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Updated: Jan 15, 2026
Investigation into Deep Breathing through Measurement of Ventilatory Parameters and Observation of Breathing Patterns
Published on: September 16, 2019
Determinants of mask ventilation grading based on upper airway indicators: a prospective observational study
1Department of Anesthesiology, ZhongDa Hospital, Southeast University, No. 87 Dingjiaqiao, Gulou District, Nanjing, Jiangsu Province, 210009, China.
Background:
Mask ventilation is a common procedure performed in the patients with general anesthesia. Various factors that can influence airways were evaluated to determine the potential value of upper airway indicators in predicting mask ventilation grading.
Methods:
This was a secondary analysis of a prospective observational study, assessing laryngoscopy exposure risk factors by combining ultrasonically measured upper airway anatomic parameters with physical examination indicators.
Results:
A total of 1120 cases were included and were divided into the easy mask ventilation group (n = 551) and the difficult mask ventilation group (n = 569) according to the peak inspiratory pressure (PIP). There were statistically significant differences found between easy and difficult mask ventilation groups in terms of patient gender (200/351 vs. 319/250, P = 0.036), age (50.04 ± 13.41 vs. 56.81 ± 10.19, P = 0.003), history of snoring (347/168/36/0 vs. 162/271/113/23, P < 0.001), BMI (22.96 ± 2.68 vs. 25.74 ± 2.85, P < 0.001), neck circumference (36.06 ± 3.43 vs. 39.67 ± 3.10, P < 0.001), thyromental distance ratio (1.44 ± 0.17 vs. 1.37 ± 0.13.10, P = 0.012), Mallampati classification (141/354/56/0 vs. 13/420/135/1, P < 0.001), ultrasonically measured tongue thickness (5.86 ± 0.59 vs. 6.22 ± 0.47, P = 0.001), tongue root distance (3.96 ± 0.66 vs. 4.23 ± 0.69, P = 0.033), epiglottis distance (1.99 ± 0.40 vs. 2.20 ± 0.37, P = 0.033) and thyrohyosteal distance (1.69 ± 0.34 vs. 1.85 ± 0.37, P = 0.019). Multiple-factor logistic regression analysis indicated that neck circumference was an independent influencing factor, with an odds ratio of 1.37 (95% CI 1.02-1.83). At a neck circumference of 37.75 cm, the sensitivity and specificity for predicting the mask ventilation grading were 0.79 and 0.64, respectively. The predictive efficiency ROC curve AUC was 0.78 (95% CI 0.69-0.86).
Conclusions:
The neck circumference was found to be an independent predictive factor for grading mask ventilation, with an odds ratio of 1.37. The best threshold value for neck circumference prediction was 37.75 cm, with a sensitivity of 0.79 and a specificity of 0.64. And the predictive efficiency ROC curve AUC was 0.78.
Trial Registration:
chictr.org.cn ChiCTR2100053826; registered on November 30, 2021.
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