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Estimating and Predicting Hemodynamic Changes During Hemodialysis

NCT01700465 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 241

Last updated 2016-12-05

No results posted yet for this study

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.

Conditions

  • Hemodialysis

Sponsors & Collaborators

  • University of Colorado, Denver

    lead OTHER

Principal Investigators

  • Steve Moulton, MD · Children's Hospital Colorado

Eligibility

Min Age
2 Years
Max Age
89 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2012-09-30
Primary Completion
2016-12-31
Completion
2016-12-31

Countries

  • United States

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT01700465 on ClinicalTrials.gov