Does neck circumference help to predict difficult intubation in obstetric patients? A prospective observational study

Influence of Curved Video Laryngoscope Blade Sizes and Patient Heights on Video Laryngoscopic Views: A Randomized Controlled Trial

This study aimed to compare the video laryngoscope views facilitated by curved blades 3 and 4 with an exploration of the relationship between these views and patient height. Conducted as a randomized controlled trial, this study enrolled adults scheduled for surgery under general anesthesia. Intubation procedures were recorded, and the percentage of glottic opening was measured before tube insertion. Multivariate analysis validated the impact of various factors, including blade size and patient height, on the percentage of glottic opening scores. A total of 192 patients were included. The median percentage of glottic opening scores for curved blades 3 and 4 were 100 and 83, respectively (p < 0.001). The unstandardized coefficient indicated a significant negative impact of blade 4 on the percentage of glottic opening scores (-13, p < 0.001). In the locally estimated scatterplot smoothing analysis, blade 3 exhibited a steady rise in glottic opening scores with increasing height, whereas blade 4 showed a peak followed by a decline around 185 cm. The unstandardized coefficient of height showed no significant association (0, p = 0.819). The study observed superior laryngoscopic views with blade 3 compared to blade 4. However, no significant association was found between laryngoscopic views and patient height.

Awake Nasal Fiberoptic Intubation in Lateral Position for Severely Obese Patients with Anticipated Difficult Airway: A Randomized Controlled Trial

BACKGROUND

Obesity is a well-recognized risk factor for difficult intubation. To safely manage and overcome airway challenges in severely obese patients with a suspected difficult airway, awake fiberoptic intubation is recommended. We aimed to investigate the utility of awake nasal fiberoptic intubation in severely obese patients with suspected difficult airway while positioning them in the lateral decubitus position.

METHODS

This randomized controlled trial compared lateral and supine positions for awake nasal fiberoptic intubation in severely obese patients with an anticipated difficult airway by assessing the success rate, time needed to secure the airway, peri-procedural adverse events, and postoperative satisfaction of patients.

RESULTS

Sixty patients with a median age of 37 [inter-quartile range (IQR): 29-44] years were included, of which 47 (78.3%) were females. The median body mass index (BMI) was 45.5 [IQR: 42.5-50.8] kg/m2. The success rate of fiberoptic intubation was 100% in both groups. The time needed to successfully secure the airway was 188 [148.8-228.8] seconds (s) in the lateral position, compared to 214.5 [181.8-280.5] s in supine position (p = 0.019). Intraprocedural cough was more common in the supine position group (n = 8; 26.7%), compared to the lateral position group (n = 3; 10%; p = 0.095). Postoperative sore throat was more common in the lateral position group (n = 12; 40%) compared to the supine position (n = 5; 16.7%; p = 0.045).

CONCLUSIONS

In conclusion, Intubation in the lateral position is a promising technique that is equivalent to the routine supine position during fiberoptic intubation. In fact, intubation in the lateral position took less time to successfully secure the airway.

Clinical and ultrasonographic assessment of airway indices among non-pregnant, normotensive pregnant and pre-eclamptic patients: a prospective observational study

BACKGROUND

Prediction of a difficult airway is of paramount importance for an anaesthesiologist. Various anatomical and physiological factors contribute to a difficult airway in pregnant females, especially those with pre-eclampsia. The aim of the study was to assess airway indices using both routinely used clinical methods and ultrasound.

METHODS

Fifty-five non-pregnant females, 55 normotensive pregnant females and 55 females with pre-eclampsia were included in this prospective study. Clinical airway assessment was the modified Mallampati score, thyromental distance, hyomental distance, hyomental distance ratio, chest circumference, neck circumference and chest-to-neck circumference ratio. Sonographic assessment included tongue width, tongue volume, anterior neck soft tissue thickness at the level of hyoid, epiglottis and vocal cords, subglottic diameter, ratio of pre-epiglottic space to anterior, posterior and midpoint of anterior and posterior vocal folds.

RESULTS

Several significant differences were observed between pregnant and non-pregnant females, with additional changes in pre-eclamptic females. These included clinical parameters such as the modified Mallampati score and sonographic measurements of tongue width, tongue volume, subglottic diameter, anterior neck soft tissue thickness at the level of hyoid, epiglottis and vocal cords, and the ratio of pre-epiglottic space to anterior, posterior and midpoint of anterior and posterior vocal folds.

