Identifying patients with opioid use disorder using International Classification of Diseases (ICD) codes: Challenges and opportunities

Osterhage, Katie P.; Hser, Yih‐Ing; Mooney, Larissa J.; Sherman, Seth; Saxon, Andrew J.; Ledgerwood, Maja; Holtzer, Caleb C.; Gehring, Margaret A.; Clingan, Sarah E.; Curtis, Megan E.; Baldwin, Laura‐Mae

doi:10.1111/add.16338

Addiction

2023

DOI: 10.1111/add.16338

|View full text |Cite

Identifying patients with opioid use disorder using International Classification of Diseases (ICD) codes: Challenges and opportunities

Katie P. Osterhage,

Yih‐Ing Hser,

Larissa J. Mooney

et al.

Abstract: Background and AimsInternational Classification of Diseases (ICD) diagnosis codes are often used in research to identify patients with opioid use disorder (OUD), but their accuracy for this purpose is not fully evaluated. This study describes application of ICD‐10 diagnosis codes for opioid use, dependence and abuse from an electronic health record (EHR) data extraction using data from the clinics' OUD patient registries and clinician/staff EHR entries.DesignCross‐sectional observational study.SettingFour rura… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 28 publications

(44 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

Predictability of buprenorphine‐naloxone treatment retention: A multi‐site analysis combining electronic health records and machine learning

Nateghi Haredasht,

Fouladvand,

Tate

et al. 2024

Addiction

View full text Add to dashboard Cite

Background and aimsOpioid use disorder (OUD) and opioid dependence lead to significant morbidity and mortality, yet treatment retention, crucial for the effectiveness of medications like buprenorphine‐naloxone, remains unpredictable. Our objective was to determine the predictability of 6‐month retention in buprenorphine‐naloxone treatment using electronic health record (EHR) data from diverse clinical settings and to identify key predictors.DesignThis retrospective observational study developed and validated machine learning‐based clinical risk prediction models using EHR data.Setting and casesData were sourced from Stanford University's healthcare system and Holmusk's NeuroBlu database, reflecting a wide range of healthcare settings. The study analyzed 1800 Stanford and 7957 NeuroBlu treatment encounters from 2008 to 2023 and from 2003 to 2023, respectively.MeasurementsPredict continuous prescription of buprenorphine‐naloxone for at least 6 months, without a gap of more than 30 days. The performance of machine learning prediction models was assessed by area under receiver operating characteristic (ROC‐AUC) analysis as well as precision, recall and calibration. To further validate our approach's clinical applicability, we conducted two secondary analyses: a time‐to‐event analysis on a single site to estimate the duration of buprenorphine‐naloxone treatment continuity evaluated by the C‐index and a comparative evaluation against predictions made by three human clinical experts.FindingsAttrition rates at 6 months were 58% (NeuroBlu) and 61% (Stanford). Prediction models trained and internally validated on NeuroBlu data achieved ROC‐AUCs up to 75.8 (95% confidence interval [CI] = 73.6–78.0). Addiction medicine specialists' predictions show a ROC‐AUC of 67.8 (95% CI = 50.4–85.2). Time‐to‐event analysis on Stanford data indicated a median treatment retention time of 65 days, with random survival forest model achieving an average C‐index of 65.9. The top predictor of treatment retention identified included the diagnosis of opioid dependence.ConclusionsUS patients with opioid use disorder or opioid dependence treated with buprenorphine‐naloxone prescriptions appear to have a high (∼60%) treatment attrition by 6 months. Machine learning models trained on diverse electronic health record datasets appear to be able to predict treatment continuity with accuracy comparable to that of clinical experts.

show abstract

Predictability of buprenorphine‐naloxone treatment retention: A multi‐site analysis combining electronic health records and machine learning

Nateghi Haredasht,

Fouladvand,

Tate

et al. 2024

Addiction

View full text Add to dashboard Cite

show abstract

Perioperative opioid prescribing and iatrogenic opioid use disorder and overdose: a state-of-the-art narrative review

Larach,

Waljee,

Bicket

et al. 2023

Reg Anesth Pain Med

View full text Add to dashboard Cite

Background/importanceConsiderable attention has been paid to identifying and mitigating perioperative opioid-related harms. However, rates of postsurgical opioid use disorder (OUD) and overdose, along with associated risk factors, have not been clearly defined.ObjectiveEvaluate the evidence connecting perioperative opioid prescribing with postoperative OUD and overdose, compare these data with evidence from the addiction literature, discuss the clinical impact of these conditions, and make recommendations for further study.Evidence reviewState-of-the-art narrative review.FindingsNearly all evidence is from large retrospective studies of insurance claims and Veterans Health Administration (VHA) data. Incidence rates of new OUD within the first year after surgery ranged from 0.1% to 0.8%, while rates of overdose events ranged from 0.01% to 0.8%. Higher rates were seen among VHA patients, which may reflect differences in data completeness and/or risk factors. Identified risk factors included those related to substance use (preoperative opioid use; non-opioid substance use disorders; preoperative sedative, anxiolytic, antidepressant, and gabapentinoid use; and postoperative new persistent opioid use (NPOU)); demographic attributes (chiefly male sex, younger age, white race, and Medicaid or no insurance coverage); psychiatric comorbidities such as depression, bipolar disorder, and PTSD; and certain medical and surgical factors. Several challenges related to the use of administrative claims data were identified; there is a need for more granular retrospective studies and, ideally, prospective cohorts to assess postoperative OUD and overdose incidence with greater accuracy.ConclusionsRetrospective data suggest an incidence of new postoperative OUD and overdose of up to 0.8% during the first year after surgery, but prospective studies are lacking.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Identifying patients with opioid use disorder using International Classification of Diseases (ICD) codes: Challenges and opportunities

Cited by 2 publications

References 28 publications

Predictability of buprenorphine‐naloxone treatment retention: A multi‐site analysis combining electronic health records and machine learning

Predictability of buprenorphine‐naloxone treatment retention: A multi‐site analysis combining electronic health records and machine learning

Perioperative opioid prescribing and iatrogenic opioid use disorder and overdose: a state-of-the-art narrative review

Contact Info

Product

Resources

About