Prediction Model for Two-Year Risk of Opioid Overdose Among Patients Prescribed Chronic Opioid Therapy

Predictive Models to Assess Risk of Persistent Opioid Use, Opioid Use Disorder, and Overdose

BACKGROUND

This systematic review summarizes the development, accuracy, quality, and clinical utility of predictive models to assess the risk of opioid use disorder (OUD), persistent opioid use, and opioid overdose.

METHODS

In accordance with Preferred Reporting Items for a Systematic Review and Meta-analysis guidelines, 8 electronic databases were searched for studies on predictive models and OUD, overdose, or persistent use in adults until June 25, 2023. Study selection and data extraction were completed independently by 2 reviewers. Risk of bias of included studies was assessed independently by 2 reviewers using the Prediction model Risk of Bias ASsessment Tool (PROBAST).

RESULTS

The literature search yielded 3130 reports; after removing 199 duplicates, excluding 2685 studies after abstract review, and excluding 204 studies after full-text review, the final sample consisted of 41 studies that developed more than 160 predictive models. Primary outcomes included opioid overdose (31.6% of studies), OUD (41.4%), and persistent opioid use (17%). The most common modeling approach was regression modeling, and the most common predictors included age, sex, mental health diagnosis history, and substance use disorder history. Most studies reported model performance via the c statistic, ranging from 0.507 to 0.959; gradient boosting tree models and neural network models performed well in the context of their own study. One study deployed a model in real time. Risk of bias was predominantly high; concerns regarding applicability were predominantly low.

CONCLUSIONS

Models to predict opioid-related risks are developed using diverse data sources and predictors, with a wide and heterogenous range of accuracy metrics. There is a need for further research to improve their accuracy and implementation.

A pilot study to identify pharmacogenomic and clinical risk factors associated with opioid related falls and adverse effects in older adults

Given the high prevalence of pain in older adults and current trends in opioid prescribing, inclusion of genetic information in risk prediction tools may improve opioid risk assessment. Our objectives were to (1) determine the feasibility of recruiting socioeconomically disadvantaged and racially diverse middle aged and older adult populations for a study seeking to identify risk factors for opioid-related falls and other serious adverse effects and (2) explore potential associations between the Risk Index for Overdose or Serious Opioid-induced Respiratory Depression (CIP-RIOSORD) risk class and other patient factors with falls and serious opioid adverse effects. This was an observational study of 44 participants discharged home from the emergency department with an opioid prescription for acute pain and followed for 30 days. We found pain interference may predict opioid-related falls or serious adverse effects within older, opioid-treated patients. If validated, pain interference may prove to be a beneficial marker for risk stratification of older adults initiated on opioids for acute pain.

Predictors of fatal and nonfatal overdose after prescription of opioids for chronic pain: a systematic review and meta-analysis of observational studies

BACKGROUND

Higher doses of opioids, mental health comorbidities, co-prescription of sedatives, lower socioeconomic status and a history of opioid overdose have been reported as risk factors for opioid overdose; however, the magnitude of these associations and their credibility are unclear. We sought to identify predictors of fatal and nonfatal overdose from prescription opioids.

METHODS

We systematically searched MEDLINE, Embase, CINAHL, PsycINFO and Web of Science up to Oct. 30, 2022, for observational studies that explored predictors of opioid overdose after their prescription for chronic pain. We performed random-effects meta-analyses for all predictors reported by 2 or more studies using odds ratios (ORs) and 95% confidence intervals (CIs).

RESULTS

Twenty-eight studies (23 963 716 patients) reported the association of 103 predictors with fatal or nonfatal opioid overdose. Moderate- to high-certainty evidence supported large relative associations with history of overdose (OR 5.85, 95% CI 3.78-9.04), higher opioid dose (OR 2.57, 95% CI 2.08-3.18 per 90-mg increment), 3 or more prescribers (OR 4.68, 95% CI 3.57-6.12), 4 or more dispensing pharmacies (OR 4.92, 95% CI 4.35-5.57), prescription of fentanyl (OR 2.80, 95% CI 2.30-3.41), current substance use disorder (OR 2.62, 95% CI 2.09-3.27), any mental health diagnosis (OR 2.12, 95% CI 1.73-2.61), depression (OR 2.22, 95% CI 1.57-3.14), bipolar disorder (OR 2.07, 95% CI 1.77-2.41) or pancreatitis (OR 2.00, 95% CI 1.52-2.64), with absolute risks among patients with the predictor ranging from 2-6 per 1000 for fatal overdose and 4-12 per 1000 for nonfatal overdose.

INTERPRETATION

We identified 10 predictors that were strongly associated with opioid overdose. Awareness of these predictors may facilitate shared decision-making regarding prescribing opioids for chronic pain and inform harm-reduction strategies SYSTEMATIC REVIEW REGISTRATION: Open Science Framework (https://osf.io/vznxj/).

Update of a Multivariable Opioid Overdose Risk Prediction Model to Enhance Clinical Care for Long-term Opioid Therapy Patients

BACKGROUND

Clinical opioid overdose risk prediction models can be useful tools to reduce the risk of overdose in patients prescribed long-term opioid therapy (LTOT). However, evolving overdose risk environments and clinical practices in addition to potential harmful model misapplications require careful assessment prior to widespread implementation into clinical care. Models may need to be tailored to meet local clinical operational needs and intended applications in practice.

OBJECTIVE

To update and validate an existing opioid overdose risk model, the Kaiser Permanente Colorado Opioid Overdose (KPCOOR) Model, in patients prescribed LTOT for implementation in clinical care.

DESIGN, SETTING, AND PARTICIPANTS

The retrospective cohort study consisted of 33, 625 patients prescribed LTOT between January 2015 and June 2019 at Kaiser Permanente Colorado, with follow-up through June 2021.

MAIN MEASURES

The outcome consisted of fatal opioid overdoses identified from vital records and non-fatal opioid overdoses from emergency department and inpatient settings. Predictors included demographics, medication dispensings, substance use disorder history, mental health history, and medical diagnoses. Cox proportional hazards regressions were used to model 2-year overdose risk.

KEY RESULTS

During follow-up, 65 incident opioid overdoses were observed (111.4 overdoses per 100,000 person-years) in the study cohort, of which 11 were fatal. The optimal risk model needed to risk-stratify patients and to be easily interpreted by clinicians. The original 5-variable model re-validated on the new study cohort had a bootstrap-corrected C-statistic of 0.73 (95% CI, 0.64-0.85) compared to a C-statistic of 0.80 (95% CI, 0.70-0.88) in the updated model and 0.77 (95% CI, 0.66-0.87) in the final adapted 7-variable model, which was also well-calibrated.

CONCLUSIONS

Updating and adapting predictors for opioid overdose in the KPCOOR Model with input from clinical partners resulted in a parsimonious and clinically relevant model that was poised for integration in clinical care.

Opioid Overdose Risk Following Hospital Discharge Among Individuals Prescribed Long-Term Opioid Therapy: a Risk Interval Analysis

BACKGROUND

Individuals prescribed long-term opioid therapy (LTOT) have increased risk of readmission and death after hospital discharge. The risk of opioid overdose during the immediate post-discharge time period is unknown.

OBJECTIVE

To examine the association between time since hospital discharge and opioid overdose among individuals prescribed LTOT.

DESIGN

Self-controlled risk interval analysis.

PARTICIPANTS

Adults prescribed LTOT with at least one hospital discharge at a safety-net health system and a non-profit healthcare organization in Colorado.

MAIN MEASURES

We identified individuals prescribed LTOT who were discharged from January 2006 through June 2019. The outcome was a composite of fatal and non-fatal opioid overdoses during a 90-day post-discharge observation period, identified using electronic health record (EHR) and vital statistics data. Risk intervals included days 0-6 after index and subsequent hospital discharges. Control intervals ranged from days 7 to 89 after index discharge and included all other time during the observation period that did not fall within a risk interval or time readmitted during a subsequent hospitalization, which was excluded. Poisson regression was used to estimate incidence rate ratios (IRR) and 95% confidence intervals (CI) for overdose events during risk in comparison to control intervals.

KEY RESULTS

We identified 7695 adults (63.3% over 55 years, 59.4% female, 20.3% Hispanic) who experienced 9499 total discharges during the study period. Twenty-one overdoses occurred during their observation periods (1174 per 100,000 person-years [9 in risk, 12 in control]). Overdose risk was significantly higher during the risk interval in comparison to the control interval (IRR 6.92; 95% CI 2.92-16.43).

