Estimating and Predicting Hemodynamic Changes During Hemodialysis
This trial has been completed.
|Sponsor||University of Colorado, Denver|
|Start date||September 2012|
|End date||December 2016|
|Trial size||241 participants|
|Trial identifier||NCT01700465, 11-1437|
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.
|United States||No locations recruiting|
|Other countries||No locations recruiting|
|Aurora, CO||Fresenius Medical Center East Denver||completed|
|Aurora, CO||Children's Hospital Colorado||completed|
|Aurora, CO||University of Colorado Hospital||completed|
|Denver, CO||Fresenius Medical Center Central||completed|
|Denver, CO||Fresenius Medical Center Rocky Mountain||completed|
Patients undergoing hemodialysis
Acute intravascular volume loss during hemodialysis
time frame: one hemodialysis session (approx 3-4 hours)
Male or female participants from 2 years up to 89 years old.
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
|Official title||Estimating and Predicting Hemodynamic Changes During Hemodialysis|
|Principal investigator||Steve Moulton, MD|
|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|
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