Airway management

Transnasal Videoendoscopy for Preoperative Airway Risk Stratification: Development and Validation of a Multivariable Risk Prediction Model

Anesth AnalgPublished Retrospective development and validation study 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 modelAUC
Mallampati0.61 (95% CI 0.57-0.65)
TVE model + Mallampati0.74 (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 →

Get them every week

New studies on this topic in My Week and in your Monday e-mail.

We e-mail you a sign-in link and a code. No password, no spam.