Prediction Of Difficult Airway Research Articles

Prediction of a Difficult Airway Using the ARNE Score and Flexible Laryngoscopy in Patients with Laryngeal Pathology.

Background and Objectives: The ARNE score was developed for the prediction of a difficult airway for both general and ear, nose and throat (ENT) surgery with a universal cut-off value. We tested the accuracy of this score in the case of laryngeal surgery and provided an insight into its effects in combination with flexible laryngoscopy. Materials and Methods: This prospective pilot clinical study included 100 patients who were being scheduled for microscopic laryngeal surgery. We calculated the ARNE score for every patient, and flexible laryngoscopy was provided preoperative. Difficult intubation was assessed according to the intubation difficulty score (IDS). Results: A total of 33% patients had difficult intubation according to the IDS. The ARNE score showed limited accuracy for the prediction of difficult intubation in laryngology with p < 0.0001 and an AUC of 0.784. Flexible laryngoscopy also showed limitations when used as an independent parameter with p < 0.0001 and an AUC of 0.766. We defined a new cut-off value of 15.50 for laryngology, according to the AUC. After the patients were divided into two groups, according to the new cut-off value and provided cut-off value, the AUC improved to 0.707 from 0.619, respectively. Flexible laryngoscopy improved the prediction model of the ARNE score to an AUC of 0.882 and of the new cut-off value to an AUC of 0.833. Conclusions: It is recommended to use flexible laryngoscopy together with the ARNE score in difficult airway prediction in patients with laryngeal pathology. Also, the universally recommended cut-off value of 11 cannot be effectively used in laryngology, and a new cut-off value of 15.50 is recommended.

Ultrasound-based assessment of tongue thickness for prediction of difficult laryngoscopy and intubation

Abstract Background and Aims: Predicting difficult airway and preparedness for the same can help prevent catastrophic situations while handling the airway. With the increasing familiarity of anaesthesiologists to the use of ultrasound machine and its easy availability and non-invasiveness, we sought to study its utility in airway assessment, by measuring the thickness of tongue, to predict difficult laryngoscopy and intubation. Material and Methods: A total of 85 patients undergoing elective surgeries under general anaesthesia with endotracheal intubation were examined preoperatively. Tongue thickness was measured using submental ultrasonography in the median sagittal plane along with other tests of airway assessment. Cormack Lehane grade on laryngoscopy and Intubation Difficulty Scale Score was recorded. The sensitivity, specificity, positive and negative predictive value, and accuracy was calculated for tongue thickness for predicting difficult intubation. Results: The tongue thickness in those with difficult intubation (4.83 ± 0.62) was significantly higher than those without difficult intubation (4.38 ± 0.65). The ratio of tongue thickness to thyromental distance was also significantly higher in difficult intubation group. The area under the receiver operating characteristic curve for predicting difficult laryngoscopy and intubation was higher for tongue thickness as compared to other clinical parameters. The sensitivity and specificity of tongue thickness to predict difficult laryngoscopy was 100% and 83%, respectively, and to predict difficult intubation was 72% and 59%, respectively, with an accuracy of 72%. Conclusion: Ultrasound based assessment of tongue thickness can be a useful predictor of difficult airway along with clinical assessment of the airway.

Reliable prediction of difficult airway for tracheal intubation from patient preoperative photographs by machine learning methods