CONCLUSION

Routinely used clinical methods of airway assessment lack sensitivity and specificity. Ultrasound can visualise anatomical structures in the supraglottic and subglottic views and is encouraging as an airway assessment tool.

Predicting difficult airway intubation in thyroid surgery using multiple machine learning and deep learning algorithms

Background

In this paper, we examine whether machine learning and deep learning can be used to predict difficult airway intubation in patients undergoing thyroid surgery.

Methods

We used 10 machine learning and deep learning algorithms to establish a corresponding model through a training group, and then verify the results in a test group. We used R for the statistical analysis and constructed the machine learning prediction model in Python.

Results

The top 5 weighting factors for difficult airways identified by the average algorithm in machine learning were age, sex, weight, height, and BMI. In the training group, the AUC values and accuracy and the Gradient Boosting precision were 0.932, 0.929, and 100%, respectively. As for the modeled effects of predicting difficult airways in test groups, among the models constructed by the 10 algorithms, the three algorithms with the highest AUC values were Gradient Boosting, CNN, and LGBM, with values of 0.848, 0.836, and 0.812, respectively; In addition, among the algorithms, Gradient Boosting had the highest accuracy with a value of 0.913; Additionally, among the algorithms, the Gradient Boosting algorithm had the highest precision with a value of 100%.

Conclusion

According to our results, Gradient Boosting performed best overall, with an AUC >0.8, an accuracy >90%, and a precision of 100%. Besides, the top 5 weighting factors identified by the average algorithm in machine learning for difficult airways were age, sex, weight, height, and BMI.

2022 American Society of Anesthesiologists Practice Guidelines for Management of the Difficult Airway

The American Society of Anesthesiologists; All India Difficult Airway Association; European Airway Management Society; European Society of Anaesthesiology and Intensive Care; Italian Society of Anesthesiology, Analgesia, Resuscitation and Intensive Care; Learning, Teaching and Investigation Difficult Airway Group; Society for Airway Management; Society for Ambulatory Anesthesia; Society for Head and Neck Anesthesia; Society for Pediatric Anesthesia; Society of Critical Care Anesthesiologists; and the Trauma Anesthesiology Society present an updated report of the Practice Guidelines for Management of the Difficult Airway.

The effect of low flow anesthesia on hemodynamic and peripheral oxygenation parameters in obesity surgery

Objectives:

To investigate the effects of low-flow anesthesia on hemodynamic parameters and recovery from anesthesia in obese individuals undergoing laparoscopic surgery.

Methods:

This randomized-controlled and prospective study included 44 obese patients who underwent laparoscopic sleeve gastrectomy operation. The patients were randomly allocated into 2 groups as low-flow and high-flow anesthesia. Further, the groups compared in terms of hemodynamic parameters, anesthesia recovery times, operation times, and arterial blood gas parameters.

Results:

The groups were similar with respect to demographic data. Heart rate, peripheral oxygen saturation, arterial blood pressure measurements, end-tidal, and CO₂, lactate levels measurements were similar in both groups during the entire procedure. There was also no statistically significant difference in terms of arterial blood gas parameters or anesthesia recovery periods.

Conclusion:

Low-flow anesthesia in laparoscopic obesity surgery seems to be safer compared to high-flow anesthesia in terms of the adequacy of tissue perfusion, depth of anesthesia, and postoperative recovery.

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

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12871-021-01343-4.

Faultless impressive results: How can I do it?

The result section of the research presents your actual work. The results could be either support or invalidate the research hypothesis. Writing in a scientific, impressive and faultless way increases the chance of acceptance in high impact factors journal. The result would be in a descriptive style around 3-4 paragraphs. The first paragraph is normally an illustration of the studied population. Followed by the second paragraph, which is a description of the main results. Expressing the secondary outcome is usually the next paragraph. Additional results not directly related to the research question are mentioned in the last paragraph. Chosen the correct format for the data (Tables and Figures) is mentioned in this review.

Airway physical examination tests for detection of difficult airway management in apparently normal adult patients

BACKGROUND

The unanticipated difficult airway is a potentially life-threatening event during anaesthesia or acute conditions. An unsuccessfully managed upper airway is associated with serious morbidity and mortality. Several bedside screening tests are used in clinical practice to identify those at high risk of difficult airway. Their accuracy and benefit however, remains unclear.

OBJECTIVES

The objective of this review was to characterize and compare the diagnostic accuracy of the Mallampati classification and other commonly used airway examination tests for assessing the physical status of the airway in adult patients with no apparent anatomical airway abnormalities. We performed this individually for each of the four descriptors of the difficult airway: difficult face mask ventilation, difficult laryngoscopy, difficult tracheal intubation, and failed intubation.

