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Predictive Model for Monitor-Independent TCI Propofol Induction Based on Loss-of- Consciousness Concentration: A Prospective Observational Study

This prospective observational study demonstrates that the propofol effect-site concentration at loss of consciousness strongly predicts the concentration required to achieve a BIS of 50 for intubation, enabling the derivation of a practical dosing formula to guide monitor-independent target-controlled infusion induction.

Original authors: Merve Yaldız Bayatlı¹, Başak Ceyda Meço¹, Süheyla Karadağ Erkoç¹, Volkan Baytaş¹, Yasemin Yavuz², Neslihan Alkış¹

Published 2026-06-24

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Merve Yaldız Bayatlı¹, Başak Ceyda Meço¹, Süheyla Karadağ Erkoç¹, Volkan Baytaş¹, Yasemin Yavuz², Neslihan Alkış¹

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you are trying to bake the perfect cake for a very specific guest. You know the guest needs a certain amount of sugar to be happy, but everyone's taste is different. If you guess the sugar amount, you might make the cake too sweet or not sweet enough.

In the world of anesthesia, doctors face a similar problem. They need to give a patient just the right amount of sleep medicine (propofol) so the patient is unconscious and can have a breathing tube inserted (intubation) without waking up or reacting painfully. Usually, they use a fancy monitor (called BIS) to tell them exactly how "asleep" the patient is. But what if that monitor isn't available? How do they know how much medicine to give?

This study is like a recipe discovery project. The researchers wanted to see if they could figure out the "intubation dose" just by watching when the patient first fell asleep.

The Experiment: Finding the "Sweet Spot"

The researchers worked with 128 healthy adults scheduled for surgery. Here is how they set up their experiment:

The Setup: They used a smart pump that slowly dripped sleep medicine (propofol) and a painkiller (remifentanil) into the patient's vein. The painkiller dose was kept the same for everyone, like a constant background hum.
The "Wake Up" Call: They slowly increased the sleep medicine. Every few seconds, they called the patient's name. The moment the patient stopped answering, they marked that exact moment as "Loss of Consciousness" (LOC). They noted exactly how much medicine was in the patient's system at that exact second.
The Goal: They kept increasing the medicine until a monitor showed the patient was "deeply asleep" enough for the tube insertion (a specific score of 50 on the monitor). They marked this second amount as the "Intubation Concentration" (INT-P).

The Big Discovery: A Predictive Formula

The researchers found a very strong connection between the two moments. It turned out that the amount of medicine needed to make someone just fall asleep was almost a perfect predictor of how much more medicine they would need to be ready for the tube.

Think of it like this: If you know how much water it takes to fill a cup to the "brim" (Loss of Consciousness), you can predict with high accuracy how much more water you need to add to fill it to the "overflow" point (Intubation).

They created a simple math formula based on this:

Intubation Dose = 0.67 + (0.97 × Loss of Consciousness Dose)

In plain English: If you know the dose that made the patient lose consciousness, you can multiply that number by almost 1, add a tiny bit, and you will have a very good guess of the dose needed for intubation. The study showed this formula worked about 81% of the time, which is a very strong match.

What Happened to the Patients?

While the formula worked well for predicting doses, the researchers noticed something interesting about how the patients' bodies reacted:

The "Jump" Reaction: Even though the patients were asleep enough to meet the monitor's target, more than half of them (about 54%) still had a physical reaction when the tube was put in. Their heart rates or blood pressure jumped up.
The "Too Low" Signal: This suggests that the "sleep level" they aimed for (the monitor score of 50) might have been just a little too light for some people to completely block the stress of the tube insertion. It's like the cake was sweet enough to be eaten, but not sweet enough to make the guest really happy.
Side Effects: A small number of patients (about 9%) had a drop in blood pressure when they first went to sleep, which is a known side effect of the medicine, but it was managed easily.

The Takeaway

The main point of this paper is that you don't always need a high-tech monitor to guess the right dose. If a doctor sees exactly when a patient falls asleep, they can use this simple formula to calculate a very good estimate of the dose needed for the next step (intubation).

However, the study also warns that while this formula helps you guess the dose, aiming for that specific "monitor score of 50" might not be deep enough to stop everyone from reacting to the tube. The researchers suggest that in the future, doctors might need to aim for a slightly deeper sleep or use a bit more painkiller to keep patients completely still and calm during the procedure.

In short: The study found a reliable "rule of thumb" to predict the right amount of sleep medicine based on the moment a patient falls asleep, but it also showed that sometimes, even with the right amount, the patient might still need a little extra help to stay completely relaxed during the surgery start.

