Intraoperative anesthetic monitoring system and method based on mass spectrum technology and medium

Abstract

The invention belongs to a mass spectrum technology, and particularly provides an intraoperative anesthetic monitoring system and method based on the mass spectrum technology, and the system comprises a mass spectrum device, a sampling module, a separation module, a calculation unit and a display unit. The method comprises the following steps: continuously collecting blood during operation, purifying the blood, rapidly ionizing the blood by a normal-pressure ionization source, and detecting the blood concentration Cp in real time by a miniaturized mass spectrum; synchronously acquiring pharmacodynamic data such as electroencephalogram; the calculation unit uses a built-in group PK-PD parameter as an initial value, uses Cp to reversely fit personalized parameters, and calculates the brain effect room concentration Ce in real time through an effect room model; the Bayesian algorithm is further adopted, and key parameters such as EC50 are dynamically optimized by comparing to-be-tested pharmacodynamic data with a model predicted value. The system can carry out prediction and safety early warning according to the Ce change trend, and can output a control signal to realize closed-loop regulation and control of the administration rate. The method has the advantages of accurate monitoring and the like.

Description

Technical Field

[0001] This invention relates to mass spectrometry technology, and particularly to intraoperative anesthetic monitoring systems, methods, and media based on mass spectrometry technology. Background Technology

[0002] Monitoring and controlling the depth of anesthesia is a crucial aspect of ensuring the safety of modern surgical procedures. Appropriate anesthesia depth is essential for ensuring successful surgery, avoiding intraoperative stress in patients, and preventing complications such as circulatory depression caused by excessively deep anesthesia. Due to variations in surgical procedures, individual patient differences (such as age, weight, and comorbidities), and the constantly changing intensity of intraoperative stimulation, patients' responses to anesthetic drugs vary significantly. Anesthesia may lead to adverse consequences such as hypotension and postoperative cognitive impairment, even endangering life; conversely, insufficient anesthesia increases the risk of intraoperative awareness and may cause long-term psychological trauma to patients. Therefore, achieving personalized, precise, and real-time intraoperative anesthesia depth monitoring and control is a critical technical challenge that urgently needs to be overcome in clinical anesthesia practice.

[0003] Currently, the main methods of anesthesia monitoring used in clinical practice include the following categories: 1. Basic vital sign monitoring, such as electrocardiogram, non-invasive / invasive blood pressure, respiratory rate and blood oxygen saturation monitoring, can reflect the basic state of the body, but the specificity for the depth of anesthesia is poor.

[0004] 2. Secondly, there is advanced functional monitoring, such as brain function monitoring based on electroencephalogram (EEG) signals, hemodynamic monitoring, and body temperature monitoring. Among these, brain function monitoring methods such as the bispectral index (BIS) and auditory evoked potentials are widely recognized as being able to reflect changes in the functional state of the cerebral cortex well and are currently commonly used tools for assessing the depth of anesthesia.

[0005] Existing monitoring technologies still have significant limitations, such as: 1. EEG monitoring indicators such as BIS are indirect physiological parameters. Their values ​​are easily affected by various non-anesthetic drug factors (such as body temperature, hypotension, electromagnetic interference, etc.), and their response characteristics to different anesthetic drugs vary, which may lead to judgment bias.

[0006] 2. Such methods fail to provide direct information on the concentration of anesthetic drugs in the body, making it impossible to achieve precise medication guidance based on pharmacokinetic / pharmacodynamic (PK / PD) models. On the other hand, although directly detecting the concentration of anesthetic drugs in the blood is the most objective indicator of the depth of anesthesia, traditional blood drug concentration analysis methods (such as gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS)) require blood samples to be collected and sent to a central laboratory for offline analysis. This process is cumbersome, takes several hours, and has a serious time lag, completely failing to meet the needs of real-time intraoperative decision-making.

