Anesthesia assisting method for promoting postoperative rapid recovery

Abstract

The invention relates to the technical field of medical control, and discloses an anesthesia assisting method for promoting postoperative rapid recovery, which comprises the following steps: constructing a basic pharmacokinetic model for a selected anesthetic; intraoperative multi-modal data of a patient are collected in real time; calculating a temperature-metabolism correction coefficient based on the thermodynamic data, and calculating a volume-dilution correction coefficient based on the hemodynamic data; correcting the basic plasma clearance rate in real time by using the temperature-metabolism correction coefficient, and correcting the basic central ventricle distribution volume in real time by using the volume-dilution correction coefficient; the corrected plasma clearance rate and the corrected central ventricle distribution volume are substituted into the basic pharmacokinetic model, and the medicine infusion rate needed for maintaining the preset target concentration is calculated. According to the method, the parameters of the pharmacokinetic model are dynamically corrected according to the real-time thermal dynamics and hemodynamics data, medicine accumulation is effectively avoided, and the postoperative awakening time is shortened.

Description

Technical Field

[0001] This invention relates to the field of medical control technology, specifically to an anesthesia-assisted method that promotes rapid postoperative recovery. Background Technology

[0002] In modern general anesthesia, target-controlled infusion (TCI) technology has become the mainstream drug delivery method. By running built-in pharmacokinetic and pharmacodynamic models through a computer system, it automatically calculates and controls the drug delivery rate of the infusion pump based on the target plasma concentration or effect-site concentration set by the physician. Existing TCI systems mainly rely on pharmacokinetic models derived from large-sample population statistics (such as the Marsh model, Schnider model, or Minto model). These models typically use patient age, sex, height, and weight as covariates to determine core parameters such as drug distribution volume and clearance. Ideally, such population-based models can predict drug metabolism in the human body relatively well.

[0003] However, existing target-controlled infusion systems suffer from model adaptability limitations in practical clinical applications. Traditional pharmacokinetic models are primarily constructed based on data from volunteers or specific patient groups under standard physiological conditions. Once initialized, their internal parameters remain fixed and cannot respond to dynamic changes in the patient's physiological environment during surgery. In actual surgical procedures, patients' physiological states often fluctuate dramatically, with changes in core body temperature and circulating volume having a particularly significant impact on pharmacokinetics.

[0004] During surgery, patients often experience intraoperative hypothermia due to the vasodilatory effects of anesthetic drugs, exposure of internal organs, or the low temperature environment of the operating room. According to the principles of enzyme reaction kinetics, a decrease in body temperature inhibits the activity of hepatic drug-metabolizing enzymes, leading to a significant decrease in the actual plasma clearance rate of drugs. Current TCI systems, lacking real-time integration of body temperature parameters, still calculate the infusion rate based on clearance rates at standard body temperature. This directly results in drug accumulation in the patient's body, causing the actual blood drug concentration to be far higher than the model's prediction, thus leading to excessively deep anesthesia and delayed postoperative recovery.

[0005] Furthermore, the surgical procedure involves bleeding, urine output, and large-volume fluid infusion, which alters the patient's effective circulating blood volume and extracellular fluid volume. The central compartment volume of distribution for drugs is not a constant value but is directly affected by hemodulation or concentration. When patients receive large-volume fluid resuscitation leading to hemodulation, the apparent volume of distribution of drugs increases. If the model still uses the initial fixed volume parameters, it will cause deviations in the calculation of drug concentrations during the induction or maintenance phases. Most current closed-loop control systems for anesthesia depth rely solely on pharmacodynamic indicators such as the bispectral index (BIS) for feedback regulation. This feedback mechanism has significant lag, typically correcting only after abnormal drug concentrations have already caused changes in the EEG state, failing to address the root cause of pharmacokinetic model inaccuracies due to changes in physiological parameters. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides an anesthesia-assisted method that promotes rapid postoperative recovery. It solves the problem that existing target-controlled infusion systems, due to their fixed pharmacokinetic model parameters, cannot dynamically adapt to the impact of intraoperative core body temperature changes on drug metabolism and clearance rates in real time, resulting in inaccurate drug infusion and delayed postoperative awakening.

[0007] To achieve the above objectives, the present invention provides the following technical solution: The first aspect of this invention provides an anesthesia-assisted method to promote rapid postoperative recovery, the method comprising the following steps: S1. Based on the patient's physiological characteristics data, construct a basic pharmacokinetic model for the selected anesthetic drug, and initialize the basic plasma clearance rate and basic central compartment distribution volume; S2. Real-time acquisition of intraoperative multimodal data of patients, including thermodynamic data reflecting the drug metabolism environment and hemodynamic data reflecting the fluid volume status; S3. Calculate the temperature-metabolic correction factor based on the thermodynamic data, and calculate the volume-dilution correction factor based on the hemodynamic data; S4. The baseline plasma clearance rate is corrected in real time using the temperature-metabolism correction coefficient to obtain the corrected plasma clearance rate; at the same time, the baseline central compartment distribution volume is corrected in real time using the volume-dilution correction coefficient to obtain the corrected central compartment distribution volume. S5. Substitute the corrected plasma clearance rate and the corrected central compartment distribution volume into the basic pharmacokinetic model, calculate the drug infusion rate required to maintain the preset target concentration, and generate infusion control commands.

