Anesthesia and analgesia
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Anesthesia and analgesia · May 2020
Parsimony of Hemodynamic Monitoring Data Sufficient for the Detection of Hemorrhage.
Individualized hemodynamic monitoring approaches are not well validated. Thus, we evaluated the discriminative performance improvement that might occur when moving from noninvasive monitoring (NIM) to invasive monitoring and with increasing levels of featurization associated with increasing sampling frequency and referencing to a stable baseline to identify bleeding during surgery in a porcine model. ⋯ Increasing hemodynamic monitoring featurization by increasing sampling frequency and referencing to personal baseline markedly improves the ability of invasive monitoring to detect bleed.
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Anesthesia and analgesia · May 2020
Predicting Deep Hypnotic State From Sleep Brain Rhythms Using Deep Learning: A Data-Repurposing Approach.
Brain monitors tracking quantitative brain activities from electroencephalogram (EEG) to predict hypnotic levels have been proposed as a labor-saving alternative to behavioral assessments. Expensive clinical trials are required to validate any newly developed processed EEG monitor for every drug and combinations of drugs due to drug-specific EEG patterns. There is a need for an alternative, efficient, and economical method. ⋯ We propose a novel method to develop hypnotic level monitors using large sleep EEG data, deep learning, and a data-repurposing approach, and for optimizing such a system for monitoring any given individual. We provide a novel data-repurposing framework to predict hypnosis levels using sleep EEG, eliminating the need for new clinical trials to develop hypnosis level monitors.
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Anesthesia and analgesia · May 2020
An Automated Algorithm Incorporating Poincaré Analysis Can Quantify the Severity of Opioid-Induced Ataxic Breathing.
Opioid-induced respiratory depression (OIRD) is traditionally recognized by assessment of respiratory rate, arterial oxygen saturation, end-tidal CO2, and mental status. Although an irregular or ataxic breathing pattern is widely recognized as a manifestation of opioid effects, there is no standardized method for assessing ataxic breathing severity. The purpose of this study was to explore using a machine-learning algorithm for quantifying the severity of opioid-induced ataxic breathing. We hypothesized that domain experts would have high interrater agreement with each other and that a machine-learning algorithm would have high interrater agreement with the domain experts for ataxic breathing severity assessment. ⋯ We concluded it may be feasible for a machine-learning algorithm to quantify ataxic breathing severity in a manner consistent with a panel of domain experts. This methodology may be helpful in conjunction with traditional measures to identify patients experiencing OIRD.