Drug infusions are an increasingly common way of delivering anesthetic agents. With inhaled anesthetic agents, the concentration in the vessel-rich group associated with adequate anesthesia spans a narrow range, and these concentrations can be readily estimated from end-tidal concentrations. Intravenous agents are far more variable in the effective concentration, and monitoring this concentration at the point of care is not likely to be available any time soon. Manufacturers of infusion systems have recognized the need for limits on the infusion rates to avoid errors in programming, and libraries of such limits are promulgated based on the judgment of individuals who may have experience in delivering these drugs … or not. Berman1 presents a methodology for establishing limits based on the documented actions of anesthesiologists participating in the Multicenter Perioperative Outcomes Group database. Presumably, anesthesiologists are rational actors, and their actions should reflect safe practice. Large databases collected from electronic medical records of multiple institutions reduce the chances of institutional bias. How can we argue against this?
Applying this methodology to driving speeds seems fairly easy: data from the GPS navigation company TomTom5 suggest that the average speed on Interstate 15 in Utah and Nevada is 77.67 miles per hour, while on the Washington DC beltway, it is 46 miles per hour. But before we develop a statistical model of highway speeds and change the signs, recall that the infusion rate is not analogous to car speed; rather, it is the gas pedal of the pharmacokinetic car. While it is possible that there is a relationship between fuel consumption and speed in a single automobile model under ideal conditions (Volkswagen has done some pioneering work in this area), applying this approach with corporate average fuel efficiency would not work well for regulating speeds on the hills of Interstate 15 or in stop-and-go traffic on the beltway.
What if we could estimate the concentrations of drugs based on their administration? Well, we can. It is called target-controlled infusion (TCI) and will eventually be available to US practitioners.2,3 With TCI, we do not really worry about infusion rates; we control plasma or effect site concentrations. Even if we do not have access to a TCI pump, there are programs such as Rugloop6 and TIVA Trainer7 that can give us these numbers. If you want to go to the pharmacokinetic speed shop, MATLAB models8 are publicly available. Using this approach, my standard induction consists of boluses of 1000 µg/kg of propofol and 1.5 µg/kg of remifentanil over 1 minute, followed by infusions of 330 µg/kg/min of propofol and 0.3 µg/kg/min until I have sufficient information to specify the maintenance infusion rates. An example is depicted in the Figure. While this approach might seem unusual to an American practitioner, this is a fairly common strategy for TCI: set the pumps to high targets for induction, then turn down the targets for maintenance. Egan and Shafer4 referred to this as “surfing the wave,” although their example of an initial hand-delivered bolus is more analogous to tow-in surfing.9
In my practice of total intravenous anesthesia, I attempt to meticulously document the infusion rates; with institutional review board permission, I reviewed 57 consecutive records from cases performed in first 2 months of 2017 and found that 35% (118 of 333) of recorded propofol rates and 43% (140 of 323) of recorded remifentanil rates exceeded the 90% upper limits reported by Berman.1 Should I be concerned about my dosing patterns? The propofol rates that exceeded the 90% cutoff comprised about 5% of the total case time, and only 0.5% of the time after induction. The impact of these brief periods when the “pedal is to the metal” have a trivial impact on the average effect site concentration of propofol; they are employed to get the effect site concentration to a higher target quickly, without overshoot. While my approach may seem complex and cumbersome, it has the advantage of allowing me to observe the transition from consciousness to unconsciousness with moderate precision. My goal is not to establish an effect site concentration that works in 50% or 90% of patients; I am trying to gauge the pharmacodynamic response for the patient in front of me. If effect site concentrations are analogous to driving speeds, pharmacodynamic responses are more analogous to following distances; on the beltway, I do not care as much about whether I am going the speed limit as whether I am too close to the car ahead of me to stop. I certainly would not try to explain away a fender bender by pointing to the dashboard indicator of my fuel efficiency.
Could large databases provide insight into response probabilities, allowing us to “crowdsource pharmacokinetics?”10 Only if we control our drugs to individualized effect site targets, rather than using the same population-based infusion rates for everyone. In other words, “don’t drive like my brother!”
Name: Jeff E. Mandel, MD, MS.
Contribution: This author wrote the manuscript.
This manuscript was handled by: Maxime Cannesson, MD, PhD.
1. Berman MF, Iyer N, Freudzon L, et al.; the Multicenter Perioperative Outcomes Group (MPOG) Perioperative Clinical Research Committee. Alarm limits for intraoperative drug infusions: a report from the Multicenter Perioperative Outcomes Group.Anesth Analg. 2017;125:1203–1211.
2. Shafer SL, Egan T. Target-controlled infusions: surfing USA redux. Anesth Analg. 2016;122:1–3.
3. Dryden PE. Target-controlled infusions: paths to approval. Anesth Analg. 2016;122:86–89.
4. Egan TD, Shafer SL. Target-controlled infusions for intravenous anesthetics: surfing USA not! Anesthesiology. 2003;99:1039–1041.