A Tool to Assess Risk of De Novo Opioid Abuse or Dependence

Mitigating Sociodemographic Bias in Opioid Use Disorder Prediction: Fairness-Aware Machine Learning Framework

BACKGROUND

Opioid use disorder (OUD) is a critical public health crisis in the United States, affecting >5.5 million Americans in 2021. Machine learning has been used to predict patient risk of incident OUD. However, little is known about the fairness and bias of these predictive models.

OBJECTIVE

The aims of this study are two-fold: (1) to develop a machine learning bias mitigation algorithm for sociodemographic features and (2) to develop a fairness-aware weighted majority voting (WMV) classifier for OUD prediction.

METHODS

We used the 2020 National Survey on Drug and Health data to develop a neural network (NN) model using stochastic gradient descent (SGD; NN-SGD) and an NN model using Adam (NN-Adam) optimizers and evaluated sociodemographic bias by comparing the area under the curve values. A bias mitigation algorithm, based on equality of odds, was implemented to minimize disparities in specificity and recall. Finally, a WMV classifier was developed for fairness-aware prediction of OUD. To further analyze bias detection and mitigation, we did a 1-N matching of OUD to non-OUD cases, controlling for socioeconomic variables, and evaluated the performance of the proposed bias mitigation algorithm and WMV classifier.

RESULTS

Our bias mitigation algorithm substantially reduced bias with NN-SGD, by 21.66% for sex, 1.48% for race, and 21.04% for income, and with NN-Adam by 16.96% for sex, 8.87% for marital status, 8.45% for working condition, and 41.62% for race. The fairness-aware WMV classifier achieved a recall of 85.37% and 92.68% and an accuracy of 58.85% and 90.21% using NN-SGD and NN-Adam, respectively. The results after matching also indicated remarkable bias reduction with NN-SGD and NN-Adam, respectively, as follows: sex (0.14% vs 0.97%), marital status (12.95% vs 10.33%), working condition (14.79% vs 15.33%), race (60.13% vs 41.71%), and income (0.35% vs 2.21%). Moreover, the fairness-aware WMV classifier achieved high performance with a recall of 100% and 85.37% and an accuracy of 73.20% and 89.38% using NN-SGD and NN-Adam, respectively.

CONCLUSIONS

The application of the proposed bias mitigation algorithm shows promise in reducing sociodemographic bias, with the WMV classifier confirming bias reduction and high performance in OUD prediction.

Electroacupuncture for abdominal pain relief in patients with acute pancreatitis: A three-arm randomized controlled trial

BACKGROUND

Electroacupuncture (EA) may reduce the severity of acute pancreatitis (AP) and provide additional pain relief in patients with chronic pancreatitis. However, the ability of EA to relieve pain in patients with AP has not been well documented.

OBJECTIVE

This study was undertaken to compare the pain-relieving effects of EA and conventional treatment in patients with AP.

DESIGN, SETTING, PARTICIPANTS AND INTERVENTIONS

This study was conducted using a randomized, controlled, three-arm, parallel-group and multi-center design. Patients diagnosed with AP were randomly and equally assigned to EA1, EA2 or control groups. All participants received conventional standard-of-care therapy for AP. Local EA alone was administered in EA1, and local plus distal EA was given in EA2. Local EA included two abdominal acupoints, while distal EA included twelve peripheral acupoints. EA groups underwent one session of EA daily for 4 days (days 1-4), or until pain was resolved or discharged.

MAIN OUTCOME MEASURES

The primary outcome measure was the change in the visual analogue scale (VAS; 0-100) pain score between baseline and day 5.

RESULTS

Eighty-nine participants were randomized into EA1, EA2 and control groups, and 88 (EA1, 30; EA2, 29; control, 29) were included in the full-analysis set. VAS score change (median [interquartile range]) on day 5 was (12.3 ± 22.5) in the EA1 group, (10.3 ± 21.5) in the EA2 group, and (8.9 ± 15.2) in the control group. There were not significant differences in the change in VAS score among treatments (P = 0.983). However, time to food intake was significantly shorter in the EA group (EA1 + EA2) than in the control group (median 2.0 days vs 3.0 days), with a hazard ratio of 0.581 (P = 0.022; 95% CI, 0.366-0.924). No significant adverse events occurred.

CONCLUSION

EA treatment did not significantly reduce pain after 4 days of treatment in patients with AP-associated abdominal pain but significantly reduced time to first food intake.

TRIAL REGISTRATION

ClinicalTrials.gov identifier NCT03173222. Please cite this article as: Jang DK, Lee JK, Jung CY, Kim KH, Kang HR, Lee YS, Yoon JH, Joo KR, Chae MK, Baek YH, Seo BK, Lee SH, Lim C. Electroacupuncture for abdominal pain relief in patients with acute pancreatitis: A three-arm randomized controlled trial. J Integr Med. 2023; 21(6): 537-542.

Acute Pain and Development of Opioid Use Disorder: Patient Risk Factors

PURPOSE OF REVIEW

Pharmacological therapy for acute pain carries the risk of opioid misuse, with opioid use disorder (OUD) reaching epidemic proportions worldwide in recent years. This narrative review covers the latest research on patient risk factors for opioid misuse in the treatment of acute pain. In particular, we emphasize newer findings and evidence-based strategies to reduce the prevalence of OUD.

RECENT FINDINGS

This narrative review captures a subset of recent advances in the field targeting the literature on patients' risk factors for OUD in the treatment for acute pain. Besides well-recognized risk factors such as younger age, male sex, lower socioeconomic status, White race, psychiatric comorbidities, and prior substance use, additional challenges such as COVID-19 further aggravated the opioid crisis due to associated stress, unemployment, loneliness, or depression. To reduce OUD, providers should evaluate both the individual patient's risk factors and preferences for adequate timing and dosing of opioid prescriptions. Short-term prescription should be considered and patients at-risk closely monitored. The integration of non-opioid analgesics and regional anesthesia to create multimodal, personalized analgesic plans is important. In the management of acute pain, routine prescription of long-acting opioids should be avoided, with implementation of a close monitoring and cessation plan.

Psychosocial problems and high amount of opioid administration are associated with opioid dependence and abuse after first exposure for chronic pain patients

Controversy surrounding the use of opioids for the treatment and the unique characteristics of chronic pain heighten the risks for abuse and dependence; however, it's unclear if higher doses of opioids and first exposure are associated with dependence and abuse. This study aimed to identify patients who developed dependence or opioid abuse after exposed to opioids for the first time and what were the risks factors associated with the outcome. A retrospective observational cohort study analyzed 2,411 patients between 2011 and 2017 who had a diagnosis of chronic pain and received opioids for the first time. A logistic regression model was used to estimate the likelihood of opioid dependence/abuse after the first exposure based on their mental health conditions, prior substance abuse disorders, demographics, and the amount of MME per day patients received. From 2,411 patients, 5.5 % of the patients had a diagnosis of dependence or abuse after the first exposure. Patients who were depressed (OR = 2.09), previous non-opioid substance dependence or abuse (OR = 1.59) or received greater than 50 MME per day (OR = 1.03) showed statistically significant relationship with developing opioid dependence or abuse, while age (OR = -1.03) showed to be a protective factor. Further studies should stratify chronic pain patients into groups who is in higher risk in developing opioid dependence or abuse and develop alternative strategies for pain management and treatments beyond opioids. This study reinforces the psychosocial problems as determinants of opioid dependence or abuse and risk factors, and the need for safer opioid prescribing practices.

Outcomes and Healthcare Utilization Among New Persistent Opioid Users and Nonopioid Users After Curative-intent Surgery for Cancer

OBJECTIVE

The aim of the study was to compare the health outcomes and resource use of cancer patients who were new persistent opioid users with those who were not, after undergoing curative intent surgery for cancer.

