Be aware of minimum alveolar concentration (MAC), or beware of MAC—is there a magic number that can seduce you?

The dosage challenge for anesthesiologists—not least in anesthesia for maxillofacial surgery

To say that individuals differ is an understatement. This is true of much of life. In the world of anesthetic dosing, it is a difficult truth to discover, and when you do, it is difficult to deal with. For superficial surgery, the stimulation is relatively modest, while for e.g., maxillofacial surgery, it is intense, with the “breakdown” of the maxilla in corrective surgery being the peak stimulation. Classically, minimum alveolar concentration (MAC) has been a way of understanding inhalational anesthetic strength and dosing.

The MAC concept

MAC is a crucial concept in understanding the pharmacodynamics of inhalational anesthetic agents, defined by Eger and Saidman in the mid-1960s (1). It represents the minimum concentration of an anesthetic required to prevent a defined response in 50% of a population, whether animals, test subjects, or patients (1). Rather, as pointed out by Hendrickx and De Wolf, MAC should be read as “median alveolar concentration (2). The original definition of MAC focused on preventing a purposeful motor response to surgical stimulation, such as a skin incision. This concept was quickly adopted as a standard procedure in anesthesia science. Initially, MAC was valuable for comparing the potency of different inhalational agents within a species or across different species. The MAC concept significantly enhanced our understanding of the potency of inhalational agents, leading to broader applications (3). However, some limitations are evident. The MAC value describes only a single point on a quantal concentration-response curve and likely reflects effects on the spinal cord more than the brain (4). Clinicians should remember that MAC represents the midpoint for a population. This concept has an equivalent in intravenous anesthesia, known as effective concentration-50 (EC₅₀) or effective dose-50 (ED₅₀). The underlying idea in the following text can also be translated to intravenous anesthesia.

What’s the problem with MAC?

The research on the MAC concept has provided two crucial clinical insights. Firstly, different stimuli require different anesthetic concentrations. Secondly, different patients require varying concentrations for the same stimulation (1,3,5) (Figure 1). Regardless of the anesthetic agent used, there is an inter-individual variation in drug concentration requirements of up to ±50% around the median value, even in a relatively fit and homogeneous population (5-8). This variation highlights the need for personalized dosing in clinical practice. Although the age-adjusted MAC is expected to decrease by approximately 6% per decade, clinical observations show a reduction of only about 3% (9). This discrepancy presents a significant challenge for anaesthetists. Another illustrative example is the log-normal distribution of extubation times, which persists even within a clinically homogeneous patient population, underscoring the complexity of predicting recovery trajectories (10). Despite these insights, the clinical limitations of the MAC concept are sometimes overlooked in everyday practice. It is essential to remember that MAC values represent medians and may not accurately reflect individual patient needs. Or rephrased, how often do we anesthetize the average or median patient?

Figure 1 Different stimuli require different anesthetic concentrations and different patients require varying concentrations for the same stimulation. This variability is illustrated in the schematic drawing of MAC for different stimulations using a specific inhalational agent. Understanding different MAC values for various stimuli is crucial in anesthesia. The weakest stimulation, a verbal command, requires an end-tidal or arterial concentration of the agent lower than 0.5× (compared to 1× for skin incision) to prevent a response (MAC awake). The steep slope of the curve and the only hint of an S-shape indicate a small difference between the most sensitive and least sensitive patients. For surgical stimulation (MAC incision), the slope of the curve is less steep, and the S-shape is more pronounced than for MAC awake, indicating a greater difference between the most sensitive and the least sensitive patients. The median concentration needed to block the most powerful stimulation, endotracheal intubation (MAC Intubation, not shown in the figure), is twice that for MAC awake. When the MAC concept is extended to include blockade of the autonomic response to skin incision (MAC BAR), an even higher concentration is required. The slope of the curve is less steep and the S-shape becomes more pronounced, indicating greater interindividual variation compared to the inhibition of motor responses alone. MAC, minimum alveolar concentration.