CONCLUSION

During the first 7 days after hospital discharge, individuals prescribed LTOT appear to be at elevated risk for opioid overdose. Clarifying mechanisms of overdose risk may help inform in-hospital and post-discharge prevention strategies.

Association Between Opioid Dose Reduction Rates and Overdose Among Patients Prescribed Long-Term Opioid Therapy

BACKGROUND

Tapering long-term opioid therapy is an increasingly common practice, yet rapid opioid dose reductions may increase the risk of overdose. The objective of this study was to compare overdose risk following opioid dose reduction rates of ≤10%, 11% to 20%, 21% to 30%, and >30% per month to stable dosing.

METHODS

We conducted a retrospective cohort study in three health systems in Colorado and Wisconsin. Participants were patients ≥18 years of age prescribed long-term opioid therapy between January 1, 2006, and June 30, 2019. Five opioid dosing patterns and drug overdoses (fatal and nonfatal) were identified using electronic health records, pharmacy records, and the National Death Index. Cox proportional hazard regression was conducted on a propensity score-weighted cohort to estimate adjusted hazard ratios (aHRs) for follow-up periods of 1, 3, 6, 9, and 12 months after a dose reduction.

RESULTS

In a cohort of 17 540 patients receiving long-term opioid therapy, 42.7% of patients experienced a dose reduction. Relative to stable dosing, a dose reduction rate of >30% was associated with an increased risk of overdose and the aHR estimates decreased as the follow-up increased; the aHRs for the 1-, 6- and 12-month follow-ups were 5.33 (95% CI, 1.98-14.34), 1.81 (95% CI,1.08-3.03), and 1.49 (95% CI, 0.97-2.27), respectively. The slower tapering rates were not associated with overdose risk.

CONCLUSIONS

Patients receiving long-term opioid therapy exposed to dose reduction rates of >30% per month had increased overdose risk relative to patients exposed to stable dosing. Results support the use of slow dose reductions to minimize the risk of overdose.

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.

Pain in People Experiencing Homelessness: A Scoping Review

BACKGROUND

Prior work suggests that people experiencing homelessness (PEH) are at heightened risk for developing pain and have a uniquely burdensome pain experience.

PURPOSE

The aim of this scoping review was to map the current peer-reviewed, published literature on the pain experience of PEH.

METHODS

In accordance with the US Annual Homeless Assessment Report, we defined homelessness as lacking shelter or a fixed address within the last year. We conceptualized the pain experience via a modified version of the Social Communication Model of Pain, which considers patient, provider, and contextual factors. Published articles were identified with CINHAL, Embase, PubMed, PsycINFO, and Web of Science databases.

RESULTS

Sixty-nine studies met inclusion criteria. Studies revealed that PEH have high rates of pain and experience high levels of pain intensity and interference. Substantially fewer studies examined other factors relevant to the pain experience, such as self-management, treatment-seeking behaviors, and pain management within healthcare settings. Nonetheless, initial evidence suggests that pain is undermanaged in PEH.

CONCLUSIONS

Future research directions to understand pain and homelessness are discussed, including factors contributing to the under-management of pain. This scoping review may inform future work to develop interventions to address the specific pain care needs of PEH.

Risk of Experiencing an Overdose Event for Patients Undergoing Treatment With Medication for Opioid Use Disorder

OBJECTIVE

Overdose risk during a course of treatment with medication for opioid use disorder (MOUD) has not been clearly delineated. The authors sought to address this gap by leveraging a new data set from three large pragmatic clinical trials of MOUD.

METHODS

Adverse event logs, including overdose events, from the three trials (N=2,199) were harmonized, and the overall risk of having an overdose event in the 24 weeks after randomization was compared for each study arm (one methadone, one naltrexone, and three buprenorphine groups), using survival analysis with time-dependent Cox proportional hazard models.

RESULTS

By week 24, 39 participants had ≥1 overdose event. The observed frequency of having an overdose event was 15 (5.30%) among 283 patients assigned to naltrexone, eight (1.51%) among 529 patients assigned to methadone, and 16 (1.15%) among 1,387 patients assigned to buprenorphine. Notably, 27.9% of patients assigned to extended-release naltrexone never initiated the medication, and their overdose rate was 8.9% (7/79), compared with 3.9% (8/204) among those who initiated naltrexone. Controlling for sociodemographic and time-varying medication adherence variables and baseline substance use, a proportional hazard model did not show a significant effect of naltrexone assignment. Significantly higher probabilities of experiencing an overdose event were observed among patients with baseline benzodiazepine use (hazard ratio=3.36, 95% CI=1.76, 6.42) and those who either were never inducted on their assigned study medication (hazard ratio=6.64, 95% CI=2.12, 19.54) or stopped their medication after initial induction (hazard ratio=4.04, 95% CI=1.54, 10.65).

CONCLUSIONS

Among patients with opioid use disorder seeking medication treatment, the risk of overdose events over the next 24 weeks is elevated among those who fail to initiate or discontinue medication and those who report benzodiazepine use at baseline.

Effect of a Predictive Analytics-Targeted Program in Patients on Opioids: a Stepped-Wedge Cluster Randomized Controlled Trial

BACKGROUND

Risk of overdose, suicide, and other adverse outcomes are elevated among sub-populations prescribed opioid analgesics. To address this, the Veterans Health Administration (VHA) developed the Stratification Tool for Opioid Risk Mitigation (STORM)-a provider-facing dashboard that utilizes predictive analytics to stratify patients prescribed opioids based on risk for overdose/suicide.

OBJECTIVE

To evaluate the impact of the case review mandate on serious adverse events (SAEs) and all-cause mortality among high-risk Veterans.

DESIGN

A 23-month stepped-wedge cluster randomized controlled trial in all 140 VHA medical centers between 2018 and 2020.

PARTICIPANTS

A total of 44,042 patients actively prescribed opioid analgesics with high STORM risk scores (i.e., percentiles 1% to 5%) for an overdose or suicide-related event.

INTERVENTION

A mandate requiring providers to perform case reviews on opioid analgesic-prescribed patients at high risk of overdose/suicide.

MAIN MEASURES

Nine serious adverse events (SAEs), case review completion, number of risk mitigation strategies, and all-cause mortality.

KEY RESULTS

Mandated review inclusion was associated with a significant decrease in all-cause mortality within 4 months of inclusion (OR: 0.78; 95% CI: 0.65-0.94). There was no detectable effect on SAEs. Stepped-wedge analyses found that mandated review patients were five times more likely to receive a case review than non-mandated patients with similar risk (OR: 5.1; 95% CI: 3.64-7.23) and received more risk mitigation strategies than non-mandated patients (0.498; CI: 0.39-0.61).

CONCLUSIONS

Among VHA patients prescribed opioid analgesics, identifying high risk patients and mandating they receive an interdisciplinary case review was associated with a decrease in all-cause mortality. Results suggest that providers can leverage predictive analytic-targeted population health approaches and interdisciplinary collaboration to improve patient outcomes.

TRIAL REGISTRATION

ISRCTN16012111.

The association between buprenorphine treatment duration and mortality: a multi-site cohort study of people who discontinued treatment

BACKGROUND AND AIMS

Buprenorphine is an effective medication for opioid use disorder that reduces mortality; however, many patients are not retained in buprenorphine treatment, and an optimal length of treatment after which patients can safely discontinue treatment has not been identified. This study measured the association between buprenorphine treatment duration and all-cause mortality among patients who discontinued treatment. Secondary objectives were to measure the association between treatment duration and drug overdose and opioid-related overdoses.

DESIGN

Multi-site cohort study.

SETTING

Eight US health systems.

PARTICIPANTS

Patients who initiated and discontinued buprenorphine treatment between 1 January 2012 and 31 December 2018 (n = 6550). Outcomes occurring after patients discontinued buprenorphine treatment were compared between patients who initiated and discontinued treatment after 8-30, 31-90, 91-180, 181-365 and > 365 days.

MEASUREMENTS

Covariate data were obtained from electronic health records (EHRs). Mortality outcomes were derived from EHRs and state vital statistics. Non-fatal opioid and drug overdoses were obtained from diagnostic codes. Four sites provided cause-of-death data to identify fatal drug and opioid-related overdoses. Adjusted frailty regression was conducted on a propensity-weighted cohort to assess associations between duration of the final treatment episode and outcomes.

