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). CONCLUSIONS: 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.

Persistent Opioid Use After Combat Injury and Subsequent Long-term Risk of Abuse: A Retrospective Cohort Study.

OBJECTIVE: To determine whether persistent opioid use after injury is associated with subsequent long-term development of clinically recognized opioid abuse. SUMMARY BACKGROUND DATA: Opioid abuse is an epidemic in the United States and trauma can initiate persistent use; however, it remains unclear whether persistent opioid use contributes to the subsequent development of opioid abuse. The care of combat casualties by the Departments of Defense and Veterans Affairs uniquely allows investigation of this long-term outcome. METHODS: This retrospective cohort study randomly selected 10,000 battle-injured United States military personnel. We excluded patients who died during initial hospitalization or within 180 days of discharge, had a preinjury opioid abuse diagnosis, or had missing data in a preselected variable. We defined persistent opioid use as filling an opioid prescription 3 to 6 months after discharge and recorded clinically recognized opioid abuse using relevant diagnosis codes. RESULTS: After exclusion, 9284 subjects were analyzed, 2167 (23.3%) of whom developed persistent opioid use. During a median follow-up time of 8 years, 631 (6.8%) patients developed clinically recognized opioid abuse with a median time to diagnosis of 3 years. Injury severity and discharge opioid prescription amount were associated with persistent opioid use after trauma. After adjusting for patient and injury-specific factors, persistent opioid use was associated with the long-term development of clinically recognized opioid abuse (adjusted hazard ratio, 2.39; 95% confidence interval, 1.99-2.86). CONCLUSIONS: Nearly a quarter of patients filled an opioid prescription 3 to 6 months after discharge, and this persistent use was associated with long-term development of opioid abuse.

Quality of life among women diagnosed with breast Cancer: A randomized waitlist controlled trial of commercially available mobile app-delivered mindfulness training.

OBJECTIVE: The primary objective was to evaluate the efficacy of commercially available mobile app-delivered mindfulness training (AMT), compared with waitlist control (WC), on quality of life (QOL) among women diagnosed with breast cancer. The secondary outcome was dispositional mindfulness. Enrollment, app utilization, and study completion are reported as feasibility objectives. METHODS: Women diagnosed with breast cancer ≤5 years (n = 112) were randomized to AMT (n = 57) or WC (n = 55), over 8 weeks, with 4 weeks of follow-up. We conducted linear mixed effects models to examine group by observation interactions on QOL and dispositional mindfulness at baseline, during intervention (5-weeks), post-intervention (9-weeks), and follow-up (12-weeks post-baseline). RESULTS: Participants assigned to AMT reported higher QOL, compared with those assigned to WC, from baseline through follow-up t(258.40) = 3.09, P < 0.01, 95% CI [2.71, 11.90]. Participants assigned to AMT also reported higher dispositional mindfulness, compared with those assigned to WC, from baseline through follow-up t(268.44) = 2.04, P = 0.04, 95% CI [0.01, 0.57]. App utilization data was obtained from 34 participants. Fewer participants assigned to AMT completed all study assessments, compared with participants assigned to WC, (χ21  = 7.07, P = 0.008). CONCLUSIONS: Findings suggest commercially available AMT may proffer some benefit to women seeking to enhance their QOL following breast cancer diagnosis.

Diabetes by Air, Land, and Sea: Effect of Deployments on HbA1c and BMI.

INTRODUCTION: Service members (SMs) in the United States (U.S.) Armed Forces have diabetes mellitus at a rate of 2-3%. Despite having a chronic medical condition, they have deployed to environments with limited medical support. Given the scarcity of data describing how they fare in these settings, we conducted a retrospective study analyzing the changes in glycated hemoglobin (HbA1c) and body mass index (BMI) before and after deployment. MATERIALS AND METHODS: SMs from the U.S. Army, Air Force, Navy, and Marine Corps with diabetes who deployed overseas were identified through the Military Health System (MHS) Management Analysis and Reporting Tool and the Defense Manpower Data Center. Laboratory and pharmaceutical data were obtained from the MHS Composite Health Care System and the Pharmacy Data Transaction Service, respectively. Paired t-tests were conducted to calculate changes in HbA1c and BMI before and after deployment. RESULTS: SMs with diabetes completed 11,325 deployments of greater than 90 days from 2005 to 2017. Of these, 474 (4.2%) SMs had both HbA1c and BMI measurements within 90 days prior to departure and within 90 days of return. Most (84.2%) required diabetes medications: metformin in 67.3%, sulfonylureas in 19.0%, dipeptidyl peptidase-4 inhibitors in 13.9%, and insulin in 5.5%. Most SMs deployed with an HbA1c < 7.0% (67.1%), with a mean predeployment HbA1c of 6.8%. Twenty percent deployed with an HbA1c between 7.0 and 7.9%, 7.2% deployed with an HbA1c between 8.0 and 8.9%, and 5.7% deployed with an HbA1c of 9.0% or higher. In the overall population and within each military service, there was no significant change in HbA1c before and after deployment. However, those with predeployment HbA1c < 7.0% experienced a rise in HbA1c from 6.2 to 6.5% (P < 0.001), whereas those with predeployment HbA1c values ≥7.0% experienced a decline from 8.0 to 7.5% (P < 0.001). Those who deployed between 91 and 135 days had a decline in HbA1c from 7.1 to 6.7% (P = 0.010), but no significant changes were demonstrated in those with longer deployment durations. BMI declined from 29.6 to 29.3 kg/m2 (P < 0.001), with other significant changes seen among those in the Army, Navy, and deployment durations up to 315 days. CONCLUSIONS: Most SMs had an HbA1c < 7.0%, suggesting that military providers appropriately selected well-managed SMs for deployment. HbA1c did not seem to deteriorate during deployment, but they also did not improve despite a reduction in BMI. Concerning trends included the deployment of some SMs with much higher HbA1c, utilization of medications with adverse safety profiles, and the lack of HbA1c and BMI evaluation proximal to deployment departures and returns. However, for SMs meeting adequate glycemic targets, we demonstrated that HbA1c remained stable, supporting the notion that some SMs may safely deploy with diabetes. Improvement in BMI may compensate for factors promoting hyperglycemia in a deployed setting, such as changes in diet and medication availability. Future research should analyze in a prospective fashion, where a more complete array of diabetes and readiness-related measures to comprehensively evaluate the safety of deploying SMs with diabetes.

