Difficult laryngoscopy caused by massive mandibular tori

Abstract Background Routine preoperative methods to assess airway such as the interincisor distance (IID), Mallampati classification, and upper lip bite test (ULBT) have a certain risk of upper respiratory tract exposure and virus spread. Condyle-tragus maximal distance(C-TMD) can be used to assess the airway, and does not require the patient to expose the upper respiratory tract, but its value in predicting difficult laryngoscopy compared to other indicators (Mallampati classification, IID, and ULBT) remains unknown. The purpose of this study was to observe the value of C-TMD to predict difficult laryngoscopy and the influence on intubation time and intubation attempts, and provide a new idea for preoperative airway assessment during epidemic. Methods Adult patients undergoing general anesthesia and tracheal intubation were enrolled. IID, Mallampati classification, ULBT, and C-TMD of each patient were evaluated before the initiation of anesthesia. The primary outcome was intubation time. The secondary outcomes were difficult laryngoscopy defined as the Cormack-Lehane Level > grade 2 and the number of intubation attempts. Results Three hundred four patients were successfully enrolled and completed the study, 39 patients were identified as difficult laryngoscopy. The intubation time was shorter with the C-TMD>1 finger group 46.8 ± 7.3 s, compared with the C-TMD<1 finger group 50.8 ± 8.6 s (p<0.01). First attempt success rate was higher in the C-TMD>1 finger group 98.9% than in the C-TMD<1 finger group 87.1% (P<0.01). The correlation between the C-TMD and Cormack-Lehane Level was 0.317 (Spearman correlation coefficient, P<0.001), and the area under the ROC curve was 0.699 (P<0.01). The C-TMD < 1 finger width was the most consistent with difficult laryngoscopy (κ = 0.485;95%CI:0.286–0.612) and its OR value was 10.09 (95%CI: 4.19–24.28), sensitivity was 0.469 (95%CI: 0.325–0.617), specificity was 0.929 (95%CI: 0.877–0.964), positive predictive value was 0.676 (95%CI: 0.484–0.745), negative predictive value was 0.847 (95%CI: 0.825–0.865). Conclusion Compared with the IID, Mallampati classification and ULBT, C-TMD has higher value in predicting difficult laryngoscopy and does not require the exposure of upper respiratory tract. Trial registration The study was registered on October 21, 2019 in the Chinese Clinical Trial Registry (ChiCTR1900026775).

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Alveolar cleft and maximum cleft width as predictors for difficult laryngoscopy and intubation in patients with unilateral complete cleft lip and palate

Ain-Shams Journal of Anesthesiology ◽

10.1186/s42077-021-00137-7 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Gamal A. Abdelhameed ◽

Wael A. Ghanem ◽

Simon H. Armanios ◽

Tamer Nabil Abdelrahman

Keyword(s):

Body Weight ◽

Odds Ratio ◽

Cleft Lip ◽

Cleft Lip And Palate ◽

Difficult Laryngoscopy ◽

Secondary Outcome ◽

Pediatric Airway ◽

Alveolar Cleft ◽

Cleft Lip Palate ◽

Width Measurements

Abstract Background Cleft lip and palate is one of the commonest congenital anomalies, which have an impact on feeding, speech, and dental development away from the significant psychosocial sequel. Early surgical repair aims to restore appearance and function, and the modern techniques can leave many defects undetectable. Therefore, the anesthetic challenge facing the pediatric airway with such abnormalities is still of a great impact. The aim of our study among 189 patients enrolled is to correlate alveolar gap and maximum cleft width measurements as predictors of difficult laryngoscopy and intubation in infants with unilateral complete cleft lip/palate aging from 1 to 6 months. As a secondary outcome, their weight is to be correlated too as another parameter. Results The alveolar gap and maximum cleft width are both of equal high predictive power (p value ≤ 0.001) with 100% sensitivity for both and specificity of 76.10% and 82.39% respectively, with a cut off value of ≤ 10 mm and 11 mm for these dimensions respectively, and odds ratio of incidence of difficult intubation is 4.18 and 5.68 respectively, while body weight ≤ 5.75 kg has an odds ratio of 2.32. Conclusion Alveolar cleft and maximum cleft width can be used as predictors for anticipation of difficult laryngoscopy and intubation infant patients with unilateral complete cleft lip and palate, while body weight ≤ 5.75 kg increases the risk more than twice.

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Development and validation of a difficult laryngoscopy prediction model using machine learning of neck circumference and thyromental height

BMC Anesthesiology ◽

10.1186/s12871-021-01343-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Jong Ho Kim ◽

Haewon Kim ◽

Ji Su Jang ◽

Sung Mi Hwang ◽

So Young Lim ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Confidence Interval ◽

Neck Circumference ◽

Difficult Laryngoscopy ◽

Gradient Boosting ◽

Test Set ◽

Equal Distribution ◽

Light Gradient ◽

Extreme Gradient Boosting

Abstract Background Predicting difficult airway is challengeable in patients with limited airway evaluation. The aim of this study is to develop and validate a model that predicts difficult laryngoscopy by machine learning of neck circumference and thyromental height as predictors that can be used even for patients with limited airway evaluation. Methods Variables for prediction of difficulty laryngoscopy included age, sex, height, weight, body mass index, neck circumference, and thyromental distance. Difficult laryngoscopy was defined as Grade 3 and 4 by the Cormack-Lehane classification. The preanesthesia and anesthesia data of 1677 patients who had undergone general anesthesia at a single center were collected. The data set was randomly stratified into a training set (80%) and a test set (20%), with equal distribution of difficulty laryngoscopy. The training data sets were trained with five algorithms (logistic regression, multilayer perceptron, random forest, extreme gradient boosting, and light gradient boosting machine). The prediction models were validated through a test set. Results The model’s performance using random forest was best (area under receiver operating characteristic curve = 0.79 [95% confidence interval: 0.72–0.86], area under precision-recall curve = 0.32 [95% confidence interval: 0.27–0.37]). Conclusions Machine learning can predict difficult laryngoscopy through a combination of several predictors including neck circumference and thyromental height. The performance of the model can be improved with more data, a new variable and combination of models.

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