Veterans

Description: In August 2021, leadership within the U.S. Department of Veterans Affairs (VA) and U.S. Department of Defense (DoD) approved a joint clinical practice guideline (CPG) for the management of substance use disorders (SUDs). This synopsis summarizes key recommendations.

Methods: In March 2020, the VA/DoD Evidence-Based Practice Work Group assembled a team to update the 2015 VA/DoD Clinical Practice Guideline for the Management of Substance Use Disorders that included clinical stakeholders and conformed to the National Academy of Medicine's tenets for trustworthy CPGs. The guideline panel developed key questions, systematically searched and evaluated the literature, created two 1-page algorithms, and distilled 35 recommendations for care using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) system. This synopsis presents the recommendations that were believed to be the most clinically impactful.

Recommendations: The scope of the CPG is broad; however, this synopsis focuses on key recommendations for the management of alcohol use disorder, use of buprenorphine in opioid use disorder, contingency management, and use of technology and telehealth to manage patients remotely.

Existing methods of screening for substance abuse (standardized questionnaires or clinician's simply asking) have proven difficult to initiate and maintain in primary care settings. This article reports on how predictive modeling can be used to screen for substance abuse using extant data in electronic health records (EHRs). We relied on data available through Veterans Affairs Informatics and Computing Infrastructure (VINCI) for the years 2006 through 2016. We focused on 4,681,809 veterans who had at least two primary care visits; 829,827 of whom had a hospitalization. Data included 699 million outpatient and 17 million inpatient records. The dependent variable was substance abuse as identified from 89 diagnostic codes using the Agency for Healthcare Quality and Research classification of diseases. In addition, we included the diagnostic codes used for identification of prescription abuse. The independent variables were 10,292 inpatient and 13,512 outpatient diagnoses, plus 71 dummy variables measuring age at different years between 20 and 90 years. A modified naive Bayes model was used to aggregate the risk across predictors. The accuracy of the predictions was examined using area under the receiver operating characteristic (AROC) curve in 20% of data, randomly set aside for the evaluation. Many physical/mental illnesses were associated with substance abuse. These associations supported findings reported in the literature regarding the impact of substance abuse on various diseases and vice versa. In randomly set-aside validation data, the model accurately predicted substance abuse for inpatient (AROC = 0.884), outpatient (AROC = 0.825), and combined inpatient and outpatient (AROC = 0.840) data. If one excludes information available after substance abuse is known, the cross-validated AROC remained high, 0.822 for inpatient and 0.817 for outpatient data. Data within EHRs can be used to detect existing or predict potential future substance abuse.

Importance: Data are limited regarding statin therapy for primary prevention of atherosclerotic cardiovascular disease (ASCVD) in adults 75 years and older.

Objective: To evaluate the role of statin use for mortality and primary prevention of ASCVD in veterans 75 years and older.

Design, setting, and participants: Retrospective cohort study that used Veterans Health Administration (VHA) data on adults 75 years and older, free of ASCVD, and with a clinical visit in 2002-2012. Follow-up continued through December 31, 2016. All data were linked to Medicare and Medicaid claims and pharmaceutical data. A new-user design was used, excluding those with any prior statin use. Cox proportional hazards models were fit to evaluate the association of statin use with outcomes. Analyses were conducted using propensity score overlap weighting to balance baseline characteristics.

Exposures: Any new statin prescription.

Main outcomes and measures: The primary outcomes were all-cause and cardiovascular mortality. Secondary outcomes included a composite of ASCVD events (myocardial infarction, ischemic stroke, and revascularization with coronary artery bypass graft surgery or percutaneous coronary intervention).

Results: Of 326 981 eligible veterans (mean [SD] age, 81.1 [4.1] years; 97% men; 91% white), 57 178 (17.5%) newly initiated statins during the study period. During a mean follow-up of 6.8 (SD, 3.9) years, a total 206 902 deaths occurred including 53 296 cardiovascular deaths, with 78.7 and 98.2 total deaths/1000 person-years among statin users and nonusers, respectively (weighted incidence rate difference [IRD]/1000 person-years, -19.5 [95% CI, -20.4 to -18.5]). There were 22.6 and 25.7 cardiovascular deaths per 1000 person-years among statin users and nonusers, respectively (weighted IRD/1000 person-years, -3.1 [95 CI, -3.6 to -2.6]). For the composite ASCVD outcome there were 123 379 events, with 66.3 and 70.4 events/1000 person-years among statin users and nonusers, respectively (weighted IRD/1000 person-years, -4.1 [95% CI, -5.1 to -3.0]). After propensity score overlap weighting was applied, the hazard ratio was 0.75 (95% CI, 0.74-0.76) for all-cause mortality, 0.80 (95% CI, 0.78-0.81) for cardiovascular mortality, and 0.92 (95% CI, 0.91-0.94) for a composite of ASCVD events when comparing statin users with nonusers.