BACKGROUND

Little is known about long-term health outcomes (overdose, mortality) and resource utilization of new persistent opioid users among cancer patients undergoing curative-intent surgery.

METHODS

This retrospective cohort study included all adults with a diagnosis of solid cancers who underwent curative-intent surgery during the study period (2011-2015) in Alberta, Canada and were opioid-naïve before surgery, with a follow-up period until December 31, 2019. The key exposure, "new persistent opioid user," was defined as a patient who was opioid-naive before surgery and subsequently filled at least 1 opioid prescription between 60 and 180 days after surgery. The primary outcome was opioid overdose that occurred within 3 years of surgery. All-cause death, noncancer caused death, and department visit (yes vs. no), and hospitalization (yes vs. no) in the follow-up periods were also included as outcomes.

RESULTS

In total, 19,219 patients underwent curative intent surgery with a median follow-up of 47 months, of whom 1530 (8.0%) were identified as postoperative new persistent opioid users. In total, 101 (0.5%) patients experienced opioid overdose within 3 years of surgery. Compared with nonopioid users, new persistent opioid users experienced a higher rate of opioid overdose (OR = 2.37, 95% CI: 1.44-3.9) within 3 years of surgery. New persistent opioid use was also associated with a greater likelihood of being hospitalized (OR = 2.03, 95% CI: 1.76-2.33) and visiting an emergency room (OR = 1.83, 95% CI: 1.62-2.06) in the first year after surgery, and a higher overall (HR = 1.28, 95% CI: 1.1-1.49) and noncancer caused mortality (HR = 1.33, 95% CI: 1.12-1.58), when compared with nonopioid users.

CONCLUSION

Postoperative new persistent opioid use among cancer patients undergoing curative-intent surgery is associated with subsequent opioid overdose, worse survival, and more health resource utilization.

Using machine learning to study the effect of medication adherence in Opioid Use Disorder

BACKGROUND

Opioid Use Disorder (OUD) and opioid overdose (OD) impose huge social and economic burdens on society and health care systems. Research suggests that Medication for Opioid Use Disorder (MOUD) is effective in the treatment of OUD. We use machine learning to investigate the association between patient's adherence to prescribed MOUD along with other risk factors in patients diagnosed with OUD and potential OD following the treatment.

METHODS

We used longitudinal Medicaid claims for two selected US states to subset a total of 26,685 patients with OUD diagnosis and appropriate Medicaid coverage between 2015 and 2018. We considered patient age, sex, region level socio-economic data, past comorbidities, MOUD prescription type and other selected prescribed medications along with the Proportion of Days Covered (PDC) as a proxy for adherence to MOUD as predictive variables for our model, and overdose events as the dependent variable. We applied four different machine learning classifiers and compared their performance, focusing on the importance and effect of PDC as a variable. We also calculated results based on risk stratification, where our models separate high risk individuals from low risk, to assess usefulness in clinical decision-making.

RESULTS

Among the selected classifiers, the XGBoost classifier has the highest AUC (0.77) closely followed by the Logistic Regression (LR). The LR has the best stratification result: patients in the top 10% of risk scores account for 35.37% of overdose events over the next 12 month observation period. PDC score calculated over the treatment window is one of the most important features, with better PDC lowering risk of OD, as expected. In terms of risk stratification results, of the 35.37% of overdose events that the predictive model could detect within the top 10% of risk scores, 72.3% of these cases were non-adherent in terms of their medication (PDC <0.8). Targeting the top 10% outcome of the predictive model could decrease the total number of OD events by 10.4%.

CONCLUSIONS

The best performing models allow identification of, and focus on, those at high risk of opioid overdose. With MOUD being included for the first time as a factor of interest, and being identified as a significant factor, outreach activities related to MOUD can be targeted at those at highest risk.

Aiding the prescriber: developing a machine learning approach to personalized risk modeling for chronic opioid therapy amongst US Army soldiers

The opioid epidemic is a major policy concern. The widespread availability of opioids, which is fueled by physician prescribing patterns, medication diversion, and the interaction with potential illicit opioid use, has been implicated as proximal cause for subsequent opioid dependence and mortality. Risk indicators related to chronic opioid therapy (COT) at the point of care may influence physicians' prescribing decisions, potentially reducing rates of dependency and abuse. In this paper, we investigate the performance of machine learning algorithms for predicting the risk of COT. Using data on over 12 million observations of active duty US Army soldiers, we apply machine learning models to predict the risk of COT in the initial months of prescription. We use the area under the curve (AUC) as an overall measure of model performance, and we focus on the positive predictive value (PPV), which reflects the models' ability to accurately target military members for intervention. Of the many models tested, AUC ranges between 0.83 and 0.87. When we focus on the top 1% of members at highest risk, we observe a PPV value of 8.4% and 20.3% for months 1 and 3, respectively. We further investigate the performance of sparse models that can be implemented in sparse data environments. We find that when the goal is to identify patients at the highest risk of chronic use, these sparse linear models achieve a performance similar to models trained on hundreds of variables. Our predictive models exhibit high accuracy and can alert prescribers to the risk of COT for the highest risk patients. Optimized sparse models identify a parsimonious set of factors to predict COT: initial supply of opioids, the supply of opioids in the month being studied, and the number of prescriptions for psychotropic medications. Future research should investigate the possible effects of these tools on prescriber behavior (e.g., the benefit of clinician nudging at the point of care in outpatient settings).

Associations Between Physician Prescribing Behavior and Persistent Postoperative Opioid Use Among Cancer Patients Undergoing Curative-intent Surgery: A Population-based Cohort Study

OBJECTIVE

This study aimed to evaluate the association between prescribers' opioid prescribing history and persistent postoperative opioid use in cancer patients undergoing curative-intent surgery.

BACKGROUND

Study has shown that patients may be over-prescribed analgesics after surgery. However, whether and how the prescriber's opioid prescribing behavior impacts persistent opioid use is unclear.

METHODS

All adults with a diagnosis of solid cancers who underwent surgery during the study period (2009-2015) in Alberta, Canada and were opioid-naïve were included. The key exposure was the historical opioid-prescribing pattern of a patient's most responsible prescriber. The primary outcome was "new persistent postoperative opioid user," was defined as a patient who was opioid-naïve before surgery and subsequently filled at least 1 opioid prescription between 60 and 180 days after surgery.

RESULTS

We identified 24,500 patients. Of these, 2106 (8.6%) patients became a new persistent opioid user after surgery. Multivariate analysis demonstrated that patients with most responsible prescribers that historically prescribed higher daily doses of opioids (≥50 vs <50 mg oral morphine equivalent) had an increased risk of new persistent opioid use after surgery (odds ratio = 2.41, P < 0.0001). In addition to the provider's prescribing pattern, other factors including younger age, comorbidities, presurgical opioid use, chemotherapy, type of tumor/surgical procedure were also found to be independently associated with new persistent postoperative opioid use.

CONCLUSIONS

Our results suggest that prescriber with a history of prescribing a higher opioid dose is an important predictor of persistent postoperative opioid use among cancer patients undergoing curative-intent surgery.

Long-Term Opioid Use Following Surgery for Symptomatic Neuroma

BACKGROUND

 Identifying patients at risk for prolonged opioid use following surgery for symptomatic neuroma would be beneficial for perioperative management. The aim of this study is to identify the factors associated with postoperative opioid use of >4 weeks in patients undergoing neuroma surgery.

