Estimating and Predicting Hemodynamic Changes During Hemodialysis

NCT ID: NCT01700465

Last Updated: 2016-12-05

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

241 participants

Study Classification

OBSERVATIONAL

Study Start Date

2012-09-30

Study Completion Date

2016-12-31

Brief Summary

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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

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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

Conditions

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Hemodialysis

Keywords

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Hemodialysis Hemodynamics

Study Design

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Observational Model Type

CASE_ONLY

Study Time Perspective

PROSPECTIVE

Study Groups

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Hemodialysis

Patients undergoing hemodialysis

No interventions assigned to this group

Eligibility Criteria

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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
Minimum Eligible Age

2 Years

Maximum Eligible Age

89 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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University of Colorado, Denver

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Steve Moulton, MD

Role: PRINCIPAL_INVESTIGATOR

Children's Hospital Colorado

Locations

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Fresenius Medical Center East Denver

Aurora, Colorado, United States

Site Status

Children's Hospital Colorado

Aurora, Colorado, United States

Site Status

University of Colorado Hospital

Aurora, Colorado, United States

Site Status

Fresenius Medical Center Central

Denver, Colorado, United States

Site Status

Fresenius Medical Center Rocky Mountain

Denver, Colorado, United States

Site Status

Countries

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United States

Other Identifiers

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11-1437

Identifier Type: -

Identifier Source: org_study_id