BackgroundEstimating the risk of a difficult tracheal intubation should help clinicians in better anaesthesia planning, to maximize patient safety. Routine bedside screenings suffer from low sensitivity. ObjectiveTo develop and evaluate machine learning (ML) and deep learning (DL) algorithms for the reliable prediction of intubation risk, using information about airway morphology. MethodsObservational, prospective cohort study enrolling n=623 patients who underwent tracheal intubation: 53/623 difficult cases (prevalence 8.51%).First, we used our previously validated deep convolutional neural network (DCNN) to extract 2D image coordinates for 27 + 13 relevant anatomical landmarks in two preoperative photos (frontal and lateral views). Here we propose a method to determine the 3D pose of the camera with respect to the patient and to obtain the 3D world coordinates of these landmarks. Then we compute a novel set of dM=59 morphological features (distances, areas, angles and ratios), engineered with our anaesthesiologists to characterize each individual's airway anatomy towards prediction.Subsequently, here we propose four ad hoc ML pipelines for difficult intubation prognosis, each with four stages: feature scaling, imputation, resampling for imbalanced learning, and binary classification (Logistic Regression, Support Vector Machines, Random Forests and eXtreme Gradient Boosting). These compound ML pipelines were fed with the dM=59 morphological features, alongside dD=7 demographic variables. Here we trained them with automatic hyperparameter tuning (Bayesian search) and probability calibration (Platt scaling). In addition, we developed an ad hoc multi-input DCNN to estimate the intubation risk directly from each pair of photographs, i.e. without any intermediate morphological description.Performance was evaluated using optimal Bayesian decision theory. It was compared against experts' judgement and against state-of-the-art methods (three clinical formulae, four ML, four DL models). ResultsOur four ad hoc ML pipelines with engineered morphological features achieved similar discrimination capabilities: median AUCs between 0.746 and 0.766. They significantly outperformed both expert judgement and all state-of-the-art methods (highest AUC at 0.716). Conversely, our multi-input DCNN yielded low performance due to overfitting. This same behaviour occurred for the state-of-the-art DL algorithms. Overall, the best method was our XGB pipeline, with the fewest false negatives at the optimal Bayesian decision threshold. ConclusionsWe proposed and validated ML models to assist clinicians in anaesthesia planning, providing a reliable calibrated estimate of airway intubation risk, which outperformed expert assessments and state-of-the-art methods. Our novel set of engineered features succeeded in providing informative descriptions for prognosis.

Prediction of Difficult Airway Using Sonographic Assessment of Pre-Epiglottic Space Versus Mallampati Grading in Correlation to Cormack Lehane Grading

Prediction of Difficult Airway Using Sonographic Assessment of Pre-Epiglottic Space Versus Mallampati Grading in Correlation to Cormack Lehane Grading

Utility of ultrasonography for prediction of difficult airway and assessment of dynamic airway changes

Background: Several clinical tools are used for preoperative airway assessment. Ultrasound is an emerging tool in anesthesia, and its role in the upper airway needs to be explored. Aims and Objectives: We aimed to assess the role of airway ultrasound in predicting difficult laryngoscopy and airway changes following induction of anesthesia. Materials and Methods: Clinical airway assessment was performed with a modified Mallampati score in 100 elective surgical patients. Ultrasound variables in pre- and post-induction of anesthesia were: distance between the skin and vocal cords at the hyoid bone (DSVC hb), distance from the skin to the thyroid isthmus, distance from the skin to the tracheal ring at suprasternal notch level, and distance between the skin and the cricothyroid (DSCM). Cormack–Lehane (CL) grades 3b and 4 were classified as having a DL. Association of clinical, ultrasonography (USG) indicators and CL grading was correlated. Dynamic changes in anterior neck tissues following induction of anesthesia were also measured. Results: Pre-induction USG variables were significantly higher in the DL group compared to the NDL group (P&lt;0.05), except DSCM. Post-induction all USG parameters were significantly higher in the DL group. Among USG variables, DSVC hb and DTSI had the highest AUCs of 0.801 and 0.772, respectively. DSVC hb had a sensitivity of 76% and a specificity of 100% for DL. DTSI had a sensitivity of 92% and a specificity of 72%. There was a significant increase in depth after anesthetic induction of all USG parameters except DSCM. Conclusions: Ultrasound of anterior airway tissues can be considered for prediction of DL and allows dynamic airway assessment.

Assessment of Different Indices as Predictors of Difficult Airway in Obese Patients.

