Concerns about opioid-related adverse events, including overdose, prompted the Veterans Health Administration (VHA) to launch an Opioid Safety Initiative and Overdose Education and Naloxone Distribution program. To mitigate risks associated with opioid prescribing, a holistic approach that takes into consideration both risk factors (e.g., dose, substance use disorders) and risk mitigation interventions (e.g., urine drug screening, psychosocial treatment) is needed. This article describes the Stratification Tool for Opioid Risk Mitigation (STORM), a tool developed in VHA that reflects this holistic approach and facilitates patient identification and monitoring. STORM prioritizes patients for review and intervention according to their modeled risk for overdose/suicide-related events and displays risk factors and risk mitigation interventions obtained from VHA electronic medical record (EMR)-data extracts. Patients' estimated risk is based on a predictive risk model developed using fiscal year 2010 (FY2010: 10/1/2009-9/30/2010) EMR-data extracts and mortality data among 1,135,601 VHA patients prescribed opioid analgesics to predict risk for an overdose/suicide-related event in FY2011 (2.1% experienced an event). Cross-validation was used to validate the model, with receiver operating characteristic curves for the training and test data sets performing well (>.80 area under the curve). The predictive risk model distinguished patients based on risk for overdose/suicide-related adverse events, allowing for identification of high-risk patients and enrichment of target populations of patients with greater safety concerns for proactive monitoring and application of risk mitigation interventions. Results suggest that clinical informatics can leverage EMR-extracted data to identify patients at-risk for overdose/suicide-related events and provide clinicians with actionable information to mitigate risk. (PsycINFO Database Record

Objective To examine the associations between stopping treatment with opioids, length of treatment, and death from overdose or suicide in the Veterans Health Administration. Design Observational evaluation. Setting Veterans Health Administration. Participants 1 394 102 patients in the Veterans Health Administration with an outpatient prescription for an opioid analgesic from fiscal year 2013 to the end of fiscal year 2014 (1 October 2012 to 30 September 2014). Main outcome measures A multivariable Cox non-proportional hazards regression model examined death from overdose or suicide, with the interaction of time varying opioid cessation by length of treatment (≤30, 31-90, 91-400, and >400 days) as the main covariates. Stopping treatment with opioids was measured as the time when a patient was estimated to have no prescription for opioids, up to the end of the next fiscal year (2014) or the patient’s death. Results 2887 deaths from overdose or suicide were found. The incidence of stopping opioid treatment was 57.4% (n = 799 668) overall, and based on length of opioid treatment was 32.0% (≤30 days), 8.7% (31-90 days), 22.7% (91-400 days), and 36.6% (>400 days). The interaction between stopping treatment with opioids and length of treatment was significant (P<0.001); stopping treatment was associated with an increased risk of death from overdose or suicide regardless of the length of treatment, with the risk increasing the longer patients were treated. Hazard ratios for patients who stopped opioid treatment (with reference values for all other covariates) were 1.67 (≤30 days), 2.80 (31-90 days), 3.95 (91-400 days), and 6.77 (>400 days). Descriptive life table data suggested that death rates for overdose or suicide increased immediately after starting or stopping treatment with opioids, with the incidence decreasing over about three to 12 months. Conclusions Patients were at greater risk of death from overdose or suicide after stopping opioid treatment, with an increase in the risk the longer patients had been treated before stopping. Descriptive data suggested that starting treatment with opioids was also a risk period. Strategies to mitigate the risk in these periods are not currently a focus of guidelines for long term use of opioids. The associations observed cannot be assumed to be causal; the context in which opioid prescriptions were started and stopped might contribute to risk and was not investigated. Safer prescribing of opioids should take a broader view on patient safety and mitigate the risk from the patient’s perspective. Factors to address are those that place patients at risk for overdose or suicide after beginning and stopping opioid treatment, especially in the first three months.

We sought to determine the sex- and age-specific risk of mortality associated with scores on the 3-item Alcohol Use Disorder Identification Test-Consumption (AUDIT-C) questionnaire using data from a national sample of Veterans Health Administration (VHA) patients. Men (N = 215,924) and women (N = 9168) who completed the AUDIT-C in a patient survey were followed for 24 months. AUDIT-C categories (0, 1-4, 5-8, 9-12) were evaluated as predictors of mortality in logistic regression models, adjusted for age, race, education, marital status, smoking, depression, and comorbidities. For women, AUDIT-C scores of 9-12 were associated with a significantly increased risk of death compared to the AUDIT-C 1-4 group (odds ratio [OR] 7.09; 95% confidence interval [CI] = 2.67, 18.82). For men overall, AUDIT-C scores of 5-8 and 9-12 were associated with increased risk of death compared to the AUDIT-C 1-4 group (OR 1.13, 95% CI = 1.05, 1.21, and OR 1.63, 95% CI = 1.45, 1.84, respectively) but these associations varied by age. These results provide sex- and age-tailored risk information that clinicians can use in evidence-based conversations with patients about the health-related risks of their alcohol consumption. This study adds to the growing literature establishing the AUDIT-C as a scaled marker of alcohol-related risk or "vital sign" that might facilitate the detection and management of alcohol-related risks and problems.

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