Literature DB >> 8063263

Mathematical modeling of erythropoietin therapy in uremic anemia. Does it improve cost-effectiveness?

R Bellazzi1, C Siviero, R Bellazzi1.   

Abstract

This paper describes the improvements in r-HuEPO therapy of uremic patients that may be obtained by using a mathematical model of patient response together with a delivery control strategy derived from the theory of industrial control. A mathematical model of r-HuEPO action is presented, and its applicability to dialytic patients is shown. Moreover, a new statistical technique for identifying the parameters of the mathematical model analyzing a patient population is summarized, and a control strategy for r-HuEPO delivery in uremic patients based on a Fuzzy Set Controller is introduced. Some results obtained from simulation, are presented.

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Year:  1994        PMID: 8063263

Source DB:  PubMed          Journal:  Haematologica        ISSN: 0390-6078            Impact factor:   9.941


  4 in total

Review 1.  Predictive modeling for improved anemia management in dialysis patients.

Authors:  Michael E Brier; Adam E Gaweda
Journal:  Curr Opin Nephrol Hypertens       Date:  2011-11       Impact factor: 2.894

2.  Simplification of an erythropoiesis model for design of anemia management protocols in end stage renal disease.

Authors:  B Nichols; R P Shrestha; J Horowitz; C V Hollot; M J Germain; A E Gaweda; Y Chait
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2011

3.  Would artificial neural networks implemented in clinical wards help nephrologists in predicting epoetin responsiveness?

Authors:  Luca Gabutti; Nathalie Lötscher; Josephine Bianda; Claudio Marone; Giorgio Mombelli; Michel Burnier
Journal:  BMC Nephrol       Date:  2006-09-18       Impact factor: 2.388

4.  Performance of a Predictive Model for Long-Term Hemoglobin Response to Darbepoetin and Iron Administration in a Large Cohort of Hemodialysis Patients.

Authors:  Carlo Barbieri; Elena Bolzoni; Flavio Mari; Isabella Cattinelli; Francesco Bellocchio; José D Martin; Claudia Amato; Andrea Stopper; Emanuele Gatti; Iain C Macdougall; Stefano Stuard; Bernard Canaud
Journal:  PLoS One       Date:  2016-03-03       Impact factor: 3.240

  4 in total

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