Introduction Difficulties with tracheal intubation contribute to the morbidity and mortality associated with anesthesia. Suggested predictors for difficult airwayinclude, history of obstructive sleep apnea, high Mallampati score, elderly, male, short neck, and high Wilson score.However, none of these has high diagnostic accuracy particularly in obese population. Parameters used to quantify obesity such as Body Mass Index(BMI), mid arm circumference, skin fold thickness,etc. have not been used as independent predictors of difficult airway. This study has been designed to evaluate the accuracy of commonly used tools to assess difficult airway and to test other obesity markers as scale for assessing difficult airway i.e. Bag mask ventilation grade ≥3 or Cormack-Lehane Grade≥3 on Direct Laryngoscopy or number of intubation attempts ≥3. Aim To assess BMI, Mallampati grading, Neck circumference and Thyromental distance as predictors of difficult airway in obese patients and to validate neck circumference to thyromental distance ratio and skin fold thickness as a tool for assessment of difficult airway (Bag mask ventilation grade ≥3 or Cormack-Lehane Grade≥3 on Direct Laryngoscopy or number of intubation attempts ≥3.) in obese patients. Methods This prospective observational study was carried out on 51 obese patients (as per BMI) of ASA gradeII, either sex, aged 23 to 57 years posted for elective surgery under general anesthesia with endotracheal intubation. After subjective assessment of difficult airway following data sets and variables were obtained - sex, weight, height, body mass index (BMI),Modified Mallampati class (MPG), Cormack-Lehane (CL) grade, adequacy of bag mask ventilation (BMV), number of endotracheal intubation attempts, patient's neck circumference (NC), thyromental distance (TMD), waist-hip ratio (WHR) and Skin fold thickness. The observations made during the study were statistically analyzed and correlated as predictors for difficult airway. Result Correlation of MPG to CL-grade (r-0.41, p-0.003), BMV (r-0.31, p-0.028) & No. of intubationattempts (r-0.37, p-0.007) was mild & statistically significant. Correlation of Neck Circumference with CL-grade (r-0.57, p-0.000), & No. of intubation attempts (r-0.62, p-0.000), found moderate & statistically significant, & with BMV was mild and statistically significant (r-0.48, p-0.000). Correlation of Thyromental Distance to CL-grade (r-0.65, p-0.000), BMV (r-0.70, p-0.000) & No. of intubationattempts (r-0.61, p-0.000) was moderate & statistically significant. Correlation of BMI to CL-grade (r-0.11, p-0.428), BMV (r-0.04, p-0.757) & No. of intubation attempts (r-0.16, p-0.257) was weak & not significant. Skin Fold Thickness showed no significant association with Difficult airway i.e., CL Grade (p-0.478), BMV (p-0.101), and No. of intubation attempts (p-0.143). Correlation of NC/TMD ratio with BMV (r-0.74, p-0.000), CL-grade (r-0.76, p-0.000), & No. of intubation attempts (r-0.77, p-0.000) wasmoderate & statistically significant. Conclusion NC, TMDand NC/TMD Ratio depicted a close association with airway difficulty in obese patients. Obesity grade is a risk factor for difficult airway but predictors of obesity including Skin Fold Thickness, individually did not showassociation with difficult airway (small sample size may be a limiting factor). None of the commonly performed tests alone has proven to be adequate in predicting difficult intubation in the obese population.

Ultrasound Versus Conventional Methods (Mallampati Score and Thyromental Distance) for Prediction of Difficult Airway in Adult Patient without Anticipated Difficult Airway

Ultrasound Versus Conventional Methods (Mallampati Score and Thyromental Distance) for Prediction of Difficult Airway in Adult Patient without Anticipated Difficult Airway

Overcoming Challenges: A Case Report of Dexmedetomidine-Ketamine Sedation and Peribulbar Block for Pediatric Ocular Surgery with Difficult Airway Predictors

Children usually require general anesthesia when undergoing ophthalmic surgery since they are unable to cooperate and remain motionless during the procedure. Although most cases are straightforward, a subset of patients may have genetic syndromes or metabolic diseases associated with difficult intubation. A 10-year-old boy with an undiagnosed neurological condition and multiple predictors of difficult airway was scheduled for a cataract surgery. The preoperative evaluation showed a history of unsuccessful intubation attempts. Therefore, we decided not to induce general anesthesia and provide a sedation with dexmedetomidine and ketamine combined with a peribulbar block. Our positive experience demonstrates that there may be safe alternatives to general anesthesia for pediatric ophthalmic surgery, particularly when securing a definitive airway poses significant challenges.