METHODS

 After retrospective identification, 77 patients who underwent surgery for symptomatic neuroma of the upper or lower extremity were enrolled. Patients completed the Patient-Reported Outcomes Measurement Information System (PROMIS) depression, Numeric Rating Scale (NRS) pain score, and a custom medication questionnaire at a median of 9.7 years (range: 2.5-16.8 years) following surgery. Neuroma excision followed by nerve implantation (n = 39, 51%), nerve reconstruction/repair (n = 18, 23%), and excision alone (n = 16, 21%) were the most common surgical treatments.

RESULTS

 Overall, 27% (n = 21) of patients reported opioid use of more than 4 weeks postoperatively. Twenty-three patients (30%) reported preoperative opioid use of which 11 (48%) did not report opioid use for >4 weeks, postoperatively. In multivariable logistic regression, preoperative opioid use was independently associated with opioid use of >4 weeks, postoperatively (odds ratio [OR] = 4.4, 95% confidence interval [CI]: 1.36-14.3, p = 0.013).

CONCLUSION

 Neuroma surgery reduces opioid use in many patients but patients who are taking opioids preoperatively are at risk for longer opioid use. Almost one-third of patients reported opioid use longer than 4 weeks, postoperatively.

A machine learning based two-stage clinical decision support system for predicting patients' discontinuation from opioid use disorder treatment: retrospective observational study

Background

Buprenorphine is a widely used treatment option for patients with opioid use disorder (OUD). Premature discontinuation from this treatment has many negative health and societal consequences.

Objective

To develop and evaluate a machine learning based two-stage clinical decision-making framework for predicting which patients will discontinue OUD treatment within less than a year. The proposed framework performs such prediction in two stages: (i) at the time of initiating the treatment, and (ii) after two/three months following treatment initiation.

Methods

For this retrospective observational analysis, we utilized Massachusetts All Payer Claims Data (MA APCD) from the year 2013 to 2015. Study sample included 5190 patients who were commercially insured, initiated buprenorphine treatment between January and December 2014, and did not have any buprenorphine prescription at least one year prior to the date of treatment initiation in 2014. Treatment discontinuation was defined as at least two consecutive months without a prescription for buprenorphine. Six machine learning models (i.e., logistic regression, decision tree, random forest, extreme-gradient boosting, support vector machine, and artificial neural network) were tested using a five-fold cross validation on the input data. The first-stage models used patients’ demographic information. The second-stage models included information on medication adherence during the early phase of treatment based on the proportion of days covered (PDC) measure.

Results

A substantial percentage of patients (48.7%) who started on buprenorphine discontinued the treatment within one year. The area under receiving operating characteristic curve (C-statistic) for the first stage models varied within a range of 0.55 to 0.59. The inclusion of knowledge regarding patients’ adherence at the early treatment phase in terms of two-months and three-months PDC resulted in a statistically significant increase in the models’ discriminative power (p-value < 0.001) based on the C-statistic. We also constructed interpretable decision classification rules using the decision tree model.

Conclusion

Machine learning models can predict which patients are most at-risk of premature treatment discontinuation with reasonable discriminative power. The proposed machine learning framework can be used as a tool to help inform a clinical decision support system following further validation. This can potentially help prescribers allocate limited healthcare resources optimally among different groups of patients based on their vulnerability to treatment discontinuation and design personalized support systems for improving patients’ long-term adherence to OUD treatment.

Morphine Equivalent Total Dosage as Predictor of Adverse Outcomes in Opioid Prescribing

OBJECTIVE

The objective of this work was to develop a risk prediction model of opioid overdoses and opioid use disorder for patients at first opioid prescription and compare the predictive accuracy of using morphine equivalent total dosage with daily dosage as predictors.

DESIGN

Records from patients aged 18-79 years with opioid prescriptions between January 1, 2016 and June 30, 2019, no prior history of adverse outcomes, and no malignant cancer diagnoses were collected from the electronic health records system of a medium-sized central Ohio health care system (n = 219,276). A Cox proportional hazards model was developed to predict the adverse outcomes of opioid overdoses and opioid use disorder from patient sociodemographic, pharmacological, and clinical diagnoses factors.

RESULTS

573 patients experienced overdoses and 2,571 patients were diagnosed with OUD in the study time frame. Morphine equivalent total dosage of opioid prescriptions was identified as a stronger predictor of adverse outcomes (C = 0.797) than morphine equivalent daily dosage (C = 0.792), with best predictions from a model that includes both predictors (C = 0.803). In the model with both daily and total dosage predictors, patients receiving a high total/low daily dosage experienced a higher risk (HR = 2.17) than those receiving a low total/high daily dosage (HR = 2.02). Those receiving a high total/high daily dosage experienced the greatest risk of all (HR = 3.09).

CONCLUSIONS

These findings demonstrate the value of including morphine equivalent total dosage as a predictor of adverse opioid outcomes and suggest total dosage may be more strongly correlated with increased risk than daily dosage.

Identifying Patterns and Predictors of Prescription Opioid Use After Total Joint Arthroplasty

INTRODUCTION

Total hip arthroplasty and total knee arthroplasty account for over 1 million procedures annually. Opioids are the mainstay of postoperative pain management for these patients. In this context, the objective of this study was to determine patterns of use and factors associated with early discontinuation of opioids after total joint arthroplasty (TJA).

METHODS

TRICARE claims data (2006-2014) were queried for adult (18-64 years) patients who underwent total hip arthroplasty or total knee arthroplasty. Prescription opioid use was identified from 6 months before and 6 months after surgical intervention. Prior opioid use was categorized as naïve, exposed (with non-sustained use), and sustained (6 month continuous use before surgery). Cox proportional-hazards models were used to identify factors associated with opioid discontinuation following TJA.

RESULTS

Among the 29,767 patients included in the study, 15,271 (51.3%) had prior opioid exposure and 3,740 (12.5%) were sustained opioid users. At 6 months after the surgical intervention, 3,171 (10.6%) continued opioid use, 3.3% were among opioid naïve, 10.2% among exposed, and 33.3% among sustained users. In risk-adjusted models, prior opioid exposure (hazards ratio: 0.65, 95% CI: 0.62-0.67) and sustained prior use (hazards ratio: 0.33, 95% CI: 0.31-0.35) were the strongest predictors of lower likelihood of opioid discontinuation. Lower socio-economic status, depression, and anxiety were also strong predictors.

CONCLUSION

Prior opioid exposure was strongly associated with continued opioid dependence after TJA. Although one-third of prior sustained users continued use after surgery, approximately 10% of previously exposed patients became sustained users, making them the prime candidates for targeted interventions to reduce the likelihood of sustained opioid use after TJA.

Identifying individuals with opioid use disorder: Validity of International Classification of Diseases diagnostic codes for opioid use, dependence and abuse

BACKGROUND

Policy evaluations and health system interventions often utilize International Classification of Diseases (ICD) codes of opioid use, dependence, and abuse to identify individuals with opioid use disorder (OUD) and assess receipt of evidence-based treatments. However, ICD codes may not map directly onto the Diagnostic and Statistical Manual of Mental Disorder (DSM-5) OUD criteria. This study investigates the positive predictive value of ICD codes in identifying patients with OUD.

METHODS

We conducted a clinical chart review on a national sample of 520 Veterans assigned ICD-9 or ICD-10 codes for opioid use, dependence, or abuse from 2012 to 2017. We extracted evidence of DSM-5 OUD criteria and opioid misuse from clinical documentation in the month preceding and three months following initial ICD code listing, and categorized patients into: 1) high likelihood of OUD, 2) limited aberrant opioid use, 3) prescribed opioid use without evidence of aberrant use, and 4) insufficient information. Positive predictive value was calculated as the percentage of individuals with these ICD codes meeting high likelihood of OUD criteria upon chart review.

