Estimating and Predicting Hemodynamic Changes During Hemodialysis

Hemodialysis Clinical Trial

Official title:

Estimating and Predicting Hemodynamic Changes During Hemodialysis

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT01700465
• Other study ID #: 11-1437
• Status: Completed
• Phase: N/A
• First received: September 27, 2012
• Last updated: December 1, 2016
• Start date: September 2012
• Est. completion date: December 2016

Study information

• Verified date: December 2016
• Source: University of Colorado, Denver
• Contact: n/a
• Is FDA regulated: No
• Health authority: United States: Institutional Review Board
• Study type: Observational

Clinical Trial 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.

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

Recruitment information / eligibility

• Status: Completed
• Enrollment: 241
• Est. completion date: December 2016
• Est. primary completion date: December 2016
• Accepts healthy volunteers: No
• Gender: Both
• Age group: 2 Years to 89 Years

Eligibility

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

Study Design

Observational Model: Case-Only, Time Perspective: Prospective

Related Conditions & MeSH terms

• Hemodialysis

Locations

Country	Name	City	State
United States	Children's Hospital Colorado	Aurora	Colorado
United States	Fresenius Medical Center East Denver	Aurora	Colorado
United States	University of Colorado Hospital	Aurora	Colorado
United States	Fresenius Medical Center Central	Denver	Colorado
United States	Fresenius Medical Center Rocky Mountain	Denver	Colorado

Sponsors (1)

Lead Sponsor	Collaborator
University of Colorado, Denver

Country where clinical trial is conducted

United States, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Acute intravascular volume loss during hemodialysis	development of algorithm to estimate acute intravascular volume loss during hemodialysis	one hemodialysis session (approx 3-4 hours)	No