A predictive risk model for nonfatal opioid overdose in a statewide population of buprenorphine patients

Association of buprenorphine retention and subsequent adverse outcomes following non-fatal overdose: An analysis using statewide linked Maryland databases

INTRODUCTION

Patients receiving buprenorphine after a non-fatal overdose have lower risk of future nonfatal or fatal overdose, but less is known about the relationship between buprenorphine retention and the risk of adverse outcomes in the post-overdose year.

OBJECTIVE

To examine the relationship between the total number of months with an active buprenorphine prescription (retention) and the odds of an adverse outcome within the 12 months following an index non-fatal overdose.

MATERIALS AND METHODS

We studied a cohort of people with an index non-fatal opioid overdose in Maryland between July 2016 and December 2020 and at least one filled buprenorphine prescription in the 12-month post-overdose observation period. We used individually linked Maryland prescription drug and hospital admissions data. Multivariable logistic regression models were used to examine buprenorphine retention and associated odds of experiencing a second non-fatal overdose, all-cause emergency department visits, and all-cause hospitalizations.

RESULTS

Of 5439 people, 25% (n=1360) experienced a second non-fatal overdose, 78% had an (n=4225) emergency department visit, and 37% (n=2032) were hospitalized. With each additional month of buprenorphine, the odds of experiencing another non-fatal overdose decreased by 4.7%, all-cause emergency department visits by 5.3%, and all-cause hospitalization decreased by 3.9% (p<.0001, respectively). Buprenorphine retention for at least nine months was a critical threshold for reducing overdose risk versus shorter buprenorphine retention.

CONCLUSIONS

Buprenorphine retention following an index non-fatal overdose event significantly decreases the risk of future overdose, emergency department use, and hospitalization even among people already on buprenorphine.

Detoxification, 12-step meeting attendance, and non-fatal opioid overdoses among a suburban/exurban population with opioid use disorder

BACKGROUND

Drug overdoses are the leading cause of injury death in the United States with an estimated 105,752 individuals dying from an overdose in the United States in a 12-month period ending October 2021. Given that people who have opioid use disorder (OUD) are at an increased risk of death, it is crucial to assess risk factors associated with opioid overdose to improve interventions.

OBJECTIVES

We examine factors associated with non-fatal overdose among a suburban/exurban population with OUD in Southern California.

METHODS

Participants were recruited by convenience sampling (n = 355) and were interviewed between November 2017 to August 2018. Participants were eligible for the study if they had a history of pharmaceutical opioid use.

RESULTS

A total of 198 (55.8%) participants reported at least one overdose in their lifetime. A total of 229 participants identified as male, 124 identified as female, and 2 identified as non-binary. When controlling for demographic factors, non-oral opioid administration at first opioid use (AOR 2.82, 95% CI 1.52-5.22), having a history of methadone detoxification, (AOR 2.23, 95% CI 1.27-3.91), history of buprenorphine detoxification (AOR 1.77, 95% CI 1.02-3.07), and history of 12 step attendance (AOR 1.89, 95% CI 1.12-3.20) were found to be independently and positively associated with lifetime opioid overdose.

CONCLUSIONS

Detoxification with buprenorphine and methadone was found to be associated with having a non-fatal opioid overdose. Buprenorphine and methadone should not be prescribed as a detoxification medication as long-term use of medication for OUD results in better outcomes than medication that is used short-term.

Outcome class imbalance and rare events: An underappreciated complication for overdose risk prediction modeling

BACKGROUND AND AIMS

Low outcome prevalence, often observed with opioid-related outcomes, poses an underappreciated challenge to accurate predictive modeling. Outcome class imbalance, where non-events (i.e. negative class observations) outnumber events (i.e. positive class observations) by a moderate to extreme degree, can distort measures of predictive accuracy in misleading ways, and make the overall predictive accuracy and the discriminatory ability of a predictive model appear spuriously high. We conducted a simulation study to measure the impact of outcome class imbalance on predictive performance of a simple SuperLearner ensemble model and suggest strategies for reducing that impact.

DESIGN, SETTING, PARTICIPANTS

Using a Monte Carlo design with 250 repetitions, we trained and evaluated these models on four simulated data sets with 100 000 observations each: one with perfect balance between events and non-events, and three where non-events outnumbered events by an approximate factor of 10:1, 100:1, and 1000:1, respectively.

MEASUREMENTS

We evaluated the performance of these models using a comprehensive suite of measures, including measures that are more appropriate for imbalanced data.

