data extracted from electronic health records. Discussion We will harness this multimodal data capture to establish meaningful patient phenotypes in order to understand the genetic and non-genetic contributions to OUD.

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Evaluation of an emergency department‐based approach to reduce subsequent opioid overdoses

Reed,

Papp,

et al. 2024

JACEP Open

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ObjectiveThe purpose of this study was to determine the association of a multi‐pronged treatment program in emergency department (ED) patients with an acute presentation of opioid use disorder (OUD) on the rate of subsequent opioid overdose (OD). This approach included ED‐initiated take‐home naloxone, prescription buprenorphine, and an ED‐based peer support and recovery program.MethodsThis was a retrospective observational analysis of adult patients presenting to the ED at a large urban hospital system from November 1, 2017 to March 17, 2023. Patients with an ED discharge diagnosis of OD or OUD were included. Outcomes determined were subsequent 90‐day OD and 180‐day OD death. Post hoc analyses were performed to identify intervention utilization throughout the study period including the COVID‐19 pandemic as well as ED characteristics associated with subsequent OD and OD death. Statistical comparisons were made using logistic regression and chi‐squared test.ResultsA total of 2634 patients presented to the ED with an opioid OD or diagnosis of OUD. Subsequent 90‐day OD decreased significantly over time (11.5%–2.3%, odds ratio [OR] 0.85, confidence interval [CI] 0.82–0.89). No single intervention was independently associated with 90‐day OD or 180‐day OD death. Resource utilization was stable during the COVID‐19 pandemic and increased afterward. A higher buprenorphine fill‐rate among all patients and the Back race subgroup was associated with a decrease in 90‐day OD.ConclusionsSubsequent OD and OD death decreased over time after implementation of a multi‐pronged treatment program to ED patients with OUD. No single intervention was associated with a decrease of subsequent OD or OD death.

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Assessment of Probable Opioid Use Disorder Using Electronic Health Record Documentation

Cited by 50 publications

References 41 publications

Two Data-Driven Approaches to Identifying the Spectrum of Problematic Opioid Use: A Pilot Study within a Chronic Pain Cohort

Two Data-Driven Approaches to Identifying the Spectrum of Problematic Opioid Use: A Pilot Study within a Chronic Pain Cohort

Genetics and prescription opioid use (GaPO): study design for consenting a cohort from an existing biobank to identify clinical and genetic factors influencing prescription opioid use and abuse

Evaluation of an emergency department‐based approach to reduce subsequent opioid overdoses

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