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Blood volume-monitored regulation of ultrafiltration in fluid-overloaded hemodialysis patients: study protocol for a randomized controlled trial

Overview of attention for article published in Trials, June 2012
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Title
Blood volume-monitored regulation of ultrafiltration in fluid-overloaded hemodialysis patients: study protocol for a randomized controlled trial
Published in
Trials, June 2012
DOI 10.1186/1745-6215-13-79
Pubmed ID
Authors

Manfred Hecking, Marlies Antlanger, Wolfgang Winnicki, Thomas Reiter, Johannes Werzowa, Michael Haidinger, Thomas Weichhart, Hans-Dietrich Polaschegg, Peter Josten, Isabella Exner, Katharina Lorenz-Turnheim, Manfred Eigner, Gernot Paul, Renate Klauser-Braun, Walter H Hörl, Gere Sunder-Plassmann, Marcus D Säemann

Abstract

Data generated with the body composition monitor (BCM, Fresenius) show, based on bioimpedance technology, that chronic fluid overload in hemodialysis patients is associated with poor survival. However, removing excess fluid by lowering dry weight can be accompanied by intradialytic and postdialytic complications. Here, we aim at testing the hypothesis that, in comparison to conventional hemodialysis, blood volume-monitored regulation of ultrafiltration and dialysate conductivity (UCR) and/or regulation of ultrafiltration and temperature (UTR) will decrease complications when ultrafiltration volumes are systematically increased in fluid-overloaded hemodialysis patients.

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Mendeley readers

The data shown below were compiled from readership statistics for 77 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 1%
Brazil 1 1%
Unknown 75 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 17%
Other 9 12%
Student > Postgraduate 8 10%
Student > Bachelor 6 8%
Student > Master 6 8%
Other 12 16%
Unknown 23 30%
Readers by discipline Count As %
Medicine and Dentistry 25 32%
Nursing and Health Professions 7 9%
Biochemistry, Genetics and Molecular Biology 3 4%
Agricultural and Biological Sciences 2 3%
Computer Science 2 3%
Other 13 17%
Unknown 25 32%