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Background: Biologic agents have demonstrated efficacy in treating ulcerative colitis (UC); however, treatment failure to tumor necrosis factor inhibitors (TNFi) is common in the real world. Data on preferential sequencing in clinical practice after failure remain limited. Objectives: This study aimed to evaluate real-world outcomes of patients cycling to TNFis or switching to non-TNFi biologics following first-line failure with TNFis. Design: Retrospective cohort study in Germany. Methods: Adult patients with UC were identified using administrative claims data from 1 May 2014 to 30 June 2022 provided by a statutory sickness fund. Patients newly initiating first-line therapy with TNFis and then switching to another agent were identified. Patients were defined as within-class switched (WCS), if they cycled to another TNFi, or outside-class switchers (OCS), if they switched to a non-TNFi biologic [ustekinumab (UST) or vedolizumab (VDZ)] and followed from index (switch date) to death, insurance end, or study end on 30 June 2022. Inverse probability of treatment weighting (IPTW) was performed to adjust for differences in baseline characteristics between groups, and weighted Cox regression models were used to compare primary (time to discontinuation and second treatment switch) and secondary outcomes (corticosteroid-free drug survival). Results: We identified 166 patients initiating TNFis and switching to a subsequent treatment (mean age: 42.9 years, 49.4% female). Following IPTW, there were 71 and 76 patients in the WCS and OCS groups, respectively. Compared to OCS, WCS were more likely to discontinue the new therapy [hazard ratio (HR), 1.82, 95% confidence interval (CI), 1.14–2.89, p = 0.012], and switch a second time (HR, 3.46, 95% CI, 1.89–6.36, p < 0.001). Moreover, WCS showed an increased likelihood of initiating prolonged corticosteroid therapy (HR, 1.42, 95% CI, 0.77–2.59, p = 0.260); however, the results were not significant. Conclusion: Following first-line TNFi failure, this study suggests that real-world outcomes among patients with UC are less favorable when cycling to another TNFi, compared to switching to a non-TNFi such as UST or VDZ. [ABSTRACT FROM AUTHOR]

Objectives: To retrospectively analyze real‐world treatment patterns in patients with relapsed/refractory multiple myeloma (RRMM) who initiated third‐line treatment in Europe. Methods: German and Italian administrative claims data were sourced from the German AOK PLUS health insurance fund and Italian local health units (2016–2020). Data for the United Kingdom (UK), France, and Spain were sourced from medical chart reviews (MCRs) from 2016 to 2018 (historical) and 2019 to 2021 (new) using electronic case report forms. Results: Across all countries, immunomodulatory imide drug (IMiD)‐based regimens were prominent in the third‐line setting. From 2016 to 2020, lenalidomide‐dexamethasone was most common in Italy (18.0%) and Germany (12.7%). From 2019 to 2021, the most common regimen was ixazomib‐lenalidomide‐dexamethasone (67.5%) in the UK, pomalidomide‐dexamethasone (17.1%) in France, and daratumumab‐bortezomib‐dexamethasone (15.0%) in Spain. In the historical data (2016–2018), third‐line lenalidomide‐ and pomalidomide‐dexamethasone doublet use across the UK (>47%), France (>46%), and Spain (>33%) was high. From historical to new, triplet use increased in Spain (>19% to >60%) as did anti‐CD38 agent use in France (15.1% to 51.9%) and Spain (19.7% to 42.1%). Conclusions: From 2016 to 2021, third‐line regimens were mostly IMiD based. The MCR data demonstrated evolving treatment choices from 2016 to 2018 and 2019 to 2021, providing insights into uptake of novel agents and current RRMM European clinical practice. [ABSTRACT FROM AUTHOR]