[0007] In summary, current clinical anesthesia depth monitoring primarily relies on indirect, delayed, and easily interfered-with physiological parameters or empirical judgments, creating a dilemma: functional monitoring is "real-time but indirect," while concentration detection is "direct but delayed." This leads to a long-term dependence on empirical inference in clinical anesthesia depth management, failing to achieve a real-time closed loop of "monitoring-prediction-regulation." Therefore, developing a technology capable of real-time, direct intraoperative drug concentration monitoring, simultaneously integrating pharmacodynamic information, and driving PK / PD models to achieve individualized dynamic predictions, is crucial to overcoming the bottleneck of precision anesthesia. Summary of the Invention

[0008] To address the shortcomings of the existing technical solutions, this invention provides an intraoperative anesthetic monitoring system and method based on mass spectrometry technology.

[0009] The objective of this invention is achieved through the following technical solution: A mass spectrometry-based intraoperative anesthetic monitoring system includes a mass spectrometer; the monitoring system further includes: The system includes a sampling module and a separation module. The sampling module is used to obtain blood samples from the patient's blood vessels during surgery and send them to the separation module. The separation module separates the anesthetic sample, which is then detected by a mass spectrometer. The calculation unit calculates the anesthetic concentration C output by the mass spectrometer. p Using the PK-PD model, the concentration C in the brain effect chamber was obtained. e And according to concentration C e The changing trend of concentration C e ; The display unit is used to synchronously display the concentration C. p and concentration C e Curve showing how it changes over time.

[0010] The present invention also aims to provide a method for monitoring intraoperative anesthetic agents based on mass spectrometry technology, which is achieved through the following technical solution: A method for monitoring intraoperative anesthetic agents based on mass spectrometry, wherein the detection method is as follows: Blood was continuously collected during the operation and monitored in real time using mass spectrometry to obtain the concentration of the anesthetic agent, C. p ; Obtain real-time drug efficacy data during surgery; Based on the C p Based on the aforementioned pharmacodynamic data, the model parameters are dynamically optimized using a PK-PD model and an adaptive algorithm to calculate the concentration C in the brain effect room. e .

[0011] Another objective of this invention is to provide a computer-readable storage medium, which is achieved through the following technical solution: A computer-readable storage medium having a computer program stored thereon, wherein the stored computer program, when executed by a processor, implements the monitoring method.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: This application integrates miniaturized mass spectrometry real-time detection technology, pharmacokinetic-pharmacodynamic (PK-PD) model algorithm, and multimodal physiological monitoring to form a synergistic organic whole, breaking through the two core technical bottlenecks of "lack of real-time concentration data" and "lag between blood drug concentration and clinical effect", and has achieved significant technological progress.

[0013] 1. This invention achieves, for the first time, real-time, online, and direct quantitative monitoring of intraoperative anesthetic drug concentrations, overcoming the fundamental technical bottleneck of lacking real-time data sources for precision anesthesia. By combining a miniaturized mass spectrometry device with continuous microfluidic sampling and purification technology, this invention successfully transforms traditional offline laboratory analysis, which takes several hours, into bedside real-time detection completed within seconds, providing an irreplaceable objective concentration indicator (Cp) for intraoperative decision-making.

[0014] 2. This invention represents a crucial leap from "blood drug concentration monitoring" to "prediction of brain effect site concentration," providing decision-making indicators that anticipate clinical effects. By introducing and solving the effect room model in real time, this invention can use real-time Cp to calculate the brain effect room concentration (Cp) directly related to the depth of anesthesia. e This effectively overcomes the physiological lag between blood drug concentration and clinical effect, enabling anesthesia management to shift from delayed feedback to proactive feedforward.

[0015] 3. This application utilizes adaptive learning algorithms (such as Bayesian optimization) to continuously and dynamically optimize key parameters (such as ECG) in the PK-PD model by comparing the inferred effect with real-time EEG (BIS) and other drug efficacy data. 50 The model evolves from "average" parameters of the population to "individual" parameters tailored to the current patient. This not only greatly improves the accuracy of individual drug administration and avoids underdosing or overdosing, but also provides unprecedented real-time data for studying the pharmacological characteristics of different individuals and disease states. It realizes the evolution from "population model" to "individual model" and lays the foundation for truly personalized precision anesthesia.

