The relative effectiveness of eribulin for advanced breast cancer treatment: a study of the southeast Netherlands advanced breast cancer registry.

Background: Eribulin provided significant overall survival (OS) benefit in heavily pretreated advanced breast cancer patients in the EMBRACE trial. We investigated the use of eribulin in daily clinical practice, the relative effectiveness of eribulin versus non-eribulin chemotherapy, and the safety of eribulin in real-world patients included in the SOutheast Netherlands Advanced BREast cancer (SONABRE) registry.Material and methods: Patients treated with eribulin and eligible patients for eribulin who received a different chemotherapy (i.e., non-eribulin group) in ten hospitals in 2013-2017 were included. A multivariate matching algorithm was applied to correct for differences in baseline characteristics between the groups, including the number of previous treatment lines. Progression-free survival (PFS) and OS of eribulin were compared with the matched non-eribulin group through Kaplan-Meier curves and multivariate Cox proportional hazard models. The occurrence of dose delay and reduction was described.Results: Forty-five patients received eribulin according to its registration criteria and 74 patients were eligible for eribulin but received non-eribulin chemotherapy. Matching increased the similarity in baseline characteristics between the eribulin and non-eribulin groups. Median PFS was 3.5 months (95% confidence interval (CI): 2.7-5.5) in the eribulin group and 3.2 months (95% CI: 2.0-4.8) in the matched non-eribulin group (adjusted hazard ratio (HR): 0.83, 95% CI: 0.49-1.38). Median OS was 5.9 months (95% CI: 4.6-11.0) and 5.2 months (95% CI: 4.6-9.5) in the eribulin and non-eribulin groups, respectively (adjusted HR: 0.66, 95% CI: 0.38-1.13). Dose delay or reduction occurred in 14 patients (31%) receiving eribulin.Conclusions: No difference in PFS and OS was observed between eribulin and non-eribulin treated patients. Eribulin had a manageable toxicity profile.

Can artificial intelligence separate the wheat from the chaff in systematic reviews of health economic articles?

OBJECTIVES: Artificial intelligence-powered tools, such as ASReview, could reduce the burden of title and abstract screening. This study aimed to assess the accuracy and efficiency of using ASReview in a health economic context. METHODS: A sample from a previous systematic literature review containing 4,994 articles was used. Previous manual screening resulted in 134 articles included for full-text screening (FT) and 50 for data extraction (DE). Here, accuracy and efficiency was evaluated by comparing the number of identified relevant articles with ASReview versus manual screening. Pre-defined stopping rules using sampling criteria and heuristic criteria were tested. Robustness of the AI-tool's performance was determined using 1,000 simulations. RESULTS: Considering included stopping rules, median accuracy for FT articles remained below 85%, but reached 100% for DE articles. To identify all relevant articles, a median of 89.9% of FT articles needed to be screened, compared to 7.7% for DE articles. Potential time savings between 49 and 59 hours could be achieved, depending on the stopping rule. CONCLUSIONS: In our case study, all DE articles were identified after screening 7.7% of the sample, allowing for substantial time savings. ASReview likely has the potential to substantially reduce screening time in systematic reviews of health economic articles.