Background: The Expanded Disability Status Scale (EDSS) quantifies disability and measures disease progression in multiple sclerosis (MS), however is not available in administrative claims databases. Objectives: To develop a claims-based algorithm for deriving EDSS and validate it against a clinical dataset capturing true EDSS values from medical records. Methods: We built a unique linked dataset combining claims data from the German AOK PLUS sickness fund and medical records from the Multiple Sclerosis Management System 3D (MSDS3D). Data were deterministically linked based on insurance numbers. We used 69 MS-related diagnostic indicators recorded with ICD-10-GMcodes within 3months before and after recorded true EDSSmeasures to estimate a claims-based EDSS proxy (pEDSS). Predictive performance of the pEDSS was assessed as an eight-fold (EDSS 1.0-7.0, ≥8.0), three-fold (EDSS 1.0-3.0, 4.0-5.0, ≥6.0), and binary classifier (EDSS <6.0, ≥6.0). For each classifier, predictive performance measures were determined, and overall performance was summarized using a macro F1-score. Finally, we implemented the algorithm to determine pEDSS among an overall cohort of patients with MS in AOK PLUS, who were alive and insured 12 months prior to and after index diagnosis. Results: We recruited 100 people withMS insured by AOK PLUS who had ≥1 EDSS measure in MSDS3D between 01/10/2015 and 30/06/2019 (620 measurements overall). Patients had a mean rescaled EDSS of 3.2 and pEDSS of 3.0. The pEDSS deviated from the true EDSS by 1.2 points, resulting in a mean squared error of prediction of 2.6. For the eight-fold classifier, the macro F1-score of 0.25 indicated low overall predictive performance. Broader severity groupings were better performing, with the three-fold and binary classifiers for severe disability achieving a F1-score of 0.68 and 0.84, respectively. In the overall AOK PLUS cohort (3,756 patients, 71.9% female, mean 51.9 years), older patients, patients with progressive forms of MS and those with higher comorbidity burden showed higher pEDSS. Conclusion: Generally, EDSS was underestimated by the algorithm as mild-to-moderate symptoms were poorly captured in claims across all functional systems. While the proxy-based approach using claims data may not allow for granular description of MS disability, broader severity groupings show good predictive performance. [ABSTRACT FROM AUTHOR]

Background: The positioning of new biologic agents for the treatment of Crohn's disease (CD) following failure of initial anti-tumor necrosis factor (anti-TNF) therapy remains a challenge in the real world. Objectives: This study aims to investigate the real-world outcomes associated with the sequential use of biologics in CD patients that newly initiate anti-TNFs, specifically comparing those that switch to another anti-TNF versus biologics with other modes of action. Design: Retrospective cohort study. Methods: We identified CD patients who newly began anti-TNF therapy between 1 October 2014 and 31 December 2018 using two German claims databases. Patients were classified as within-class switchers (WCS) if they switched to another anti-TNF or outside-class switchers (OCS) if they switched to vedolizumab (VDZ) or ustekinumab (UST). To compare WCS and OCS, baseline covariates were adjusted through inverse probability of treatment weighting (IPTW), and time-to-event analyses were performed using Cox Proportional Hazard regressions. Results from both databases were meta-analyzed using an inverse variance model. Results: Overall, 376 prevalent adult CD patients who initiated anti-TNFs and switched to another biologic were identified. After IPTW, there were 152 and 177 patients in the WCS and OCS group, respectively. WCS were more likely to receive prolonged corticosteroid therapy [hazard ratio (HR): 1.63, 95% confidence interval (CI): 1.17–2.27, p = 0.004], switch a second time to a different biologic (HR: 2.44, 95% CI: 1.63–3.66, p < 0.001), and discontinue treatment (HR: 1.71, 95% CI: 1.25–2.34, p = 0.001) than OCS. Conclusion: This study suggests that CD patients exhibit more favorable outcomes when switching outside the anti-TNF class to VDZ or UST after initial anti-TNF failure than switching to a second anti-TNF. With loss of response to anti-TNFs as a concern in the real world, comparative evidence from claims data assessing sequential use of biologics can help optimize treatment algorithms of patients after anti-TNF failure. [ABSTRACT FROM AUTHOR]