Caihua Liang

Purpose: The study aims to develop and validate algorithms to identify and classify opioid overdoses using claims and other coded data, and clinical text extracted from electronic health records using natural language processing (NLP).Methods: Primary data were derived from Kaiser Permanente Northwest (2008–2014), an integrated health care system (~n > 475 000 unique individuals per year). Data includedInternational Classification of Diseases, Ninth Revision(ICD‐9) codes for nonfatal diagnoses,International Classification of Diseases, Tenth Revision(ICD‐10) codes for fatal events, clinical notes, and prescription medication records. We assessed sensitivity, specificity, positive predictive value, and negative predictive value for algorithms relative to medical chart review and conducted assessments of algorithm portability in Kaiser Permanente Washington, Tennessee State Medicaid, and Optum.Results: Code‐based algorithm performance was excellent for opioid‐related overdoses (sensitivity = 97.2%, specificity = 84.6%) and classification of heroin‐involved overdoses (sensitivity = 91.8%, specificity = 99.0%). Performance was acceptable for code‐based suicide/suicide attempt classifications (sensitivity = 70.7%, specificity = 90.5%); sensitivity improved with NLP (sensitivity = 78.7%, specificity = 91.0%). Performance was acceptable for the code‐based substance abuse‐involved classification (sensitivity = 75.3%, specificity = 79.5%); sensitivity improved with the NLP‐enhanced algorithm (sensitivity = 80.5%, specificity = 76.3%). The opioid‐related overdose algorithm performed well across portability assessment sites, with sensitivity greater than 96% and specificity greater than 84%. Cross‐site sensitivity for heroin‐involved overdose was greater than 87%, specificity greater than or equal to 99%.Conclusions: Code‐based algorithms developed to detect opioid‐related overdoses and classify them according to heroin involvement perform well. Algorithms for classifying suicides/attempts and abuse‐related opioid overdoses perform adequately for use for research, particularly given the complexity of classifying such overdoses. The NLP‐enhanced algorithms for suicides/suicide attempts and abuse‐related overdoses perform significantly better than code‐based algorithms and are appropriate for use in settings that have data and capacity to use NLP.

A retrospective cohort study, supplemented with a nested case‐control study, was performed using two administrative databases from commercial health plans in the United States to compare the incidence of pancreatic and thyroid cancer among users of exenatide versus other antidiabetic drugs (OADs). Patients with type 2 diabetes who initiated exenatide or OADs between 1 June 2005 and 30 June 2015 were included. Pancreatic and thyroid cancers were identified using chart‐validated algorithms in the cohort study. Cases in the nested case‐control study were chart‐confirmed pancreatic or thyroid cancers, and controls were sampled using risk‐set sampling. The time‐fixed analyses comparing 33 629 exenatide initiators with 49 317 propensity‐score‐matched OAD initiators yielded hazard ratios of 0.76 (95% confidence interval [CI] 0.47‐1.21) for pancreatic cancer and 1.46 (95% CI 0.98‐2.19) for thyroid cancer. Results in the time‐dependent analyses by cumulative duration or dose were similar. Nested case‐control analyses yielded rate ratios of 0.61 (95%CI, 0.37–1.00) for pancreatic cancer and 0.89 (95% CI, 0.64–1.24) for thyroid cancer. This observational study suggested exenatide use was not associated with an increased risk of pancreatic or thyroid cancer.

Nab-Paclitaxel is an albumin-bound formulation of paclitaxel approved for the treatment of metastatic breast cancer (MBC). This analysis was designed to characterize the treatment patterns, efficacy, and safety of nab-paclitaxel for MBC treatment using health claims data from US health plans associated with Optum. Women aged ≥ 18 years who initiated nab-paclitaxel for MBC treatment from January 1, 2005, to September 30, 2012, and who met eligibility criteria were selected from the Optum Research Database for this analysis. Patients were required to have complete medical coverage and pharmacy benefits, ≥ 6 months of continuous enrollment, and a diagnosis of MBC prior to nab-paclitaxel initiation. The pattern of use for nab-paclitaxel (eg, regimen, schedule, duration, and administration) and claims-captured toxicities were characterized by line of therapy. Overall survival (OS) and time to next therapy or death (TNTD) were described by line of therapy, regimen, and schedule. Of the 664 nab-paclitaxel patients, 172 (25.9 %) received it as first-line therapy, 211 (31.8 %) as second-line therapy, and 281 (42.3 %) as third-line or later therapy. Overall, the majority of patients received monotherapy (61 %) and followed a weekly (71 %) rather than an every 3 weeks treatment schedule. nab-Paclitaxel was often (31.7 %) combined with targeted therapy (57.5 % with bevacizumab and 23.9 % with trastuzumab or lapatinib). The median duration of therapy was 128 days (4.2 months). For the overall population, median OS was 17.4 months (22.7, 17.4, and 15.1 months in first-, second-, and third-line or later therapy, respectively). Median TNTD was 6.1 months (7.1, 6.6, and 5.3 months in first-, second-, and third-line or later therapy, respectively). For patients aged ≤ 50 years or with ≥ 3 metastatic sites, median OS was 15.6 months. No new safety signal was identified. In this US healthcare system, the majority of patients received nab-paclitaxel as second-line or later therapy, monotherapy, and weekly treatment. The efficacy and safety outcomes of nab-paclitaxel observed in this real-world setting appear consistent with those from clinical trial data.

Purpose Clinical trials of olmesartan for prevention of progression of renal disease in patients with diabetes showed renal protection but an unexpected imbalance in cardiac deaths. The US Food and Drug Administration requested from the manufacturer a cohort study of olmesartan, other angiotensin‐receptor blockers (ARBs), and angiotensin‐converting enzyme (ACE) inhibitors in a large population. Methods A retrospective cohort study was conducted with the cooperation of a US health insurer. Subject entry and follow‐up ran from 2002 through 2009. In propensity‐matched cohorts, the primary analysis considered continuous current users. Endpoints were sudden cardiac death (SCD) and all‐cause mortality, identified through the US National Death Index, supplemented by insurance and hospital discharge data. Statistical estimation was based on proportional hazards analyses with 95% confidence intervals. Power calculations had shown that 25 000 olmesartan initiators would be required to detect relative risks (RRs) of SCD of twofold or greater. Results A total of 57 123 initiators of olmesartan were matched 1:2 to initiators of other ARBs and 41 801 to initiators of ACE inhibitors. Average follow‐up time ranged from 8 to 9 months. Olmesartan initiators and comparators experienced similar patterns of both outcomes, with RRs ≤1.0 and upper confidence bounds ≤1.6. Among persons with prior use of hypoglycemic agents, in comparison with other ARBs, the RR of SCD for olmesartan users was 0.8, with an upper confidence bound of 2.2. Conclusion The results of this well‐powered study do not raise concerns for the risk of SCD or death from all causes among olmesartan users in comparison with users of other ARBs or ACE inhibitors. Copyright © 2013 John Wiley & Sons, Ltd.