Three‐dimensional facial phenotyping for difficult facemask ventilation: from association to clinical utility

We read with interest the prospective study by Wünsch et al. evaluating pre-operative three-dimensional facial scanning for prediction of difficult facemask ventilation [1]. Converting facial morphology into interpretable Faces Learned with an Articulated Model and Expressions (FLAME) coefficients is innovative, and the reported optimism-corrected area under the receiver operator curve (AUROC) of 0.74 for facial features alone is encouraging. However, several points may help clarify whether the increase in AUROC from 0.73 to 0.76 after adding three facial features to DIFFMASK represents clinically useful incremental information. The primary outcome was an alert entered in the health record by the airway operator, based on clinical judgement and without predefined physiological or procedural criteria. The operator could observe facial morphology and several DIFFMASK components, including obesity and beard and mandibular configurations. Predictor assessment and outcome classification were therefore not independent, creating scope for observer expectancy bias and differential outcome misclassification. Independent intra-operative observation does not fully address this concern because the definitive binary label remained the operator's alert. Definitions of difficult facemask ventilation vary substantially and influence reported prevalence and associations [2]. Facemask ventilation is also a patient-operator-technique interaction. Operator experience ranged from 1 to 286 months, while positioning, jaw thrust, airway adjuncts, two-handed ventilation, manual ventilation and senior takeover were discretionary. If individual operators treated multiple participants, outcomes and alert thresholds may have been clustered. Reporting the number of operators and cases per operator, together with mixed-effects modelling or an operator-level cluster bootstrap, would help separate patient morphology from operator and technique effects. There were 43 outcome events, but adaptive best-subset selection searched 100 facial coefficients to select 10 for the facial model and three for the enriched model. The sample size justification based on three degrees of freedom does not appear to reflect this larger data-driven search. Crucially, the entire pipeline, including feature selection, should be repeated within every bootstrap sample; otherwise, residual optimism may remain. New guidance recommends transparent reporting of model building and internal validation procedures [3]. Selection frequencies, coefficient stability, shrinkage and nested resampling would therefore be informative. Finally, a likelihood-ratio p value and a small AUROC increase do not establish clinical usefulness. Reporting the paired AUROC difference with its confidence interval, calibration-in-the-large, calibration slope, a flexible calibration plot, Brier score and decision-curve analysis would show whether scanning improves decisions at clinically relevant thresholds [4]. Similarly, the stated external validation of DIFFMASK would be more complete if discrimination were accompanied by calibration after applying the original model without refitting.