Prolonged opioid use among opioid-naive individuals after prescription for nonspecific low back pain in the emergency department

Hayden, Jill A.; Ellis, Jenna; Asbridge, Mark; Ogilvie, Rachel; Merdad, Roah; Grant, Daniel; Stewart, Samuel Alan; Campbell, Samuel

doi:10.1097/j.pain.0000000000002075

Cited by 16 publications

(23 citation statements)

References 36 publications

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“…The increased risk remained significant, even after adjusting for pain scores. A similar trend was found in another retrospective cohort study of opioid‐naïve patients presenting with ALBP, identifying patients aged ≥55 years and females being at the highest risk for ongoing opioid use 13 …”

Section: What Is Known and Objectivesupporting

confidence: 83%

“…A similar trend was found in another retrospective cohort study of opioid-naïve patients presenting with ALBP, identifying patients aged ≥55 years and females being at the highest risk for ongoing opioid use. 13 In spite of this evidence, one study reported that 52.6% of patients with ALBP received an opioid medication in the ED. 14 Positive predictors of opioid prescribing included white race, severe pain, radicular pain, sciatica, psychosocial risk factors for future disability, or lack of insurance.…”

Section: What Is K Nown and Objec Tivementioning

confidence: 99%

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Prevalence, predictors and trends of opioid prescribing for lower back pain in United States emergency departments

et al. 2020

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What is known and objective: Current evidence-based guidelines for the treatment of acute low back pain (ALBP) recommend the use of opioid medications only after failure of nonpharmacological therapy, non-steroidal anti-inflammatory drugs and skeletal muscle relaxants and after thorough evaluation of risks and benefits. Despite this recommendation and the state of the opioid epidemic in the United States (US), opioids remain a common drug of choice for ALBP in the emergency department (ED). The purpose of this study was to quantify the prevalence and identify predictors of opioid prescribing for acute lower back pain (ALBP) in emergency departments (EDs) in the United States.Methods: This was a national, cross-sectional study of the National Hospital Ambulatory Care Survey from 2013-2016. ED visits for patients aged ≥18 years treated for ALBP were included. Patients presenting with specified reasons that an opioid may be indicated were excluded. The primary endpoint was frequency of opioids prescribed. A multivariate logistic regression model identified patient-and provider-level predictors of opioid use. Results and discussion:This analysis included 2260 visits for ALBP. Opioids were prescribed in 32.3% of visits. Positive predictors of opioid prescribing were pain score of 7-10 (OR 1.85; 95% CI 1.26-2.70), and patients seen in the Southern (OR 2.53; 95% CI 1.47-4.36) or Western US (OR 2.10; 95% CI 1.19-3.70). Opioids were prescribed less often to patients who received a NSAID or acetaminophen (OR 0.38; 95% CI 0.28-0.52 and OR 0.03; 95% CI 0.01-0.10, respectively). What is new and conclusion:Opioid prescribing rates for ALBP remain high and the predictors identified demonstrate that this prescribing pattern is not uniformly distributed across the patient and provider characteristics studied. K E Y W O R D S low back pain, opioid, opioid prescribing, pain | 699 WALKERLY Et AL. Received an NSAID a 0.36 0.25-0.51 Received acetaminophen a 0.03 0.01-0.10 History of alcohol use disorder 0.51 0.19-1.35a Indicates a statistically significant predictor of receiving an opioid via the logistic regression model.

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Section: What Is Known and Objectivesupporting

confidence: 83%

Section: What Is K Nown and Objec Tivementioning

confidence: 99%

Prevalence, predictors and trends of opioid prescribing for lower back pain in United States emergency departments

et al. 2020

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“…These results are consistent with recent trends in the acceptance of opioid use for the management of chronic physical pain. 29 , 30 These trends raise concern about the possibility of chronic opioid use in patients with physical pain. Unlike cancer or postoperative pain, chronic back or arthritis pain often persists for years, which could result in long-term opioid use and increase the risk of opioid dependence and disorder.…”

Section: Discussionmentioning

confidence: 99%

Trends in potentially inappropriate opioid prescribing and associated risk factors among Korean noncancer patients prescribed non-injectable opioid analgesics

