Predicting optimal endotracheal tube size and depth in pediatric patients using demographic data and machine learning techniques

Artificial intelligence in anesthesiology: a bibliometric analysis

The application of artificial intelligence (AI) in anesthesiology has become increasingly widespread. However, no previous study has analyzed this field from the bibliometric analysis dimension. The objective of this paper was to assess the global research trends in AI in anesthesiology using bibliometric software. Literatures relevant to AI and anesthesiology were retrieved from the Web of Science until 10 April 2024 and were visualized and analyzed using Excel, CiteSpace, and VOSviewer. After screening, 491 studies were included in the final bibliometric analysis. The growth rate of publications, countries, institutions, authors, journals, literature co-citations, and keyword co-occurrences was computed. The number of publications increased annually since 2018, with the most significant contributions from the USA, China, and England. The top 3 institutions were Yuan Ze University, National Taiwan University, and Brunel University London. The top three journals were Anesthesia & Analgesia, BMC Anesthesiology, and the British Journal of Anaesthesia. The researches on the application of AI in predicting hypotension have been extensive and represented a hotspot and frontier. In terms of keyword co-occurrence cluster analysis, keywords were categorized into four clusters: ultrasound-guided regional anesthesia, postoperative pain and airway management, prediction, depth of anesthesia (DoA), and intraoperative drug infusion. This analysis provides a systematic analysis on the literature regarding the AI-related research in the field of anesthesiology, which may help researchers and anesthesiologists better understand the research trend of anesthesia-related AI.

The development of prediction model for cuffed tracheal tube size from the middle finger in pediatrics: a concise and feasible approach

Background

Selecting the optimal tracheal tube size is critically important for pediatric patients. Age-based formulas are often used, but still have limitations. The aim of this prospective study was to investigate whether middle finger measurements correlate with cuffed tracheal tube size and to further develop a prediction model based on these measurements.

Methods

Patients under 12 years of age scheduled for elective surgery involving tracheal intubation were enrolled in the study. The length was determined from the tip of the distal metacarpal to the palm's root on the palm side, while the circumference was measured at the base of the palm using a soft tape measure. The appropriate cuffed tracheal tube size was determined based on specific criteria. If the tube encountered resistance during insertion or required an airway pressure >25 cmH2O to detect an audible leak, it was replaced with a tube 0.5 mm smaller. Conversely, if an audible leak occurred at an airway pressure <10 cmH2O, or peak pressure >25 cmH2O, or the cuff pressure >25 cmH2O to achieve a seal, the tube was exchanged for one with a 0.5 mm larger. Linear regression analysis was used to examine the association between middle finger circumference and length with the cuffed tracheal tube size. Subsequently, regression equations were constructed based on the results of the linear regression analysis and their predictive performance was compared to the conventional age-based formulas, including the Khine formula and Motoyama formula. The predictive performance was evaluated by mean absolute error (MAE), root mean square error (RMSE), and prediction accuracy.

Results

A total of 261 patients were analyzed in our study. The mean age of the patients was 46.19±35.83 months. The linear relationship was observed between the cuffed tracheal tube size and the middle finger circumference and middle finger length with R2 values of 0.77 and 0.73, respectively. In comparison to conventional age-based formulas, both middle finger circumference and middle finger length demonstrated superior predictive performance, characterized by lower MAE and RMSE, as well as higher prediction accuracy. Notably, the regression equation based on the middle finger circumference obtained the higher predictive accuracy of 0.590, with an MAE of 0.259 and an RMSE of 0.333 as opposed to the predictive accuracy of 0.391, MAE of 0.349, and RMSE of 0.473 derived from conventional age-based formulas. Based on the regression coefficients of linear regression, simplified formulas were proposed, with the middle finger circumference-based formula emerging as the most accurate and simple option.

Conclusions

The appropriate cuffed tracheal tube size could be predicted by the middle finger circumference. Our proposed formula 'cuffed tracheal tube internal diameter (mm) = middle finger circumference (cm) - 0.2' has the potential to improve the selection of the cuffed tracheal tube size in pediatric patients.