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The Capital Region of Denmark - a part of Copenhagen University Hospital
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The DIFFMASK score for predicting difficult facemask ventilation: a cohort study of 46,804 patients

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Facemask ventilation is an essential part of airway management. Correctly predicting difficulties in facemask ventilation may reduce the risk of morbidity and mortality among patients at risk. We aimed to develop and evaluate a weighted risk score for predicting difficult facemask ventilation during anaesthesia. We analysed a cohort of 46,804 adult patients who were assessed pre-operatively airway for 13 predictors of difficult airway management and subsequently underwent facemask ventilation during general anaesthesia. We developed the Difficult Facemask (DIFFMASK) score in two consecutive steps: first, a multivariate regression analysis was performed; and second, the regression coefficients of the adjusted regression model were converted into a clinically applicable weighted point score. The predictive accuracy of the DIFFMASK score was evaluated by assessment of receiver operating characteristic curves. The prevalence of difficult facemask ventilation was 1.06% (95%CI 0.97-1.16). Following conversion of regression coefficients into 0, 1, 2 or 3 points, the cumulated DIFFMASK score ranged from 0 to 18 points and the area under the receiver operating characteristic curve was 0.82. The Youden index indicated a sum score ≥ 5 as an optimal cut-off value for prediction of difficult facemask ventilation giving a sensitivity of 85% and specificity of 59%. The DIFFMASK score indicated that a score of 6-10 points represents a population of patients who may require heightened attention when facemask ventilation is planned, compared with those patients who are obviously at a high- or low risk of difficulties. The DIFFMASK score may be useful in a clinical context but external, prospective validation is needed.

Original languageEnglish
JournalAnaesthesia
Volume74
Issue number10
Pages (from-to)1267-1276
ISSN0003-2409
DOIs
Publication statusPublished - 2019

ID: 58154838