Construct validity and reproducibility of the Prescription Opioid Misuse And Abuse Questionnaire (POMAQ)

Identification of prescription opioid misuse and abuse behaviors and risk factors in chronic pain patients using the Prescription Opioid Misuse and Abuse Questionnaire (POMAQ)

OBJECTIVES

To identify patient risk factors associated with prescription opioid misuse and abuse as well as groupings of misuse and abuse behaviors as measured by the Prescription Opioid Misuse and Abuse Questionnaire (POMAQ).

METHODS

Adults with chronic pain requiring long-term treatment with opioids completed the POMAQ and other study questionnaires. Latent class analysis (LCA) was used to examine underlying subgroups exhibiting particular risk profiles. Patient demographic and clinical characteristics were examined as covariates and the concordance between the identified latent classes at-risk classifications and the POMAQ clinical scoring algorithm was assessed.

RESULTS

Analysis of data from 809 patients revealed four classes: "chronic pain, low risk" (n = 473, low to no prevalence of POMAQ behaviors), "chronic pain, comorbid condition" (n = 152, high prevalence of anti-anxiety, sleeping pill, and antihistamine use), "at risk" (n = 154, taking more opioids than prescribed and drinking alcohol with opioids more frequently than other groups), and "high risk" (n = 30, highest prevalence of each behavior). The "high risk" group was associated with being younger, less educated, and unemployed compared to other groups. When examining the LCA classes by groups defined by the original POMAQ clinical scoring algorithm, the "high risk" class had the highest proportion of participants identified with abuse behaviors (46.7%), compared to just 4.7% in the "chronic pain, low risk" group.

CONCLUSIONS

Findings suggest there are four distinct subgroups of patients defined by chronic opioid misuse and abuse behaviors and support the use of the POMAQ to identify risk factors associated with prescription opioid misuse and abuse.

Substance use and common contributors to morbidity: A genetics perspective

Excessive substance use and substance use disorders (SUDs) are common, serious and relapsing medical conditions. They frequently co-occur with other diseases that are leading contributors to disability worldwide. While heavy substance use may potentiate the course of some of these illnesses, there is accumulating evidence suggesting common genetic architectures. In this narrative review, we focus on four heritable medical conditions - cardiometabolic disease, chronic pain, depression and COVID-19, which are commonly overlapping with, but not necessarily a direct consequence of, SUDs. We find persuasive evidence of underlying genetic liability that predisposes to both SUDs and chronic pain, depression, and COVID-19. For cardiometabolic disease, there is greater support for a potential causal influence of problematic substance use. Our review encourages de-stigmatization of SUDs and the assessment of substance use in clinical settings. We assert that identifying shared pathways of risk has high translational potential, allowing tailoring of treatments for multiple medical conditions. FUNDING: SSR acknowledges T29KT0526, T32IR5226 and DP1DA054394; RLK acknowledges AA028292; AA acknowledges DA054869 & K02DA032573. The funders had no role in the conceptualization or writing of the paper.

Clinical scoring algorithm for the prescription opioid misuse and abuse questionnaire (POMAQ)

OBJECTIVE

The Prescription Opioid Misuse and Abuse Questionnaire (POMAQ) was developed to identify prescription opioid misuse and abuse among patients with chronic pain. A clinical scoring algorithm was developed and refined to align with the patient experience.

METHODS

This study utilized data from the POMAQ validation study (3033-4, NCT02660606) conducted on a sample of patients with chronic pain living in the United States. The study was carried out in two phases. Two purposefully enriched patient samples, one for each phase, were created based on patient responses to select POMAQ items and the availability of urine and hair samples. Two clinical experts (SHS, SFB) reviewed patient data to classify prescription opioid use behavior. Classification differences were adjudicated by a third clinical expert (JTF). Comparisons were made between the final clinical classification determined by the experts and the proposed classification based on the POMAQ algorithm.

RESULTS

Sixty patients were included in Phase I (only POMAQ data) and 52 in Phase II (including POMAQ and ancillary sources [e.g. electronic medical records, urine toxicity screen]). Refinements were made to the POMAQ scoring algorithm following discussions with clinical experts to ensure it was clinically relevant. For both phases, classifications were reviewed and discussed to achieve maximal concordance of classifications across experts. The proposed scoring algorithm was then modified to maximize agreement with the expert interpretation of clinically relevant patient experiences.

CONCLUSION

The clinical scoring algorithm for the POMAQ was developed and refined to reflect clinically relevant patient behaviors identified by expert review. Future testing is needed to determine the sensitivity and specificity of this measure.