CASE PRESENTATION: A 23-year-old trauma patient with closed fracture of left femoral shaft and left humerus presented to our emergency department (ED). 11 h after admission to ED, patient became confused, hypoxic and hypotensive. He was then intubated for respiratory failure and mechanically ventilated. Transesophageal ultrasound revealed hyperdynamic heart, dilated right ventricle with no regional wall abnormalities and no major aorta injuries. Whole-body computed tomography was normal. During central venous cannulation of right internal jugular vein (IJV), we found free floating mobile hyperechoic spots, located at the anterior part of the vein. A diagnosis of fat embolism syndrome later was made based on the clinical presentation of long bone fractures and fat globulin in the blood. Despite aggressive fluid resuscitation, patient was a non-responder and needed vasopressor infusion for persistent shock. Blood aspirated during cannulation from the IJV revealed a fat globule. Patient underwent uneventful orthopedic procedures and was discharged well on day 5 of admission.
CONCLUSIONS: Point-of-care ultrasound findings of fat embolism in central vein can facilitate and increase the suspicion of fat embolism syndrome.
MATERIALS AND METHODS: An electronic search of the scientific literature from January 2005 to June 2016 was done using Web of Science, Dentistry & Oral Sciences Source and PubMed databases. A combination of search terms "rapid maxillary expansion", "nasal", "airway" and "breathing" were used. Studies that involved surgical or combined RME-surgical treatments and patients with craniofacial anomalies were excluded.
RESULTS: The initial screening yielded a total of 183 articles. After evaluation of the titles, abstracts and accessing the full text, a total of 20 articles fulfilled both inclusion/exclusion criteria and possessed adequate evidence to be incorporated into this review.
CONCLUSIONS: Non-surgical RME was found to improve breathing, increase nasal cavity geometry and decrease nasal airway resistance in children and adolescents.
METHOD: The CAE model was trained using 12,170,655 simulated SB flow and normal flow data (NB). The paired SB and NB flow data were simulated using a Gaussian Effort Model (GEM) with 5 basis functions. When the CAE model is given a SB flow input, it is capable of predicting a corresponding NB flow for the SB flow input. The magnitude of SB effort (SBEMag) is then quantified as the difference between the SB and NB flows. The CAE model was used to evaluate the SBEMag of 9 pressure control/ support datasets. Results were validated using a mean squared error (MSE) fitting between clinical and training SB flows.
RESULTS: The CAE model was able to produce NB flows from the clinical SB flows with the median SBEMag of the 9 datasets being 25.39% [IQR: 21.87-25.57%]. The absolute error in SBEMag using MSE validation yields a median of 4.77% [IQR: 3.77-8.56%] amongst the cohort. This shows the ability of the GEM to capture the intrinsic details present in SB flow waveforms. Analysis also shows both intra-patient and inter-patient variability in SBEMag.
CONCLUSION: A Convolutional Autoencoder model was developed with simulated SB and NB flow data and is capable of quantifying the magnitude of patient spontaneous breathing effort. This provides potential application for real-time monitoring of patient respiratory drive for better management of patient-ventilator interaction.
METHODS: Gaussian effort model (GEM) is a derivative of the single-compartment model with basis function. GEM model uses a linear combination of basis functions to model the nonlinear pressure waveform of spontaneous breathing patients. The GEM model estimates respiratory mechanics such as Elastance and Resistance along with the magnitudes of basis functions, which accounts for patient inspiratory effort.
RESULTS AND DISCUSSION: The GEM model was tested using both simulated data and a retrospective observational clinical trial patient data. GEM model fitting to the original airway pressure waveform is better than any existing models when reverse triggering asynchrony is present. The fitting error of GEM model was less than 10% for both simulated data and clinical trial patient data.
CONCLUSION: GEM can capture the respiratory mechanics in the presence of patient effect in volume control ventilation mode and also can be used to assess patient-ventilator interaction. This model determines basis functions magnitudes, which can be used to simulate any waveform of patient effort pressure for future studies. The estimation of parameter identification GEM model can further be improved by constraining the parameters within a physiologically plausible range during least-square nonlinear regression.