No abstract

We performed a case-controlled, double-blind study to examine the performance of three multivariate clinical models (Wilson, Arné, and Naguib models) in the prediction of unanticipated difficult intubation. The study group consisted of 97 patients in whom an unanticipated difficult intubation had occurred. For each difficult intubation patient, a matched control patient was selected in whom tracheal intubation had been easily accomplished. Postoperatively, a blinded investigator evaluated both patients. The clinical assessment included the patient's weight, height, age, Mallampati score, interincisor gap, thyromental distance, thyrosternal distance, neck circumference, Wilson risk sum score, history of previous difficult intubation, and diseases associated with difficult laryngoscopy or intubation. The Naguib model was significantly more sensitive (81.4%; P < 0.0001) than the Arné (54.6%) or Wilson (40.2%) models. Both the Naguib (76.8%) and Arné (74.7%) model classified more intubations correctly (P = 0.01) than the Wilson model (66.5%). The specificity of Arné, Wilson, and Naguib model was 94.9%, 92.8%, and 72.2%, respectively (P < 0.0001). The corresponding area under the receiver operating characteristic curve was 0.87, 0.79, and 0.82, respectively. Our new model for prediction of difficult intubation was developed using logistic regression and includes thyromental distance, Mallampati score, interincisor gap, and height. This model is 82.5% sensitive and 85.6% specific with an area under the receiver operating characteristic curve of 0.90.

Combined Nd:YAG laser incision and balloon dilation in an awake, spontaneously breathing patient is a safe and effective management tool in the treatment of laryngotracheostenosis. This technique may be particularly beneficial in patients who are at increased risk for general anesthesia such as those with morbid obesity or who have had a history of airway problems during anesthesia.

In conclusion, micro-CT imaging provides a unique platform for in vivo longitudinal assessment of pulmonary lung cancer progression and potentially tracking of therapies at very high resolutions. The ability to evaluate the same subject over time provides for a sensitive assay that can be carried out on a smaller sample size. When integrated with image processing and analysis routines as detailed in this study, the data acquired from micro-CT imaging can now provide a very powerful assessment of pulmonary disease outcomes.

Respiratory research with mice using micro-computed tomography (micro-CT) has been predominantly hindered by the limited resolution and signal-to-noise ratio (SNR) as a result of respiratory motion artefacts. In this study, we develop a novel technique for capturing the lung microstructure in vivo using micro-CT, through a computer-controlled intermittent iso-pressure breath hold (IIBH), to reduce respiratory motion, increasing resolution and SNR of reconstructed images. We compare four gating techniques, i.e. no gating, late expiratory (LE) gating, late inspiratory (LI) gating and finally intermittent iso-pressure breath hold (IIBH) gating. Quantitatively, we compare several common image analysis methods used to extract valuable physiologic and anatomic information from the respiratory system, and show that the IIBH technique produces the most representative and repeatable results.