Patients exhibiting a documented ORADE had greater overall costs, longer hospitalizations, and increased likelihood for readmission. These results highlight the economic impact associated with opioid use for postsurgical pain management.

Inbred mouse strains differ in sensitivity to a first dose of nicotine and in the development of tolerance to nicotine. The experiments reported here used six inbred mouse strains (A, BUB, C3H, C57BL/6, DBA/2, ST/b) that differ in sensitivity to an acute challenge dose of nicotine to determine whether differences in oral self-selection of nicotine exist. Animals were presented with solutions containing nicotine or vehicle (water or 0.2% saccharin) and their daily intake of the two fluids was measured for 4 days starting with a 10 micrograms/ml nicotine solution. This was followed by sequential 4-day testing with 20, 35, 50, 65, 80, 100, 125, 160 and 200 micrograms/ml nicotine solutions. The strains differed dramatically in their self-selection of nicotine and in maximal daily dose (mg/kg); the rank order of the strains was C57BL/6 > DBA > BUB > A > or = C3H > or = ST/b for both the tap water and 0.2% saccharin choice experiments. Correlations between nicotine consumption and sensitivity to nicotine, as measured by a battery of behavioral and physiological responses, were also calculated. Strain differences in nicotine intake were highly correlated with sensitivity to nicotine-induced seizures. As sensitivity to nicotine-induced seizures increases, oral self-selection of nicotine decreases. This finding may suggest that this toxic action of nicotine serves to limit intake.

The goal of this study was to develop an algorithm for detecting epilepsy cases in managed care organizations (MCOs). A data set of potential epilepsy cases was constructed from an MCO's administrative data system for all health plan members continuously enrolled in the MCO for at least 1 year within the study period of July 1, 1996 through June 30, 1998. Epilepsy status was determined using medical record review for a sample of 617 cases. The best algorithm for detecting epilepsy cases was developed by examining combinations of diagnosis, diagnostic procedures, and medication use. The best algorithm derived in the exploratory phase was then applied to a new set of data from the same MCO covering the period of July 1, 1998 through June 30, 2000. A stratified sample based on ethnicity and age was drawn from the preliminary algorithm-identified epilepsy cases and non-cases. Medical record review was completed for 644 cases to determine the accuracy of the algorithm. Data from both phases were combined to permit refinement of logistic regression models and to provide more stable estimates of the parameters. The best model used diagnoses and antiepileptic drugs as predictors and had a positive predictive value of 84% (sensitivity 82%, specificity 94%). The best model correctly classified 90% of the cases. A stable algorithm that can be used to identify epilepsy patients within MCOs was developed. Implications for use of the algorithm in other health care settings are discussed.