Evaluation of difficult laryngoscopy with ultrasonography in pediatric patients: Prospective study
Medicine, vol.105, no.9, 2026 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 105 Issue: 9
- Publication Date: 2026
- Doi Number: 10.1097/md.0000000000047776
- Journal Name: Medicine
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, CINAHL, EMBASE, Directory of Open Access Journals
- Keywords: airway ultrasonography, difficult airway, difficult laryngoscopy, pediatric airway, ultrasonography
- Eskisehir Osmangazi University Affiliated: Yes
Abstract
Effective airway management is a critical aspect of anesthesia, with difficult laryngoscopy (DL) posing significant challenges, particularly in pediatric patients, due to anatomical and physiological differences. This study aimed to evaluate the utility of ultrasonographic measurements of the upper airway as predictors of DL in children. Pediatric patients aged 2 to 8 years who underwent endotracheal intubation under general anesthesia were included. According to the Cormack-Lehane classification (CL), grades 3 and 4 are defined as difficult laryngoscopies. Preoperative ultrasonographic measurements of the hyoid bone-to-skin distance (DSHB), epiglottis-to-skin distance (DSE), and vocal cord anterior commissure-to-skin distance (DSAC) were obtained. The DSAC/DSE ratio was calculated and its potential for predicting DL was assessed. A total of 121 pediatric patients were included in the final analysis, and 6 patients had DL. This study found no correlation between ultrasonographic measurements and the difficulty of laryngoscopy, as classified by the CL score. However, the Mallampati score was a predictor of DL and showed a positive correlation with higher difficulty grades. In this study, the effectiveness of DSHB, DSAC, DSE measurements, and the DSAC/DSE ratio in predicting DL in pediatric patients was evaluated; however, no significant correlation was found. Further research is needed to validate these findings and improve predictive models for difficult pediatric airway management.