FINDINGS

Increasing imbalance tended to spuriously improve overall accuracy (using a high threshold to classify events vs non-events, overall accuracy improved from 0.45 with perfect balance to 0.99 with the most severe outcome class imbalance), but diminished predictive performance was evident using other metrics (corresponding positive predictive value decreased from 0.99 to 0.14).

CONCLUSION

Increasing reliance on algorithmic risk scores in consequential decision-making processes raises critical fairness and ethical concerns. This paper provides broad guidance for analytic strategies that clinical investigators can use to remedy the impacts of outcome class imbalance on risk prediction tools.

Sociodemographic and prescribing characteristics that impact long-term retention in buprenorphine treatment for opioid use disorder among a statewide population

BACKGROUND AND AIMS

Partial opioid agonist medications for opioid use disorder reduce mortality and morbidity, however long-term retention in treatment is challenging. The objective of this study was to identify patient and prescription characteristics associated with long-term buprenorphine treatment retention.

METHODS

We used data from the Rhode Island prescription drug monitoring program to identify residents who initiated buprenorphine treatment and determine if they were retained in long-term buprenorphine treatment 12-months after treatment initiation. Multivariable logistic regression models were used to identify sociodemographic and prescription characteristics associated with long-term buprenorphine retention.

FINDINGS

During the study period 4898 unique Rhode Island residents initiated buprenorphine treatment, of whom 37.8 % were retained in treatment at 12-months. Demographic factors associated with a higher odds of long-term buprenorphine retention included older age, female sex, Medicaid insurance (vs private), and living closer to the pharmacy where the prescription was filled. Individuals who were prescribed the tablet formulation (aOR: 0.82 [95 % CI 0.72, 0.93]) or received a non-buprenorphine opioid during the follow-up window (aOR: 0.37 [95 % CI 0.31, 0.44]) had lower odds of long-term treatment at 12-months. Individuals who received at least one day of overlapping benzodiazepine and buprenorphine prescriptions (aOR: 2.00 [95 % CI 1.70, 2.34]) and those given a longer days supply (aOR: 1.26 [95 % CI 1.01, 1.56]) had higher odds of long-term treatment at 12-months. Findings were similar for treatment retention at 6-months in sensitivity analyses.

CONCLUSIONS

These findings highlight several modifiable prescribing practices associated with long-term buprenorphine retention, suggesting that clinicians and public health practitioners can help remove barriers to long-term retention.

Evaluation of the Effectiveness of Buprenorphine-Naloxone on Opioid Overdose and Death among Insured Patients with Opioid Use Disorder in the United States

Opioid use disorder (OUD) is a chronic disease requiring long-term treatment and is associated with opioid overdose and increased risk of mortality. However, existing randomized clinical trials focused on short-term treatment engagement and detoxification rather than overdose or mortality risk due to limited follow-up time and ethical considerations. We used a hypothetical trial framework to conduct a retrospective cohort study to assess the effectiveness of time-varying buprenorphine-naloxone on opioid overdose and death. We identified 58,835 insured adult patients with OUD diagnosis in the US, 2010-2017. We fit a marginal structural model using inverse probability weighting methods to account for measured baseline and time-varying confounders, as well as selection bias due to possibly differential loss-to-follow-up. We found that receipt of buprenorphine-naloxone was associated with reduced risk of opioid overdose (hazard ratio (HR) = 0.66, 95% confidence interval (CI): 0.49, 0.91), death (HR = 0.24, 95% CI: 0.08, 0.75), and overdose or death (HR = 0.58, 95% CI: 0.40, 0.84). The E-value for death was 7.8, which was larger than the upper 95% CI of the association between each measured baseline variable and all-cause death, which implies that the unmeasured confounding itself may not explain away the estimated effect of treatment on the endpoint of all-cause mortality.

Development and validation of a risk-score model for opioid overdose using a national claims database

Opioid overdose can be serious adverse effects of opioid analgesics. Thus, several strategies to mitigate risk and reduce the harm of opioid overdose have been developed. However, despite a marked increase in opioid analgesic consumption in Korea, there have been no tools predicting the risk of opioid overdose in the Korean population. Using the national claims database of the Korean population, we identified patients who were incidentally prescribed non-injectable opioid analgesic (NIOA) at least once from 2017 to 2018 (N = 1,752,380). Among them, 866 cases of opioid overdose occurred, and per case, four controls were selected. Patients were randomly allocated to the development (80%) and validation (20%) cohort. Thirteen predictive variables were selected via logistic regression modelling, and a risk-score was assigned for each predictor. Our model showed good performance with c-statistics of 0.84 in the validation cohort. The developed risk score model is the first tool to identify high-risk patients for opioid overdose in Korea. It is expected to be applicable in the clinical setting and useful as a national level surveillance tool due to the easily calculable and identifiable predictors available from the claims database.

