Method for regulating and controlling degradation performance of degradable implantable interventional medical device through structural design

Abstract

The invention provides a method for regulating and controlling degradation performance of a degradable implantable interventional medical device through structural design. The method comprises the following steps: extracting key design parameters of the degradable implantable interventional medical device based on an axiom design method; the method comprises the following steps: establishing a corrosion model based on a finite element method, simulating a continuous medium mechanics-based corrosion model and an elementary corrosion model of various corrosion mechanisms such as uniform galvanic corrosion, pitting corrosion, stress corrosion, fatigue corrosion and the like, and quantitatively calculating degradation kinetic parameters such as degradation rate, period, uniformity and the like of different structures under physiological conditions; in combination with clinical requirements on degradation performance, a structure optimization method is used, key design parameters are used as optimization geometric parameters, clinical degradation performance requirements are used as optimization targets, and the degradation performance of the implantable interventional medical device is regulated and controlled through the structure optimization method. The method can effectively solve the problem that the degradation performance of the existing degradable implantable interventional medical device is uncontrollable, the degradation uniformity is improved, and the curative effect of the degradable implantable interventional medical device is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of implantable medical device design, and in particular relates to a method for structurally designing and regulating the degradation performance of a degradable implantable medical device. Technical Background

[0002] Degradable implantable medical devices can avoid a series of problems caused by long-term retention of permanent medical devices in the body, such as chronic inflammatory reactions, infections, foreign body reactions, etc.; they can also avoid secondary surgery, reduce patient pain and treatment costs. The degradable materials currently used in research are mainly degradable metals and degradable polymers. Degradable alloys mainly include magnesium alloys, zinc alloys and iron alloys. Degradable polymers mainly include polylactic acid PLA, polylactic acid-glycolic acid PLGA, polycarbonate, polycaprolactone, polyhydroxybutyric acid, polyacetylglutamic acid, etc. Degradable implantable medical devices provide support functions within a certain period of time. After being implanted in the body, they are gradually degraded and absorbed by metabolism or excreted from the body. At the same time, the tissues and organs at the implantation site undergo functional reconstruction, and the physiological functions are restored when the device degradation is completed. Therefore, for a good degradable implantable medical device, the degradation kinetic parameters such as degradation rate, degradation cycle, and degradation uniformity should be as controllable as possible, so that the degradation rate is consistent with the tissue reconstruction speed.

[0003] However, currently, degradable implantable medical devices generally have problems such as uncontrollable degradation rate and uneven degradation, which lead to mismatch between degradation rate and the recovery growth rate of the tissue itself and premature local fracture, affecting the efficacy of degradable implantable medical devices. If the degradation rate of degradable implantable medical devices is too fast, the degradation products may exceed the maximum limit that the surrounding tissue can bear, which may cause the body to have a hypersensitivity reaction, affect the implant effect and produce side effects; and the mechanical decay is too fast, the effective mechanical support time of the implant does not match the repair time of the damaged tissue, and the implant loses its mechanical support before the damaged tissue is fully repaired. If the degradation rate of the degradable medical device is too slow, it may lead to insufficient drug release or poor support effect, reduce the treatment effect, and there are long-term risks, increasing the risk of complications such as infection and thrombosis.

[0004] The existing technology regulates the degradation performance of degradable medical devices, mainly in the process of device design, by regulating alloying, material coating technology, polymer material molecular weight and monomer types, etc. However, how to effectively control the degradation kinetic parameters of degradable medical devices, such as degradation rate, degradation cycle and degradation uniformity, and improve the degradation uniformity of the device, is still a difficulty in the current research of degradable implantable medical devices. Summary of the invention

[0005] The present application aims to solve the technical problem that it is difficult for the prior art to effectively control the degradation kinetic parameters of degradable medical devices and to achieve uniform degradation of the devices. The present application further provides a method for controlling the degradation performance of degradable medical devices so that their degradation rate matches the recovery growth rate of their own tissues, while effectively improving the uniformity of degradation by structural design and regulating the degradation performance of degradable implantable medical devices.

[0006] The technical solution adopted by this application to solve the above technical problems is:

[0007] A method for structural design and regulation of the degradation performance of a degradable implantable medical device, comprising:

[0008] (1) Extract the key design parameters of degradable implantable medical devices based on the axiomatic design method; (2) Establish a corrosion model based on the degradation mechanism of different degradable materials, and quantitatively calculate the degradation kinetic parameters of different structures, which include degradation rate, degradation cycle and degradation uniformity; (3) Take the requirements of clinical degradation performance as the optimization goal, and use the key design parameters as the geometric design parameters to use the structural optimization algorithm to regulate the degradation performance of degradable implantable medical devices.

[0009] In step (1), the step of extracting key design parameters includes:

[0010] i. Define the user domain of the implantable medical device according to the clinical use purpose and the anatomical structure constraints of the implantation location; ii. Map the user domain to the functional domain to form the top-level performance requirements and ethical constraints, and map the top-level performance requirements to the physical domain to form the top-level design parameters of the implantable medical device; iii. Perform a Z-shaped mapping transformation between the functional domain and the physical domain, decompose the various performance requirements of the implantable medical device, and obtain the leaf-level functional requirement indicators and leaf-level design parameters of the various performance of the implantable medical device; iv. Express the mapping relationship between the leaf-level functional requirement indicators and the leaf-level design parameters of the implantable medical device through a design matrix, establish design equations for each level, and establish an association between the performance indicators and design parameters at each level; v. Combined with the experimental results, the leaf-level design parameters of the implantable medical device are evaluated by sensitivity coefficients and / or error reduction ratios to determine the degree to which the corresponding leaf-level functional requirement indicators are achieved, and the key leaf-level design parameters are determined.

[0011] Furthermore, the influence of quantitative leaf-level design parameters on the realization of corresponding leaf-level functional requirement indicators is measured by sensitivity coefficient; the influence of categorical leaf-level design parameters on the realization of corresponding leaf-level functional requirement indicators is measured by error elimination ratio.

[0012] In step v, the ethical constraint experiment includes numerical simulation experiment, mechanical experiment, degradation experiment, material experiment and biocompatibility experiment.

[0013] The corrosion model in step (2) includes a corrosion model based on continuum mechanics and a phenomenological corrosion model.

[0014] The corrosion model based on continuum mechanics is aimed at degradable materials with surface corrosion mechanism. By defining a scalar field damage variable D that can quantify the accumulation of material damage, the geometric discontinuity phenomenon of the material degradation process is transformed into a reduction in the macroscopic mechanical properties of the material.

