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Voice parameters for difficult mask ventilation evaluation: an observational study.

Shuang Cao1, Ming Xia1, Ren Zhou1

  • 1Department of Anesthesiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Annals of Translational Medicine
|January 24, 2022
PubMed
Summary

Predicting difficult mask ventilation (DMV) is crucial for patient safety. This study found that voice parameters show potential as novel predictors of DMV, aiding anesthesiologists in airway management.

Keywords:
Difficult airwaydifficult mask ventilation (DMV)voice parameters

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Area of Science:

  • Anesthesiology
  • Airway Management
  • Medical Diagnostics

Background:

  • Difficult mask ventilation (DMV) poses a significant risk for perioperative hypoxic brain injury.
  • Predicting DMV remains a challenge in clinical practice.
  • Novel predictors for DMV are needed to improve patient safety.

Purpose of the Study:

  • To investigate the potential of voice parameters as novel predictors of difficult mask ventilation (DMV).
  • To assess the association between extracted voice parameters and the occurrence of DMV.
  • To evaluate the diagnostic performance of a model incorporating voice parameters for DMV prediction.

Main Methods:

  • 1,160 adult patients undergoing general anesthesia were included.
  • Voice recordings of various phonemes were analyzed for formants and bandwidths.
  • Univariate and multivariate logistic regression, along with ROC curve analysis, were used to evaluate voice parameters' predictive value for DMV.

Main Results:

  • The prevalence of DMV was 18.8% (218/1,160 patients).
  • A stepwise forward model incorporating specific voice parameters achieved an Area Under the Curve (AUC) of 0.779.
  • The model demonstrated a sensitivity of 75.0% and specificity of 71.0% for predicting DMV.

Conclusions:

  • Voice parameters demonstrate potential as alternative predictors for difficult mask ventilation (DMV).
  • Further research is necessary to validate these initial findings and their clinical utility.
  • Voice analysis may offer a non-invasive method to identify patients at risk for DMV.