Ensemble learning to predict opioid-related overdose using statewide prescription drug monitoring program and hospital discharge data in the state of Tennessee

Objective

To develop and validate algorithms for predicting 30-day fatal and nonfatal opioid-related overdose using statewide data sources including prescription drug monitoring program data, Hospital Discharge Data System data, and Tennessee (TN) vital records. Current overdose prevention efforts in TN rely on descriptive and retrospective analyses without prognostication.

Materials and Methods

Study data included 3 041 668 TN patients with 71 479 191 controlled substance prescriptions from 2012 to 2017. Statewide data and socioeconomic indicators were used to train, ensemble, and calibrate 10 nonparametric “weak learner” models. Validation was performed using area under the receiver operating curve (AUROC), area under the precision recall curve, risk concentration, and Spiegelhalter z-test statistic.

Results

Within 30 days, 2574 fatal overdoses occurred after 4912 prescriptions (0.0069%) and 8455 nonfatal overdoses occurred after 19 460 prescriptions (0.027%). Discrimination and calibration improved after ensembling (AUROC: 0.79–0.83; Spiegelhalter P value: 0–.12). Risk concentration captured 47–52% of cases in the top quantiles of predicted probabilities.

Discussion

Partitioning and ensembling enabled all study data to be used given computational limits and helped mediate case imbalance. Predicting risk at the prescription level can aggregate risk to the patient, provider, pharmacy, county, and regional levels. Implementing these models into Tennessee Department of Health systems might enable more granular risk quantification. Prospective validation with more recent data is needed.

Conclusion

Predicting opioid-related overdose risk at statewide scales remains difficult and models like these, which required a partnership between an academic institution and state health agency to develop, may complement traditional epidemiological methods of risk identification and inform public health decisions.

Using administrative data to predict cessation risk and identify novel predictors among new entrants to opioid agonist treatment

BACKGROUND

Longer retention in opioid agonist treatment (OAT) is associated with improved treatment outcomes but 12-month retention rates are often low. Innovative approaches are needed to strengthen retention in OAT. We develop and compare traditional and deep learning-extensions of Cox regression to examine the potential for predicting time in OAT at individuals' first episode entry.

METHODS

Retrospective cohort study in New South Wales, Australia including 16,576 people entering OAT for the first time between January 2006 and December 2017. We develop 12-month OAT cessation prediction models using traditional and deep learning-extensions of the Cox regression algorithm with predictors evaluated from linked administrative datasets. Proportion of explained variation, calibration, and discrimination are compared using 5 × 2 cross-validation.

RESULTS

Twelve-month cessation rate was 58.4%. The largest hazard ratios for earlier cessation from the deep learning model were observed for treatment factors, including private dosing points (HR=1.54, 95% CI=1.49-1.60) and buprenorphine medication (HR=1.43, 95% CI=1.39-1.46). Diagnostic codes for homelessness (HR=1.09, 95% CI=1.04-1.13), outpatient treatment for drug use disorders (HR=1.10, 95% CI=1.06-1.15), and occupant of vehicle accident (HR=1.04, 95% CI=1.01-1.07) from past-year health service presentations were identified as significant predictors of retention. We observed no improvement in performance of the deep learning model over traditional Cox regression.

CONCLUSIONS

Deep learning may be more useful in identifying novel risk factors of OAT retention from administrative data than evaluating individual-level risk. An increased focus on addressing structural issues at the population level and considering alternate models of care may be more effective at improving retention than delivering fully personalised OAT.

Assessing opioid overdose risk: a review of clinical prediction models utilizing patient-level data

Drug, and specifically opioid-related, overdoses remain a major public health problem in the United States. Multiple studies have examined individual risk factors associated with overdose risk, but research developing clinical risk prediction tools for overdose has only emerged in the last few years. We conducted a comprehensive review of the literature on patient-level factors associated with opioid-related overdose risk, with an emphasis on clinical risk prediction models for opioid-related overdose in the United States. Studies that developed and/or validated clinical prediction models were closely reviewed and evaluated to determine the state of the field. We identified 12 studies that reported risk prediction models for opioid-related overdose risk. Published models were developed from a variety of data sources, including Veterans Health Administration data, Medicare data, commercial insurance data, and statewide linked datasets. Studies reported model performance using measures of discrimination, usually at good-to-excellent levels, though they did not always assess calibration. C-statistics were better for models that included clinical predictors (c-statistics: 0.75-0.95) compared to models without them (c-statistics: 0.69-0.82). External validation of models was rare, and we found no studies evaluating implementation of models or risk prediction tools into clinical practice. A common feature of these models was a high rate of false positives, largely because opioid-related overdose is rare in the general population. Thus, efforts to implement prediction models into practice should take into account that published models overestimate overdose risk for many low-risk patients. Future prediction models assessing overdose risk should employ external validation and address model calibration. In order to translate findings from prediction models into clinical public health benefit, future studies should focus on developing clinical prediction tools based on prediction models, implementing these tools into clinical practice, and evaluating the impact of these models on treatment decisions, patient outcomes, and, ultimately, opioid overdose rates.

