Development of a Risk Index for Serious Prescription Opioid-Induced Respiratory Depression or Overdose in Veterans' Health Administration Patients

Evaluating the Risk Index for Serious Prescription Opioid-Induced Respiratory Depression or Overdose in Patients with Cancer

The Commercially Insured health Plan Risk Index for Overdose or Serious Opioid-induced Respiratory Depression (CIP-RIOSORD) is an evidence-based tool to determine serious opioid-induced respiratory depression (OIRD) or overdose risk. The CIP-RIOSORD total score determines a risk class and estimates the probability for an OIRD event within the next 6 months. We performed a single-center, retrospective analysis to determine CIP-RIOSORD baseline scores and the most common predictive factors in patients with cancer. Patients (n = 160) were split into new consultations (n = 83, Group 1) versus the first documented follow-up consultation (n = 77, Group 2). Most patients were Caucasian women with metastatic gastrointestinal cancer. CIP-RIOSORD scores for Group 1 patients were 14.8 ± 15.2 (mean ± SD, risk class 4). Group 2 patients had higher CIP-RIOSORD scores (16.6 ± 14.9, risk class 4). For Group 1, the most common CIP-RIOSORD predictive factors were use of a long-acting opioid formulation (n = 24, 29%) and daily oral morphine equivalent (OME) ≥100 (n = 20, 24%); for Group 2, predictive factors were use of an antidepressant (n = 34, 44%) and a long-acting opioid formulation (n = 27, 35%). Based on the CIP-RIOSORD, there is a 15% probability of experiencing a serious OIRD event or overdose within the next 6 months.

Implementation of Opioid Safety Efforts: Influence of Academic Detailing on Adverse Outcomes Among Patients in the Veterans Health Administration

BACKGROUND

The Veterans Health Administration (VA) implemented academic detailing (AD) to support safer opioid prescribing and overdose prevention initiatives.

METHODS

Patient-level data were extracted monthly from VA's electronic health record to evaluate whether AD implementation was associated with changes in all-cause mortality, opioid poisoning inpatient admissions, and opioid poisoning emergency department (ED) visits in an observational cohort of patients with long-term opioid prescriptions (≥45-day supply of opioids 6 months prior to a given month with ≤15 days between prescriptions). A single-group interrupted time series analysis using segmented logistic regression for mortality and Poisson regression for counts of inpatient admissions and ED visits was used to identify whether the level and slope of these outcomes changed in response to AD implementation.

RESULTS

Among 955 376 unique patients (19 431 241 person-months), there were 53 369 deaths (29 025 pre-AD; 24 344 post-AD), 1927 opioid poisoning inpatient admissions (610 pre-AD; 1317 post-AD), and 408 opioid poisoning ED visits (207 pre-AD; 201 post-AD). Immediately after AD implementation, there was a 5.8% reduction in the odds of all-cause mortality (95% confidence interval [CI]: 0.897, 0.990). However, patients had a significantly increased incidence rate of inpatient admissions for opioid poisoning immediately after AD implementation (incidence rate ratio = 1.523; 95% CI: 1.118, 2.077). No significant differences in ED visits for opioid poisoning were observed.

CONCLUSIONS

AD was associated with decreased all-cause mortality but increased inpatient hospitalization for opioid poisoning among patients prescribed long-term opioids. Mechanisms via which AD's efforts influenced opioid-related outcomes should be explored.

Healthcare costs and use before and after opioid overdose in Veterans Health Administration patients with opioid use disorder

AIMS

To compare healthcare costs and use between United States (US) Veterans Health Administration (VHA) patients with opioid use disorder (OUD) who experienced an opioid overdose (OD cohort) and patients with OUD who did not experience an opioid overdose (non-OD cohort).

DESIGN

This is a retrospective cohort study of administrative and clinical data.

SETTING

The largest integrated national health-care system is the US Veterans Health Administration's healthcare systems.

PARTICIPANTS

We included VHA patients diagnosed with OUD from October 1, 2017 through September 30, 2018. We identified the index date of overdose for patients who had an overdose. Our control group, which included patients with OUD who did not have an overdose, was randomly assigned an index date. A total of 66 513 patients with OUD were included for analysis (OD cohort: n = 1413; non-OD cohort: n = 65 100).

MEASUREMENTS

Monthly adjusted healthcare-related costs and use in the year before and after the index date. We used generalized estimating equation models to compare patients with an opioid overdose and controls in a difference-in-differences framework.

FINDINGS

Compared with the non-OD cohort, an opioid overdose was associated with an increase of $16 890 [95% confidence interval (CI) = $15 611-18 169; P < 0.001] in healthcare costs for an estimated $23.9 million in direct costs to VHA (95% CI = $22.1 million, $25.7 million) within the 30 days following overdose after adjusting for baseline characteristics. Inpatient costs ($13 515; 95% CI = $12 378-14 652; P < 0.001) reflected most of this increase. Inpatient days (+6.15 days; 95% CI, = 5.33-6.97; P < 0.001), inpatient admissions (+1.01 admissions; 95% CI = 0.93-1.10; P < 0.001) and outpatient visits (+1.59 visits; 95% CI = 1.34-1.84; P < 0.001) also increased in the month after opioid overdose. Within the overdose cohort, healthcare costs and use remained higher in the year after overdose compared with pre-overdose trends.

CONCLUSIONS

The US Veterans Health Administration patients with opioid use disorder (OUD) who have experienced an opioid overdose have increased healthcare costs and use that remain significantly higher in the month and continuing through the year after overdose than OUD patients who have not experienced an overdose.

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/).

Retrospective Cohort Study of Safety Outcomes Associated with Opioid Rotations to Buprenorphine

The objective of this study was to understand the effect buprenorphine rotations have on respiratory risk and other safety outcomes. This was a retrospective observational study evaluating Veterans who underwent an opioid rotation from full-agonist opioids to buprenorphine products or to alternative opioids. The primary endpoint was change in the Risk Index for Overdose or Serious Opioid-induced Respiratory Depression (RIOSORD) score from baseline to six months post-rotation. Median baseline RIOSORD scores were 26.0 and 18.0 in the Buprenorphine Group and the Alternative Opioid Group, respectively. There was no statistically significant difference between groups in baseline RIOSORD score. At six months post-rotation, median RIOSORD scores were 23.5 and 23.0 in the Buprenorphine Group and Alternative Opioid Group, respectively. The difference in change in RIOSORD scores between groups was not statistically significant (p = 0.23). However, based on changes in RIOSORD risk class, an 11% and 0% decrease in respiratory risk was observed in the Buprenorphine and Alternative Opioid groups, respectively. This finding may be considered clinically significant given a change in risk was observed as predicted by RIOSORD score. Further research is needed to clarify the effect that opioid rotations have on respiratory depression risk and other safety outcomes.

Video-telecare collaborative pain management during COVID-19: a single-arm feasibility study

BACKGROUND

Chronic pain is among the most common conditions presenting to primary care and guideline-based care faces several challenges. A novel pain management program, Video-Telecare Collaborative Pain Management (VCPM), was established to support primary care providers and meet new challenges to care presented by the COVID-19 pandemic.

METHODS

The present single-arm feasibility study aimed to evaluate the feasibility and acceptability of VCPM and its components among U.S. veterans on long-term opioid therapy for chronic pain at ≥ 50 mg morphine equivalent daily dose (MEDD). VCPM consists of evidence-based interventions, including opioid reassessment and tapering, rotation to buprenorphine and monitoring, and encouraging behavioral pain and opioid-use disorder self-management.

