Prediction of Future Chronic Opioid Use Among Hospitalized Patients

Ten Priorities for Research Addressing the Intersections of Brain Injury, Mental Health and Addictions: A Stakeholder-Driven Priority-Setting Study

OBJECTIVES

The purpose of this study was to engage key stakeholders in a health research priority-setting process to identify, prioritize and produce a community-driven list of research questions addressing intersectional issues on mental health and addictions (MHA) in acquired brain injury (ABI).

METHODS

A multiphasic health research priority-setting process was co-designed and executed with community-based stakeholders, including researchers, health professionals, clinicians, service providers, representatives from brain injury associations, policy makers and people with lived experience of ABI and MHA, including patients and their family members. Stakeholders' ideas led to the generation of research questions, which were prioritized at a 1-day workshop.

RESULTS

Fifty-nine stakeholders participated in the priority-setting activity during the workshop, which resulted in a rank-ordered list of the top 10 questions for research addressing the intersections of ABI and MHA. Questions identified touched on several pressing issues (e.g., opioid crisis, homelessness), encompassed multiple subtypes of ABI (e.g., hypoxic-ischaemic, mild traumatic), and involved different domains (e.g., identification, intervention) of health research.

CONCLUSIONS

This community-driven health research priority-setting study identified and prioritized research questions addressing the intersections of ABI and MHA. Researchers and funding agencies should use this list to inform their agendas and address stakeholders' most urgent needs, fostering meaningful improvements to clinical services.

PATIENT OR PUBLIC CONTRIBUTION

An 11-person working group comprised of people with lived experience, service providers, researchers, healthcare professionals and other key stakeholders collaboratively developed and informed the scope, design, methodology and interpretation of this study. Over 50 community-based stakeholders contributed to the research priority-setting activity. One co-author is a person with lived experience.

Short-term healthcare resource utilization associated with receipt of CDC-informed opioid thresholds among commercially insured new chronic opioid users

OBJECTIVE

To evaluate the impact of recent changes to the Centers for Disease Control and Prevention (CDC) morphine milligram equivalent (MME)/day threshold recommendations on healthcare utilization.

DESIGN

A retrospective cohort study of new chronic opioid users (NCOUs).

SETTING

Commercially insured plans across the United States using IQVIA PharMetrics® Plus for Academics database with new use between January 2014 and March 2015.

PATIENTS

NCOUs with ≥60-day coverage of opioids within a 90-day period with ≥30-day opioid-free period prior to the date of the first qualifying opioid -prescription.

INTERVENTIONS

NCOU categorized by the CDC three-tiered risk-based average MME/day thresholds: low (>0 to <50), medium (≥50 to <90), and high (≥90).

MAIN OUTCOME MEASURES

Multivariable logistic regression was used to calculate adjusted odds of incurring an acute care encounter (ACE) (all-cause and opioid-related) between the thresholds (adjusted odds, 95 percent confidence interval).

RESULTS

In adjusted analyses, when compared to low threshold, there was no difference in the odds of all-cause ACE across the medium (1.01, 0.94-1.28) and high (1.01, 0.84-1.22) thresholds. When compared to low threshold, a statistically insignificant increase was observed when evaluating opioid-related ACE among medium (1.86, 0.86-4.02) and high (1.51, 0.65-3.52) thresholds.

CONCLUSIONS

There was no difference in odds of an all-cause or opioid-related ACE associated with the thresholds. Early-intervention programs and policies exploring reduction of MME/day among NCOUs may not result in short-term reduction in all-cause or opioid-related ACEs. Further assessment of potential long-term reduction in ACEs among this cohort may be insightful.

Predictors of persistent opioid use in non-cancer older adults: a retrospective cohort study

BACKGROUND

Long-term opioid use and associated adverse outcomes have increased dramatically in recent years. Limited research is available on long-term opioid use in older adults.

OBJECTIVE

We aimed to determine the incidence and predictors of long-term or persistent opioid use (POU) amongst opioid-naïve older adults without a cancer diagnosis.

METHODS

This was a retrospective cohort study using five national administrative healthcare databases in New Zealand. We included all opioid-naïve older adults (≥65 years) who were initiated on opioid therapy between January 2013 and June 2018. The outcome of interest was POU, defined as having continuously filled ≥1 opioid prescription within 91-180 days after the index opioid prescription. Multivariable logistic regression was used to examine the predictors of POU.

RESULTS

The final sample included 268,857 opioid-naïve older adults; of these, 5,849(2.2%) developed POU. Several predictors of POU were identified. The use of fentanyl (adjusted odds ratio (AOR) = 3.61; 95% confidence interval (CI) 2.63-4.95), slow-release opioids (AOR = 3.02; 95%CI 2.78-3.29), strong opioids (AOR = 2.03; 95%CI 1.55-2.65), Charlson Comorbidity Score ≥ 3 (AOR = 2.09; 95% CI 1.78-2.46), history of substance abuse (AOR = 1.52; 95%CI 1.35-1.72), living in most socioeconomically deprived areas (AOR = 1.40; 95%CI 1.27-1.54), and anti-epileptics (AOR = 2.07; 95%CI 1.89-2.26), non-opioid analgesics (AOR = 2.05; 95%CI 1.89-2.21), antipsychotics (AOR = 1.96; 95%CI 1.78-2.17) or antidepressants (AOR = 1.50; 95%CI 1.41-1.59) medication use were the strongest predictors of POU.

CONCLUSION

A significant proportion of patients developed POU, and several factors were associated with POU. The findings will enable healthcare providers and policymakers to target early interventions to prevent POU and related adverse events.

Predictors of frequent emergency department visits among hospitalized cancer patients: a comparative cohort study using integrated clinical and administrative data to improve care delivery

BACKGROUND

Frequent emergency department (FED) visits by cancer patients represent a significant burden to the health system. This study identified determinants of FED in recently hospitalized cancer patients, with a particular focus on opioid use.

METHODS

A prospective cohort discharged from surgical/medical units of the McGill University Health Centre was assembled. The outcome was FED use (≥ 4 ED visits) within one year of discharge. Data retrieved from the universal health insurance system was analyzed using Cox Proportional Hazards (PH) model, adopting the Lunn-McNeil approach for competing risk of death.

RESULTS

Of 1253 patients, 14.5% became FED users. FED use was associated with chemotherapy one-year pre-admission (adjusted hazard ratio (aHR) 2.60, 95% CI: 1.80-3.70), ≥1 ED visit in the previous year (aHR: 1.80, 95% CI 1.20-2.80), ≥15 pre-admission ambulatory visits (aHR 1.54, 95% CI 1.06-2.34), previous opioid and benzodiazepine use (aHR: 1.40, 95% CI: 1.10-1.90 and aHR: 1.70, 95% CI: 1.10-2.40), Charlson Comorbidity Index ≥ 3 (aHR: 2.0, 95% CI: 1.2-3.4), diabetes (aHR: 1.60, 95% CI: 1.10-2.20), heart disease (aHR: 1.50, 95% CI: 1.10-2.20) and lung cancer (aHR: 1.70, 95% CI: 1.10-2.40). Surgery (cardiac (aHR: 0.33, 95% CI: 0.16-0.66), gastrointestinal (aHR: 0.34, 95% CI: 0.14-0.82) and thoracic (aHR: 0.45, 95% CI: 0.30-0.67) led to a decreased risk of FED use.