[0008] Preferably, in step S1, the physiological characteristic data specifically includes: The patient's age, gender, height, and weight; The physiological characteristic data is used to match an applicable set of population pharmacokinetic parameters from a pre-set database, serving as the basis for constructing the basic pharmacokinetic model.

[0009] Preferably, in step S1, the initialization of the baseline plasma clearance rate and baseline central compartment distribution volume specifically includes: Based on the physiological characteristic data, the initial central compartment distribution volume and initial plasma clearance rate of the patient under standard conditions are calculated using a three-compartment model algorithm. Acquire a marker signal indicating whether to implement nerve blockade; if the marker signal confirms the implementation of nerve blockade, then acquire a preset synergistic ratio coefficient. The preset target concentration in the basic pharmacokinetic model is multiplied by the synergistic ratio coefficient based on the standard concentration to obtain the adjusted preset target concentration, and this adjusted preset target concentration is used as the target maintenance value for subsequent calculation of drug infusion rate.

[0010] Preferably, in step S2, the thermodynamic data specifically includes: By implanting a temperature sensor into the patient's body, the core body temperature value at the current moment is collected in real time at a preset sampling frequency and marked with a timestamp. A standard reference body temperature value is pre-stored in the system database. This standard reference body temperature value is defined as the temperature point at which the metabolic enzyme of the selected anesthetic drug has a baseline reaction rate, and is used as a benchmark reference for calculating enzyme activity deviation.

[0011] Preferably, in step S2, the hemodynamic data specifically includes: Real-time fluid input volume obtained through the infusion pump system; Real-time fluid output volume obtained through excretion monitoring devices or suction metering devices; The patient's initial blood volume is estimated based on the patient's weight.

[0012] Preferably, in step S4, the real-time correction of the baseline plasma clearance rate specifically includes: A preset temperature sensitivity coefficient is obtained, wherein the temperature sensitivity coefficient characterizes the multiple relationship between the enzyme-catalyzed reaction rate and the temperature. Calculate the difference between the core body temperature value and the standard reference body temperature value in the thermodynamic data; Using the temperature sensitivity coefficient as the base and the ratio of the difference to a specific temperature step as the exponent, the temperature-metabolism correction coefficient is obtained by performing an exponential calculation. The baseline plasma clearance rate is multiplied by the temperature-metabolic correction factor, and the product is taken as the corrected plasma clearance rate.

[0013] Preferably, in step S4, the real-time correction of the basic central room distribution volume specifically includes: The difference between the real-time fluid input and real-time fluid output in the hemodynamic data is integrated over time to obtain the cumulative net fluid balance at the current moment. Calculate the ratio of the cumulative net fluid balance to the initial blood volume, and multiply this ratio by a preset drug distribution characteristic coefficient to obtain the volume deviation factor; Adding the capacity deviation factor to the value 1 yields the capacity-dilution correction coefficient; Multiply the basic central chamber distribution volume by the capacity-dilution correction factor, and use the product as the corrected central chamber distribution volume.

[0014] Preferably, in step S5, the basic pharmacokinetic model specifically includes: A set of differential equations describing drug transport between the central compartment, the fast peripheral compartment, and the slow peripheral compartment; The system of differential equations includes a first rate constant characterizing the transport from the central ventricle to the fast peripheral ventricle, and a second rate constant characterizing the transport from the central ventricle to the slow peripheral ventricle. During calculation, the basic pharmacokinetic model uses the modified central compartment distribution volume to synchronously update the first rate constant and the second rate constant, so as to maintain the conservation of substance transport within the model.

[0015] Preferably, in step S5, calculating the drug infusion rate required to maintain the preset target concentration specifically includes: Multiply the preset target concentration by the corrected plasma clearance rate to obtain the elimination flux; The distribution flux is calculated using the preset target concentration, the corrected central chamber distribution volume, and the updated inter-chamber transport rate constant. The elimination flux is added to the distribution flux to obtain the drug infusion rate required to maintain the current preset target concentration.

[0016] A second aspect of the present invention provides an anesthesia support system for promoting rapid postoperative recovery, the system comprising: The model initialization module is used to construct a basic pharmacokinetic model for a selected anesthetic drug based on the patient's physiological characteristics data, and to initialize the basic plasma clearance rate and the basic central compartment distribution volume. A multimodal data acquisition module, connected to the model initialization and construction module, is used to acquire intraoperative multimodal data of the patient in real time. The intraoperative multimodal data includes thermodynamic data reflecting the drug metabolism environment and hemodynamic data reflecting the fluid volume status. A coupling correction coefficient calculation module, connected to the multimodal data acquisition module, is used to calculate the temperature-metabolism correction coefficient based on the thermodynamic data and the volume-dilution correction coefficient based on the hemodynamic data. The pharmacokinetic parameter real-time update module is connected to the coupling correction coefficient calculation module. It is used to correct the baseline plasma clearance rate in real time using the temperature-metabolism correction coefficient to obtain the corrected plasma clearance rate; at the same time, it uses the volume-dilution correction coefficient to correct the baseline central compartment distribution volume in real time to obtain the corrected central compartment distribution volume. The infusion control command generation module, connected to the pharmacokinetic parameter real-time update module, is used to substitute the corrected plasma clearance rate and the corrected central compartment distribution volume into the basic pharmacokinetic model, calculate the drug infusion rate required to maintain the preset target concentration, and generate infusion control commands.

