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.

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.