The Risk of Prior Opioid Exposure on Future Opioid Use and Comorbidities in Individuals With Non-Acute Musculoskeletal Knee Pain

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

OBJECTIVES

Emergency 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/PARTICIPANTS

In 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).

OUTCOME

The primary outcome was the diagnosis of OUD.

RESULTS

Among 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.

CONCLUSIONS

Patients 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.

A Call for Interdisciplinary Collaboration to Promote Musculoskeletal Health: The Creation of the International Musculoskeletal Mental and Social Health Consortium (I-MESH)

Despite increasing recognition of psychosocial factors in musculoskeletal conditions, its impact on reducing the global toll of musculoskeletal symptoms has been only incremental. It is time to bring together clinicians and researchers with heterogeneous backgrounds, unified by a commitment to reduce the global impact of musculoskeletal illness by addressing mental and social health factors. In 2020, we initiated the International Musculoskeletal Mental and Social Health Consortium. Our current key priority areas are: (1) Develop best practices for uniform terminology, (2) Understand barriers to mental and social health care for musculoskeletal conditions, (3) Develop clinical and research resources. The purpose of this paper is to render a call to interdisciplinary collaboration on the psychological aspects of musculoskeletal health. We believe this international interdisciplinary collaboration is pivotal to the advancement of the biopsychosocial model of musculoskeletal care and has the potential to improve the health of individuals with musculoskeletal conditions globally.

Endogenous opiates and behavior: 2020

This paper is the forty-third consecutive installment of the annual anthological review of research concerning the endogenous opioid system, summarizing articles published during 2020 that studied the behavioral effects of molecular, pharmacological and genetic manipulation of opioid peptides and receptors as well as effects of opioid/opiate agonists and antagonists. The review is subdivided into the following specific topics: molecular-biochemical effects and neurochemical localization studies of endogenous opioids and their receptors (1), the roles of these opioid peptides and receptors in pain and analgesia in animals (2) and humans (3), opioid-sensitive and opioid-insensitive effects of nonopioid analgesics (4), opioid peptide and receptor involvement in tolerance and dependence (5), stress and social status (6), learning and memory (7), eating and drinking (8), drug abuse and alcohol (9), sexual activity and hormones, pregnancy, development and endocrinology (10), mental illness and mood (11), seizures and neurologic disorders (12), electrical-related activity and neurophysiology (13), general activity and locomotion (14), gastrointestinal, renal and hepatic functions (15), cardiovascular responses (16), respiration and thermoregulation (17), and immunological responses (18).

Risk Factors Associated With the Prescription of Opioids Among Service Members Following a First Mild Traumatic Brain Injury

OBJECTIVE

To evaluate factors impacting opioid receipt among active-duty service members (SMs) following a first mild traumatic brain injury (mTBI).

SETTING

Active-duty SMs receiving care within the Military Health System.

PARTICIPANTS

In total, 14 757 SMs who have sustained an initial mTBI, as documented within electronic health records (EHRs), between 2016 and 2017.

DESIGN

A retrospective analysis of EHR metadata.

MAIN MEASURES

Multivariable logistic regression assessed factors impacting opioid receipt and initiation. Factors include demographics, military characteristics, and preexisting clinical conditions, including prior opioid prescription.

RESULTS

Of the sample population, 33.4% (n = 4927) were prescribed opioids after their initial mTBI, of which, 60.6% (n = 2985) received opioids for the first time following injury. Significant risk factors associated with the increased probability of opioid receipt included age, gender, and preexisting behavioral health and musculoskeletal conditions. Military characteristics also exhibited changes in the probability of opioid receipt, both among initiation and new prescription. No changes were observed among race, nor among those with preexisting headaches or migraines.

CONCLUSION

Despite concerns about the negative impact on recovery, the prescribing of opioids is common in this population of active-duty SMs first diagnosed with an mTBI. As several demographic and preexisting health conditions are factors in the receipt of opioids post-mTBI, the entire medical history of these patients should be considered prior to prescription. Understanding these factors may further inform policy for opioid use in the Military Health System.