Transnasal Videoendoscopy for Preoperative Airway Risk Stratification: Development and Validation of a Multivariable Risk Prediction Model
Authors' conclusion
Stored images and videos from TVE examinations can be reused for the purpose of predicting risk associated with airway management. Vestibular fold, supraglottic, and arytenoid lesions are most concerning, especially if they are accompanied by secretion retention or restrict the glottic view. Our data indicate that the TVE model improves discrimination of the Mallampati score
Study context
- Population
- Patients with electronically stored TVE videos undergoing otorhinolaryngologic surgery, University Medical Centre Hamburg-Eppendorf, 2011–2018
- Participants
- 1,099 patients · 1,231 surgeries
- Comparison
- TVE model combined with Mallampati vs Mallampati score
- Outcome
- Difficult airway management
Methodological detailssingle-centre, blinded review, Least Absolute Shrinkage and Selection Operator (LASSO) regression, cross validation
- Single-centre
- Blinded review
- LASSO regression
- Cross validation
Performance
As reported in the abstract
Discrimination for difficult airway management (AUC)
| Test or model | AUC |
|---|---|
| Mallampati | 0.61 (95% CI 0.57-0.65) |
| TVE model + Mallampati | 0.74 (95% CI 0.71-0.78) |
AUC
Mallampati0.61 (95% CI 0.57-0.65)
TVE model + Mallampati0.74 (95% CI 0.71-0.78)
- TVE model + Mallampati vs Mallampati: P < 0.001
Further findings
Prevalence of difficult airway management
- Prevalence
- 24.7% (304/1231)
TVE risk factors selected by LASSO (ß-coefficients)
- Vestibular fold lesions
- 0.123
- Supraglottic lesions
- 0.161
- Arytenoid lesions
- 0.063
- Viewing restriction on rima glottidis covering ≥50% of glottis area
- 0.485
- Pharyngeal secretion retention
- 0.372
- Lesions at vocal cords, epiglottis or hypopharynx not selected
Original publication
Transnasal Videoendoscopy for Preoperative Airway Risk Stratification: Development and Validation of a Multivariable Risk Prediction Model. Anesth Analg. Published 19 April 2023. doi:10. 1213/ ane. 0000000000006418
Composed by AI from the published abstract and checked against it. How Briefs are made →