[0017] This invention provides an anesthesia-assisted method to promote rapid postoperative recovery. It has the following beneficial effects: 1. This invention establishes a dynamic mapping relationship between core body temperature and drug plasma clearance rate by introducing a temperature-metabolism correction coefficient. This mechanism can automatically correct metabolic parameters in the pharmacokinetic model based on the principle of enzyme-catalyzed reaction kinetics to address potential intraoperative patient temperature fluctuations. This solves the model prediction bias problem caused by the assumption of a constant patient body temperature in traditional target-controlled infusion systems, effectively preventing drug accumulation caused by reduced activity of drug-metabolizing enzymes in hypothermic states. This ensures that the blood drug concentration decreases at the expected rate upon postoperative discontinuation, shortening the patient's recovery time.

[0018] 2. This invention utilizes real-time acquired hemodynamic data to calculate the volume-dilution correction factor, achieving dynamic compensation for the distribution volume of the central compartment in the model. This method fully considers the impact of intraoperative fluid management on blood dilution or concentration, correcting the concentration prediction error generated by the fixed-parameter model when blood volume changes. By adjusting the distribution volume parameter in real time according to the net fluid balance, the system can ensure that the calculated drug infusion rate matches the patient's actual fluid distribution during rapid fluid exchange, maintaining the stability of intraoperative blood drug concentration.

[0019] 3. This invention constructs a feedforward compensation control logic that incorporates non-pharmacological physiological parameters, unlike lagging feedback regulation that relies solely on anesthesia depth monitoring values. By monitoring physical environmental factors affecting pharmacokinetics in real time, the system can proactively detect and correct differences in drug metabolism or distribution caused by changes in the physiological environment before substantial fluctuations occur in anesthesia depth indicators. This causal predictive regulation mechanism improves the accuracy of anesthetic drug administration, ensuring the depth of surgical anesthesia while avoiding overdose caused by model inaccuracies, thus helping to reduce the probability of postoperative drug residue-related complications. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a system architecture diagram of the present invention.

[0021] Among them, 100 is the model initialization and construction module; 200 is the multimodal data acquisition module; 300 is the coupling correction coefficient calculation module; 400 is the pharmacokinetic parameter real-time update module; and 500 is the infusion control command generation module. Detailed Implementation

[0022] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Reference Figure 1 , Figure 1 This is a general flowchart of an anesthesia assistance method for promoting rapid postoperative recovery according to an embodiment of the present invention. The present invention provides an anesthesia assistance method for promoting rapid postoperative recovery, specifically including the following steps: Step S100: Based on the patient's physiological characteristics data, construct a basic pharmacokinetic model for the selected anesthetic drug, and initialize the basic plasma clearance rate and basic central compartment distribution volume.

[0024] In step S100, the system first obtains the patient's age, gender, height, and weight through a human-computer interaction interface or a hospital information system interface. The system's internal database stores population pharmacokinetic parameter sets for various anesthetic drugs. Based on the input physiological characteristic data, the system retrieves and matches applicable parameter sets from the database, thereby establishing a basic three-compartment model describing the drug's distribution and elimination in the body. This basic three-compartment model includes well-defined baseline plasma clearance parameters and baseline central compartment distribution volume parameters, serving as benchmark values ​​for subsequent calculations.

[0025] Step S200: Real-time acquisition of intraoperative multimodal data of the patient, including thermodynamic data reflecting the drug metabolism environment and hemodynamic data reflecting the fluid volume status.

[0026] In step S200, the system reads data from the multimodal physiological parameter monitoring device and the fluid metering and management device at a preset sampling frequency. Thermodynamic data primarily comes from temperature sensors implanted in the patient, reflecting the patient's current body temperature. Hemodynamic data primarily comes from real-time statistics of fluid input and output, reflecting the patient's current fluid balance. These two types of data constitute the physical basis for environmental correction of the pharmacokinetic model.

[0027] Step S300: Calculate the temperature-metabolic correction factor based on thermodynamic data, and calculate the volume-dilution correction factor based on hemodynamic data.

[0028] In step S300, the system incorporates a mapping algorithm between physical and pharmacological parameters. The system compares the collected thermodynamic data with standard reference values ​​to calculate a temperature-metabolic correction factor characterizing changes in enzyme activity. Simultaneously, the system calculates the cumulative net balance of the liquid and converts it into a volume-dilution correction factor characterizing the degree of blood dilution or concentration. Both correction factors are dimensionless numerical factors used to quantify the influence of non-pharmacological factors on drug metabolism and pharmacokinetics.

[0029] Step S400: The baseline plasma clearance rate is corrected in real time using the temperature-metabolism correction coefficient to obtain the corrected plasma clearance rate; at the same time, the baseline central compartment distribution volume is corrected in real time using the volume-dilution correction coefficient to obtain the corrected central compartment distribution volume.

[0030] In step S400, the system executes parameter update logic. The basal plasma clearance rate is multiplied by a temperature-metabolic correction factor to generate a corrected plasma clearance rate that reflects the current body temperature. The basal central compartment distribution volume is multiplied by a volume-dilution correction factor to generate a corrected central compartment distribution volume that reflects the current blood volume status. This step S400 implements the dynamic time-varying characteristics of the pharmacokinetic model parameters, enabling them to adjust in response to changes in the patient's physiological state.

