Pilot study of a preliminary criterion standard for prescription opioid misuse

Exploring the Psychometric Properties of the Current Opioid Misuse Measure Among Adults With Chronic Pain and Opioid Use

OBJECTIVES

The opioid epidemic is a significant public health problem that is associated with overdose and death. The increase in opioid-related problems can largely be attributed to increases in opioid prescriptions for the treatment of chronic pain. Unfortunately, there is not a consensus on a definition of opioid misuse in the context of chronic pain, making measurement a challenge. One commonly used measure to assess opioid misuse in the context of chronic pain is the Current Opioid Misuse Measure (COMM). The COMM was designed to assess opioid misuse generally, as captured by psychiatric symptoms and aberrant drug use behaviors. Although studies have examined cross-validation using correlations, little psychometric work has been conducted, and therefore it is currently unknown what domains the measure is assessing.

MATERIALS AND METHODS

The current study examined the factor structure of the COMM using confirmatory and exploratory factor analysis among 445 opioid-using adults with chronic pain.

RESULTS

The results did not support the widely accepted 1-factor opioid misuse solution; rather they supported a 2-factor, psychiatric problems and aberrant-drug-use-problems factor structure. Convergent and divergent validity were also examined at the bivariate level.

DISCUSSION

Given the importance and relevance for opioid misuse in the context of chronic pain, it is important for researchers to continue assessing and providing psychometric evidence for the COMM.

External validation of an opioid misuse machine learning classifier in hospitalized adult patients

BACKGROUND

Opioid misuse screening in hospitals is resource-intensive and rarely done. Many hospitalized patients are never offered opioid treatment. An automated approach leveraging routinely captured electronic health record (EHR) data may be easier for hospitals to institute. We previously derived and internally validated an opioid classifier in a separate hospital setting. The aim is to externally validate our previously published and open-source machine-learning classifier at a different hospital for identifying cases of opioid misuse.

METHODS

An observational cohort of 56,227 adult hospitalizations was examined between October 2017 and December 2019 during a hospital-wide substance use screening program with manual screening. Manually completed Drug Abuse Screening Test served as the reference standard to validate a convolutional neural network (CNN) classifier with coded word embedding features from the clinical notes of the EHR. The opioid classifier utilized all notes in the EHR and sensitivity analysis was also performed on the first 24 h of notes. Calibration was performed to account for the lower prevalence than in the original cohort.

RESULTS

Manual screening for substance misuse was completed in 67.8% (n = 56,227) with 1.1% (n = 628) identified with opioid misuse. The data for external validation included 2,482,900 notes with 67,969 unique clinical concept features. The opioid classifier had an AUC of 0.99 (95% CI 0.99-0.99) across the encounter and 0.98 (95% CI 0.98-0.99) using only the first 24 h of notes. In the calibrated classifier, the sensitivity and positive predictive value were 0.81 (95% CI 0.77-0.84) and 0.72 (95% CI 0.68-0.75). For the first 24 h, they were 0.75 (95% CI 0.71-0.78) and 0.61 (95% CI 0.57-0.64).

CONCLUSIONS

Our opioid misuse classifier had good discrimination during external validation. Our model may provide a comprehensive and automated approach to opioid misuse identification that augments current workflows and overcomes manual screening barriers.

Publicly available machine learning models for identifying opioid misuse from the clinical notes of hospitalized patients

Background

Automated de-identification methods for removing protected health information (PHI) from the source notes of the electronic health record (EHR) rely on building systems to recognize mentions of PHI in text, but they remain inadequate at ensuring perfect PHI removal. As an alternative to relying on de-identification systems, we propose the following solutions: (1) Mapping the corpus of documents to standardized medical vocabulary (concept unique identifier [CUI] codes mapped from the Unified Medical Language System) thus eliminating PHI as inputs to a machine learning model; and (2) training character-based machine learning models that obviate the need for a dictionary containing input words/n-grams. We aim to test the performance of models with and without PHI in a use-case for an opioid misuse classifier.

Methods

An observational cohort sampled from adult hospital inpatient encounters at a health system between 2007 and 2017. A case-control stratified sampling (n = 1000) was performed to build an annotated dataset for a reference standard of cases and non-cases of opioid misuse. Models for training and testing included CUI codes, character-based, and n-gram features. Models applied were machine learning with neural network and logistic regression as well as expert consensus with a rule-based model for opioid misuse. The area under the receiver operating characteristic curves (AUROC) were compared between models for discrimination. The Hosmer-Lemeshow test and visual plots measured model fit and calibration.

Results

Machine learning models with CUI codes performed similarly to n-gram models with PHI. The top performing models with AUROCs > 0.90 included CUI codes as inputs to a convolutional neural network, max pooling network, and logistic regression model. The top calibrated models with the best model fit were the CUI-based convolutional neural network and max pooling network. The top weighted CUI codes in logistic regression has the related terms ‘Heroin’ and ‘Victim of abuse’.

