There is ever greater interest in mitigating medical errors, particularly through cognitive aids and checklist-system long-used in the aviation industry.
Jelacic and team instituted a computerised pre-induction checklist, using an observational before-and-after study design across 1,570 cases. This is the first study of a computerised anaesthesia checklist in a real clinical environment.
They found an absolute risk reduction of almost 4% of failure-to-perform critical pre-induction steps, along with reduction in non-routine events and several examples of pre-induction mistake identification through checklist use.
Although the researchers claim the results “strongly argue for the routine use of a pre-induction anaesthesia checklist” this overstates the case a little. This study, like many similar, struggles with confounder effects on anaesthesia vigilance that may explain some of the results, particularly as arising from observational, non-randomised, non-blinded research.
The challenge for cognitive aid research is that commonly it must use surrogate markers (workflow step failure; behavioural deviations; efficiency; time spent on task etc.) rather than the safety outcomes that actually matter to patients: death and injury.
There will continue to be tension between those pro-checklist and those against. The irony is that both camps share a similar rationale for their position: the advocates for routine checklists point to the safety benefits of reducing cognitive load, whereas those opposing argue that enforced use is anti-individual and itself adds additional task and cognitive burden for clinicians.summary
What’s so interesting?
De Carvalho and co. show that pre-operative voice analysis can be predictive of difficult laryngoscopy.
I’d never thought about that...
The authors describe how different frequency components and acoustic qualities of the voice are, at least partly, determined by the shape and size of different anatomical areas of the vocal tract. By analysing the most intense frequencies (voice formants) within the voice spectrum they were able to correlate components with difficult laryngoscopy, namely Cormack & Lehane grade 3 or 4.
During pre-anaesthetic assessment, 467 elective general surgical patients were asked to pronounce each of the five vowels, corresponding to base phonemes. This was recorded on a smartphone and then later processed and analysed on a laptop computer.1
A model using voice ‘formants’ could reliably predict difficult laryngoscopy with a ROC-AUC of 0.761 (ie. 76% probability that it correctly classifies a patient as difficult or not). When combined with the modified Mallampati this improved to 92%.
The big picture
While interesting, it’s worth remembering that using voice formants (76%) did not perform as well as modified Mallampati alone (87%), and that this performance is also surprisingly much better than those from the most recent Cochrane meta-analysis (2018) of bedside airway assessment. Over 133 studies the Cochrane review reported a summary sensitivity of only 53% and specificity of 80% for the modified Mallampati (vs 100% and 75% respectively for this study).
Although this is an interesting and novel new test, it’s just not that simple... Screening for an uncommon outcome using tests with imperfect sensitivity and specificity is already problematic, but doubly so when we are not always certain which outcome we should be screening for (laryngoscopy, intubation, ventilation, oxygenation...).
As an airway screening test, voice analysis is both different and also more of the same.
It would also be feasible for recording, analysis and reporting to occur entirely at the bedside on a smartphone. ↩