[0016] 4. It achieves intelligent fusion and cross-validation of multimodal data, providing unprecedented insights into clinical decision-making; the system can identify complex pharmacodynamic abnormalities such as "high concentration - light anesthesia" (indicating tolerance) or "low concentration - deep anesthesia" (indicating hypersensitivity) and issue specific alerts. This deep integration and cross-validation capability is impossible to achieve with a single technical means.

[0017] 5. A complete technical framework and core inputs are provided for realizing a safe and precise closed-loop automated anesthesia control system. Based on a real-time concentration trend prediction algorithm, the system can provide early warnings that anticipate physiological changes, achieving a shift from "passive feedback" to "active prediction." The high-quality, real-time concentration output signal is the core input for building the next generation of intelligent anesthesia systems, providing the most critical technical foundation for ultimately realizing a closed-loop system that automatically adjusts drug administration according to individual real-time needs, and is expected to completely revolutionize the anesthesia management paradigm. Attached Figure Description

[0018] The disclosure of this invention will become more readily understood with reference to the accompanying drawings. It will be readily understood by those skilled in the art that these drawings are merely illustrative of the technical solutions of this invention and are not intended to limit the scope of protection of this invention. In the drawings: Figure 1 This is a flowchart illustrating the monitoring method of Embodiment 1 of the present invention. Detailed Implementation

[0019] Figure 1 The following description illustrates optional embodiments of the invention to teach those skilled in the art how to implement and reproduce the invention. Some conventional aspects have been simplified or omitted to teach the technical solutions of the invention. Those skilled in the art should understand that variations or substitutions derived from these embodiments will be within the scope of the invention. Those skilled in the art should understand that the following features can be combined in various ways to form multiple variations of the invention. Therefore, the invention is not limited to the optional embodiments described below, but is defined only by the claims and their equivalents.

[0020] Example 1

[0021] The intraoperative anesthetic monitoring system based on mass spectrometry technology in this embodiment includes: Mass spectrometry devices, such as miniaturized mass spectrometry devices.

[0022] The sampling module is used to connect blood vessels, and the blood sample is sent to the separation module; the separation module separates the anesthetic, and the mass spectrometry device detects the separated anesthetic.

[0023] The calculation unit calculates the anesthetic concentration C based on the output of the mass spectrometer. p Using the PK-PD model, the concentration C in the brain effect chamber was obtained. e And according to concentration C e The changing trend of concentration C e .

[0024] The display unit is used to synchronously display the concentration C. p and concentration C e Curve showing how it changes over time.

[0025] To reduce volume, the separation module employs microfluidic technology and incorporates a filter membrane and a dialysis membrane.

[0026] To accommodate the rapid ionization of common volatile and non-volatile anesthetics during surgery, atmospheric pressure direct ionization sources such as DBDI, DART, PDESI, APCI, and nanoESI are used, eliminating the need for complex vacuum interfaces and simplifying the system.

[0027] The intraoperative anesthetic monitoring method based on mass spectrometry technology in this embodiment of the invention, i.e., the working method of the system in this embodiment, wherein the detection method is as follows: The sampling module takes samples from the patient's blood vessels during the operation and sends them to the separation module.

[0028] The separated anesthetic was sent to a mass spectrometer to obtain the anesthetic concentration C. p .

[0029] Obtain real-time drug efficacy data during surgery; The calculation unit utilizes concentration C p Using the PK-PD model and adaptive algorithm, the concentration C in the brain effect chamber was obtained. e And according to concentration C e The changing trend of concentration C e .

[0030] The PK-PD model incorporates population pharmacokinetic and pharmacodynamic parameters for the anesthetic. The pharmacokinetic parameters include micro-rate constants, and the pharmacodynamic parameters include the effect-site equilibrium rate constant k. eo .

[0031] The display unit synchronously displays the concentration C. p and concentration C e Curve showing how it changes over time.

[0032] During the above process, real-time drug efficacy data, such as the Bis-Brain Index (BIS), is obtained.

[0033] Comparison of concentration C e And changes in data.

[0034] If the changes are not synchronized, adjust the injection rate of the anesthetic to achieve the desired concentration C. e Synchronize with changes in data.

