We continue to read with great interest the description of the drug titration paradox by Minto et al.  They are, in essence, describing a classical control theory problem on how to tune a control system, with an anesthesiologist being at the center of a control-feedback loop.

It turns out that we anesthesiologists, at a population level, behave like an overdamped control system trying to slowly identify the correct target with no a priori knowledge (fig. 1). There appears to be a natural reluctance that biases using a preconceived delivery schedule instead of dosing to clinical effect. This may be due to clinical concerns because overshooting our target potentially risks either underdosing the patient and causing awareness or overdosing the patient, leading to burst suppression and hypotension.

Fig. 1.
Computer simulation examples for different propofol dosing strategies. In all cases, the simulated patient received a bolus dose at the beginning of the simulation. (Left) Patient sensitive to propofol (relatively lower effect site concentration to achieve a Bispectral Index [BIS] value of 50). (Right) Patient “resistant” to propofol (relatively higher effect site concentration to achieve a BIS value of 50). (Top) Conventional dosing strategy with slow changes in dosing being updated every 5 min. Note that values are skewed in line with the drug titration paradox. (Bottom) Computer-controlled closed-loop dosing strategy using an optimized regulator. The two bottom plots show identical BIS values after 20 min despite targeting different effect site concentrations.

Computer simulation examples for different propofol dosing strategies. In all cases, the simulated patient received a bolus dose at the beginning of the simulation. (Left) Patient sensitive to propofol (relatively lower effect site concentration to achieve a Bispectral Index [BIS] value of 50). (Right) Patient “resistant” to propofol (relatively higher effect site concentration to achieve a BIS value of 50). (Top) Conventional dosing strategy with slow changes in dosing being updated every 5 min. Note that values are skewed in line with the drug titration paradox. (Bottom) Computer-controlled closed-loop dosing strategy using an optimized regulator. The two bottom plots show identical BIS values after 20 min despite targeting different effect site concentrations.

We would argue that if we were to seek better knowledge as to patient dosing or if we were more aggressive in our dosing strategy, we would not see the feature. In addition, closed-loop control anesthesia may also alter the results of this paradox. If we implemented a differently tuned controller, we might lose the negative correlation described in the paradox (fig. 1) or even see a positive correlation.

In summary, the drug titration paradox is not a property of the observed patient population or the drug model. It is a property of the anesthesia teams delivering the anesthetic. Any conclusion drawn from population data concerning dosing should be understood in this context. We otherwise agree with the assessment that big data should very cautiously approach this problem.