Journal of clinical monitoring and computing
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J Clin Monit Comput · Dec 2020
Performance of a capnodynamic method estimating cardiac output during respiratory failure - before and after lung recruitment.
Respiratory failure may cause hemodynamic instability with strain on the right ventricle. The capnodynamic method continuously calculates cardiac output (CO) based on effective pulmonary blood flow (COEPBF) and could provide CO monitoring complementary to mechanical ventilation during surgery and intensive care. The aim of the current study was to evaluate the ability of a revised capnodynamic method, based on short expiratory holds (COEPBFexp), to estimate CO during acute respiratory failure (LI) with high shunt fractions before and after compliance-based lung recruitment. ⋯ Bias (levels of agreement) and percentage error between COEPBFexp and COTS changed from 0.5 (- 0.5 to 1.5) L/min and 30% at HLP5 to - 0.6 (- 2.3 to 1.1) L/min and 39% during LIP5 and finally 1.1 (- 0.3 to 2.5) L/min and 38% at LIPadj. Concordance during CO changes improved from 87 to 100% after lung recruitment and PEEP adjustment. COEPBFexp could possibly be used for continuous CO monitoring and trending in hemodynamically unstable patients with increased shunt and after recruitment manoeuvre.
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J Clin Monit Comput · Dec 2020
Randomized Controlled TrialShortening of the twitch stabilization period by tetanic stimulation in acceleromyography in infants, children and young adults (STSTS-Study): a prospective randomised, controlled trial.
Acceleromyography is characterised by an increase of the twitch response T1 (first twitch of the train-of-four [TOF]) during first 30 min of monitoring known as the staircase phenomenon. In adults the staircase phenomenon can be avoided by tetanic prestimulation. This study examined, if tetanic prestimulation eliminates the staircase phenomenon in children. ⋯ Tetanic prestimulation prevents the staircase phenomenon in these age groups. The stability of the TOFR reading confirms its value to monitor neuromuscular function over time. Registration: The study was registered as NCT02552875 on Clinical Trials.gov on July 29, 2014.
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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
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.