[0035] During the above process, real-time drug efficacy data, such as the Bis-Brain Index (BIS), is obtained.

[0036] Compare the data with the efficacy data predicted by the model.

[0037] If the deviation exceeds the threshold, the PD parameter is adjusted to make the deviation less than the threshold. The PD parameter includes the effect-room concentration EC at which 50% of the maximum effect is achieved. 50 .

[0038] use The estimated value of θ is updated using the measured value of E, so that the predicted value tends to the measured value of E.

[0039] E represents measured drug efficacy data, such as the Bisigma Index (BIS) on electroencephalogram (EEG), and θ represents the personalized parameter vector to be optimized [EC]. 50 [,γ];P(θ) is the population probability distribution of θ;P(E|θ) represents the probability of observing the current data E given the parameter θ;P(θ|E) is the latest probability distribution of parameter θ after observing data E.

[0040] EC 50 It is the effect-room concentration at which 50% of the maximum effect is achieved, and γ is a shape parameter that determines the steepness of the curve.

[0041] The computing unit runs built-in software whose execution logic includes: receiving the real-time Cp sequence output by the mass spectrometer; using population pharmacokinetic parameters (such as the micro-rate constant of the three-compartment model) as initial values, and using the Cp sequence to estimate personalized parameters in real time; running the personalized pharmacokinetic model to generate the central compartment simulated concentration C1(t); and comparing C1(t) with the effect compartment equilibrium constant k. e0 Substituting into the differential equation dC e / dt = k e0 ·[C1(t) - C e Perform real-time numerical integration (e.g., using the Euler method), and output continuous C. e value.

[0042] A computer-readable storage medium having a computer program stored thereon, wherein the stored computer program, when executed by a processor, implements the monitoring method of this embodiment.

[0043] Example 2

[0044] Application examples of the system and method according to Example 1.

[0045] Taking a 50-year-old female patient undergoing laparoscopic cholecystectomy and receiving propofol target-controlled infusion (TCI) anesthesia as an example, blood was continuously drawn and analyzed through her radial artery at a flow rate of 10 μL / min.

[0046] After the surgery began, the system performed real-time calculations. For example, at t=5 min, the mass spectrometer measured Cp(5)=4.2 μg / mL. The system's internal state was: based on the data from the first 4 minutes, the individualized clearance rate Cl for this patient had been inversely fitted to be approximately 1.8 L / min (the population value is approximately 2.0 L / min). Running the individualized PK model yielded the simulated central chamber concentration C1(5)=4.3 μg / mL at this moment. Assuming k e0 Take the population value as 0.46 min⁻¹, and the previous time step C. e (4.5) = 1.5 μg / mL, then C is calculated using the Euler method: e (5) ≈C e (4.5) + 0.46 × [4.3 - 1.5] × 0.5 ≈ 2.1 μg / mL. This value represents the concentration in the brain effect room at t=5 min.

[0047] During the procedure, the system displays the blood drug concentration C in real time. p The concentration fluctuated between 3.8 and 4.5 μg / mL, and the concentration C in the brain effect chamber was estimated. e After 10 minutes, the value reached a stable value of 3.2 μg / mL, at which point the BIS value of the monitor dropped to 50, confirming the predictive effectiveness of this application.

[0048] At 30 minutes into the surgery, wound irritation intensified, and the BIS value surged to 65, but C p No decrease was observed. The system triggered an adaptive algorithm, determined that the effect room concentration was "relatively insufficient," issued a yellow alert, and predicted that C would decrease in 5 minutes. e The concentration will drop to 2.8 μg / mL. Based on this, the doctor increased the target concentration from 4.0 μg / mL to 4.8 μg / mL in advance.

[0049] About 3 minutes later, C p Rising to 4.7 μg / mL, C e The concentration subsequently rose and stabilized at 3.6 μg / mL, while the BIS value fell and stabilized at 55. Intraoperative risk awareness was successfully avoided, achieving precise intervention.

[0050] The procedure was completed, and the infusion was stopped. The system detected C. p and C e A rapid decline, the predicted curve shows C after 5 minutes e The concentration was below the awakening threshold (1.2 μg / mL). The patient woke up as expected approximately 4 minutes later, confirming the accuracy of the model's prediction.