CONCLUSIONS

Cancer patients with higher co-morbidity, frequent use of the healthcare system, and opioid use were at increased risk of FED use. High-risk patients should be flagged for preventive intervention.

External validation of a model to predict future chronic opioid use among hospitalized patients

BACKGROUND

Previous research demonstrates an association between opioid prescribing at hospital discharge and future chronic opioid use. Various opioid guidelines and policies contributed to changes in opioid prescribing practices. How this affected hospitalized patients remains unknown.

OBJECTIVE

Externally validate a prediction model to identify hospitalized patients at the highest risk for future chronic opioid therapy (COT).

DESIGNS

Retrospective analysis of health record data from 2011 to 2022 using logistic regression.

PARTICIPANTS

Hospitalized adults with limited to no opioid use 1-year prior to hospitalization.

SETTINGS

A statewide healthcare system.

MAIN MEASUREMENTS

Used variables associated with progression to COT in a derivation cohort from a different healthcare system to predict expected outcomes in the validation cohort.

KEY RESULTS

The derivation cohort included 17,060 patients, of whom 9653 (56.6%) progressed to COT 1 year after discharge. Compared to the derivation cohort, in the validation cohort, patients who received indigent care (odds ratio [OR] = 0.40, 95% confidence interval [CI] =  0.27-0.59, p < .001) were least likely to progress to COT. Among variables assessed, opioid receipt at discharge was most strongly associated with progression to COT (OR = 3.74, 95% CI = 3.06-4.61, p < .001). The receiver operating characteristic curve for the validation set using coefficients from the derivation cohort performed slightly better than chance (AUC = 0.55).

CONCLUSIONS

Our results highlight the importance of externally validating a prediction model prior to use outside of the derivation population. Periodic updates to models are necessary as policy changes and clinical practice recommendations may affect model performance.

Risk factors for opioid use disorder after severe burns in adults

INTRODUCTION

Risk factors for opioid dependence amongst burn patients have not been well-explored compared to other surgical fields.

METHODS

The TrinetX database was queried for patients diagnosed with opioid use disorder (OUD) after thermal or chemical burn. Propensity score matching was performed. Opioid and non-opioid analgesia use, ICU care, surgery, and comparative risks among common opiates were examined using descriptive and univariate regression models, including odds ratios. Subgroup analysis evaluated the impact of multimodal analgesia.

RESULTS

Odds of receiving IV opioids for acute analgesia (p = <0.0001, OR = 1.80, CI = 1.45-2.25), undergoing surgery (p = <0.0001, OR = 1.58, CI = 1.26-1.98), and ICU care (p = <0.0001, OR = 3.60, CI = 2.00-3.83) after burn injury were higher in patients who developed OUD. Patients receiving multimodal therapy within 24 hours of admission had lower odds of developing OUD (OR = 0.74, CI = 2.76-4.68, p = 0.0001) and chronic pain (OR = 0.89, CI = 0.78-1.00, p = 0.05) regardless of TBSA.

CONCLUSION

Patients who developed opioid use disorder following burn injury had higher odds of receiving opioid exclusive pain management, more frequent surgery, ICU care.

Intended and unintended consequences: Changes in opioid prescribing practices for postsurgical, acute, and chronic pain indications following two policies in North Carolina, 2012-2018 - Controlled and single-series interrupted time series analyses

BACKGROUND

The potential misapplication of current opioid prescribing policies remains understudied and may have substantial adverse implications for patient safety.

METHODS

We used autoregressive integrated moving average models to assess level and trend changes in monthly 1) prescribing rates, 2) days' supply, and 3) daily morphine milligram equivalents (MME) of incident opioid prescriptions relative to 1) a state medical board initiative to reduce high-dose and -volume opioid prescribing and 2) legislation to limit initial opioid prescriptions for acute and postsurgical pain. We examined outcomes by pain indication overall and by cancer history, using prescribing patterns for benzodiazepines to control for temporal trends. We used large private health insurance claims data to include North Carolina residents, aged 18-64, insured at any point between January 2012 and August 2018.

RESULTS

After the medical board initiative, prescribing patterns for chronic pain patients did not change; conversely, acute and postsurgical pain patients experienced immediate declines in daily MME. Post-legislation prescription rates did not decline for those with acute, postsurgical, and non-cancer pain, but instead declined among cancer patients with chronic pain. Chronic pain patients experienced the largest days' supply declines post-legislation, instead of acute and postsurgical pain patients.

CONCLUSIONS

We found mixed evidence on the potential impact of two opioid prescribing policies, with some observed declines in a group not intended to be impacted by the policy. This study provides evidence of the need for clearer opioid prescribing policies to ensure impacts on intended populations and avoid unintended consequences.

Use of Predictive Analytics to Identify Unhealthy Opioid Use and Guide Intervention

OBJECTIVE

The authors aimed to use the newly developed Opioid Risk Stratification Tool to identify individuals who may be at risk for unhealthy opioid use and to examine the impact of applying a mailing and engagement intervention to this population and their prescribers, with the goal of reducing high-risk prescribing behaviors, opioid medication use, and mortality rates.

METHODS

A nonrandomized controlled study was conducted with members from two Medicaid managed care organizations. In both the intervention (N=131) and control (N=187) groups, an algorithm identified members at moderate to high risk for hazardous opioid use. Members at increased risk in the intervention group and their prescribers received a letter from the managed care organization, and members still at risk 3 months after the mailing were contacted by a care coordinator. Individuals in the control group were not contacted. Medicaid claims data were used to compare opioid use and prescribing practices between groups before and after the intervention.

RESULTS

Individuals in the intervention group were less likely to have any opioid prescription postintervention compared with those in the control group (OR=0.55, p<0.001), and the intervention group had a greater reduction in the number of individuals with concurrent opioid and benzodiazepine prescriptions (OR=0.49, p=0.042). Practices such as multiple opioid prescriptions and multiple prescribers of opioids were not affected by the intervention.

CONCLUSIONS

An intervention targeting individuals at risk for hazardous opioid use was associated with a reduction in some high-risk prescribing practices. Future research should determine the ideal mix of interventions to reduce as many risk factors as possible.

Identifying Patients Vulnerable to Inadequate Pain Resolution After Cardiac Surgery

Acute postoperative pain (APOP) is often evaluated through granular parameters, though monitoring postoperative pain using trends may better describe pain state. We investigated acute postoperative pain trajectories in cardiac surgical patients to identify subpopulations of pain resolution and elucidate predictors of problematic pain courses. We examined retrospective data from 2810 cardiac surgical patients at a single center. The k-means algorithm for longitudinal data was used to generate clusters of pain trajectories over the first 5 postoperative days. Patient characteristics were examined for association with cluster membership using ordinal and multinomial logistic regression. We identified 3 subgroups of pain resolution after cardiac surgery: 37.7% with good resolution, 44.2% with moderate resolution, and 18.2% exhibiting poor resolution. Type I diabetes (2.04 [1.00-4.16], p = 0.05), preoperative opioid use (1.65 [1.23-2.22], p = 0.001), and illicit drug use (1.89 [1.26-2.83], p = 0.002) elevated risk of membership into worse pain trajectory clusters. Female gender (1.72 [1.30-2.27], p < 0.001), depression (1.60 [1.03-2.50], p = 0.04) and chronic pain (3.28 [1.79-5.99], p < 0.001) increased risk of membership in the worst pain resolution cluster. This study defined 3 APOP resolution subgroups based on pain score trend after cardiac surgery and identified factors that predisposed patients to worse resolution. Patients with moderate or poor pain trajectory consumed more opioids and received them for longer before discharge. Future studies are warranted to determine if altering postoperative pain monitoring and management improve postoperative course of patients at risk of moderate or poor pain resolution.

