CN115227923A - Model construction method for evaluating patient airway resistance and lung compliance, terminal device and storage medium - Google Patents
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Abstract
The application relates to the technical field of medical equipment, in particular to a model construction method, terminal equipment and storage medium for evaluating airway resistance and lung compliance of a patient. And then the calculated airway resistance value and compliance value are utilized to provide a beneficial intelligent breathing control mode for clinical treatment, and the coordination and synchronization of the respiratory system of the human body and the breathing machine are realized.
Description
Technical Field
The present application relates to the technical field of medical devices, and in particular, to a model construction method, a terminal device, and a storage medium for evaluating airway resistance and lung compliance of a patient.
Background
When the life support system supplies air to the lungs, airway resistance and lung compliance are the major factors that cause changes in the air pressure and flow in the airways. The airway resistance refers to the pressure difference generated by unit flow in the airway, and is suitable for various obstructive ventilation dysfunctional diseases, mechanical ventilation, respiratory monitoring and other conditions in clinic. The lung compliance refers to the change of lung volume caused by unit pressure change, and represents the influence of the chest cavity on the lung volume, and can be divided into static lung compliance and dynamic lung compliance, wherein the lung compliance refers to the difficulty degree of change under the action of external force, the lung compliance is large, and the lung compliance shows that the lung has strong deformability, namely large deformation is caused under the action of small external force, and large compliance shows that the lung has large expansibility and can cause large change of the cavity volume under the action of small transmural pressure, and the static compliance reflects the elasticity of lung tissues, and the dynamic compliance has double influences on the elasticity of lung tissues and the resistance of air passages. At the same time, the measurement of respiratory parameters can be used to monitor and diagnose the progression of respiratory diseases and to generate treatment recommendations.
Only by automatically, quickly and accurately calculating the respiratory system parameters of the patient, namely airway resistance and lung compliance, can safe, reliable and accurate life support be provided for the patient, and the ventilation mode of the life support equipment is effectively adjusted and controlled, so that the ventilation device is suitable for various lung forms under different symptoms, and the complications of the patient using the life support equipment are reduced.
Airway resistance is typically calculated using the formula R = Δ P/Δ F, and air volume is calculated using the formula C = Δ V/Δ P; wherein Δ P = Ppeak-Pplate in R, Δ F represents the peak flow rate, Δ V in C represents the tidal volume, Δ P = Ppeak-PEEP; the method can calculate the R and C values only in a VCV mode with a breath holding function, has great algorithm limitation, low precision and wide application range, cannot be applied to other ventilation algorithms, and cannot comprehensively reflect the interaction process of a human respiratory system and a breathing machine.
Disclosure of Invention
In order to improve the ventilation effect of equipment such as a breathing machine, an anesthesia machine and the like, a model construction method, terminal equipment and a storage medium for evaluating the airway resistance and lung compliance of a patient are provided through a mechanical ventilation modeling technology and the identification of breathing system parameters, an excellent intelligent breathing control mode is provided by utilizing the calculated airway resistance and lung compliance values, and the coordination and synchronization of the breathing machine are realized.
In a first aspect: the application provides a model construction method for evaluating patient airway resistance and lung compliance, which adopts the following technical scheme:
a model construction method for assessing patient airway resistance and lung compliance, comprising:
acquiring relevant parameters influencing the real-time airway pressure value;
constructing a first model based on the obtained related parameters, wherein the first model is as follows: p (t) = E × V (t) + R × dv/dt, wherein P (t) represents t real-time airway pressure value, V (t) represents t real-time capacity, dv/dt represents instant flow rate at time t and is denoted as F (t), E represents conductance, R represents airway resistance; transforming based on the constructed first model to obtain a second model, wherein the second model is as follows: p (t) = V (t)/C + R F (t), wherein E =1/C, C represents lung compliance,
constructing a third model based on the second model, wherein the third model is as follows: p (t + 1) = V (t + 1)/C + R x F (t + 1), where P (t + 1) represents the t +1 real-time airway pressure value, V (t + 1) represents the t +1 real-time capacity, and F (t + 1) represents the instant flow rate at the t +1 moment;
constructing a model IV and a model V based on the model II and the model III: the model four is as follows: r = (V (t) × P (t + 1) -V (t + 1) × P (t))/(F (t + 1) × V (t) -F (t) × V (t + 1)), and the model V is: c = (V (t + 1) × F (t) -V (t) × F (t + 1))/(F (t) × P (t + 1) -F (t + 1) × P (t)).
