Journal of clinical monitoring and computing
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J Clin Monit Comput · Apr 2021
Randomized Controlled TrialPhotoplethysmography-derived approximate entropy and sample entropy as measures of analgesia depth during propofol-remifentanil anesthesia.
The ability to monitor the physiological effect of the analgesic agent is of interest in clinical practice. Nonstationary changes would appear in photoplethysmography (PPG) during the analgesics-driven transition to analgesia. The present work studied the properties of nonlinear methods including approximate entropy (ApEn) and sample entropy (SampEn) derived from PPG responding to a nociceptive stimulus under various opioid concentrations. ⋯ The result showed that low Ceremi (0 and 1 ng·ml-1) could be differentiated from high Ceremi (3 and 5 ng·ml-1) by ApEn and SampEn. Depending on the coefficient employed in algorithm: ApEn with k = 0.15 yielded the largest PK value (0.875) whereas SampEn gained its largest PK of 0.867 with k = 0.2. Thus, PPG-based ApEn and SampEn with appropriate k values have the potential to offer good quantification of analgesia depth under general anesthesia.
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J Clin Monit Comput · Apr 2021
Observational StudyOscillometric versus invasive blood pressure measurement in patients with shock: a prospective observational study in the emergency department.
In emergency medicine, blood pressure is often measured by an oscillometric device using an upper arm cuff. However, measurement accuracy of this technique in patients suffering from hypotensive shock has not been sufficiently evaluated. We designed a prospective observational study investigating the accuracy of an oscillometric device in hypotensive patients admitted to the resuscitation area of the emergency department. ⋯ In 64% of readings, values obtained by the upper arm cuff were not able to detect hypotension. Oscillometric blood pressure measurement is not able to reliably detect hypotension in emergency patients. Therefore, direct measurement of blood pressure should be established as soon as possible in patients suffering from shock.
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J Clin Monit Comput · Apr 2021
An effective pressure-flow characterization of respiratory asynchronies in mechanical ventilation.
Ineffective effort during expiration (IEE) occurs when there is a mismatch between the demand of a mechanically ventilated patient and the support delivered by a Mechanical ventilator during the expiration. This work presents a pressure-flow characterization for respiratory asynchronies and validates a machine-learning method, based on the presented characterization, to identify IEEs. 1500 breaths produced by 8 mechanically-ventilated patients were considered: 500 of them were included into the training set and the remaining 1000 into the test set. Each of them was evaluated by 3 experts and classified as either normal, artefact, or containing inspiratory, expiratory, or cycling-off asynchronies. ⋯ The software classified IEEs with sensitivity 0.904, specificity 0.995, accuracy 0.983, positive and negative predictive value 0.963 and 0.986, respectively. The Cohen's kappa is 0.983 (with 95% confidence interval (CI): [0.884, 0.962]). The pressure-flow characterization of respiratory cycles and the monitoring technique proposed in this work automatically identified IEEs in real-time in close agreement with the experts.
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J Clin Monit Comput · Apr 2021
A physiology-based mathematical model for the selection of appropriate ventilator controls for lung and diaphragm protection.
Mechanical ventilation is used to sustain respiratory function in patients with acute respiratory failure. To aid clinicians in consistently selecting lung- and diaphragm-protective ventilation settings, a physiology-based decision support system is needed. To form the foundation of such a system, a comprehensive physiological model which captures the dynamics of ventilation has been developed. ⋯ Finally, the model is seen to be able to provide robust predictions of esophageal pressure, transpulmonary pressure and blood pH for patient parameters with realistic variability. The LDPV model is a robust physiological model which produces outputs which directly target and reflect the risk of ventilator-induced lung and diaphragm injury. Ventilation and sedation parameters are seen to modulate the model outputs in accordance with what is currently known in literature.