Estimating and Predicting Hemodynamic Changes During Hemodialysis
| Status: | Active, not recruiting |
|---|---|
| Conditions: | Renal Impairment / Chronic Kidney Disease |
| Therapuetic Areas: | Nephrology / Urology |
| Healthy: | No |
| Age Range: | 2 - 89 |
| Updated: | 4/21/2016 |
| Start Date: | September 2012 |
| End Date: | 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.
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.
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
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
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
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