Background: The EMPA-REG OUTCOME trial showed that empagliflozin, a sodium-glucose co-transporter-2 inhibitor (SGLT2i), reduces the risk of hospitalization for heart failure (HHF) by 35%, on top of standard of care in patients with type 2 diabetes (T2D) and established CV disease (CVD). The EMPagliflozin compaRative effectIveness and SafEty (EMPRISE) study aims to assess empagliflozin's effectiveness, safety, and healthcare utilization in routine care from 08/2014 through 09/2019. In this first interim analysis, we investigated the risk of HHF among T2D patients initiating empagliflozin vs. sitagliptin, a dipeptidyl peptidase-4 inhibitor (DPP-4i). Methods: Within two commercial and one federal (Medicare) claims data sources in the U.S., we identified a 1:1 propensity-score (PS) matched cohort of T2D patients ≥18 years initiating empagliflozin or sitagliptin from 08/2014 through 09/2016. The HHF outcome was defined as a HF discharge diagnosis in the primary position (HHF-specific); a broader definition was based on a HF discharge diagnosis in any position (HHF-broad). Hazard ratios (HR) and 95% confidence intervals (CI) were estimated controlling for over 140 baseline characteristics in each data source and pooled by fixed-effects meta-analysis. Results: After PS-matching, we identified 16,443 patient pairs who initiated empagliflozin or sitagliptin. Average age was approximately 59 years, almost 54% of the participants were males, and approximately 25% had records of existing cardiovascular disease. Compared to sitagliptin, the initiation of empagliflozin decreased the risk of HHF-specific by 50% (HR = 0.50; 95% CI = 0.28-0.91), and the risk of HHF-broad by 49% (HR: 0.51;95% CI: 0.39-0.68), over a mean follow-up of 5.3 months. Results were consistent in patients with and without baseline cardiovascular disease, and for both empagliflozin 10 mg or 25mg daily dose; analyses comparing

ObjectiveTo investigate the rate of serious bacterial, viral or opportunistic infection in patients with rheumatoid arthritis (RA) starting tocilizumab (TCZ) versus tumour necrosis factor inhibitors (TNFi) or abatacept.MethodsUsing claims data from US Medicare from 2010 to 2015, and IMS and MarketScan from 2011 to 2015, we identified adults with RA who initiated TCZ or TNFi (primary comparator)/abatacept (secondary comparator) with prior use of ≥1 different biologic drug or tofacitinib. The primary outcome was hospitalised serious infection (SI), including bacterial, viral or opportunistic infection. To control for >70 confounders, TCZ initiators were propensity score (PS)-matched to TNFi or abatacept initiators. Database-specific HRs were combined by a meta-analysis.ResultsThe primary cohort included 16 074 TCZ PS-matched to 33 109 TNFi initiators. The risk of composite SI was not different between TCZ and TNFi initiators (combined HR 1.05, 95% CI 0.95 to 1.16). However, TCZ was associated with an increased risk of serious bacterial infection (HR 1.19, 95% CI 1.07 to 1.33), skin and soft tissue infections (HR 2.38, 95% CI 1.47 to 3.86), and diverticulitis (HR 2.34, 95% CI 1.64 to 3.34) versus TNFi. An increased risk of composite SI, serious bacterial infection, diverticulitis, pneumonia/upper respiratory tract infection and septicaemia/bacteraemia was observed in TCZ versus abatacept users.ConclusionsThis large multidatabase cohort study found no difference in composite SI risk in patients with RA initiating TCZ versus TNFi after failing ≥1 biologic drug or tofacitinib. However, the risk of serious bacterial infection, skin and soft tissue infections, and diverticulitis was higher in TCZ versus TNFi initiators. The risk of composite SI was higher in TCZ initiators versus abatacept.

