Nonmedical Opioid Pain Relievers and All-Cause Mortality: A 27-Year Follow-Up From the Epidemiologic Catchment Area Study.

OBJECTIVES: We investigated whether nonmedical opioid pain reliever use is associated with higher mortality in the general US population. METHODS: We assessed the history of nonmedical opioid pain reliever use among 9985 people interviewed at baseline of the Epidemiologic Catchment Area Program initiated in 1981 to 1983 in Baltimore, Maryland; St. Louis, Missouri; and Durham, North Carolina. We linked the data with the National Death Index through 2007. RESULTS: Nonmedical opioid pain reliever use was 1.4%. Compared with no nonmedical drug use, mortality was increased for nonmedical opioid pain reliever use (hazard ratio [HR] = 1.60; 95% confidence interval [CI] = 1.01, 2.53) or nonmedical use of other drugs (HR = 1.31; 95% CI = 1.07, 1.62). Mortality was also higher for males and for those beginning nonmedical opioid pain reliever use before aged 15 years. CONCLUSIONS: A history of nonmedical opioid pain reliever use was associated with increased mortality, in particular for males and early onset users.

Uric acid and the prediction models of tumor lysis syndrome in AML.

We investigated the ability of serum uric acid (SUA) to predict laboratory tumor lysis syndrome (LTLS) and compared it to common laboratory variables, cytogenetic profiles, tumor markers and prediction models in acute myeloid leukemia patients. In this retrospective study patients were risk-stratified for LTLS based on SUA cut-off values and the discrimination ability was compared to current prediction models. The incidences of LTLS were 17.8%, 21% and 62.5% in the low, intermediate and high-risk groups, respectively. SUA was an independent predictor of LTLS (adjusted OR 1.12, CI95% 1.0-1.3, p = 0.048). The discriminatory ability of SUA, per ROC curves, to predict LTLS was superior to LDH, cytogenetic profile, tumor markers and the combined model but not to WBC (AUCWBC 0.679). However, in comparisons between high-risk SUA and high-risk WBC, SUA had superior discriminatory capability than WBC (AUCSUA 0.664 vs. AUCWBC 0.520; p <0.001). SUA also demonstrated better performance than the prediction models (high-risk SUAAUC 0.695, p<0.001). In direct comparison of high-risk groups, SUA again demonstrated superior performance than the prediction models (high-risk SUAAUC 0.668, p = 0.001) in predicting LTLS, approaching that of the combined model (AUC 0.685, p<0.001). In conclusion, SUA alone is comparable and highly predictive for LTLS than other prediction models.