RESULTS

Only 57.7 % of patients were categorized as high likelihood of OUD; 16.5 % were categorized as limited aberrant opioid use, 18.9 % prescribed opioid use without evidence of aberrant use, and 6.9 % insufficient information.

CONCLUSIONS

Patients assigned ICD codes for opioid use, dependence, or abuse often lack documentation of meeting OUD criteria. Many receive long-term opioid therapy for chronic pain without evidence of misuse. Robust methods of identifying individuals with OUD are crucial to improving access to clinically appropriate treatment.

Patient Factors Associated With Opioid Consumption in the Month Following Major Surgery

OBJECTIVE

The aim of this study was to determine preoperative patient characteristics associated with postoperative outpatient opioid use and assess the frequency of postoperative opioid overprescribing.

SUMMARY BACKGROUND DATA

Although characteristics associated with inpatient opioid use have been described, data regarding patient factors associated with opioid use after discharge are lacking. This hampers the development of individualized approaches to postoperative prescribing.

METHODS

We included opioid-naïve patients undergoing hysterectomy, thoracic surgery, and total knee and hip arthroplasty in a single-center prospective observational cohort study. Preoperative phenotyping included self-report measures to assess pain severity, fibromyalgia survey criteria score, pain catastrophizing, depression, anxiety, functional status, fatigue, and sleep disturbance. Our primary outcome measure was self-reported total opioid use in oral morphine equivalents. We constructed multivariable linear-regression models predicting opioids consumed in the first month following surgery.

RESULTS

We enrolled 1181 patients; 1001 had complete primary outcome data and 913 had complete phenotype data. Younger age, non-white race, lack of a college degree, higher anxiety, greater sleep disturbance, heavy alcohol use, current tobacco use, and larger initial opioid prescription size were significantly associated with increased opioid consumption. Median total oral morphine equivalents prescribed was 600 mg (equivalent to one hundred twenty 5-mg hydrocodone pills), whereas median opioid consumption was 188 mg (38 pills).

CONCLUSIONS

In this prospective cohort of opioid-naïve patients undergoing major surgery, we found a number of characteristics associated with greater opioid use in the first month after surgery. Future studies should address the use of non-opioid medications and behavioral therapies in the perioperative period for these higher risk patients.

Identifying sociodemographic profiles of veterans at risk for high-dose opioid prescribing using classification and regression trees

OBJECTIVE

To identify sociodemographic profiles of patients prescribed high-dose opioids.

DESIGN

Cross-sectional cohort study.

SETTING/PATIENTS

Veterans dually-enrolled in Veterans Health Administration and Medicare Part D, with ≥1 opioid pre-scription in 2012.

MAIN OUTCOME MEASURES

We identified five patient-level demographic characteristics and 12 community variables re-flective of region, socioeconomic deprivation, safety, and internet connectivity. Our outcome was the proportion of vet-erans receiving >120 morphine milligram equivalents (MME) for ≥90 consecutive days, a Pharmacy Quality Alliance measure of chronic high-dose opioid prescribing. We used classification and regression tree (CART) methods to identify risk of chronic high-dose opioid prescribing for sociodemographic subgroups.

RESULTS

Overall, 17,271 (3.3 percent) of 525,716 dually enrolled veterans were prescribed chronic high-dose opioids. CART analyses identified 35 subgroups using four sociodemographic and five community-level measures, with high-dose opioid prescribing ranging from 0.28 percent to 12.1 percent. The subgroup (n = 16,302) with highest frequency of the outcome included veterans who were with disability, age 18-64 years, white or other race, and lived in the Western Census region. The subgroup (n = 14,835) with the lowest frequency of the outcome included veterans who were with-out disability, did not receive Medicare Part D Low Income Subsidy, were >85 years old, and lived in communities within the second and sixth to tenth deciles of community public assistance.

CONCLUSIONS

Using CART analyses with sociodemographic and community-level variables only, we identified sub-groups of veterans with a 43-fold difference in chronic high-dose opioid prescriptions. Interactions among disability, age, race/ethnicity, and region should be considered when identifying high-risk subgroups in large populations.

New Persistent Opioid and Benzodiazepine Use After Curative-Intent Treatment in Patients With Breast Cancer

BACKGROUND

Opioid and benzodiazepine use and abuse is a national healthcare crisis to which patients with cancer are particularly vulnerable. Long-term use and risk factors for opioid and benzodiazepine use in patients with breast cancer is poorly characterized.

METHODS

We conducted a retrospective population-based study of patients with breast cancer diagnosed between 2008 and 2015 undergoing curative-intent treatment identified through the SEER-Medicare linked database. Primary outcomes were new persistent opioid use and new persistent benzodiazepine use. Factors associated with new opioid and benzodiazepine use were investigated by univariate and multivariable logistic regression.

RESULTS

Among opioid-naïve patients, new opioid use was observed in 22,418 (67.4%). Of this group, 611 (2.7%) developed persistent opioid use at 3 months and 157 (0.7%) at 6 months after treatment. Risk factors for persistent use at 3 and 6 months included stage III disease (odds ratio [OR], 2.16; 95% CI, 1.49-3.12, and OR, 3.48; 95% CI, 1.58-7.67), surgery plus chemotherapy (OR, 1.44; 95% CI, 1.10-1.88, and OR, 2.28; 95% CI, 1.40-3.71), surgery plus chemoradiation therapy (OR, 1.47; 95% CI, 1.10-1.96, and OR, 2.34; 95% CI, 1.38-3.96), and initial tramadol use (OR, 2.66; 95% CI, 2.05-3.46, and OR, 3.12; 95% CI, 1.93-5.04). Among benzodiazepine-naïve patients, new benzodiazepine use was observed in 955 (10.3%), and 111 (11.6%) developed new persistent use at 3 months. Tamoxifen use was statistically significantly associated with new persistent benzodiazepine use at 3 months.

CONCLUSIONS

A large percentage of patients receiving curative-intent treatment of breast cancer were prescribed new opioids; however, only a small number developed new persistent opioid use. In contrast, a smaller proportion of patients received a new benzodiazepine prescription; however, new persistent use after completion of treatment was more likely and particularly related to concurrent treatment with tamoxifen.

Predictors of long-term opioid use and opioid use disorder among construction workers: Analysis of claims data

BACKGROUND

Construction workers have high rates of work-related musculoskeletal disorders, which lead to frequent opioid use and opioid use disorder (OUD). This paper quantified the incidence of opioid use and OUD among construction workers with and without musculoskeletal disorders.

METHODS

We conducted a retrospective study using union health claims from January 2015 to June 2018 from 19,909 construction workers. Claims for diagnoses of chronic musculoskeletal disorders, acute musculoskeletal injuries, musculoskeletal surgery, and other conditions were linked to new opioid prescriptions. We examined the effects of high doses (≥50 morphine mg equivalents per day), large supply (more than 7 days per fill), long-term opioid use (60 or more days supplied within a calendar quarter), and musculoskeletal disorders, on the odds of a future OUD.

RESULTS

There were high rates (42.8% per year) of chronic musculoskeletal disorders among workers, of whom 24.1% received new opioid prescriptions and 6.3% received long-term opioid prescriptions per year. Workers receiving opioids for chronic musculoskeletal disorders had the highest odds of future OUD: 4.71 (95% confidence interval 3.09-7.37); workers prescribed long-term opioids in any calendar quarter had a nearly 10-fold odds of developing an OUD.

CONCLUSIONS

Among construction workers, opioids initiated for musculoskeletal pain were strongly associated with incident long-term opioid use and OUD. Musculoskeletal pain from physically demanding work is likely one driver of the opioid epidemic in occupations like construction. Prevention of work injuries and alternative pain management are needed for workers at risk for musculoskeletal injuries.