[0031] Step S500: Substitute the corrected plasma clearance rate and the corrected central compartment distribution volume into the basic pharmacokinetic model, calculate the drug infusion rate required to maintain the preset target concentration, and generate infusion control instructions.

[0032] In step S500, the system re-solves the pharmacokinetic differential equation or its analytical solution using the corrected parameters. The system calculates the instantaneous drug input required at the current moment to maintain the plasma concentration or effect-site concentration in the patient at a preset target concentration level. This calculation result is converted into an infusion control command containing a specific flow rate value and sent to the target-controlled infusion actuator via a data interface to drive the infusion pump to adjust the injection rate.

[0033] Reference Figure 2 , Figure 2 This is an architectural diagram of an anesthesia support system for promoting rapid postoperative recovery according to an embodiment of the present invention. The present invention provides an anesthesia support system for promoting rapid postoperative recovery, specifically comprising: The model initialization module 100 is used to establish the basic computational framework. This module has a data input interface for receiving patients' physiological characteristic data, specifically including age, gender, height, and weight. Internally, the model initialization module 100 stores a population pharmacokinetic parameter database. Based on the input physiological characteristic data, it matches the applicable parameter set and constructs a basic pharmacokinetic model for the selected anesthetic drug. Simultaneously, the model initialization module 100 performs initialization calculations, determining the baseline plasma clearance rate and baseline central compartment volume, and outputs these two basic parameters to subsequent modules.

[0034] A multimodal data acquisition module 200, connected to the model initialization and construction module 100, has a communication interface with external hardware devices. This multimodal data acquisition module 200 is configured to acquire intraoperative multimodal data of the patient in real time. The intraoperative multimodal data is divided into two categories: one is thermodynamic data reflecting the drug metabolism environment, specifically obtained by connecting a temperature sensor to acquire core body temperature values; the other is hemodynamic data reflecting the fluid volume status, specifically obtained by connecting an infusion pump system and an excretion monitoring device to acquire real-time fluid input and output volumes. The multimodal data acquisition module 200 filters and performs time synchronization processing on the acquired raw signals to form a continuous data stream.

[0035] The coupling correction coefficient calculation module 300 is connected to the multimodal data acquisition module 200. This module is configured to perform specific mathematical operations, converting the acquired physical quantities into dimensionless correction factors. First, based on the difference between the core body temperature and the standard reference body temperature in the thermodynamic data, the coupling correction coefficient calculation module 300 calculates the temperature-metabolic correction coefficient using the built-in Arrhenius exponent algorithm. Simultaneously, it integrates the net fluid balance volume in the hemodynamic data and, combined with the patient's initial blood volume, calculates the volume-dilution correction coefficient. These two correction coefficients are output to the next-level module in real time.

[0036] A pharmacokinetic parameter real-time update module 400 is connected to a coupling correction coefficient calculation module 300. This module 400 is configured to perform dynamic corrections to the model parameters. It receives base parameters from the model initialization construction module 100 and correction coefficients from the coupling correction coefficient calculation module 300. The module 400 uses a temperature-metabolism correction coefficient to multiply and correct the base plasma clearance rate to obtain the corrected plasma clearance rate; it also uses a volume-dilution correction coefficient to multiply and correct the base central compartment distribution volume to obtain the corrected central compartment distribution volume. Furthermore, the module 400 is also responsible for synchronously updating the intercompartmental transport rate constant in the model based on the principle of mass conservation.

[0037] An infusion control command generation module 500 is connected to a pharmacokinetic parameter real-time update module 400. This module is configured to calculate the final control signal. It substitutes the corrected plasma clearance rate, the corrected central compartment distribution volume, and the updated transport rate constant into the differential equations of the basic pharmacokinetic model. Based on a preset target concentration, the module calculates the instantaneous drug infusion rate required to maintain that concentration and encodes the result as an infusion control command. This command is sent to an external target-controlled infusion pump via the system output interface to control the motor speed and execute the drug delivery operation.

[0038] Reference Figure 1 Step S100 specifically includes the following: The system first receives the patient's demographic parameters through a data interface, specifically including age. Actual weight ,height and gender For pharmacokinetic calculations, the system does not directly use total body weight, but instead calculates lean body mass based on the parameters mentioned above. The system has built-in formulas for calculating lean body mass for different genders. For example, for male patients, the system calculates lean body mass based on the James formula, which uses the square relationship between weight and height.

[0039] After obtaining the lean body mass, the system retrieves a pre-stored set of population pharmacokinetic parameters for anesthetic drugs. Taking remifentanil as an example, the system uses the Minto pharmacokinetic model as the base model. Based on the patient's age and lean body mass, the system calculates the central compartment distribution volume at baseline. and basal plasma clearance rate .

[0040] Basic central room distribution volume The calculations were configured to show a linear positive correlation with the patient's lean body mass, adjusted for age. Basal plasma clearance rate The calculation is also configured to be related to lean body mass and reduced according to a specific non-linear decay curve with age.

