Identifying Opioid Use Disorder in the Emergency Department: Multi-System Electronic Health Record-Based Computable Phenotype Derivation and Validation Study

Identifying Opioid Use Disorder in the Emergency Department: Multi-System Electronic Health Record-Based Computable Phenotype Derivation and Validation Study

Deploying accurate computable phenotypes in pragmatic trials requires a trade-off between precise and clinically sensical variable selection. In particular, evaluating the medical encounter to assess a pattern leading to clinically significant impairment or distress indicative of disease is a diffic...

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Bibliographic Details
Published in:JMIR medical informatics Vol. 7; no. 4; p. e15794
Main Authors: Chartash, David, Paek, Hyung, Dziura, James D, Ross, Bill K, Nogee, Daniel P, Boccio, Eric, Hines, Cory, Schott, Aaron M, Jeffery, Molly M, Patel, Mehul D, Platts-Mills, Timothy F, Ahmed, Osama, Brandt, Cynthia, Couturier, Katherine, Melnick, Edward
Format: Journal Article
Language:English
Published: Canada JMIR Publications 31-10-2019
Subjects:
Alcohol
Algorithms
Classification
Codes
Data dictionaries
Drug withdrawal
Electronic health records
Emergency medical care
Genotype & phenotype
Heroin
Hospitals
Information technology
Intervention
Narcotics
Original Paper
Poisoning
Structured Query Language-SQL
Substance use disorder
Validation studies
United States > US
phenotype
algorithms
electronic health records
opioid-related disorders
emergency medicine
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Summary:Deploying accurate computable phenotypes in pragmatic trials requires a trade-off between precise and clinically sensical variable selection. In particular, evaluating the medical encounter to assess a pattern leading to clinically significant impairment or distress indicative of disease is a difficult modeling challenge for the emergency department. This study aimed to derive and validate an electronic health record-based computable phenotype to identify emergency department patients with opioid use disorder using physician chart review as a reference standard. A two-algorithm computable phenotype was developed and evaluated using structured clinical data across 13 emergency departments in two large health care systems. Algorithm 1 combined clinician and billing codes. Algorithm 2 used chief complaint structured data suggestive of opioid use disorder. To evaluate the algorithms in both internal and external validation phases, two emergency medicine physicians, with a third acting as adjudicator, reviewed a pragmatic sample of 231 charts: 125 internal validation (75 positive and 50 negative), 106 external validation (56 positive and 50 negative). Cohen kappa, measuring agreement between reviewers, for the internal and external validation cohorts was 0.95 and 0.93, respectively. In the internal validation phase, Algorithm 1 had a positive predictive value (PPV) of 0.96 (95% CI 0.863-0.995) and a negative predictive value (NPV) of 0.98 (95% CI 0.893-0.999), and Algorithm 2 had a PPV of 0.8 (95% CI 0.593-0.932) and an NPV of 1.0 (one-sided 97.5% CI 0.863-1). In the external validation phase, the phenotype had a PPV of 0.95 (95% CI 0.851-0.989) and an NPV of 0.92 (95% CI 0.807-0.978). This phenotype detected emergency department patients with opioid use disorder with high predictive values and reliability. Its algorithms were transportable across health care systems and have potential value for both clinical and research purposes.
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ISSN:2291-9694
2291-9694
DOI:10.2196/15794