Further: and recording the volume V, the pressure P and the flow rate F in real time, recording the volume V, the pressure P and the flow rate F at the previous moment, and applying the recorded data to the model four and the model five.
By adopting the technical scheme, the obtained airway resistance R and lung compliance C value are processed, the airway resistance and compliance existing in a pipeline in a respiratory system are substituted into a model, and influence parameters influencing the airway resistance R and the lung compliance C are processed.
By adopting the technical scheme: and constructing a relation model between the respiratory system and the airway resistance R and the lung compliance C, and processing the obtained data of the airway resistance R and the lung compliance C.
Further: and realizing self-tuning of parameters in the models I to V based on the particle swarm optimization.
By adopting the technical scheme: when the pipeline parameters of the breathing machine and the anesthesia machine are selected, the particle swarm optimization technology is used to obtain the pipeline value of the optimal parameter.
Further: calling parameters for realizing self-tuning in a Kalman pre-estimation controller, and estimating the called parameters to obtain an estimated value;
the memory cells stored in the controller are sorted based on the magnitude of the estimated value.
Further: acquiring data influencing the storage in an inspiration period;
and solving the normal distribution based on the acquired related parameters to obtain the value of the required estimated airway resistance R and the lung compliance C.
Further: calculating cycle time limits of the airway resistance R and the lung compliance C in real time in an inspiratory state; the loop calculation is performed within 5 to 500ms based on the calculation period.
In a second aspect, the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor loads and executes the computer program, and the model construction method for evaluating airway resistance and lung compliance of a patient is adopted.
In a third aspect, the present application provides a computer-readable storage medium, in which a computer program is stored, and when the computer program is loaded and executed by a processor, the above model construction method for evaluating airway resistance and lung compliance of a patient is adopted.
The beneficial effect of this application mainly does: the model construction method, the terminal equipment and the storage medium for evaluating the airway resistance and the lung compliance of the patient can be used for analyzing the pressure, the flow and the volume monitored in the air supply stage of the patient in real time, storing analysis data after filtering and estimation through an algorithm, and carrying out statistical induction on the stored analysis data in the non-air supply stage of the patient to realize the estimation of the parameter airway resistance and the lung compliance value of the respiratory system of the patient. The method can be applied to various intelligent breathing control modes and has the advantages of wide application range, high estimation precision and the like.
Drawings
Fig. 1 is a diagram of a method for constructing a model for evaluating airway resistance and lung compliance of a patient according to an embodiment of the present application.
Fig. 2 is a flow chart of a construction of an embodiment of the present application for automatic, rapid and accurate estimation of patient airway resistance and lung compliance.
Fig. 3 is a view of an RRCC model in a model construction method for evaluating airway resistance and lung compliance of a patient according to an embodiment of the present application.
Detailed Description
As is well known in the art, it is in many cases necessary or desirable to deliver a flow of breathing gas to the airway of a patient in a non-invasive manner, i.e., without intubating the patient or surgically inserting a tracheal tube into the esophagus thereof. Such treatment is commonly referred to as non-invasive ventilation (NIV) treatment.
For example, it is known to deliver Continuous Positive Airway Pressure (CPAP) or variable airway pressure that varies with the patient's respiratory cycle in a non-invasive manner to treat medical disorders such as sleep apnea syndrome, in particular Obstructive Sleep Apnea (OSA) or congestive heart failure. NIV therapy involves placing a patient interface including a mask component onto the face of a patient, wherein the patient interface couples a ventilator or pressure support device with the airway of the patient. As is also known in the art, there are also many situations where it is necessary or desirable to deliver a flow of breathing gas to the airway of a patient in an invasive manner, i.e., where the patient is intubated or has a surgically inserted tracheal tube.