[0015] The phenomenological corrosion model is aimed at degradable materials with bulk corrosion mechanism. Through in vitro mechanical loading, in vitro degradation experiments under gradient stress are carried out to establish a mathematical model between mass loss rate, stress and degradation time during material degradation, and the fitted degradation model is verified using experimental data.

[0016] The corrosion model uses the subroutine VUMAT of the user-defined material constitutive relationship in the ABAQUS software to update the material properties and delete the units during the degradation process. Based on the degradation model, as the degradation time changes, the damage variable of the VUMAT subroutine begins to accumulate damage. When the damage accumulates to a certain extent, the units of the degradable implantable medical device begin to be lost, that is, degradation begins. As the service time increases, the mechanical properties of the degradable implantable medical device decrease until it breaks.

[0017] The structural optimization method in step (3) comprises the following steps:

[0018] a. Based on the corrosion model, a functional relationship between the leaf-level design parameters and the degradation kinetic parameter targets is established; b. The functional relationship is used as the optimization target, and the leaf-level design parameters are optimized in combination with the structural optimization algorithm to obtain the optimized leaf-level design parameters; c. The degradation performance of the implantable medical device with the optimized leaf-level design parameters is evaluated. If the evaluation results meet the expected requirements, the optimized leaf-level design parameters are determined to be the optimal leaf-level design parameters; if the evaluation results do not meet the requirements, the leaf-level design parameters are adjusted again according to the evaluation results, and the optimization is performed again until the expected degradation performance is achieved.

[0019] In step a, a machine learning method is used to obtain known information using a corrosion model to establish the functional relationship.

[0020] The method for structural design and regulation of the degradation performance of degradable implantable medical devices described in the present invention has the following technical effects:

[0021] This application uses the axiomatic design method to extract the key design parameters of degradable implantable medical devices, quantifies the influence of various leaf-level functional requirements on the leaf-level design parameters through sensitivity analysis, and selects the key design parameters. In addition, a corrosion model is established based on the finite element numerical method to analyze the degradation behavior of degradable implantable medical devices under different degradation modes and physiological loads. The degradation rate, degradation cycle, and degradation uniformity degradation kinetic parameters can be calculated, and the fracture area and fracture time during the degradation process can be predicted, which helps to further understand the degradation behavior in the physiological environment, and can provide a large number of data samples for machine learning of the relationship between leaf-level design parameters and degradation rate. At the same time, this application combines machine learning with structural optimization algorithms, and optimizes the leaf-level design parameters based on the clinical requirements for degradation performance such as degradation rate, degradation cycle, and degradation uniformity, and obtains the optimal structural design parameters, providing a method for regulating the degradation rate of degradable implantable medical devices. Compared with in vivo and in vitro studies, numerical simulation studies can fully capture the degradation behavior of degradable medical devices. Moreover, compared with the structural optimization method based only on in vitro experimental design and empirical attempts, the optimization method of constructing a functional model between structural parameters and the target performance of degradable implantable medical devices is more scientific and effective. It can reduce time costs, improve the efficiency of structural optimization, and help guide designers to regulate the degradation performance of degradable implantable medical devices, providing a more reasonable and scientific basis.

[0022] As a preferred embodiment, the corrosion model in the present application includes a corrosion model based on continuous medium mechanics and a phenomenological corrosion model, which has the advantage that the corrosion model based on continuous medium mechanics considers the relationship between the internal microstructure and macroscopic mechanical properties of the material, deeply studies the corrosion mechanism of different materials, and can more accurately predict the corrosion rate and corrosion behavior of the material; the phenomenological corrosion model establishes a model through experimental data and empirical laws, which can simplify the complex corrosion process, provide a more intuitive method for understanding and predicting corrosion behavior, and is more convenient and practical in practice. At the same time, the corrosion model preferably described in the present application realizes the update of material properties and the deletion of units during the degradation process through the subroutine VUMAT of the user-defined material constitutive relationship in ABAQUS software, and the advantage is that the VUMAT subroutine can customize the material constitutive relationship according to specific needs, and the flexibility of defining material properties is high, and a more complex description of material behavior can be realized; at the same time, the update of material properties and the deletion of units can be controlled in real time, the degradation process of the material can be simulated, and a more sophisticated description and simulation of material behavior can be realized, and the accuracy and reliability of the degradation simulation results can be improved, and the simulation of the degradation process of the degradable medical device after being implanted in the body can be realized in a simple and effective way.

[0023] In order to make the technical solution of the method for structural design and regulation of degradation performance of degradable implantable medical devices described in the present invention more clear, the present invention is further described below in conjunction with specific implementation methods. DETAILED DESCRIPTION

[0024] The present embodiment provides a method for structural design to regulate the degradation performance of degradable implantable medical devices, comprising the following steps: (1) extracting key design parameters of degradable implantable medical devices based on an axiomatic design method; (2) establishing a finite element corrosion model for the degradation mechanism of different degradable materials, providing a basis for quantitatively calculating the degradation kinetic parameters of medical devices with different structures; (3) using a structural optimization algorithm to regulate the degradation performance of degradable implantable medical devices with the requirements of clinical degradation performance as the optimization target.

[0025] Among them, step (1) is a method for extracting design parameters of controllable degradable implantable medical devices based on axiomatic design, specifically including: i. Defining the user domain CA of the implantable medical device according to the clinical use purpose and the anatomical structure constraints of the implantation position. ii. Mapping the user domain of the implantable medical device to the functional domain to form the top-level performance requirement FR0, and the ethical constraint Cs, FR0 is mapped to the physical domain to form the top-level design parameter DP0 of the implantable medical device. iii. Performing a Z-shaped mapping transformation between the functional domain and the physical domain, decomposing the biomechanical performance requirements, material performance requirements, biological performance requirements and structural design parameters, and material design parameters of the implantable medical device, and obtaining the leaf-level functional requirement indicators FRs and leaf-level design parameters DPs of various performances of the implantable medical device. iv. Representing the mapping relationship between the leaf-level functional requirement indicators FRs and the leaf-level design parameters DPs of the implantable medical device through the design matrix, establishing the design equations of each level, and establishing an association between the performance indicators of each level and the design parameters.