Technical Summary: Predictive Model for Monitor-Independent TCI

Problem Statement
In total intravenous anaesthesia (TIVA) using target-controlled infusion (TCI), clinicians lack universally accepted depth parameters comparable to the Minimum Alveolar Concentration (MAC) used in inhalational anaesthesia. While Bispectral Index (BIS) monitoring allows for tailored propofol administration, many clinical settings operate without depth-of-anaesthesia monitoring. In such scenarios, the specific propofol effect-site concentration required to achieve adequate depth for tracheal intubation remains uncertain. The study addresses the need to determine if the propofol effect-site concentration at the loss of consciousness (LOC-P) can serve as a reliable predictor for the concentration required to achieve a target BIS of 50 for intubation (INT-P), thereby enabling individualized dosing without continuous monitoring.

Methodology
This was a prospective observational study conducted at Ankara University İbni Sina Hospital between June 2021 and April 2022.

Participants: 128 adults (ASA physical status I–II, aged 18–65 years) scheduled for elective non-cardiac surgery. Seven patients were excluded due to incomplete neuromuscular blockade (TOF count >0) at the time intubation criteria were met.
Protocol: Anaesthesia was induced using TCI of propofol (Schnider model) and remifentanil (Minto model). Remifentanil effect-site concentration ( $C_e$ ) was fixed at 2 ng mL⁻¹. Propofol $C_e$ was initiated at 2 µg mL⁻¹ and titrated upward by 0.2 µg mL⁻¹ every 20 seconds.
Measurements:
- LOC-P: Recorded as the propofol $C_e$ at the moment of loss of verbal response.
- INT-P: Recorded as the propofol $C_e$ when BIS reached 50 and remained stable for ≥30 seconds.
- Neuromuscular Blockade: Rocuronium (1 mg kg⁻¹) was administered immediately after LOC-P determination. Intubation occurred only when TOF count was zero.
Analysis: The primary analysis utilized Pearson correlation and linear regression to evaluate the relationship between LOC-P and INT-P. Secondary outcomes included induction timing, hemodynamic stability (MAP and HR), and adverse events.

Key Results

Predictive Correlation: A strong positive correlation was found between LOC-P and INT-P ( $r = 0.90$ , 95% CI 0.86–0.93; $P < 0.001$ ).
Dosing Formula: Linear regression yielded the following predictive equation:
$INT\text{-}P = 0.67 + 0.974 \times LOC\text{-}P$
The model demonstrated a high coefficient of determination ( $R^2 = 0.81$ ), with a regression slope ( $\beta = 0.974$ ) indicating an approximately 1:1 relationship between the two concentrations.
Concentration Values: The mean LOC-P was $2.66 \pm 0.35$ µg mL⁻¹, and the mean INT-P was $3.26 \pm 0.37$ µg mL⁻¹.
Hemodynamics:
- Mean arterial pressure (MAP) decreased significantly from baseline at all post-LOC time points.
- Hypotension (≥20% decrease in MAP) occurred in 12 patients (9.4%), primarily between LOC and intubation.
- A hemodynamic response to intubation (≥20% increase in MAP and/or HR) was observed in 69 patients (53.9%), despite the BIS target of 50 and remifentanil $C_e$ of 2 ng mL⁻¹.
Timing: The mean time from induction start to intubation was $9.48 \pm 2.28$ minutes.

Significance and Claims
The authors claim that LOC-P is a strong predictor of the propofol concentration required for intubation, allowing for the derivation of a practical dosing formula. This formula ( $INT\text{-}P = 0.67 + 0.974 \times LOC\text{-}P$ ) offers a method to individualize induction and anticipate intubation readiness in settings lacking depth-of-anaesthesia monitoring.

However, the study modestly qualifies its findings regarding clinical safety. The high incidence of hemodynamic responses to intubation (53.9%) suggests that a BIS target of 50, combined with a fixed remifentanil concentration of 2 ng mL⁻¹, may not provide sufficient anesthetic depth to suppress the stress response in a substantial proportion of patients. The authors suggest that deeper targets (e.g., BIS 40) or higher opioid concentrations might be necessary to mitigate this response, though these specific adjustments were not tested in this study.

The authors emphasize that these findings are hypothesis-generating and require external validation across diverse populations (e.g., older patients, ASA ≥III, emergency settings) and different opioid regimens before widespread clinical adoption. The study does not claim to replace BIS monitoring but rather to provide a reliable anchor for titration when such monitoring is unavailable.

The Experiment: Finding the "Sweet Spot"

The Big Discovery: A Predictive Formula

What Happened to the Patients?

The Takeaway

Technical Summary: Predictive Model for Monitor-Independent TCI

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