Differential patterns of opioid misuse between younger and older adults - a retrospective observational study using data from South Carolina's prescription drug monitoring program

Background: Most research on opioid misuse focuses on younger adults, yet opioid-related mortality has risen fastest among older Americans over age 55.Objectives: To assess whether there are differential patterns of opioid misuse over time between younger and older adults and whether South Carolina's mandatory Prescription Drug Monitoring Program (PDMP) affected opioid misuse differentially between the two groups.Methods: We used South Carolina's Reporting and Identification Prescription Tracking System from 2010 to 2018 to calculate an opioid misuse score for 193,073 patients (sex unknown) using days' supply, morphine milligram equivalents (MME), and the numbers of unique prescribers and dispensaries. Multivariable regression was used to assess differential opioid misuse patterns by age group over time and in response to implementation of South Carolina's mandatory PDMP in 2017.Results: We found that between 2011 and 2018, older adults received 57% (p < .01) more in total MME and 25.4 days more (p < .01) in supply, but received prescriptions from fewer doctors (-0.063 doctors, p < 01) and pharmacies (-0.11 pharmacies, p < 01) per year versus younger adults. However, older adults had lower odds of receiving a high misuse score (OR 0.88, p < .01). After the 2017 legislation, misuse scores fell among younger adults (OR 0.79, p < .01) relative to 2011, but not among older adults.Conclusion: Older adults may misuse opioids differently compared to younger adults. Assessment of policies to reduce opioid misuse should take into account subgroup differences that may be masked at the population level.

Identifying High-Risk Comorbidities Associated with Opioid Use Patterns Using Electronic Health Record Prescription Data

Introduction

Opioid use disorders (OUDs) constitute a major public health issue, and we urgently need alternative methods for characterizing risk for OUD. Electronic health records (EHRs) are useful tools for understanding complex medical phenotypes but have been underutilized for OUD because of challenges related to underdiagnosis, binary diagnostic frameworks, and minimally characterized reference groups. As a first step in addressing these challenges, a new paradigm is warranted that characterizes risk for opioid prescription misuse on a continuous scale of severity, i.e., as a continuum.

Methods

Across sites within the PsycheMERGE network, we extracted prescription opioid data and diagnoses that co-occur with OUD (including psychiatric and substance use disorders, pain-related diagnoses, HIV, and hepatitis C) for over 2.6 million patients across three health registries (Vanderbilt University Medical Center, Mass General Brigham, Geisinger) between 2005 and 2018. We defined three groups based on levels of opioid exposure: no prescriptions, minimal exposure, and chronic exposure and then compared the comorbidity profiles of these groups to the full registries and to those with OUD diagnostic codes.

Results

Our results confirm that EHR data reflects known higher prevalence of substance use disorders, psychiatric disorders, medical, and pain diagnoses in patients with OUD diagnoses and chronic opioid use. Comorbidity profiles that distinguish opioid exposure are strikingly consistent across large health systems, indicating the phenotypes described in this new quantitative framework are robust to health systems differences.

Conclusion

This work indicates that EHR prescription opioid data can serve as a platform to characterize complex risk markers for OUD using existing data.

Opioid and benzodiazepine prescribing after COVID-19 hospitalization

Opioid and benzodiazepine prescribing after COVID-19 hospitalization is not well understood. We aimed to characterize opioid and benzodiazepine prescribing among naïve patients hospitalized for COVID and to identify the risk factors associated with a new prescription at discharge. In this retrospective study of patients across 39 Michigan hospitals from March to November 2020, we identified 857 opioid- and benzodiazepine-naïve patients admitted with COVID-19 not requiring mechanical ventilation. Of these, 22% received opioids, 13% received benzodiazepines, and 6% received both during the hospitalization. At discharge, 8% received an opioid prescription, and 3% received a benzodiazepine prescription. After multivariable adjustment, receipt of an opioid or benzodiazepine prescription at discharge was associated with the length of inpatient opioid or benzodiazepine exposure. These findings suggest that hospitalization represents a risk of opioid or benzodiazepine initiation among naïve patients, and judicious prescribing should be considered to prevent opioid-related harms.

Patient and medication-related factors associated with opioid use disorder after inpatient opioid administration

BACKGROUND

Examine baseline factors associated with a new diagnosis of opioid use disorder (OUD) within 12 months postdischarge among opioid-naïve patients who received an opioid prescription in the inpatient setting.

DESIGN/SETTING

Retrospective cohort (surgery and nonsurgery) study of opioid-naive patients who had at least one prescription for an opioid during an inpatient hospitalist between 2014 and 2017.

PARTICIPANTS

Twenty-three thousand and thirty-three patients were included.

OBJECTIVE

The primary objective was to determine baseline factors associated with a new OUD diagnosis within 12 months of discharge. Baseline covariates included demographic information, clinical characteristics, medication use, characteristics related to index hospital encounter, and discharge location.

FINDINGS

2.1% of the sample had a new diagnosis of OUD within a year after receiving an opioid during hospital admission. Patients between ages 25 and 34 had higher odds of a new OUD diagnosis compared to those 65 years of age and older (odds ratio [OR]: 6.98, 95% confidence interval [CI]: 4.02-12.1 [nonsurgery] and 4.69, 95% CI: 2.63-8.37 [surgery]). Patients from a high opioid geo-rank region had higher odds of OUD diagnosis (OR: 2.08, 95% CI: 1.31-3.31 [nonsurgery] and 1.80, 95% CI: 1.03-3.15 [surgery]). History of nonopioid-related drug disorder, tobacco use disorder, mental health conditions, and gabapentin use 12 months prior to index date and white race were associated with higher odds of new OUD diagnosis.

CONCLUSIONS

It is important to identify and evaluate factors associated with developing a new diagnosis of OUD following hospitalization. This can inform pain management strategies within the hospital and at discharge, and prompt clinicians to screen for risk of OUD.

