Background:In 2005, a national registry was established to collect epidemiological and clinical data for patients with beta-thalassemia living in France. The objective of this data collection was to better understand patient characteristics, treatment patterns, and clinical outcomes among patients followed in France. Completeness is achieved by cross-checking with different sources. The registry collects data on patients with beta-thalassemia major TM) (defined as requiring ≥8 RBC transfusions per year) and thalassemia intermedia (TI). Data is updated every 2 years. Aims: Here, we first describe the whole population included in the registry with the number and causes of death during the period 2005-2017; second, we focus on a sub-group of transfusion-dependent thalassemia (TDT) patients followed from 2009/2010 for 2 to 6 years. Methods: Patients were included between 01/01/2005 and 31/01/2019. A cross sectional study and a longitudinal study were performed in February 2019. For the longitudinal study, TDT (either TM or TI) patients included in the registry before 2010 were selected. Baseline data corresponds to data collected in 2009 or 2010 and 2-, 4-, and 6-year data corresponds to data collected during follow-up. Iron overload was assessed by serum ferritin (SF) level and hepatic and cardiac MRI. Iron measurement risk category cutoffs based on published literature were ≤20 ms (intermediate risk) and ≤10 ms (high risk) for cardiac T2 * and ≥7
The derivative-free nonlinear Kalman filter is proposed for state estimation and fault diagnosis in distributed parameter systems of the wave-type and particularly in the Peyrard-Bishop-Dauxois model of DNA dynamics. At a first stage, a nonlinear filtering approach is introduced for estimating the dynamics of the Peyrard-Bishop-Dauxois 1D nonlinear wave equation, through the processing of a small number of measurements. It is shown that the numerical solution of the associated partial differential equation results in a set of nonlinear ordinary differential equations. With the application of a diffeomorphism that is based on differential flatness theory it is shown that an equivalent description of the system is obtained in the linear canonical (Brunovsky) form. This transformation enables to obtain local estimates about the state vector of the DNA model through the application us of the standard Kalman filter recursion. At a second stage, the local statistical approach to fault diagnosis is used to perform fault diagnosis for this distributed parameter system by processing with statistical tools the differences (residuals) between the output of the Kalman filter and the measurements obtained from the distributed parameter system. Optimal selection of the fault threshold is succeeded by using the local statistical approach to fault diagnosis. The efficiency of the proposed filtering approach in the problem of fault diagnosis for parametric change detection, in nonlinear wave-type models of DNA dynamics, is confirmed through simulation experiments.
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