International journal of pediatric otorhinolaryngology · 2026
Evaluation of anatomical and ultrasonographic parameters to predict difficult airway in pediatric patients.
СтатьяНаблюдательное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Altun Demet, Canbaz Mert, Dinçer Müşerref Beril, Demirel Ebru Emre, Altun Dilek, Kara Hakan, Sivrikoz Nükhet, Çamcı Emre
Аннотация
BACKGROUND: Predicting a difficult airway in pediatric patients remains challenging due to unique anatomical and physiological characteristics. Traditional bedside predictors show inconsistent accuracy in children, and ultrasonography has emerged as a promising, objective tool for airway evaluation. This study aimed to assess the diagnostic performance of anatomical and ultrasonographic parameters in predicting difficult intubation in pediatric patients.
METHODS: In this prospective, double-blind observational study, children aged 1-12 years undergoing elective surgery with endotracheal intubation were evaluated. Clinical assessments included anthropometric measurements and airway tests (Mallampati, thyromental distance (TMD), sternomental distance (SMD), hyomental distance (HMD)). Sonographic measurements-skin-epiglottis distance (S-ED) and skin-hyoid distance (S-HD)-were obtained preoperatively. Difficult intubation was defined as Intubation Difficulty Score (IDS) > 5. ROC curve analyses were used to evaluate diagnostic performance; logistic regression identified independent predictors.
RESULTS: A total of 240 children were included, with a difficult-intubation incidence of 20%. S-ED demonstrated the highest predictive accuracy (AUC 0.941), followed by S-HD (AUC 0.912), HMD (AUC 0.898), and Mallampati classification (AUC 0.860). TMD and SMD showed moderate predictive ability. Combining significant clinical and sonographic parameters improved diagnostic performance, with S-ED + S-HD yielding an AUC of 0.961. Multivariable regression identified S-ED (OR 4.61), S-HD (OR 1.76), Mallampati ≥ III, and facial anomalies as independent predictors of difficult intubation. The final model correctly classified 95.4% of cases.
CONCLUSIONS: Ultrasonographic parameters, particularly S-ED and S-HD, are highly accurate predictors of difficult intubation in children and outperform traditional clinical tests. Combining sonographic with anatomical assessments significantly enhances diagnostic performance. Airway ultrasound provides an objective, reproducible, and non-cooperation-dependent tool that can improve preoperative risk stratification in pediatric anesthesia.
METHODS: In this prospective, double-blind observational study, children aged 1-12 years undergoing elective surgery with endotracheal intubation were evaluated. Clinical assessments included anthropometric measurements and airway tests (Mallampati, thyromental distance (TMD), sternomental distance (SMD), hyomental distance (HMD)). Sonographic measurements-skin-epiglottis distance (S-ED) and skin-hyoid distance (S-HD)-were obtained preoperatively. Difficult intubation was defined as Intubation Difficulty Score (IDS) > 5. ROC curve analyses were used to evaluate diagnostic performance; logistic regression identified independent predictors.
RESULTS: A total of 240 children were included, with a difficult-intubation incidence of 20%. S-ED demonstrated the highest predictive accuracy (AUC 0.941), followed by S-HD (AUC 0.912), HMD (AUC 0.898), and Mallampati classification (AUC 0.860). TMD and SMD showed moderate predictive ability. Combining significant clinical and sonographic parameters improved diagnostic performance, with S-ED + S-HD yielding an AUC of 0.961. Multivariable regression identified S-ED (OR 4.61), S-HD (OR 1.76), Mallampati ≥ III, and facial anomalies as independent predictors of difficult intubation. The final model correctly classified 95.4% of cases.
CONCLUSIONS: Ultrasonographic parameters, particularly S-ED and S-HD, are highly accurate predictors of difficult intubation in children and outperform traditional clinical tests. Combining sonographic with anatomical assessments significantly enhances diagnostic performance. Airway ultrasound provides an objective, reproducible, and non-cooperation-dependent tool that can improve preoperative risk stratification in pediatric anesthesia.