Expectations of Lumbar Surgery Outcomes among Opioid Users Compared with Non-Users

STUDY DESIGN

Matched cohort study.

PURPOSE

To compare and describe the effect of opioid usage on the expectations of lumbar surgery outcomes among patients taking opioids and patients not taking opioids.

OVERVIEW OF LITERATURE

Chronic opioid use is common among lumbar-spine surgery patients. The decision to undergo elective lumbar surgery is influenced by the expected surgery outcomes. However, the effects of opioids on patients' expectations of lumbar surgery outcomes remain to be rigorously assessed.

METHODS

A total of 77 opioid users grouped according to dose and duration (54 "higher users," 30 "lower users") were matched 2:1 to 154 non-opioid users based on age, sex, marital status, chiropractic care, disability, and diagnosis. All patients completed a validated 20-item Expectations Survey measuring expected improvement with regard to symptoms, function, psychological well-being, and anticipated future spine condition. "Greater expectations" was defined as a higher survey score (possible range, 0-100) based on the number of items expected and degree of improvement expected.

RESULTS

The mean Expectations Survey scores for all opioid users and all non-users were similar (73 vs. 70, p=0.18). Scores were different, however, for lower users (79) compared with matched non-users (69, p=0.01) and compared with higher users (70, p=0.01). In multivariable analysis, "reater expectations" was independently associated with having had chiropractic care (p=0.03), being more disabled (p=0.002), and being a lower-dose opioid user (p=0.03). Compared with higher users, lower users were also more likely to expect not to need pain medications 2 years after surgery (47% vs. 83%, p=0.003).

CONCLUSIONS

Patient expectations of lumbar surgery are associated with diverse demographic and clinical variables. A lower dose and shorter duration of opioid use were associated with expecting more items and expecting more complete improvement compared with non-users. In addition, lower opioid users had greater overall expectations compared with higher users.

Using machine learning to predict risk of incident opioid use disorder among fee-for-service Medicare beneficiaries: A prognostic study

OBJECTIVE

To develop and validate a machine-learning algorithm to improve prediction of incident OUD diagnosis among Medicare beneficiaries with ≥1 opioid prescriptions.

METHODS

This prognostic study included 361,527 fee-for-service Medicare beneficiaries, without cancer, filling ≥1 opioid prescriptions from 2011-2016. We randomly divided beneficiaries into training, testing, and validation samples. We measured 269 potential predictors including socio-demographics, health status, patterns of opioid use, and provider-level and regional-level factors in 3-month periods, starting from three months before initiating opioids until development of OUD, loss of follow-up or end of 2016. The primary outcome was a recorded OUD diagnosis or initiating methadone or buprenorphine for OUD as proxy of incident OUD. We applied elastic net, random forests, gradient boosting machine, and deep neural network to predict OUD in the subsequent three months. We assessed prediction performance using C-statistics and other metrics (e.g., number needed to evaluate to identify an individual with OUD [NNE]). Beneficiaries were stratified into subgroups by risk-score decile.

RESULTS

The training (n = 120,474), testing (n = 120,556), and validation (n = 120,497) samples had similar characteristics (age ≥65 years = 81.1%; female = 61.3%; white = 83.5%; with disability eligibility = 25.5%; 1.5% had incident OUD). In the validation sample, the four approaches had similar prediction performances (C-statistic ranged from 0.874 to 0.882); elastic net required the fewest predictors (n = 48). Using the elastic net algorithm, individuals in the top decile of risk (15.8% [n = 19,047] of validation cohort) had a positive predictive value of 0.96%, negative predictive value of 99.7%, and NNE of 104. Nearly 70% of individuals with incident OUD were in the top two deciles (n = 37,078), having highest incident OUD (36 to 301 per 10,000 beneficiaries). Individuals in the bottom eight deciles (n = 83,419) had minimal incident OUD (3 to 28 per 10,000).

CONCLUSIONS

Machine-learning algorithms improve risk prediction and risk stratification of incident OUD in Medicare beneficiaries.

Engineering Approaches for Addressing Opioid Use Disorder in the Community

Many communities in the United States are struggling to deal with the negative consequences of illicit opioid use. Effectively addressing this epidemic requires the coordination and support of community stakeholders in a change process with common goals and objectives, continuous engagement with individuals with opioid use disorder (OUD) through their treatment and recovery journeys, application of systems engineering principles to drive process change and sustain it, and use of a formal evaluation process to support a learning community that continuously adapts. This review presents strategies to improve OUD treatment and recovery with a focus on engineering approaches grounded in systems thinking.

Chronic Opioid Use after Laryngeal Cancer Treatment: A VA Study

OBJECTIVE

To investigate opioid utilization in veterans undergoing laryngeal cancer treatment and describe the risk of chronic use after treatment cessation.

STUDY DESIGN

A retrospective cohort study.

SETTING

A single Veterans Health Administration site.

SUBJECTS AND METHODS

Veterans with newly diagnosed and treated laryngeal cancer with attributable opioid use from 2005 to 2015. Milligram morphine equivalents (MMEs) were calculated from 90 days prior to diagnosis for up to 1 year. Adjuvant pain medications filled 30 days prior to and up to a year from the date of diagnosis were assessed.

RESULTS

Of 74 veterans with biopsy-proven laryngeal carcinoma, 73 (98.6%) were male and 71 (96%) were white. Forty-three (58%) patients were stage 0/I/II; 31 (42%) were III/IV. Eleven (14.9%) were treated with surgery alone, 35 (47.3%) with radiation alone, and 28 (38%) with multimodal therapy. Twenty-four (32.4%) patients had preexisting opioid use prior to cancer diagnosis. Patients who used opioids more than 30 days prior to date of diagnosis were found to be 10 times more likely to have persistent opioid use at 90 days (P = .0024) and 8 times more likely to have chronic use at 360 days (P = .0041). Maximum MMEs within 1 year of diagnosis were significantly associated with chronic use at 90 days (P = .00045) and chronic use at 360 days (P = .0006).

CONCLUSION

Preexisting opioid use and maximum MMEs are strongly associated with chronic opioid use among veterans treated for laryngeal carcinoma independent of stage and treatment type.

Wisdom of the CROUD: Development and validation of a patient-level prediction model for opioid use disorder using population-level claims data

OBJECTIVE

Some patients who are given opioids for pain could develop opioid use disorder. If it was possible to identify patients who are at a higher risk of opioid use disorder, then clinicians could spend more time educating these patients about the risks. We develop and validate a model to predict a person's future risk of opioid use disorder at the point before being dispensed their first opioid.

METHODS

A cohort study patient-level prediction using four US claims databases with target populations ranging between 343,552 and 384,424 patients. The outcome was recorded diagnosis of opioid abuse, dependency or unspecified drug abuse as a proxy for opioid use disorder from 1 day until 365 days after the first opioid is dispensed. We trained a regularized logistic regression using candidate predictors consisting of demographics and any conditions, drugs, procedures or visits prior to the first opioid. We then selected the top predictors and created a simple 8 variable score model.

RESULTS

We estimated the percentage of new users of opioids with reported opioid use disorder within a year to range between 0.04%-0.26% across US claims data. We developed an 8 variable Calculator of Risk for Opioid Use Disorder (CROUD) score, derived from the prediction models to stratify patients into higher and lower risk groups. The 8 baseline variables were age 15-29, medical history of substance abuse, mood disorder, anxiety disorder, low back pain, renal impairment, painful neuropathy and recent ER visit. 1.8% of people were in the high risk group for opioid use disorder and had a score > = 23 with the model obtaining a sensitivity of 13%, specificity of 98% and PPV of 1.14% for predicting opioid use disorder.

