Anaesthesia · 2026
Pre-operative three-dimensional face scans for predicting difficult facemask ventilation: a prospective development study.
СтатьяНаблюдательное исследованиеНазвание и аннотация: Перевод в очереди
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Wünsch Viktor A, Bommes Hannes, Germer Sofia, Piltan Milan, Breitfeld Philipp, Latus Sarah, Krause Linda, Zöllner Christian, Gundler Christopher, Petzoldt Martin
Аннотация
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.
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.