RESULTS

Of the 133 patients outreached for VPCM, 44 completed an initial intake (33%) and 19 attended multiple VCPM appointments (14%). Patients were generally satisfied with VCPM, virtual modalities, and provider interactions. Nearly all patients who attended multiple appointments maintained a buprenorphine switch or tapered opioids (16/19; 84%), and buprenorphine switches were generally reported as acceptable by patients. Patients completing an initial intake with VCPM had reduced morphine equivalent daily dose after three months (means = 109 mg MEDD vs 78 mg), with greater reductions among those who attended multiple appointments compared to intake only (ΔMEDD = -58.1 vs. -8.40). Finally, 29 referrals were placed for evidence-based non-pharmacologic interventions.

CONCLUSION

Pre-defined feasibility and acceptability targets for VCPM and its components were broadly met, and preliminary data are encouraging. Novel strategies to improve enrollment and engagement and future directions are discussed.

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.

High suicidality predicts overdose events among people with substance use disorder: A latent class analysis

Introduction

Suicide is the tenth leading cause of death in the United States and continues to be a major public health concern. Suicide risk is highly prevalent among individuals with co-occurring substance use disorders (SUD) and mental health disorders, making them more prone to adverse substance use related outcomes including overdose. Identifying individuals with SUD who are suicidal, and therefore potentially most at risk of overdose, is an important step to address the synergistic epidemics of suicides and overdose fatalities in the United States. The current study assesses whether patterns of suicidality endorsement can indicate risk for substance use and overdose.

Methods

Latent class analysis (LCA) was used to assess patterns of item level responses to the Concise Health Risk Tracking Self-Report (CHRT-SR), which measures thoughts and feelings associated with suicidal propensity. We used data from 2,541 participants with SUD who were enrolled across 8 randomized clinical trials in the National Drug Abuse Treatment Clinical Trials Network from 2012 to 2021. Characteristics of individuals in each class were assessed, and multivariable logistic regression was performed to examine class membership as a predictor of overdose. LCA was also used to analyze predictors of substance use days.

Results

Three classes were identified and discussed: Class (1) Minimal Suicidality, with low probabilities of endorsing each CHRT-SR construct; Class (2) Moderate Suicidality, with high probabilities of endorsing pessimism, helplessness, and lack of social support, but minimal endorsement of despair or suicidal thoughts; and Class (3) High Suicidality with high probabilities of endorsing all constructs. Individuals in the High Suicidality class comprise the highest proportions of males, Black/African American individuals, and those with a psychiatric history and baseline depression, as compared with the other two classes. Regression analysis revealed that those in the High Suicidality class are more likely to overdose as compared to those in the Minimal Suicidality class (p = 0.04).

Conclusion

Suicidality is an essential factor to consider when building strategies to screen, identify, and address individuals at risk for overdose. The integration of detailed suicide assessment and suicide risk reduction is a potential solution to help prevent suicide and overdose among people with SUD.

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.

Clinician Perspectives on Referring Medicaid Back Pain Patients to Integrative and Complementary Medicine: A Qualitative Study

Objective: To increase understanding of referral processes from primary care to integrative and complementary medicine (ICM) under an Oregon Medicaid policy that restricted opioids and expanded access to ICM for back pain patients. Methods: Four asynchronous online focus groups with 48 medical clinicians were conducted. Themes were constructed using thematic analysis. Results: Three themes were constructed related to the clinician's experience: (1) high patient receptivity to ICM, (2) difficulty finding ICM providers who accept Medicaid beneficiaries, and (3) uncertainty of the effectiveness of ICM among clinicians. Conclusions: Findings suggest that health systems expanding access to ICM for Medicaid beneficiaries may benefit from establishing and supporting linkages between clinicians and ICM providers, especially in rural areas.

Integration of Pharmacy Student Interns into a Naloxone Telephone Outreach Service

Opioid overdose remains a significant public health issue in the United States and is the leading cause of accidental death. Naloxone has become increasingly accessible, with greater emphasis placed by health systems and pharmacies on distribution of the safety tool. While the utility of pharmacists in advancing this work is clear, there is limited research on the success of integrating pharmacy students into a naloxone outreach program. The purpose of this project was to implement and evaluate the success of integrating pharmacy student interns into a naloxone telephone outreach service for Veteran patients at risk for opioid overdose. A telephone outreach protocol was developed and reviewed by Clinical Pharmacist Practitioners (CPPs) at the site. Pharmacy student interns were trained to complete naloxone outreach calls, which were supervised by a CPP. In the first three months, 160 patients were identified for outreach based on prescription opioid risk factors. Of the 118 reached by telephone, 92 (78.0%) accepted naloxone and 26 (22.0%) declined. In total, 150 (93.8%) patients received naloxone education via either telephone discussion or letter. Integrating supervised pharmacy student interns into a naloxone telephone outreach service was feasible for interns and CPPs and resulted in a high naloxone acceptance rate.

Suicidality as a Predictor of Overdose among Patients with Substance Use Disorders

Increasing rates of overdose and overdose deaths are a significant public health problem. Research has examined co-occurring mental health conditions, including suicidality, as a risk factor for intentional and unintentional overdose among individuals with substance use disorder (SUD). However, this research has been limited to single site studies of self-reported outcomes. The current research evaluated suicidality as a predictor of overdose events in 2541 participants who use substances enrolled across eight multi-site clinical trials completed within the National Drug Abuse Treatment Clinical Trials Network between 2012 to 2021. The trials assessed baseline suicidality with the Concise Health Risk Tracking Self-Report (CHRT-SR). Overdose events were determined by reports of adverse events, cause of death, or hospitalization due to substance overdose, and verified through a rigorous adjudication process. Multivariate logistic regression was performed to assess continuous CHRT-SR score as a predictor of overdose, controlling for covariates. CHRT-SR score was associated with overdose events (p = 0.03) during the trial; the likelihood of overdose increased as continuous CHRT score increased (OR 1.02). Participants with lifetime heroin use were more likely to overdose (OR 3.08). Response to the marked rise in overdose deaths should integrate suicide risk reduction as part of prevention strategies.

Effect of Paroxetine or Quetiapine Combined With Oxycodone vs Oxycodone Alone on Ventilation During Hypercapnia: A Randomized Clinical Trial

IMPORTANCE

Opioids can cause severe respiratory depression by suppressing feedback mechanisms that increase ventilation in response to hypercapnia. Following the addition of boxed warnings to benzodiazepine and opioid products about increased respiratory depression risk with simultaneous use, the US Food and Drug Administration evaluated whether other drugs that might be used in place of benzodiazepines may cause similar effects.

OBJECTIVE

To study whether combining paroxetine or quetiapine with oxycodone, compared with oxycodone alone, decreases the ventilatory response to hypercapnia.

DESIGN, SETTING, AND PARTICIPANTS

Randomized, double-blind, crossover clinical trial at a clinical pharmacology unit (West Bend, Wisconsin) with 25 healthy participants from January 2021 through May 25, 2021.

INTERVENTIONS

Oxycodone 10 mg on days 1 and 5 and the following in a randomized order for 5 days: paroxetine 40 mg daily, quetiapine twice daily (increasing daily doses from 100 mg to 400 mg), or placebo.

