Predicting Time to Death after Withdrawal of Life Sustaining Measures using Variability Analysis of Vital Signs Waveform Data
Investigators: Nathan Scales, Ottawa Hospital Research Institute; Christophe Herry, Ottawa Hospital Research Institute; Laura Hornby, Children's Hospital of Eastern Ontario; Sonny Dhanani, Children's Hospital of Eastern Ontario; Andrew Seely, The Ottawa Hospital
Purpose:
To our knowledge, few studies have evaluated the predictive value of variability (HRV and BPV) in determining whether a patient will die within the timelines required for organ donation after withdrawal of life sustaining measures (WLSM). In this present study, we aim to derive a prediction model combining continuous HRV and BPV monitoring to predict time to death for potential organ donors using data from the Death Physiology and Prediction after Removal of Therapies (DePPaRT) study, an international study that included the capture of waveform data from patients prior to and after the removal of life supporting measures. We compare the results of our model with a model employing the clinical features used in the models by Brieva et al. (2013).
To our knowledge, few studies have evaluated the predictive value of variability (HRV and BPV) in determining whether a patient will die within the timelines required for organ donation after withdrawal of life sustaining measures (WLSM). In this present study, we aim to derive a prediction model combining continuous HRV and BPV monitoring to predict time to death for potential organ donors using data from the Death Physiology and Prediction after Removal of Therapies (DePPaRT) study, an international study that included the capture of waveform data from patients prior to and after the removal of life supporting measures. We compare the results of our model with a model employing the clinical features used in the models by Brieva et al. (2013).