CONCLUSIONS

CROUD could be used by clinicians to obtain personalized risk scores. CROUD could be used to further educate those at higher risk and to personalize new opioid dispensing guidelines such as urine testing. Due to the high false positive rate, it should not be used for contraindication or to restrict utilization.

Patient-level factors associated with chronic opioid use in cancer: a population-based cohort study

PURPOSE

Concerns around chronic opioid use (COU), misuse, and harms have led to increased scrutiny of opioid prescribing in oncology. There is lack of research examining patient-level factors associated with COU. Our aim was to examine patient-level factors associated with COU in newly diagnosed cancer patients.

METHODS

Population-based retrospective cohort study using administrative health data of patients in Alberta, Canada, diagnosed between February 2016 and October 2017. Adult cancer patients who completed a symptom survey within ± 60 days of diagnosis were included. Patients were divided into two groups: COU (defined as continuous opioid prescriptions for at least 90 days post-diagnosis) and non-chronic opioid use (NCOU). Logistic regression was used to evaluate factors associated with COU.

RESULTS

We included 694 patients (mean age 65 years; 51% female). Most had breast (20%), colorectal (13%), and lung (33%) cancers. Of the 14% with COU, 79% were opioid naïve at diagnosis. Those in the COU group were more often diagnosed with advanced cancer (66% versus 40%), had lung cancer (47%), and were opioid tolerant (> 90 days of continuous opioids within one-year pre-diagnosis). A total of 64% of COU versus 27% of NCOU had moderate to severe pain at diagnosis (p < 0.001). Irrespective of treatment type or stage, those with moderate to severe pain, were opioid tolerant at diagnosis, or had multiple prescribers were at greater risk for COU.

CONCLUSIONS

Specific patient groups were at increased risk of COU and should be the focus of adaptive prescribing approaches to ensure that opioid use is appropriate.

The Value of Shorter Initial Opioid Prescriptions: A Simulation Evaluation

BACKGROUND

During the period from 1999 to 2016, more than 350,000 Americans died from overdoses related to the use of prescription opioids. To the extent that supply is directly related to overprescribing, policy interventions aimed at changing prescriber behavior, such as the recent Centers for Disease Control and Prevention guideline, are clearly warranted. Although these could plausibly reduce the prevalence of opioid overuse and dependency, little is known about their economic and health-related impacts.

OBJECTIVE

The aim of this study was to quantify the efficacy of a policy intervention aimed at reducing the length of initial opioid prescriptions.

STUDY DESIGN AND METHODS

A Markov decision process model was fitted on a retrospective cohort of 827,265 patients, and patient cost and health trajectories were simulated over a 24-month period. The model's parameters were based on patients who received short (≤ 3 days) or long (> 7 days) initial opioid prescriptions, matched using propensity score methods.

STUDY POPULATION

All active-duty US Army soldiers from 2011 to 2014; the data contained detailed medical and administrative information on over 11 million soldier-months corresponding to 827,265 individual soldiers.

MAIN OUTCOME MEASURE

Overall costs of a policy change, quality-adjusted life-years (QALYs) gained, and $/QALY gained.

RESULTS

Over a 2-year horizon, a reassignment of 10,000 patients to short initial duration would generate a cost saving in the vicinity of $3.1 million (excluding program costs), and would also lead to an estimated 4451 additional opioid-free months, i.e. months without any opioid prescriptions.

CONCLUSION

The analysis found that efforts to change prescriber behavior can be cost effective, and further studies into the implementation of such policies are warranted.

Acupuncture for Acute Pancreatitis: A Systematic Review and Meta-analysis

OBJECTIVE

The objective of this study was to assess the efficacy and safety of acupuncture plus routine treatment (RT) for acute pancreatitis (AP).

METHODS

Literature searches were performed in 8 databases up to October 31, 2018. Randomized controlled trials comparing acupuncture plus RT with RT alone for AP were included.

RESULTS

Twelve eligible studies were included finally. The meta-analysis showed that acupuncture plus RT compared with RT alone could significantly improve the total effective rate and gastrointestinal function and reduce the Acute Physiology, Age, Chronic Health Evaluation II score, tumor necrosis factor α count, the time of resuming to diets, and the length of hospital stay. Only 3 of the studies reported adverse events or reactions.

CONCLUSIONS

This study suggested that acupuncture combined with RT may be effective for AP. However, more rigorously designed randomized controlled trials are warranted to confirm the current findings.

Opioid Abuse or Dependence Increases 30-day Readmission Rates after Major Operating Room Procedures: A National Readmissions Database Study

BACKGROUND

Although opioids remain the standard therapy for the treatment of postoperative pain, the prevalence of opioid misuse is rising. The extent to which opioid abuse or dependence affects readmission rates and healthcare utilization is not fully understood. It was hypothesized that surgical patients with a history of opioid abuse or dependence would have higher readmission rates and healthcare utilization.

METHODS

A retrospective cohort analysis was performed of patients undergoing major operating room procedures in 2013 and 2014 using the National Readmission Database. Patients with opioid abuse or dependence were identified using International Classification of Diseases codes. The primary outcome was 30-day hospital readmission rate. Secondary outcomes included hospital length of stay and estimated hospital costs.

RESULTS

Among the 16,016,842 patients who had a major operating room procedure whose death status was known, 94,903 (0.6%) had diagnoses of opioid abuse or dependence. After adjustment for potential confounders, patients with opioid abuse or dependence had higher 30-day readmission rates (11.1% vs. 9.1%; odds ratio 1.26; 95% CI, 1.22 to 1.30), longer mean hospital length of stay at initial admission (6 vs. 4 days; P < 0.0001), and higher estimated hospital costs during initial admission ($18,528 vs. $16,617; P < 0.0001). Length of stay was also higher at readmission (6 days vs. 5 days; P < 0.0001). Readmissions for infection (27.0% vs. 18.9%; P < 0.0001), opioid overdose (1.0% vs. 0.1%; P < 0.0001), and acute pain (1.0% vs. 0.5%; P < 0.0001) were more common in patients with opioid abuse or dependence.

CONCLUSIONS

Opioid abuse and dependence are associated with increased readmission rates and healthcare utilization after surgery.

VISUAL ABSTRACT

An online visual overview is available for this article at http://links.lww.com/ALN/B704.

Short- and Long-term Opioid Use in Patients with Oral and Oropharynx Cancer

OBJECTIVE

Opioid use and abuse is a national health care crisis, yet opioids remain the cornerstone of pain management in cancer. We sought to determine the risk of acute and chronic opioid use with head and neck squamous cell cancer (HNSCC) treatment.

STUDY DESIGN

Retrospective population-based study.

SETTING

Surveillance, Epidemiology and End Results (SEER)-Medicare database from 2008 to 2011.

SUBJECTS AND METHODS

In total, 976 nondistant metastatic oral cavity and oropharynx patients undergoing cancer-directed treatment enrolled in Medicare were included. Opiate use was the primary end point. Univariate and multivariable logistic analyses were completed to determine risk factors.

RESULTS

Of the patients, 811 (83.1%) received an opioid prescription during the treatment period, and 150 patients (15.4%) had continued opioid prescriptions at 3 months and 68 (7.0%) at 6 months. Opioid use during treatment was associated with prescriptions prior to treatment (odds ratio [OR], 3.28; 95% confidence interval [CI], 2.11-5.12) and was least likely to be associated with radiation treatment alone (OR, 0.35; 95% CI, 0.18-0.68). Risk factors for continued opioid use at both 3 and 6 months included tobacco use (OR, 2.23; 95% CI, 1.05-4.71 and OR, 3.84; 95% CI, 1.44-10.24) and opioids prescribed prior to treatment (OR, 3.84; 95% CI, 2.45-5.91 and OR, 3.56; 95% CI, 1.95-6.50). Oxycodone prescribed as the first opioid was the least likely to lead to ongoing use at 3 and 6 months (OR, 0.33; 95% CI, 0.17-0.62 and OR, 0.26; 95% CI, 0.10-0.67).