MAIN OUTCOMES AND MEASURES

Ventilation at end-tidal carbon dioxide of 55 mm Hg (hypercapnic ventilation) using rebreathing methodology assessed for paroxetine or quetiapine with oxycodone, compared with placebo and oxycodone, on days 1 and 5 (primary) and for paroxetine or quetiapine alone compared with placebo on day 4 (secondary).

RESULTS

Among 25 participants (median age, 35 years [IQR, 30-40 years]; 11 female [44%]), 19 (76%) completed the trial. The mean hypercapnic ventilation was significantly decreased with paroxetine plus oxycodone vs placebo plus oxycodone on day 1 (29.2 vs 34.1 L/min; mean difference [MD], -4.9 L/min [1-sided 97.5% CI, -∞ to -0.6]; P = .01) and day 5 (25.1 vs 35.3 L/min; MD, -10.2 L/min [1-sided 97.5% CI, -∞ to -6.3]; P < .001) but was not significantly decreased with quetiapine plus oxycodone vs placebo plus oxycodone on day 1 (33.0 vs 34.1 L/min; MD, -1.2 L/min [1-sided 97.5% CI, -∞ to 2.8]; P = .28) or on day 5 (34.7 vs 35.3 L/min; MD, -0.6 L/min [1-sided 97.5% CI, -∞ to 3.2]; P = .37). As a secondary outcome, mean hypercapnic ventilation was significantly decreased on day 4 with paroxetine alone vs placebo (32.4 vs 41.7 L/min; MD, -9.3 L/min [1-sided 97.5% CI, -∞ to -3.9]; P < .001), but not with quetiapine alone vs placebo (42.8 vs 41.7 L/min; MD, 1.1 L/min [1-sided 97.5% CI, -∞ to 6.4]; P = .67). No drug-related serious adverse events were reported.

CONCLUSIONS AND RELEVANCE

In this preliminary study involving healthy participants, paroxetine combined with oxycodone, compared with oxycodone alone, significantly decreased the ventilatory response to hypercapnia on days 1 and 5, whereas quetiapine combined with oxycodone did not cause such an effect. Additional investigation is needed to characterize the effects after longer-term treatment and to determine the clinical relevance of these findings.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT04310579.

Long-Term Prescription Opioid Use After Injury in Washington State 2015-2018

BACKGROUND

Patients with injury may be at high risk of long-term opioid use due to the specific features of injury (e.g., injury severity), as well as patient, treatment, and provider characteristics that may influence their injury-related pain management.

OBJECTIVES

Inform prescribing practices and identify high-risk populations through studying chronic prescription opioid use in the trauma population.

METHODS

Using the Washington State All-Payer Claims Database (WA-APCD) data, we included adults aged 18-65 years with an incident injury from October 1, 2015-December 31, 2017. We compared patient, injury, treatment, and provider characteristics by whether or not the patients had long-term (≥ 90 days continuous prescription opioid use), or no opioid use after injury.

RESULTS

We identified 191,130 patients who met eligibility criteria and were included in our cohort; 5822 met criteria for long-term use. Most had minor injuries, with a median Injury Severity Score = 1, with no difference between groups. Almost all patients with long-term opioid use had filled an opioid prescription in the year prior to their injury (95.3%), vs. 31.3% in the no-use group (p < 0.001). Comorbidities associated with chronic pain, mental health, and substance use conditions were more common in the long-term than the no-use group.

CONCLUSION

Across this large cohort of multiple, mostly minor, injury types, long-term opioid use was relatively uncommon, but almost all patients with chronic use post injury had preinjury opioid use. Long-term opioid use after injury may be more closely tied to preinjury chronic pain and pain management than acute care pain management.

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.

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

Introduction

The aim of this study was to investigate trends in the prevalence of potentially inappropriate opioid prescribing (PIOP) and identify potential risk factors among Korean noncancer patients.

Methods

We conducted a cross-sectional study of annual national patient sample data from the Korean Health Insurance Review and Assessment Service (HIRA-NPS) for the period 2012-2018. Noncancer patients who were prescribed non-injectable opioid analgesics (NIOAs) at least once were included. The proportion of patients with at least one PIOP in terms of concurrent use of benzodiazepines or gabapentinoids, substance use disorder, treatment duration, and dosage was evaluated. Multivariable logistic regression was performed to identify the risk factors associated with PIOP.

Results

Of the 9,772,503 noncancer patients, 1,583,444 (16.2%) were prescribed NIOAs at least once. Among them, 15.7% were exposed to PIOP, and the prevalence was much higher (31.6%) in the elderly group (age: ⩾65 years). The prevalence of PIOP increased 1.1-fold over 7 years (14.8-16.8%) among the total NIOA users and was more pronounced in non-tramadol NIOA users (a 1.5-fold increase, from 13.2% to 19.4%). Multivariable logistic regression indicated that older age, beneficiaries of medical aid or national meritorious service, exposure to polypharmacy, psychological disorder, chronic pain indication, and concomitant sedative use were independently associated with higher odds of PIOP.

Discussion and Conclusion

We found that the prevalence of PIOP was 15.7% among Korean noncancer patients, and it increased over the 7-year study period. This increasing trend is alarming because it was more drastic with non-tramadol NIOAs compared with that with tramadol. Several patient-level risk factors associated with PIOP would be useful in targeted management strategies for the safe use of opioids.

Plain Language Summary

Potentially inappropriate opioid prescribing and related risk factors among noncancer patients prescribed non-injectable opioids in Korea In Korea, the prevalence of non-injectable opioid analgesic (NIOA) use in noncancer patients steadily increased from 15.3% in 2012 to 17.1% in 2018.Also, the prevalence of potentially inappropriate opioid prescribing (PIOP) increased from 14.8% in 2012 to 16.8% in 2018.The following factors were associated with a markedly increased risk of PIOP: age, beneficiaries of medical aid or national meritorious service, polypharmacy, psychological disorder, chronic pain, and concomitant medications.

Evaluation of an Opioid Overdose Composite Risk Score Cutoff in Active Duty Military Service Members

OBJECTIVE

To evaluate the current cutoff score and a recalibrated adaptation of the Veterans Health Administration (VHA) Risk Index for Serious Prescription Opioid-Induced Respiratory Depression or Overdose (RIOSORD) in active duty service members.

DESIGN

Retrospective case-control.

SETTING

Military Health System.

SUBJECTS

Active duty service members dispensed ≥ 1 opioid prescription between January 1, 2018 and December 31, 2019.

METHODS

Service members with a documented opioid overdose were matched 1:10 to controls. An active duty-specific (AD) RIOSORD was constructed using the VHA RIOSORD components. Analyses examined the risk stratification and predictive characteristics of two RIOSORD versions (VHA and AD).

RESULTS

Cases (n = 95) were matched with 950 controls. Only 6 of the original 17 elements were retained in the AD RIOSORD. Long-acting or extended-release opioid prescriptions, antidepressant prescriptions, hospitalization, and emergency department visits were associated with overdose events. The VHA RIOSORD had fair performance (C-statistic 0.77, 95% CI 0.75, 0.79), while the AD RIOSORD did not demonstrate statistically significant performance improvement (C-statistic 0.78, 95% CI, 0.77, 0.80). The DoD selected cut point (VHA RIOSORD > 32) only identified 22 of 95 ORD outcomes (Sensitivity 0.23) while an AD-specific cut point (AD RIOSORD > 16) correctly identified 53 of 95 adverse events (Sensitivity 0.56).