[0041] In determining and Subsequently, the system further initializes the intercompartmental transport rate constant based on the three-compartment model principle. The system calculates the central compartment elimination rate constant under baseline conditions. This value equals the baseline plasma clearance rate divided by the baseline central compartment distribution volume. Simultaneously, the system determines the transport rate constant between the central compartment and the rapid peripheral compartment based on fixed coefficients in the parameter set or as a function of age. , and the transit rate constant between the central chamber and the slow peripheral chamber. , The above parameters together constitute the initial state-space equations when there is no interference from intraoperative physiological fluctuations.

[0042] After initializing the basic model parameters, the system executes the logical judgment for setting the nerve block coordination. The system checks whether a discrete signal confirming the implementation of nerve block exists at the input port. This signal corresponds to whether regional or peripheral nerve block was performed on the patient during clinical operation.

[0043] If the system detects a false or null value for the marker signal, the system will preset the target concentration. The standard induction concentration for general anesthesia of this anesthetic drug is set at... .

[0044] If the system detects a logically true flag, it indicates that the patient has received a nerve block, and the pain afferent pathways are partially inhibited. The system reads the coordination ratio coefficient from the preset storage unit. The value of this coefficient is limited to a range greater than 0 and less than 1, with the specific value set according to the type of nerve block and the expected range of block, for example, a value between 0.6 and 0.8. The system performs the following calculations to determine the adjusted preset target concentration. : In the formula, Preset target concentration; This is the standard concentration for inducing general anesthesia; This is the synergistic ratio coefficient.

[0045] The system will adjust the preset target concentration This value is locked as the target tracking value in the subsequent closed-loop control algorithm. Through this step, the system introduces a multi-modal analgesia dose reduction mechanism at the beginning of the pharmacokinetic calculation, so that the output command of the basic model is adapted to the reduced opioid demand level in the initial stage.

[0046] Reference Figure 1 Step S200 specifically includes the data acquisition and preprocessing of thermodynamic and hemodynamic data.

[0047] For thermodynamic data acquisition, the system obtains core body temperature signals by connecting to temperature sensors implanted inside the patient. These sensors are typically located in the lower esophagus, nasopharynx, or bladder to ensure that the acquired values ​​accurately reflect the thermal state of the body's core region. The system operates at a preset sampling frequency. (For example, collect data every 5 seconds) Read the analog voltage signal from the sensor and convert it from analog to digital form to obtain the core body temperature value. The system adds a timestamp to each collected body temperature value. .

[0048] The system calls the preset standard reference body temperature value in memory. This value is defined as the ambient temperature at which the baseline clearance rate of the metabolic enzymes of a selected anesthetic drug is determined in in vitro experiments or population pharmacokinetic studies, typically set at 37 degrees Celsius. This standard reference body temperature is used as a static baseline for subsequent calculations of the relative deviation of enzyme activity.

[0049] The system primarily acquires hemodynamic data by real-time monitoring of fluid intake and output.

[0050] For liquid input, the system connects to the intelligent infusion pump station via a communication interface. The system reads the cumulative infusion volume of each channel pump in real time and sums the values ​​of each channel to obtain the current value. Real-time liquid input This value includes the total input volume of crystalloid solutions, colloidal solutions, and blood products.

[0051] For fluid output, the system is connected to an excretion metering device equipped with a precision weighing or level sensor. The system reads the fluid volume in the urine collection bag and surgical drainage bottle in real time to obtain the current volume. Real-time liquid output In the absence of automatic metering devices, the system provides a manual input interface to receive periodically entered output data and convert it into continuous time series data through a linear interpolation algorithm.

[0052] In addition, the system estimates the patient's initial blood volume based on the weight data obtained in step S100. The system uses the Nadler formula for estimation. For male patients, the formula for calculating the initial blood volume is: For female patients, the formula for calculating the initial blood volume is: In the formula, This represents the initial blood volume value. For height; Total actual weight; Calculation results The unit is liters. This initial blood volume value serves as the denominator for subsequent calculations of the blood dilution ratio.

[0053] The system collects , as well as A moving average filter is applied to eliminate transient spikes caused by sensor noise, ensuring that the data used for subsequent correction calculations is smooth and stable.

[0054] Reference Figure 1 Step S300 specifically includes the following: The system first calculates the temperature-metabolic correction factor. The calculation is based on the exponential relationship between chemical reaction rates and temperature described by the Arrhenius equation. The system reads the temperature sensitivity coefficients for the current anesthetic drug from a database. This coefficient characterizes the fold change in the rate of drug-metabolizing enzyme-catalyzed reactions for every 10-degree Celsius change in temperature. For drugs such as remifentanil, which are primarily metabolized by non-specific esterases in blood and tissues, this coefficient... The value is usually set between 2.0 and 3.0.

[0055] The system calculates the core body temperature at the current moment. Compared with standard reference body temperature The difference between The system then performs an exponential operation to determine the correction factor. Based on the base, using the temperature difference value The ratio to a specific temperature step size (here, a constant of 10) is the exponent, calculated using the following formula: In the formula, This represents the temperature-metabolism correction factor; The current core body temperature; Standard reference body temperature; This is the temperature sensitivity coefficient.

[0056] When the patient is in a state of hypothermia, the index part is negative, and the calculated value is... A value less than 1 indicates a decreased metabolic rate. Conversely, if the patient's body temperature is elevated, A value greater than 1 indicates an increase in metabolic rate.

