ArticleAnnals of translational medicine2021
Voice parameters for difficult mask ventilation evaluation: an observational study.
Article in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 6 citations in OpenAlex.
- Pre-operative three-dimensional face scans for predicting difficult facemask ventilation: a prospective development study.Anaesthesia · 2026Observational
- Association between upper airway computed tomography measurement parameters and mask ventilation in patients undergoing oral and maxillofacial surgery.Quantitative imaging in medicine and surgery · 2026Article
- An ultrasound-based artificial intelligence framework for difficult airway prediction: A two-model, three-step decision framework.PloS one · 2026Article
- Voice Analysis as a Method for Preoperatively Predicting a Difficult Airway Based on Machine Learning Algorithms: Original Research Report.Health science reports · 2024Article
- Scope of artificial intelligence in airway management.Indian journal of anaesthesia · 2024Article
- Expert consensus on difficult airway assessment.Hepatobiliary surgery and nutrition · 2023Review
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMask ventilation (MV) is an essential component of airway management. Difficult mask ventilation (DMV) is a major cause for perioperative hypoxic brain injury; however, predicting DMV remains a challenge. This study aimed to determine the potential value of voice parameters as novel predictors of DMV in patients scheduled for general anesthesia.
methodsWe included 1,160 adult patients scheduled for elective surgery under general anesthesia. The clinical variables usually reported as predictors of DMV were collected before surgery. Voice sample of phonemes ([a], [o], [e], [i], [u], [ü], [ci], [qi], [chi], [le], [ke], and [en]) were recorded and their formants (f1-f4) and bandwidths (bw1-bw4) were extracted. The definition of DMV was the inability of an unassisted anesthesiologist to ensure adequate ventilation during MV under general anesthesia. Univariate and multivariate logistic regression analyses were used to explore the association between voice parameters and DMV. The predictive value of the voice parameters was evaluated by assessment of area under the curve (AUC) of receiver operating characteristic (ROC) curves of a stepwise forward model.
resultsThe prevalence of DMV was 218/1,160 (18.8%). The AUC of the stepwise forward model (including o_f4, e_bw2, i_f3, u_pitch, u_f1, u_f4, ü_bw4, ci_f1, qi_f1, qi_f4, qi_bw4, chi_f1, chi_bw2, chi_bw4, le_pitch, le_bw3, ke_bw2, en_pitch, and en_f2, en_bw4) attained a value of 0.779. The sensitivity and specificity of the model were 75.0% and 71.0%, respectively.
conclusionsVoice parameters may be considered as alternative predictors of DMV, but additional studies are needed to confirm the initial findings.
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