Effectiveness of four ultrasonographic parameters as predictors of difficult intubation in patients without anticipated difficult airway

Agarwal, Rishabh; Jain, Gaurav; Agarwal, Ankit; Govil, Nishith

doi:10.4097/kja.20114

Cited by 18 publications

(34 citation statements)

References 13 publications

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“…The sensitivity and specificity of individual tests were lower for most of the studies in this domain in comparison with TTD and APD assessments. Two of these studies’ authors commented that OSD tests demonstrate limited accuracy (16, 23). A potential reason could be that ultrasound cannot adequately visualize the dimensions of an air-filled oropharynx irrespective of the size of the tongue and other soft tissues adjacent to it.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Difficult Laryngoscopy Using Ultrasound: A Systematic Review and Meta-Analysis

Bhargava

Rockwell

Tawfik

et al. 2022

Critical Care Medicine

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OBJECTIVES: Evaluate associations between ultrasound measures and difficult laryngoscopy. DATA SOURCES: MEDLINE, Embase, Google Scholar, Web of Science, and the Cochrane Library were searched using MeSH terms and keywords. STUDY SELECTION: Studies published in English describing the use of airway ultrasound for identifying difficult laryngoscopy, with sufficient data to calculate sensitivity and specificity using 2 × 2 tables. DATA EXTRACTION: We assigned the described indices of airway dimension to one of three domains based on methodology characteristics: anterior tissue thickness domain, anatomical position domain, and oral space domain. We then performed a bivariate random-effects meta-analysis, deriving pooled sensitivity, specificity, diagnostic odds ratio, positive likelihood ratio, and negative likelihood ratio estimates. We assessed risks of bias using Quality Assessment of Diagnostic Accuracy Studies-2 analysis. DATA SYNTHESIS: Thirty-three studies evaluating 27 unique indices were included in the meta-analysis. The ultrasound protocols of the included studies were heterogeneous. Anterior tissue thickness demonstrated a pooled sensitivity of 76% (95% CI, 71–81%), specificity of 77% (95% CI, 72–81%), and an area under the receiver operating characteristic curve (AUROC) of 0.83 (95% CI, 0.80–0.86). Anatomical position demonstrated a pooled sensitivity of 74% (95% CI, 61–84%), specificity of 86% (95% CI, 78–91%), and an AUROC of 0.87 (95% CI, 0.84–0.90). Oral space demonstrated a pooled sensitivity of 53% (95% CI, 0.36–0.69), specificity of 77% (95% CI, 0.67–0.85), and an AUROC of 0.73 (95% CI, 0.69–0.77). CONCLUSIONS: Airway ultrasound metrics associate with difficult laryngoscopy in three domains: anterior tissue thickness, anatomic position, and oral space. An assessment instrument combining clinical and ultrasound assessments may be an accurate screening tool for difficult laryngoscopy.

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Section: Discussionmentioning

confidence: 99%

Prediction of Difficult Laryngoscopy Using Ultrasound: A Systematic Review and Meta-Analysis

Bhargava

Rockwell

Tawfik

et al. 2022

Critical Care Medicine

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“…The hyomental distance can directly reflect whether the patient’s mandibular space is sufficient and is an effective indicator for predicting difficult airways [ 20 , 21 ]. Ultrasound can accurately locate the hyoid bone and measure the hyomental distance, thereby improving the prediction of difficult airways [ 22 ]. Tongue hypertrophy can interfere with laryngeal exposure, thus leading to difficult intubation.…”

Section: Discussionmentioning

confidence: 99%

“…We selected mandibular condylar mobility, the hyomental distance, and tongue thickness to demonstrate the ability of individual parameters and combinations of parameters based on upper airway ultrasonography to predict difficult airways. Agarwal et al [ 22 ] studied the performance of the ultrasonic measurement of multiple airway parameters in predicting difficult airways and proved that ultrasonic comprehensive prediction methods can effectively predict difficult airways.…”

Section: Discussionmentioning

confidence: 99%

The value of multiparameter combinations for predicting difficult airways by ultrasound