Evaluation of Thyromental Height Test for Predicting Difficult Laryngoscopy in Morbid Obese Patients Candidate for Bariatric Surgery: An Observational Study

Upper airway management in morbid obese patients undergoing bariatric surgery is a major challenge during induction of anesthesia. Bed side Thyromental Height Test (TMHT) is an easy and valid predictor for prediction of difficult airway in non‑obese patients, but its precision in morbid obese patients haven’t been evaluated yet. The purpose of present study is to find the accuracy of TMHT for the prediction of difficult laryngoscopy in morbid obese patients undergoing bariatric surgery. The present prospective observational study was performed on 95 morbid obese patients at Sina hospital during 2020. Preoperative exams of patients include the assessment of Mallampati classification, thyromental height, thyromental, sternomental and interincisor distances. After induction of anesthesia, the laryngoscopy view was evaluated using the Cormack Lehane classification, and the relationship between these tests to prediction of difficult laryngoscopy view based on the Cormack Lehane degrees (grades 3,4) were evaluated. A total of 95 morbid obese patients with a mean BMI of 44.7±5.6 kg/m2.were included in the study. 67.3% of them were women. The incidence of difficult laryngoscopy (C &amp; L III, IV) was 16.8%. TMHT less than 56.5 mm, with 98% positive predictive value and 93% accuracy was the best predictor of difficult intubation in these patients. The accuracy of thyromental, sternomental and interincisor distance, with cut off value less than 51.5 mm, 89.5 mm, 41.5 mm respectively was less than TMHT in prediction of difficult laryngoscopy in morbid obese patients. In morbid obese patients with a BMI greater than 40, thyromental height less than 56.5 mm with 98% positive predictive value and 93% accuracy is the best predictor for difficult laryngoscopy view.

Comparison between clinical and ultrasonographic parameters in predicting difficult airway: An observational prospective study

Introduction : Various clinical tests are applied at the bedside to predict difficult airway during the pre-anaesthetic examination. Ultrasonography can be used to evaluate the airway and predict difficulty in laryngoscopy and endotracheal intubation.&#x0D; Objective : To compare the various clinical and ultrasonographic parameters in predicting difficult airway.&#x0D; Methodology: This prospective observational study was carried out at Birat Medical College and Teaching Hospital. The clinical parameters obtained were Modified Mallampati Grade (MMP), Upper Lip Bite Test (ULBT) and Thyromental Distance (TMD) and the ultrasonographic (USG) parameters used were Tongue Volume (TV), Hyomental Distance Ratio (HMDR) and distance of soft tissue from skin to hyoid bone (DSHB).These results were correlated with Cormack-Lehane (CL) grade during laryngoscopy of 200 patients. The sensitivity, specificity, Receiver Operating Characteristic (ROC) curve along with Area under the curve (AUC) was calculated for each parameter.&#x0D; Result: Incidence of difficult airway was 15.5%. The sensitivity and specificity of ultrasonographic tongue volume (TV) was higher among all parameters in predicting difficult airway. The specificity of HMDR and DSHB were better compared to clinical parameters.&#x0D; Conclusion: The use of ultrasound in pre-anaesthetic examination can be a viable tool along with clinical parameters for prediction of difficult airway.

Evaluation of Ultrasound Airway Assessment Parameters in Pregnant Patients and their Comparison With that of Nonpregnant Women: A Prospective Cohort Study

(Int J Obstet Anesth. 2023;53:103623) Pregnancy-induced changes in airway anatomy and physiology can make airway management more difficult in this population. Clinical airway assessment parameters are used to predict difficult airways; however, literature evaluating ultrasound (US) airway parameters in pregnant patients compared with nonpregnant patients is limited. This study aimed to evaluate and compare US airway assessment parameters in pregnant and non‐pregnant women. This study also investigated the associations between established difficult airway predictors and US parameters in the two groups.

