Literature DB >> 1751236

Mathematical modeling of erythropoietin therapy.

L J Garred1, R Pretlac.   

Abstract

A simple mathematical model to describe hemoglobin (Hb) concentration response to recombinant human erythropoietin (EPO) therapy is proposed. The model is based on the assumption that Hb production increases linearly with EPO dose level. The resulting equation contains two patient parameters: 1) S, the proportionality constant between g Hb generated/L blood/wk and IU EPO administered/kg body weight/wk; and 2) tau, the patient erythrocyte lifetime in weeks. The model was applied retrospectively to 67 patients from the Canadian Erythropoietin Study, yielding an average error of 5.5 g/L between 27 measured and predicted Hb value pairs over the 27 week study. The model parameters, S (mean +/- SD = 0.015 +/- 0.005) and tau (14.0 +/- 4.1), varied over an order of magnitude. The model was also used to predict the EPO dose required to reach a target Hb of 110 g/L; the EPO requirements varied from 55 to 742 IU EPO/kg/wk (mean +/- SD = 225 +/- 124). It is recommended, based upon the model results, that EPO therapy be initiated at 3 IU EPO/kg/wk for each g/L difference between target and baseline Hb, with subsequent EPO dose adjustment guided by patient modeling.

Entities:  

Mesh:

Substances:

Year:  1991        PMID: 1751236

Source DB:  PubMed          Journal:  ASAIO Trans        ISSN: 0889-7190


  3 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

Review 2.  Nephrology, dialysis and transplantation.

Authors:  K Farrington; P Sweny
Journal:  Postgrad Med J       Date:  1993-07       Impact factor: 2.401

3.  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

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.