[0026] v. Conduct numerical simulation, mechanical experiment, degradation experiment, material experiment and biocompatibility experiment under ethical constraints, and evaluate the degree of realization of corresponding leaf-level functional requirement indicators by leaf-level design parameters of degradable implantable medical devices through sensitivity coefficient and / or reduction error ratio in combination with the experimental results. The degree to which each leaf-level functional requirement indicator is affected by leaf-level design parameters is measured by sensitivity coefficient E, and the calculation method of sensitivity coefficient E is as follows:

[0027]

[0028] Wherein, ΔY is the rate of change of the leaf-level design parameter, and ΔX is the rate of change of the leaf-level functional requirement index corresponding to the change of the leaf-level design parameter. The absolute value of the sensitivity coefficient is compared with the preset sensitivity coefficient benchmark, and the design parameters whose absolute value of the sensitivity coefficient is less than or equal to the benchmark are eliminated. For each leaf-level design parameter of a certain category, the correlation ratio E between the leaf-level design parameter and each leaf-level functional requirement index is calculated. 2The error reduction ratio PRE is calculated and compared with the preset error reduction ratio benchmark, and the leaf-level design parameters with error reduction ratios less than the benchmark are eliminated, and finally the key leaf-level design parameters of the implantable medical device are extracted. Among them, the preset sensitivity coefficient benchmark is 1, and the preset error reduction ratio benchmark is 0.1.

[0029] Specifically, when the degradable implantable medical device is a vascular stent, the leaf-level functional requirement indicators include: the stiffness and strength performance of the stent itself, the compliance of the stent through complex curved blood vessels during implantation, the axial shortening rate and radial rebound rate after stent implantation, the dog bone rate to avoid warping at both ends after stent implantation, the fatigue resistance after stent implantation, the stress distribution and plastic strain distribution after stent implantation. The mass loss rate during stent degradation, the maximum support time during stent degradation, the degradation uniformity and support performance during stent degradation. The blood flow velocity distribution after stent implantation, the wall shear force (WSS) distribution after stent implantation, the integrated average value of wall shear stress (TAWSS) within one cardiac cycle after stent implantation, the blood flow oscillation index (OSI) after stent implantation, the ECAP after stent implantation (the ratio of OSI to TAWSS), the circumferential stress distribution after stent implantation, and the pressure distribution after stent implantation. The distribution of degradation products in the blood during stent degradation, and the distribution of red blood cells and macromolecules in the blood during stent degradation. The blade-level design parameters include: the outer diameter of the bracket, the overall length of the bracket, the thickness between the outer diameter and the inner diameter of the bracket, the number of support rings in the axial direction of the bracket, the number of connecting ribs in the axial direction of the bracket, the number of support ring repeating units in the circumferential direction of the bracket, the number of connecting ribs in the circumferential direction of the bracket, the shape of the bracket support ring repeating units, the inner diameter of the local corrugated ring and the length of the rectangular parallelepiped of the bracket support ring repeating units, the width of the bracket support ring, the length of the bracket connecting rib, the width of the bracket connecting rib, the shape of the bracket connecting rib, the inner diameter of the local annular structure of the bracket connecting rib, the material type and component content of the bracket main body, the Young's modulus and Poisson's ratio of the bracket main body material, and the yield strength of the bracket main body material.

[0030] When the degradable implantable medical device is a vena cava filter, the leaf-level functional requirements include: stiffness and strength performance, good radial support capacity, anti-displacement performance, fatigue resistance after implantation, stress distribution and plastic strain distribution after implantation, mass loss rate during degradation, maximum support time during degradation, degradation uniformity during degradation, hemodynamic performance, and thrombus interception rate. The leaf-level design parameters include: overall shape design of the degradable vena cava filter, outer diameter of the filter, overall length of the filter, filter size design, filter mesh structure design, number of filter rods, cross-sectional shape of the filter rods, width of the filter rods, thickness of the filter rods, filter barb shape design, material type and component content, Young's modulus and Poisson's ratio of the material, and yield strength of the material.

[0031] The finite element corrosion model, including the corrosion model based on continuous damage mechanics and the phenomenological corrosion model, can quantitatively calculate the degradation kinetic parameters such as the degradation rate, degradation cycle and degradation uniformity of different structures. The corrosion model based on continuous damage mechanics, by defining a scalar field damage variable D that can quantify the accumulation of material damage, transforms the geometric discontinuity phenomenon of the degradation process of degradable medical devices into the reduction of the macroscopic mechanical properties of the material, and establishes a model to describe the loss of mechanical strength of the material during the degradation process. The damage variable D is a gradually accumulated monotonically increasing function, and the effective stress during the damage process is calculated by formula (1).

[0032]

[0033] Where σ is the effective stress, is the undamaged stress, and D is a damage variable that increases monotonically from 0 to 1. The material properties during degradation are linearly adjusted by D. When D is equal to 0, the material is not damaged; when D is approximately equal to 1, the material is completely damaged and the unit is deleted from the model.

[0034] The corrosion model based on continuous damage mechanics described in step (2) can describe various forms of corrosion mechanisms of surface corrosion of degradable materials by changing the definition of the damage variable D, including uniform corrosion, pitting corrosion, stress corrosion, fatigue corrosion and other corrosion forms and the synergistic effect of multiple corrosion mechanisms. Uniform galvanic corrosion describes the chemical reaction and electrochemical reaction on the surface of the degradable material when it is exposed to a corrosive environment, causing the overall degradation of the structure. The corrosion rate on the entire free surface is similar. It is the most common form of global corrosion and the corrosion is relatively uniform. Pitting corrosion describes the random local severe corrosion caused by factors such as uneven distribution of degradable materials caused by processing conditions and different concentrations of degradation liquids in the corrosive environment. Degradable implantable medical devices are often implanted in the body through minimally invasive surgery. The medical device must first be compressed and gripped into a small diameter delivery sheath. After reaching the target location in the body through the blood vessels, the medical device is expanded to its original size through balloon dilation. Therefore, the compression-expansion process during the implantation process causes residual stress inside the degradable implantable medical device. This residual stress and the mechanical action in the physiological environment can cause stress concentration inside the device, causing localized non-uniform corrosion. The above corrosion mechanism can be described by stress corrosion. Fatigue corrosion describes the corrosion caused by the physiological cyclic loads that the degradable implantable medical device is subjected to after being implanted in the body. In addition to the above degradation models, multiple types of corrosion mechanisms can be coupled according to the application scenarios and physiological environments of degradable implantable medical devices to more accurately simulate the degradation process of degradable materials in the body.

[0035] At the same time, the corrosion model based on continuous damage mechanics described in this embodiment uses one or more corrosion models similar to the actual corrosion type for simulation according to the actual use scenarios and main corrosion mechanisms of degradable implantable medical devices. The subroutine VUMAT of the custom material constitutive relationship of the ABAQUS software is used to update the material properties during the degradation process. During the explicit dynamics solution, when the global damage parameter D>0.9 of a unit is considered to have completed degradation, it is deleted, and then the corrosion layer is automatically updated, the global damage parameters of each unit are calculated, and the stress and strain states are continuously updated in a cycle.