Noh

Heo

et al. 2022

Therapeutic Advances in Drug Safety

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Introduction: The aim of this study was to investigate trends in the prevalence of potentially inappropriate opioid prescribing (PIOP) and identify potential risk factors among Korean noncancer patients. Methods: We conducted a cross-sectional study of annual national patient sample data from the Korean Health Insurance Review and Assessment Service (HIRA-NPS) for the period 2012–2018. Noncancer patients who were prescribed non-injectable opioid analgesics (NIOAs) at least once were included. The proportion of patients with at least one PIOP in terms of concurrent use of benzodiazepines or gabapentinoids, substance use disorder, treatment duration, and dosage was evaluated. Multivariable logistic regression was performed to identify the risk factors associated with PIOP. Results: Of the 9,772,503 noncancer patients, 1,583,444 (16.2%) were prescribed NIOAs at least once. Among them, 15.7% were exposed to PIOP, and the prevalence was much higher (31.6%) in the elderly group (age: ⩾65 years). The prevalence of PIOP increased 1.1-fold over 7 years (14.8–16.8%) among the total NIOA users and was more pronounced in non-tramadol NIOA users (a 1.5-fold increase, from 13.2% to 19.4%). Multivariable logistic regression indicated that older age, beneficiaries of medical aid or national meritorious service, exposure to polypharmacy, psychological disorder, chronic pain indication, and concomitant sedative use were independently associated with higher odds of PIOP. Discussion and Conclusion: We found that the prevalence of PIOP was 15.7% among Korean noncancer patients, and it increased over the 7-year study period. This increasing trend is alarming because it was more drastic with non-tramadol NIOAs compared with that with tramadol. Several patient-level risk factors associated with PIOP would be useful in targeted management strategies for the safe use of opioids. Plain Language Summary Potentially inappropriate opioid prescribing and related risk factors among noncancer patients prescribed non-injectable opioids in Korea In Korea, the prevalence of non-injectable opioid analgesic (NIOA) use in noncancer patients steadily increased from 15.3% in 2012 to 17.1% in 2018. Also, the prevalence of potentially inappropriate opioid prescribing (PIOP) increased from 14.8% in 2012 to 16.8% in 2018. The following factors were associated with a markedly increased risk of PIOP: age, beneficiaries of medical aid or national meritorious service, polypharmacy, psychological disorder, chronic pain, and concomitant medications.

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“…Such distinctions are important to consider in the context of the physiologic adaptations (i.e., opioid tolerance and dependence). No standard definition exists to classify individuals on this spectrum, and existing ones used in the literature vary widely 31 – 33 . The classification definitions used were informed by prior literature and content expert consensus, and focused on identification of extreme outliers (i.e., those that most clearly fit into either extreme end of the spectrum) with many participants falling into the middle category (occasional opioid use).…”

Section: Methodsmentioning

confidence: 99%

Impact of individual and treatment characteristics on wearable sensor-based digital biomarkers of opioid use

et al. 2022

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Opioid use disorder is one of the most pressing public health problems of our time. Mobile health tools, including wearable sensors, have great potential in this space, but have been underutilized. Of specific interest are digital biomarkers, or end-user generated physiologic or behavioral measurements that correlate with health or pathology. The current manuscript describes a longitudinal, observational study of adult patients receiving opioid analgesics for acute painful conditions. Participants in the study are monitored with a wrist-worn E4 sensor, during which time physiologic parameters (heart rate/variability, electrodermal activity, skin temperature, and accelerometry) are collected continuously. Opioid use events are recorded via electronic medical record and self-report. Three-hundred thirty-nine discreet dose opioid events from 36 participant are analyzed among 2070 h of sensor data. Fifty-one features are extracted from the data and initially compared pre- and post-opioid administration, and subsequently are used to generate machine learning models. Model performance is compared based on individual and treatment characteristics. The best performing machine learning model to detect opioid administration is a Channel-Temporal Attention-Temporal Convolutional Network (CTA-TCN) model using raw data from the wearable sensor. History of intravenous drug use is associated with better model performance, while middle age, and co-administration of non-narcotic analgesia or sedative drugs are associated with worse model performance. These characteristics may be candidate input features for future opioid detection model iterations. Once mature, this technology could provide clinicians with actionable data on opioid use patterns in real-world settings, and predictive analytics for early identification of opioid use disorder risk.

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Prolonged opioid use among opioid-naive individuals after prescription for nonspecific low back pain in the emergency department

Cited by 16 publications

References 36 publications

Prevalence, predictors and trends of opioid prescribing for lower back pain in United States emergency departments

Prevalence, predictors and trends of opioid prescribing for lower back pain in United States emergency departments

Trends in potentially inappropriate opioid prescribing and associated risk factors among Korean noncancer patients prescribed non-injectable opioid analgesics

Impact of individual and treatment characteristics on wearable sensor-based digital biomarkers of opioid use

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