FINDINGS

The mortality rate after buprenorphine treatment was 1.82 per 100 person-years (n = 191 deaths). In regression analyses with > 365 days as the reference group, treatment duration was not associated with all-cause mortality and drug overdose (P > 0.05 for both). However, compared with > 365 days of treatment, 91-180 days of treatment was associated with increased opioid overdose risk (hazard ratio = 2.94, 95% confidence interval = 1.11-7.79).

CONCLUSIONS

Among patients who discontinue buprenorphine treatment, there appears to be no treatment duration period associated with a reduced risk for all-cause mortality. Patients who discontinue buprenorphine treatment after 91-180 days appear to be at heightened risk for opioid overdose compared with patients who discontinue after > 365 days of treatment.

Opioid Dose Trajectories and Associations With Mortality, Opioid Use Disorder, Continued Opioid Therapy, and Health Plan Disenrollment

IMPORTANCE

Uncertainty remains about the longer-term benefits and harms of different opioid management strategies, such as tapering and dose escalation. For instance, opioid tapering could help patients reduce opioid exposure to prevent opioid use disorder, but patients may also seek care elsewhere and engage in nonprescribed opioid use.

OBJECTIVE

To evaluate the association between opioid dose trajectories observed in practice and patient outcomes.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cohort study was conducted in 3 health systems in Colorado and Wisconsin. The study population included patients receiving long-term opioid therapy between 50 and 200 morphine milligram equivalents between August 1, 2014, and July 31, 2017. Follow-up ended on December 31, 2019. Data were analyzed from January 2020 to August 2022.

EXPOSURES

Group-based trajectory modeling identified 5 dosing trajectories over 1 year: 1 decreasing, 1 high-dose increasing, and 3 stable.

MAIN OUTCOMES AND MEASURES

Primary outcomes assessed after the trajectory period were 1-year all-cause mortality, incident opioid use disorder, continued opioid therapy at 1 year, and health plan disenrollment. Associations were tested using Cox proportional hazards regression and log-binomial models, adjusting for baseline covariates.

RESULTS

A total of 3913 patients (mean [SD] age, 59.2 [14.4] years; 2767 White non-Hispanic [70.7%]; 2237 female patients [57.2%]) were included in the study. Compared with stable trajectories, the decreasing dose trajectory was negatively associated with opioid use disorder (adjusted hazard ratio [aHR], 0.40; 95% CI, 0.29-0.55) and continued opioid therapy (site 1: adjusted relative risk [aRR], 0.39; 95% CI, 0.34-0.44), but was positively associated with health plan disenrollment (aHR, 1.66; 95% CI, 1.24-2.22). The decreasing trajectory was not associated with mortality (aHR, 1.28; 95% CI, 0.87-1.86). In contrast, the high-dose increasing trajectory was positively associated with mortality (aHR, 2.19; 95% CI, 1.44-3.32) and opioid use disorder (aHR, 1.81; 95% CI, 1.39-2.37) but was not associated with disenrollment (aHR, 0.90; 95% CI, 0.56-1.42) or continued opioid therapy (site 1: aRR, 0.98; 95% CI, 0.94-1.03).

CONCLUSIONS AND RELEVANCE

In this cohort study, decreasing opioid dose was associated with reduced risk of opioid use disorder and continued opioid therapy but increased risk of disenrollment compared with stable dosing, whereas the high-dose increasing trajectory was associated with an increased risk of mortality and opioid use disorder. These findings can inform opioid management decision-making.

Naloxone Co-Dispensing with Opioids: a Cluster Randomized Pragmatic Trial

BACKGROUND

Although naloxone prevents opioid overdose deaths, few patients prescribed opioids receive naloxone, limiting its effectiveness in real-world settings. Barriers to naloxone prescribing include concerns that naloxone could increase risk behavior and limited time to provide necessary patient education.

OBJECTIVE

To determine whether pharmacy-based naloxone co-dispensing affected opioid risk behavior. Secondary objectives were to assess if co-dispensing increased naloxone acquisition, increased patient knowledge about naloxone administration, and affected opioid dose and other substance use.

DESIGN

Cluster randomized pragmatic trial of naloxone co-dispensing.

SETTING

Safety-net health system in Denver, Colorado, between 2017 and 2020.

PARTICIPANTS

Seven pharmacies were randomized. Pharmacy patients (N=768) receiving opioids were followed using automated data for 10 months. Pharmacy patients were also invited to complete surveys at baseline, 4 months, and 8 months; 325 survey participants were enrolled from November 15, 2017, to January 8, 2019.

INTERVENTION

Intervention pharmacies implemented workflows to co-dispense naloxone while usual care pharmacies provided usual services.

MAIN MEASURES

Survey instruments assessed opioid risk behavior; hazardous drinking; tobacco, cannabis, and other drug use; and knowledge. Naloxone dispensings and opioid dose were evaluated using pharmacy data among pharmacy patients and survey participants. Intention-to-treat analyses were conducted using generalized linear mixed models accounting for clustering at the pharmacy level.

KEY RESULTS

Opioid risk behavior did not differ by trial group (P=0.52; 8-month vs. baseline adjusted risk ratio [ARR] 1.07; 95% CI 0.78, 1.47). Compared with usual care pharmacies, naloxone dispensings were higher in intervention pharmacies (ARR 3.38; 95% CI 2.21, 5.15) and participant knowledge increased (P=0.02; 8-month vs. baseline adjusted mean difference 1.05; 95% CI 0.06, 2.04). There was no difference in other substance use by the trial group.

CONCLUSION

Co-dispensing naloxone with opioids effectively increased naloxone receipt and knowledge but did not increase self-reported risk behavior.

TRIAL REGISTRATION

Registered at ClinicalTrials.gov ; Identifier: NCT03337100.

Conceptualizing the Socio-Built Environment: An Expanded Theoretical Framework to Promote a Better Understanding of Risk for Nonmedical Opioid Overdose Outcomes in Urban and Non-Urban Settings

Nonmedical opioid (NMO) use has been linked to significant increases in rates of NMO morbidity and mortality in non-urban areas. While there has been a great deal of empirical evidence suggesting that physical features of built environments represent strong predictors of drug use and mental health outcomes in urban settings, there is a dearth of research assessing the physical, built environment features of non-urban settings in order to predict risk for NMO overdose outcomes. Likewise, there is strong extant literature suggesting that social characteristics of environments also predict NMO overdoses and other NMO use outcomes, but limited research that considers the combined effects of both physical and social characteristics of environments on NMO outcomes. As a result, important gaps in the scientific literature currently limit our understanding of how both physical and social features of environments shape risk for NMO overdose in rural and suburban settings and therefore limit our ability to intervene effectively. In order to foster a more holistic understanding of environmental features predicting the emerging epidemic of NMO overdose, this article presents a novel, expanded theoretical framework that conceptualizes "socio-built environments" as comprised of (a) environmental characteristics that are applicable to both non-urban and urban settings and (b) not only traditional features of environments as conceptualized by the extant built environment framework, but also social features of environments. This novel framework can help improve our ability to identify settings at highest risk for high rates of NMO overdose, in order to improve resource allocation, targeting, and implementation for interventions such as opioid treatment services, mental health services, and care and harm reduction services for people who use drugs.

Changes in predicted opioid overdose risk over time in a state Medicaid program: a group-based trajectory modeling analysis

BACKGROUND AND AIMS

The time lag encountered when accessing health-care data is one major barrier to implementing opioid overdose prediction measures in practice. Little is known regarding how one's opioid overdose risk changes over time. We aimed to identify longitudinal patterns of individual predicted overdose risks among Medicaid beneficiaries after initiation of opioid prescriptions.

DESIGN, SETTING AND PARTICIPANTS

A retrospective cohort study in Pennsylvania, USA among Pennsylvania Medicaid beneficiaries aged 18-64 years who initiated opioid prescriptions between July 2017 and September 2018 (318 585 eligible beneficiaries (mean age = 39 ± 12 years, female = 65.7%, White = 62.2% and Black = 24.9%).