Evaluation and comparison of sonographic difficult airway assessment parameters with clinical airway predictors

Abstract Background and Aims: None of the clinical difficult airway predictors are 100% sensitive and specific. Ultrasound is being used for airway assessment, but there is still no established parameters or model to predict difficult laryngoscopy. This observational study was planned to determine the predictive ability of clinical and sonography-based airway assessment parameters for difficult laryngoscopy and intubation. Material and Methods: A total of 130 patients of 18–60 years of age undergoing elective intubation were included. The distribution of Cormack–Lehane (CL) grade and intubation difficulty scale (IDS) was correlated with the clinical and sonographic screening parameters for difficult airways. Results The prevalence of difficult laryngoscopy and difficult intubation in our study was 17.6% and 11.5%, respectively. Mallampati grade (MMG), upper lip bite test (ULBT), neck circumference, hyomental distance ratio (HMDR), tongue thickness (TT), skin to epiglottis/epiglottis to vocal cord distance (SED/E-VC), and mandibular condylar mobility (MCM) had significant association with the difficult laryngoscopy and MMG, neck circumference, SED, SED/E-VC; MCM had significant association with the difficult intubation. The combination of these predictors showed better diagnostic ability for difficult airways. Model 1 based on ultrasound parameters showed an area under the curve (AUC) of 0.848 (CI- 0.748-0.947, P value &lt; 0.0001) and model 2 based on combined clinical and ultrasound parameters showed an AUC of 0.755 (95% CI- 0.631-0.879, P value &lt; 0.0001). Conclusions: Ultrasound-based airway predictors can help in predicting difficult laryngoscopy and intubation along with the clinical parameters. Individual sonographic predictors have moderately satisfactory diagnostic profiles. The models based on combined tests have better diagnostic value.

Distance from skin to epiglottis measurement with ultrasonography as a predictor of difficult laryngoscopy: A diagnostic test accuracy meta-analysis

BackgroundThe studies investigating distance from the skin to epiglottis measurements by ultrasonography (US-DSEM) for prediction of difficult laryngoscopy are inconclusive and non-standardised. AimWe aimed to assess the diagnostic test accuracy (DTA) of US-DSEM relative to Cormack Lehane 3–4 for diagnosing difficult laryngoscopy using a meta-analysis method. MethodStrict inclusion criteria were used to provide clear definition of how to measure US-DSEM. PubMed, Cochrane Library, Embase and Google Scholar were searched for papers published between January 2005 and March 2021 to perform a systematic review and DTA meta-analysis. Publications reporting data on US-DSEM in difficult laryngoscopy prediction in adults with normal airway were included. Exclusions were obese or pregnant patients and patients with difficult airway predictors and those in which the measurements were made in a position other than supine and neutral positions. The pooled sensitivity, specificity, positive/negative likelihood ratios (PLR/NLR), diagnostic odds ratio (DOR), and area under the hierarchic summary receiver operating characteristics curve (AU-HSROC) were determined. Results1244 patients from nine studies were included. Pooled sensitivity: 0.68 (95%CI 0.39–0.87), specificity: 0.76 (95%CI 0.68–0.83), PLR: 2.87 (95%CI 2.20–3.73), NLR: 0.42 (%95CI 0.20–0.87), DOR: 6.80 (95%CI 2.72–17.3), and AU-HSROC: 0.79 (0.75–0.82) were the results. No threshold effect (r = 0.53, p = 0.137) and publication bias (p = 0.540) existed. ConclusionThis meta-analysis showed that US-DSEM measured with linear probe placed transversely at the thyrohyoid membrane level with midline approach and neutral head position is accurate and has diagnostic value in predicting difficult laryngoscopy. However, its sensitivity and specificity are not sufficient to replace clinical tests.

Comparison of Intubating Conditions on the basis of Neuromuscular Monitoring versus Clinical Assessment Guided Tracheal Intubation: A Randomized Interventional Study

