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Background: Oxymorphone's metabolism does not involve the hepatic cytochrome P450 (CYP) system. The effect of this pharmacokinetic feature of oxymorphone on opioid prescribing is unknown., Objective: To assess the relative frequency with which oxymorphone and oxycodone (a CYP3A-metabolized opioid analgesic) were each prescribed to patients concomitantly receiving CYP3A-modifying drugs (i.e., inducers and inhibitors) to characterize opioid-prescribing patterns in patients at risk for CYP3A-related drug interactions., Methods: We analyzed the Sentinel Distributed Database from January 1, 2013, to December 31, 2016, to identify the proportion of patients with concomitant dispensing of selected CYP3A modifiers among initiators of oxymorphone. We then repeated the analysis using oxycodone instead of oxymorphone. We conducted sensitivity analyses that varied the washout periods for each opioid to account for potential opioid switching., Results: In the primary analysis, the proportion of patients with concomitant incident dispensings of oxymorphone and selected CYP3A modifiers was 3.26% (95% CI = 3.09%-3.43%), and the proportion of patients with incident dispensings of oxycodone and selected CYP3A modifiers was 2.82% (95% CI = 2.79%-2.85%). The difference between proportions was 0.43% (95% CI = 0.26%-0.60%). Sensitivity analyses that varied the washout periods for each opioid with respect to the other opioid to account for switching yielded similar results., Conclusions: We observed similar proportions of patients using selected CYP3A modifiers concomitantly with both oxymorphone and oxycodone. While the CIs of the point estimates did not overlap, the absolute differences between the proportions were small., Disclosures: This project was supported by Task Order HHSF22301001T under Master Agreement HHSF223201400030I from the U.S. Food and Drug Administration (FDA). The FDA approved the study protocol, including the statistical analysis plan, and reviewed and approved the manuscript. Coauthors from the FDA participated in the results interpretation and in the preparation and decision to submit the manuscript for publication. Coyle, Money, Staffa, Meyer, and Woods are employed by the FDA. The other authors have no financial conflicts of interest to report. The views expressed are those of the authors and not necessarily those of the U.S. Department of Health and Human Services, U.S. Food and Drug Administration.

The US Sentinel System and the Canadian Network for Observational Drug Effect Studies (CNODES) are two medical product safety surveillance networks. Using Sentinel's preprogrammed, parameterizable analytic tools, we reproduced two protocol-based studies conducted by CNODES to assess the risks of acute pancreatitis and heart failure (HF) associated with the use of incretin-based drugs, compared with use of ≥ 2 oral hypoglycemic agents. Results from the replication new-user cohort analyses aligned with those from the CNODES nested case-control studies. The adjusted hazard ratios were 0.95 (0.81-1.12; vs. 1.03 (0.87-1.22) in CNODES) for acute pancreatitis and 0.91 (0.84-1.00; vs. 0.82 (0.67-1.00) in CNODES) for HF among patients without HF history. The CNODES's common protocol approach allows studies tailored to specific safety questions, whereas the Sentinel's common data model plus pretested program approach enables more rapid analysis. Despite these differences, it is possible to obtain comparable results using both approaches., (© 2019 The Authors. Clinical Pharmacology & Therapeutics © 2019 American Society for Clinical Pharmacology and Therapeutics.)

