Daily Anesthesiology Research Analysis

INTRODUCTION: Difficult facemask ventilation is an entity that lacks a robust definition, leading to inconsistent identification in clinical practice and research. The aim of this study was to develop and validate an objective classification and numeric score for difficult facemask ventilation. METHODS: Four hundred patients who required tracheal intubation for ear, nose and throat or maxillofacial surgery participated in this prospective single centre study. After induction of anaesthesia, facemask ventilation was attempted using pressure-controlled mechanical ventilation. The primary outcome was difficult facemask ventilation documented as an alert in the patient health records. An independent observer assessed for potential indicators of difficult facemask ventilation: two-handed grip; oral airway use; jaw thrust; senior consultant anaesthetist taking over; conversion to manual rescue ventilation; peripheral oxygen saturation drop; tidal volume; and leak fraction. RESULTS: Difficult facemask ventilation occurred in 43 (10.8%) patients. Five eligible indicators were selected by cross-validated least absolute shrinkage selector operator regression and used to develop a multivariable logistic regression model (MASCAN model) and score (MASCAN score). These indicators were: two-handed grip; oral airway use; jaw thrust; tidal volume ≤ 2 ml.kg DISCUSSION: The MASCAN score is an objective, data-driven classification for difficult facemask ventilation with clearly defined thresholds that may improve airway documentation and inform future airway management. WHAT WE DID: Researchers studied 400 patients having head and neck surgery. After the patients were given an anaesthetic, doctors used a facemask to ventilate them. The researchers looked for signs that facemask ventilation was difficult, such as using two hands to hold the mask, using an oral airway tool, doing a jaw thrust, low airflow into the lungs or seeing oxygen levels drop. They then created a new classification system called the MASCAN score to help measure how difficult facemask ventilation was. WHY DID WE DO IT: Doctors sometimes find it hard to ventilate patients with a facemask during anaesthesia. There has not been a clear and reliable way to measure and document this problem. The researchers wanted to create a simple and objective system that doctors could use to communicate difficult facemask ventilation more easily and accurately. WHAT WE FOUND: About 1 in 10 patients had difficult facemask ventilation. The researchers found five important signs that reliably describe difficult facemask ventilation. These include using two hands to hold the mask, using an oral airway tool, doing a jaw thrust, low airflow into the lungs and a drop in oxygen levels. The new MASCAN score was very good at classifying difficult facemask ventilation and may help doctors improve their record keeping and future patient care.

INTRODUCTION: Facemask ventilation is a key airway management skill but predicting difficulty can be challenging. Pre-operative three-dimensional face scanning may have diagnostic value. We aimed to identify interpretable facial shape features and to quantify their value for predicting difficult facemask ventilation. METHODS: In this prospective observational single-centre study, pre-operative three-dimensional face scans were obtained, and a structured airway assessment was performed on patients undergoing ear, nose and throat or maxillofacial surgery. The primary outcome was difficult facemask ventilation documented as an alert in the patient health record. After postprocessing, three-dimensional face scans were fitted to an established, non-clinical facial model to identify interpretable shape coefficients. The area under the receiver operating characteristic (AUROC) curve for the DIFFMASK score was calculated before and after enrichment with three facial shape features and the added diagnostic value was assessed using likelihood ratios. RESULTS: Data from 398 patients were analysed. The optimism-corrected AUROC was 0.73 (95%CI 0.65-0.80) for the DIFFMASK score and 0.74 (95%CI 0.66-0.82) for selected facial shape features. Enrichment of the DIFFMASK score with three facial shape features improved goodness of model fit (p = 0.002) and achieved an optimism-corrected AUROC of 0.76 (95%CI 0.68-0.82). Generated face meshes with superimposed colour mapping revealed that morphological features of the nose, lower mandible, neck region and facial convexity were most predictive of difficult facemask ventilation. DISCUSSION: Pre-operative three-dimensional face scans predicted difficult facemask ventilation at least as well as the DIFFMASK score. Integrating the features of three selected facial shapes enriched the DIFFMASK score and improved its diagnostic value. Digital phenotyping can complement traditional clinical assessment. WHAT WE DID: Researchers studied 398 patients having head and neck surgery. Before surgery, they used a special 3D scanner to record and study each patient's face and carried out normal airway checks. After the patients were given anaesthesia, doctors used a facemask to ventilate them. Doctors recorded when facemask ventilation was difficult. The researchers then looked for facial features in the 3D scans that might help predict when facemask ventilation would be difficult. WHY DID WE DO IT: Ventilation with a facemask is an important part of anaesthesia, but it can sometimes be difficult. Doctors wanted to know if 3D face scans could help spot patients in advance before anaesthesia who may develop difficulties. This could help doctors prepare and improve patient safety. WHAT WE FOUND: The 3D face scans worked at least as well as the usual clinical scoring system at predicting difficult facemask ventilation. When the researchers combined the face scan information with the usual clinical scoring system, the prediction became even better. Certain facial features, such as the shape of the nose, jaw, neck and face, were linked with more difficult facemask ventilation. The study showed that 3D face scanning could be a useful extra tool for doctors when planning anaesthetic care.

PURPOSE: Advances in neuromuscular monitoring and reversal agents have facilitated the widespread clinical adoption of deep neuromuscular blockade (dNMB). Nevertheless, whether dNMB improves postoperative recovery quality compared with moderate neuromuscular blockade (mNMB) remains unclear. METHODS: We systematically searched PubMed, EMBASE, and the Cochrane Library up to January 31, 2026. Eligible studies were randomized controlled trials (RCTs) enrolling adult surgical patients, comparing dNMB versus mNMB, and reporting postoperative recovery quality measured by the Quality of Recovery-40 (QoR-40) or Quality of Recovery-15 (QoR-15) questionnaire. Pooled standardized mean differences (SMDs) or mean differences (MDs) with 95% confidence intervals (CIs) were calculated. The Cochrane RoB 2 tool was used to assess risk of bias, and the GRADE approach was applied to evaluate the certainty of evidence. The study protocol was registered in PROSPERO (CRD420261302047). RESULTS: A total of five RCTs encompassing 507 patients were included in the analysis. dNMB did not demonstrate superior postoperative recovery quality on postoperative day 1 (POD 1) (SMD = 0.36, 95% CI =-0.01 to 0.72) or POD 2 (SMD = -0.04, 95% CI = -0.35 to 0.27). CONCLUSION: Compared with mNMB, dNMB did not appear to improve recovery quality on the first or second postoperative day. Nevertheless, owing to the low certainty of evidence, well-designed, large-scale RCTs are still needed to validate these results.