MEASUREMENTS

We first applied a previously developed and validated machine-learning algorithm to obtain risk scores for opioid overdose emergency room or hospital visits in 3-month intervals for each beneficiary who initiated opioid therapy, until disenrollment from Medicaid, death or the end of observation (December 2018). We performed group-based trajectory modeling to identify trajectories of these predicted overdose risk scores over time.

FINDINGS

Among eligible beneficiaries, 0.61% had one or more occurrences of opioid overdose in a median follow-up of 15 months. We identified five unique opioid overdose risk trajectories: three trajectories (accounting for 92% of the cohort) had consistent overdose risk over time, including consistent low-risk (63%), consistent medium-risk (25%) and consistent high-risk (4%) groups; another two trajectories (accounting for 8%) had overdose risks that substantially changed over time, including a group that transitioned from high- to medium-risk (3%) and another group that increased from medium- to high-risk over time (5%).

CONCLUSIONS

More than 90% of Medicaid beneficiaries in Pennsylvania USA with one or more opioid prescriptions had consistent, predicted opioid overdose risks over 15 months. Applying opioid prediction algorithms developed from historical data may not be a major barrier to implementation in practice for the large majority of individuals.

Novel digital approaches to the assessment of problematic opioid use

The opioid epidemic continues to contribute to loss of life through overdose and significant social and economic burdens. Many individuals who develop problematic opioid use (POU) do so after being exposed to prescribed opioid analgesics. Therefore, it is important to accurately identify and classify risk factors for POU. In this review, we discuss the etiology of POU and highlight novel approaches to identifying its risk factors. These approaches include the application of polygenic risk scores (PRS) and diverse machine learning (ML) algorithms used in tandem with data from electronic health records (EHR), clinical notes, patient demographics, and digital footprints. The implementation and synergy of these types of data and approaches can greatly assist in reducing the incidence of POU and opioid-related mortality by increasing the knowledge base of patient-related risk factors, which can help to improve prescribing practices for opioid analgesics.

Development and Validation of a Model to Predict Postdischarge Opioid Use After Cesarean Birth

A model with three predictors readily found in the electronic health record—inpatient opioid use, tobacco use, and depression or anxiety—accurately estimated postdischarge opioid use.

OBJECTIVE:

To develop and validate a prediction model for postdischarge opioid use in patients undergoing cesarean birth.

METHODS:

We conducted a prospective cohort study of patients undergoing cesarean birth. Patients were enrolled postoperatively, and they completed pain and opioid use questionnaires 14 days after cesarean birth. Clinical data were abstracted from the electronic health record (EHR). Participants were prescribed 30 tablets of hydrocodone 5 mg–acetaminophen 325 mg at discharge and were queried about postdischarge opioid use. The primary outcome was total morphine milligram equivalents used. We constructed three proportional odds predictive models of postdischarge opioid use: a full model with 34 predictors available before hospital discharge, an EHR model that excluded questionnaire data, and a reduced model. The reduced model used forward selection to sequentially add predictors until 90% of the full model performance was achieved. Predictors were ranked a priori based on data from the literature and prior research. Predictive accuracy was estimated using discrimination (concordance index).

RESULTS:

Between 2019 and 2020, 459 participants were enrolled and 279 filled the standardized study prescription. Of the 398 with outcome measurements, participants used a median of eight tablets (interquartile range 1–18 tablets) after discharge, 23.5% used no opioids, and 23.0% used all opioids. Each of the models demonstrated high accuracy predicting postdischarge opioid use (concordance index range 0.74–0.76 for all models). We selected the reduced model as our final model given its similar model performance with the fewest number of predictors, all obtained from the EHR (inpatient opioid use, tobacco use, and depression or anxiety).

CONCLUSION:

A model with three predictors readily found in the EHR—inpatient opioid use, tobacco use, and depression or anxiety—accurately estimated postdischarge opioid use. This represents an opportunity for individualizing opioid prescriptions after cesarean birth.

Comparing person-level matching algorithms to identify risk across disparate datasets among patients with a controlled substance prescription: retrospective analysis

Background

The opioid epidemic in the United States has precipitated a need for public health agencies to better understand risk factors associated with fatal overdoses. Matching person-level information stored in public health, medical, and human services datasets can enhance the understanding of opioid overdose risk factors and interventions.

Objective

This study compares approximate match versus exact match algorithms to link disparate datasets together for identifying persons at risk from an applied perspective.

Methods

This study used statewide prescription drug monitoring program (PDMP), arrest, and mortality data matched at the person-level using an approximate match and 2 exact match algorithms. Impact of matching was assessed by analyzing 3 independent concepts: (1) the prevalence of key risk indicators used by PDMP programs in practice, (2) the prevalence of arrests and fatal opioid overdose, and (3) the performance of a multivariate logistic regression for fatal opioid overdose. The PDMP key risk indicators included (1) multiple provider episodes (MPE), or patients with prescriptions from multiple prescribers and dispensers, (2) high morphine milligram equivalents (MMEs), which represents an opioid’s potency relative to morphine, and (3) overlapping opioid and benzodiazepine prescriptions.

Results

Prevalence of PDMP-based risk indicators were higher in the approximate match population for MPEs (n = 4893/1 859 445 [0.26%]) and overlapping opioid/benzodiazepines (n = 57 888/1 859 445 [4.71%]), but the exact-basic match population had the highest prevalence of individuals with high MMEs (n = 664/1 910 741 [3.11%]). Prevalence of arrests and deaths were highest for the approximate match population compared with the exact match populations. Model performance was comparable across the 3 matching algorithms (exact-basic validation area under the receiver operating characteristic curve [AUC]: 0.854; approximate validation AUC: 0.847; exact + zip validation AUC: 0.826) but resulted in different cutoff points balancing sensitivity and specificity.

Conclusions

Our study illustrates the specific tradeoffs of different matching methods. Further research should be performed to compare matching algorithms and its impact on the prevalence of key risk indicators in an applied setting that can improve understanding of risk within a population.

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.

Using data science to improve outcomes for persons with opioid use disorder

Medication treatment for opioid use disorder (MOUD) is an effective evidence-based therapy for decreasing opioid-related adverse outcomes. Effective strategies for retaining persons on MOUD, an essential step to improving outcomes, are needed as roughly half of all persons initiating MOUD discontinue within a year. Data science may be valuable and promising for improving MOUD retention by using "big data" (e.g., electronic health record data, claims data mobile/sensor data, social media data) and specific machine learning techniques (e.g., predictive modeling, natural language processing, reinforcement learning) to individualize patient care. Maximizing the utility of data science to improve MOUD retention requires a three-pronged approach: (1) increasing funding for data science research for OUD, (2) integrating data from multiple sources including treatment for OUD and general medical care as well as data not specific to medical care (e.g., mobile, sensor, and social media data), and (3) applying multiple data science approaches with integrated big data to provide insights and optimize advances in the OUD and overall addiction fields.

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.

The leading neighborhood-level predictors of drug overdose: A mixed machine learning and spatial approach

BACKGROUND

Drug overdose is a leading cause of unintentional death in the United States and has contributed significantly to a decline in life expectancy during recent years. To combat this health issue, this study aims to identify the leading neighborhood-level predictors of drug overdose and develop a model to predict areas at the highest risk of drug overdose using geographic information systems and machine learning (ML) techniques.

METHOD

Neighborhood-level (block group) predictors were grouped into three domains: socio-demographic factors, drug use variables, and protective resources. We explored different ML algorithms, accounting for spatial dependency, to identify leading predictors in each domain. Using geographically weighted regression and the best-performing ML algorithm, we combined the output prediction of three domains to produce a final ensemble model. The model performance was validated using classification evaluation metrics, spatial cross-validation, and spatial autocorrelation testing.

RESULTS

The variables contributing most to the predictive model included the proportion of households with food stamps, households with an annual income below $35,000, opioid prescription rate, smoking accessories expenditures, and accessibility to opioid treatment programs and hospitals. Compared to the error estimated from normal cross-validation, the generalized error of the model did not increase considerably in spatial cross-validation. The ensemble model using ML outperformed the GWR method.

CONCLUSION

This study identified strong neighborhood-level predictors that place a community at risk of experiencing drug overdoses, as well as protective factors. Our findings may shed light on several specific avenues for targeted intervention in neighborhoods at risk for high drug overdose burdens.