It is important to recognize the clinical limitations of the MAC concept. To repeat, MAC is originally defined as the MAC of an agent that produces immobility in 50% of subjects exposed to a noxious stimulus. The key point here is the 50% “success rate”. In clinical practice, a 50% “success rate” is not acceptable. Early in the evolution of MAC, attempts were made to increase its clinical utility by defining MAC₉₅, the MAC that prevents 95% of a population from responding to stimulation (11). However, this still leaves 5% of patients dissatisfied—sometimes with catastrophic consequences—while others, at the opposite end of the sensitivity spectrum, risk receiving an excessive dose. The implications of overdosing are significant. Recovery might be delayed, and more seriously, the controlled intoxication of the brain may no longer be well controlled. If X% of an organic solvent is enough to prevent a response to surgical stimulation, then 1.5X% of the compound might not be healthy (12)?

The MAC concept was originally applied to monotherapy-based anesthesia (1,3,11). However, monotherapy has been seriously questioned due to delayed recovery and its failure to prevent both movement and hemodynamic responses to noxious stimuli, even at high end-tidal concentrations (6,13). Monotherapy based on an analgesic agent is not a complete anesthetic (14-16). Instead, it might serve as a model for awareness research (17,18). Attempts to assess the contribution of analgesics, in terms of MAC multiples, to the overall effect of balanced anesthesia have been unsuccessful (19-22). When dealing with individual patients, quantifying anesthetic drug interactions is problematic. Data from population studies cannot be directly applied to fit a particular patient for a specific type of surgery with varying stimuli. Therefore, aiming for a specific MAC value is not a goal in itself. The sensitivity scale for anesthetic dose requirements is broad, both inter- and intra-individually. This variability makes dosing a significant challenge in clinical practice.

Relying solely on end-tidal gas concentration monitoring exhibits similar limitations as using MAC multiples, although a good pedagogical attempt to use multiples of MAC or fractions of MAC (fMAC) has been presented (2). The end-tidal/arterial gradient is unpredictable due to varying ventilation-perfusion ratios (23,24). Due to this unpredictability, we cannot determine—during the initial hours of anaesthesia, before blood-brain equilibrium is achieved—the extent to which this pharmacokinetic factor may bias our readings for a given patient. Nor can we fully account for the influence of other pharmacokinetic factors. As a result, we cannot be confident about the concentration of the anesthetic in the patient’s brain based on a known dose (vapor setting). Additionally, we may not know the exact concentration needed to achieve the desired effect in a single patient. Therefore, it is not useful to focus on a “magic number”, whether it is the vapor setting, the end-tidal concentration, or the digital MAC number on the monitor, or for that matter, the estimated effect-site concentrations of propofol and remifentanil in intravenous anesthesia. Instead, we should focus on the pharmacodynamic effect: does the patient respond to surgery in the way we expect and prefer?

What is the alternative to MAC when dosing anesthesia?

Traditionally, the hemodynamic response has been used as the primary variable for dosing anesthesia (25). However, good anesthesia is not solely about hemodynamic stability. It is obvious that the hemodynamic response is unreliable when monitoring anesthetic depth in a patient with a disease involving the cardiovascular system and perhaps taking one or several drugs acting on that system. Additionally, our use of drugs like anti-cholinergics can lead to confusion when interpreting hemodynamic responses. Autonomic responses do not always correlate perfectly with the level of consciousness in every patient and situation (13). The degree of central nervous system intoxication may not be accurately reflected by the intoxication of the cardiovascular system. Returning to the original MAC concept, which focuses on motor response, we often paralyze patients during surgery. This practice removes an important indicator that warns us when anesthesia is too light (26).