Conclusions and relevance: Among US veterans 75 years and older and free of ASCVD at baseline, new statin use was significantly associated with a lower risk of all-cause and cardiovascular mortality. Further research, including from randomized clinical trials, is needed to more definitively determine the role of statin therapy in older adults for primary prevention of ASCVD.

Conflict of interest statement

Conflict of Interest Disclosures: Dr Djousse reported receiving grants from Merck. No other disclosures were reported.

Abstract

Description:

In September 2019, the U.S. Department of Veterans Affairs (VA) and the U.S. Department of Defense (DoD) approved a new joint clinical practice guideline for assessing and managing patients with chronic insomnia disorder and obstructive sleep apnea (OSA). This guideline is intended to give health care teams a framework by which to screen, evaluate, treat, and manage the individual needs and preferences of VA and DoD patients with either of these conditions.

Methods:

In October 2017, the VA/DoD Evidence-Based Practice Work Group initiated a joint VA/DoD guideline development effort that included clinical stakeholders and conformed to the Institute of Medicine's tenets for trustworthy clinical practice guidelines. The guideline panel developed key questions, systematically searched and evaluated the literature, created three 1-page algorithms, and advanced 41 recommendations using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) system.

Recommendations:

This synopsis summarizes the key recommendations of the guideline in 3 areas: diagnosis and assessment of OSA and chronic insomnia disorder, treatment and management of OSA, and treatment and management of chronic insomnia disorder. Three clinical practice algorithms are also included.

The National Institutes of Health has estimated that insomnia and obstructive sleep apnea (OSA) are 2 of the most common sleep disorders in the general U.S. population and in the military and veteran populations (1). Insomnia symptoms are the most common sleep symptoms among U.S. adults, occurring in approximately 20% to 30% of adults, and the prevalence of chronic insomnia disorder ranges from 6% to 10% (2–6). The prevalence of OSA ranges from 9% to 38% and is associated with older age, higher body mass index, male sex, and menopause.

Sleep disorders are more prevalent in the populations served by the U.S. Department of Veterans Affairs (VA) and the U.S. Department of Defense (DoD) than in the general civilian population. In the RAND report “Sleep in the Military: Promoting Healthy Sleep Among U.S. Servicemembers,” 48.6% of military personnel surveyed had poor sleep quality (Pittsburgh Sleep Quality Index score >5) (7). The prevalence of insomnia symptoms has been reported to be as high as 41% in service members deployed to combat and 25% in noncombatants (8). In a large cohort of soldiers preparing for deployment, 19.9% met criteria for insomnia according to the Insomnia Severity Index (ISI) (8). A more recent study evaluated the incidence of insomnia and OSA in the entire population of U.S. Army soldiers from 1997 to 2011 (9) and showed unprecedented increases in the incidence of both conditions (652% and 600%, respectively) during this period. In military personnel referred for sleep evaluations, sleep-disordered breathing is the most frequently diagnosed disorder, and some studies have found that military personnel have high rates of comorbid insomnia and OSA (10, 11). Further, military personnel with sleep disorders often also have posttraumatic stress disorder (PTSD), symptoms of anxiety and depression, and traumatic brain injury, which can complicate diagnosis and management (11–13).

Sleep disturbances are also common in veterans (14–16). Similar to findings from active-duty service members, the National Veteran Sleep Disorder Study found that the number of veterans diagnosed with sleep disorders increased nearly 6-fold from 2000 to 2010. In this study, 4.5% of veterans were diagnosed with sleep-disordered breathing, and 2.5% were diagnosed with insomnia. However, the actual prevalence of insomnia disorder among veterans is likely to be considerably higher (17) because it is often not documented in the medical record (18, 19). Comorbid PTSD was associated with a 7.6-fold greater risk for OSA and a 6.3-fold greater risk for insomnia (15). Because veterans have high rates of cardiovascular disease and PTSD, and because OSA is more prevalent in patients with these disorders (20), there is likely a large percentage of veterans who have not yet been diagnosed with OSA (21).