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Estimating and Predicting Hemodynamic Changes During Hemodialysis

This study has been completed.
Information provided by (Responsible Party):
University of Colorado, Denver Identifier:
First received: September 27, 2012
Last updated: December 1, 2016
Last verified: December 2016
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.


Study Type: Observational
Study Design: Observational Model: Case-Only
Time Perspective: Prospective
Official Title: Estimating and Predicting Hemodynamic Changes During Hemodialysis

Resource links provided by NLM:

Further study details as provided by University of Colorado, Denver:

Primary Outcome Measures:
  • Acute intravascular volume loss during hemodialysis [ Time Frame: one hemodialysis session (approx 3-4 hours) ]
    development of algorithm to estimate acute intravascular volume loss during hemodialysis

Enrollment: 241
Study Start Date: September 2012
Study Completion Date: December 2016
Primary Completion Date: December 2016 (Final data collection date for primary outcome measure)
Patients undergoing hemodialysis

Detailed Description:
  1. Collect physiological waveform data from patients undergoing hemodialysis at the University of Colorado Hospital, Children's Hospital Colorado, and Fresenius Medical Centers using non-invasive monitoring techniques.
  2. Combine the physiological data from patient monitors with clinical and demographic data, including age, gender, race, problem list, reason for dialysis, estimated dry weight, volume removed, arterial and venous pressures, etc. for use in developing mathematical models of hemodialysis.
  3. Develop robust, real-time, computational models for:

    • estimating acute intravascular volume loss during hemodialysis
    • predicting an optimal, individual specific, intravascular volume to be removed during a hemodialysis session
    • predicting intradialytic hypotension
  4. Determine:

    • which non-invasive signals are relevant to each model type
    • which features extracted from these signals are relevant
    • which algorithms are capable of using the extracted features for each decision type

Ages Eligible for Study:   2 Years to 89 Years   (Child, Adult, Senior)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
Adult and pediatric patients undergoing hemodialysis at Fresenius Medical Centers, University of Colorado Hospital or Children's Hospital Colorado will be the population base for enrollment in this study. Patients may have acute kidney injury or end stage renal disease. Subjects may be inpatients or outpatients.

Inclusion Criteria:

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

Exclusion Criteria:

  • 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
  Contacts and Locations
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Please refer to this study by its identifier: NCT01700465

United States, Colorado
Fresenius Medical Center East Denver
Aurora, Colorado, United States, 80011
Children's Hospital Colorado
Aurora, Colorado, United States, 80045
University of Colorado Hospital
Aurora, Colorado, United States, 80045
Fresenius Medical Center Central
Denver, Colorado, United States, 80209
Fresenius Medical Center Rocky Mountain
Denver, Colorado, United States, 80220
Sponsors and Collaborators
University of Colorado, Denver
Principal Investigator: Steve Moulton, MD Children's Hospital Colorado
  More Information

Responsible Party: University of Colorado, Denver Identifier: NCT01700465     History of Changes
Other Study ID Numbers: 11-1437
Study First Received: September 27, 2012
Last Updated: December 1, 2016

Keywords provided by University of Colorado, Denver:
Hemodynamics processed this record on May 25, 2017