CONCLUSION

Results highlight the need to continually recalibrate predictive models and to consider multiple measures of performance. Although both models had similar overall performance with respect to the C-statistic, an AD-specific index threshold improves sensitivity. The calibrated AD RIOSORD does not represent an end-state, but a bridge to a future model developed on a wider range of patient variables, taking into consideration features that capture both care received, and care that was not received.

Incidence and Predictors of Persistent Opioid Use in Survivors of Head and Neck Cancer Treated With Curative Radiation

PURPOSE

We sought to characterize the incidence of chronic opioid dependence among head and neck cancer survivors treated by radiation, as well as to identify patient and treatment factors associated with persistent use.

MATERIALS AND METHODS

The medical records of patients with head and neck cancer who received radiation therapy from January 2012 to July 2016 were reviewed. All patients received 60 to 70 Gy with curative intent. Patients who progressed or died within 1 year were intentionally excluded. Opioid doses were calculated in morphine equivalent daily doses in milligrams (mg). Univariate and multivariate regression models were used to identify associations between demographic, medical, disease, and persistent opioid use.

RESULTS

Two hundred and sixty-one patients were included. The median follow-up was 39 months (range: 12 to 83 mo). Two hundred and eleven patients (80%) received opioids for pain control during radiation. The median morphine equivalent daily dose during treatment was 73.8 mg (range: 5 to 561 mg). Rates of persistent opioid use at 6 months, 1 year, and 2 years from completion of radiation were 41.8%, 30.1%, and 26.0%, respectively. On multivariate analysis, only preradiation opioid use correlated with persistent opioid use at all 3 time points (P<0.05). Smoking history and a Charlson comorbidity index ≥2 predicted for persistent opioid use at some time points, but not all.

CONCLUSIONS

High rates of persistent opioid use exist in patients with head and neck cancer after radiation therapy. Early interventions to appropriately wean patients should be further investigated.

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.

Naloxone prescribing practices in the Military Health System before and after policy implementation

Supplemental Digital Content is Available in the Text. Naloxone prescribing rates for patients meeting the criteria for risk indicators in the U.S. Military Health System increased from January 2017 to February 2021. Bayesian time series analyses indicated that increases in naloxone prescribing after the release of 2 Defense Health Agency policies (June 2018) were not significantly different than forecasted.

Introduction:

Objectives:

Methods:

Results:

Conclusion:

Opioid overdose counseling and prescribing of naloxone in rural community pharmacies: A pilot study

Introduction

Fatal overdoses from opioids increased four-fold from 1999 to 2009, and they are now the leading cause of death among Americans under 50. Legislation has been passed by every state to increase access to naloxone but dispensing by community pharmacies remains low.

Objectives

The objective of this study was to pilot test a proactive opioid overdose counseling intervention and a passive naloxone intervention, and the implementation strategies developed to support their delivery, in rural community pharmacies on relevant implementation outcomes.

Methods

The interventions, implementation strategies, and the overall pilot study approach were developed in a collaborative partnership with a regional supermarket pharmacy chain. They selected 2 rural pharmacies to participate in the pilot study and 2 non-intervention pharmacies to serve as comparison sites. Two interventions were pilot tested in the 2 intervention pharmacies: 1)a proactive opioid overdose counseling intervention and 2) a passive naloxone intervention. An explanatory sequential mixed-methods design was utilized to evaluate adoption, feasibility, acceptability, and appropriateness outcomes after the 3-month observation period.

Results

Between the 2 intervention pharmacies, 130 patients received the opioid overdose counseling intervention. 44 (33.8%) were prescribed and dispensed naloxone. Zero naloxone prescriptions were written or dispensed at the comparison pharmacies. Interviews with pharmacy staff found the interventions to be feasible, acceptable, and appropriate in their settings.

Conclusion

This small scale pilot study in partnership with a regional supermarket pharmacy chain had positive results with a third of patients who received the opioid overdose counseling intervention being dispensed naloxone. However, the majority of patients did not receive naloxone indicating additional revisions to the intervention components and/or implementation strategies are needed to improve the overall impact of the interventions.

Balanced Opioid Prescribing via a Clinical Trade-Off: Pain Relief vs. Adverse Effects of Discomfort, Dependence, and Tolerance/Hypersensitivity

In the backdrop of the opioid epidemic, opioid prescribing has distinct medical and social challenges. Overprescribing contributes to the ongoing opioid epidemic, whereas underprescribing yields inadequate pain relief. Moreover, opioids have serious adverse effects including tolerance and increased sensitivity to pain, paradoxically inducing more pain. Prescribing trade-offs are recognized but not modeled in the literature. We study the prescribing decisions for chronic, acute, and persistent pain types to minimize the cumulative pain that incorporates opioid adverse effects (discomfort and dependence) and the risk of tolerance or hypersensitivity (THS) developed with opioid use. After finding closed-form solutions for each pain type, we analytically investigate the sensitivity of acute pain prescriptions and examine policies on incorporation of THS, patient handover, and adaptive treatments. Our analyses show that the role of adverse effects in prescribing decisions is as critical as that of the pain level. Interestingly, we find that the optimal prescription duration is not necessarily increasing with the recovery time. We show that not incorporating THS or information curtailment at patient handovers leads to overprescribing that can be mitigated by adaptive treatments. Last, using real-life pain and opioid use data from two sources, we estimate THS parameters and discuss the proximity of our model to clinical practice. This paper has a pain management framework that leads to tractable models. These models can potentially support balanced opioid prescribing after their validation in a clinical setting. Then, they can be helpful to policy makers in assessment of prescription policies and of the controversy around over- and underprescribing.

Approaches to opioid prescribing in cancer survivors: Lessons learned from the general literature

LAY SUMMARY

Guidance on how to approach opioid decisions for people beyond active cancer treatment is lacking. This editorial discusses strategies from the general literature that can be thoughtfully tailored to cancer survivors to provide patient-centered pain and opioid care.

Prevalence of co-occurring mental illness and substance use disorder and association with overdose: a linked data cohort study among residents of British Columbia, Canada

AIMS

To estimate the treated prevalence of mental illness, substance use disorder (SUD) and dual diagnosis and the association between dual diagnosis and fatal and non-fatal overdose among residents of British Columbia (BC), Canada.

DESIGN

A retrospective cohort study using linked health, income assistance, corrections and death records.

SETTING

British Columbia (BC), Canada.

PARTICIPANTS

A total of 921 346 BC residents (455 549 males and 465 797 females) aged 10 years and older.

MEASUREMENTS

Hospital and primary-care administrative data were used to identify a history of mental illness only, SUD only, dual diagnosis or no history of SUD or mental illness (2010-14) and overdoses resulting in medical care (2015-17). We calculated crude incidence rates of non-fatal and fatal overdose by dual diagnosis history. Andersen-Gill and competing risks regression were used to examine the association between dual diagnosis and non-fatal and fatal overdose, respectively, adjusting for age, sex, comorbidities, incarceration history, social assistance, history of prescription opioid and benzodiazepine dispensing and region of residence.

