Measurement of Hemodynamic Variables Under Spinal Anesthesia With Varied Positioning
NCT02883075
Anesthesia, Pregnancy
Galveston, Texas, United States
View Trial DetailsNCT Number: NCT01699243
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) predict cardiovascular collapse well ahead of any clinically significant changes in standard vital signs, 2) monitor and estimate fluid resuscitation needs, 3) estimate acute blood loss volume, and 4) estimate intracranial pressure. The investigators hypothesize that these same methods can be used to predict functional hypovolemia during regional anesthesia for labor or fetal intervention.
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Notify Me14 year–44 year
Female
Observational
Children's Hospital Colorado, Aurora, Colorado, United States
Specific aims:
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
Time frame: during epidural, 1-4 hours
Develop algorithm for estimating effective intravascular volume loss during maternal regional anesthesia
University of Colorado, Denver
Other
A Continuous, Noninvasive Method for Estimating and Predicting Maternal and Fetal Hemodynamic Changes During Regional Anesthesia
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