Chronic Pain Prevalence and Factors Associated With High Impact Chronic Pain following Total Joint Arthroplasty: An Observational Study

Hip, knee, and shoulder arthroplasty are among the most frequently performed orthopaedic procedures in the United States. High impact and bothersome chronic pain rates following total joint arthroplasty (TJA) are unknown; as are factors that predict these chronic pain outcomes. This retrospective observational study included individuals that had a TJA from January 2014 to January 2020 (n = 2,638). Pre-operative and clinical encounter information was extracted from the electronic health record and chronic pain state was determined by email survey. Predictor variables included TJA location, number of surgeries, comorbidities, tobacco use, BMI, and pre-operative pain intensity. Primary outcomes were high impact and bothersome chronic pain. Rates of high impact pain (95% CI) were comparable for knee (9.8-13.3%), hip (8.3-11.8%) and shoulder (7.6-16.3%). Increased risk of high impact pain included non-white race, two or more comorbidities, age less than 65 years, pre-operative pain scores 5/10 or higher, knee arthroplasty, and post-operative survey completion 24 months or less. Rates of bothersome chronic pain (95% CI) were also comparable for knee (24.9-29.9%) and hip (21.3-26.3%) arthroplasty; but higher for shoulder (26.9-39.6%). Increased risk of bothersome chronic pain included non-white race, shoulder arthroplasty, knee arthroplasty, current or past tobacco use, and being female. PERSPECTIVE: In this cohort more than 1/3rd of individuals reported high impact or bothersome chronic pain following TJA. Non-white race and knee arthroplasty were the only two variables associated with both chronic pain outcomes.

Risk factors of chronic opioid use after surgical procedures in noncancer patients: A nationwide case-control study

BACKGROUND

Surgery is an indication for opioid prescription in noncancer patients, and chronic use of opioids is associated with overdose and abuse.

OBJECTIVES

We aimed to evaluate the prevalence and risk factors associated with chronic opioid use (COU) following surgery among noncancer patients.

DESIGN

A nationwide case-control study.

SETTING

Retrospective analysis of the annual national patient sample data from 2012 to 2018 in South Korea.

PATIENTS

Adults without cancer who had undergone surgery and received noninjectable opioids during hospital stay.

MAIN OUTCOME MEASURES

COU during 3 months following surgery.

RESULTS

A total of 15 543 participants were included, and the prevalence overall and in opioid-naïve users was 8.1 and 5.7%, respectively. Prior exposure patterns of opioids [intermittent user, adjusted odds ratio (aOR) 2.35; 95% CI, 2.00 to 2.77, and continuous user, aOR 8.58; 95% CI, 6.54 to 11.24] and concomitant use of benzodiazepine (in continuous user, aOR 18.60; 95% CI 11.70 to 29.55) were strongly associated with COU compared with naïve users. Morphine milligram equivalent, type of opioid strength at discharge and prescription of nonopioid analgesics at discharge were also associated with COU. Compared with minor surgery, knee (aOR 1.49; 95% CI 1.17 to 1.89), spine (aOR 1.65; 95% CI 1.33 to 2.06) and shoulder (aOR 2.54; 95% CI 1.97 to 3.27) procedures showed a significantly positive association with COU. Sensitivity analysis in opioid-naïve patients showed similar results.

CONCLUSION

About 8.1% of noncancer patients who had undergone surgery and were prescribed noninjectable opioids became chronic opioid users in Korea. Identified risk factors could be used to derive strategies for safe opioid use in noncancer patients in the future.

The path from big data analytics capabilities to value in hospitals: a scoping review

BACKGROUND

As the uptake of health information technologies increased, most healthcare organizations have become producers of big data. A growing number of hospitals are investing in the development of big data analytics (BDA) capabilities. If the promises associated with these capabilities are high, how hospitals create value from it remains unclear. The present study undertakes a scoping review of existing research on BDA use in hospitals to describe the path from BDA capabilities (BDAC) to value and its associated challenges.

METHODS

This scoping review was conducted following Arksey and O'Malley's 5 stages framework. A systematic search strategy was adopted to identify relevant articles in Scopus and Web of Science. Data charting and extraction were performed following an analytical framework that builds on the resource-based view of the firm to describe the path from BDA capabilities to value in hospitals.

RESULTS

Of 1,478 articles identified, 94 were included. Most of them are experimental research (n=69) published in medical (n=66) or computer science journals (n=28). The main value targets associated with the use of BDA are improving the quality of decision-making (n=56) and driving innovation (n=52) which apply mainly to care (n=67) and administrative (n=48) activities. To reach these targets, hospitals need to adequately combine BDA capabilities and value creation mechanisms (VCM) to enable knowledge generation and drive its assimilation. Benefits are endpoints of the value creation process. They are expected in all articles but realized in a few instances only (n=19).

CONCLUSIONS

This review confirms the value creation potential of BDA solutions in hospitals. It also shows the organizational challenges that prevent hospitals from generating actual benefits from BDAC-building efforts. The configuring of strategies, technologies and organizational capabilities underlying the development of value-creating BDA solutions should become a priority area for research, with focus on the mechanisms that can drive the alignment of BDA and organizational strategies, and the development of organizational capabilities to support knowledge generation and assimilation.

Predictive Modelling of Susceptibility to Substance Abuse, Mortality and Drug-Drug Interactions in Opioid Patients

Objective: Opioids are a class of drugs that are known for their use as pain relievers. They bind to opioid receptors on nerve cells in the brain and the nervous system to mitigate pain. Addiction is one of the chronic and primary adverse events of prolonged usage of opioids. They may also cause psychological disorders, muscle pain, depression, anxiety attacks etc. In this study, we present a collection of predictive models to identify patients at risk of opioid abuse and mortality by using their prescription histories. Also, we discover particularly threatening drug-drug interactions in the context of opioid usage.

Methods and Materials: Using a publicly available dataset from MIMIC-III, two models were trained, Logistic Regression with L2 regularization (baseline) and Extreme Gradient Boosting (enhanced model), to classify the patients of interest into two categories based on their susceptibility to opioid abuse. We’ve also used K-Means clustering, an unsupervised algorithm, to explore drug-drug interactions that might be of concern.

Results: The baseline model for classifying patients susceptible to opioid abuse has an F1 score of 76.64% (accuracy 77.16%) while the enhanced model has an F1 score of 94.45% (accuracy 94.35%). These models can be used as a preliminary step towards inferring the causal effect of opioid usage and can help monitor the prescription practices to minimize the opioid abuse.

Discussion and Conclusion: Results suggest that the enhanced model provides a promising approach in preemptive identification of patients at risk for opioid abuse. By discovering and correlating the patterns contributing to opioid overdose or abuse among a variety of patients, machine learning models can be used as an efficient tool to help uncover the existing gaps and/or fraudulent practices in prescription writing. To quote an example of one such incidental finding, our study discovered that insulin might possibly be interacting with opioids in an unfavourable way leading to complications in diabetic patients. This indicates that diabetic patients under long term opioid usage might need to take increased amounts of insulin to make it more effective. This observation backs up prior research studies done on a similar aspect. To increase the translational value of our work, the predictive models and the associated software code are made available under the MIT License.

A Risk Prediction Model for Long-term Prescription Opioid Use

BACKGROUND

Tools are needed to aid clinicians in estimating their patients' risk of transitioning to long-term opioid use and to inform prescribing decisions.

OBJECTIVE

The objective of this study was to develop and validate a model that predicts previously opioid-naive patients' risk of transitioning to long-term use.

RESEARCH DESIGN

This was a statewide population-based prognostic study.

SUBJECTS

Opioid-naive (no prescriptions in previous 2 y) patients aged 12 years old and above who received a pill-form opioid analgesic in 2016-2018 and whose prescriptions were registered in the California Prescription Drug Monitoring Program (PDMP).

