Estimating and Predicting Hemodynamic Changes During Hemodialysis

Completed

Conditions studied: Hemodialysis

In brief

Machine learning techniques and algorithms originally developed for use in the field of robotics can be applied to continuous, noninvasive physiological waveform data to discover hidden, hemodynamic relationships. Newly developed algorithms can, in real-time: 1) estimate acute blood loss volume, 2) monitor and estimate fluid resuscitation needs, 3) predict cardiovascular collapse well ahead of any clinically significant changes in standard vital signs, and 4) estimate intracranial pressure. We hypothesize that these same methods can be used to monitor volume loss during hemodialysis, as well as predict intradialytic hypotension, well before it occurs.

Key facts

Study ID

NCT01700465

Run by

University of Colorado, Denver

People needed

241

Starts

2012-09-01

Expected to finish

2016-12-01

Last updated by the study team

2016-12-05

Who can join

Age: 2 and older, up to 89. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

• Age: 2 - 89 years
• Undergoing hemodialysis at the Fresenius Medical Centers, University of Colorado Hospital or Children's Hospital Colorado

You may not qualify if…

• Pregnant
• Incarcerated
• Decisionally challenged
• Positive for hepatitis B surface antigen
• Limited access to or compromised monitoring sites for non-invasive finger and ear or forehead sensors

Where it is running

• Fresenius Medical Center East Denver — Aurora, Colorado, United States
• Children's Hospital Colorado — Aurora, Colorado, United States
• University of Colorado Hospital — Aurora, Colorado, United States
• Fresenius Medical Center Central — Denver, Colorado, United States
• Fresenius Medical Center Rocky Mountain — Denver, Colorado, United States

Full record on ClinicalTrials.gov

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