[0036] The corrosion model based on continuous damage mechanics described in this embodiment needs to obtain the initial mechanical parameters and degradation rate of the degradable material through in vitro degradation experiments, and the data obtained from the degradation test are used for parameter calibration of the corrosion model, so that the corrosion model matches the degradable material used with the actual physiological environment conditions, and the accuracy of the numerical simulation degradation process is guaranteed. Immersion test is a common method for studying the corrosion of biodegradable materials. Various electrolyte buffers are used to simulate body fluids, including 0.9% sodium chloride solution, simulated body fluid (SBF), Hank's solution, phosphate buffered saline (PBS), cell culture medium (DMEM), etc. Select multiple immersion time nodes, measure the mass change of the sample before and after the immersion experiment to evaluate the corrosion rate, and measure the mass change of multiple samples at the same time node to reduce the error, and finally obtain the mass loss curve of the sample, and obtain the degradation rate of the material, which is used for the calibration of parameters in uniform corrosion, pitting corrosion, stress corrosion, and fatigue corrosion.

[0037] The phenomenological corrosion model is subjected to in vitro mechanical loading, and an in vitro degradation experiment under gradient stress is carried out on the degradable material specimen, and the apparent morphology, mechanical properties (Young's elastic modulus, tensile strength, etc.) and mass loss of the degradable material during the degradation process are observed and counted, and a mathematical model between the mass loss rate f(σ, t), stress σ, and degradation time t during the material degradation process is established, and the fitted in vitro degradation law is verified using the in vitro degradation experimental data. The mathematical relationship between the mass loss rate f(σ, t), stress σ, and degradation time t of the stent material during the in vitro degradation experiment is as shown in Formula (2).

[0038] f(σ,t)=at+bt 2 +ct 3 +dt 4 +eσt+fσ 2 t+gσ 2 t 2 +hσ 2 t 3 (2)

[0039] Among them, ah is the experimental constant.

[0040] The phenomenological corrosion model calculates the cumulative loss D of unit k between degradation time [0, T] according to formula (3): k According to formula (4), the failure limit D of unit k is calculated kc The larger the unit volume, the longer the degradation time. kcProportional to the unit volume. The unit degradation model is controlled by equation (5). For unit k, when the mass loss accumulates to a certain extent, the material is completely degraded and deleted from the model.

[0041]

[0042] Where T is the total degradation time, D k is the total mass loss rate of unit k when degradation reaches time T, D kc is the critical value of k unit degradation, σ k is the tensile stress on unit k, V k is the volume of unit k, and V is the maximum unit volume in the model.

[0043] The phenomenological corrosion model described in this embodiment uses the ABAQUS subroutine VUMAT to define the constitutive relationship of the degradable material, realize damage accumulation of the unit, and determine the deletion of the unit.

[0044] The phenomenological corrosion model needs to be based on the use scenario of the degradable implantable medical device, and an in vitro degradation experiment with gradient stress applied to the degradable material is carried out, and the mass loss is measured at different degradation time points to obtain the mathematical relationship between mass loss rate-applied stress-degradation time. The gradient stress includes tensile stress, compressive stress, shear stress or a combination of multiple forms of stress. According to the applied stress, an in vitro degradation device capable of applying stress to the degradable material sample needs to be independently designed. The in vitro degradation test, including the immersion test, can use various electrolyte buffers to simulate body fluids, including 0.9% sodium chloride solution, simulated body fluid (SBF), Hank's solution, phosphate buffered saline (PBS), cell culture medium (DMEM), etc.

[0045] The method for regulating the degradation performance of a degradable implantable medical device by an optimization algorithm includes: (1) functional relationship modeling: based on the corrosion model, a variety of models such as polynomial response surface, polynomial interpolation, Kriging interpolation, radial basis function interpolation, neural network, support vector machine regression, etc. can be used to establish a functional relationship between leaf-level design parameters and degradation rate targets. (2) Determine the structural optimization algorithm: take the established functional relationship as the optimization target, and optimize the leaf-level design parameters in combination with the structural optimization algorithm. Various structural optimization algorithms, such as genetic algorithms, particle swarm algorithms, etc., can be used to search for the optimal leaf-level design parameters. (3) Evaluation of optimization results: Based on the optimal leaf-level design parameters obtained by the optimization algorithm, the degradation performance is evaluated. Experimental verification, simulation and other methods can be used to verify the effectiveness and feasibility of the optimization results. (4) Iterative optimization: If the optimization results do not meet the requirements, iterative optimization can be performed. According to the evaluation results, the leaf-level design parameters are adjusted again and optimized again until the expected degradation performance is achieved.

[0046] Example 1

[0047] This embodiment takes a PLGA degradable vascular stent as an example to explain in detail the method for structural design and regulation of the degradation performance of a degradable implantable medical device according to the present invention. The specific process of this method is as follows.

[0048] (1) Extract key design parameters of biodegradable implantable medical devices based on axiomatic design method, including:

[0049] i. Define the user domain CA of implantable medical devices based on the clinical purpose of use and the anatomical constraints of the implantation location. Define user requirements based on the clinical purpose of use of degradable implantable medical devices and the anatomical constraints of the implantation location. For degradable vascular stents, the stent must maintain its support for the blood vessel within the specified time of implantation, and degrade and disappear after the vessel is reconstructed. Therefore, the user requirements of degradable vascular stents can be summarized as follows: a. Provide sufficient support during reconstruction at the stenotic site of the vessel; b. The stent does not shift significantly after intervention; c. The stent has little interference with blood flow after intervention to avoid restenosis within the stent; d. The degradation rate matches the rate of reconstruction of the mechanical strength of the vessel; e. The stent is completely degraded when the vascular reconstruction is completed; f. The degradation products do not accumulate and are non-toxic and harmless; g. The stent has good biocompatibility.

[0050] ii. According to the above user needs, determine the top-level performance requirements FR0 of the implantable medical device, map the top-level performance requirements to the physical domain, and form the top-level design parameters DP0 of the implantable medical device. This embodiment defines:

[0051] Top-level performance requirement FR0: Have appropriate performance;

[0052] Top-level design parameter DP0: overall design of the degradable vascular stent.

