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Introduction: During the COVID‐19 pandemic, inpatient electronic health records (EHRs) have been used to conduct public health surveillance and assess treatments and outcomes. Invasive mechanical ventilation (MV) and supplemental oxygen (O2) use are markers of severe illness in hospitalized COVID‐19 patients. In a large US system (n = 142 hospitals), we assessed documentation of MV and O2 use during COVID‐19 hospitalization in administrative data versus nursing documentation. Methods: We identified 319 553 adult hospitalizations with a COVID‐19 diagnosis, February 2020–October 2022, and extracted coded, administrative data for MV or O2. Separately, we developed classification rules for MV or O2 supplementation from semi‐structured nursing documentation. We assessed MV and O2 supplementation in administrative data versus nursing documentation and calculated ordinal endpoints of decreasing COVID‐19 disease severity. Nursing documentation was considered the gold standard in sensitivity and positive predictive value (PPV) analyses. Results: In nursing documentation, the prevalence of MV and O2 supplementation among COVID‐19 hospitalizations was 14% and 75%, respectively. The sensitivity of administrative data was 83% for MV and 41% for O2, with both PPVs above 91%. Concordance between sources was 97% for MV (κ = 0.85), and 54% for O2 (κ = 0.21). For ordinal endpoints, administrative data accurately identified intensive care and MV but underestimated hospitalizations with O2 requirements (42% vs. 18%). Conclusions: In comparison to nursing documentation, administrative data under‐ascertained O2 supplementation but accurately estimated severe endpoints such as MV. Nursing documentation improved ascertainment of O2 among COVID‐19 hospitalizations and can capture oxygen requirements in adults hospitalized with COVID‐19 or other respiratory illnesses. [ABSTRACT FROM AUTHOR]

Purpose: Immediate‐release forms of generic mixed amphetamine salts (MAS) have been the subject of passive surveillance reports signaling lack of effectiveness. We examined switching patterns that might suggest whether long‐term users of specific MAS are more likely to switch away or switch back after use of the MAS of interest in the FDA's Sentinel Distributed Database. Methods: We required at least 60‐day continuous supply of selected MAS grouped by Abbreviated New Drug Application (ANDA) to describe patterns of switching away from and to generics approved under the ANDAs of interest among individuals ages 15–64 years with attention deficit hyperactivity disorder or narcolepsy during 2013–2019. Results: We observed the greatest number of treatment episodes for ANDA 040422 (n = 525 771), followed by ANDA 202424 (n = 181 693), ANDA 040439 (n = 62 363), ANDA 040440 (n = 21 143), and ANDA 040480 (n = 8792). Of those with switches away from their original ANDA, episodes initiated on generic products under ANDA 040422 (48.6%) and ANDA 202424 (43.0%) were most likely to switch back, while those initiated on generic product under ANDA 040480 were least likely (24.1%). Of those episodes with switches to a generic under an ANDA of interest, about one‐third (range 27.1% to 37.0%) switched back to the same product. These switches back had a median time to switch of about 30 days. Conclusions: These descriptive analyses, although subject to limitations, did not suggest increased switching away or switching back after use of the generics of interest. Continued post‐marketing surveillance is warranted. [ABSTRACT FROM AUTHOR]

Purpose Health plan claims may provide complete longitudinal data for timely, real‐world population‐level COVID‐19 assessment. However, these data often lack laboratory results, the standard for COVID‐19 diagnosis. Methods: We assessed the validity of ICD‐10‐CM diagnosis codes for identifying patients hospitalized with COVID‐19 in U.S. claims databases, compared to linked laboratory results, among six Food and Drug Administration Sentinel System data partners (two large national insurers, four integrated delivery systems) from February 20–October 17, 2020. We identified patients hospitalized with COVID‐19 according to five ICD‐10‐CM diagnosis code‐based algorithms, which included combinations of codes U07.1, B97.29, general coronavirus codes, and diagnosis codes for severe symptoms. We calculated the positive predictive value (PPV) and sensitivity of each algorithm relative to laboratory test results. We stratified results by data source type and across three time periods: February 20–March 31 (Time A), April 1–30 (Time B), May 1–October 17 (Time C). Results: The five algorithms identified between 34 806 and 47 293 patients across the study periods; 23% with known laboratory results contributed to PPV calculations. PPVs were high and similar across algorithms. PPV of U07.1 alone was stable around 93% for integrated delivery systems, but declined over time from 93% to 70% among national insurers. Overall PPV of U07.1 across all data partners was 94.1% (95% CI, 92.3%–95.5%) in Time A and 81.2% (95% CI, 80.1%–82.2%) in Time C. Sensitivity was consistent across algorithms and over time, at 94.9% (95% CI, 94.2%–95.5%). Conclusion: Our results support the use of code U07.1 to identify hospitalized COVID‐19 patients in U.S. claims data. [ABSTRACT FROM AUTHOR]