FINDINGS

Of the 921 346 people in the cohort, 176 780 (19.2%), 6147 (0.7%) and 15 269 (1.7%) had a history of mental illness only, SUD only and dual diagnosis, respectively; 4696 (0.5%) people experienced 688 fatal and 6938 non-fatal overdoses. In multivariable analyses, mental illness only, SUD only and dual diagnosis were associated with increased rate of non-fatal [hazard ratio (HR) = 1.8, 95% confidence interval (CI) = 1.6-2.1; HR = 9.0, 95% CI = 7.0-11.5, HR = 8.7, 95% CI = 6.9-10.9, respectively] and fatal overdose (HR = 1.6, 95% CI = 1.3-2.0, HR = 4.3, 95% CI = 2.8-6.5, HR = 4.1, 95% CI = 2.8-6.0, respectively) compared with no history.

CONCLUSIONS

In a large sample of residents of British Columbia (Canada), approximately one in five people had sought care for a substance use disorder or mental illness in the past 5 years. The rate of overdose was elevated in people with a mental illness alone, higher again in people with a substance use disorder alone and highest in people with a dual diagnosis. The adjusted hazard rates were similar for people with substance use disorder only and people with a dual diagnosis.

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.

Pain Management and Risks Associated With Substance Use: Practice Recommendations

Assessing and managing pain while evaluating risks associated with substance use and substance use disorders continues to be a challenge faced by health care clinicians. The American Society for Pain Management Nursing and the International Nurses Society on Addictions uphold the principle that all persons with co-occurring pain and substance use or substance use disorders have the right to be treated with dignity and respect, and receive evidence-based, high quality assessment, and management for both conditions. The American Society for Pain Management Nursing and International Nurses Society on Addictions have updated their 2012 position statement on this topic supporting an integrated, holistic, multidimensional approach, which includes nonopioid and nonpharmacological modalities. Opioid use disorder is used as an exemplar for substance use disorders and clinical recommendations are included with expanded attention to risk assessment and mitigation with interventions targeted to minimize the risk for relapse or escalation of substance use. Opioids should not be excluded for anyone when indicated for pain management. A team-based approach is critical, promotes the active involvement of the person with pain and their support systems, and includes pain and addiction specialists whenever possible. Health care systems should establish policies and procedures that facilitate and support the principles and recommendations put forth in this article.

Characteristics of opioid prescribing to outpatients with chronic liver diseases: A call for action

Background

Chronic liver disease (CLD) is among the strongest risk factors for adverse prescription opioid-related events. Yet, the current prevalence and factors associated with high-risk opioid prescribing in patients with chronic liver disease (CLD) remain unclear, making it challenging to address opioid safety in this population. Therefore, we aimed to characterize opioid prescribing patterns among patients with CLD.

Methods

This retrospective cohort study included patients with CLD identified at a single medical center and followed for one year from 10/1/2015-9/30/2016. Multivariable, multinomial regression was used identify the patient characteristics, including demographics, medical conditions, and liver-related factors, that were associated with opioid prescriptions and high-risk prescriptions (≥90mg morphine equivalents per day [MME/day] or co-prescribed with benzodiazepines).

Results

Nearly half (47%) of 12,425 patients with CLD were prescribed opioids over a one-year period, with 17% of these receiving high-risk prescriptions. The baseline factors significantly associated with high-risk opioid prescriptions included female gender (adjusted incident rate ratio, AIRR = 1.32, 95% CI = 1.14–1.53), Medicaid insurance (AIRR = 1.68, 95% CI = 1.36–2.06), cirrhosis (AIRR = 1.22, 95% CI = 1.04–1.43) and baseline chronic pain (AIRR = 3.40, 95% CI = 2.94–4.01), depression (AIRR = 1.93, 95% CI = 1.60–2.32), anxiety (AIRR = 1.84, 95% CI = 1.53–2.22), substance use disorder (AIRR = 2.16, 95% CI = 1.67–2.79), and Charlson comorbidity score (AIRR = 1.27, 95% CI = 1.22–1.32). Non-alcoholic fatty liver disease was associated with decreased high-risk opioid prescriptions (AIRR = 0.56, 95% CI = 0.47–0.66).

Conclusion

Opioid medications continue to be prescribed to nearly half of patients with CLD, despite efforts to curtail opioid prescribing due to known adverse events in this population.

Predicting opioid-induced oversedation in hospitalised patients: a multicentre observational study

OBJECTIVES

Opioid-induced respiratory depression (OIRD) and oversedation are rare but potentially devastating adverse events in hospitalised patients. We investigated which features predict an individual patient's risk of OIRD or oversedation; and developed a risk stratification tool that can be used to aid point-of-care clinical decision-making.

DESIGN

Retrospective observational study.

SETTING

Twelve acute care hospitals in a large not-for-profit integrated delivery system.

PARTICIPANTS

All inpatients ≥18 years admitted between 1 July 2016 and 30 June 2018 who received an opioid during their stay (163 190 unique hospitalisations).

MAIN OUTCOME MEASURES

The primary outcome was occurrence of sedation or respiratory depression severe enough that emergent reversal with naloxone was required, as determined from medical record review; if naloxone reversal was unsuccessful or if there was no evidence of hypoxic encephalopathy or death due to oversedation, it was not considered an oversedation event.

RESULTS

Age, sex, body mass index, chronic obstructive pulmonary disease, concurrent sedating medication, renal insufficiency, liver insufficiency, opioid naïvety, sleep apnoea and surgery were significantly associated with risk of oversedation. The strongest predictor was concurrent administration of another sedating medication (adjusted HR, 95% CI=3.88, 2.48 to 6.06); the most common such medications were benzodiazepines (29%), antidepressants (22%) and gamma-aminobutyric acid analogue (14.7%). The c-statistic for the final model was 0.755. The 24-point Oversedation Risk Criteria (ORC) score developed from the model stratifies patients as high (>20%, ≥21 points), moderate (11%-20%, 10-20 points) and low risk (≤10%, <10 points).

CONCLUSIONS

The ORC risk score identifies patients at high risk for OIRD or oversedation from routinely collected data, enabling targeted monitoring for early detection and intervention. It can also be applied to preventive strategies-for example, clinical decision support offered when concurrent prescriptions for opioids and other sedating medications are entered that shows how the chosen combination impacts the patient's risk.

Clinical pharmacist involvement in expanding naloxone distribution in a veteran population

DISCLAIMER

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time.

PURPOSE

To describe the process used in a clinical pharmacist specialist (CPS)-led Opioid Overdose Education and Naloxone Distribution (OEND) program initiative to increase naloxone distribution to veterans at high risk for overdose via provider education and identification of barriers to naloxone distribution.

SUMMARY

Drug overdose is the leading cause of accidental death in the United States. One step toward counteracting the epidemic includes expanding access to and use of naloxone. The Veterans Health Administration has developed initiatives to target veterans at risk for opioid overdose, such as the Veterans Affairs (VA) OEND program. Pharmacists can play a unique role in OEND by both prescribing naloxone and educating patients and providers on risk mitigation strategies. Through provider education, patient education, and facility-wide initiatives, naloxone prescribing was increased by 9-fold from August 2016 to August 2018. In addition, the number of new naloxone prescribers increased by almost 7-fold during the intervention period. Naloxone distribution to high-risk groups drastically increased across all target groups.

CONCLUSION

CPS involvement in promoting OEND at VAPHS drastically increased rates of prescribing of naloxone kits to veterans at risk for opioid overdose. This initiative showed that a CPS can play multiple roles in supporting OEND outreach at a large healthcare setting.