Risk Factors Associated With Nonfatal Opioid Overdose Leading to Intensive Care Unit Admission: A Cross-sectional Study

BACKGROUND

Opioid overdose (OD) and related deaths have significantly increased in the United States over the last 2 decades. Existing studies have mostly focused on demographic and clinical risk factors in noncritical care settings. Social and behavioral determinants of health (SBDH) are infrequently coded in the electronic health record (EHR) and usually buried in unstructured EHR notes, reflecting possible gaps in clinical care and observational research. Therefore, SBDH often receive less attention despite being important risk factors for OD. Natural language processing (NLP) can alleviate this problem.

OBJECTIVE

The objectives of this study were two-fold: First, we examined the usefulness of NLP for SBDH extraction from unstructured EHR text, and second, for intensive care unit (ICU) admissions, we investigated risk factors including SBDH for nonfatal OD.

METHODS

We performed a cross-sectional analysis of admission data from the EHR of patients in the ICU of Beth Israel Deaconess Medical Center between 2001 and 2012. We used patient admission data and International Classification of Diseases, Ninth Revision (ICD-9) diagnoses to extract demographics, nonfatal OD, SBDH, and other clinical variables. In addition to obtaining SBDH information from the ICD codes, an NLP model was developed to extract 6 SBDH variables from EHR notes, namely, housing insecurity, unemployment, social isolation, alcohol use, smoking, and illicit drug use. We adopted a sequential forward selection process to select relevant clinical variables. Multivariable logistic regression analysis was used to evaluate the associations with nonfatal OD, and relative risks were quantified as covariate-adjusted odds ratios (aOR).

RESULTS

The strongest association with nonfatal OD was found to be drug use disorder (aOR 8.17, 95% CI 5.44-12.27), followed by bipolar disorder (aOR 2.69, 95% CI 1.68-4.29). Among others, major depressive disorder (aOR 2.57, 95% CI 1.12-5.88), being on a Medicaid health insurance program (aOR 2.26, 95% CI 1.43-3.58), history of illicit drug use (aOR 2.09, 95% CI 1.15-3.79), and current use of illicit drugs (aOR 2.06, 95% CI 1.20-3.55) were strongly associated with increased risk of nonfatal OD. Conversely, Blacks (aOR 0.51, 95% CI 0.28-0.94), older age groups (40-64 years: aOR 0.65, 95% CI 0.44-0.96; >64 years: aOR 0.16, 95% CI 0.08-0.34) and those with tobacco use disorder (aOR 0.53, 95% CI 0.32-0.89) or alcohol use disorder (aOR 0.64, 95% CI 0.42-1.00) had decreased risk of nonfatal OD. Moreover, 99.82% of all SBDH information was identified by the NLP model, in contrast to only 0.18% identified by the ICD codes.

CONCLUSIONS

This is the first study to analyze the risk factors for nonfatal OD in an ICU setting using NLP-extracted SBDH from EHR notes. We found several risk factors associated with nonfatal OD including SBDH. SBDH are richly described in EHR notes, supporting the importance of integrating NLP-derived SBDH into OD risk assessment. More studies in ICU settings can help health care systems better understand and respond to the opioid epidemic.

Suicide by Opioid: Exploring the Intentionality of the Act

Opioid toxicity can result in life-threatening respiratory depression. Opioid-overdose mortality in the United States is high and increasing, but it is difficult to determine what proportion of those deaths might actually be suicides. The exact number of Americans who died of an opioid overdose but whose deaths might be classified as suicide remains unknown. It is important to differentiate between those who take opioids with the deliberate and unequivocal objective of committing suicide, that is, those with active intent, from those with passive intent. The passive-intent group understands the risks of opioid consumption and takes dangerous amounts, but with a more ambiguous attitude toward suicide. Thus, among decedents of opioid overdose, a large population dies by accident, whereas a small population dies intending to commit suicide; but between them exists a sub-population with equivocal intentions, waxing and waning between their desire to live and the carelessness about death. There may be a passive as well as active intent to commit suicide, but less is known about the passive motivation. It is important for public health efforts aimed at reducing both suicides and opioid-use disorder to better understand the range of motivations behind opioid-related suicides and how to combat them.

Development and validation of a prediction model for opioid use disorder among youth

BACKGROUND

Youth are vulnerable to opioid use initiation and its complications. With growing rates of opioid overdose, strategies to identify youth at risk of opioid use disorder (OUD) to efficiently focus prevention interventions are needed. This study developed and validated a prediction model of OUD in youth aged 14-18 years.

METHODS

The model was developed in a Colorado healthcare system (derivation site) using Cox proportional hazards regression analysis. Model predictors and outcomes were identified using electronic health record data. The model was externally validated in a separate Denver safety net health system (validation site). Youth were followed for up to 3.5 years. We evaluated internal and external validity using discrimination and calibration.

RESULTS

The derivation cohort included 76,603 youth, of whom 108 developed an OUD diagnosis. The model contained 3 predictors (smoking status, mental health diagnosis, and non-opioid substance use or disorder) and demonstrated good calibration (p = 0.90) and discrimination (bootstrap-corrected C-statistic = 0.76: 95 % CI = 0.70, 0.82). Sensitivity and specificity were 57 % and 84 % respectively with a positive predictive value (PPV) of 0.49 %. The validation cohort included 45,790 youth of whom, 74 developed an OUD diagnoses. The model demonstrated poorer calibration (p < 0.001) but good discrimination (C-statistic = 0.89; 95 % CI = 0.84, 0.95), sensitivity of 87.8 % specificity of 68.6 %, and PPV of 0.45 %.

CONCLUSIONS

In two Colorado healthcare systems, the prediction model identified 57-88 % of subsequent OUD diagnoses in youth. However, PPV < 1% suggests universal prevention strategies for opioid use in youth may be the best health system approach.

Opioids and Chronic Pain: An Analytic Review of the Clinical Evidence

We conducted an analytic review of the clinical scientific literature bearing on the use of opioids for treatment of chronic non-cancer pain in the United States. There is substantial, albeit not definitive, scientific evidence of the effectiveness of opioids in treating pain and of high variability in opioid dose requirements and side effects. The estimated risk of death from opioid treatment involving doses above 100 MMED is ~0.25%/year. Multiple large studies refute the concept that short-term use of opioids to treat acute pain predisposes to development of opioid use disorder. The prevalence of opioid use disorder associated with prescription opioids is likely &lt;3%. Morbidity, mortality, and financial costs of inadequate treatment of the 18 million Americans with moderate to severe chronic pain are high. Because of the absence of comparative effectiveness studies, there are no scientific grounds for considering alternative non-pharmacologic treatments as an adequate substitute for opioid therapy but these treatments might serve to augment opioid therapy, thereby reducing dosage. There are reasons to question the ostensible risks of co-prescription of opioids and benzodiazepines. As the causes of the opioid crisis have come into focus, it has become clear that the crisis resides predominantly in the streets and that efforts to curtail it by constraining opioid treatment in the clinic are unlikely to succeed.

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.

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.

Restrictive opioid prescribing policies and evolving risk environments: A qualitative study of the perspectives of patients who experienced an accidental opioid overdose

BACKGROUND

Despite policy efforts to prevent overdose, accidental overdoses among individuals prescribed opioids continue to occur. Guided by Rhodes' Risk Environment Framework, we examined the unintended consequences of restrictive policies by identifying macro policy and micro-level contextual factors that patients prescribed opioids for pain identified as contributing to overdose events.

METHODS

Semi-structured interviews were conducted with 31 patients prescribed opioids who experienced an accidental opioid overdose between April 2017 and June 2019 in two health systems.

RESULTS

We identified three interrelated factors that emerged within an evolving risk environment and may have increased patients' vulnerability for an accidental opioid overdose: desperation from persistent pain and comorbidities; limited knowledge about opioid medication safety and effectiveness; and restrictive opioid prescribing policies that exacerbated stigma, fear and mistrust and prevented open patient-clinician communication. When experiencing persistent pain, patients took matters into their own hands by taking more medications or in different intervals than prescribed, mixing them with other substances, or using illicitly obtained opioids.

CONCLUSION

For some patients, macro-level policies and guidelines designed to reduce opioid overdoses by restricting opioid supply may have paradoxically created a micro-level risk environment that contributed to overdose events in a subset of patients.

Modifying and Evaluating the Opioid Overdose Knowledge Scale for Prescription Opioids: A Pilot Study of the Rx-OOKS

OBJECTIVE

To develop a validated instrument that measures knowledge about prescription opioid overdose.