[0057] Meanwhile, the system calculates the capacity-dilution correction factor. This calculation is based on the principles of fluid mass conservation and dilution. The system first processes the real-time fluid input from the hemodynamic data. With real-time liquid output The difference can be integrated over time, or the current time can be obtained by accumulating discrete sampling points. Cumulative net liquid balance : In the formula, Indicates the current time The cumulative net liquid balance; Indicates time Real-time liquid input volume; Indicates time Real-time liquid output; This is the current calculation time.

[0058] The system calculates the cumulative net liquid balance. Compared with the initial blood volume value estimated in step S200 The ratio, which reflects the relative expansion or contraction of the intravascular volume, is used in the system. A drug distribution characteristic coefficient is also introduced. For water-soluble drugs that are mainly distributed in plasma and extracellular fluid, The value should be close to 1; for drugs with high lipid solubility or high plasma protein binding rate, The value is set based on the ratio of its apparent volume of distribution to blood volume, and is usually between 0.5 and 1.0.

[0059] The system calculates the capacity-dilution correction factor using the following formula. : In the formula, This is the capacity-dilution correction factor; This is the drug distribution characteristic coefficient; Indicates the current time The cumulative net liquid balance; This represents the initial blood volume value.

[0060] When the intraoperative fluid intake exceeds the output, resulting in a positive balance... It is a positive value. A value greater than 1 indicates a relative increase in the apparent volume of distribution of the drug due to blood dilution. Conversely, a negative equilibrium indicates a decrease in the apparent volume of distribution. A value less than 1 indicates a decrease in distribution volume due to blood concentration. The system outputs the calculated value in real time. and To the parameter update module.

[0061] Reference Figure 1 Step S400 specifically implements the time-varying update mechanism for model parameters.

[0062] The system receives basic parameters from step S100. , and the correction factor from step S300 , .

[0063] Regarding the correction of plasma clearance rate, the system will adjust the baseline plasma clearance rate. With temperature-metabolic correction factor Perform multiplication to obtain the corrected plasma clearance rate at the current moment. : In the formula, This is the corrected plasma clearance rate; Basic plasma clearance rate; This is the temperature-metabolism correction factor.

[0064] This operation simulates the direct impact of body temperature changes on the activity of drug-metabolizing enzymes by adjusting the elimination rate parameter. For example, under deep hypothermic anesthesia, this correction will reduce the drug elimination rate in the model, preventing accumulation in the body that would occur with administration at room temperature.

[0065] Regarding the correction of the central room distribution volume, the system will adjust the basic central room distribution volume. With capacity-dilution correction factor Perform multiplication to obtain the corrected central chamber distribution volume at the current moment. : In the formula, This refers to the corrected distribution volume of the central chamber. The basic central chamber distribution volume; This is the capacity-dilution correction factor.

[0066] This operation simulates the physical effects of blood dilution or concentration on drug concentration by adjusting volume parameters.

[0067] Updated and After the two principal parameters are established, the system must synchronously update their associated intercompartmental transport rate constants to maintain the mathematical consistency of the pharmacokinetic differential equations. For the central compartment elimination rate constant... This is defined as the ratio of the clearance rate to the central chamber volume. Therefore, the system calculates the current moment's clearance rate using the following formula: : In the formula, This represents the central chamber elimination rate constant at the current moment; This refers to the corrected distribution volume of the central chamber. The basic central chamber distribution volume; This is the capacity-dilution correction factor; This represents the temperature-metabolism correction factor; This is the corrected plasma clearance rate; Based on plasma clearance rate.

[0068] For the transit rate constants from the central chamber to the fast peripheral chamber (chamber 2) and the slow peripheral chamber (chamber 3) and Due to the volume of the peripheral chamber , The corresponding microtransport coefficients are usually considered to be relatively constant or less affected by hemodynamics. Based on the principle of conservation of mass, the system adjusts the transport rate constant inversely according to the change in the volume of the central chamber.

[0069] Specifically, the flux of drug from the central chamber to the peripheral chambers should remain continuous. If the central chamber volume... Increase (e.g., due to infusion dilution), with the total drug volume remaining constant, the central compartment drug concentration It will decrease instantaneously. To maintain the physical processes of mass exchange described by the model, the transport rate constant needs to be adjusted accordingly. The system updates using the following formula. and : In the formula, The current transit rate constant from the central chamber to the rapid peripheral chamber (chamber 2); The current transit rate constant from the central chamber to the slow peripheral chamber (chamber 3); The basic inter-ventricular transport rate constant (1-2); Basic central chamber distribution volume; The basic inter-ventricular transport rate constant (1-3); This refers to the corrected distribution volume of the central chamber. This is the capacity-dilution correction factor.

[0070] For the rate constant of the return from the peripheral chamber to the central chamber and Since it primarily depends on the properties of the peripheral compartment and the rate of drug release from the tissue, this embodiment maintains the baseline value unchanged, or performs secondary adjustments based on a more complex tissue blood perfusion model. This embodiment adopts a strategy of keeping the baseline unchanged, i.e. , .

[0071] Through the above steps, the system constructs a set of time-varying parameters. Dynamically changing set of rate constants This parameter set will be used to solve the differential equation in the next time step.

[0072] Reference Figure 1 Step S500 is specifically responsible for converting the updated pharmacokinetic parameters into actual equipment control signals and introducing a safety feedback mechanism.

