Journal of clinical monitoring and computing
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J Clin Monit Comput · Apr 2021
Observational StudyUltrasonography for predicting a difficult laryngoscopy. Getting closer.
Our objective was to evaluate the usefulness of five ultrasound measurements to predict a difficult laryngoscopy (DL). Prospective observational study. 50 patients underwent scheduled surgery under general anesthesia with orotracheal intubation with classical laryngoscopy at the University Hospital of Jaén (Spain). Sociodemographic variables, classic preintubation screening tests and ultrasound measurements of the neck soft tissue from skin to hyoid (DSH), epiglottis (DSE) and glottis (DSG) were obtained, as well as two measurements derived from the above: DSH + DSE and DSE - DSG. ⋯ It was established that DSE ≥ 3 cm, could predict a DL with a positive predictive value (PPV) of 69.23% [95%CI 40.3-98.2], and DSE - DSG ≥ 1.9 cm would do so with a PPV of 78.57% [95%CI 53.31-100%]. The multivariate analysis endorsed that DSE and DSE - DSG combined with classic tests (the Modified Mallampati score, the thyromental distance and the upper lip bite test) improved the preoperative detection of a DL. The inclusion of DSE and DSE - DSG in a multivariate model with classic parameters may offer the anesthesiologist better information for detecting a DL preoperatively.
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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.