Endogenous opiates and behavior: 2019

This paper is the forty-second consecutive installment of the annual anthological review of research concerning the endogenous opioid system, summarizing articles published during 2019 that studied the behavioral effects of molecular, pharmacological and genetic manipulation of opioid peptides and receptors as well as effects of opioid/opiate agonists and antagonists. The review is subdivided into the following specific topics: molecular-biochemical effects and neurochemical localization studies of endogenous opioids and their receptors (1), the roles of these opioid peptides and receptors in pain and analgesia in animals (2) and humans (3), opioid-sensitive and opioid-insensitive effects of nonopioid analgesics (4), opioid peptide and receptor involvement in tolerance and dependence (5), stress and social status (6), learning and memory (7), eating and drinking (8), drug abuse and alcohol (9), sexual activity and hormones, pregnancy, development and endocrinology (10), mental illness and mood (11), seizures and neurologic disorders (12), electrical-related activity and neurophysiology (13), general activity and locomotion (14), gastrointestinal, renal and hepatic functions (15), cardiovascular responses (16), respiration and thermoregulation (17), and immunological responses (18).

A spatio-temporal Bayesian model to estimate risk and evaluate factors related to drug-involved emergency department visits in the greater Baltimore metropolitan area

The ongoing opioid overdose epidemic in the United States presents a major public health challenge. Opioid-involved morbidity, especially nonfatal emergency department (ED) visits, are a key opportunity to prevent mortality and measure the extent of the problem in the local substance use landscape. Data on the rate of ED visits is normally distributed by federal agencies. However, state- and substate-level rates of ED visit demonstrate significant geographic variation. This study uses an ongoing sample of ED visits from four hospitals in the University of Maryland Medical System from January 2016 to December 2019 to provide locally sensitive information on ED visit rates and risk for drug-related health outcomes. Using exploratory spatial data analysis and spatio-temporal Bayesian models, this study analyzes both the frequency and risk of heroin-, methadone-, and cocaine-involved ED visits across the greater Baltimore Maryland area at the Zip Code Tabulation Area-level (ZCTA). The Global Moran's I for total heroin-, methadone-, and cocaine-involved ED visits in 2019 was 0.44, 0.56, and 0.53, demonstrating strong positive spatial autocorrelation. Spatio-temporal Bayesian models indicated that ZCTA with a higher score in a deprivation index, with a higher share of Centers for Medicare Services claims, and adjacent to a sampled UMMS hospital had an increased risk of ED visits, with variation in the magnitude of this increased risk depending on the drug-demographic strata. Modeled disease risk surfaces - including posterior median risk and posterior exceedance probabilities - showed distinctly different risk surfaces between the substances of interest, probabilistically identifying ZCTA with a lower or higher risk of ED visits. The modeling approach used a sample of ED visits from a larger health system to estimate recent, locally sensitive drug-related morbidity across a large metropolitan area.

Big data and predictive modelling for the opioid crisis: existing research and future potential

A need exists to accurately estimate overdose risk and improve understanding of how to deliver treatments and interventions in people with opioid use disorder in a way that reduces such risk. We consider opportunities for predictive analytics and routinely collected administrative data to evaluate how overdose could be reduced among people with opioid use disorder. Specifically, we summarise global trends in opioid use and overdoses; describe the use of big data in research into opioid overdose; consider the potential for predictive modelling, including machine learning, for prevention and monitoring of opioid overdoses; and outline the challenges and risks relating to the use of big data and machine learning in reducing harms that are related to opioid use. Future research for improving the coverage and provision of existing interventions, treatments, and resources for opioid use disorder requires collaboration of multiple agencies. Predictive modelling could transport the concept of stratified medicine to public health through novel methods, such as predictive modelling and emulated trials for evaluating diagnoses and prognoses of opioid use disorder, predicting treatment response, and providing targeted treatment recommendations.