Conclusions

We demonstrate good test characteristics for an opioid misuse computable phenotype that is void of any PHI and performs similarly to models that use PHI. Herein we share a PHI-free, trained opioid misuse classifier for other researchers and health systems to use and benchmark to overcome privacy and security concerns.

Factors predicting development of opioid use disorders among individuals who receive an initial opioid prescription: mathematical modeling using a database of commercially-insured individuals

BACKGROUND

Prescription drug abuse in the United States and elsewhere in the world is increasing at an alarming rate with non-medical opioid use, in particular, increasing to epidemic proportions over the past two decades. It is imperative to identify individuals most likely to develop opioid abuse or dependence to inform large-scale, targeted prevention efforts.

METHODS

The present investigation utilized a large commercial insurance claims database to identify demographic, mental health, physical health, and healthcare service utilization variables that differentiate persons who receive an opioid abuse or dependence diagnosis within two years of filling an opioid prescription (OUDs) from those who do not receive such a diagnosis within the same time frame (non-OUDs).

RESULTS

When compared to non-OUDs, OUDs were more likely to: (1) be male (59.9% vs. 44.2% for non-OUDs) and younger (M=37.9 vs. 47.7); (2) have a prescription history of more opioids (1.7 vs. 1.2), and more days supply of opioids (M=272.5, vs. M=33.2; (3) have prescriptions filled at more pharmacies (M=3.3 per year vs. M=1.3); (4) have greater rates of psychiatric disorders; (5) utilize more medical and psychiatric services; and (6) be prescribed more concomitant medications. A predictive model incorporating these findings was 79.5% concordant with actual OUDs in the data set.

CONCLUSIONS

Understanding correlates of OUD development can help to predict risk and inform prevention efforts.

Classification and definition of misuse, abuse, and related events in clinical trials: ACTTION systematic review and recommendations

As the nontherapeutic use of prescription medications escalates, serious associated consequences have also increased. This makes it essential to estimate misuse, abuse, and related events (MAREs) in the development and postmarketing adverse event surveillance and monitoring of prescription drugs accurately. However, classifications and definitions to describe prescription drug MAREs differ depending on the purpose of the classification system, may apply to single events or ongoing patterns of inappropriate use, and are not standardized or systematically employed, thereby complicating the ability to assess MARE occurrence adequately. In a systematic review of existing prescription drug MARE terminology and definitions from consensus efforts, review articles, and major institutions and agencies, MARE terms were often defined inconsistently or idiosyncratically, or had definitions that overlapped with other MARE terms. The Analgesic, Anesthetic, and Addiction Clinical Trials, Translations, Innovations, Opportunities, and Networks (ACTTION) public-private partnership convened an expert panel to develop mutually exclusive and exhaustive consensus classifications and definitions of MAREs occurring in clinical trials of analgesic medications to increase accuracy and consistency in characterizing their occurrence and prevalence in clinical trials. The proposed ACTTION classifications and definitions are designed as a first step in a system to adjudicate MAREs that occur in analgesic clinical trials and postmarketing adverse event surveillance and monitoring, which can be used in conjunction with other methods of assessing a treatment's abuse potential.

Depression and prescription opioid misuse among chronic opioid therapy recipients with no history of substance abuse

PURPOSE

Opioid misuse in the context of chronic opioid therapy (COT) is a growing concern. Depression may be a risk factor for opioid misuse, but it has been difficult to tease out the contribution of co-occurring substance abuse. This study aims to examine whether there is an association between depression and opioid misuse in patients receiving COT who have no history of substance abuse.

METHODS

A telephone survey was conducted at Group Health Cooperative and Kaiser Permanente of Northern California. We interviewed 1,334 patients on COT for noncancer pain who had no history of substance abuse. Patients were asked about 3 forms of opioid misuse: (1) self-medicating for symptoms other than pain, (2) self-increasing doses, and (3) giving to or getting opioids from others. Depression was evaluated by the 8-item Patient Health Questionnaire (PHQ-8).

RESULTS

Compared with patients who were not depressed (PHQ-8 score 0 to 4), patients with moderate depression (PHQ-8 score 10 to 14) and severe depression (PHQ-8 score 15 or higher) were 1.8 and 2.4 times more likely, respectively, to misuse their opioid medications for non-pain symptoms. Patients with mild (PHQ-8 score 5 to 9), moderate, and severe depression were 1.9, 2.9, and 3.1 times more likely, respectively, to misuse their opioid medications by self-increasing their dose. There was no statistically significant association between depression and giving opioids to or getting them from others.

CONCLUSION

In patients with no substance abuse history, depressive symptoms are associated with increased rates of some forms of self-reported opioid misuse. Clinicians should be alert to the risk of patients with depressive symptoms using opioids to relieve these symptoms and thereby using more opioids than prescribed.