Wang

et al. 2022

BMC Anesthesiol

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Background Based on the upper airway anatomy and joint function parameters examined by ultrasound, a multiparameter ultrasound model for difficult airway assessment (ultrasound model) was established, and we evaluated its ability to predict difficult airways. Methods A prospective case-cohort study of difficult airway prediction in adult patients undergoing elective surgery with endotracheal intubation under general anesthesia, and ultrasound phantom examination for difficult airway assessment before anesthesia, including hyomental distance, tongue thickness, mandibular condylar mobility, mouth opening, thyromental distance, and modified Mallampati tests, was performed. Receiver operating characteristic (ROC) curve analysis was used to evaluate the effectiveness of the ultrasound model and conventional airway assessment methods in predicting difficult airways. Results We successfully enrolled 1000 patients, including 51 with difficult laryngoscopy (DL) and 26 with difficult tracheal intubation (DTI). The area under the ROC curve (AUC) for the ultrasound model to predict DL was 0.84 (95% confidence interval [CI]: 0.82–0.87), and the sensitivity and specificity were 0.75 (95% CI: 0.60–0.86) and 0.82 (95% CI: 0.79–0.84), respectively. The AUC for predicting DTI was 0.89 (95% CI: 0.87–0.91), and the sensitivity and specificity were 0.85 (95% CI: 0.65–0.96) and 0.81 (95% CI: 0.78–0.83), respectively. Compared with mouth opening, thyromental distance, and modified Mallampati tests, the ultrasound model predicted a greater AUC for DL (P < 0.05). Compared with mouth opening and modified Mallampati tests, the ultrasound model predicted a greater AUC for DTI (P < 0.05). Conclusions The ultrasound model has good predictive performance for difficult airways. Trial registration This study is registered on chictr.org.cn (ChiCTR-ROC-17013258); principal investigator: Jianling Xu; registration date: 06/11/2017).

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“…[27] Especially, ultrasound provides real-time imaging. [28][29][30][31][32][33] However, we only measured GHJCCSA and GHJCT on S-MRI. Third, there might be some bias associated with measuring GHJCCSA and GHJCT on S-MRI.…”

Section: Discussionmentioning

confidence: 99%

The value of the glenohumeral joint cross-sectional area as a morphological parameter of glenohumeral osteoarthritis

et al. 2022

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Glenohumeral joint (GHJ) space narrowing has been demonstrated to be an important morphologic parameter of glenohumeral osteoarthritis (GHO). However, the morphology of GHJ space is irregular because of degeneration of subchondral bone and articular cartilage. Thus, we devised GHJ cartilage cross-sectional area (GHJCCSA) as a new diagnostic morphological parameter to assess the irregular morphologic change of GHJ. GHJ samples were acquired from 33 patients with GHO and from 33 normal controls without evidence of GHO based on shoulder magnetic resonance imaging. T2-weighted coronal MRIs were collected at the GHJ level for all individuals. GHJCCSA and GHJ cartilage thickness (GHJCT) at the GHJ were measured on MRIs using a graphic measuring system. The GHJCCSA was measured as the whole cartilage cross-sectional area of the GHJ. The average GHJCCSA was 115.28 ± 17.36 mm 2 in normal individuals and 61.77 ± 13.74 mm 2 in the GHO group. The mean GHJCT was 2.06 ± 0.35 mm in normal individuals and 1.50 ± 0.28 mm in the GHO group. GHO patients had significantly lower GHJCCSA (P < .001) and GHJCT (P < .001) than normal individuals. Receiver operator characteristics curve analysis revealed that the optimal cutoff score of the GHJCCSA was 82.21 mm 2 , with a sensitivity of 97.0%, a specificity of 97.0%, and an area under the curve of 0.99 (95% CI: 0.97-1.00). Although GHJCCSA and GHJCT were both significantly associated with GHO, the GHJCCSA was a more sensitive measurement parameter.Abbreviations: AUC = area under the curve, CI = confidence interval, GHJ = glenohumeral joint, GHJCCSA = glenohumeral joint cartilage cross-sectional area, GHJCT = glenohumeral joint cartilage space thickness, GHO = glenohumeral osteoarthritis, ROC = receiver operating characteristic, S-MRI = shoulder magnetic resonance imaging.

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Effectiveness of four ultrasonographic parameters as predictors of difficult intubation in patients without anticipated difficult airway

Cited by 18 publications

References 13 publications

Prediction of Difficult Laryngoscopy Using Ultrasound: A Systematic Review and Meta-Analysis

Prediction of Difficult Laryngoscopy Using Ultrasound: A Systematic Review and Meta-Analysis

The value of multiparameter combinations for predicting difficult airways by ultrasound

The value of the glenohumeral joint cross-sectional area as a morphological parameter of glenohumeral osteoarthritis

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