Estimating and Predicting Hemodynamic Changes During Hemodialysis
1 other identifier
observational
241
1 country
5
Brief Summary
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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Sep 2012
Longer than P75 for all trials
5 active sites
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
Study Start
First participant enrolled
September 1, 2012
CompletedFirst Submitted
Initial submission to the registry
September 27, 2012
CompletedFirst Posted
Study publicly available on registry
October 4, 2012
CompletedPrimary Completion
Last participant's last visit for primary outcome
December 1, 2016
CompletedStudy Completion
Last participant's last visit for all outcomes
December 1, 2016
CompletedDecember 5, 2016
December 1, 2016
4.3 years
September 27, 2012
December 1, 2016
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
Acute intravascular volume loss during hemodialysis
development of algorithm to estimate acute intravascular volume loss during hemodialysis
one hemodialysis session (approx 3-4 hours)
Study Arms (1)
Hemodialysis
Patients undergoing hemodialysis
Eligibility Criteria
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.
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
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (5)
Fresenius Medical Center East Denver
Aurora, Colorado, 80011, United States
Children's Hospital Colorado
Aurora, Colorado, 80045, United States
University of Colorado Hospital
Aurora, Colorado, 80045, United States
Fresenius Medical Center Central
Denver, Colorado, 80209, United States
Fresenius Medical Center Rocky Mountain
Denver, Colorado, 80220, United States
Study Officials
- PRINCIPAL INVESTIGATOR
Steve Moulton, MD
Children's Hospital Colorado
Study Design
- Study Type
- observational
- Observational Model
- CASE ONLY
- Time Perspective
- PROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
September 27, 2012
First Posted
October 4, 2012
Study Start
September 1, 2012
Primary Completion
December 1, 2016
Study Completion
December 1, 2016
Last Updated
December 5, 2016
Record last verified: 2016-12