A NOVEL DIFFICULT-AIRWAY PREDICTION TOOL FOR EMERGENCY AIRWAY MANAGEMENT: VALIDATION OF THE HEAVEN CRITERIA IN A LARGE AIR MEDICAL COHORT

摘要

Background: Difficult-airway prediction tools help identify optimal airway techniques, but were derived in elective surgery patients and may not be applicable to emergency rapid sequence intubation (RSI). The HEAVEN criteria (Hypoxemia, Extremes of size, Anatomic abnormalities, Vomit/blood/fluid, Exsanguination, Neck mobility issues) may be more relevant to emergency RSI patients. Objective: To validate the HEAVEN criteria for difficult-airway prediction in emergency RSI using a large air medical cohort. Methods: This was a retrospective analysis using a large air medical airway registry using data from 160 bases over a 1-year period. Standard test characteristics (sensitivity, specificity, positive predictive value, negative predictive value [NPV]) for the HEAVEN criteria were calculated for overall intubation success, first-attempt success, and first-attempt success without desaturation. In addition, multivariable logistic regression was used to quantify the independent association between each of the HEAVEN criteria, as well as the total number of criteria present and intubation success after adjusting for age, gender, and clinical category (burn, medical, trauma, nontraumatic shock). Results: A total of 2419 patients undergoing air medical RSI were included. Excellent NPV was observed (97% for each of the HEAVEN criteria except "Exsanguination," which had an NPV of 87% but specificity of 99%). First-attempt success was lower for each of the HEAVEN criteria, with an inverse relationship observed between total HEAVEN criteria and intubation success (first-attempt success with no criteria = 94% and with 5 + criteria = 43%). Multivariable logistic regression revealed independent associations between each of the HEAVEN criteria, as well as total number of criteria and intubation success. Conclusions: The HEAVEN criteria seem to be a useful tool to predict difficult airways in emergency RSI. (C) 2017 Elsevier Inc. All rights reserved.