Adherence to buprenorphine: An analysis of prescription drug monitoring program data

BACKGROUND

Although buprenorphine is an evidence-based treatment for opioid use disorder (OUD), many individuals discontinue treatment soon after starting. This study assesses predictors of buprenorphine adherence using Prescription Drug Monitoring Program (PDMP) data.

METHODS

PDMP data for Philadelphia, Pennsylvania were used to measure 180-day adherence to buprenorphine among new initiates. Adherence was classified using percent days covered (PDC), and new initiates with PDC ≥ 0.80 were classified as adherent. Multivariable logistic regression was conducted to determine factors associated with buprenorphine adherence.

RESULTS

Between January 2017 and December 2018, 10,669 Philadelphia residents initiated buprenorphine and 26.6 % remained adherent after 180 days. Demographic factors associated with greater odds of adherence included age category and female sex (aOR: 1.37; 95 % CI: 1.25-1.50). Those filling an opioid prescription, other than buprenorphine, during the follow-up period had lower odds of adherence than those who did not fill an opioid prescription (aOR: 0.62; 95 % CI: 0.50-0.77). Odds of adherence was greater for those on the film formulation (aOR: 1.37; 95 % CI: 1.25-1.50) than the tablet formulation. Those filling medium (aOR: 1.76; 95 % CI: 1.55-2.00) and high dose (aOR: 5.11; 95 % CI: 4.30-6.17) buprenorphine prescriptions had higher odds of adherence than those filling low dose prescriptions.

CONCLUSIONS

Individual demographics, receipt of an opioid prescription, buprenorphine formulation, and buprenorphine dose were all associated with adherence to buprenorphine. Ongoing strategies to address OUD need to prioritize increasing retention in long-term evidence-based buprenorphine treatment while also encouraging providers to regularly consult the PDMP to ensure patient compliance.

Predictive Modeling of Opioid Overdose Using Linked Statewide Medical and Criminal Justice Data

IMPORTANCE

Responding to the opioid crisis requires tools to identify individuals at risk of overdose. Given the expansion of illicit opioid deaths, it is essential to consider risk factors across multiple service systems.

OBJECTIVE

To develop a predictive risk model to identify opioid overdose using linked clinical and criminal justice data.

DESIGN, SETTING, AND PARTICIPANTS

A cross-sectional sample was created using 2015 data from 4 Maryland databases: all-payer hospital discharges, the prescription drug monitoring program (PDMP), public-sector specialty behavioral treatment, and criminal justice records for property or drug-associated offenses. Maryland adults aged 18 to 80 years with records in any of 4 databases were included, excluding individuals who died in 2015 or had a non-Maryland zip code. Logistic regression models were estimated separately for risk of fatal and nonfatal opioid overdose in 2016. Model performance was assessed using bootstrapping. Data analysis took place from February 2018 to November 2019.

EXPOSURES

Controlled substance prescription fills and hospital, specialty behavioral health, or criminal justice encounters.

MAIN OUTCOMES AND MEASURES

Fatal opioid overdose defined by the state medical examiner and 1 or more nonfatal overdoses treated in Maryland hospitals during 2016.

RESULTS

There were 2 294 707 total individuals in the sample, of whom 42.3% were male (n = 970 019) and 53.0% were younger than 50 years (647 083 [28.2%] aged 18-34 years and 568 160 [24.8%] aged 35-49 years). In 2016, 1204 individuals (0.05%) in the sample experienced fatal opioid overdose and 8430 (0.37%) experienced nonfatal opioid overdose. In adjusted analysis, the factors mostly strongly associated with fatal overdose were male sex (odds ratio [OR], 2.40 [95% CI, 2.08-2.76]), diagnosis of opioid use disorder in a hospital (OR, 2.93 [95% CI, 2.17-3.80]), release from prison in 2015 (OR, 4.23 [95% CI, 2.10-7.11]), and receiving opioid addiction treatment with medication (OR, 2.81 [95% CI, 2.20-3.86]). Similar associations were found for nonfatal overdose. The area under the curve for fatal overdose was 0.82 for a model with hospital variables, 0.86 for a model with both PDMP and hospital variables, and 0.89 for a model that further added behavioral health and criminal justice variables. For nonfatal overdose, the area under the curve using all variables was 0.85.

CONCLUSIONS AND RELEVANCE

In this analysis, fatal and nonfatal opioid overdose could be accurately predicted with linked administrative databases. Hospital encounter data had higher predictive utility than PDMP data. Model performance was meaningfully improved by adding PDMP records. Predictive models using linked databases can be used to target large-scale public health programs.