METHODS

Within an integrated health care system, we adapted, piloted, and tested the reliability and predictive validity of a modified Opioid Overdose Knowledge Scale (OOKS) instrument specific to prescription opioids (Rx-OOKS) with a patient population prescribed long-term opioid therapy and potentially at risk of opioid overdose. We used an interdisciplinary team approach and patient interviews to adapt the instrument. We then piloted the survey on a patient sample and assessed it using Cronbach's alpha and logistic regression.

RESULTS

Rx-OOKS (N = 56) resulted in a three-construct, 25-item instrument. Internal consistency was acceptable for the following constructs: "signs of an overdose" (10 items) at α = 0.851, "action to take with opioid overdose" (seven items) at α = 0.692, and "naloxone use knowledge" (eight items) at α = 0.729. One construct, "risks of an overdose" (three items), had an α of 0.365 and was subsequently eliminated from analysis due to poor performance. We conducted logistic regression to determine if any of the constructs was strongly associated with future naloxone receipt. Higher scores on "actions to take in an overdose" had nine times the odds of receiving naloxone (odds ratio [OR] = 9.00, 95% confidence interval [CI] = 1.42-57.12); higher "naloxone use knowledge" scores were 15.8 times more likely to receive naloxone than those with lower scores (OR = 15.83, 95% CI = 1.68-149.17).

CONCLUSIONS

The Rx-OOKS survey instrument can reliably measure knowledge about prescription opioid overdose recognition and naloxone use. Further, knowledge about actions to take during an opioid overdose and naloxone use were associated with future receipt of naloxone.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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.

Using ICD-10-CM codes to detect illicit substance use: A comparison with retrospective self-report

BACKGROUND

Understanding whether International Classification of Disease, 10th Revision, Clinical Modification (ICD-10-CM) codes can be used to accurately detect substance use can inform their use in future surveillance and research efforts.

METHODS

Using 2015-2018 data from a retrospective cohort study of 602 safety-net patients prescribed opioids for chronic non-cancer pain, we calculated the sensitivity and specificity of using ICD-10-CM codes to detect illicit substance use compared to retrospective self-report by substance (methamphetamine, cocaine, opioids [heroin or non-prescribed opioid analgesics]), self-reported use frequency, and type of healthcare encounter.

RESULTS

Sensitivity of ICD-10-CM codes for detecting self-reported substance use was highest for methamphetamine (49.5 % [95 % confidence interval: 39.6-59.5 %]), followed by cocaine (44.4 % [35.8-53.2 %]) and opioids (36.3 % [28.8-44.2 %]); higher for participants who reported more frequent methamphetamine (intermittent use: 27.7 % [14.6-42.6 %]; ≥weekly use: 67.2 % [53.7-79.0 %]) and opioid use (intermittent use: 21.4 % [13.2-31.7 %]; ≥weekly use: 52.6 % [40.8-64.2 %]); highest for outpatient visits (methamphetamine: 43.8 % [34.1-53.8 %]; cocaine: 36.8 % [28.6-45.6 %]; opioids: 33.1 % [25.9-41.0 %]) and lowest for emergency department visits (methamphetamine: 8.6 % [4.0-15.6 %]; cocaine: 5.3 % [2.1-10.5 %]; opioids: 6.3 % [3.0-11.2 %]). Specificity was highest for methamphetamine (96.4 % [94.3-97.8 %]), followed by cocaine (94.0 % [91.5-96.0 %]) and opioids (85.0 % [81.3-88.2 %]).

CONCLUSIONS

ICD-10-CM codes had high specificity and low sensitivity for detecting self-reported substance use but were substantially more sensitive in detecting frequent use. ICD-10-CM codes to detect substance use, particularly those from emergency department visits, should be used with caution, but may be useful as a lower-bound population measure of substance use or for capturing frequent use among certain patient populations.

Patient, prescriber, and Community factors associated with filled naloxone prescriptions among patients receiving buprenorphine 2017-18

BACKGROUND

Prescribing naloxone to patients at increased opioid overdose risk is a key component of opioid overdose prevention efforts, but little is known about naloxone fills among patients receiving buprenorphine for opioid use disorder, one such high risk group.

METHODS

This retrospective cross-sectional study used de-identified pharmacy claims representing 90% of all prescriptions filled at retail pharmacies in 50 states and the District of Columbia. We performed a multivariable logistic regression to examine filled naloxone prescriptions among patients receiving buprenorphine treatment and assessed how filled naloxone prescriptions vary by patient, prescriber, and community characteristics.

RESULTS

Filled naloxone prescriptions occurred among 4.5% of buprenorphine treatment episodes. Episodes paid through Medicaid (aOR 2.40, 95%CI 2.33-2.47) and Medicare (aOR 1.53, 95%CI 1.46-1.60) had higher odds of filled naloxone prescriptions than commercial insurance episodes. Compared to episodes where the primary prescriber was an adult primary care physician, odds of filling a naloxone prescription were higher among episodes prescribed by addiction specialists (aOR 1.30, 95% CI 1.24-1.37) and physician assistants/nurse practitioners (aOR 1.57, 95% CI 1.53-1.61).

CONCLUSIONS

Prescribing naloxone to patients receiving buprenorphine represents a tangible clinical action that can be taken to help prevent opioid overdose deaths. However, despite recommendations to co-prescribe naloxone to patients at increased risk for opioid overdose, rates of filling naloxone prescriptions remain low among patients dispensed buprenorphine. States, insurers, and health systems should consider implementing strategies to facilitate increased co-prescribing of naloxone to at-risk individuals.

Integrating human services and criminal justice data with claims data to predict risk of opioid overdose among Medicaid beneficiaries: A machine-learning approach

Health system data incompletely capture the social risk factors for drug overdose. This study aimed to improve the accuracy of a machine-learning algorithm to predict opioid overdose risk by integrating human services and criminal justice data with health claims data to capture the social determinants of overdose risk. This prognostic study included Medicaid beneficiaries (n = 237,259) in Allegheny County, Pennsylvania enrolled between 2015 and 2018, randomly divided into training, testing, and validation samples. We measured 290 potential predictors (239 derived from Medicaid claims data) in 30-day periods, beginning with the first observed Medicaid enrollment date during the study period. Using a gradient boosting machine, we predicted a composite outcome (i.e., fatal or nonfatal opioid overdose constructed using medical examiner and claims data) in the subsequent month. We compared prediction performance between a Medicaid claims only model to one integrating human services and criminal justice data with Medicaid claims (i.e., integrated model) using several metrics (e.g., C-statistic, number needed to evaluate [NNE] to identify one overdose). Beneficiaries were stratified into risk-score decile subgroups. The samples (training = 79,087, testing = 79,086, validation = 79,086) had similar characteristics (age = 38±18 years, female = 56%, white = 48%, having at least one overdose = 1.7% during study period). Using the validation sample, the integrated model slightly improved on the Medicaid claims only model (C-statistic = 0.885; 95%CI = 0.877–0.892 vs. C-statistic = 0.871; 95%CI = 0.863–0.878), with small corresponding improvements in the NNE and positive predictive value. Nine of the top 30 most important predictors in the integrated model were human services and criminal justice variables. Using the integrated model, approximately 70% of individuals with overdoses were members of the top risk decile (overdose rates in the subsequent month = 47/10,000 beneficiaries). Few individuals in the bottom 9 deciles had overdose episodes (0-12/10,000). Machine-learning algorithms integrating claims and social service and criminal justice data modestly improved opioid overdose prediction among Medicaid beneficiaries for a large U.S. county heavily affected by the opioid crisis.

