Accuracy of thyromental height test for predicting difficult intubation in elderly

Mostafa, Maha; Saeed, Mai; Hasanin, Ahmed; Badawy, S. H.; Khaled, Dalia

doi:10.1007/s00540-019-02721-6

Cited by 15 publications

(32 citation statements)

References 15 publications

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“…These findings support that the NC and TMHT may be predictors of DL. Several studies showed promising results, even with a single predictor [4,[16][17][18][19][20][21][22]. However, the previous studies are different from those of ours.…”

Section: Discussionmentioning

confidence: 72%

“…The vast majority of the studies on prediction of difficult airway using NC is on obese patients so data in non-obese are insufficient [8,16,17]. There were also differences in the primary outcome (difficult intubation vs. DL) [8,18,[20][21][22]. There may be differences in some TMHT studies because the patient population is of different races from the patient population in our study.…”

Section: Discussionmentioning

confidence: 83%

“…AUROC and AUPRC, which evaluate the model's performance, showed the best performance in the model to which BRF was applied but did not show excellent In many studies, the NC has been associated with difficult airway intubation in obese patients [8,16,17]. Thyromental height has also been reported as a predictor of difficult airway management [4,[16][17][18][19][20]. These findings support that the NC and TMHT may be predictors of DL.…”

Section: Discussionmentioning

confidence: 90%

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

Kim

Jang

et al. 2021

BMC Anesthesiol

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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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Section: Discussionmentioning

confidence: 72%

Section: Discussionmentioning

confidence: 83%

Section: Discussionmentioning

confidence: 90%

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

Kim

Jang

et al. 2021

BMC Anesthesiol

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“…The laryngoscopy view was assessed by the same senior anesthesiologist not involved in the preoperative radiological data with Macintosh laryngoscope. The result was determined by the Cormack-Lehane (C-L) grade [9]. Patients with Cormack-Lehane grade > 2 were assigned to the di cult laryngoscopy group, and patients with Cormack-Lehane grade I or II were assigned to the easy laryngoscopy group.…”

Section: Methodsmentioning

confidence: 99%

Preoperative X-ray C2C6AR is Applicable for Prediction of Difficult Laryngoscopy in Patients With Cervical Spondylosis

Zhou

Han

et al. 2020

Preprint

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Background: Airway management is one of the most important techniques in clinical anesthesia practice, and inappropriate airway management is related with airway injury, brain hypoxia, and even death. The patients with cervical spondylosis are often confronted with difficult laryngoscopy and are more prone to appear unexpected difficult airway, so it is important to figure out the most valuable predictor of difficult laryngoscopy in these patients.Methods: We randomly enrolled 270 patients undergoing elective cervical spine surgery and analyzed the cervical mobility data in predicting difficult laryngoscopy. The preoperative X-ray radiological indicators were measured by an attending radiologist. Cormack–Lehane scales were assessed during intubation, and patients with a class III or IV view were assigned to the difficult laryngoscopy group.Results: Univariate analysis showed that the hyomental distance (HMD, the distance between the hyoid bone and the tip of the chin) and the hyomental distance ratio (HMDR, the ratio between HMD in the extension position and the one in the neutral position) might not be suitable indicators in patients with cervical spondylosis. Binary multivariate logistic regression (backward-Wald) analyses identified two independent risk factors from the cervical mobility indicators that correlated best as a predictor of difficult laryngoscopy: modified Mallampati test (MMT) and C2C6AR (the ratio of the angle between a line passing through the bottom of the second cervical vertebra and a line passing through the bottom of the sixth cervical vertebra in the extension position over the one in the neutral position). The odds ratio (OR) and 95% CI were 2.292(1.093-4.803) and 0.493 (0.306-0.793), respectively. C2C6AR exhibited the largest area under the curve (0.714; 95% CI 0.633–0.794).Conclusions: C2C6AR based on preoperative X-ray images may be the most accurate predictor of cervical mobility indicators for difficult laryngoscopy in patients with cervical spondylosis.Trial registration: The study was registered at the Chinese Clinical Trial Registry (http://www.chictr.org.cn; identifier: ChiCTR-ROC-16008598) on June 6, 2016.

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“…In recent article of Mostafa et al [1], we noted several issues that would have made interpretation of their findings difficult. First, limited neck extension (LNE) is a known risk factor for difficult airways.…”

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confidence: 89%

Accuracy of predictive factors for difficult intubation

2020

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Accuracy of thyromental height test for predicting difficult intubation in elderly

Cited by 15 publications

References 15 publications

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

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

Preoperative X-ray C2C6AR is Applicable for Prediction of Difficult Laryngoscopy in Patients With Cervical Spondylosis

Accuracy of predictive factors for difficult intubation

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