In providing ventilatory assistance to a patient, it is often helpful and/or necessary to be able to obtain an estimate of the patient's upper airway resistance in the various ventilation therapies described above. However, estimating the upper airway resistance of mechanically ventilated patients with spontaneous respiratory action is rather complicated, mainly due to the fact that the forces applied to the respiratory system need to be known and to the fact that: in ventilated patients with spontaneous breathing action, the force comprises a component related to the pressure (Pin) generated by the respiratory muscles, which varies continuously during the inflation phase of ventilation.
Although there are many known methods for airway resistance measurement/estimation for a user patient, including the well-known chopper and forced oscillation techniques, such methods all have their drawbacks and limitations. Therefore, the construction method for automatically, accurately and quickly estimating the airway resistance and the lung compliance of the patient is provided, the airway resistance and the lung compliance are estimated, an excellent respiration control mode is provided for clinical treatment, and the patient can be conveniently treated.
The application discloses a model construction method for evaluating airway resistance and lung compliance of a patient, terminal equipment and a storage medium. Referring to fig. 1, a model construction method for evaluating airway resistance and lung compliance of a patient includes:
s1: according to the relationship between the resistance and compliance of the air passage and the air pressure and the air flow, a lung model is established into a nonlinear model which can well reflect the respiratory function of the lung, the nonlinear model is used for reflecting the respiratory function of the lung, the model is established in a process that a respiratory system of a person is composed of the air passage and the alveoli, relevant parameters are obtained according to the prior art, the respiratory process is that the person breathes to do work, the alveoli are enlarged, the pressure in the alveoli is reduced, and therefore the air pressure difference is generated in the air passage, and the air flow for supplying air to the lung is formed. The respiratory tract and alveoli can thus be equivalently concatenated, whereas the effect of the respiratory tract on airflow is mainly reflected as airway resistance and the effect of the alveoli on airflow is mainly reflected as lung compliance (C = 1/E), i.e. airway resistance concatenated with lung compliance. Let P (t) be airway pressure, obtain relevant parameter that influences airway resistance and lung compliance, relevant parameter in this embodiment all obtains through prior art, obtains lung respiratory system parameter model one:
P(t)＝E*V(t)+R*dv/dt，
wherein P (t) represents t real-time airway pressure value, V (t) represents t real-time capacity, dv/dt represents t moment instantaneous flow rate and makes F (t), E represents conductance, R represents airway resistance, and the above related parameters are obtained by the prior art.
S2: transforming the model, wherein E =1/C, C represents lung compliance, and forming a second model after transformation:
P(t)＝V(t)/C+R*F(t)；
s3: and obtaining a third model by using the second model:
P(t+1)＝V(t+1)/C+R*F(t+1)；
s4: obtaining a model IV from the model II and the model III:
R＝(V(t)*P(t+1)-V(t+1)*P(t))/(F(t+1)*V(t)-F(t)*V(t+1))；
s5: obtaining a model V from the model II and the model III:
C＝(V(t+1)*F(t)-V(t)*F(t+1))/(F(t)*P(t+1)-F(t+1)*P(t))；
s6: through the capacity V, the pressure P and the flow rate F in the real-time monitoring system and the recording of the capacity V, the pressure P and the flow rate F at the previous moment, the approximate airway resistance R and the lung compliance C value can be calculated through the 6 parameters, and the airway resistance R and the lung compliance C value are the approximate airway resistance R and the lung compliance C value. The volume V, pressure P, flow rate F are all obtained by the prior art.
S7: the calculated values of the airway resistance R and the lung compliance C cannot be directly applied, in this embodiment, the breathing system is exemplified by a ventilator and an anesthesia machine, the pipelines and the airway of the ventilator and the anesthesia machine also have certain resistance to the airflow, and meanwhile, the pipelines also have certain lung compliance, so that the airway resistance and the lung compliance of the ventilator and the anesthesia machine pipeline need to be added into the lung model, the airway resistance and the lung compliance in the pipelines of the ventilator and the anesthesia machine are added into the lung model, according to the flow direction of the airflow, the airway resistance and the lung compliance of the trachea of the ventilator can be connected in series to the front of the lung model, and according to the established relationship model between the airway resistance and the lung compliance and the flow and the pressure.