Opioid Overdose Risk in Patients Returning to the Emergency Department for Pain

OBJECTIVE

Using the Risk Index for Overdose or Serious Opioid-induced Respiratory Depression (CIP-RIOSORD) in patients returning to the emergency department (ED) for pain and discharged with an opioid prescription, we assessed overall opioid overdose risk and compared risk in opioid naive patients to those who are non-opioid naive.

DESIGN

This was a secondary analysis from a prospective observational study of patients ≥ 18 years old returning to the ED within 30 days. Data were collected from patient interviews and chart reviews. Patients were categorized as Group 1 (not using prescription opioids) or Group 2 (consuming prescription opioids). Statistical analyses were performed using Fisher's exact and Wilcoxon's rank sum tests. Risk class and probability of overdose was determined using Risk Index for Overdose or Serious Opioid-induced Respiratory Depression (CIP-RIOSORD).

RESULTS

Of the 389 enrollees who returned to the ED due to pain within 30 days of an initial visit, 67 (17%) were prescribed opioids. The majority of these patients were in Group 1 (60%). Both Group 1 (n = 40) and Group 2 (n = 27) held an average CIP-RIOSORD risk class of 3. Race significantly differed between groups; the majority of Group 1 self-identified as African American (80%) (P = .0267). There were no differences in age, gender, or CIP-RIOSORD risk class between groups. However, Group 2 had nearly double the number of predictive factors (median = 1.93) as Group 1 (median = 1.18) (P = .0267).

CONCLUSIONS

A substantial proportion of patients (25%) were high risk for opioid overdose. CIP-RIOSORD may prove beneficial in risk stratification of patients discharged with prescription opioids from the ED.

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.

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.

Impact of a Multidisciplinary Long-Term Opioid Therapy Safety Program at a Military Tertiary Academic Medical Center

OBJECTIVE

In light of the ongoing opioid crisis, Naval Medical Center Portsmouth (NMCP) created the Long-Term Opioid Therapy Safety (LOTS) program to reduce risks and improve long-term opioid therapy outcomes. Our primary outcome was change in compliance with the recommended safety metrics.

DESIGN

This is a retrospective cohort study performed at NMCP, a large military academic medical center providing comprehensive medical care to DoD beneficiaries. The NMCP LOTS program provides both patient and provider narcotic education as well as medical record auditing. The NMCP LOTS program promotes adherence to published CDC, the DVA, and DoD guidelines.

METHODS

Anonymized data were compiled each fiscal quarter and were analyzed retrospectively. Adult patients prescribed opioids for at least 90 days without a gap of 30 days between prescriptions were included in this study. The investigators recorded and reported provider compliance with LOTS metrics over the same period.

RESULTS

Compliance with the recommended safety metrics improved. We noted a decrease in the number of long-term opioid patients, concurrent benzodiazepine prescriptions, and patients prescribed greater than 90 morphine equivalents per day during the observation period. The number of naloxone prescriptions for LOTS patients also increased, reflecting improved guideline adherence.

CONCLUSION

Systematic education and feedback to providers are effective in creating a system and culture of opioid reduction, safe opioid prescribing, and system accountability. This article presents a comprehensive approach to modifying prescribing patterns of long-term opioids in a large healthcare system.

Development, implementation, and evaluation of a clinical decision support tool to improve naloxone coprescription within military health system pharmacies

DISCLAIMER

In an effort to expedite the publication of articles related to the COVID-19 pandemic, AJHP is posting these manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time.

PURPOSE

To describe the development, implementation, and evaluation of a pharmacy clinical decision support tool designed to increase naloxone coprescription among people at risk for opioid overdose in a large healthcare system.

SUMMARY

The Military Health System Opioid Registry and underlying presentation layer were used to develop a clinical decision support capability to improve naloxone coprescription at the pharmacy point of care. Pharmacy personnel use a patient identification card barcode scanner or manually enter a patient's identification number to quickly visualize information on a patient's risk for opioid overdose and medical history related to pain and, when appropriate, receive a recommendation to coprescribe naloxone. The tool was made available to military treatment facility pharmacy locations. An interactive dashboard was developed to support monitoring, utilization, and impact on naloxone coprescription to patients at risk for opioid overdose.

CONCLUSION

Initial implementation of the naloxone tool was slow from a lack of end-user awareness. Efforts to increase utilization were, in part, successful owing to a number of enterprise-wide educational initiatives. In early 2020, the naloxone tool was used in 15% of all opioid prescriptions dispensed at a military pharmacy. Data indicate that the frequency of naloxone coprescription to patients at risk for opioid overdose was significantly higher when the naloxone tool was used than when the tool was not used.

Opioid Overdose in the Hospital Setting: A Systematic Review

OBJECTIVE

Our objective was to determine the percentage of opioid overdose events among medical and surgical inpatient admissions, and to identify risk factors associated with these events.

METHODS

We searched PubMed and CINAHL databases from inception through July 30, 2017 and identified additional studies from reference lists and other reviews. Articles were included if they reported original research on the rate of opioid overdoses or opioid-related adverse events, and the adverse events occurred in a general medical hospital during an inpatient stay. We extracted information on study population, design, results, and risk for bias using the Newcastle-Ottawa Quality Assessment Scale. We performed this review in accordance with recently suggested standards and report our findings as per the Meta-Analyses and Systematic Reviews of Observational Studies guidelines.

RESULTS

Thirteen studies met our eligibility criteria. The percentage of opioid overdoses ranged from 0.06% to 2.50% of hospitalizations. The majority of studies used only 1 method of event detection. Risk factors for overdose included older age, infancy, medical comorbidity, substance use disorder diagnosis, combining opioids with other sedatives, and admission to hospitals with higher opioid-prescribing rates.

CONCLUSIONS

Opioid overdose in the inpatient setting is a serious preventable harm and is likely underestimated in much of the current literature. Improved detection methods are needed to more accurately measure the rate of inpatient opioid overdose. Refined estimates of opioid overdose should be used to drive safety and quality improvement initiatives in hospitals.

Opioid Management in Older Adults: Lessons Learned From a Geriatric Patient-Centered Medical Home

Background

The United States continues to confront an opioid crisis that also affects older adults. Best practices for prescription opioid management in older adults are challenging to implement in this population. We present our experience with a 1-year management of 48 high-risk older patients who received guideline-based best practices for chronic prescription opioid therapy at a US Department of Veterans Affairs (VA) patient aligned care team (PACT) patient-centered medical home.

Methods

The GeriPACT population at the Nashville Campus of the VA Tennessee Valley Healthcare System has an enrollment of 745 patients of whom 48 (6.5%) receive chronic prescription opioid therapy. The practice is supported by the VA Computerized Patients Record System, including the electronic patient portal, My healtheVet, and telemedicine capabilities. Data were collected by chart review and operations data.

Results

The mean (range) age of patients was 70.4 (66-93) years. Many patients had comorbid conditions, such as diabetes mellitus (35%), congestive heart failure (18.6%), and dementia (8.3%). More than half had an estimated glomerular filtration rates (eGFR) < 60 mL/min, indicating at least stage 3 chronic kidney disease, 41.7% used mental health services (41.7%), and 20.8% had a history of opioid use disorder. Most indications for chronic pain were for musculoskeletal pain (95.8%). The mean (range) morphine equivalent daily dose was 37 mg (10-109). More than half had been seen in the emergency department, and 20.8% had been hospitalized in the previous year for an opioid-related hospitalization, and 3% had expired. Over the year, dose reductions of benzodiazepines or narcotics was performed for 12.5% of patients, accidental overdoses occurred in 4.2%, and positive urine drug screens (UDSs) for cocaine and cannabinoid/tetrahydrocannabinol occurred in 10.4%. One patient was terminated from the program for multiple positive UDSs.