Purpose Lymphoma is a health outcome of interest for drug safety studies. Studies using administrative claims data require the accurate identification of lymphoma cases. We developed and validated an International Classification of Diseases, Tenth Revision, Clinical Modification (ICD‐10‐CM)‐based algorithm to identify lymphoma in healthcare claims data. Methods: We developed a three‐component algorithm to identify patients aged ≥15 years who were newly diagnosed with Hodgkin (HL) or non‐Hodgkin (NHL) lymphoma from January 2016 through July 2018 among members of four Data Partners within the FDA's Sentinel System. The algorithm identified potential cases as patients with ≥2 ICD‐10‐CM lymphoma diagnosis codes on different dates within 183 days; ≥1 procedure code for a diagnostic procedure (e.g., biopsy, flow cytometry) and ≥1 procedure code for a relevant imaging study within 90 days of the first lymphoma diagnosis code. Cases identified by the algorithm were adjudicated via chart review and a positive predictive value (PPV) was calculated. Results: We identified 8723 potential lymphoma cases via the algorithm and randomly sampled 213 for validation. We retrieved 138 charts (65%) and adjudicated 134 (63%). The overall PPV was 77% (95% confidence interval: 69%–84%). Most cases also had subtype information available, with 88% of cases identified as NHL and 11% as HL. Conclusions: Seventy‐seven percent of lymphoma cases identified by an algorithm based on ICD‐10‐CM diagnosis and procedure codes and applied to claims data were true cases. This novel algorithm represents an efficient, cost‐effective way to target an important health outcome of interest for large‐scale drug safety and public health surveillance studies. [ABSTRACT FROM AUTHOR]

Purpose To describe utilization of filgrastim and infliximab, the first two products with biosimilars approved in the United States. Methods: We identified use of filgrastim (reference, tbo‐filgrastim, and filgrastim‐sndz) and infliximab (reference, infliximab‐dyyb, and infliximab‐abda) in the Sentinel Distributed Database using Healthcare Common Procedure Coding System (HCPCS) codes and National Drug Codes (NDCs) from January 2015 to August 2018. We calculated the proportion of use by code type and assessed uptake over time. We compared baseline patient characteristics and treatment indications. Among patients with >1 exposure episode, we characterized gaps between episodes. Results: Use was identified primarily via HCPCS codes (filgrastim: 86.4%‐97.7%; infliximab: 87.8%‐100%) although some was identified via NDCs (filgrastim: 2.2%‐13.5%; infliximab: <0.1%‐6.5%). Filgrastim reference product use declined from 89.4% in January 2015 to 30.3% in June 2018, with corresponding increases in filgrastim‐sndz (0% to 49.3%) and tbo‐filgrastim (10.6% to 20.4%). Infliximab biosimilar uptake was low (9.7% in June 2018). We identified 94 846 filgrastim reference product, 27 143 tbo‐filgrastim, and 38 264 filgrastim‐sndz users. For infliximab, we identified 125 412 reference product, 1034 infliximab‐dyyb, 49 infliximab‐abda, and 4855 undetermined biosimilar users. Patients receiving filgrastim products were largely similar, but differences in age, sex, and indication were observed across infliximab product users. The median exposure episode gap ranged from 1 to 3 days for filgrastim and 48 to 50 days for infliximab. Conclusion: Use of biosimilar filgrastim has increased in the United States, but infliximab biosimilar use remains low. Data on identification of biosimilars in claims data and observed gaps between exposure episodes can be used to support drug safety studies of biosimilars. [ABSTRACT FROM AUTHOR]

Purpose US Food and Drug Administration approval for generic drugs relies on demonstrating pharmaceutical equivalence and bioequivalence; however, some drug products have unique attributes that necessitate product-specific approval pathways. We evaluated rates of patients' switching back to brand-name versions from generic versions of four drugs approved via such approaches. Methods We used data from Optum LifeSciences Research Database to identify patients using a brand-name version of a study drug (acarbose tablets, salmon calcitonin nasal spray, enoxaparin sodium injection, and venlafaxine extended release tablets) or a control drug. We followed patients to identify switching to generic versions and then followed those who switched to identify whether they switched back to brand-name versions. We calculated switch and switch-back rates and used Kaplan-Meier and log-rank tests to compare rates between study and control drugs. Results Our cohort included 201 959 eligible patients. Brand-to-generic switch rates ranged from 66 to 106 switches per 100 person-years for study drugs and 80 to 110 for control drugs. Rates of switch-back to brand-name versions ranged from 5 to 37 among study drugs and 3 to 53 among control drugs. Switch-back rates were higher for venlafaxine vs. sertraline ( p < 0.01) and calcitonin vs. alendronate ( p = 0.01). Switch-back rates were lower for venlafaxine vs. paroxetine ( p < 0.01) and acarbose vs. nateglinide ( p < 0.01). Rates were similar for acarbose vs. glimepiride ( p = 0.97) and for enoxaparin vs. fondiparinux ( p = 0.11). Conclusion As compared to control drugs, patients were not more likely to systematically switch back from generic to brand-name versions of the four study drugs. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]