S8: assuming that voltage represents pressure, current represents flow, resistance represents airway resistance, and capacitance represents lung compliance, a respiratory system lumped parameter RRCC model is developed that accounts for the effects of different sizes of airway tubes and air leaks. Referring to FIG. 3, a lumped parameter RRCC model is presented for a respiratory system to process factors affecting airway resistance and lung compliance values in the respiratory system, where C C Represents lung compliance, R 1 Representing the airway resistance, R C And R L Representing the airway resistance of the conduit in the respiratory system, R in this example C And R L Respectively, the airway resistances of the pipelines in the respirator and the anesthesia machine, Q (t) represents the real-time airway pressure value, P M Representing the airway pressure value, and the related parameters are obtained by the prior art. By establishing the RRCC model, namely connecting the airway resistance and the lung compliance of the trachea of the respiratory system in series in front of the lung model, the airway resistance and the compliance of the pipeline in the respiratory system are processed, and available airway resistance and compliance values are calculated.
S9: and filtering the obtained data, transmitting the airway resistance R and the lung compliance C value obtained at each moment into a Kalman pre-estimation controller for estimation, and storing estimation results into a storage unit in the controller according to the numerical value classification.
S10: and after the period of inhalation is finished, the stored airway resistance R and lung compliance C value samples are subjected to normal distribution to obtain the required estimated airway resistance R and lung compliance C value.
S11: calculating the cycle time limit of the airway resistance R and the lung compliance C value in real time in an inspiration state: and performing cyclic calculation within 5-500 ms to obtain an optimal calculation period.
Referring to fig. 2, in the embodiment, a model construction method for evaluating airway resistance and lung compliance of a patient is further described, first, the airway resistance and the lung compliance are identified and acquired, and whether an inspiration state is finished is judged;
when the inspiration state is judged to be finished, reading and storing results of the estimated airway resistance R and the lung compliance C value, and performing normal distribution processing on the read results of the airway resistance R and the lung compliance C;
and outputting the mean value of the airway resistance R and the lung compliance C of the widest point of the positive distribution as a result of the airway resistance R and the lung compliance C.
Judging whether a timing calculation time is reached or not when the inspiration state is not finished, wherein the timing calculation time is a preset value;
after the timing calculation time is reached, using model four: r = (V (t) × P (t + 1) -V (t + 1) × P (t))/(F (t + 1) × V (t) -F (t) × V (t + 1)), calculating an airway resistance R value;
using the model five: c = (V (t + 1) × F (t) -V (t) × F (t + 1))/(F (t) × P (t + 1) -F (t + 1) × P (t)), a lung compliance C value is calculated;
transmitting the calculated airway resistance R and lung compliance C values into a Kalman estimation controller for estimation, storing estimation results into a storage unit in the controller, reading results of the stored estimated airway resistance R and lung compliance C values, and performing normal distribution processing on the read airway resistance R and lung compliance C results;
and outputting the mean value of the airway resistance R and the lung compliance C of the widest point of the positive distribution as a result of the airway resistance R and the lung compliance C.
The device for acquiring the airway resistance comprises a medical image acquisition module, an examination result acquisition module, an airway model acquisition module, a first resistance acquisition module and a second resistance acquisition module, wherein the medical image acquisition module is used for acquiring a lung medical image of a patient, the resolution of the lung medical image at least can identify an airway with the diameter larger than a diameter threshold, the related diameter threshold can be set according to experience, the airway model acquisition module is connected with the medical image acquisition module and is used for acquiring a model of the first airway according to the lung medical image of the patient, and the first airway is an airway with the diameter larger than the diameter threshold. The first resistance obtaining module is connected with the airway model obtaining module and the checking result obtaining module and used for obtaining the resistance of the first airway according to the model of the first airway and the first second exhalation volume, the second resistance obtaining module is connected with the first resistance obtaining module and the airway model obtaining module and used for obtaining the resistance of the second airway according to the total airway resistance and the resistance of the first airway, wherein the second airway is an airway with the diameter smaller than or equal to a diameter threshold value and comprises a terminal small airway of a patient. Specifically, the resistance of the second airway is related to a difference between the total airway resistance and the resistance of the first airway, and therefore the second resistance obtaining module can obtain the resistance of the second airway according to the total airway resistance and the resistance of the first airway, for example, the second resistance obtaining module can obtain the resistance of the second airway by subtracting the total airway resistance and the resistance of the first airway.