Conclusions

Guideline-based patient-centered medical home management of patients with chronic pain who were treated with opioids can be an effective model contributing to the health and well-being of older patients. Complex older patients on chronic opioid treatment are best managed by an interdisciplinary team.

Understanding Buprenorphine for Use in Chronic Pain: Expert Opinion

Objective

An expert panel convened to reach a consensus on common misconceptions surrounding buprenorphine, a Schedule III partial µ-opioid receptor agonist indicated for chronic pain. The panel also provided clinical recommendations on the appropriate use of buprenorphine and conversion strategies for switching to buprenorphine from a full µ-opioid receptor agonist for chronic pain management.

Methods

The consensus panel met on March 25, 2019, to discuss relevant literature and provide recommendations on interpreting buprenorphine as a partial µ-opioid receptor agonist, prescribing buprenorphine before some Schedule II, III, or IV options, perioperative/trauma management of patients taking buprenorphine, and converting patients from a full µ-opioid receptor agonist to buprenorphine.

Results

The panel recommended that buprenorphine’s classification as a partial µ-opioid receptor agonist not be clinically translated to mean partial analgesic efficacy. The panel also recommended that buprenorphine be considered before some Schedule II, III, or IV opioids in patients with a favorable risk/benefit profile on the basis of metabolic factors, abuse potential, and tolerability and that buprenorphine be continued during the perioperative/trauma period. In addition, switching patients from a full µ-opioid receptor agonist to buprenorphine should be considered with no weaning period at starting doses that are based on the previous opioid dose.

Conclusions

These recommendations provide a framework for clinicians to address most clinical scenarios regarding buprenorphine use. The overall consensus of the panel was that buprenorphine is a unique Schedule III opioid with favorable pharmacologic properties and a safety profile that may be desirable for chronic pain management.

Predicting Mortality Risk After a Hospital or Emergency Department Visit for Nonfatal Opioid Overdose

BACKGROUND

Survivors of opioid overdose have substantially increased mortality risk, although this risk is not evenly distributed across individuals. No study has focused on predicting an individual's risk of death after a nonfatal opioid overdose.

OBJECTIVE

To predict risk of death after a nonfatal opioid overdose.

DESIGN AND PARTICIPANTS

This retrospective cohort study included 9686 Pennsylvania Medicaid beneficiaries with an emergency department or inpatient claim for nonfatal opioid overdose in 2014-2016. The index date was the first overdose claim during this period.

EXPOSURES, MAIN OUTCOME, AND MEASURES

Predictor candidates were measured in the 180 days before the index overdose. Primary outcome was 180-day all-cause mortality. Using a gradient boosting machine model, we classified beneficiaries into six subgroups according to their risk of mortality (< 25th percentile of the risk score, 25th to < 50th, 50th to < 75th, 75th to < 90th, 90th to < 98th, ≥ 98th). We then measured receipt of medication for opioid use disorder (OUD), risk mitigation interventions (e.g., prescriptions for naloxone), and prescription opioids filled in the 180 days after the index overdose, by risk subgroup.

KEY RESULTS

Of eligible beneficiaries, 347 (3.6%) died within 180 days after the index overdose. The C-statistic of the mortality prediction model was 0.71. In the highest risk subgroup, the observed 180-day mortality rate was 20.3%, while in the lowest risk subgroup, it was 1.5%. Medication for OUD and risk mitigation interventions after overdose were more commonly seen in lower risk groups, while opioid prescriptions were more likely to be used in higher risk groups (both p trends < .001).

CONCLUSIONS

A risk prediction model performed well for classifying mortality risk after a nonfatal opioid overdose. This prediction score can identify high-risk subgroups to target interventions to improve outcomes among overdose survivors.

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.

Top Ten Tips Palliative Care Clinicians Should Know About End-Stage Liver Disease

End-stage liver disease (ESLD) is an increasingly prevalent condition with high morbidity and mortality, especially for those ineligible for liver transplantation. Patients with ESLD, along with their family caregivers, have significant needs related to their quality of life, and there is increasing attention being paid to integration of palliative care (PC) principles into routine care throughout the disease spectrum. To provide upstream care for these patients and their family caregivers, it is essential for PC providers to understand their complex psychosocial and physical needs and to be aware of the unique challenges around medical decision making and end-of-life care for this patient population. This article, written by a team of liver and PC experts, shares 10 high-yield tips to help PC clinicians provide better care for patients with advanced liver disease.

Ventilatory Response to Hypercapnia as Experimental Model to Study Effects of Oxycodone on Respiratory Depression

BACKGROUND

Opioid analgesics used to treat pain can cause respiratory depression. However, this effect has not been extensively studied, and life- threatening, opioid-induced respiratory depression remains difficult to predict. We tested the ventilatory response to hypercapnia for evaluating the pharmacodynamic effect of a drug on respiratory depression.

METHODS

We conducted a randomized, placebo-controlled, double-blind, crossover, study in 12 healthy adult males. Subjects received 2 treatments (placebo and immediate-release oxycodone 30 mg) separated by a 24-hour washout period. Subjects inhaled a mixture of 7% carbon dioxide, 21% oxygen, and 72% nitrogen for 5 minutes to assess respiratory depression. Minute ventilation, respiratory rate, tidal volume, flow rate, end-tidal CO2, and oxygen saturation were recorded continuously at pre-dose and 30, 60, 120, and 180 minutes post-dose. The primary endpoint was the effect on ventilatory response to hypercapnia at 60 minutes post-dose, as assessed by the slope of the linear relationship between minute ventilation and end-tidal CO2.

RESULTS

At 60 minutes post-dose, subjects had a mean slope of 2.4 in the oxycodone crossover period, compared to 0.1 in the placebo period (mean difference, 2.3; 95%CI: 0.2 to 4.5; p = 0.035). Statistical significance was likewise achieved at the secondary time points (30, 120, and 180 minutes post-dose, p <0.05).

CONCLUSIONS

This model for testing ventilatory response to hypercapnia discriminated the effect of 30 mg of oxycodone vs. placebo for up to 3 hours after a single dose. It may serve as a method to predict the relative effect of a drug on respiratory depression.

Exploring Factors Associated With Long-Term Opioid Therapy in Cancer Survivors: An Integrative Review

CONTEXT

The prevalence of chronic pain in cancer survivors is double that of the general U.S.

POPULATION

Opioids have been the foundation of cancer pain management for decades; however, there is a paucity of literature on long-term opioid therapy (LTOT) in cancer survivors. An understanding of factors related to LTOT use in cancer survivors is needed to address chronic pain and balance opioid harms in the expanding population of cancer survivors.

OBJECTIVES

To analyze the research of LTOT utilization and factors associated with persistent opioid use in cancer survivors.

METHODS

A five-stage integrative review process was adapted from Whittemore and Knafl. Data sources searched included Web of Science, PubMed, Embase, Cochrane, and Google Scholar. Quantitative research studies from 2010 to present related to cancer survivors managed on LTOT were included. Editorials, reviews, or abstracts were excluded.