CONCLUSION

Patients with oral/oropharyngeal cancer are at a very high risk for receiving opioids as part of symptom management during treatment, and a significant portion continues use at 3 and 6 months after treatment completion.

Predicting opioid dependence from electronic health records with machine learning

BACKGROUND

The opioid epidemic in the United States is averaging over 100 deaths per day due to overdose. The effectiveness of opioids as pain treatments, and the drug-seeking behavior of opioid addicts, leads physicians in the United States to issue over 200 million opioid prescriptions every year. To better understand the biomedical profile of opioid-dependent patients, we analyzed information from electronic health records (EHR) including lab tests, vital signs, medical procedures, prescriptions, and other data from millions of patients to predict opioid substance dependence.

RESULTS

We trained a machine learning model to classify patients by likelihood of having a diagnosis of substance dependence using EHR data from patients diagnosed with substance dependence, along with control patients with no history of substance-related conditions, matched by age, gender, and status of HIV, hepatitis C, and sickle cell disease. The top machine learning classifier using all features achieved a mean area under the receiver operating characteristic (AUROC) curve of ~ 92%, and analysis of the model uncovered associations between basic clinical factors and substance dependence. Additionally, diagnoses, prescriptions, and procedures prior to the diagnoses of substance dependence were analyzed to elucidate the clinical profile of substance-dependent patients, relative to controls.

CONCLUSIONS

The predictive model may hold utility for identifying patients at risk of developing dependence, risk of overdose, and opioid-seeking patients that report other symptoms in their visits to the emergency room.

Associations of Early Treatments for Low-Back Pain with Military Readiness Outcomes

BACKGROUND

Chronic low-back pain (LBP) is a frequent cause of work absence and disability, and is frequently associated with long-term use of opioids.

OBJECTIVE

To describe military readiness-related outcomes at follow-up in soldiers with LBP grouped by the type of early treatment received for their LBP. Treatment groups were based on receipt of opioid or tramadol prescription and receipt of nonpharmacologic treatment modalities (NPT). Design, Subjects, Measures: A retrospective longitudinal analysis of U.S. soldiers with new LBP episodes persisting more than 90 days between October 2012 and September 2014. Early treatment groups were constructed based on utilization of services within 30 days of the first LBP claim. Outcomes were measured 91-365 days after the first LBP claim. Outcomes were constructed to measure five indicators of limitations of military readiness: military duty limitations, pain-related hospitalization, emergency room visit for LBP, pain score of moderate/severe, and prescription for opioid/tramadol.

RESULTS

Among soldiers with no opioid receipt in the prior 90 days, there were 30,612 new episodes of LBP, which persisted more than 90 days. Multivariable logistic regression models found that compared to the reference group (no NPT, no opioids/tramadol receipt), soldiers who received early NPT-only had lower likelihoods for military duty limitations, pain-related hospitalization, and opioid/tramadol prescription at follow-up, while soldiers' that started with opioid receipt (at alone or follow-up in conjunction with NPT) exhibited higher likelihoods on many of these negative outcomes.

CONCLUSION

This observational study of soldiers with a new episode of LBP and no opioid receipt in the prior 90 days suggests that early receipt of NPT may be associated with small, significant gains in ability to function as a soldier and reduced reliance on opioid/tramadol medication. While further research is warranted, increased access to NPT at the beginning of LBP episodes should be considered.

Pain in Head and Neck Cancer Survivors: Prevalence, Predictors, and Quality-of-Life Impact

Objectives

Pain is common among patients with cancer, stemming from both malignancy and side effects of treatment. The extent to which pain persists after treatment has received little attention. We examined the prevalence, predictors, and impact on quality of life (QOL) caused by pain among survivors of head and neck cancer.

Study Design

Cohort study.

Setting

Tertiary head and neck cancer survivorship clinic.

Subjects and Methods

We identified survivors of head and neck cancer ≥1 year after diagnosis and examined the prevalence and risk factors for development of pain. Pain and QOL were assessed with multiple QOL instruments. Ordinal regression modeling examined predictors of pain in survivors.

Results

We identified 175 patients at a median of 6.6 years after diagnosis. Among survivors, 45.1% reported pain, and 11.5% reported severe pain. Among patients with current pain, 46% reported low overall QOL versus only 12% of those without pain ( P < .001). On multivariable analysis after adjustment for age, sex, and stage of disease, pain was associated with trimodality treatment (odds ratio [OR], 3.55; 95% CI, 1.06-12.77). Multivariable analysis of QOL issues revealed that pain was associated with major depression (OR, 3.91; 95% CI, 1.68-9.11), anxiety (OR, 4.22; 95% CI, 2.28-7.81), poor recreation (OR, 3.31; 95% CI, 1.70-6.48), and low overall QOL (OR, 2.20; 95% CI, 1.12-4.34).

Conclusions

Years after head and neck cancer treatment, pain remains a significant problem and is associated with worse QOL. Future efforts should focus on preventing pain from treatment and comprehensive management.

Cracking the Code: Using Data to Combat the Opioid Crisis

The goal of this article is to understand the value of data and to call for efforts to explore improved data sharing and collection among local, state, and federal agencies. It discusses the data available and existing barriers to sharing it. It also looks at examples of data sharing initiatives and analysis, such as mapping and visualization tools. The article then examines relevant regulations and calls for reforms. Finally, the article considers objections, including privacy interests, data security, and the costs and benefits of data sharing initiatives.

Incidence and predictive factors for additional opioid prescription after endoscopic sinus surgery

BACKGROUND

Excessive postoperative opioid prescription is a source of prescription diversion in the United States opioid crisis and may contribute to chronic opioid use. Efficient prescription by the surgeon can mitigate opioid abuse and improve postoperative pain control. In this study we sought to better characterize the incidence and predictive baseline characteristics associated with the need for additional opioid prescription after endoscopic sinus surgery (ESS) for chronic rhinosinusitis.

METHODS

A retrospective review was performed on subjects undergoing ambulatory ESS between November 2016 and August 2017. The medical and Controlled Substance Utilization Review and Evaluation System (CURES) records were reviewed. Uni- and multivariable logistic regressions were performed to evaluate factors associated with additional opioid prescription within 60 days of surgery.

RESULTS

A total of 121 patients were included. Additional prescriptions were seen in 22 patients (18%). Surgical factors, including sinuses operated, septoplasty, revision, or extended procedure (Draf IIB/III), were not associated with additional prescription. On multivariate logistic regression, preoperative opioid use (odds ratio [OR], 23.45; 95% CI, 1.52-362.63), greater number of prescribed tablets (OR, 1.13; 95% CI, 1.01-1.26), and lower preoperative health status (ASA score) (OR, 11.21; 95% CI, 1.49-84.30) were associated with additional prescription (p < 0.05).

CONCLUSION

A need for extension of postoperative opioid pain control is not uncommon after ESS. Patient baseline clinical characteristics are predictive of a need for re-prescription of opioids. Surgical extent is not associated with need for prolonged postoperative opioid pain management.

Electroacupuncture for abdominal pain relief in patients with acute pancreatitis: study protocol for a randomized controlled trial

BACKGROUND

Previous studies have shown that electroacupuncture (EA) reduces the severity of acute pancreatitis. However, the effect of EA for pain relief in patients with acute pancreatitis has not been evaluated yet. The purpose of this study was to prove the efficacy of EA for pain relief in patients with acute pancreatitis compared with conventional treatment.

