BACKGROUND: Numerous studies have investigated the best method of selecting the appropriate size of endotracheal tube (ETT) for children. However, none of the methods or formulae for selection of ETT size have shown better prediction over another, and they have required complex formulae calculation or even use of cumbersome equipment. Recursive partitioning analysis creates a decision tree that is more likely to enable clearer and easier visualization of decision charts compared to other data mining methods. OBJECTIVES: The aim of the current study was to develop a clinically practical and intuitive chart for prediction of ETT size. METHODS: Pediatric patients aged 2-9 years undergoing general anesthesia were intubated with uncuffed ETT. The tube size was considered optimal when a tracheal leak was detected at an inflation pressure between 10 and 25 cmH2 O. The observed ETT size was compared with the predicted ETT size calculated using Cole's formula, multivariate regression analysis, ultrasonographic measurements, and recursive partitioning tree structure analysis. Preference among the prediction methods was also investigated by asking physicians about their preference of methods. RESULTS: Correct prediction rates were 33.3%, 50%, 61.9%, and 59.5%, and close prediction rates were 61.9%, 83.3%, 88.1%, and 93.7% for Cole's formulae, multivariate regression analysis, ultrasonographic measurements, and recursive partitioning tree model, respectively. Fourteen of 16 physicians prefer to use the easy-to-interpret tree model. CONCLUSIONS: Analysis of the tree model by recursive partitioning structure analysis accomplished a high correct and close prediction rate for selection of an appropriate ETT size. The intuitive and easy-to-interpret tree model would be a quick and helpful tool for selection of an ETT tube for pediatric patients.
BACKGROUND: Numerous studies have investigated the best method of selecting the appropriate size of endotracheal tube (ETT) for children. However, none of the methods or formulae for selection of ETT size have shown better prediction over another, and they have required complex formulae calculation or even use of cumbersome equipment. Recursive partitioning analysis creates a decision tree that is more likely to enable clearer and easier visualization of decision charts compared to other data mining methods. OBJECTIVES: The aim of the current study was to develop a clinically practical and intuitive chart for prediction of ETT size. METHODS: Pediatric patients aged 2-9 years undergoing general anesthesia were intubated with uncuffed ETT. The tube size was considered optimal when a tracheal leak was detected at an inflation pressure between 10 and 25 cmH2 O. The observed ETT size was compared with the predicted ETT size calculated using Cole's formula, multivariate regression analysis, ultrasonographic measurements, and recursive partitioning tree structure analysis. Preference among the prediction methods was also investigated by asking physicians about their preference of methods. RESULTS: Correct prediction rates were 33.3%, 50%, 61.9%, and 59.5%, and close prediction rates were 61.9%, 83.3%, 88.1%, and 93.7% for Cole's formulae, multivariate regression analysis, ultrasonographic measurements, and recursive partitioning tree model, respectively. Fourteen of 16 physicians prefer to use the easy-to-interpret tree model. CONCLUSIONS: Analysis of the tree model by recursive partitioning structure analysis accomplished a high correct and close prediction rate for selection of an appropriate ETT size. The intuitive and easy-to-interpret tree model would be a quick and helpful tool for selection of an ETT tube for pediatric patients.