BACKGROUND-Although increased attention has been paid to sex and racial differences in the management of myocardial infarction, it is unknown whether these differences have narrowed over time.

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.

Background The current ability to predict readmissions in patients with heart failure is modest at best. It is unclear whether machine learning (ML) techniques that address higher dimensional, non-linear relationships among variables would enhance prediction. We sought to compare the effectiveness of several ML algorithms for predicting readmissions. Methods and Results Using data from the Telemonitoring to Improve Heart Failure Outcomes trial, we compared the effectiveness of Random Forests (RF), Boosting, RF combined hierarchically with Support Vector Machines (SVM) or Logistic Regression (LR) and Poisson Regression against traditional LR to predict 30-day and 180-day all-cause and heart fauilre-only readmissions. We randomly selected 50% of patients for a derivation set and the remaining patients comprised a validation set, repeated 100 times. We compared c-statistics for discrimination and distributions of observed outcomes in risk deciles for predictive range. In 30-day all-cause readmission prediction, the best performing ML model, RF, provided a 17.8% improvement over LR (mean c-statistics 0.628 and 0.533, respectively). For readmissions due to heart failure, Boosting improved the c-statistic by 24.9% over LR (mean c-statistic 0.678 and 0.543, respectively). For 30-day all cause readmission, the observed readmission rates in the lowest and highest deciles of predicted risk with RF (7.8% and 26.2%, respectively) showed a much wider separation than LR (14.2% and 16.4%, respectively). Conclusions ML methods improved the prediction of readmission after hospitalization for heart failure compared with LR and provided the greatest predictive range in observed readmission rates.

Awareness of the science of the neuroplasticity effects of repeated use of opioids is necessary to better manage these patients with complex challenges.

Collaborative on Countering the US Opioid Epidemic [8] has been focusing on comprehensive and collaborative efforts to fundamentally address the opioid epidemic crisis. All of these major initiatives emphasize pain education as a key component in the fight against the dual crises of chronic pain and the opioid epidemic. I am honored to represent the AAPM on the HHS Pain Management Task Force and the NAM Action Collaborative and contribute to these important initiatives of our nation on your behalf.

Future research focused on identifying further correlates of treatment retention is required to help devise interventions to improve treatment continuation. (Am J Addict 2017;26:572-580).

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Buprenorphine treatment retention declined markedly in the first year and was substantially lower than in comparable studies from publicly funded health care systems, apparently largely due to disenrollment. The association of psychotherapy with greater retention suggests that it may be an important complement to opioid agonist treatment.