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. AbstractBackground: The EMPA-REG OUTCOME trial showed that empagliflozin reduced the risk of cardiovascular death and hospitalization for heart failure (HHF) in diabetic patients with cardiovascular disease. EMPRISE is a study programme on the effectiveness, safety and healthcare utilization of empagliflozin in routine care, leveraging real-world data from two commercial and one federal US data sources from 2014 to 2019.Objectives: To describe rationale and design of EMPRISE, assess ability to minimize confounding and evaluate the time to reach sufficient statistical power for a key study outcome, HHF, using baseline information from the first year of EMPRISE. Methods:In 3 claims data sets, we identified a 1:1 propensity score (PS)-matched cohort of diabetic patients ≥18 years initiating empagliflozin or a dipeptidyl peptidase-4 inhibitor (DPP4i), resulting in 6643 total pairs. The PS model included >140 baseline covariates. We measured covariate balance via standardized differences (SD) and postmatching c-statistic. We computed the incidence rate (IR) of HHF, predicted exposure accrual over time and calculated expected power.Results: After PS matching, patient characteristics were balanced with SD <0.1 and cstatistic between 0.54 and 0.59. The population IR of HHF was 4.4 per 1000 personyears using a specific HHF definition and 14.8 using a broader HHF definition. In our projection, 80%-powered analyses would require a minimum of 169 HHF events, expected to accumulate by year 3 (specific definition) or year 2 (broader definition). Conclusion:Baseline information from EMPRISE provided evidence of solid confounding control and adequate exposure accrual with expected powered analyses for the primary outcomes. K E Y W O R D Scomparative effectiveness, confounding (epidemiology), empagliflozin, heart failure, realworld data, study validity, type 2 diabetes

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Purpose: An increasing number of new medications are being developed and approved for psoriatic arthritis (PsA). To generate real-world evidence on comparative safety and effectiveness of these drugs, a claims-based algorithm that can accurately identify PsA is greatly needed.Methods: To identify patients with PsA, we developed seven claims-based algorithms based on a combination of diagnosis codes and medication dispensing using the claims data from Medicare parts A/B/D linked to electronic medical records (2012)(2013)(2014). Two physicians independently conducted a chart review using the treating physician's diagnosis of PsA as the gold standard. We calculated the positive predictive value (PPV) and 95% confidence intervals of each algorithm.Results: Of the total 2157 records identified by the seven algorithms, 45% of the records had relevant clinical data to determine the presence of PsA. The PPV of the algorithms ranged from 75.2% (algorithm 1: ≥2 diagnosis codes for PsA and ≥1 diagnosis code for psoriasis) to 88.6% (algorithm 7: ≥2 diagnosis codes for PsA with ≥1 code by rheumatologist and ≥1 dispensing for PsA medication). Having ≥2 diagnosis codes and ≥1 dispensing for PsA medications (algorithm 6) also had PPV of 82.4%.Conclusions: All seven claims-based algorithms demonstrated a moderately high PPV of 75% to 89% in identifying PsA. The use of ≥2 diagnosis codes plus ≥1 prescription claim for PsA appears to be a valid and efficient tool in identifying PsA patients in the claims data, while broader algorithms based on diagnoses without a prescription claim also have reasonably good PPVs. K E Y W O R D S claims database, Medicare, pharmacoepidemiology, psoriasis, psoriatic arthritis

Purpose: The Centers for Medicare and Medicaid Services (CMS) mandated the transition from ICD-9 to ICD-10 codes on October 1, 2015. Postmarketing surveillance of newly marketed drugs, including novel biologics and biosimilars, requires a robust approach to convert ICD-9 to ICD-10 codes for study variables. We examined three mapping methods for health conditions (HCs) of interest to the Biologics and Biosimilars Collective Intelligence Consortium (BBCIC) and compared their prevalence. Methods: Using CMS General Equivalence Mappings, we applied forwardbackward mapping (FBM) to 108 HCs and secondary mapping (SM) and tertiary mapping (TM) to seven preselected HCs. A physician reviewed the mapped ICD-10 codes. The prevalence of the 108 HCs defined by ICD-9 versus ICD-10 codes was examined in BBCIC's distributed research network (September 1, 2012 to March 31, 2018). We visually assessed prevalence trends of these HCs and applied a threshold of 20% level change in ICD-9 versus ICD-10 prevalence. Results: Nearly four times more ICD-10 codes were mapped by SM and TM than FBM, but most were irrelevant or nonspecific. For conditions like myocardial infarction, SM or TM did not generate additional ICD-10 codes. Through visual inspection, one-fifth of the HCs had inconsistent ICD-9 versus ICD-10 prevalence trends. 13% of HCs had a level change greater than +/−20%. Conclusion: FBM is generally the most efficient way to convert ICD-9 to ICD-10 codes, yet manual review of converted ICD-10 codes is recommended even for FBM. The lack of existing guidance to compare the performance of ICD-9 with ICD-10 codes led to challenges in empirically determining the quality of conversions.