Two key questions arise: what is meant by anesthetic depth, and how can we properly monitor it once defined? Various processed electroencephalography (EEG) variables have been evaluated and commercialized, although they do not yet fully reveal the interaction between hypnotics and analgesics (27,28). Training clinicians to interpret raw EEG waveforms alongside numerical indices can significantly enhance patient safety. Such training is strongly recommended and may eventually be supported or supplanted by artificial intelligence technologies (29,30). The cost of level of consciousness monitoring is another issue to consider, as processed EEG monitoring has become practically mandatory (27). This raises the question of which monitors, or other equipment, might be sacrificed due to limited resources. On the other hand, what is more important to monitor in general anesthesia than the target organ?

Can we do more than use processed EEG?

Is it possible to increase the accuracy of clinical monitoring to shift the focus from the “magic figure” on the gas monitor to the patient’s response? At the very least, we should sharpen our approach to avoid under-dosage, thereby reducing the risk of awareness. In the absence of processed EEG, using the motor response as an early warning sign may help in the individual case, provided that we have avoided the routine use of neuromuscular blocking agents (NMBAs). It has been shown that NMBA can be avoided even in some abdominal surgeries without making it difficult for the surgeon (31). If this is not feasible for a particular patient in a particular procedure, the isolated forearm technique might serve the same purpose (32). Another strategy is to carefully titrate the NMBA dose guided by a neuromuscular transmission monitor to avoid completely blocking the motor response (33). This approach ensures that the patient’s motor response can still provide valuable feedback. Finally, if endotracheal intubation is performed without NMBA, just by a good anesthetic, knowing that this is the most severe stimulation, worse than skin incision, can help identify patients with higher dose demands than average (31). Conversely, slowing down the tempo during induction and titrating the anesthetics to the patient’s response can help identify those with lower dose demands. The information learned during these critical moments can guide future anesthetic needs when NMBA must be used for specific surgical reasons. NMBAs are especially critical during procedures such as head fixation, to avoid spinal cord injury, and cardiac or great vessel surgeries, where movement may cause severe complications. Since general anesthesia is mostly induced with intravenous drugs, we must assume that patients have similar sensitivity to both intravenous and inhalational hypnotics to adopt this approach.

The inter-individual variation in dose demands is significantly reduced when monotherapy is replaced by balanced anesthesia (2,5,8). This approach involves combining a hypnotic with one or more opioids, or a hypnotic with a good local, peripheral, or regional block. Remifentanil can further reduce the inter-individual variation in hypnotic dose demands. Its exceptionally low context-sensitive half-time allows it to effectively cover pain stimulation without causing a prolonged recovery (34). Studies measuring hypnotic expenditure in conjunction with remifentanil show low standard deviations, supporting this explanation (35,36). Overall, the total hypnotic expenditure is markedly reduced with the use of remifentanil, which automatically reduces the risk of over-dosage of the hypnotic agent (34-36). It may be possible to approximate the synergism between an inhalational hypnotic agent and an opioid, similar to the synergism observed with intravenously administered drugs (37-39).

Recently, a promising attempt was presented to understand the overall potency of multiple anesthetics administered simultaneously by calculating an indicator based on response surface models that has an equivalent probability as the MAC fraction (abbreviated as eMAC fraction) for lack of response to skin incision (40). This new tool must be thoroughly evaluated.

Conclusions

The MAC concept is more of a scientific tool than a clinical one. Therefore, we should be aware of MAC for what it is worth in science, and beware of MAC in daily clinical practice, and not rely on a number on an ever-so-seductive digital display.

Acknowledgments

None.

Footnote

Provenance and Peer Review: This article was commissioned by the editorial office, Journal of Oral and Maxillofacial Anesthesia. The article has undergone external peer review.

Peer Review File: Available at https://joma.amegroups.com/article/view/10.21037/joma-25-13/prf

Funding: None.

Conflicts of Interest: The author has completed the ICMJE uniform disclosure form (available at https://joma.amegroups.com/article/view/10.21037/joma-25-13/coif). M.E. has received compensation for tuition from Mälardalen University and consulting fee from Nimbelle AB. The author has no other conflicts of interest to declare.

Ethical Statement: The author is accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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