METHODS

This study is a randomized, controlled, three-arm, parallel-group, multi-center trial. Patients diagnosed with acute pancreatitis are enrolled and randomly assigned to EA 1, EA 2, or a control group in a 1:1:1 ratio. All the enrolled patients basically receive the conventional standard-of-care therapy for acute pancreatitis. Local EA is given in group EA 1, while local with additional distal EA is given in group EA 2. Local EA includes two acupoints, Zhong Wan (CV12) and Shang Wan (CV13), located in the abdomen, while distal EA includes 12 peripheral acupoints, Zhong Wan (CV12), Shang Wan (CV13), He Gu (LI4), Nei Guan (PC6), San Yin Jiao (SP6), Xuan Zhong (GB39), Zu San Li (ST36), and Shang Ju Xu (ST37). The patients randomized to the EA 1 and EA 2 groups undergo one session of EA daily from day 1 until day 4, or until pain resolves. The primary endpoint is the Visual Analog Scale (VAS) change for pain on day 5. Secondary endpoints include daily VAS, requirement of analgesics, changes of inflammatory markers, time to pain disappearance, and hospital days.

DISCUSSION

The results of this trial are expected to prove the efficacy of EA for pain relief in patients with acute pancreatitis. Based upon the results, EA would be applied to a variety of clinical practices for reducing pain.

TRIAL REGISTRATION

This trial is registered at ClinicalTrials.gov, ID: NCT03173222 . Registered on 1 August 2017.

Postsurgical prescriptions for opioid naive patients and association with overdose and misuse: retrospective cohort study

OBJECTIVE

To quantify the effects of varying opioid prescribing patterns after surgery on dependence, overdose, or abuse in an opioid naive population.

DESIGN

Retrospective cohort study.

SETTING

Surgical claims from a linked medical and pharmacy administrative database of 37 651 619 commercially insured patients between 2008 and 2016.

PARTICIPANTS

1 015 116 opioid naive patients undergoing surgery.

MAIN OUTCOME MEASURES

Use of oral opioids after discharge as defined by refills and total dosage and duration of use. The primary outcome was a composite of misuse identified by a diagnostic code for opioid dependence, abuse, or overdose.

RESULTS

568 612 (56.0%) patients received postoperative opioids, and a code for abuse was identified for 5906 patients (0.6%, 183 per 100 000 person years). Total duration of opioid use was the strongest predictor of misuse, with each refill and additional week of opioid use associated with an adjusted increase in the rate of misuse of 44.0% (95% confidence interval 40.8% to 47.2%, P<0.001), and 19.9% increase in hazard (18.5% to 21.4%, P<0.001), respectively.

CONCLUSIONS

Each refill and week of opioid prescription is associated with a large increase in opioid misuse among opioid naive patients. The data from this study suggest that duration of the prescription rather than dosage is more strongly associated with ultimate misuse in the early postsurgical period. The analysis quantifies the association of prescribing choices on opioid misuse and identifies levers for possible impact.

Empowering Post-Surgical Patients to Improve Opioid Disposal: A Before and After Quality Improvement Study

BACKGROUND

Our country is in the midst of an opioid epidemic. Although the problem is multifactorial, one issue is the presence of excess prescription opioid medications circulating in our communities. Our objective was to determine whether dissemination of an educational brochure would improve the disposal of unused opioids after surgery.

STUDY DESIGN

Eligible surgery patients from an upper extremity/peripheral nerve clinic were enrolled into this prospective before and after study between February 2017 and September 2017. Patients who reported opioid use preoperatively were excluded from this study. The same survey was administered to the group of patients who did not receive the intervention and to those who did receive the intervention. Our primary endpoint was the proportion of patients who disposed of unused opioid medications.

RESULTS

A total of 334 patients were studied: 164 who did not receive the brochure and 170 who received the brochure. Seventy-six patients were excluded for preoperative opioid use. After dissemination of the brochure, there was a significant increase in the proportion of patients who disposed of their unused opioids (11% vs 22%, p = 0.02). Of those who disposed of their opioids, there was no significant difference in the proportion of patients from each group who disposed in a manner that was recommended by the brochure (43% vs 64%, p = 0.19).

CONCLUSIONS

Dissemination of the educational brochure improved disposal of unused opioids after surgery. This low-cost, easily implemented intervention can improve disposal of unused opioids and ultimately, decrease the amount of excess opioids circulating in our communities.

Dose and Duration of Opioid Use in Propensity Score-Matched, Privately Insured Opioid Users With and Without Spinal Cord Injury

OBJECTIVES

To (1) compare the opioid utilization patterns in opioid users with spinal cord injury (SCI) to a propensity score-matched general population of opioid users without SCI; and (2) identify characteristics of persons with SCI associated with long-term and/or high-dose use of opioids.

DESIGN

Quasi-experimental analysis of archival data.

SETTING

Data used for the analysis were derived from Thompson Reuters MarketScan Commercial Claims and Encounters Databases for the years 2012 to 2013.

PARTICIPANTS

Participants (N=2908; aged 18-64y) included opioid users with SCI (n=1454) and propensity score-matched opioid users without SCI (n=1454). The cohorts were matched using demographics including comorbidities, hospital admissions, age, sex, and geographic region.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

Medical and pharmacy claims from 2012 to 2013 MarketScan data were analyzed to characterize whether persons were short-term (<90d) or long-term (≥90d) opioid users, and whether persons had high (≥120mg) or low (<120mg) average daily morphine equivalents.

RESULTS

Persons with SCI were significantly more likely to be long-term users of low-dose, short-acting opioids (P<.0001) and more likely to be taking high morphine-equivalent doses of long-acting opioids (P<.0001) than matched controls. Among persons with SCI, those with lumbar/sacral injuries had more days' supply of high-dose, long-acting opioids than did persons with thoracic or cervical injuries.

CONCLUSIONS

Persons with SCI are prescribed opioids for longer durations and at higher morphine-equivalent doses than controls, which may increase the risk of opioid dependence or adverse drug events. Findings should be considered in the development of practice guidelines for alternate pain management options or opioid dependence interventions for persons with SCI.

Who is Overdosing? An Updated Picture of Overdose Deaths From 2008 to 2015

PURPOSE

To determine the role of opioids in drug overdose deaths in Allen County, Indiana between January 1, 2008, and December 31, 2015.

METHODS

File review of 418 overdose deaths was performed using Indiana State Department of Health death certificates available through the Allen County Coroner's Office. Data from autopsy and toxicology reports and coroner-requested prescribing data from Indiana's Prescription Monitoring Program were reviewed. Cause of death and available data were analyzed to identify patterns and trends related to overdose deaths.

RESULTS

Four hundred eighteen drug overdose deaths were identified (336 accidental, 66 intentional, and 16 undetermined). Mean age was 42.5 years, 88.5% were Caucasian, and 68.7% were employed. The majority of deaths occurred at a place of residence (71.4%) and with other people present (57.5% of the time). Depression was the most common comorbidity identified. The most common drug classes identified by toxicology were opioids, followed by benzodiazepines. Significant increases in both heroin (35% of deaths in 2015 versus 8.2% in 2013) and fentanyl (30% of deaths in 2015 versus 2.2% in 2011) were observed.

CONCLUSIONS

Drug overdose continues to be a significant cause of death in Allen County. The majority of deaths were accidental and in relatively young, employed individuals. Prevention and awareness strategies should be encouraged, given that the majority of overdose deaths occurred at a place of residence with other people frequently present. Additional concerns about patterns of drug use were confirmed with marked increases in both heroin and fentanyl contributing to overdose deaths in the latter part of the study.