IMPORTANCEThe US Food and Drug Administration (FDA) Amendments Act of 2007 authorized the FDA to impose safety requirements on drugs with important risks, such as prescriber certification or routine laboratory testing, to ensure that the benefits of use outweighed the risks. However, little is known about patient and caregiver experiences with these Risk Evaluation and Mitigation Strategy (REMS) programs with Elements to Assure Safe Use (ETASU). OBJECTIVE To understand patient and caregiver experiences with and perceptions of REMS programs with ETASU. DESIGN, SETTING, AND PARTICIPANTS This qualitative study included semistructured qualitative phone interviews conducted between 2016 and 2017, with initial analysis performed in 2017 and reanalysis performed in 2021. Adult patients prescribed natalizumab or sodium oxybate, adult patients or caregivers of adult patients prescribed vigabatrin, and adult female patients of reproductive age prescribed riociguat were included. MAIN OUTCOMES AND MEASURESAssessment of knowledge, decision-making, medication access, and perceptions of medical privacy. RESULTS Among 63 participants, 46 (73%) were female. Twenty-five participants (40%) had taken natalizumab, 10 (16%) riociguat, 15 (24%) sodium oxybate, and 10 (16%) vigabatrin. One participant had taken both natalizumab and vigabatrin; 4 (6%) were caregivers of patients using vigabatrin. Most participants expressed knowledge of REMS program requirements, but many lacked the insight that these requirements were part of an FDA-mandated special safety program and expressed difficulty understanding program education materials. REMS requirements made some participants more likely to initiate treatment. However, many reported burdens accessing medication, including the need to travel to certified prescribers or pharmacies. Manufacturer access to personal health information was also controversial, although some participants expressed an altruistic desire to assist others. CONCLUSIONS AND RELEVANCEThis qualitative study found that REMS programs with ETASU reassured patients and their caregivers about drug safety and helped support medication initiation.However, steps are needed to improve the quality of REMS educational materials, promote efficient medication access, and protect patient privacy.

the Centers for Medicare and Medicaid Services (CMS) mandated the transition from ICD-9 to ICD-10 codes. Post-marketing surveillance of originator biologics and their corresponding biosimilars by the Biologics & Biosimilars Collective Intelligence Consortium (BBCIC) requires a robust approach to convert ICD-9 to ICD-10 codes used to define study variables. We examined three ICD-9 to ICD-10 mapping methods for health conditions (HCs) of BBCIC's interest and compared their incidence in BBCIC's distributed research network (DRN). Methods: We applied forward-backward mapping (FBM), using direct links of forward and backward General Equivalence Mappings developed by CMS, to 110 HCs. Secondary mapping (SM) and tertiary mapping (TM), based on iterations of FBM, were tested for 7 selected variables. A physician reviewed the mapped ICD-10 codes from the three methods. Incidence of the 110 HCs defined by ICD-9 versus ICD-10 codes was examined in the DRN during 9/1/2012-3/31/2018. We visually assessed incidence trends before and after October 2015 and used a threshold of 20% level change to examine the performance of ICD-9-to-ICD-10 conversion. Results: Nearly 4 times more ICD-10 codes were mapped by SM and TM compared to FBM. However, the additional codes identified by SM or TM were mostly irrelevant or non-specific.For distinct conditions such as myocardial infarction, SM or TM did not add any ICD-10 codes. Through visual inspection, 22% HCs had inconsistent ICD-9 versus ICD-10 trends; in general, ICD-10 algorithms led to a higher incidence. 15% HCs had an incidence level change greater than +/-20% between ICD-9 and ICD-10 algorithms. Conclusions: FBM is generally the most efficient way to convert ICD-9 to ICD-10 codes, yet manual review of converted ICD-10 codes is recommended even for FBM. No existing guidance is available to compare the performance of ICD-9 versus ICD-10 codes, leading to challenges in empirically determining the quality of conversions.