[0073] The system calculates and maintains the preset target concentration based on a set of differential equations from a three-compartment model. Required drug infusion rate According to the law of conservation of mass, in order to maintain the drug concentration in the central compartment at the target level, the rate of external drug input must be able to fully compensate for drug elimination losses and net drug transfer losses to the peripheral compartment.

[0074] The system first calculates the elimination flux, which is the amount of drug removed from the central chamber via metabolic pathways per unit time. This value is the preset target concentration. Compared with the current revised plasma clearance rate The product of.

[0075] The system calculates the distribution flux, which is the net amount of drug exchanged between the central and peripheral compartments per unit time. During the steady-state maintenance phase, this component typically approaches zero; however, during target concentration adjustment or model parameter adjustments... This component cannot be ignored when a step change occurs. The system utilizes the current inter-chamber transport rate constant. , The calculations are performed based on parameters such as the estimated drug quantity in each chamber.

[0076] Combining the above two parts, the system generates the basic infusion rate based on the following control algorithm. : In the formula, Base infusion rate; Preset target concentration; This refers to the corrected distribution volume of the central chamber. The central chamber elimination rate constant at the current moment; This represents the sum of the total rate constants flowing out of the central chamber; This represents the total drug flux returning from all peripheral chambers to the central chamber; This represents the current total amount of medication in the central storage room; This represents the total amount of drug in each peripheral compartment; This represents the rate constant returning from the peripheral chamber to the central chamber. In simplified calculations, if the system is assumed to be in a quasi-steady state, the infusion rate is mainly determined by... Decide.

[0077] After generating the baseline infusion rate, the system enters a safety verification closed-loop subroutine. This procedure is designed to prevent abnormal depth of anesthesia due to model prediction bias or individual specificity.

[0078] The system reads the bispectral index (BIS) values ​​from the multimodal monitoring device in real time. The system sets a safe range for the BIS, for example, between 40 and 60.

[0079] If the monitored BIS value is within the safe range, the system determines that the current model calculation is accurate and directly... It is converted into infusion control commands and sent to the infusion pump.

[0080] If the monitored BIS value deviates from the safe range, for example, a value consistently above 60 indicates insufficient anesthesia or a value below 40 indicates excessive anesthesia, the system activates a proportional-integral-derivative (PID) feedback controller. The system calculates the BIS deviation value. The PID controller calculates the compensation rate based on this deviation. .

[0081] In the formula, Calculate the compensation rate for the deviation; This is the proportional gain coefficient; This represents the BIS deviation value at the current moment. This is the integral gain coefficient; This is the integral term of the BIS deviation value; The differential gain coefficient; This is the differential term of the BIS deviation value.

[0082] The system adds the base infusion rate to the compensation rate to obtain the final execution rate. .

[0083] Furthermore, this closed-loop logic also includes a physical intervention trigger mechanism for hypothermia. If the system detects an excessively low BIS value (excessive anesthesia) and simultaneously detects core body temperature... If the temperature falls below a preset threshold (e.g., 35.5 degrees Celsius), the system logic determines that the excessive anesthesia is partly due to metabolic inhibition caused by hypothermia. In this case, in addition to reducing the drug infusion rate, the system will send a warming control command to the patient's surrounding warming blanket or liquid warmer via an IoT interface. This command includes a target set temperature and a signal to initiate heating, aiming to restore the activity of the patient's drug-metabolizing enzymes through physical rewarming, correcting pharmacokinetic deviations at the physiological source, and achieving dual closed-loop control of drug infusion and physical therapy.

Claims

1. An anesthetic adjuvant method to promote rapid postoperative recovery, characterized in that, Includes the following steps: S1. Based on the patient's physiological characteristics data, construct a basic pharmacokinetic model for the selected anesthetic drug, and initialize the basic plasma clearance rate and basic central compartment distribution volume; S2. Real-time acquisition of intraoperative multimodal data of patients, including thermodynamic data reflecting the drug metabolism environment and hemodynamic data reflecting the fluid volume status; S3. Calculate the temperature-metabolic correction factor based on the thermodynamic data, and calculate the volume-dilution correction factor based on the hemodynamic data; S4. The baseline plasma clearance rate is corrected in real time using the temperature-metabolism correction coefficient to obtain the corrected plasma clearance rate; at the same time, the baseline central compartment distribution volume is corrected in real time using the volume-dilution correction coefficient to obtain the corrected central compartment distribution volume. S5. Substitute the corrected plasma clearance rate and the corrected central compartment distribution volume into the basic pharmacokinetic model, calculate the drug infusion rate required to maintain the preset target concentration, and generate infusion control commands.

2. The anesthesia adjuvant method for promoting rapid postoperative recovery according to claim 1, characterized in that, In step S1, the physiological characteristic data specifically includes: The patient's age, gender, height, and weight; The physiological characteristic data is used to match an applicable set of population pharmacokinetic parameters from a pre-set database, serving as the basis for constructing the basic pharmacokinetic model.

3. The anesthesia adjuvant method for promoting rapid postoperative recovery according to claim 1, characterized in that, In step S1, the initialization of the baseline plasma clearance rate and baseline central compartment distribution volume specifically includes: Based on the physiological characteristic data, the initial central compartment distribution volume and initial plasma clearance rate of the patient under standard conditions are calculated using a three-compartment model algorithm. Acquire a marker signal indicating whether to implement nerve blockade; if the marker signal confirms the implementation of nerve blockade, then acquire a preset synergistic ratio coefficient. The preset target concentration in the basic pharmacokinetic model is multiplied by the synergistic ratio coefficient based on the standard concentration to obtain the adjusted preset target concentration, and this adjusted preset target concentration is used as the target maintenance value for subsequent calculation of drug infusion rate.