[0053] In addition, in this step, it is also necessary to define ethical restrictions in the design and evaluation of biodegradable vascular stents in the functional domain according to the ethical requirements in the regulatory standard system of implantable medical devices. The ethical restrictions Cs are mainly based on the "animal protection requirements" in the ISO10993 system, which include restrictions on animal experiments to be used in the evaluation process, including: a. Qualification review of experimental personnel; b. Care of animals before, during and after surgery; c. Humane treatment of animals; d. Reducing unnecessary repeated experiments.

[0054] iii. Decompose between the physical domain and the functional domain through Z-shaped mapping transformation to obtain the structural design parameters and material design parameters corresponding to the biomechanical performance requirements, material performance requirements and biological performance requirements of the implantable medical device, and decompose them until the leaf-level structural design parameters and material design parameters DPs that can be directly reflected in the mechanical, material and biological test models are obtained, as well as various leaf-level performance measurement indicators (leaf-level functional requirement indicators) FRs obtained through test result analysis.

[0055] Decomposing FR0 into the first-level functional requirements of biodegradable vascular stents:

[0056] FR11: has good biomechanical properties; FR12: has appropriate degradation properties.

[0057] In the physical domain, DP0 can be decomposed into the first-level design parameters of the degradable vascular stent:

[0058] DP11: Overall design of the bracket; DP12: Unit design of the bracket.

[0059] The degradable vascular stent described in FR11 needs to have good biomechanical properties, which can be specifically divided into the following aspects: a. Good radial support capacity to reduce radial rebound and prevent stent breakage; b. Good expansion performance; c. Good flexibility to improve the stent's wall adhesion and adaptability to vascular deformation; d. Appropriate hemodynamic parameters. FR11 is further subdivided into the second-level functional requirements: FR111: Good radial support capacity; FR112: Good expansion performance; FR113: Good flexibility; FR114: Appropriate hemodynamic parameters.

[0060] For degradable vascular stents, the stents must not fail within the specified time after being implanted in the blood vessel, maintain the support function for the blood vessel, and have a certain mechanical strength. After the blood vessel is reconstructed, it must degrade and disappear, and must have a suitable degradation speed. This embodiment further refines FR12 into the second-level functional requirements as follows:

[0061] FR121: has a degradation rate that matches vascular repair; FR122: has good degradation performance.

[0062] In the physical domain, the design parameters that affect the above functional requirements in the design of degradable vascular stents are further refined. For DP11, the overall design elements of the vascular stent mainly include the overall material design and structural design of the stent. Through the design of these two, the mechanical properties and degradation properties that the stent should have can be achieved. Therefore, DP11 can be further refined into the second-level design parameters as follows:

[0063] DP111: Bracket material design; DP112: Bracket overall structure design.

[0064] For DP12, the local size design elements of the stent mainly include the geometric structure design of the stent support ring and the geometric structure design of the connecting ribs. DP12 can be further refined into the second-level design parameters as follows:

[0065] DP121: Support ring geometry design; DP122: Connecting rib geometry design.

[0066] Regarding the good radial support performance that the stent needs to have as described in FR111, specifically, after the stent intervention surgery is completed and the balloon is withdrawn from the body, the stent must withstand the radial squeezing force from the diseased tissue and blood vessels. The stent must be able to withstand this squeezing force without deformation or damage to maintain smooth blood flow. Regarding the stress on the stent in the body. Therefore, the third-level functional requirements of FR111 are refined as follows:

[0067] FR1111: Radial support force F of vascular stent.

[0068] The good expansion performance that the FR112 stent needs to have is mainly divided into radial expansion performance and axial expansion performance. Specifically, if the expansion diameter is too small during the balloon expansion of the stent, the blood vessel is very likely to restenosis, and if the expansion is large, it may cause certain damage to the blood vessel. When the stent expands to the maximum diameter, the balloon is withdrawn and no longer provides support for the stent. The stent has a certain rebound under the action of the blood vessel wall and itself. If the rebound is large, a larger diameter should be expected before implantation, which will cause greater damage to the blood vessel. At the same time, the axial direction of the stent will also be shortened to a certain extent during the expansion process. During the implantation process, it is generally hoped that the length of the stent is as small as possible while ensuring that the blocked blood vessel can be opened. A longer stent may damage the branch blood vessels on the side. If the axial shortening of the stent after implantation is large, a longer stent should be expected before implantation. Therefore, a smaller axial shortening rate and radial rebound rate are more conducive to clinical physicians to judge the expansion state of the stent after the balloon is withdrawn, so as to select a suitable stent according to the lesion. The third-layer functional requirements of FR112 are refined as follows:

[0069] FR1121: Reduce axial shortening after stent intervention;

[0070] FR1122: Reducing radial rebound rate after stent intervention.

[0071] The good flexibility that the stent needs to have is divided into axial flexibility, that is, the ability to bend and deform, and radial flexibility, that is, wall adhesion performance. These two are two important biomechanical properties that affect the therapeutic effect of vascular stents: First, if the axial flexibility of the stent is poor, it will lead to low adhesion with the host blood vessel, which is one of the reasons for the slippage and internal leakage of the vascular stent, and should be avoided as much as possible; second, the stent should match the radial compliance of the host blood vessel to reduce the possibility of slippage and internal leakage. In addition, it can also reduce the internal blood flow disorder and reduce the occurrence of other vascular diseases. Therefore, the third-level functional requirements of FR113 are refined as follows:

[0072] FR1131: The stent has good axial flexibility; FR1132: The stent has good radial flexibility.

[0073] Regarding the appropriate hemodynamic parameters mentioned in FR114, existing studies have shown that after stent intervention, too small WSS may cause inflammatory response and platelet deposition in the arterial endothelium, and too large WSS may induce platelet activation and damage the arterial wall. The wall shear force should be within an appropriate range, and the degree of disturbance to blood flow after stent intervention should be reduced, the possibility of local mural thrombosis after stent intervention should be reduced, and the material transmission capacity in the blood after stent intervention should be increased. Therefore, the third-level functional requirements of FR114 are refined as follows:

[0074] FR1141: Appropriate range of wall shear force; FR1142: Reduce the degree of disturbance to blood flow after stent intervention; FR1143: Reduce the possibility of local mural thrombosis after stent intervention; FR1144: Increase the material transfer capacity in the blood after stent intervention.

[0075] Regarding FR121, the stent should have a degradation speed that matches the vascular repair. Existing technical studies have shown that narrowed blood vessels will generally complete vascular repair and remodeling within 6 to 12 months, and the support capacity of the stent will drop significantly after the initial rupture. Therefore, the ideal degradable vascular stent should be completely degraded 24-36 months after intervention. The degradation rate should not be too fast, otherwise the stent will lose its support performance before the vascular repair is completed; it should not be too slow, otherwise it will easily cause problems such as late thrombosis of the diseased blood vessels. Therefore, FR121 can refine the third-layer functional requirements as follows:

[0076] FR1211: suitable degradation rate of the stent before initial fracture;

[0077] FR1212: Suitable degradation rate for complete degradation of the stent.

[0078] According to FR122, the stent should have good degradation performance, which means that the degradation of the stent should be uniform and it should still have certain mechanical properties during the degradation process. Therefore, FR122 can refine the third-layer functional requirements as follows:

[0079] FR1221: Degradation uniformity of the stent; FR1222: Mechanical properties of the stent during degradation.

[0080] The materials of degradable vascular stents mainly include polymers and metals. Therefore, the third-layer design parameters of DP111 can be refined as follows:

[0081] DP1111: polymer stent material design; DP1112: metal stent material design.

[0082] Similarly, the overall structural design elements of the stent include its overall geometric size design and overall characteristic parameter design, so DP112 can refine the third-level design parameters as follows:

[0083] DP1121: Design of overall geometric dimensions of the stent; DP1122: Design of overall characteristic parameters of the stent.

[0084] The design elements of the support ring structure include its geometric size design and characteristic parameter design, so the third-layer design parameters of DP121 can be refined as follows:

[0085] DP1211: Design of support ring geometry; DP1212: Design of support ring characteristic parameters.

[0086] The geometric structure design elements of the connecting ribs include their geometric dimension design and characteristic parameter design. Therefore, DP122 can refine the third-level design parameters as follows:

[0087] DP1221: Design of connecting rib geometry; DP1222: Design of connecting rib characteristic parameters.

[0088] The radial support force F of the vascular stent described in FR1111 can be measured by the radial compression method, and can therefore be subdivided into the fourth level of functional requirements:

[0089] FR11111: The radial support force F of the vascular stent is calculated as follows:

[0090]

[0091] Among them, f i is the contact normal stress at the bracket node i in the radial compression method measurement, and n is the total number of nodes.

[0092] Regarding the reduction in the degree of axial shortening after stent intervention as described in FR1121, it can be measured by the axial shortening rate Fr, and therefore can be subdivided into the fourth level of functional requirements:

[0093] FR11211: The axial shortening rate Fr after stent intervention is calculated as follows:

[0094]

[0095] L 初始 and L 最终 They represent the initial length of the stent before balloon loading and the length of the stent after balloon unloading, respectively.

[0096] The degree of radial elastic recoil reduction after stent intervention as described in FR1122 can be measured by the radial rebound rate RR, and can therefore be subdivided into the fourth level of functional requirements:

[0097] FR11221: The radial rebound rate RR after stent intervention is calculated as follows:

[0098]

[0099] Among them, R 扩张 and R 回弹 They represent the stent radius at the end of balloon dilation and the stent radius after complete balloon unloading, respectively.

[0100] The stent described in FR1131 has good axial flexibility, which can be evaluated by measuring the diameter change rate and elastic rebound force of the stent when it is bent using an in vitro test method. Based on this, FR1131 can be subdivided into the fourth level of functional requirements:

[0101] FR11311: diameter change rate DR when the stent is bent; FR11312: stent elastic rebound force RF.

[0102] The stent described in FR1132 has good radial flexibility, which can be evaluated using the test method specified in the ISO 7198-2016 standard. Based on this, FR1132 can be divided into the fourth level of functional requirements:

[0103] FR11321: The radial compliance C of the stent is calculated as follows:

[0104]

[0105] The test system includes a water circulation system that simulates the human blood circulation pathway, a motor system for applying pulsating pressure to the sample, a laser system for reading the sample diameter, and a pressure test system for monitoring the real-time pressure applied to the sample. The two ends of the sample to be tested are fixed on the sample loading device. The water flow direction inside the sample is from left to right, and the water flow rate is adjustable. Among them, p1 (mmHg) is the low pressure value; p2 (mmHg) is the high pressure value; R p1 is the radius at low pressure; R p2 is the radius at high pressure.

[0106] Regarding the avoidance of shear force within an appropriate range as described in FR1141, this can be achieved by controlling the wall shear stress WSS after stent intervention. Therefore, this functional requirement can be subdivided into the fourth level of functional requirements:

[0107] FR11411: Controlling wall shear stress WSS after stent insertion.

[0108] In view of the reduction of the degree of disturbance of blood flow after stent intervention as described in FR1142, OSI can characterize the degree of disturbance of blood flow, so FR1142 can refine the fourth layer function requirements:

[0109] FR11421: Reducing the OSI value after stent intervention. The OSI is defined as:

[0110]

[0111] Regarding the possibility of reducing local mural thrombosis after stent intervention as described in FR1143, ECAP is the ratio of OSI to TAWSS. TAWSS is the average value of WSS integral in one cardiac cycle. ECAP is used to characterize the possibility of mural thrombosis. The larger the ECAP (generally 1.4Pa-1 is used as a critical value), the greater the probability of thrombosis. Therefore, FR1143 can be refined as follows:

[0112] FR11431: Reducing ECAP values ​​after stent intervention.

[0113] Similarly, in order to increase the material transport capacity in the blood after stent intervention as described in FR1144, the blood flow spiral strength h is defined as follows:

[0114]

[0115] In the formula, v(s, t) and ω(s, t) are the velocity vector and the curl vector at time t, respectively. This formula represents the average spiral degree of each unit (whose volume is Vi) in a finite volume unit within a cardiac cycle Ti, that is, the blood flow spiral intensity h. The spiral flow structure in the blood flow is conducive to the transport of substances in the blood and reduces the risk of vascular lesions. Therefore, FR1144 can be refined as:

[0116] FR11441: Increase the helical strength h of blood flow after stent intervention.

[0117] The degradation rate of the first fracture of the stent described in FR1211 can be controlled by controlling the time t1 of the first fracture of the stent. Therefore, FR1211 can be refined as follows:

[0118] FR12111: Time t1 when the stent first fracture occurs.

[0119] The degradation rate of the stent completely degraded as described in FR1212 can be controlled by controlling the time t2 when the stent is completely degraded. Therefore, FR1212 can be refined as follows:

[0120] FR12121: Time t2 when the stent is completely degraded.

[0121] The degradation uniformity of the stent described in FR1212 can be controlled by controlling the degradation uniformity M of the stent, which defines the mass loss rate of the stent when the stent is first fractured. The higher the value, the more uniform the stent degradation. Therefore, FR1221 can be refined as follows: FR12211: Degradation uniformity M of the stent.

[0122] The mechanical properties of the stent during the degradation process described in FR1222 can be reflected by the radial support force F2 of the stent when the blood vessel has completed a certain degree of repair. Therefore, FR1222 can be refined as follows:

[0123] FR12221: The radial support force F2 of the stent when the blood vessel has completed a certain degree of repair.

[0124] The above fourteen items are the fourth-level functional requirements of the degradable vascular stent. So far, the leaf-level functional requirement indicators of the degradable vascular stent have been decomposed.

[0125] In the physical domain, the third-layer design parameters of the biodegradable vascular stent are decomposed and refined. The overall geometric design elements of the stent include the overall length of the stent, the inner diameter of the stent when expanded, and the overall thickness of the stent. Therefore, DP1121 can be refined as follows:

[0126] DP11211: overall length of the stent L; DP11212: inner diameter of the stent R;

[0127] DP11213: Overall thickness T of the bracket.

[0128] The width, length, thickness, cross-sectional shape and other geometric structural parameters of the support ribs and connecting ribs of the vascular stent will affect the mechanical properties of the stent. The main features of the stent are that the support ribs are a corrugated ring structure, and the support ribs are connected by straight connecting ribs. The overall characteristic parameters of the stent include the number of circumferentially distributed support rings, the number of axially distributed support rings and the window length. The window length refers to the farthest distance between two adjacent rows of support rib corrugated rings along the axial direction. Therefore, DP1122 can be refined as follows:

[0129] DP11221: number of circumferential support rings of the stent m; DP11222: number of axial support rings of the stent n;

[0130] DP11223: Bracket window length l.

[0131] The geometric dimensions of the support ring mainly include its width and the inner and outer diameters of the corrugated ring, so DP1211 can be refined as follows:

[0132] DP12111: support ring width w1; DP12112: support ring wave ring inner diameter ri;

[0133] DP12113: Support ring corrugated ring outer diameter ro.

[0134] The support ring features specifically refer to its cross-sectional shape, so DP1212 can be broken down into:

[0135] DP12121: Support ring cross-sectional shape.

[0136] The geometric dimensions of the connecting rib mainly include its width and length, so DP1221 can be refined as follows:

[0137] DP12221: connecting rib width w2; DP12212: connecting rib length l2.

[0138] The characteristics of the connecting rib specifically refer to its cross-sectional shape, so DP1132 can be refined as follows:

[0139] DP12221: Cross-sectional shape of connecting ribs.

[0140] The above 18 design parameters can be directly reflected and evaluated in the test model and test device, so they are used as the leaf-level design parameters DPs of the degradable magnesium alloy stent with polymer coating. At this point, the FR-DP decomposition of the degradable vascular stent is completed.

[0141] iv. The mapping relationship between the leaf-level functional requirement index FRs and the leaf-level design parameter DPs of the biodegradable vascular stent is represented by a design matrix, and the design equations of each level are established to associate the performance index of each level with the design parameters. The specific form of the design matrix is ​​as follows:

[0142]

[0143]

[0144] FR i (i=1,2,…,n),DP j (j=1,2,…,m) are the functional requirements and design parameters at the same level. The element A in the design matrix ij Represents DP j Design parameters for FR i The degree of influence of functional requirement indicators.

[0145] v. Evaluate the degree to which the design parameters of implantable medical devices meet their functional requirements through numerical simulation, mechanical tests, materials tests and biocompatibility tests. Before the experiment, it should be verified whether each test and evaluation process meets the ethical constraints Cs in the functional domain, and the experiment should be conducted using an experimental method that meets the ethical constraints Cs. Based on the test results, for quantitative leaf-level design parameters, such as length, width, thickness and other structural design parameters, sensitivity analysis is used to determine the degree of influence on each leaf-level functional requirement indicator when these design parameters change. The sensitivity coefficient is used to measure it and compare it with the preset sensitivity coefficient benchmark. The design parameters with a sensitivity coefficient lower than the benchmark are eliminated. Taking the sensitivity analysis process of the leaf-level biomechanical functional requirement indicator FR11111 (radial support force F of the vascular stent) of the degradable vascular stent to the leaf-level structural design parameter DP11213 (stent thickness T) as an example, the specific explanation is as follows:

[0146] a. Determine the sensitivity index of the functional requirement: Here, the sensitivity index of the functional requirement FR11111 can be defined as "keeping the other leaf-level structural design parameters unchanged, and changing the value of the thickness T of the degradable vascular stent, the rate of change ΔF of the radial retraction rate F after the stent intervention". b. Determine the rate of change of the structural design parameters: Here, the rate of change ΔT of the thickness T of the degradable vascular stent is set to ±10%. c. Determine the sensitivity coefficient benchmark: Here, the benchmark E0 of the sensitivity coefficient is set to ±1. d. Calculate the sensitivity coefficient: E=ΔF / ΔT, E>0, indicating that the functional requirement FR11111 and the structural design parameter DP11213 change in the same direction; E<0, indicating that the two change in opposite directions. The larger |E| is, the higher the sensitivity coefficient is, and the more sensitive the functional requirement FR11111 is to the structural design parameter DP11213. e. Compare the calculated sensitivity coefficient E with the benchmark E0: When the sensitivity coefficient E calculated according to the test results is in the range of [-1,1], it is considered that FR11111 is insensitive to the change of DP11213, and DP11213 is listed as a "non-critical design parameter" affecting FR11111; when the sensitivity coefficient is outside the range of [-1,1], it is considered that FR11111 is sensitive to the change of DP11213, and DP11213 is listed as a "critical design parameter" affecting FR11111.