In S7, the parameter values of the breathing machine pipeline and the anesthesia machine pipeline directly influence the precision of the breathing system model, the parameter values are generally completed by engineering technicians with rich experience, and the uncertainty of an actual object makes the parameter setting have certain difficulty.
And S6, the implementation monitoring system comprises a pressure sensor, a temperature sensor, a humidity sensor and a gas flowmeter and is used for detecting the pressure, the gas temperature, the gas humidity and the gas flow of the gas, respectively detecting the volume V, the pressure P and the flow rate F in real time, recording a plurality of groups of data, wherein every two adjacent moments are a group of data, and each group of data comprises the volume V, the pressure P, the flow rate F and other data. And sending the monitored data to the models obtained by the models 2 and 3, and respectively calculating an airway resistance R value and a lung compliance C value.
The life support system further comprises a control unit, wherein the control unit comprises a data acquisition module, a data processing module and a data sending module, the data acquisition module transmits the estimated airway resistance R value and the estimated compliance C value to the data processing module, the data processing module analyzes and processes corresponding data, and then sends a processing result to the data sending module. And the data sending module sends the processed information to an air supply unit in the life support system, and adjusts various output values so that the life support system is coordinated and synchronized.
The implementation principle of the model construction method for evaluating patient airway resistance and lung compliance in the embodiment is as follows: the method comprises the steps of carrying out real-time analysis on airway resistance and lung compliance acquired according to the prior art, storing analysis data after algorithm filtering estimation, carrying out statistics and summarization on the stored analysis data in a non-air supply stage of a patient, realizing estimation on respiratory system parameter airway resistance R values and compliance C values of the patient, further providing a beneficial intelligent respiratory control mode by utilizing the calculated airway resistance R values and compliance C values, and realizing coordination and synchronization of a human respiratory system and a breathing machine.
The embodiment also discloses a terminal device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program by adopting the model construction method for evaluating the patient airway resistance and lung compliance of the embodiment.
The terminal device may adopt a computer device such as a desktop computer, a notebook computer, or a cloud server, and the terminal device includes but is not limited to a processor and a memory, for example, the terminal device may further include an input/output device, a network access device, a bus, and the like.
The processor may be a Central Processing Unit (CPU), and of course, according to an actual use situation, other general processors, digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), ready-made programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and the like may also be used, and the general processor may be a microprocessor or any conventional processor, and the application does not limit the present invention.
The memory may be an internal storage unit of the terminal device, for example, a hard disk or a memory of the terminal device, or an external storage device of the terminal device, for example, a plug-in hard disk, a smart card memory (SMC), a secure digital card (SD) or a flash memory card (FC) equipped on the terminal device, and the memory may also be a combination of the internal storage unit of the terminal device and the external storage device, and the memory is used for storing a computer program and other programs and data required by the terminal device, and the memory may also be used for temporarily storing data that has been output or will be output, which is not limited in this application.
The method for constructing the enterprise big data system based on the physical location according to the embodiment is stored in the memory of the terminal device through the terminal device, and is loaded and executed on the processor of the terminal device, so that the terminal device is convenient for a user to use.
The embodiment of the application also discloses a computer readable storage medium, and the computer readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the model construction method for evaluating the patient airway resistance and the lung compliance of the embodiment is adopted.
