For any practitioner of airway management, predicting a difficult intubation in advance, reliably and with confidence, has obvious benefits. Know it in advance and you can plan for it. You can work out your plan A, as well as your plan B, plan C and plan D, refresh your memory of the appropriate algorithms, check the appropriate equipment is available and consult with colleagues. All in advance. All relatively stress free.
There are many predictive tests for difficult intubation, including thyromental distance, the mallampati classification and sternomental distance, as well as mnemonic difficult airway identifiers, such as LEMON (Look externally, Evaluate, Mallampati, Obstruction, Neck mobility) for difficult direct laryngoscopy, and MOANS (Mask seal, Obstruction, Age, No teeth, Stiff lungs) for difficult mask ventilation. For anticipation of difficult supraglottic/extraglottic device use, there is RODS (Restricted mouth opening, Obstruction of the upper airway, Distorted airway, Stiff lungs) and for difficult surgical cricothyrotomy, there is SHORT (previous neck Surgery, Haematoma, Obese, previous Radiation therapy, Tumour).
For tests such as thyromental distance, sternomental distance and the mallampati classification, the sensitivity and specificity vary, but range from poor to fair. Combinations of these, such as thyromental distance and mallampati, can be a useful predictor of difficult intubation, but still have their limitations, particularly since there can be variation in measurement conditions. These issues are explored in the meta-analysis Predicting Difficult Intubation in Apparently Normal Patients by Shiga et al, published in Anesthesiology. The full text is free to download.
Last month, an exciting project to develop a computer algorithm to accurately predict how difficult (or easy) it would be to intubate a patient using digital images was revealed by Tufts Medical Center in Boston, USA. This project follows on from a previous study at Tufts, which used computerised facial structure analysis, combined with thyromental distance, to produce a model for predicting difficult intubation. Their model accurately classified 70/80 airways, compared with 47/80 for mallampati + thyromental distance. The computing power needed might currently exceed the capability of today’s mobile phones, but the objective is to produce a computer algorithm that could utilise high speed computers, perhaps over a network, and the digital camera in a mobile phone for the image/photographic input. The dream scenario though has to be – data input and computation of the data in a totally self contained handheld device. Of course, even if this becomes possible, the algorithm will need to demonstrate a clear superiority to current techniques to be clinically useful. All very exciting!
You can keep up to date with progress of this study, ‘Facial Analysis to Classify Difficult Intubation’ at ClinicalTrials.gov.