Clinical trial NCT01700465: Estimating and Predicting Hemodynamic Changes During Hemodialysis
Official title: Estimating and Predicting Hemodynamic Changes During Hemodialysis
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Hemodialysis
Keywords: Hemodialysis
Hemodynamics
Lead sponsor: University of Colorado, Denver
Sponsor class: OTHER
First posted: 2012-10-04
Enrollment: 241
Sex: ALL
Minimum age (years): 2
Maximum age (years): 89
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_ONLY
Time perspective: PROSPECTIVE
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.

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.

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

Eligibility criteria: 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

Primary outcomes:
measure: Acute intravascular volume loss during hemodialysis
timeFrame: one hemodialysis session (approx 3-4 hours)
Secondary outcomes:
Other outcomes:
Locations:
city: Aurora
country: United States
facility: Fresenius Medical Center East Denver
state: Colorado
city: Aurora
country: United States
facility: Children's Hospital Colorado
state: Colorado
city: Aurora
country: United States
facility: University of Colorado Hospital
state: Colorado
city: Denver
country: United States
facility: Fresenius Medical Center Central
state: Colorado
city: Denver
country: United States
facility: Fresenius Medical Center Rocky Mountain
state: Colorado
Study officials:
affiliation: Children's Hospital Colorado
name: Steve Moulton, MD
role: PRINCIPAL_INVESTIGATOR
References: