Development and validation of an electronic health records-based opioid use disorder algorithm by expert clinical adjudication among patients with prescribed opioids.

Abstract

Background

In the US, over 200 lives are lost from opioid overdoses each day. Accurate and prompt diagnosis of opioid use disorders (OUD) may help prevent overdose deaths. However, international classification of disease (ICD) codes for OUD are known to underestimate prevalence, and their specificity and sensitivity are unknown. We developed and validated algorithms to identify OUD in electronic health records (EHR) and examined the validity of OUD ICD codes.

Methods

Through four iterations, we developed EHR-based OUD identification algorithms among patients who were prescribed opioids from 2014 to 2017. The algorithms and OUD ICD codes were validated against 169 independent "gold standard" EHR chart reviews conducted by an expert adjudication panel across four healthcare systems. After using 2014-2020 EHR for validating iteration 1, the experts were advised to use 2014-2017 EHR thereafter.

Results

Of the 169 EHR charts, 81 (48%) were reviewed by more than one expert and exhibited 85% expert agreement. The experts identified 54 OUD cases. The experts endorsed all 11 OUD criteria from the Diagnostic and Statistical Manual of Mental Disorders-5, including craving (72%), tolerance (65%), withdrawal (56%), and recurrent use in physically hazardous conditions (50%). The OUD ICD codes had 10% sensitivity and 99% specificity, underscoring large underestimation. In comparison our algorithm identified OUD with 23% sensitivity and 98% specificity.

Conclusions and relevance

This is the first study to estimate the validity of OUD ICD codes and develop validated EHR-based OUD identification algorithms. This work will inform future research on early intervention and prevention of OUD.

Department

Description

Provenance

Citation

Published Version (Please cite this version)

10.1002/pds.5591

Publication Info

Ranapurwala, Shabbar I, Ishrat Z Alam, Brian W Pence, Timothy S Carey, Sean Christensen, Marshall Clark, Paul R Chelminski, Li-Tzy Wu, et al. (2023). Development and validation of an electronic health records-based opioid use disorder algorithm by expert clinical adjudication among patients with prescribed opioids. Pharmacoepidemiology and drug safety, 32(5). pp. 577–585. 10.1002/pds.5591 Retrieved from https://hdl.handle.net/10161/28950.

This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.

Scholars@Duke

Wu

Li-Tzy Wu

Professor in Psychiatry and Behavioral Sciences

Education/Training: Pre- and post-doctoral training in mental health service research, psychiatric epidemiology (NIMH T32), and addiction epidemiology (NIDA T32) from Johns Hopkins University School of Public Health (Maryland); Fellow of the NIH Summer Institute on the Design and Conduct of Randomized Clinical Trials.

Director: Duke Community Based Substance Use Disorder Research Program.

Research interests: COVID-19, Opioid misuse, Opioid overdose, Opioid use disorder, Opioid addiction prevention and treatment, Pain and addiction, Chronic diseases and substance use disorders, diabetes, pharmacy-based care models and services, medication treatment for opioid use disorder (MOUD), Drug overdose, Polysubstance use and disorders, cannabis, alcohol, tobacco, hallucinogens, stimulants, e-cigarette, SBIRT (substance use Screening, Brief Intervention, Referral to Treatment), EHR-based research and intervention, data science, psychometric analysis (IRT), epidemiology of addictions and comorbidity, behavioral health care integration, health services research (mental health disorders, substance use disorders, chronic diseases), nosology, research design, HIV risk behavior. 

FUNDED Research projects (Principal Investigator [PI], Site PI, or Sub-award PI): 
R03: Substance use/dependence (PI).
R21: Treatment use for alcohol use disorders (PI).
R21: Inhalant use & disorders (PI).
R01: MDMA/hallucinogen use/disorders (PI).
R01: Prescription pain reliever (opioids) misuse and use disorders (PI).
R01: Substance use disorders in adolescents (PI).
R21: CTN Substance use diagnoses & treatment (PI).
R33: CTN Substance use diagnoses & treatment (PI).
R01: Evolution of Psychopathology in the Population (ECA Duke site PI).
R01: Substance use disorders and treatment use among Asian Americans and Pacific Islanders (PI).
UG1: SBIRT in Primary Care (NIDA, PI).
UG1: TAPS Tool, Substance use screening tool validation in primary care (NIDA, PI).
UG1: NIDA CTN Mid-Southern Node (Clinical Trials Network, PI).
UG1: EHR Data Element Study (NIDA, PI).
UG1: Buprenorphine Physician-Pharmacist Collaboration in the Management of Patients With Opioid Use Disorder (NIDA, PI).
PCORI: INSPIRE-Integrated Health Services to Reduce Opioid Use While Managing Chronic Pain (Site PI).
CDC R01: Evaluation of state-mandated acute and post-surgical pain-specific CDC opioid prescribing (Site PI).
Pilot: Measuring Opioid Use Disorders in Secondary Electronic Health Records Data (Carolinas Collaborative Grant: Duke PI).
R21: Developing a prevention model of alcohol use disorder for Pacific Islander young adults (Subaward PI, Investigator).
UG1: Subthreshold Opioid Use Disorder Prevention Trial (NIH HEAL Initiative) (NIDA supplement, CTN-0101, Investigator).
NIDA: A Pilot Study to Permit Opioid Treatment Program Physicians to Prescribe Methadone through Community Pharmacies for their Stable Methadone Patients (NIDA/FRI: Study PI).
UG1: Integrating pharmacy-based prevention and treatment of opioid and other substance use disorders: A survey of pharmacists and stakeholder (NIH HEAL Initiative, NIDA, PI).
UG1: NorthStar Node of the Clinical Trials Network (NIDA, Site PI).
R34: Intervention Development and Pilot Study to Reduce Untreated Native Hawaiian and Pacific Islander Opioid Use Disorders (Subaward PI, Investigator).
UG1: Optimal Policies to Improve Methadone Maintenance Adherence Longterm (OPTIMMAL Study) (NIDA, Site PI).
R01: Increasing access to opioid use disorder treatment by opening pharmacy-based medication units of opioid treatment programs (NIDA, PI)

Greenblatt

Lawrence Howard Greenblatt

Professor of Medicine

Dr. Greenblatt focuses his professional efforts in 3 domains.  First, he provides care to a busy general internal medicine panel utilizing an approach that is both patient-centered and evidence-based.  Second, he is an active educator routinely providing clinical teaching to students and residents.   He routinely regularly provides faculty development in teaching and other skills for medical educators across many professions both in Durham and in the Academic Medicine Education Institute in Singapore.  Third, he has a community focus.  He serves as Medical Director for Northern Piedmont Community Care which provides practice support, care management, and population management primarily for Medicaid recipients.  He is currently working on developing systems and policy to improve opioid safety both for Duke Health System and the State of North Carolina.

Bowlby

Lynn Anne Bowlby

Adjunct Associate Professor in the Department of Medicine

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