Comparative Safety of Originator and Biosimilar Epoetin Alfa Drugs: An Observational Prospective Multicenter Study

Background: Erythropoiesis-stimulating agents (ESAs) are biological molecules approved for the treatment of anemia associated with chronic renal failure. Biosimilars were licensed for use in Europe in 2007. Aim: This study aimed to compare the safety profile of biosimilars with respect to the reference product in a nephrology setting. Methods: A prospective study was conducted in four Italian regions between 1 October 2013 and 30 June 2015. The study population included patients aged 65 18 years undergoing hemodialysis and treated with epoetins as per the clinical practice of the participating centers. The two comparison cohorts included patients treated with either an originator or a biosimilar epoetin alfa. Each patient was followed up until occurrence of any safety outcome of interest (grouped into three major categories), switch to a different ESA product, transplant or peritoneal dialysis, death, or end of the study period, whichever came first. Results: Overall, 867 subjects were included in the study (originator: N = 423; biosimilar: N = 444). Biosimilar users were older than originator users (median age of 76 vs 64 years, respectively), more frequently affected by arrhythmia (29.3 vs 22.5%), and less frequently candidates for transplantation (3.8 vs 18.2%). Cox-regression analysis showed no increase in risk of safety outcomes in biosimilar users, even after adjusting for confounding factors: 1.0 (95% confidence interval [CI] 0.7\u20131.3) for any outcomes; 1.1 (95% CI 0.7\u20131.8) for problems related to dialysis device; 0.9 (95% CI 0.6\u20131.5) for cardio- and cerebro-vascular conditions; 0.9 (95% CI 0.6\u20131.5) for infections. Conclusion: This study confirms the comparable safety profiles of originator and biosimilar epoetin alfa drugs when used in patients receiving dialysis

Real-time rainfall maps based on satellite broadcast signal attenuation

In spite of the variety of existing methods to measure precipitation, the retrieval of rainfall fields is still a matter of research, because of the high number of applications in different fields critically dependent on rainfall data and thus demanding for upgraded precisions in quantity estimation, spatial distribution and resolution, as well as for homogeneous retrieval over large domains. Telecommunication technologies can bring relevant information on rainfall rate, through the measurement of the attenuation caused by raindrops on broadcast satellite signals, albeit not specifically developed for this. NEFOCAST is a FAR-FAS research project funded by Regione Toscana, which exploits this feature through innovative two-way (i.e., transmit/receive) devices named Smart Low-Noise Block converter (SmartLNB), that are going to constitute a free-of-charge network of sensors, densely distributed in urbanised areas. Usage of smart LNBs has many advantages in terms of cost and setup and has a great potential for application worldwide including areas lacking of meteorological data, providing also an efficient data transmission solution. In NEFOCAST an experimental network of SmartLNBs has been deployed in Florence and analysed through a co-located rain gauge network and a Doppler polarimetric X-band radar for cal/val objectives. The high rate of attenuation measurements provided by the SmartLNBs (in our case 1 min.), suggests to approach the rainfall retrieval problem similarly to a trajectory assessment in a phase space, using a Kalman filter to achieve the rainfall field over a target domain. SmartLNBs provide an average measurement along a non-nadir path, so that information on the structure of the intercepted rainfall system are needed to retrieve ground precipitation, and MSG satellite observations can be used at the purpose. In this work we will present the measurement concept, the signal processing algorithm, and the method to estimate the rainfall fields. Firstly, some significant synthetic case studies will be introduced, featuring some events with different precipitation patterns; then, real SmartLNB measurements, acquired during different meteorological conditions, will be discussed, analysing also the impacts of SmartLNB density, satellite link geometry and structure of rainfall systems

Real-time high resolution rainfall maps from a network of ground-based interactive satellite terminals: the NEFOCAST project

Rainfall estimation and its spatial distribution are key elements for agriculture. Actually, rainfall maps over cultivated areas are needed for efficient water resources management, while prediction and monitoring of severe precipitation events are required for the estimation of possible damages and risks for crops, animals and infrastructures. Spatial and temporal accuracies of rainfall estimates are crucial, especially in case of intense and localized phenomena. Conventional instruments such as rain gauges provide point estimations, but the setup of a dense network requires high installation and maintenance costs. On the other hand, techniques based on satellite remote sensing or weather radars present specific limitations either in terms of data availability, sources of error, cost, or spatial and temporal resolution. A promising alternative is the exploitation of modern telecommunication technologies that, albeit not specifically developed for rainfall estimation, can bring relevant information through the measurement of the attenuation caused by raindrops on broadcast satellite signals. NEFOCAST1 is a research project funded by Tuscany Region (Italy) which implements such an approach based upon a dense population of ground-based Interactive Satellite Terminals (ISTs). The IST employed in the project is an innovative two-way (i.e., transmit/receive) device named Smart Low-Noise Block converter (SmartLNB). Usage of smart LNBs has many advantages in terms of cost and setup and has a great potential for application worldwide including areas where hydro-meteorological networks are not fully developed. In the framework of this project, an experimental network of SmartLNBs has been installed in Tuscany Region in Central Italy and a dedicated platform (NEFOCAST Service Center) has been set up where the data is collected (via ground-to-satellite link), processed and shared with a number of value-added service providers (VASPs). Real-time estimates (with 1 minute update) of rain rates as ‘seen’ by the SmartLNBs are produced through a processing algorithm based on the relationship between the rain rate and the signal attenuation with respect to clear-sky conditions. The real-time point estimates are filtered with a space-time Kalman filter to predict the pattern and evolution of the rainfall field and produce high resolution maps. This work is focused on the simulation of a set of case studies, featuring several storms with different spatial-temporal patterns and intensities. The 3D simulated rainfall fields are used as a virtual reality for the synthetic reconstruction of a set of SmartLNBs measurements over randomly located points. The relevant rainfall maps are then produced by use of the above mentioned Kalman filter approach over the set of synthetic SmartLNBs measurements. Finally a simple linear model of the storm evolution is introduced to test the ability of such algorithm to reconstruct the dynamic of the precipitation system. The impacts of various factors such as SmartLNB density, satellite link geometry, rainfall characteristics (e.g. horizontal/vertical structure, convective/stratiform event), are investigated and the potential for practical applications is eventually discussed

Kalman Tracking of GEO Satellite Signal for Opportunistic Rain Rate Estimation

In the NEFOCAST project we aim at estimating rainfall by the opportunistic use of the signal attenuation due to the propagation channel in satellite communications. The estimation is performed by reverse engineering the effects of the various propagation phenomena on the satellite signal. However, the accuracy of the estimation is affected by several factors: in first place the rapid fluctuations in signal amplitude caused by small-scale irregularities in the tropospheric refractive index; secondly, the perturbations of the orbit of GEO satellites, such as the gravitational effects of the moon and the sun, which, even if periodically counteracted by correction maneuvers, nevertheless cause residual orbit inclinations. The problem with all these factors is that they can cause large deviations in the clear-sky measurements that can be misinterpreted as rain events. In this paper we address these problems by employing two Kalman filters designed to track slow and fast changes of the received signal energy, so that the rain events can be reliably estimated