Journal of clinical monitoring and computing
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J Clin Monit Comput · Dec 2024
Observational StudyPost-anesthesia care unit hypotension in low-risk patients recovering from non-cardiac surgery: a prospective observational study.
Intraoperative hypotension is common and associated with organ injury. Hypotension can not only occur during surgery, but also thereafter. After surgery, most patients are treated in post-anesthesia care units (PACU). ⋯ The median volume of crystalloid fluid patients were given during PACU treatment was 200 (100 to 400) ml. None was given colloids or a vasopressor during PACU treatment. In low-risk patients recovering from non-cardiac surgery, the incidence of PACU hypotension was very low and the few episodes of PACU hypotension were short and of modest severity.
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J Clin Monit Comput · Dec 2024
Intraoperative use of processed electroencephalogram in a quaternary center: a quality improvement audit.
Although intraoperative electroencephalography (EEG) is not consensual among anesthesiologists, growing evidence supports its use to titrate anesthetic drugs, assess the level of arousal/consciousness, and detect ischemic cerebrovascular events; in addition, intraoperative EEG monitoring may decrease the incidence of postoperative neurocognitive disorders. Based on the known and potential benefits of intraoperative EEG monitoring, an educational program dedicated to staff anesthesiologists, residents of Anesthesiology and anesthesia technicians was started at Cleveland Clinic Abu Dhabi in May 2022 and completed in June 2022, aiming to have all patients undergoing general anesthesia with adequate brain monitoring and following international initiatives promoting perioperative brain health. All the surgical cases performed under General Anesthesia at 24 daily locations were prospectively inspected during 15 consecutive working days in March 2023. ⋯ Of note, in the Neuroradiology suite, no processed EEG monitor was used in cases under General Anesthesia. We identified a reasonable use of EEG monitoring during general anesthesia, unfortunately not reaching our target of 100%. The educational and support program previously implemented within the Anesthesiology Institute needs to be continued and improved, including workshops, online discussions, and journal club sessions, to increase the use of EEG monitoring in underused areas.
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J Clin Monit Comput · Dec 2024
Relationships between the qNOX, qCON, burst suppression ratio, and muscle activity index of the CONOX monitor during total intravenous anesthesia: a pilot study.
Processed electroencephalographic (EEG) indices can help to navigate general anesthesia. The CONOX (Fresenius Kabi) calculates two indices, the qCON (hypnotic level) and the qNOX (nociception). The CONOX also calculates indices for electromyographic (EMG) activity and EEG burst suppression (BSR). Because all EEG parameters seem to influence each other, our goal was a detailed description of parameter relationships. ⋯ We could describe relationships between qCON, qNOX, EMG, BSR, ceP, and ceR, which may help the anaesthesiologist better interpret the information provided. One major finding is the dependence of qCON > 80 on EMG activity. This may limit the possibility of detecting wakefulness in the absence of EMG. Further, qNOX seems generally higher than qCON, but high opioid doses may lead to higher qCON than qNOX indices.
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J Clin Monit Comput · Dec 2024
Imitating the respiratory activity of the brain stem by using artificial neural networks: exploratory study on an animal model of lactic acidosis and proof of concept.
Artificial neural networks (ANNs) are versatile tools capable of learning without prior knowledge. This study aims to evaluate whether ANN can calculate minute volume during spontaneous breathing after being trained using data from an animal model of metabolic acidosis. Data was collected from ten anesthetized, spontaneously breathing pigs divided randomly into two groups, one without dead space and the other with dead space at the beginning of the experiment. ⋯ The best architecture of ANN had 17 intermediate neurons; the best performance of the finally trained ANN had a linear regression with R2 of 0.99, an MSE of 0.001 [L/min], a B-A analysis with bias ± standard deviation of 0.006 ± 0.039 [L/min]. ANNs can accurately estimate ΔVM using the same information that arrives at the respiratory centers. This performance makes them a promising component for the future development of closed-loop artificial ventilators.