Commentary on Karnik <i>et al</i>.: Harmonization now—the need for consistent, validated measures to identify opioid use disorder in observational data

Morgan, Jake R.; Larochelle, Marc R.

doi:10.1111/add.15977

Cited by 5 publications

(7 citation statements)

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“…Others have developed algorithmic approaches for identifying patients with OUD [34,35], and further work applying these algorithms in real‐world settings is needed. These algorithmic and NLP‐supported approaches begin to address the call for consistent, validated measures to identify OUD in observational data by Morgan and LaRochelle [36] in their 2022 commentary in Addiction .…”

Section: Discussionmentioning

confidence: 99%

“…Without consistent application of ICD codes specific to the diagnosis of OUD, researchers may not be able to use these codes to identify appropriate cohorts of patients with OUD for their studies [17]. There is a critical need for the field to develop consistent and clinically validated approaches to identifying patients with OUD in administrative claims and electronic health record (EHR) data [18].…”

Section: Introductionmentioning

confidence: 99%

“…algorithmic and NLP-supported approaches begin to address the call for consistent, validated measures to identify OUD in observational data by Morgan and LaRochelle[36] in their 2022 commentary in Addiction.This study has several limitations. Research team or clinic staff decisions may have impacted our study findings.…”

mentioning

confidence: 99%

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

Osterhage,

Hser,

Mooney

et al. 2023

Addiction

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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 rural primary care clinics in Washington and Idaho, USA.Participants307 patients.MeasurementsThis study used three data sources from each clinic: (1) a limited dataset extracted from the EHR, (2) a clinic‐based registry of patients with OUD and (3) the clinician/staff interface of the EHR (e.g. progress notes, problem list). Data source one included records with six commonly applied ICD‐10 codes for opioid use, dependence and abuse: F11.10 (opioid abuse, uncomplicated), F11.20 (opioid dependence, uncomplicated), F11.21 (opioid dependence, in remission), F11.23 (opioid dependence with withdrawal), F11.90 (opioid use, unspecified, uncomplicated) and F11.99 (opioid use, unspecified with unspecified opioid‐induced disorder). Care coordinators used data sources two and three to categorize each patient identified in data source one: (1) confirmed OUD diagnosis, (2) may have OUD but no confirmed OUD diagnosis, (3) chronic pain with no evidence of OUD and (4) no evidence for OUD or chronic pain.FindingsF11.10, F11.21 and F11.99 were applied most frequently to patients who had clinical diagnoses of OUD (64%, 89% and 79%, respectively). F11.20, F11.23 and F11.90 were applied to patients who had a diagnostic mix of OUD and chronic pain without OUD. The four clinics applied codes inconsistently.ConclusionsLack of uniform application of ICD diagnosis codes make it challenging to use diagnosis code data from EHR to identify a research population of persons with opioid use disorder.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Osterhage,

Hser,

Mooney

et al. 2023

Addiction

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“…These limitations emphasise and draw attention to earlier calls for development of validated algorithms to accurately identify patients with OUD in observational data. 40 Additionally, the protocols for OUD screening in an ED and resources to support them might be health system specific and not generalisable to other settings. Our study used EHRs coded as structured data.…”

Section: Discussionmentioning

confidence: 99%

“…Because our EHR data contain records from only one healthcare system, there is a possibility of variable misclassification due to missing medications and diagnoses from encounters outside of the studied health system. These limitations emphasise and draw attention to earlier calls for development of validated algorithms to accurately identify patients with OUD in observational data 40. Additionally, the protocols for OUD screening in an ED and resources to support them might be health system specific and not generalisable to other settings.…”

Section: Discussionmentioning

confidence: 99%

Quickly identifying people at risk of opioid use disorder in emergency departments: trade-offs between a machine learning approach and a simple EHR flag strategy

2022

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ObjectivesEmergency departments (EDs) are an important point of contact for people with opioid use disorder (OUD). Universal screening for OUD is costly and often infeasible. Evidence on effective, selective screening is needed. We assessed the feasibility of using a risk factor-based machine learning model to identify OUD quickly among patients presenting in EDs.Design/settings/participantsIn this cohort study, all ED visits between January 2016 and March 2018 for patients aged 12 years and older were identified from electronic health records (EHRs) data from a large university health system. First, logistic regression modelling was used to describe and elucidate the associations between patient demographic and clinical characteristics and diagnosis of OUD. Second, a Gradient Boosting Classifier was applied to develop a predictive model to identify patients at risk of OUD. The predictive performance of the Gradient Boosting algorithm was assessed using F1 scores and area under the curve (AUC).OutcomeThe primary outcome was the diagnosis of OUD.ResultsAmong 345 728 patient ED visits (mean (SD) patient age, 49.4 (21.0) years; 210 045 (60.8%) female), 1.16% had a diagnosis of OUD. Bivariate analyses indicated that history of OUD was the strongest predictor of current OUD (OR=13.4, CI: 11.8 to 15.1). When history of OUD was excluded in multivariate models, baseline use of medications for OUD (OR=3.4, CI: 2.9 to 4.0) and white race (OR=2.9, CI: 2.6 to 3.3) were the strongest predictors. The best Gradient Boosting model achieved an AUC of 0.71, accuracy of 0.96 but only 0.45 sensitivity.ConclusionsPatients who present at the ED with OUD are high-need patients who are typically smokers with psychiatric, chronic pain and substance use disorders. A machine learning model did not improve predictive ability. A quick review of a patient’s EHR for history of OUD is an efficient strategy to identify those who are currently at greatest risk of OUD.

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Opioid-related emergency admissions in people with opioid dependence/use disorder with and without sickle cell disease: An analysis of multi-state insurance claims

Liu,

Brown,

King

et al. 2024

General Hospital Psychiatry

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Commentary on Karnik et al.: Harmonization now—the need for consistent, validated measures to identify opioid use disorder in observational data

Abstract: Consistent and accurate methods of identifying opioid use disorder and its sequelae are critical for research, population health management and clinical interventions.

Cited by 5 publications

References 8 publications

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

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

Quickly identifying people at risk of opioid use disorder in emergency departments: trade-offs between a machine learning approach and a simple EHR flag strategy

Opioid-related emergency admissions in people with opioid dependence/use disorder with and without sickle cell disease: An analysis of multi-state insurance claims

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