Journal of clinical monitoring and computing
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J Clin Monit Comput · Dec 2020
Randomized Controlled TrialHierarchical Poincaré analysis for anaesthesia monitoring.
Although the degree of dispersion in Poincaré plots of electroencephalograms (EEG), termed the Poincaré-index, detects the depth of anaesthesia, the Poincaré-index becomes estranged from the bispectral index (BIS) at lighter anaesthesia levels. The present study introduces Poincaré-index20-30 Hz, targeting the 20- to 30-Hz frequency, as the frequency range reported to contain large electromyogram (EMG) portions in frontal EEG. We combined Poincaré-index20-30 Hz with the conventional Poincaré-index0.5-47 Hz using a deep learning technique to adjust to BIS values, and examined whether this layered Poincaré analysis can provide an index of anaesthesia level like BIS. ⋯ We then evaluated the trained MLPNN model using the test dataset, by comparing the measured BIS (mBIS) with BIS predicted from the model (PredBIS). The relationship between mBIS and PredBIS using the two Poincaré-indices showed a tight linear regression equation: mBIS = 1.00 × PredBIS + 0.15, R = 0.87, p < 0.0001, root mean square error (RMSE) = 7.09, while the relationship between mBIS and PredBIS simply using the original Poincaré-index0.5-47 Hz was weaker (R = 0.82, p < 0.0001, RMSE = 7.32). This suggests the 20- to 30-Hz hierarchical Poincaré analysis has potential to improve on anaesthesia depth monitoring constructed by simple Poincaré analysis.
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J Clin Monit Comput · Dec 2020
Heart rate variability and surgical pleth index under anesthesia in poor and normal sleepers.
Poor sleep quality is associated with autonomic dysfunctions and altered pain perception and tolerance. To investigate whether autonomic dysregulations related to insomnia would still exist under general anesthesia, we adopt heart rate variability (HRV) analysis to evaluate ANS activity and surgical pleth index (SPI) to compare nociceptive/anti-nociceptive balance. We enrolled 61 adult females scheduled for gynecological surgeries under general anesthesia. ⋯ Patients with different sleep qualities did not exhibit different SPI levels in all four periods. Poor sleepers exhibited attenuated parasympathetic activities at the baseline but no differences after the induction. Nociceptive/anti-nociceptive balance seems not be altered by poor sleep condition under general anesthesia.
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J Clin Monit Comput · Dec 2020
Reliability of B-line quantification by different-level observers and a software algorithm using point-of-care lung ultrasound.
Quantification of B-lines on lung ultrasonographs is operator-dependent and considered a semi-quantitative method. To avoid this variability, we designed a software algorithm for counting B-lines. We compared the number of B-lines obtained in real-time by observers with three different levels of experience and by the software algorithm, and analyzed intra-rater variability in terms of the estimated number of B-lines in two successive examinations. ⋯ For all lung zones, the intraclass correlation for B-lines counting between OB1 and OB2 was 0.663; between OB1 and OB3, 0.559; and between OB1 and OBS, 0.710. OBS had a better concordance coefficient (0.752) between the first and the second measurements than did the human observers. Our results show that the software algorithm for B-lines counting is a potentially promising alternative when observers have little lung ultrasound experience.
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J Clin Monit Comput · Dec 2020
Validation of an automated system for detecting ineffective triggering asynchronies during mechanical ventilation: a retrospective study.
We compare the sensitivity and specificity of clinician visual waveform analysis against an automated system's waveform analysis in detecting ineffective triggering in mechanically ventilated intensive care unit patients when compared against a reference label set based upon analysis of respiratory muscle activity. Electrical activity of the diaphragm or esophageal/transdiaphragmatic pressure waveforms were available to a single clinician for the generation of a reference label set indicating the ground truth, that is, presence or absence of ineffective triggering, on a breath-by-breath basis. Pressure and flow versus time tracings were made available to (i) a group of three clinicians; and (ii) the automated Syncron-E™ system capable of detecting patient-ventilator asynchrony in real-time, in order to obtain breath-by-breath labels indicating the presence or absence of ineffective triggering. ⋯ Specificity for clinicians and the automated system were high (99.3% for clinician and 98.5% for the automated system). The automated system had a significantly higher sensitivity (83.2%) compared to clinicians (41.1%). Ineffective triggering detected by the automated system, which has access only to airway pressure and flow versus time tracings, is in substantial agreement with a reference detection derived from analysis of invasively measured patient effort waveforms.
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J Clin Monit Comput · Dec 2020
Multicenter StudyEffects of a standardized distraction on caregivers' perceptive performance with avatar-based and conventional patient monitoring: a multicenter comparative study.
Patient monitoring requires constant attention and may be particularly vulnerable to distractions, which frequently occur during perioperative work. In this study, we compared anesthesia providers' perceptive performance and perceived workload under distraction for conventional and avatar-based monitoring, a situation awareness-based technology that displays patient status as an animated patient model. In this prospective, multicenter study with a within-subject design, 38 participants evaluated scenarios of 3- and 10-s durations using conventional and avatar-based monitoring, under standardized distraction in the form of a simple calculation task. ⋯ Participants rated perceived workload lower under distraction with the avatar in the 3-s scenario: 65 (IQR 40-79) vs. 75 (IQR 51-88), p = 0.007, MoD: 9 (95% CI 3 to 15), and in the 10-s scenario: 68 (IQR 50-80) vs. 75 (IQR 65-86), p = 0.019, MoD: 10 (95% CI 2 to 18). Avatar-based monitoring improved anesthesia providers' perceptive performance under distraction and reduced perceived workload. This technology could help to improve caregivers' situation awareness, especially in high-workload situations.