The computer program may be stored in a computer readable medium, the computer program includes computer program code, the computer program code may be in a source code form, an object code form, an executable file or some intermediate form, and the like, and the computer readable medium includes any entity or device capable of carrying the computer program code, a recording medium, a usb disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a Read Only Memory (ROM), a Random Access Memory (RAM), an electrical carrier signal, a telecommunication signal, a software distribution medium, and the like.
The method for constructing the enterprise big data system based on the physical location according to the embodiment is stored in the computer-readable storage medium through the computer-readable storage medium, and is loaded and executed on the processor, so that the storage and the application of the method for constructing the enterprise big data system based on the physical location are facilitated.
The above embodiments are preferred embodiments of the present application, and the protection scope of the present application is not limited by the above embodiments, so: equivalent changes in structure, shape and principle of the present application shall be covered by the protection scope of the present application.
Claims (8)
1. A model building method for assessing patient airway resistance and lung compliance, comprising:
acquiring relevant parameters influencing the real-time airway pressure value;
constructing a first model based on the obtained relevant parameters, wherein the first model is as follows: p (t) = E × V (t) + R × dv/dt, where P (t) represents t real-time airway pressure value, V (t) represents t real-time volume, dv/dt represents instant flow rate at time t and is denoted as F (t), E represents conductance, and R represents airway resistance; transforming based on the constructed first model to obtain a second model, wherein the second model is as follows: p (t) = V (t)/C + R F (t), wherein E =1/C, C represents lung compliance,
constructing a third model based on the second model, wherein the third model is as follows: p (t + 1) = V (t + 1)/C + R × F (t + 1), wherein P (t + 1) represents the t +1 real-time airway pressure value, V (t + 1) represents the t +1 real-time capacity, and F (t + 1) represents the instant flow rate at the t +1 moment;
constructing a model IV and a model V based on the model II and the model III: the model four is as follows: r = (V (t) × P (t + 1) -V (t + 1) × P (t))/(F (t + 1) × V (t) -F (t) × V (t + 1)), and model V is: c = (V (t + 1) × F (t) -V (t) × F (t + 1))/(F (t) × P (t + 1) -F (t + 1) × P (t)).
2. The model building method for evaluating patient airway resistance and lung compliance as claimed in claim 1, wherein:
and recording the volume V, the pressure P and the flow rate F in real time, recording the volume V, the pressure P and the flow rate F at the previous moment, and applying the recorded data to the model four and the model five.
3. The method of claim 2, wherein the model for evaluating airway resistance and lung compliance of the patient comprises:
and realizing self-tuning of parameters in the models from the first model to the fifth model based on a particle swarm optimization.
4. The model building method for evaluating patient airway resistance and lung compliance as claimed in claim 3, wherein:
calling parameters for realizing self-tuning in a Kalman pre-estimation controller, and estimating the called parameters to obtain an estimated value;
the memory cells stored in the controller are sorted based on the magnitude of the estimated value.
5. The method of claim 4, wherein the model for evaluating airway resistance and lung compliance of the patient comprises:
acquiring data influencing the storage in an inspiration period;
and solving the normal distribution based on the acquired related parameters to obtain the value of the required estimated airway resistance R and the lung compliance C.
6. The model building method for evaluating patient airway resistance and lung compliance as claimed in claim 5, wherein:
calculating cycle time limits in real time for airway resistance R and lung compliance C in an inspiratory state;
the loop calculation is performed within 5 to 500ms based on the calculation period.
7. A terminal device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: the processor, when loaded and executing a computer program, implements the method of model construction for assessing patient airway resistance and lung compliance as claimed in any one of claims 1 to 6.
8. A computer-readable storage medium having a computer program stored therein, the computer program characterized by: the computer program, when loaded and executed by a processor, implements a model construction method for assessing patient airway resistance and lung compliance as claimed in any one of claims 1 to 6.
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| CN109785953A (en) * | 2017-11-10 | 2019-05-21 | 北京航天长峰股份有限公司 | General-purpose algorithm for the monitoring of anesthesia respirator R value C value |
| US20210145307A1 (en) * | 2018-04-17 | 2021-05-20 | Artiq | Method and apparatus for determining airflow limitation |
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