Performance of a Predictive Model versus Prescription-Based Thresholds in Identifying Patients at Risk of Fatal Opioid Overdose

Background: Prescription Drug Monitoring Programs (PDMPs) collect controlled substance prescriptions dispensed within a state. Many PDMP programs perform targeted outreach (i.e., "unsolicited reporting") for patients who exceed numerical thresholds, however, the degree to which patients at highest risk of fatal opioid overdose are identified has not been compared with one another or with a predictive model. Methods: A retrospective analysis was performed using statewide PDMP data for Maryland residents aged 18 to 80 years with an opioid fill between April to June 2015. The outcome was opioid-related overdose death in 2015 or 2016. A multivariable logistic regression model and three PDMP thresholds were evaluated: (1) multiple provider episodes; (2) high daily average morphine milligram equivalents (MME); and (3) overlapping opioid and benzodiazepine prescriptions. Results: The validation cohort consisted of 170,433 individuals and 244 deaths. The predictive model captured more individuals who died (46.3% of total deaths) and had a higher death rate (7.12 per 1000) when the risk score cutoff (0.0030) was selected for a comparable size of high-risk individuals (n = 15,881) than those meeting the overlapping opioid/benzodiazepine prescriptions (n = 17,440; 33.2% of total deaths; 4.64 deaths per 1000) and high MME (n = 14,675; 24.6% of total deaths; 4.09 deaths per 1000) thresholds. Conclusions: The predictive model identified more individuals at risk of fatal opioid overdose as compared with PDMP thresholds commonly used for unsolicited reporting. PDMP programs could improve their targeting of unsolicited reports to reach more individuals at risk of overdose by using predictive models instead of simple threshold-based approaches.

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.

Chronic Opioid Therapy: A Scoping Literature Review on Evolving Clinical and Scientific Definitions

The management of chronic noncancer pain (CNCP) with chronic opioid therapy (COT) is controversial. There is a lack of consensus on how COT is defined resulting in unclear clinical guidance. This scoping review identifies and evaluates evolving COT definitions throughout the published clinical and scientific literature. Databases searched included PubMed, Embase, and Web of Science. A total of 227 studies were identified from 8,866 studies published between January 2000 and July 2019. COT definitions were classified by pain population of application and specific dosage/duration definition parameters, with results reported according to PRISMA-ScR. Approximately half of studies defined COT as "days' supply duration >90 days" and 9.3% defined as ">120 days' supply," with other days' supply cut-off points (>30, >60, or >70) each appearing in <5% of total studies. COT was defined by number of prescriptions in 63 studies, with 16.3% and 11.0% using number of initiations or refills, respectively. Few studies explicitly distinguished acute treatment and COT. Episode duration/dosage criteria was used in 90 studies, with 7.5% by Morphine Milligram Equivalents + days' supply and 32.2% by other "episode" combination definitions. COT definitions were applied in musculoskeletal CNCP (60.8%) most often, and typically in adults aged 18 to 64 (69.6%). The usage of ">90 days' supply" COT definitions increased from 3.2 publications/year before 2016 to 20.7 publications/year after 2016. An increasing proportion of studies define COT as ">90 days' supply." The most recent literature trends toward shorter duration criteria, suggesting that contemporary COT definitions are increasingly conservative. PERSPECTIVE: This study summarized the most common, current definition criteria for chronic opioid therapy (COT) and recommends adoption of consistent definition criteria to be utilized in practice and research. The most recent literature trends toward shorter duration criteria overall, suggesting that COT definition criteria are increasingly stringent.

Ensuring Patient Protections When Tapering Opioids: Consensus Panel Recommendations

Long-term opioid therapy has the potential for serious adverse outcomes and is often used in a vulnerable population. Because adverse effects or failure to maintain benefits is common with long-term use, opioid taper or discontinuation may be indicated in certain patients. Concerns about the adverse individual and population effects of opioids have led to numerous strategies aimed at reductions in prescribing. Although opioid reduction efforts have had generally beneficial effects, there have been unintended consequences. Abrupt reduction or discontinuation has been associated with harms that include serious withdrawal symptoms, psychological distress, self-medicating with illicit substances, uncontrolled pain, and suicide. Key questions remain about when and how to safely reduce or discontinue opioids in different patient populations. Thus, health care professionals who reduce or discontinue long-term opioid therapy require a clear understanding of the associated benefits and risks as well as guidance on the best practices for safe and effective opioid reduction. An interdisciplinary panel of pain clinicians and one patient advocate formulated recommendations on tapering methods and ongoing pain management in primary care with emphasis on patient-centered, integrated, comprehensive treatment models employing a biopsychosocial perspective.

Nonconsensual Dose Reduction Mandates are Not Justified Clinically or Ethically: An Analysis

This manuscript describes the institutional and clinical considerations that apply to the question of whether to mandate opioid dose reduction in patients who have received opioids long-term. It describes how a calamitous rise in addiction and overdose involving opioids has both led to a clinical recalibration by healthcare providers, and to strong incentives favoring forcible opioid reduction by policy making agencies. Neither the 2016 Guideline issued by the Centers for Disease Control and Prevention nor clinical evidence can justify or promote such policies as safe or effective.

Association of Opioids Prescribed to Family Members With Opioid Overdose Among Adolescents and Young Adults

IMPORTANCE

Family members are cited as a common source of prescription opioids used for nonmedical reasons. However, the overdose risk associated with exposure to opioids prescribed to family members among adolescents and young adults is not well established.

OBJECTIVE

To assess the association of opioids prescribed to family members with pharmaceutical opioid overdose among youth.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study included 45 145 family units with a total of 72 040 adolescents and young adults aged 11 to 26 years enrolled in a Kaiser Permanente Colorado health plan in 2006 and observed through June 2018.

EXPOSURES

Opioid prescriptions and dosage dispensed to family members and youth in the past month.

MAIN OUTCOMES AND MEASURES

Fatal pharmaceutical opioid overdoses identified in vital records and nonfatal pharmaceutical opioid overdoses identified in emergency department and inpatient settings. Time to first overdose was modeled using Cox regression.

RESULTS

The study population consisted of 72 040 adolescents and young adults (mean [SD] age across follow-up, 19.3 [3.7] years; 36 646 [50.9%] girls and women) nested in 45 145 family units. Youth were more commonly exposed to prescription opioids dispensed to a family member than through their own prescriptions. During follow-up, 26 284 youth (36.5%) filled at least 1 opioid prescription, and 47 461 youth (65.9%) had at least 1 family member with a prescription. Exposure to family members with opioid prescriptions in the past month was associated with increased risk of pharmaceutical opioid overdose (adjusted hazard ratio [aHR], 2.17; 95% CI, 1.24-3.79) independent of opioids prescribed to youth (aHR, 6.62; 95% CI, 3.39-12.91). Concurrent exposure to opioid prescriptions from youth and family members was associated with substantially increased overdose risk (aHR, 12.99; 95% CI, 5.08-33.25). High dosage of total morphine milligram equivalents (MME) prescribed to family members in the past month was associated with youth overdose (0 MME vs >0 to <200 MME: aHR, 1.39; 95% CI, 0.51-3.81; 0 MME vs 200 to <600 MME: aHR, 1.49; 95% CI, 0.59-3.77; 0 MME vs ≥600 MME: aHR, 2.93; 95% CI, 1.55-5.56).

CONCLUSIONS AND RELEVANCE

In this study of youth linked to family members, exposure to family members' prescribed opioids was associated with increased risk of pharmaceutical opioid overdose, independent of opioids prescribed to youth. Further interventions targeting youth and families are needed, including counseling patients about the risks of opioids to youth in their families.

Physician Responses to Enhanced Prescription Drug Monitoring Program Profiles

OBJECTIVE

Many states have begun implementing enhancements to PDMP patient profiles such as summaries or graphics to highlight issues of concern and enhance comprehension. The purpose of this study was to examine how physicians respond to sample enhanced PDMP profiles based on patient vignettes.

DESIGN

Brief semistructured interviews with physicians.

SETTING

Three national medical conferences for targeted specialties.

SUBJECTS

Ninety-three physicians practicing in primary care, emergency medicine, or pain management.

METHODS

We presented participants with one of three patient vignettes with corresponding standard and enhanced PDMP profiles and conducted brief interviews.

RESULTS

Findings indicated that enhanced profiles could increase ease of comprehension, reduce time burden, and aid in communicating with patients about opioid risks. Physicians also expressed concern about liability for prescribing when the enhanced profile indicates risk and cautioned against any implication that risk warnings should override clinical judgment based on the patient's complete medical history or presenting condition. Physicians emphasized the need for transparency of measures and evidence of validation of risk scores. We found little indication that enhanced profiles would change opioid prescribing decisions, though decisions varied by physician.

CONCLUSIONS

Our study underscores the importance of involving prescribers in developing and testing PDMP profile enhancements, as well as providing guidance in the interpretation and clinical use of enhanced profiles. Reduced time burden is an important benefit to consider as the number of states mandating PDMP use increases.

