Journal of clinical monitoring and computing
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J Clin Monit Comput · Feb 2022
Frontal electroencephalogram based drug, sex, and age independent sedation level prediction using non-linear machine learning algorithms.
Brain monitors which track quantitative electroencephalogram (EEG) signatures to monitor sedation levels are drug and patient specific. There is a need for robust sedation level monitoring systems to accurately track sedation levels across all drug classes, sex and age groups. Forty-four quantitative features estimated from a pooled dataset of 204 EEG recordings from 66 healthy adult volunteers who received either propofol, dexmedetomidine, or sevoflurane (all with and without remifentanil) were used in a machine learning based automated system to estimate the depth of sedation. ⋯ Nonlinear machine-learning models using quantitative EEG features can accurately predict sedation levels. The results obtained in this study may provide a useful reference for developing next generation EEG based sedation level prediction systems using advanced machine learning algorithms. Clinical trial registration: NCT02043938 and NCT03143972.
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J Clin Monit Comput · Feb 2022
Anesthetics affect peripheral venous pressure waveforms and the cross-talk with arterial pressure.
Analysis of peripheral venous pressure (PVP) waveforms is a novel method of monitoring intravascular volume. Two pediatric cohorts were studied to test the effect of anesthetic agents on the PVP waveform and cross-talk between peripheral veins and arteries: (1) dehydration setting in a pyloromyotomy using the infused anesthetic propofol and (2) hemorrhage setting during elective surgery for craniosynostosis with the inhaled anesthetic isoflurane. PVP waveforms were collected from 39 patients that received propofol and 9 that received isoflurane. ⋯ The k-NN prediction models had 82% and 77% accuracy for detecting propofol and MAC, respectively. The cross-talk relationship at each stage was: (a) ρ = 0.95, (b) ρ = 0.96, and (c) could not be evaluated using this cohort. Future research should consider anesthetic agents when analyzing PVP waveforms developing future clinical monitoring technology that uses PVP.
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J Clin Monit Comput · Feb 2022
Intraoperative evoked potentials in patients with ossification of posterior longitudinal ligament.
Preoperative somatosensory evoked potentials (preSEPs) are used to evaluate the severity of myelopathy, and intraoperative neurophysiological monitoring (IONM) is used to reduce iatrogenic damage during operations. However, the correlation between preSEPs and IONM on postoperative neurologic deterioration (PND) in ossification of the posterior longitudinal ligament (OPLL) has not been studied. Thus, under the hypothesis that the patients with deteriorated preSEPs would be more likely to have significant changes in intraoperative SEPs (ioSEPs), and that this would be correlated with PND, we investigated the prognostic value of preSEPs on IONM and PND. ⋯ There was a positive correlation between amount of blood loss and maximum percentage of ioSEPs latency prolongation and a negative correlation with PMD at 48 h and 4 weeks postoperatively. PreSEPs predict significant changes in ioSEPs. Furthermore, bleeding control is important to reduce PMD in OPLL.
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J Clin Monit Comput · Feb 2022
Observational StudyEvaluation of respiratory rate monitoring using a microwave Doppler sensor mounted on the ceiling of an intensive care unit: a prospective observational study.
Continuous monitoring of the respiratory rate is crucial in an acute care setting. Contact respiratory monitoring modalities such as capnography and thoracic impedance pneumography are prone to artifacts, causing false alarms. Moreover, their cables can restrict patient behavior or interrupt patient care. ⋯ Compared to visual counting, the microwave Doppler sensor showed small bias; however, the limits of agreement were similar to those observed in other conventional methods. Our monitor and the conventional ones are not interchangeable with visual counting. Trial registration number: UMIN000032021, March/30/2018.
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J Clin Monit Comput · Feb 2022
Clinical evaluation of a wearable sensor for mobile monitoring of respiratory rate on hospital wards.
A wireless and wearable system was recently developed for mobile monitoring of respiratory rate (RR). The present study was designed to compare RR mobile measurements with reference capnographic measurements on a medical-surgical ward. The wearable sensor measures impedance variations of the chest from two thoracic and one abdominal electrode. ⋯ Error grid analysis showed that the proportions of RR measurements done with the wearable system were 89.7% in zone A (no risk), 9.6% in zone B (low risk) and < 1% in zones C, D and E (moderate, significant and dangerous risk). The wearable method detected RR values > 20 (tachypnea) with a sensitivity of 81% and a specificity of 93%. In ward patients, the wearable sensor enabled accurate and precise measurements of RR within a relatively broad range (6-36 b/min) and the detection of tachypnea with high sensitivity and specificity.