MEASURES

A multiple logistic regression approach was used to construct a prediction model with long-term (ie, >90 d) opioid use as the outcome. Models were developed using 2016-2017 data and validated using 2018 data. Discrimination (c-statistic), calibration (calibration slope, intercept, and visual inspection of calibration plots), and clinical utility (decision curve analysis) were evaluated to assess performance.

RESULTS

Development and validation cohorts included 7,175,885 and 2,788,837 opioid-naive patients with outcome rates of 5.0% and 4.7%, respectively. The model showed high discrimination (c-statistic: 0.904 for development, 0.913 for validation), was well-calibrated after intercept adjustment (intercept, -0.006; 95% confidence interval, -0.016 to 0.004; slope, 1.049; 95% confidence interval, 1.045-1.053), and had a net benefit over a wide range of probability thresholds.

CONCLUSIONS

A model for the transition from opioid-naive status to long-term use had high discrimination and was well-calibrated. Given its high predictive performance, this model shows promise for future integration into PDMPs to aid clinicians in formulating opioid prescribing decisions at the point of care.

Impact of Opioid Administration in the Intensive Care Unit and Subsequent Use in Opioid-Naïve Patients

BACKGROUND

Opioids are a mainstay of therapy for patients in the intensive care unit (ICU) as part of the analgesia-first approach to sedation. Despite knowledge of acute consequences of opioid based analgosedation, less is known about the potential long-term consequences, including the effect of opioid administration in the ICU on subsequent opioid use in opioid-naïve patients.

OBJECTIVE

To evaluate the relationship between ICU opioid administration to opioid-naïve patients and subsequent opioid use following discharge.

METHODS

A query of the electronic medical record was performed to identify opioid-naïve adult patients admitted directly to an ICU. Patients who received continuous intravenous infusion of fentanyl, hydromorphone, or morphine were screened for inclusion into the analysis.

RESULTS

Of the 342 patients included for analysis, 164 (47.1%) received an opioid at hospital discharge. In total, 17 of the 342 patients (5.0%) became long-term users, noted to be more common in patients who received an opioid prescription at discharge (8.7% vs 1.6%; P = 0.006). Neither total ICU morphine milligram equivalent (MME) nor average daily ICU MME administration were found to correlate with daily MME prescription quantity at discharge (R² = 0.008 and R² = 0.03, respectively). Following control for potentially confounding variables, total ICU MME administration remained an insignificant predictor of subsequent receipt of an opioid prescription at discharge and long-term opioid use.

CONCLUSION AND RELEVANCE

This study failed to find a significant relationship between ICU opioid use in opioid-naïve patients and subsequent opioid use. These findings highlight the need to focus on transitions points between the ICU and discharge as potential opportunities to reduce inappropriate opioid continuation.

Identifying risk of opioid use disorder for patients taking opioid medications with deep learning

OBJECTIVE

The United States is experiencing an opioid epidemic. In recent years, there were more than 10 million opioid misusers aged 12 years or older annually. Identifying patients at high risk of opioid use disorder (OUD) can help to make early clinical interventions to reduce the risk of OUD. Our goal is to develop and evaluate models to predict OUD for patients on opioid medications using electronic health records and deep learning methods. The resulting models help us to better understand OUD, providing new insights on the opioid epidemic. Further, these models provide a foundation for clinical tools to predict OUD before it occurs, permitting early interventions.

METHODS

Electronic health records of patients who have been prescribed with medications containing active opioid ingredients were extracted from Cerner's Health Facts database for encounters between January 1, 2008, and December 31, 2017. Long short-term memory models were applied to predict OUD risk based on five recent prior encounters before the target encounter and compared with logistic regression, random forest, decision tree, and dense neural network. Prediction performance was assessed using F1 score, precision, recall, and area under the receiver-operating characteristic curve.

RESULTS

The long short-term memory (LSTM) model provided promising prediction results which outperformed other methods, with an F1 score of 0.8023 (about 0.016 higher than dense neural network (DNN)) and an area under the receiver-operating characteristic curve (AUROC) of 0.9369 (about 0.145 higher than DNN).

CONCLUSIONS

LSTM-based sequential deep learning models can accurately predict OUD using a patient's history of electronic health records, with minimal prior domain knowledge. This tool has the potential to improve clinical decision support for early intervention and prevention to combat the opioid epidemic.

Effect of preoperative long-term opioid therapy on patient outcomes after total knee arthroplasty: an analysis of multicentre population-based administrative data

Background

Up to 40% of patients are receiving opioids at the time of total knee arthroplasty (TKA) in the United States despite evidence suggesting opioids are ineffective for pain associated with arthritis and have substantial risks. Our primary objective was to determine whether preoperative opioid users had worse knee pain and physical function outcomes 12 months after TKA than patients who were opioid-naive preoperatively; our secondary objective was to determine the prevalence of opioid use before and after TKA in Alberta, Canada.

Methods

In this retrospective analysis of population-based data, we identified adult patients who underwent TKA between 2013 and 2015 in Alberta. We used multivariable linear regression to examine the association between preoperative opioid use and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain and physical function scores 12 months after TKA, adjusting for potentially confounding variables.

Results

Of the 1907 patients, 592 (31.0%) had at least 1 opioid dispensed before TKA, and 124 (6.5%) were classified as long-term opioid users. Long-term opioid users had worse adjusted WOMAC pain and physical function scores 12 months after TKA than patients who were opioid-naive preoperatively (pain score β = 7.7, 95% confidence interval [CI] 4.0 to 11.6; physical function score β = 7.8, 95% CI 4.0 to 11.6; p < 0.001 for both). The majority (89 ([71.8%]) of patients who were long-term opioid users preoperatively were dispensed opioids 180–360 days after TKA, compared to 158 (12.0%) patients who were opioid-naive preoperatively.

Conclusion

A substantial number of patients were dispensed opioids before and after TKA, and patients who received opioids preoperatively had worse adjusted pain and functional outcome scores 12 months after TKA than patients who were opioid-naive preoperatively. These results suggest that patients prescribed opioids preoperatively should be counselled judiciously regarding expected outcomes after TKA.

Variations in Long-term Opioid Therapy Definitions: A Systematic Review of Observational Studies Using Routinely Collected Data (2000-2019)

Routinely collected data have been increasingly used to assess long-term opioid therapy (LTOT) patterns, with very little guidance on how to measure LTOT from these data sources. We conducted a systematic review of studies published between January 2000 and July 2019 to catalogue LTOT definitions, the rationale for definitions and LTOT rates in observational research using routinely collected data in nonsurgical settings. We screened 4056 abstracts, 210 full-text manuscripts and included 128 studies, mostly from the United States (81%) and published between 2015 and 2019 (69%). We identified 78 definitions of LTOT, commonly operationalised as 90 days of use within a year (23%). Studies often used multiple criteria to derive definitions (60%), mostly based on measures of duration, such as supply days/days of use (66%), episode length (21%) or prescription fills within specified time periods (12%). Definitions were based on previous publications (63%), clinical judgment (16%) or empirical data (3%); 10% of studies applied more than one definition. LTOT definition was not provided with enough details for replication in 14 studies and 38 studies did not specify the opioids evaluated. Rates of LTOT within study populations ranged from 0.2% to 57% according to study design and definition used. We observed a substantial rise in the last 5 years in studies evaluating LTOT with large variability in the definitions used and poor reporting of the rationale and implementation of definitions. This variation impacts on research reproducibility, comparability of findings and the development of strategies aiming to curb therapy that is not guideline-recommended.