Association of Opioid Overdose Risk Factors and Naloxone Prescribing in US Adults

BACKGROUND

Prescribing naloxone to patients is a key strategy to prevent opioid overdoses, but little is known about the reach of naloxone prescribing.

OBJECTIVE

Determine patient factors associated with receiving naloxone and trends over time in patients with key overdose risk factors.

DESIGN

Retrospective observational study.

PARTICIPANTS

Using the Clinformatics DataMart, a US-wide health insurance claims dataset, we compared adults who received opioids and naloxone (opioid+naloxone) from January 2014 to June 2017 with adults who received opioids without naloxone (opioids only), matched on gender, age ± 5 years, month/year of opioid fill, and number of opioid claims.

MAIN MEASURES

Key patient-level opioid overdose risk factors included receipt of high-dosage opioids, concurrent benzodiazepines, history of opioid and other substance use disorders, and history of opioid overdose.

RESULTS

We included 3963 opioid+naloxone and 19,815 opioid only patients. Key factors associated with naloxone fills included high opioid daily dosage (50 to < 90 morphine milligram equivalents (MME): AOR = 2.43, 95% CI 2.15-2.76 and ≥ 90 MME: AOR = 3.94, 95% CI 3.47-4.46; reference: < 50 MME), receiving concurrent benzodiazepines (AOR = 1.27, 95% CI 1.16-1.38), and having a diagnosis of opioid use disorder (AOR = 1.56, 95% CI 1.40-1.73). History of opioid overdose was not associated with naloxone (AOR = 0.92, 95% CI 0.74-1.15). The percent of patients receiving naloxone increased, yet less than 2% of patients in any of the key overdose risk factor groups received naloxone by the last 6 months of the study period.

CONCLUSIONS

Naloxone prescribing has increased and was more likely to be co-prescribed to patients with some risk factors for overdose. However, overall prescribing remains minimal. Additional efforts are needed across health systems to increase naloxone prescribing for patients at risk for opioid overdose.

Predictors of Overdose Death Among High-Risk Emergency Department Patients With Substance-Related Encounters: A Data Linkage Cohort Study

STUDY OBJECTIVE

Persons with substance use disorders frequently utilize emergency department (ED) services, creating an opportunity for intervention and referral to addiction treatment and harm-reduction services. However, EDs may not have the appropriate tools to distinguish which patients are at greatest risk for negative outcomes. We link hospital ED and medical examiner mortality databases in one state to identify individual-level risk factors associated with overdose death among ED patients with substance-related encounters.

METHODS

This retrospective cohort study linked Maryland statewide ED hospital claims records for adults with nonfatal overdose or substance use disorder encounters in 2014 to 2015 with medical examiner mortality records in 2015 to 2016. Logistic regression was used to identify factors in hospital records associated with risk of opioid overdose death. Predicted probabilities for overdose death were calculated for hypothetical patients with different combinations of overdose and substance use diagnostic histories.

RESULTS

A total of 139,252 patients had substance-related ED encounters in 2014 to 2015. Of these patients, 963 later experienced an opioid overdose death, indicating a case fatality rate of 69.2 per 10,000 patients, 6 times higher than that of patients who used the ED for any cause. Factors most strongly associated with death included having both an opioid and another substance use disorder (adjusted odds ratio 2.88; 95% confidence interval 2.04 to 4.07), having greater than or equal to 3 previous nonfatal overdoses (adjusted odds ratio 2.89; 95% confidence interval 1.54 to 5.43), and having a previous nonfatal overdose involving heroin (adjusted odds ratio 2.24; 95% confidence interval 1.64 to 3.05).

CONCLUSION

These results highlight important differences in overdose risk among patients receiving care in EDs for substance-related conditions. The findings demonstrate the potential utility of incorporating routine data from patient records to assess risk of future negative outcomes and identify primary targets for initiation and linkage to lifesaving care.

Predicting Opioid Overdose Deaths Using Prescription Drug Monitoring Program Data

INTRODUCTION

Prescription Drug Monitoring Program data can provide insights into a patient's likelihood of an opioid overdose, yet clinicians and public health officials lack indicators to identify individuals at highest risk accurately. A predictive model was developed and validated using Prescription Drug Monitoring Program prescription histories to identify those at risk for fatal overdose because of any opioid or illicit opioids.

METHODS

From December 2018 to July 2019, a retrospective cohort analysis was performed on Maryland residents aged 18-80 years with a filled opioid prescription (n=565,175) from January to June 2016. Fatal opioid overdoses were identified from the Office of the Chief Medical Examiner and were linked at the person-level with Prescription Drug Monitoring Program data. Split-half technique was used to develop and validate a multivariate logistic regression with a 6-month lookback period and assessed model calibration and discrimination.

RESULTS

Predictors of any opioid-related fatal overdose included male sex, age 65-80 years, Medicaid, Medicare, 1 or more long-acting opioid fills, 1 or more buprenorphine fills, 2 to 3 and 4 or more short-acting schedule II opioid fills, opioid days' supply ≥91 days, average morphine milligram equivalent daily dose, 2 or more benzodiazepine fills, and 1 or more muscle relaxant fills. Model discrimination for the validation cohort was good (area under the curve: any, 0.81; illicit, 0.77).

CONCLUSIONS

A model for predicting fatal opioid overdoses was developed using Prescription Drug Monitoring Program data. Given the recent national epidemic of deaths involving heroin and fentanyl, it is noteworthy that the model performed equally well in identifying those at risk for overdose deaths from both illicit and prescription opioids.

Would You Be Surprised If This Patient Died This Year? Advance Care Planning in Substance Use Disorders

Primary care physicians are increasingly incorporating screening tools for substance use disorders (SUDs) and referral to treatment into their practice. Despite efforts to provide access to treatment, patients with SUDs remain at an increased risk of mortality, both from overdose and from general medical conditions. Advance care planning (ACP) is recommended for patients with chronic, progressive medical conditions such as malignancies or heart failure. Though SUDs are widely acknowledged to be chronic diseases associated with an increased risk of mortality, there has been little discussion on ACP in this population. ACP is a discussion regarding future care, often including selection of a surrogate decision-maker and completion of an advanced directive. ACP has been associated with better quality of end-of-life and care more consistent with patient preferences. Studies in other vulnerable populations have shown that marginalized and high-risk individuals may be less likely to receive ACP. Similarly, patients with SUDs may employ different decision-makers than that defined by law (i.e., friend vs. family member), increasing the importance of discussing patient values and social structure. Physicians should routinely conduct ACP conversations with patients with SUDs, especially those with chronic, progressive medical conditions and/or severe, uncontrolled substance use disorders.

"You can see those concentric rings going out": Emergency personnel's experiences treating overdose and perspectives on policy-level responses to the opioid crisis in New Hampshire

BACKGROUND

In parallel to a substantial increase in opioid overdose deaths in New Hampshire (NH), emergency personnel experienced an increase in opioid-related encounters. To inform public health responses to this crisis, insights into the experiences and perspectives of those emergency personnel who treat opioid-related overdoses are warranted.

AIMS

Systematically examine emergency personnel's experiences treating opioid overdoses and obtain their perspectives on policy-level responses to the opioid crisis in NH.

METHODS

Semi-structured qualitative interviews were conducted with 18 first responders [firefighters (n = 6), police officers (n = 6), emergency medical service providers (n = 6)] and 18 emergency department personnel employed in six NH counties. Interviews focused on emergency personnel's perspectives on fentanyl/heroin formulations, experiences treating overdoses, harm reduction strategies, and experiences with treatment referral. Interviews were audio recorded, transcribed verbatim, and analyzed using content analysis.

RESULTS

Emergency personnel cited the potency and inconsistency of fentanyl-laced heroin as primary drivers of opioid overdose. Increases in overdose-related encounters took a substantial emotional toll on emergency personnel, who described a range of responses including feelings of burnout, exhaustion, and helplessness. While some emergency personnel felt conflicted about the implementation of harm reduction strategies like syringe services programs, others emphasized the necessity of these services. Emergency personnel expressed frustration with barriers to treatment referral in the state and recommended immediate treatment access after overdose events.

CONCLUSIONS

Findings suggest that interventions addressing trauma and burnout are necessary to support emergency personnel, while expanded harm reduction and treatment access are critical to support those who experience opioid overdose in NH.