Literature DB >> 32383041

The Role of Feedback Control Design in Developing Anemia Management Protocols.

Yossi Chait1, Michael J Germain2,3,4, Christopher V Hollot5, Joseph Horowitz6.   

Abstract

The optimal use of erythropoiesis stimulating agents to treat anemia of end-stage renal disease remains difficult due to reported associations with adverse events. A patient's hemoglobin response to these agents cannot be accurately described using population-level models due to many individual factors including chronic inflammation, red blood cell lifespan, and acute blood loss. As a consequence, it is generally understood that current one-size-fits-all anemia management protocols result in suboptimal outcomes. In this paper, we report on our collaboration with the medical community in designing anemia management protocols. In clinical implementation, these new dosing protocols have led to improved outcomes due to their use of control-relevant modelling, model parameter identification, and principles of feedback control. This is an example of medical professionals and control engineers working together to positively affect the performance of anemia management protocols in end-stage renal disease.

Entities:  

Keywords:  Anemia management; Biomedical control systems; Drug dosing algorithms; Erythropoiesis stimulating agents feedback algorithms

Mesh:

Substances:

Year:  2020        PMID: 32383041      PMCID: PMC7647949          DOI: 10.1007/s10439-020-02520-1

Source DB:  PubMed          Journal:  Ann Biomed Eng        ISSN: 0090-6964            Impact factor:   3.934


  23 in total

1.  Individualized anemia management reduces hemoglobin variability in hemodialysis patients.

Authors:  Adam E Gaweda; George R Aronoff; Alfred A Jacobs; Shesh N Rai; Michael E Brier
Journal:  J Am Soc Nephrol       Date:  2013-09-12       Impact factor: 10.121

2.  Pharmacodynamic models for agents that alter production of natural cells with various distributions of lifespans.

Authors:  Wojciech Krzyzanski; Sukyung Woo; William J Jusko
Journal:  J Pharmacokinet Pharmacodyn       Date:  2006-03-25       Impact factor: 2.745

3.  Managing anemia in dialysis patients: hemoglobin cycling and overshoot.

Authors:  Ajay K Singh; Edgar Milford; Steven Fishbane; Sai Ram Keithi-Reddy
Journal:  Kidney Int       Date:  2008-03-12       Impact factor: 10.612

4.  Recovery of hemoglobin mass after blood donation.

Authors:  Torben Pottgiesser; Wolfgang Specker; Markus Umhau; Hans-Hermann Dickhuth; Kai Roecker; Yorck O Schumacher
Journal:  Transfusion       Date:  2008-05-02       Impact factor: 3.157

5.  Use of 12x/month haemoglobin monitoring with a computer algorithm reduces haemoglobin variability.

Authors:  Warren R Ho; Michael J Germain; Jane Garb; Sue Picard; Molly-Kate Mackie; Cherry Bartlett; Eric J Will
Journal:  Nephrol Dial Transplant       Date:  2010-02-22       Impact factor: 5.992

6.  Anemia management in patients receiving chronic hemodialysis.

Authors:  Mayuri Thakuria; Norma J Ofsthun; Claudy Mullon; Jose A Diaz-Buxo
Journal:  Semin Dial       Date:  2011 Sep-Oct       Impact factor: 3.455

7.  Hemoglobin cycling in hemodialysis patients treated with recombinant human erythropoietin.

Authors:  Steven Fishbane; Jeffrey S Berns
Journal:  Kidney Int       Date:  2005-09       Impact factor: 10.612

Review 8.  Erythropoiesis stimulatory agent- resistant anemia in dialysis patients: review of causes and management.

Authors:  Mehmet Kanbay; Mark A Perazella; Benan Kasapoglu; Mustafa Koroglu; Adrian Covic
Journal:  Blood Purif       Date:  2009-10-08       Impact factor: 2.614

9.  An international observational study suggests that artificial intelligence for clinical decision support optimizes anemia management in hemodialysis patients.

Authors:  Carlo Barbieri; Manuel Molina; Pedro Ponce; Monika Tothova; Isabella Cattinelli; Jasmine Ion Titapiccolo; Flavio Mari; Claudia Amato; Frank Leipold; Wolfgang Wehmeyer; Stefano Stuard; Andrea Stopper; Bernard Canaud
Journal:  Kidney Int       Date:  2016-06-02       Impact factor: 10.612

10.  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

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