Aspiration pneumonitis and aspiration pneumonia are clinical syndromes caused by aspiration. These conditions are clinically significant due to their high morbidity and mortality. However, aspiration as a preceding event are often unwitnessed, particularly in cases of asymptomatic or silent aspiration. Furthermore, despite the difference in treatment approaches for managing aspiration pneumonitis and aspiration pneumonia, these two disease entities are often difficult to discriminate from one another, resulting in inappropriate treatment. The use of unclear terminologies hinders the comparability among different studies, making it difficult to produce evidence-based conclusions and practical guidelines. We reviewed the most recent studies to define aspiration, aspiration pneumonitis, and aspiration pneumonia, and to further assess these conditions in terms of incidence and epidemiology, pathophysiology, risk factors, diagnosis, management and treatment, and prevention.

During ECG-guided central venous catheterization, the tallest peaked P wave may be used to place the CVC tip at the SVC/RA junction, the normally-shaped P wave identifies the mid to upper SVC, and biphasic P waves identify RA localization.

Background The discrepancy between predicted effect-site concentration and measured bispectral index is problematic during intravenous anesthesia with target-controlled infusion of propofol and remifentanil. We hypothesized that bispectral index during total intravenous anesthesia would be more accurately predicted by a deep learning approach. Methods Long short-term memory and the feed-forward neural network were sequenced to simulate the pharmacokinetic and pharmacodynamic parts of an empirical model, respectively, to predict intraoperative bispectral index during combined use of propofol and remifentanil. Inputs of long short-term memory were infusion histories of propofol and remifentanil, which were retrieved from target-controlled infusion pumps for 1,800 s at 10-s intervals. Inputs of the feed-forward network were the outputs of long short-term memory and demographic data such as age, sex, weight, and height. The final output of the feed-forward network was the bispectral index. The performance of bispectral index prediction was compared between the deep learning model and previously reported response surface model. Results The model hyperparameters comprised 8 memory cells in the long short-term memory layer and 16 nodes in the hidden layer of the feed-forward network. The model training and testing were performed with separate data sets of 131 and 100 cases. The concordance correlation coefficient (95% CI) were 0.561 (0.560 to 0.562) in the deep learning model, which was significantly larger than that in the response surface model (0.265 [0.263 to 0.266], P < 0.001). Conclusions The deep learning model–predicted bispectral index during target-controlled infusion of propofol and remifentanil more accurately compared to the traditional model. The deep learning approach in anesthetic pharmacology seems promising because of its excellent performance and extensibility.

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During central venous catheterization via the right IJV, landmark guidance was comparable with ECG guidance with regard to CVC tip positioning in the superior vena cava.