A spatiotemporal analysis of opioid poisoning mortality in Ohio from 2010 to 2016

Opioid-related deaths have severely increased since 2000 in the United States. This crisis has been declared a public health emergency, and among the most affected states is Ohio. We used statewide vital statistic data from the Ohio Department of Health (ODH) and demographics data from the U.S. Census Bureau to analyze opioid-related mortality from 2010 to 2016. We focused on the characterization of the demographics from the population of opioid-related fatalities, spatiotemporal pattern analysis using Moran's statistics at the census-tract level, and comorbidity analysis using frequent itemset mining and association rule mining. We found higher rates of opioid-related deaths in white males aged 25-54 compared to the rest of Ohioans. Deaths tended to increasingly cluster around Cleveland, Columbus and Cincinnati and away from rural regions as time progressed. We also found relatively high co-occurrence of cardiovascular disease, anxiety or drug abuse history, with opioid-related mortality. Our results demonstrate that state-wide spatiotemporal and comorbidity analysis of the opioid epidemic could provide novel insights into how the demographic characteristics, spatiotemporal factors, and/or health conditions may be associated with opioid-related deaths in the state of Ohio.

Evaluation of a novel metric for personalized opioid prescribing after hospitalization

Background

The duration of an opioid prescribed at hospital discharge does not intrinsically account for opioid needs during a hospitalization. This discrepancy may lead to patients receiving much larger supplies of opioids on discharge than they truly require.

Objective

Assess a novel discharge opioid supply metric that adjusts for opioid use during hospitalization, compared to the conventional discharge prescription signature.

Design, setting, & participants

Retrospective study using electronic health record data from June 2012 to November 2018 of adults who received opioids while hospitalized and after discharge from a single academic medical center.

Measures & analysis

We ascertained inpatient opioids received and milligrams of opioids supplied after discharge, then determined days of opioids supplied after discharge by the conventional prescription signature opioid-days (“conventional days”) and novel hospital-adjusted opioid-days (“adjusted days”) metrics. We calculated descriptive statistics, within-subject difference between measurements, and fold difference between measures. We used multiple linear regression to determine patient-level predictors associated with high difference in days prescribed between measures.

Results

The adjusted days metric demonstrates a 2.4 day median increase in prescription duration as compared to the conventional days metric (9.4 vs. 7.0 days; P<0.001). 95% of all adjusted days measurements fall within a 0.19 to 6.90-fold difference as compared to conventional days measurements, with a maximum absolute difference of 640 days. Receiving a liquid opioid prescription accounted for an increased prescription duration of 135.6% by the adjusted days metric (95% CI 39.1–299.0%; P = 0.001). Of patients who were not on opioids prior to admission and required opioids during hospitalization but not in the last 24 hours, 325 (8.6%) were discharged with an opioid prescription.

Conclusions

The adjusted days metric, based on inpatient opioid use, demonstrates that patients are often prescribed a supply lasting longer than the prescription signature suggests, though with marked variability for some patients that suggests potential under-prescribing as well. Adjusted days is more patient-centered, reflecting the reality of how patients will take their prescription rather than providers’ intended prescription duration.

Development of a machine learning algorithm for early detection of opioid use disorder

BACKGROUND

Opioid use disorder (OUD) affects an estimated 16 million people worldwide. The diagnosis of OUD is commonly delayed or missed altogether. We aimed to test the utility of machine learning in creating a prediction model and algorithm for early diagnosis of OUD.

SUBJECTS AND METHODS

We analyzed data gathered in a commercial claim database from January 1, 2006, to December 31, 2018 of 10 million medical insurance claims from 550 000 patient records. We compiled 436 predictor candidates, divided to six feature groups - demographics, chronic conditions, diagnosis and procedures features, medication features, medical costs, and episode counts. We employed the Word2Vec algorithm and the Gradient Boosting trees algorithm for the analysis.

RESULTS

The c-statistic for the model was 0.959, with a sensitivity of 0.85 and specificity of 0.882. Positive Predictive Value (PPV) was 0.362 and Negative Predictive Value (NPV) was 0.998. Significant differences between positive OUD- and negative OUD- controls were in the mean annual amount of opioid use days, number of overlaps in opioid prescriptions per year, mean annual opioid prescriptions, and annual benzodiazepine and muscle relaxant prescriptions. Notable differences were the count of intervertebral disc disorder-related complaints per year, post laminectomy syndrome diagnosed per year, and pain disorders diagnosis per year. Significant differences were also found in the episodes and costs categories.

CONCLUSIONS

The new algorithm offers a mean 14.4 months reduction in time to diagnosis of OUD, at potential saving in further morbidity, medical cost, addictions and mortality.

Demographic, Clinical, and Prescribing Characteristics Associated with Future Opioid Use in an Opioid-Naive Population in an Integrated Health System

INTRODUCTION

Health systems and prescribers need additional tools to reduce the risk of opioid dependence, abuse, and overdose. Identifying opioid-naive individuals who are at risk of opioid dependence could allow for the development of needed interventions.

METHODS

We conducted a retrospective cohort analysis of 23,804 adults in an integrated health system who had received a first opioid prescription between 2010 and 2015. We compared the demographic, clinical, and prescribing characteristics of individuals who later received a third opioid dispense at least 27 days later, indicating long-term opioid use, with those who did not.

RESULTS

The strongest predictors of continued opioid use were an initial prescription dosage of 90 morphine milligram equivalence or more; prescription of extended-release opioids, rather than short-release; and being prescribed outside of a hospital setting. Patients with a third prescription were also more likely to be older than 45 years, white, and non-Hispanic and to have physical comorbidities or prior substance abuse or mental health diagnoses.

DISCUSSION

Our findings are largely consistent with prior research but provide new insight into differences in continued opioid use by opioid type, prescribing location, ethnicity, and comorbidities. Together with previous research, our data support a pattern of higher opioid use among older adults but higher rates of diagnosed opioid abuse among younger adults.

CONCLUSIONS

By identifying population characteristics associated with continued opioid use following a first prescription, our data pave the way for quality improvement interventions that target individuals who are at higher risk of opioid dependence.

Assessment of Probable Opioid Use Disorder Using Electronic Health Record Documentation

IMPORTANCE

Electronic health records are a potentially valuable source of information for identifying patients with opioid use disorder (OUD).

OBJECTIVE

To evaluate whether proxy measures from electronic health record data can be used reliably to identify patients with probable OUD based on Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (DSM-5) criteria.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cross-sectional study analyzed individuals within the Geisinger health system who were prescribed opioids between December 31, 2000, and May 31, 2017, using a mixed-methods approach. The cohort was identified from 16 253 patients enrolled in a contract-based, Geisinger-specific medication monitoring program (GMMP) for opioid use, including patients who maintained or violated contract terms, as well as a demographically matched control group of 16 253 patients who were prescribed opioids but not enrolled in the GMMP. Substance use diagnoses and psychiatric comorbidities were assessed using automated electronic health record summaries. A manual medical record review procedure using DSM-5 criteria for OUD was completed for a subset of patients. The analysis was conducted beginning from June 5, 2017, until May 29, 2020.