[0051] Example 3

[0052] Application examples of the system and method according to Example 1.

[0053] Using real-time blood drug concentration Cp Using real-time pharmacodynamic data (BIS) as input for calibration, the brain effect room concentration C is continuously and dynamically calculated through a PK-PD model. e This is used to precisely guide medication application. The algorithm for personalized parameter fitting and effect-site concentration calculation is illustrated using a patient (weighing 70 kg, currently receiving propofol target-controlled infusion) as an example: Initialization: Set the population PK parameters (e.g., the rate constant k of the three-compartment model). 10 =0.12, k 12 =0.35, k 21 =0.25…), population PD parameter (k e0 =0.46 min⁻¹, EC 50 =2.0 μg / mL, γ=2.0).

[0054] Input real-time data: At t=2min, input the mass spectrometry measured Cp(2)=4.0 μg / mL and the BIS measured value E(2)=80.

[0055] Personalized PK parameter fitting (inverse fitting): The system uses Cp data from the past 2 minutes as observations and employs least squares or extended Kalman filtering to fit the PK model state (concentration in each compartment) and parameters (such as k). 10 Perform joint estimation. Example output: Estimate the current k of the patient. 10 =0.10 min⁻¹ (Clearing is slightly slower).

[0056] Calculate C1(t) and C e (t): using the estimated k 10 Running the PK model with equal parameters, the optimal estimated concentration in the central chamber was obtained as C1(2) = 4.1 μg / mL. Substituting this into the effect chamber equation, with C... e Using (1.5) = 0.5 μg / mL (the value at the previous moment) as the initial value, calculate: C e (2) = C e (1.5) + 0.46 × [4.1 - 0.5] × 0.5 ≈ 1.7 μg / mL.

[0057] Prediction-to-measurement deviation calculation: C e (2) = 1.7 μg / mL, substituted into Sigmoid E max The model yields the predicted BIS: E_pred = 100 - (100 × 1.7²) / (1.7²+2.0²) ≈ 52. The deviation from the measured E(2)=80 is 28.

[0058] Bayesian optimization of PD parameters: Bayesian updates are performed based on bias. Example: The algorithm calculates the current parameters (EC... 50 The likelihood of a bias of 28 is extremely low when γ = 2.0 and γ = 2.0. Through iterative sampling (such as MCMC), a new parameter that maximizes the posterior probability is found: EC. 50 =4.2 μg / mL, γ'=2.1.

[0059] Model Update and Output: Automatic EC Optimization 50 This ensures that the predicted BIS value closely matches the measured data. Subsequently, to maintain the doctor's target BIS=50, the system calculates the required target brain concentration C in reverse. e The infusion rate is set to approximately 1.8 μg / mL, and the infusion rate is adjusted accordingly to achieve individualized and precise anesthesia. The system updates the PD parameters to personalized values.

[0060] Subsequently, Ce estimation and efficacy prediction will be based on the new parameters, significantly improving prediction accuracy.

[0061] Example 4

[0062] Application examples of the system and method according to Example 1.

[0063] This embodiment utilizes an adaptive algorithm to continuously update the estimated value of parameter θ using the measured data E, making the model predictions increasingly accurate.

[0064] Adaptive Algorithm: .

[0065] θ is the personalized parameter vector to be optimized, typically [EC] 50 , γ].

[0066] P(θ) is the prior probability, i.e., the population probability distribution of the parameters (known).

[0067] P(E|θ) is the likelihood function, which represents the probability of observing the current data E given the parameter θ.

[0068] P(θ|E) is the posterior probability, which is the latest probability distribution of parameter θ after observing data E.

[0069] Taking a patient using propofol as an example, with a target BIS of 50 and an initial population parameter of k... eo =0.46 min⁻¹, EC 50 =2.0 μg / mL, γ=1.8.

[0070] When t=2min, the miniaturized mass spectrometer measured C p =4.0μg / mL, C is calculated eThe actual value was approximately 1.7 μg / mL, but the BIS measured value was 80, which deviated significantly from the model prediction value of 52.

