Background. Intravenous (i.v.) iron sucrose similar (ISS) preparations are available but clinical comparisons with the originator iron sucrose (IS) are lacking.Methods. The impact of switching from IS to ISS on anaemia and iron parameters was assessed in a sequential observational study comparing two periods of 27 weeks each in 75 stable haemodialysis (HD) patients receiving i.v. iron weekly and an i.v. erythropoiesis-stimulating agent (ESA) once every 2 weeks. Patients received IS in the first period (P1) and ISS in the second period (P2).Results. Mean haemoglobin value was 11.78 ± 0.99 g/dL during P1 and 11.48 ± 0.98 g/dL during P2 (P = 0.01). Mean serum ferritin was similar for both treatment periods (P1, 534 ± 328 μg/L; P2, 495 ± 280 μg/L, P = 0.25) but mean TSAT during P1 (49.3 ± 10.9%) was significantly higher than during P2 (24.5 ± 9.4%, P <0.0001). The mean dose of i.v. iron per patient per week was 45.58 ± 32.55 mg in P1 and 61.36 ± 30.98 mg in P2 (+34.6%), while the mean ESA dose was 0.58 ± 0.52 and 0.66 ± 0.64 μg/kg/week, respectively (+13.8%). Total mean anaemia drug costs increased in P2 by 11.9% compared to P1.Conclusions. The switch from the originator IS to an ISS preparation led to destabilization of a well-controlled population of HD patients and incurred an increase in total anaemia drug costs. Prospective comparative clinical studies are required to prove that ISS are as efficacious and safe as the originator i.v. IS.

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Electric vehicles (EVs) represent one of the promising solutions to face environmental and energy concerns in transportation. Due to the limited range of EVs, deploying a charging infrastructure enabling EV drivers to carry out long distance trips is a key step to foster the widespread adoption of EVs. In this paper, we study the problem of locating EV fast charging stations so as to satisfy as much recharging demand as possible within the available investment budget. We focus on incorporating two important features into the optimization problem modeling: a multi-period decision making horizon and uncertainties on the recharging demand in terms of both the number of EVs to recharge and the set of long-distance trips to cover. Our objective is to determine the charging stations to be opened at each time period so as to maximize the expected value of the satisfied recharging demand over the entire planning horizon. To model the problem, we propose a multi-stage stochastic integer programming approach based on the use of a scenario tree to represent the uncertainties on the recharging demand. To solve the resulting large-size integer linear program, we develop two solution algorithms: an exact solution method based on a Benders decomposition and a heuristic approach based on a genetic algorithm. Our numerical results show that both methods perform well as compared to a standalone mathematical programming solver. Moreover, we provide the results of additional simulation experiments showing the practical benefit of the proposed multi-stage stochastic programming model as compared to a simpler multi-period deterministic model.

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