Background: Laryngoscopy and endotracheal intubation have been associated with marked hemodynamic responses and hazards. This study was undertaken with the purpose to compare the intubating conditions when the suitable time for intubation was judged by either clinical assessment or train-of-four monitoring.&#x0D; Methods: 60 patients without any difficult airway predictors, posted for surgery under general anaesthesia, were randomised into two groups. In Group A patients, the trachea was intubated after train of four counts became zero in adductor pollicis muscle, whereas in Group B patients, intubation was done after clinically judging jaw muscle relaxation. The primary objective was to compare Intubating conditions and mean duration of time between the administration of a neuromuscular blocker and endotracheal intubation. The secondary objectives included number of attempts, changes in hemodynamic parameters. Results were analysed by the Analysis of variance and chi-square tests.&#x0D; Results: In all Group A patients excellent and good intubating conditions were observed, whereas 25 out of 30 patients (83%) in Group B showed excellent and good intubation conditions. The mean time required for intubation was significantly longer in Group A compared to Group B (369 ± 79 s vs. 191 ± 5 s). HR and mean arterial pressure were significantly higher in Group B as compared to Group A after laryngoscopy and tracheal intubation (P &lt; 0.05).&#x0D; Conclusion: Better intubating conditions and more haemodynamic stability are seen after attaining complete relaxation of laryngeal muscles, as detected by neuromuscular monitoring of adductor pollicis muscle.

Sanyar Video Laryngoscope Improved Time and First Pass Success of Tracheal Intubation in Intensive Care Unit in Compared to Direct Laryngoscopy

Background: Airway management and tracheal intubation in the ICU is a difficult procedure that may be concomitant with major complications. The purpose of this study was to evaluate the effect of the SANYAR ® video laryngoscope(S-VL) on laryngeal view and first Pass Success of tracheal Intubation compared with direct laryngoscopy.&#x0D; Methods: This comparative, prospective clinical study was conducted on 120 adult patients in a single-center, in a surgical ICU under the supervision of an anesthesiologist in a university hospital. Difficult airway predictors, glottic view, first Pass Success of tracheal Intubation and time of intubation were evaluated with Macintosh laryngoscopy (ML) or the SANYAR® Video Laryngoscope(S-VL).&#x0D; Results: Tracheal intubation was performed in 58 critically ill patients using ML and 62 patients using S-VL. According to Cormack and Lehane (C&amp;L) grading glottic visualization was more difficult using ML (41%, C&amp;L grade 3 and 4) compared with S-VL (13%, C&amp;L grade 3 and 4) p&lt;0.001. Intubation of trachea was more successful in the first attempt, in patients with at least one difficult airway predictor with a S-VL compared to ML (87% vs. 38%; P = 0.001), time of intubation was also shorter by using S-VL.&#x0D; Conclusion: Among critically ill patients in the intensive care unit, who require intubation, the SANYAR video laryngoscopy improved glottis view compared to the Macintosh direct laryngoscopy and first-pass orotracheal intubation rate especially in patients with potentially difficult airways.

Improving Clinical Decision-Making: A Focus on Early Warning Systems and Scoring Methods