[0071] The system triggers the Bayesian optimization algorithm to optimize the parameters to EC. 50 =4.2 μg / mL, γ=2.1, the new predicted value E(80) is a perfect match with the measured value E(80), so that the predicted value is matched with the measured value again.

[0072] The system determined that the patient was not sensitive to propofol and updated their personalized parameters to EC. 50 =4.2μg / mL, γ=2.1.

[0073] To achieve the target BIS=50, C needs to be... p Maintain at 6.5 μg / mL, corresponding to C e ≈3.8 μg / mL. After adjusting the target concentration, the system successfully maintained the BIS at the target value, achieving precise closed-loop control.

Claims

1. An intraoperative anesthetic monitoring system based on mass spectrometry technology, comprising a mass spectrometry device; characterized in that, The monitoring system also includes: The system includes a sampling module and a separation module. The sampling module is used to obtain blood samples from the patient's blood vessels during surgery and send them to the separation module. The separation module separates the anesthetic sample, which is then detected by a mass spectrometer. The calculation unit calculates the concentration C of the anesthetic agent output by the mass spectrometer. p Using the PK-PD model, the concentration C in the brain effect chamber was obtained. e And according to concentration C e The changing trend of C, predicting the concentration C e ; The display unit is used to synchronously display the concentration C. p and concentration C e Curve showing how it changes over time.

2. The monitoring system according to claim 1, characterized in that, The sampling module is connected to the patient's arterial pressure measurement tubing to achieve continuous sampling; the separation module adopts microfluidic technology and is equipped with a filter membrane and a dialysis membrane.

3. The monitoring system according to claim 1 or 2, characterized in that, The mass spectrometer uses an atmospheric pressure direct ionization source.

4. A method for monitoring intraoperative anesthetic agents based on mass spectrometry, characterized in that, The detection method is as follows: Blood was continuously collected during the operation and monitored in real time using mass spectrometry to obtain the concentration of the anesthetic agent, C. p ; Obtain real-time drug efficacy data during surgery; Based on the C p Based on the aforementioned pharmacodynamic data, the model parameters are dynamically optimized using a PK-PD model and an adaptive algorithm to calculate the concentration C in the brain effect room. e .

5. The monitoring method according to claim 4, characterized in that, Obtain concentration C e The method is as follows: Using the group PK parameter as the initial value, and utilizing real-time C p Personalized pharmacokinetic parameters describing the current patient's drug administration process are obtained through inverse fitting. A pharmacokinetic model based on these personalized parameters is then run to obtain the central compartment simulated concentration C1(t). This is combined with the effect compartment equilibrium rate constant k from the pharmacodynamic (PD) model. e0 Solve for dC e / dt=k eo ·[C1(t)-C e [Obtain the concentration C in the brain effect chamber] e .

6. The monitoring method according to claim 4, characterized in that, The dynamic optimization model parameters are estimated using a Bayesian algorithm. By maximizing the posterior probability, the pharmacokinetic parameter EC is optimized using the pharmacodynamic data. 50 and γ.

7. The monitoring method according to claim 4, characterized in that, The efficacy data mentioned are electroencephalogram (EEG) depth index data.

8. The method according to claim 7, characterized in that, The parameters of the dynamic optimization model include: Based on the current PK-PD model and C e The calculated predicted drug efficacy data is compared with the real-time monitored EEG depth index data; If the deviation between the two exceeds a set threshold, the pharmacokinetic parameters in the PK-PD model are adjusted using the adaptive algorithm to make the deviation less than the threshold. The PD parameters include the effect-site concentration EC at which 50% of the maximum effect is achieved. 50 .

9. The monitoring method according to claim 7 or 8, characterized in that, It also includes intelligent control and early warning steps: Compare the C e The real-time trend of the EEG depth index data; According to the C e The real-time trend of change can be used to predict the concentration value at a specified future time point; When the C e The current value or the predicted future concentration value exceeds the preset safe concentration range, or the C e When the trend of changes in EEG data is not synchronized, an early warning signal is triggered and / or an anesthetic infusion rate control signal is calculated and output.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 4 to 9.

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