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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) 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.


Clinical Trial Description

Specific aims: 1. Collect noninvasive physiological waveform data from patients undergoing regional anesthesia for labor or fetal intervention at the University of Colorado Hospital and Children's Hospital Colorado. 2. Combine the physiological data from patient monitors with clinical and demographic data, including maternal problem list, medications, volume infused, use of vasopressors, arterial and venous pressures, fetal heart rate, fetal umbilical artery Doppler velocimetry, maternal uterine artery Doppler waveform, fetal and neonatal outcomes etc. for use in developing mathematical model for early detection of maternal functional hypovolemia. 3. Develop robust, real-time, computational models for: - estimating maternal volume status prior to administration of epidural anesthesia - estimating effective intravascular volume loss during maternal regional anesthesia - predicting an optimal, individual specific requirement for IV resuscitation and/or need for vasopressor agents while providing adequate analgesia using regional techniques and optimizing the fetal outcomes - identifying mothers susceptible to epidural induced hypotension ;


Study Design


Related Conditions & MeSH terms


NCT number NCT01699243
Study type Observational
Source University of Colorado, Denver
Contact
Status Completed
Phase
Start date September 1, 2012
Completion date June 1, 2016

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