Dear Editor, Examinations, laboratory tests, radiology, and clinical experience are required to make the most appropriate clinical decisions. There is no single universal clinical decision-making method advocated in routine medical literature. Often, this process is driven by experience, exploration, and clinical gestalt. Clinician management serves as a subjective decision tool in disease management. It has been extensively studied in the literature, particularly in entities such as pulmonary embolism, difficult airway prediction, and severe COVID-19 (1). By combining parameters such as laboratory-vital parameters and combined hematological parameters, clinical decision-making tools have been developed (2-3). Early warning systems (EWS) help predict which patients will require critical care by evaluating physiological parameters in busy and crowded workspaces. These scores, which can be measured through vital parameters and a simple physical examination, ensure the effective use of resources (4). In intensive care units (ICUs), complex systems are used to predict mortality. APACHE II is the most well-known score used in intensive care, consisting of many parameters that are computationally difficult. Vital parameters, biochemical tests, and blood gas analysis results are used when calculating APACHE II (5). On the other hand, important features of scores used in the emergency department are their quick and easy calculation, requiring no professional knowledge. The VitalPAC Early Warning Score (ViEWS), developed by Prytherch et al. in 2010, is an EWS that includes six physiological parameters. This score records the pulse rate, systolic blood pressure, respiratory minute rate, body temperature, level of consciousness (assessed by AVPU), and peripheral oxygen saturation (SpO2) parameters. ViEWS is created based on the degree of deviation of these six parameters from their normal ranges. Additional points are given if the patient receives oxygen support. The total ViEWS value, calculated by considering each parameter, provides information about the patient's prognosis (6). By adding the rapid lactate level to ViEWS (ViEWS-L), a score that better predicts outcomes in the geriatric population was developed (7). The Modified Early Warning Score (MEWS) is one of the triage scoring methods used to identify patients in need of intensive and critical care, and to expedite their transfer to the intensive care unit. It evaluates systolic blood pressure, heart rate, respiratory rate, body temperature, and neurological status. MEWS is included in routine nursing care procedures in some countries and is routinely assessed by nurses. ICU admission rates and mortality rates increase for those who score 5 or more on this scale, although this threshold may vary in different populations or clinical situations. In a Korean study analyzing sepsis patients, combining lactate with the MEWS score improved the prediction of the need for intensive care (8). The National Early Warning Score (NEWS) is another scoring system used to determine clinical deterioration and follow-up level for all hospitalized patients, as well as to predict clinically high-risk patients. It is calculated by evaluating respiratory rate, oxygen saturation, oxygen support status, blood pressure, body temperature, and neurological status. The NEWS-L score, obtained by combining NEWS with lactate, has been reported to be superior to NEWS alone in geriatric critically ill patients (9). However, contrary to the geriatric patient study, Özkan's study reported that NEWS-L was not superior to NEWS and that they exhibited similar predictive abilities (10). As a result, researchers are actively working on new parameters and scoring systems to optimize resource utilization and predict critical illness. Researchers should be encouraged to investigate studies on EWS lactate combinations or other combinations.

Efficacy and adverse events profile of videolaryngoscopy in critically ill patients: subanalysis of the INTUBE study

BackgroundTracheal intubation is a high-risk procedure in the critically ill, with increased intubation failure rates and a high risk of other adverse events. Videolaryngoscopy might improve intubation outcomes in this population, but evidence remains conflicting, and its impact on adverse event rates is debated. MethodsThis is a subanalysis of a large international prospective cohort of critically ill patients (INTUBE Study) performed from 1 October 2018 to 31 July 2019 and involving 197 sites from 29 countries across five continents. Our primary aim was to determine the first-pass intubation success rates of videolaryngoscopy. Secondary aims were characterising (a) videolaryngoscopy use in the critically ill patient population and (b) the incidence of severe adverse effects compared with direct laryngoscopy. ResultsOf 2916 patients, videolaryngoscopy was used in 500 patients (17.2%) and direct laryngoscopy in 2416 (82.8%). First-pass intubation success was higher with videolaryngoscopy compared with direct laryngoscopy (84% vs 79%, P=0.02). Patients undergoing videolaryngoscopy had a higher frequency of difficult airway predictors (60% vs 40%, P<0.001). In adjusted analyses, videolaryngoscopy increased the probability of first-pass intubation success, with an OR of 1.40 (95% confidence interval [CI] 1.05–1.87). Videolaryngoscopy was not significantly associated with risk of major adverse events (odds ratio 1.24, 95% CI 0.95–1.62) or cardiovascular events (odds ratio 0.78, 95% CI 0.60–1.02). ConclusionsIn critically ill patients, videolaryngoscopy was associated with higher first-pass intubation success rates, despite being used in a population at higher risk of difficult airway management. Videolaryngoscopy was not associated with overall risk of major adverse events. Clinical trial registrationNCT03616054.

Global research trends in prediction of difficult airways: A bibliometric and visualization study.

Many tools are used to predict difficult airway, including bedside screening tests, radiological variables, and ultrasonography. However, the "gold standard" to identify difficult airway before intubation has not been established. The assessment and prediction of difficult airway is receiving increasing attention in clinical practice due to the devastating results of failed oxygenation or intubation. A literature visualization study is necessary to understand the research trend and help tailor future research directions. Science citation index-expanded web of Science database were used to search for literature related to assessment and prediction of difficult airways published before May 9th, 2022. VOS viewer software was used for visual analysis, including literature statistics, and co-occurrence analysis. A total of 2609 articles were included. The amount of relevant research interest and literature is increasing every year. According to co-occurrence network analysis, the research results can be grouped into the following 5 clusters, intubation approaches, intubation in special populations, difficult airway assessment tests, intubation in critical care/emergency settings and education, and laryngoscopes. Co-occurrence overlay analysis showed that video laryngoscopes and index prediction (including computed tomography and ultrasonography), emerged recently and comprised an important percentage of current studies. It can be predicted that future studies should focus on understanding the upper airway anatomy and constructing risk index predictions. Based on current global research trends, risk index predictions are the next hot topics in the evaluation and prediction of difficult airways, and video laryngoscopes will continue to be a hot topic in this field.