4. The anesthesia adjuvant method for promoting rapid postoperative recovery according to claim 1, characterized in that, In step S2, the thermodynamic data specifically includes: By implanting a temperature sensor into the patient's body, the core body temperature value at the current moment is collected in real time at a preset sampling frequency and marked with a timestamp. A standard reference body temperature value is pre-stored in the system database. This standard reference body temperature value is defined as the temperature point at which the metabolic enzyme of the selected anesthetic drug has a baseline reaction rate, and is used as a benchmark reference for calculating enzyme activity deviation.

5. The anesthesia adjuvant method for promoting rapid postoperative recovery according to claim 1, characterized in that, In step S2, the hemodynamic data specifically includes: Real-time fluid input volume obtained through the infusion pump system; Real-time fluid output volume obtained through excretion monitoring devices or suction metering devices; The patient's initial blood volume is estimated based on the patient's weight.

6. The anesthesia adjuvant method for promoting rapid postoperative recovery according to claim 1, characterized in that, In step S4, the real-time correction of the baseline plasma clearance rate specifically includes: A preset temperature sensitivity coefficient is obtained, wherein the temperature sensitivity coefficient characterizes the multiple relationship between the enzyme-catalyzed reaction rate and the temperature. Calculate the difference between the core body temperature value and the standard reference body temperature value in the thermodynamic data; Using the temperature sensitivity coefficient as the base and the ratio of the difference to a specific temperature step as the exponent, the temperature-metabolism correction coefficient is obtained by performing an exponential calculation. The baseline plasma clearance rate is multiplied by the temperature-metabolic correction factor, and the product is taken as the corrected plasma clearance rate.

7. The anesthesia adjuvant method for promoting rapid postoperative recovery according to claim 1, characterized in that, In step S4, the real-time correction of the basic central room distribution volume specifically includes: The difference between the real-time fluid input and real-time fluid output in the hemodynamic data is integrated over time to obtain the cumulative net fluid balance at the current moment. Calculate the ratio of the cumulative net fluid balance to the initial blood volume, and multiply this ratio by a preset drug distribution characteristic coefficient to obtain the volume deviation factor; Adding the capacity deviation factor to the value 1 yields the capacity-dilution correction coefficient; Multiply the basic central chamber distribution volume by the capacity-dilution correction factor, and use the product as the corrected central chamber distribution volume.

8. The anesthesia adjuvant method for promoting rapid postoperative recovery according to claim 1, characterized in that, In step S5, the basic pharmacokinetic model specifically includes: A set of differential equations describing drug transport between the central compartment, the fast peripheral compartment, and the slow peripheral compartment; The system of differential equations includes a first rate constant characterizing the transport from the central ventricle to the fast peripheral ventricle, and a second rate constant characterizing the transport from the central ventricle to the slow peripheral ventricle. During calculation, the basic pharmacokinetic model uses the modified central compartment distribution volume to synchronously update the first rate constant and the second rate constant, so as to maintain the conservation of substance transport within the model.

9. The anesthesia adjuvant method for promoting rapid postoperative recovery according to claim 1, characterized in that, In step S5, calculating the drug infusion rate required to maintain the preset target concentration specifically includes: Multiply the preset target concentration by the corrected plasma clearance rate to obtain the elimination flux; The distribution flux is calculated using the preset target concentration, the corrected central chamber distribution volume, and the updated inter-chamber transport rate constant. The elimination flux is added to the distribution flux to obtain the drug infusion rate required to maintain the current preset target concentration.

10. An anesthesia support system for promoting rapid postoperative recovery, applied to the anesthesia support method for promoting rapid postoperative recovery as described in any one of claims 1-9, characterized in that, include: The model initialization module is used to construct a basic pharmacokinetic model for a selected anesthetic drug based on the patient's physiological characteristics data, and to initialize the basic plasma clearance rate and the basic central compartment distribution volume. A multimodal data acquisition module, connected to the model initialization and construction module, is used to acquire intraoperative multimodal data of the patient in real time. The intraoperative multimodal data includes thermodynamic data reflecting the drug metabolism environment and hemodynamic data reflecting the fluid volume status. A coupling correction coefficient calculation module, connected to the multimodal data acquisition module, is used to calculate the temperature-metabolism correction coefficient based on the thermodynamic data and the volume-dilution correction coefficient based on the hemodynamic data; The pharmacokinetic parameter real-time update module is connected to the coupling correction coefficient calculation module. It is used to correct the baseline plasma clearance rate in real time using the temperature-metabolism correction coefficient to obtain the corrected plasma clearance rate; at the same time, it uses the volume-dilution correction coefficient to correct the baseline central compartment distribution volume in real time to obtain the corrected central compartment distribution volume. The infusion control command generation module, connected to the pharmacokinetic parameter real-time update module, is used to substitute the corrected plasma clearance rate and the corrected central compartment distribution volume into the basic pharmacokinetic model, calculate the drug infusion rate required to maintain the preset target concentration, and generate infusion control commands.

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