There are several factors that complicate interpreting these results. First, while the review utilized Bayesian methods, this has limitations including the need for estimating prior probability (the likelihood of one agent being worse or better based on information available before the analysis), which can be difficult and subjective but directly informs numerical results. The review found ketamine has a 68.6% probability of lowering mortality by up to 1% and a probability of 41.6% that ketamine reduced mortality by ≥1% when compared to etomidate, but a traditional frequentist interpretation would characterize this as “no difference” (see comments below for more on Bayesian vs. frequentist). Second, most larger trials were open label, introducing bias. Third, there was variation in the timepoints for mortality assessment in the included studies, with assessment including 28 days, 30 days, and at hospital discharge. Fourth, there was no standardization of peri-intubation interventions (e.g., paralytics, opioids, vasopressors), introducing further potential confounders. Fifth, one study combined ketamine with propofol, confounding the drug's effects.
Of note, a 2022 meta-analysis of nine studies found less postintubation hypotension with etomidate, but included six retrospective studies, while a 2023 meta-analysis of 11 trials found increased mortality with etomidate but evaluated multiple agents (propofol, midazolam, thiopental, and ketamine). Based on weak methodology in the first review, and the desire to focus on comparing the two agents head to head, we did not summarize these meta-analyses.
On the reliability and utility of “Bayesian” versus traditional “frequentist” methods of analyzing, the issue is hotly debated. For the purposes of this paper, one distinction is crucial. Traditional frequentist methods demand that any difference in numerical results cross a certain threshold (usually 95% mathematical likelihood) to be considered a “true” difference, providing a dichotomous (yes/no) answer. Conversely, Bayesian methods assign a sliding scale of probability to the existence of a difference, providing a percentage likelihood. In both cases; however, the calculations are embedded with assumptions such as a total absence of bias and methodologic perfection in the underlying data. Of course, this might not be true, but mathematical representations of study results are two dimensional—they are just numbers. This is why studies require careful, expert interpretation, and numerical results must be placed in context.
Based on current data it is unclear whether ketamine lowers mortality compared to etomidate. Therefore, we have selected a color recommendation of yellow (equal efficacy) for the use of ketamine in critically ill adult patients requiring induction for ETI. Further data are needed using clearly defined patient populations, outcomes, and standardized peri-induction management and dosing of etomidate and ketamine.
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