SEARCH METHODS

We searched major electronic databases including CENTRAL, MEDLINE, Embase, ISI Web of Science, CINAHL, as well as regional, subject specific, and dissertation and theses databases from inception to 16 December 2016, without language restrictions. In addition, we searched the Science Citation Index and checked the references of all the relevant studies. We also handsearched selected journals, conference proceedings, and relevant guidelines. We updated this search in March 2018, but we have not yet incorporated these results.

SELECTION CRITERIA

We considered full-text diagnostic test accuracy studies of any individual index test, or a combination of tests, against a reference standard. Participants were adults without obvious airway abnormalities, who were having laryngoscopy performed with a standard laryngoscope and the trachea intubated with a standard tracheal tube. Index tests included the Mallampati test, modified Mallampati test, Wilson risk score, thyromental distance, sternomental distance, mouth opening test, upper lip bite test, or any combination of these. The target condition was difficult airway, with one of the following reference standards: difficult face mask ventilation, difficult laryngoscopy, difficult tracheal intubation, and failed intubation.

DATA COLLECTION AND ANALYSIS

We performed screening and selection of the studies, data extraction and assessment of methodological quality (using QUADAS-2) independently and in duplicate. We designed a Microsoft Access database for data collection and used Review Manager 5 and R for data analysis. For each index test and each reference standard, we assessed sensitivity and specificity. We produced forest plots and summary receiver operating characteristic (ROC) plots to summarize the data. Where possible, we performed meta-analyses to calculate pooled estimates and compare test accuracy indirectly using bivariate models. We investigated heterogeneity and performed sensitivity analyses.

MAIN RESULTS

We included 133 (127 cohort type and 6 case-control) studies involving 844,206 participants. We evaluated a total of seven different prespecified index tests in the 133 studies, as well as 69 non-prespecified, and 32 combinations. For the prespecified index tests, we found six studies for the Mallampati test, 105 for the modified Mallampati test, six for the Wilson risk score, 52 for thyromental distance, 18 for sternomental distance, 34 for the mouth opening test, and 30 for the upper lip bite test. Difficult face mask ventilation was the reference standard in seven studies, difficult laryngoscopy in 92 studies, difficult tracheal intubation in 50 studies, and failed intubation in two studies. Across all studies, we judged the risk of bias to be variable for the different domains; we mostly observed low risk of bias for patient selection, flow and timing, and unclear risk of bias for reference standard and index test. Applicability concerns were generally low for all domains. For difficult laryngoscopy, the summary sensitivity ranged from 0.22 (95% confidence interval (CI) 0.13 to 0.33; mouth opening test) to 0.67 (95% CI 0.45 to 0.83; upper lip bite test) and the summary specificity ranged from 0.80 (95% CI 0.74 to 0.85; modified Mallampati test) to 0.95 (95% CI 0.88 to 0.98; Wilson risk score). The upper lip bite test for diagnosing difficult laryngoscopy provided the highest sensitivity compared to the other tests (P < 0.001). For difficult tracheal intubation, summary sensitivity ranged from 0.24 (95% CI 0.12 to 0.43; thyromental distance) to 0.51 (95% CI 0.40 to 0.61; modified Mallampati test) and the summary specificity ranged from 0.87 (95% CI 0.82 to 0.91; modified Mallampati test) to 0.93 (0.87 to 0.96; mouth opening test). The modified Mallampati test had the highest sensitivity for diagnosing difficult tracheal intubation compared to the other tests (P < 0.001). For difficult face mask ventilation, we could only estimate summary sensitivity (0.17, 95% CI 0.06 to 0.39) and specificity (0.90, 95% CI 0.81 to 0.95) for the modified Mallampati test.

AUTHORS' CONCLUSIONS

Bedside airway examination tests, for assessing the physical status of the airway in adults with no apparent anatomical airway abnormalities, are designed as screening tests. Screening tests are expected to have high sensitivities. We found that all investigated index tests had relatively low sensitivities with high variability. In contrast, specificities were consistently and markedly higher than sensitivities across all tests. The standard bedside airway examination tests should be interpreted with caution, as they do not appear to be good screening tests. Among the tests we examined, the upper lip bite test showed the most favourable diagnostic test accuracy properties. Given the paucity of available data, future research is needed to develop tests with high sensitivities to make them useful, and to consider their use for screening difficult face mask ventilation and failed intubation. The 27 studies in 'Studies awaiting classification' may alter the conclusions of the review, once we have assessed them.