RESULTS

After reviewing 315 articles, 21 articles were included. We found that there were several definitions of LTOT in the reviewed studies, but the duration of opioid use (i.e., more than three months after completion of curative treatment) was the most common. The reviewed literature describes a relationship between LTOT and important biopsychosocial factors (cancer type, socioeconomic factors, and comorbidities).

CONCLUSION

The studies in this review shed light on the factors associated with LTOT in cancer survivors. LTOT was common in certain populations of cancer survivors and those with a collection of patient-specific characteristics. This review suggests that there is a critical need for specialized research on chronic cancer pain and opioid safety in cancer survivors.

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.

Current state of opioid stewardship

PURPOSE

The opioid epidemic continues to result in significant morbidity and mortality even within hospitals where opioids are the second most common cause of adverse events. Opioid stewardship represents one model for hospitals to promote safe and rational prescribing of opioids to mitigate preventable adverse events in alliance with new Joint Commission standards. The purpose of this study was to identify the prevalence of current hospital practices to improve opioid use.

METHODS

A cross-sectional survey of hospital best practices for opioid use was electronically distributed via electronic listservs in March 2018 to examine the presence of an opioid stewardship program and related practices, including formulary restrictions, specialist involvement for high-risk patients, types of risk factors screened, and educational activities.

RESULTS

Among 133 included hospitals, 23% reported a stewardship program and 14% reported a prospective screening process to identify patients at high risk of opioid-related adverse events (ORAEs). Among those with a prospective screening process, there was variability in ORAE risk factor screening. Formulary restrictions were dependent on specific opioids and formulations. Patient-controlled analgesia was restricted at 45% of hospitals. Most hospitals reported having a pain management service (90%) and a palliative care service providing pain management (67%).

CONCLUSION

The absence of opioid stewardship and prospectively screening ORAEs represents a gap in current practice at surveyed hospitals. Hospitals have an opportunity to implement and refine best practices such as access to pain management specialists, use of formulary restrictions, and retrospective and prospective monitoring of adverse events to improve opioid use.

The Opioid Epidemic In Veterans Who Were Homeless Or Unstably Housed

Veterans who are homeless or unstably housed are at increased risk for opioid-related morbidity and mortality. However, there is a limited understanding of the scope of the opioid epidemic and gaps in care for these populations. We conducted a retrospective observational study to examine opioid use disorder (OUD) in a national sample of veterans who accessed specialized homeless programs in the Veterans Health Administration. Additionally, in a subgroup of veterans with a history of OUD, we examined several opioid-related measures: opioid dose, concomitant opioid-benzodiazepine prescribing, and receipt of medication for addiction treatment (MAT) and overdose prevention medication (naloxone). Rates of OUD history varied significantly across age, gender, and program type. Among the subgroup of homeless veterans with an OUD history, prescribing practices and rates of MAT and naloxone receipt varied significantly by age and specialized homeless program. Rates of receipt of MAT and naloxone were moderate and low, respectively, indicating opportunities for program-specific interventions.

Independent Validation in a Large Privately Insured Population of the Risk Index for Serious Prescription Opioid-Induced Respiratory Depression or Overdose

OBJECTIVE

To assess the generalizability of the overdose or serious opioid-induced respiratory depression risk index (VHA-RIOSORD), created by Zedler et al., using claims data from a large private insurer.

DESIGN

A retrospective nested case-control analysis of health care claims data.

SUBJECTS

Commercially insured individuals with a claim for an opioid prescription between October 1, 2014, and September 30, 2016 (N = 1,431,737).

METHODS

An overdose or serious opioid-induced respiratory depression (OSORD) occurred in 1,097 patients. Ten controls were selected per case (N = 10,970). Items and the assignment of point values to predictors were consistent with those determined by Zedler et al. Modeling of risk index scores produced predicted probabilities of OSORD; risk classes were defined by the predicted probability distribution.

RESULTS

All 15 items of the VHA-RIOSORD were used to determine a member's risk of OSORD. The average predicted probability of experiencing OSORD ranged from 3% in the lowest risk decile to 90% in the highest, with excellent agreement between predicted and observed incidence across risk classes. The model's C-statistic was 0.88.

CONCLUSIONS

Consistent with the findings of its developers, the VHA-RIOSORD performed well in identifying members of a large private insurance company who were medical users of prescription opioids at elevated risk of overdose or life-threatening respiratory depression, those most likely to benefit from preventive interventions.

Naloxone acceptance by outpatient veterans: A risk-prioritized telephone outreach event

BACKGROUND

Opioid overdose is a major public health concern in the United States. Naloxone education and distribution can decrease the risk of overdose deaths. A previous study showed that a longitudinal, multi-attempt telephone intervention by a single pharmacy resident was effective for distributing naloxone to a high-risk veteran population.

OBJECTIVES

The purpose of this project was to investigate whether a team-based, single-attempt telephone outreach event is effective for distributing naloxone to at-risk outpatient veterans.

METHODS

The Risk Index for Overdose or Serious Opioid-Induced Respiratory Depression (RIOSORD) tool was used to identify patients with risk class ≥4. Pharmacy trainees contacted 164 patients and offered naloxone. The primary outcome was the proportion of patients with RIOSORD risk class ≥4 who had naloxone before versus after the intervention.

RESULTS

The proportion of patients with RIOSORD class ≥4 who had a naloxone kit before and after the event was 0.28 and 0.63, respectively (difference = 0.35, p < 1 × 10^-6). Per-protocol analysis showed that of 164 patients contacted, 67% were reached (n = 109) and 80 patients accepted naloxone, corresponding to a 73% acceptance rate for those reached.

CONCLUSIONS

A team-based telephone outreach event is an effective method for distributing naloxone to at-risk outpatient veterans.

Improving Intranasal Naloxone Prescribing Through EMR Modification and Automation

BACKGROUND

In 2017, approximately 11.4 million Americans used opioids inappropriately. Nearly 47,600 deaths in 2017 were attributable to overdose on opioids. Intranasal naloxone was approved by the Food and Drug Administration in 2015 as a rescue medication for opioid overdose. New York State launched a prescription drug monitoring program in 2012, the Internet System for Tracking Over-Prescribing (I-STOP), that required completion before dispensing any controlled substance. Currently, prescribing naloxone at our institution requires 10 clicks and 2 free text boxes. The goal of this project was to increase the prescribing of intranasal naloxone by utilizing EMR automation and visualization tools.

METHODS

Our intervention embedded a section within the required I-STOP note, displaying the last date naloxone was prescribed and an option to "prescribe intranasal naloxone." If checked, a prepopulated order dialog box was generated.

RESULTS

Intranasal naloxone orders for the institution totaled 65 for 2 months before the intervention and 203 for 2 months after the intervention, with 112 (55%) coming directly from the I-STOP note modification. Ease of prescribing improved as total clicks were reduced from 10 to 2, and free text boxes from 2 to 0.

CONCLUSIONS

Our findings suggest that a clinical decision support system can be an effective way to increase hospital-wide naloxone prescribing rates. We were able to increase prescribing rates by more than three-fold, significantly increasing the availability of a rescue medication to individuals at high-risk for overdose. Intranasal naloxone prescribing increased with the implementation of a visual reminder and a more intuitive ordering experience while preserving provider autonomy.