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Anesthesiology. 2010 Oct;113(4):819-24. doi: 10.1097/ALN.0b013e3181ef6757.

Prediction of pediatric endotracheal tube size by ultrasonography.

Author information

1
Department of Anesthesiology and Intensive Care, Kyoto Prefectural University of Medicine, Kyoto, Japan. mshiba@koto.kpu-m.ac.jp

Abstract

BACKGROUND:

Formulas based on age and height often fail to reliably predict the proper endotracheal tube (ETT) size in pediatric patients. We, thus, tested the hypothesis that subglottic diameter, as determined by ultrasonography, better predicts optimal ETT size than existing methods.

METHODS:

A total of 192 patients, aged 1 month to 6 yr, who were scheduled for surgery and undergoing general anesthesia were enrolled and divided into development and validation phases. In the development group, the optimal ETT size was selected according to standard age-based formulas for cuffed and uncuffed tubes. Tubes were replaced as necessary until a good clinical fit was obtained. Via ultrasonography, the subglottic upper airway diameter was determined before tracheal intubation. We constructed a regression equation between the subglottic upper airway diameter and the outer diameter of the ETT finally selected. In the validation group, ETT size was selected after ultrasonography using this regression equation. The primary outcome was the fraction of initial cuffed and uncuffed tube sizes, as selected through the regression formula, that proved clinically optimal.

RESULTS:

Subglottic upper airway diameter was highly correlated with outer ETT diameter deemed optimal on clinical grounds. The rate of agreement between the predicted ETT size based on ultrasonic measurement and the final ETT size selected clinically was 98% for cuffed ETTs and 96% for uncuffed ETTs.

CONCLUSIONS:

Measuring subglottic airway diameter with ultrasonography facilitates the selection of appropriately sized ETTs in pediatric patients. This selection method better predicted optimal outer ETT diameter than standard age- and height-based formulas.

PMID:
20808208
DOI:
10.1097/ALN.0b013e3181ef6757
[Indexed for MEDLINE]

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