MAIN OUTCOMES AND MEASURES

The primary outcome was the prevalence of OUD as defined by proxy measures for DSM-5 criteria for OUD as well as the prevalence of comorbidities among patients prescribed opioids within an integrated health system.

RESULTS

Among the 16 253 patients enrolled in the GMMP (9309 women [57%]; mean [SD] age, 52 [14] years), OUD diagnoses as defined by diagnostic codes were present at a much lower rate than expected (291 [2%]), indicating the necessity for alternative diagnostic strategies. The DSM-5 criteria for OUD can be assessed using manual medical record review; a manual review of 200 patients in the GMMP and 200 control patients identifed a larger percentage of patients with probable moderate to severe OUD (GMMP, 145 of 200 [73%]; and control, 27 of 200 [14%]) compared with the prevalence of OUD assessed using diagnostic codes.

CONCLUSIONS AND RELEVANCE

These results suggest that patients with OUD may be identified using information available in the electronic health record, even when diagnostic codes do not reflect this diagnosis. Furthermore, the study demonstrates the utility of coding for DSM-5 criteria from medical records to generate a quantitative DSM-5 score that is associated with OUD severity.

Utility of accumulated opioid supply days and individual patient factors in predicting probability of transitioning to long-term opioid use: An observational study in the Veterans Health Administration

Initial supply days dispensed to new users is strongly predictive of future long‐term opioid use (LTO). The objective was to examine whether a model integrating additional clinical variables conferred meaningful improvement in predicting LTO, beyond a simple approach using only accumulated supply. Three cohorts were created using Veteran's Health Administration data based on accumulated supply days during the 90 days following opioid initiation: (a) <30 days, (b) ≥30 days, (c) ≥60 days. A base, unadjusted probability of subsequent LTO (days 91‐365) was calculated for each cohort, along with an associated risk range based on midpoint values between cohorts. Within each cohort, log‐binomial regression modeled the probability of subsequent LTO, using demographic, diagnostic, and medication characteristics. Each patient's LTO probability was determined using their individual characteristic values and model parameter estimates, where values falling outside the cohort's risk range were considered a clinically meaningful change in predictive value. Base probabilities for subsequent LTO and associated risk ranges by cohort were as follows: (a) 3.92% (0%‐10.75%), (b) 17.59% (10.76%‐28.05%), (c) 38.53% (28.06%‐47.55%). The proportion of patients whose individual probability fell outside their cohort's risk range was as follows: 1.5%, 4.6%, and 9.2% for cohorts 1, 2, and 3, respectively. The strong relationship between accumulated supply days and future LTO offers an opportunity to leverage electronic healthcare records for decision support in preventing the initiation of inappropriate LTO through early intervention. More complex models are unlikely to meaningfully guide decision making beyond the single variable of accumulated supply days.

Common substance use disorders in older adults

Substance use disorder prevalence in older adults is increasing as the baby boom generation ages. Of the different substances with concern for misuse and use disorder, alcohol, prescription drugs, and illicit drugs are the leading causes. High-risk drinking and alcohol use disorder is the leading substance use disorder in older adults. Prescription drug misuse and use disorder in older adults are the second leading cause for substance use disorder and most commonly involves prescription opioids and benzodiazepines. Illicit drug use in older adults is also increasing. Substance use disorders are difficult to recognize in older adults due to medical comorbidity, neurocognitive impairment, and functional decline. Older adults are also more susceptible to drug effects due to decreased hepatic and renal clearance of the substances. Older adults should be screened and assessed for substance use disorders, and when diagnosed, non-pharmacologic as well as pharmacologic intervention should be performed.

Chronic opioid use in women following hysterectomy: Patterns and predictors

BACKGROUND

Most women are prescribed an opioid after hysterectomy. The goal of this study was to determine the association between initial opioid prescribing characteristics and chronic opioid use after hysterectomy.

METHODS

This study included women enrolled in a commercial health plan who had a hysterectomy between 1 July 2010 and 31 March 2015. We used trajectory models to define chronic opioid use as patients with the highest probability of having an opioid prescription filled during the 6 months post-surgery. A multivariable logistic regression was applied to examine the association between initial opioid dispensing (amount prescribed and duration of treatment) and chronic opioid use after adjusting for potential confounders.

RESULTS

A total of 693 of 50 127 (1.38%) opioid-naïve women met the criteria for chronic opioid use following hysterectomy. The baseline variables and initial opioid prescription characteristics predicted the pattern of long-term opioid use with moderate discrimination (c statistic = 0.70). Significant predictors of chronic opioid use included initial opioid daily dose (≥60 MME vs <40 MME, aOR: 1.43, 95% CI: 1.14-1.79) and days' supply (4-7 days vs 1-3 days, aOR: 1.28, 95% CI: 1.06-1.54; ≥8 days vs 1-3 days, aOR: 1.41, 95% CI: 1.05-1.89). Other significant baseline predictors included older age, abdominal or laparoscopic/robotic hysterectomy, tobacco use, psychiatric medication use, back pain, and headache.

CONCLUSION

Initial opioid prescribing characteristics are associated with the risk of chronic opioid use after hysterectomy. Prescribing lower daily doses and shorter days' supply of opioids to women after hysterectomy may result in lower risk of chronic opioid use.

Predicting Opioid Use, Increased Health Care Utilization and High Costs for Musculoskeletal Pain: What Factors Mediate Pain Intensity and Disability?

This study determined the predictive capabilities of pain intensity and disability on health care utilization (number of condition-specific health care visits, incident, and chronic opioid use) and costs (total condition-specific and overall medical costs) in the year following an initial evaluation for musculoskeletal pain. We explored pain catastrophizing and spatial distribution of symptoms (ie, body diagram symptom score) as mediators of these relationships. Two hundred eighty-three military service members receiving initial care for a musculoskeletal injury completed a region-specific disability measure, numeric pain rating scale, Pain Catastrophizing Scale, and body pain diagram. Pain intensity predicted all outcomes, while disability predicted incident opioid use only. No mediation effects were observed for either opioid use outcome, while pain catastrophizing partially mediated the relationship between pain intensity and number of health care visits. Pain catastrophizing and spatial distribution of symptoms fully mediated the relationship between pain intensity and both cost outcomes. The mediation effects of pain catastrophizing and spatial distribution of symptoms are outcome specific, and more consistently observed for cost outcomes. Higher pain intensity may drive more condition-specific health care utilization and use of opioids, while higher catastrophizing and larger spatial distribution of symptoms may drive higher costs for services received. PERSPECTIVE: This article examines underlying characteristics that help explain relationships between pain intensity and disability, and the outcomes of health care utilization and costs. Health care systems can use these findings to refine value-based prediction models by considering factors that differentially influence outcomes for health care use and cost of services.

Opioid Utilization and Perception of Pain Control in Hospitalized Patients: A Cross-Sectional Study of 11 Sites in 8 Countries

BACKGROUND

Hospitalized patients are frequently treated with opioids for pain control, and receipt of opioids at hospital discharge may increase the risk of future chronic opioid use.