Reducing coprescriptions of benzodiazepines and opioids in a veteran population.

OBJECTIVES: Combination opioid and benzodiazepine (BZD) therapy is associated with poor treatment outcomes and increased risk of overdose death. There is currently limited literature detailing well-implemented strategies to minimize dual prescribing of these agents. The following describes the implementation processes and outcomes of a health system quality improvement project that aimed to reduce combination BZD and opioid therapy. STUDY DESIGN: A retrospective chart review-based quality improvement project. METHODS: All patients within a single healthcare system of the Department of Veterans Affairs treated with long-term (>90 days) combination therapy were identified. A psychiatric pharmacist submitted a 1-time chart review note for each patient, which briefly outlined patient-specific considerations and recommendations for alternatives to BZD treatment. After a 30-day period following entry of the chart review notes, data were collected regarding the number of providers who (1) acknowledged the chart review notes by providing their additional signature and (2) committed to the recommended interventions by initiating taper schedules. RESULTS: During the 30-day follow-up period, 47.5% (n = 29) of chart review notes were acknowledged and 11.5% (n = 7) of prescriptions were tapered by providers. Mental health providers were less likely to provide their additional signature to the chart review notes (χ2 = 4.62, df = 1, P = .0316; Fisher exact test, P = .0215) and to initiate taper schedules (χ2 = 5.51, df = 1, P = .0189; Fisher exact test, P = .0410) compared with primary care providers. CONCLUSIONS: Chart review note recommendations were frequently disregarded by providers and are likely insufficient as a primary intervention tool for reducing long-term combination BZD and opioid therapy.

Opioid Use Patterns Among Active Duty Service Members and Civilians: 2006-2014.

Introduction: Between 2001 and 2009, opioid analgesic prescriptions in the Military Health System quadrupled to 3.8 million. The sheer quantity of opioid analgesics available sets the stage for issues related to misuse, abuse, and diversion. To address this issue, the Department of Defense implemented several directives and clinical guidelines to improve access to appropriate pain care and safe opioid prescribing. Unfortunately, little has been done to characterize changing patterns of opioid use in active duty service members (ADSM), so little is known about how combat operations and military health care policy may have influenced this significant problem. We examined changes in opioid use for ADSM between 2006 and 2014, compared trends with the civilian population, and explored the potential role of military-specific factors in changes in opioid use in the Military Health System. Materials and Methods: After obtaining Institutional Review Board approval, administrative prescription records (Pharmacy Data Transaction Records) for non-deployed ADSM were used to determine the number of opioid prescriptions dispensed each year and the proportion of ADSM who received at least one prescription per month between 2006 and 2014. Based on the observation and the literature, we identified December 2011 as the demarcation point (the optimal point to identify the downturn in opioid use) and used it to compare opioid use trends before and after. We used an autoregressive forecast model to verify changes in opioid use patterns before and after 2011. Several interrupted time series models examined whether military system-level factors were associated with changes in opioid use. Results: Between 2006 and 2014, 1,516,979 ADSM filled 7,119,945 opioid prescriptions, either in military treatment facilities or purchased through TRICARE. Both active duty and civilian populations showed signs of decreasing use after 2011, but this change was much more pronounced among ADSM. The forecast model showed a significant difference after 2011 between the projected and actual proportion of ADSM filling an opioid prescription, confirming 2011 as a point of divergence in opioid use. Interrupted time series models showed that the deflection point was associated with significant decreases. A significant increase of 0.261% in opioid prescriptions was seen for every 1,000 wounded in action service members in a given month. Troops returning from Operation Enduring Freedom, Operation Iraqi Freedom, or Operation New Dawn did not appear to influence the rates of use. Even after accounting for returning troops from Operation Enduring Freedom/Operation Iraqi Freedom/Operation New Dawn and wounded in action counts, the deflection point was associated with a lower proportion of ADSM who filled an opioid prescription, leading to a decrease of 1.61% by the end of the observation period (December 2014). Conclusion: After December 2011, opioid use patterns significantly decreased in both civilian and ADSM populations, but more so in the military population. Many factors, such as numbers of those wounded in action and the structural organization of the Military Health System, may have caused the decline, although more than likely the decrease was influenced by many factors inside and outside of the military, including policy directives and cultural changes.