USG guided indices as a tool for prediction of difficult airway in obese: an observational study

Background: Now a days, Ultrasound has been the gold standard and an invaluable tool in the field of regional anaesthesia, especially for upper limb and truncal blocks where general anesthesia is associated with high risk and also in patients posted for general anesthesia with anticipated difficult airway. Recently, few studies have published the importance of Ultrasound in the evaluation of airway and related procedures. Aim of this study is to evaluate the use of USG guided indices for prediction of difficult airway in obese patients and correlating them with clinical parameters. Methods: This study was a prospective observational study done in a tertiary health care center, Government General hospital, Kakinada over a duration of two months from August 2022 to September 2022. Eight Ultrasound parameters like tongue thickness, skin to hyoid distance, pre-epiglottic space, skin to midpoint of vocal cords, skin to thyroid isthmus, anterior soft tissue thickness at suprasternal notch, hyomental distance and thyromental distance were correlated with clinical evaluation (Cormacke-lehane grading) in this study and conclusions were drawn. Results: Out of 30 cases studied, 20 (70%) were predicted to be difficult with Ultrasound airway examination and 16 (53.3%) were found to have difficulty in intubation clinically. Conclusions: The following USG indices like pre-epiglottic space, Skin to midpoint of vocal cords, Anterior soft tissue thickness at Suprasternal notch, Hyomental distance in mid-extended position and thyromental distance were found to be clinically correlated to predict difficult airway and intubation in obese patients.

A fully-automatic semi-supervised deep learning model for difficult airway assessment

BackgroundDifficult airway conditions represent a substantial challenge for clinicians. Predicting such conditions is essential for subsequent treatment planning, but the reported diagnostic accuracies are still quite low. To overcome these challenges, we developed a rapid, non-invasive, cost-effective, and highly-accurate deep-learning approach to identify difficult airway conditions through photographic image analysis. MethodsFor each of 1000 patients scheduled for elective surgery under general anesthesia, images were captured from 9 specific and different viewpoints. The collected image set was divided into training and testing subsets in the ratio of 8:2. We used a semi-supervised deep-learning method to train and test an AI model for difficult airway prediction. ResultsWe trained our semi-supervised deep-learning model using only 30% of the labeled training samples (with the remaining 70% used without labels). We evaluated the model performance using metrics of accuracy, sensitivity, specificity, F1-score, and the area under the ROC curve (AUC). The numerical values of these four metrics were found to be 90.00%, 89.58%, 90.13%, 81.13%, and 0.9435, respectively. For a fully-supervised learning scheme (with 100% of the labeled training samples used for model training), the corresponding values were 90.50%, 91.67%, 90.13%, 82.25%, and 0.9457, respectively. When three professional anesthesiologists conducted comprehensive evaluation, the corresponding results were 91.00%, 91.67%, 90.79%, 83.26%, and 0.9497, respectively. It can be seen that the semi-supervised deep learning model trained by us with only 30% labeled samples can achieve a comparable effect with the fully supervised learning model, but the sample labeling cost is smaller. Our method can achieve a good balance between performance and cost. At the same time, the results of the semi-supervised model trained with only 30% labeled samples were very close to the performance of human experts. ConclusionsTo the best of our knowledge, our study is the first one to apply a semi-supervised deep-learning method in order to identify the difficulties of both mask ventilation and intubation. Our AI-based image analysis system can be used as an effective tool to identify patients with difficult airway conditions. Clinical trial registrationChiCTR2100049879 (URL: http://www.chictr.org.cn).