Introduction: Neurological complications including seizures have been reported with ranolazine. We sought to quantify the risk of seizure-related hospitalizations or emergency department events following ranolazine exposure in the Sentinel System (2006-2015)., Study Design and Setting: Eligibility criteria were new use of ranolazine after 183 days washout period and absence of seizure diagnoses, anti-epileptic drugs, or seizure-related disorders during the baseline period., Results: Among 52,155 ranolazine users, we identified 28 seizures in the 1-32 days after new ranolazine dispensing: 12 occurring in days 1-10 (high-risk window), 11 in days 11-20 (moderate-risk window) and 5 in the control window (days 21-32). Assuming an equal likelihood of seizure events across the 32-day observation window, we estimate an attributable risk of 0.9 excess cases per 10,000 exposed users. Using a self-controlled risk interval design with exact logistic regression, seizures were elevated in the high-risk window (relative risk [RR] 2.88 (95% confidence interval [CI] 1.01-8.33) compared with the control window. No significant increased risk was observed in the moderate window. Half of the seizure cases had a diagnosis of renal disease, although seizure risk was not significant (RR 3.20 [CI 0.82-14.01]). A majority of patients in both risk windows were 75 years or older., Conclusion: Our study suggests risk among younger ranolazine patients is rare. Given the imprecision of the risk estimates, we interpret the elevated seizure risk following ranolazine exposure with caution. Further analysis in a larger elderly population is warranted.

Purpose: To describe the consistency in the frequency of 5 health outcomes across the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) and Tenth Revision, Clinical Modification (ICD-10-CM) eras in the US., Methods: We examined the incidence of 3 acute conditions (acute myocardial infarction [AMI], angioedema, ischemic stroke) and the prevalence of 2 chronic conditions (diabetes, hypertension) during the final 5 years of the ICD-9-CM era (January 2010-September 2015) and the first 15 months of the ICD-10-CM era (October 2015-December 2016) in 13 electronic health care databases in the Sentinel System. For each health outcome reviewed during the ICD-10-CM era, we evaluated 4 definitions, including published algorithms derived from other countries, as well as simple-forward, simple-backward, and forward-backward mapping using the General Equivalence Mappings. For acute conditions, we also compared the incidence between April to December 2014 and April to December 2016., Results: The analyses included data from approximately 172 million health plan members. While the incidence or prevalence of AMI and hypertension performed similarly across the 2 eras, the other 3 outcomes did not demonstrate consistent trends for some or all the ICD-10-CM definitions assessed., Conclusions: When using data from both the ICD-9-CM and ICD-10-CM eras, or when using results from ICD-10-CM data to compare to results from ICD-9-CM data, researchers should test multiple ICD-10-CM outcome definitions as part of sensitivity analysis. Ongoing assessment of the impact of ICD-10-CM transition on identification of health outcomes in US electronic health care databases should occur as more data accrue., (© 2018 John Wiley & Sons, Ltd.)

Background: The combined comorbidity score, which merges the Charlson and Elixhauser comorbidity indices, uses the ninth revision of the International Classification of Diseases, Clinical Modification (ICD-9-CM). In October 2015, the United States adopted the 10th revision (ICD-10-CM)., Objective: The objective of this study is to examine different coding algorithms for the ICD-10-CM combined comorbidity score and compare their performance to the original ICD-9-CM score., Methods: Four ICD-10-CM coding algorithms were defined: 2 using General Equivalence Mappings (GEMs), one based on ICD-10-CA (Canadian modification) codes for Charlson and Elixhauser measures, and one including codes from all 3 algorithms. We used claims data from the Clinfomatics Data Mart to identify 2 cohorts. The ICD-10-CM cohort comprised patients who had a hospitalization between January 1, 2016 and March 1, 2016. The ICD-9-CM cohort comprised patients who had a hospitalization between January 1, 2015 and March 1, 2015. We used logistic regression models to predict 30-day hospital readmission for the original score in the ICD-9-CM cohort and for each ICD-10-CM algorithm in the ICD-10-CM cohort., Results: Distributions of each version of the score were similar. The algorithm based on ICD-10-CA codes [c-statistic, 0.646; 95% confidence interval (CI), 0.640-0.653] had the most similar discrimination for readmission to the ICD-9-CM version (c, 0.646; 95% CI, 0.639-0.653), but combining all identified ICD-10-CM codes had the highest c-statistic (c, 0.651; 95% CI, 0.644-0.657)., Conclusions: We propose an ICD-10-CM version of the combined comorbidity score that includes codes identified by ICD-10-CA and GEMs. Compared with the original score, it has similar performance in predicting readmission in a population of United States commercially insured individuals.