OBJECTIVE

To compare inpatient analgesic prescribing patterns and patients' perception of pain control in the United States and non-US hospitals.

DESIGN

Cross-sectional observational study.

SETTING

Four hospitals in the US and seven in seven other countries.

PARTICIPANTS

Medical inpatients reporting pain.

MEASUREMENTS

Opioid analgesics dispensed during the first 24-36 hours of hospitalization and at discharge; assessments and beliefs about pain.

RESULTS

We acquired completed surveys for 981 patients, 503 of 719 patients in the US and 478 of 590 patients in other countries. After adjusting for confounding factors, we found that more US patients were given opioids during their hospitalization compared with patients in other countries, regardless of whether they did or did not report taking opioids prior to admission (92% vs 70% and 71% vs 41%, respectively; P < .05), and similar trends were seen for opioids prescribed at discharge. Patient satisfaction, beliefs, and expectations about pain control differed between patients in the US and other sites.

LIMITATIONS

Limited number of sites and patients/country.

CONCLUSIONS

In the hospitals we sampled, our data suggest that physicians in the US may prescribe opioids more frequently during patients' hospitalizations and at discharge than their colleagues in other countries, and patients have different beliefs and expectations about pain control. Efforts to curb the opioid epidemic likely need to include addressing inpatient analgesic prescribing practices and patients' expectations regarding pain control.

The Influence of Allowable Refill Gaps on Detecting Long-Term Opioid Therapy: An Analysis of Population-Based Administrative Dispensing Data Among Patients with Knee Arthritis Awaiting Total Knee Arthroplasty

BACKGROUND

It is challenging to detect long-term opioid therapy (LTOT) using administrative data, as refill gaps can disrupt opioid utilization episodes. Previous studies have used various methods to define LTOT and allowable refill gaps with little supporting evidence.

OBJECTIVE

To describe the effect of allowable refill gaps on detecting LTOT among a cohort of patients with arthritis awaiting total knee arthroplasty (TKA) using 3 different methods.

METHODS

A retrospective analysis of multicenter population-based data between January 1, 2012, and December 31, 2016, identified patients prescribed opioids before TKA in Alberta, Canada. We described 3 methods to detect LTOT based on a (1) fixed number of days between prescriptions; (2) fraction of the preceding prescription length; and (3) combination method that selected whichever refill gap was greatest. We then compared the number of patients classified as long-term opioid users by varying the number of days between prescriptions from 1-90 days (fixed method) or 0.04-3.2 times the duration (fraction method) for each method and refill gap.

RESULTS

Of the 14,252 patients included in our cohort, 4,393 patients (31%) had an opioid prescription within 180 days before TKA. Detection of LTOT varied from 4.4% to 14.6% (fixed method), 4.2% to 13.2% (fraction method), and 4.5% to 15.1% (mixed method) as refill gaps varied from minimum to maximum. As refills gaps increased, the dose and duration of opioids in the utilization episode decreased for all 3 methods.

CONCLUSIONS

The allowable refill gap between opioid prescriptions can influence the estimated rate of LTOT when using administrative pharmaceutical dispensing data. Definitional parameters should be carefully considered when using administrative data to define consistent opioid use.

DISCLOSURES

This work was supported by the Department of Surgery's Clinical Research Grant at the University of Alberta (RES0039945). The authors have no potential conflicts of interest.

Acute postoperative opioid consumption trajectories and long-term outcomes in pediatric patients after spine surgery

Background: The days following surgery are a critical period where the use of opioids predicts long-term outcomes in adults. It is currently unknown as to whether opioid consumption throughout the acute postoperative period is associated with long-term outcomes in pediatric patients. The aims of this study were to characterize opioid consumption trajectories in the acute postoperative period, identify predictors of trajectory membership and determine associations between opioid consumption trajectories and long-term patient outcomes.

Materials and methods: Medication use, pain and mental health status were assessed at baseline in adolescents with idiopathic scoliosis who were scheduled for spinal fusion surgery. Cumulative 6-hr opioid consumption was recorded for up to 5 days after spinal surgery. At 6 months after surgery, medication use, pain and functional activity were evaluated. Growth mixture modeling was used to identify opioid trajectories.

Results: One hundred and six patients were included in the study. Mean cumulative 6-hr opioid consumption in the acute postoperative period was 13.23±5.20 mg/kg. The model with the best fit contained 5 acute postoperative trajectories and a quadratic term (AIC =6703.26, BIC =6767.19). Two types of patient behaviors were identified: high opioid consumers (trajectories 4 and 5) and low opioid consumers (trajectories 1, 2 and 3). Intraoperative intrathecal morphine dose was a predictor of trajectory membership (p=0.0498). Opioid consumption during the acute postoperative period was not significantly associated with pain, functional activity or pain medication use at 6 months after surgery.

Conclusion: In pediatric patients, intraoperative intrathecal morphine dose predicts opioid consumption in the acute postoperative period. Importantly, opioid consumption during this period does not affect long-term outcomes in pediatric patients after a spine surgery.

Long-Term Opioid Therapy in Older Cancer Survivors: A Retrospective Cohort Study

OBJECTIVES

To examine the rates and predictors of long-term opioid therapy in older cancer survivors.

DESIGN

Retrospective cohort study.

SETTING

Texas, United States.

PARTICIPANTS

Cancer survivors (5 years or more postcancer diagnosis) diagnosed from 1995 to 2008 and who were also Medicare Parts A, B, and D beneficiaries.

MEASUREMENTS

We used Medicare Part D event data to calculate the proportion of cancer survivors with a prolonged opioid prescription (90-day or more supply of opioids/year). Adjusted odds ratios were calculated to identify predictors of prolonged opioid prescribing. All analyses were repeated with a subcohort of opioid-naïve cancer survivors.

RESULTS

The rate of prolonged opioid therapy for cancer patients diagnosed in 2008 was 7.1% prior to cancer diagnosis; it rose to 9.8% within a year of cancer treatments, and to 13.3% at 5 years postdiagnosis. The rate at the sixth year varied by cancer sites: 19.4% in lung cancer and 9.6% in prostate cancer. Among opioid-naïve survivors, the rate increased from 1.4% to 7.1%, from 5 to 18 years postcancer diagnosis. Cancer survivors diagnosed in 2004 to 2008 had higher rates of opioid prescribing compared to those diagnosed in 1995 to 1998 and 1999 to 2003. Years since diagnosis, a later year of diagnosis, female sex, urban location, lung cancer diagnosis, disability as reason for Medicare entitlement, Medicaid eligibility, one or more comorbidity, and history of depression or drug abuse were predictors of prolonged opioid therapy. Among opioid-naïve cancer survivors, diagnosis in 2004 to 2008 was the strongest predictor, while a history of drug abuse was the strongest predictor for all the survivors.

CONCLUSION

The rates of prolonged opioid prescribing for older cancer survivors remained high at 5 or more years after cancer diagnosis. Our findings have potential to inform the development of clinical guidelines and public policy to ensure safer and more effective pain treatment in older cancer survivors. J Am Geriatr Soc 67:945-952, 2019.