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ImportanceSuboptimal adherence to endocrine therapy (ET) among patients with hormone-receptor–positive breast cancer significantly affects survival outcomes and is associated with higher hospitalization rates and health care costs. Weak adherence to long-term treatments has multiple determinants, including disease characteristics, treatment adverse effects, and patients’ attributes, such as age and comorbidities.ObjectiveTo examine whether potential drug-drug interactions (PDDI) with tamoxifen or aromatase inhibitor were associated with adherence to ET in patients with early and advanced breast cancer.Design, Setting, and ParticipantsThis cohort study used anonymized health record data of women with breast cancer who received ET in a private observational primary care database. Patients eligible for analysis included women aged 18 years or older who had a reported diagnosis of breast cancer and received ET with tamoxifen or aromatase inhibitor between 1994 and 2021. Data were analyzed 2021.ExposuresAdherence to ET during a given year was defined by a medication possession ratio of 80% or greater over 1-year prescription periods. PDDI were categorized into absent, minor (a combination to take into account), moderate (combination requiring precautions for use), major (combination not recommended), and contraindicated according to guidelines in the Claude Bernard Drug Database.Main Outcomes and MeasuresWe used regression models to estimate odds ratios (ORs) and 95% CIs for the associations between adherence and age, baseline comorbidities, PDDI, and adherence to ET during the previous year.ResultsA total of 10 863 patients who were prescribed ET for breast cancer were eligible for the analysis (age 70 years or older, 3509 patients [32.3%]). In the tamoxifen cohort (3564 patients), PDDI were reported in 497 of 3670 patients (13.5%) at baseline (moderate, 254 patients [51.1%]; major, 227 patients [45.7%]), 2047 of 4831 patients (42.4%) at year 1, 1127 of 2751 patients (41.0%) at year 2, 761 of 1861 patients (40.9%) at year 3, 376 of 1058 patients (35.5%) at year 4, and 201 of 593 patients (33.9%) at year 5. In the aromatase inhibitor cohort (7299 patients), PDDI were reported in 592 of 7437 patients (8.0%) at baseline (moderate in 588 of 592 patients [99.3%]), which reached 2875 of 9031 patients (31.8%) at year 1 and ranged between 31.4% (1802 of 5730 patients in year 2) and 32.8% (791 of 2411 in year 4) throughout the study period. No association between adherence and PDDI was found in the tamoxifen (OR, 0.99; 95% CI, 0.91-1.08) or aromatase inhibitor (OR, 1.05; 95% CI, 0.95-1.15) cohort.Conclusions and RelevanceIn this cohort of patients with hormone-receptor–positive breast cancer, PDDI with tamoxifen and aromatase inhibitors were not associated with adherence to ET.

International audience; In this paper, we propose a study of the decision horizon duration for rolling stock mission assignment and maintenance planning in a prognostics and health management (PHM) context. The aim is to determine the best decision horizon duration that allows the con- struction of a suitable schedule that assigns railway vehicles to missions and integrates required maintenance operations accord- ing to the current and future health of the vehicles. A genetic algorithm is used to minimize the overall cost of the joint schedule as a function of the decision horizon. The results are compared to three proposed heuristics to study the influence of the resolution method on the decision horizon duration. The best decision horizon duration is given for each used method for an illustration case.

This paper address the problem of using prognostic information in the decision-making process of a single multi-purpose machine. The prognostics and health management method is compared to condition-based maintenance combined with a genetic algorithm to determine the joint schedule of maintenance and production. The paper presents a methodology to select the adequate strategy while considering several factors that influence the functioning of the machine. The results show that operational and conditions variability influence the choice of the suitable methods. In the presented case, we show configurations where prognostic information is useless or useful.

Prognostics and health management have become increasingly important in recent years. Many research studies focus on a crucial phase consisting of predicting the remaining useful life of equipment or a component. However, this step is often carried out without taking into account the decisions that will be taken later. This article aims to propose a modification of the existing PHM framework to combine the prognostics and decision-making phases in a closed loop. In this paper, the presented framework is described and some elements for its implementation are proposed. A simplifiedexample is developed to illustrate the presented methodology of post-prognostic decision enhancement.