Dataset Information

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

ABSTRACT:

Background

Mask 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.

Methods

We 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.

Results

The 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.

Conclusions

Voice parameters may be considered as alternative predictors of DMV, but additional studies are needed to confirm the initial findings.

SUBMITTER: Cao S

PROVIDER: S-EPMC8743704 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Publications

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

Cao Shuang S Xia Ming M Zhou Ren R Wang Jie J Jin Chen-Yu CY Pei Bei B Zhou Zhi-Kai ZK Qian Yan-Min YM Jiang Hong H

Annals of translational medicine 20211201 23

<h4>Background</h4>Mask 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.<h4>Methods</h4>We included 1,160 adult patients scheduled for elective surgery under general anesthesia. The clinical variables usual ...[more]

PMID: 35071434

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Project description:Background Unanticipated difficult mask ventilation (DMV) is a potentially life-threatening event in anesthesia. Nevertheless, predicting DMV currently remains a challenge. This study aimed to verify whether three dimensional (3D) facial scans could predict DMV in patients scheduled for general anesthesia. Methods The 3D facial scans were taken on 669 adult patients scheduled for elective surgery under general anesthesia. Clinical variables currently used as predictors of DMV were also collected. The DMV was defined as the inability to provide adequate and stable ventilation. Spatially dense landmarks were digitized on 3D scans to describe sufficient details for facial features and then processed by 3D geometric morphometrics. Ten different machine learning (ML) algorithms, varying from simple to more advanced, were introduced. The performance of ML models for DMV prediction was compared with that of the DIFFMASK score. The area under the receiver operating characteristic curves (AUC) with its 95% confidence interval (95% CI) as well as the specificity and sensitivity were used to evaluate the predictive value of the model. Results The incidence of DMV was 35/669 (5.23%). The logistic regression (LR) model performed best among the 10 ML models. The AUC of the LR model was 0.825 (95% CI, 0.765–0.885). The sensitivity and specificity of the model were 0.829 (95% CI, 0.629–0.914) and 0.733 (95% CI, 0.532–0.819), respectively. The LR model demonstrated better predictive performance than the DIFFMASK score, which obtained an AUC of 0.785 (95% CI, 0.710–0.860) and a sensitivity of 0.686 (95% CI, 0.578–0.847). Notably, we identified a significant morphological difference in the mandibular region between the DMV group and the easy mask ventilation group. Conclusion Our study indicated a distinct morphological difference in the mandibular region between the DMV group and the easy mask ventilation group. 3D geometric morphometrics with ML could be a rapid, efficient, and non-invasive tool for DMV prediction to improve anesthesia safety.

Project description:BackgroundArtificial intelligence has provided new opportunities for human interactions with technology for the practice of medicine. Among the recent artificial intelligence innovations, personal voice assistants have been broadly adopted. This highlights their potential for health care-related applications such as behavioral counseling to promote healthy lifestyle habits and emotional well-being. However, the use of voice-based applications for behavioral therapy has not been previously evaluated.ObjectiveThis study aimed to conduct a formative user evaluation of Lumen, a virtual voice-based coach developed as an Alexa skill that delivers evidence-based, problem-solving treatment for patients with mild to moderate depression and/or anxiety.MethodsA total of 26 participants completed 2 therapy sessions-an introductory (session 1) and a problem-solving (session 2)-with Lumen. Following each session with Lumen, participants completed user experience, task-related workload, and work alliance surveys. They also participated in semistructured interviews addressing the benefits, challenges and barriers to Lumen use, and design recommendations. We evaluated the differences in user experience, task load, and work alliance between sessions using 2-tailed paired t tests. Interview transcripts were coded using an inductive thematic analysis to characterize the participants' perspectives regarding Lumen use.ResultsParticipants found Lumen to provide high pragmatic usability and favorable user experience, with marginal task load during interactions for both Lumen sessions. However, participants experienced a higher temporal workload during the problem-solving session, suggesting a feeling of being rushed during their communicative interactions. On the basis of the qualitative analysis, the following themes were identified: Lumen's on-demand accessibility and the delivery of a complex problem-solving treatment task with a simplistic structure for achieving therapy goals; themes related to Lumen improvements included streamlining and improved personalization of conversations, slower pacing of conversations, and providing additional context during therapy sessions.ConclusionsOn the basis of an in-depth formative evaluation, we found that Lumen supported the ability to conduct cognitively plausible interactions for the delivery of behavioral therapy. Several design suggestions identified from the study including reducing temporal and cognitive load during conversational interactions, developing more natural conversations, and expanding privacy and security features were incorporated in the revised version of Lumen. Although further research is needed, the promising findings from this study highlight the potential for using Lumen to deliver personalized and accessible mental health care, filling a gap in traditional mental health services.

Dataset Information

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

Background

Methods

Results

Conclusions

Publications

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

Similar Datasets

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