[0147] For each leaf-level design parameter of a certain category, such as material type, surface texture shape and other design parameters, the correlation ratio E between the leaf-level design parameter and each leaf-level functional requirement index is calculated. 2 The error reduction ratio PRE is calculated and compared with the preset error reduction ratio benchmark, and the design parameters with an impact lower than the benchmark are eliminated. 2 It is often used to measure the correlation between a categorical independent variable and a quantitative dependent variable by calculating the correlation ratio E 2 PRE can measure the relative importance of categorical design parameters. PRE means that when variable X and dependent variable Y are assuming a correlation, the correlation ratio E12 obtained when predicting Y with data from X can reduce the error compared to the correlation ratio E22 obtained when predicting Y without data from X. The larger the PRE, the greater the influence of variable X on dependent variable Y. The calculation process of the error reduction ratio PRE of the correlation ratio E2 between the leaf-level structural design parameter DP12221 (connecting rib cross-sectional shape) of the biodegradable vascular stent and the leaf-level biomechanical functional requirement index FR11111 (radial shrinkage rate RR after stent intervention) is taken as an example, and the specific explanation is as follows:

[0148] a. Calculate the relevant ratio E12 of FR11111 when the cross-sectional shape of the connecting rib of the degradable vascular stent is a quadrilateral. b. Calculate the relevant ratio E22 of FR11111 when the cross-sectional shape of the connecting rib of the degradable vascular stent is an arc. c. Set the benchmark PRE0 of the subtracted error ratio: PRE0 is set to 0.1 here. d. Calculate the subtracted error ratio PRE of the correlation ratio in the two cases and compare it with PRE0. For example: if the subtracted error ratio PRE of the correlation ratio E2 between DP12221 and FR11111 is less than 0.1, it is considered that the influence of DP12221 on FR11111 is negligible, and DP12221 is listed as a "non-critical design parameter" affecting FR11111; otherwise, DP12221 is listed as a "critical design parameter" affecting FR11111, so as to finally extract the key design parameters affecting the performance of the degradable vascular stent.

[0149] (2) According to the degradation mechanism of different degradable materials, a finite element corrosion model is established to provide a basis for quantitatively calculating the degradation performance of medical devices with different structures. In this embodiment, a corrosion model is established by taking a PLGA degradable vascular stent as an example. Since tensile stress has a great influence on the degradation process of PLGA, and after the stent enters the human blood vessel, the expansion and contraction effect from the vascular wall is more significant, this embodiment takes the application of tensile stress of different sizes as an example to analyze the tensile strength, Young's elastic modulus, surface morphology and mass loss of PLGA materials during the degradation process. In this embodiment, a PLGA film sample is first prepared and a degradation experimental device for applying tensile stress is designed. The tensile mechanical loading device consists of a base, a clamp, a fixed pulley and a standard weight. The device is placed in ultrapure water with pH = 7.4, and the degradation temperature is controlled at room temperature 25°C. The PLGA specimen samples are divided into six groups, and the tensile stress loads are 0.1, 0.2, 0.3, 0.4, and 0.5 MPa respectively. No load is applied to the control group. During the degradation process, samples are taken once a week, 5 samples are taken under each load condition, and the degradation time is 8 weeks. The sample specimens were cut into 50mm×10mm size for mechanical tensile testing, and the elastic modulus and tensile strength were measured, and the mass loss was determined. Polynomial fitting was performed through MATLAB to describe the functional relationship between mass loss rate, tensile stress and time.

[0150] f(σ,t)=at+bt 2 +ct 3 +dt 4 +eσt+fσ 2 t+gσ 2 t 2 +hσ 2 t 3 (12)

[0151] Among them, ah is the experimental constant.

[0152] The degradation process of the stent material is described by the birth-death unit method, and the VUMAT subroutine of ABAQUS is used to update the material properties during the degradation process. The cumulative loss D of unit k between the degradation time [0, T] is calculated according to formula (13): k According to formula (14), the failure limit D of unit k is calculated kc The larger the unit volume, the longer the degradation time. kc Proportional to the unit volume. The unit degradation model is controlled by equation (15). For unit k, when the mass loss accumulates to a certain extent, the material is completely degraded and deleted from the model.

[0153]

[0154] Where T is the total degradation time, D k is the total mass loss rate of unit k when degradation reaches time T, D kc is the critical value of k unit degradation, σ k is the tensile stress on unit k, V k is the volume of unit k, and V is the maximum unit volume in the model.

[0155] The constitutive relation of the material in the elastic stage of the degradation model is as follows:

[0156]

[0157] Among them, the Lamé constant Where E is the elastic modulus, μ is Poisson's ratio, G is the shear modulus, and volume strain θ = ε x +ε y +ε z .

[0158] Compute the stress tensor invariants:

[0159]

[0160] Substituting into the characteristic equation I1σ2+I2σ–I3=0, we obtain the principal stresses σ1, σ2, σ3.

[0161] In the plastic stage of the degradation model, the constitutive relation of the material is based on the Mises yield criterion, and the stress tensor invariant is:

[0162]

[0163] like It is determined that the material has undergone plastic deformation. At this time, the principal stresses need to be recalculated. Substitute J1 and J2 obtained from formula (18) into the new characteristic equation σ′3-J1σ′-J2=0 to obtain new principal stresses σ1′, σ2′, σ3′, and use the new principal stresses to replace the original principal stresses for the next calculation.

[0164] (3) Next, the degradation performance of degradable implantable medical devices is regulated by using an optimization algorithm, taking the clinical requirements for degradation performance as the optimization target. Based on the key design parameters that affect the performance of degradable vascular stents, a machine learning model between key design parameters and degradation performance targets is constructed. Under the condition of meeting other leaf-level functional targets and design parameters, the fourth-level leaf-level design parameters of the degradable stent, the overall length L of the DP11211 stent, the inner diameter R of the DP11212 stent, and the overall thickness T of the DP11213 stent are used as leaf-level key design parameters, and the fourth-level leaf-level functional requirements of the degradable stent, FR12111: the time t1 when the stent first breaks, FR12121: the time t2 when the stent is completely degraded, and FR12211: the uniformity of stent degradation M are used as optimization targets.

[0165] This embodiment uses the Latin Hypercube Sampling (LHS) method to sample the design variables, obtain a data set, and perform training and testing. LHS is a method for obtaining an approximate random distribution from a multivariate parameter distribution, which belongs to stratified sampling and is one of the most widely used sampling methods. This method ensures the comprehensiveness and uniformity of the sample projection in the probability space from the perspective of spatial coverage. The key is to stratify each design variable with equal probability and then sample from it.

[0166] The model between key design parameters and optimization objectives is constructed based on the RBF radial basis neural network algorithm. The RBF network can approximate any nonlinear function, can handle the regularity that is difficult to analyze in the system, has good generalization ability, and has a fast learning convergence speed. It has been successfully applied to nonlinear function approximation. The four leaf-level functional objectives are dimensionless and normalized, and the corresponding coefficients c are added. i (i=1,2,3,4), and add the corresponding proportional weight coefficient w i (i=1,2,3,4), construct the objective function P as follows:

[0167] P=w1c1t1+w2c2t2+w3c3M+w4c4F2 (19)

[0168] The above mathematical model is optimized and solved using a genetic algorithm to obtain the optimal values ​​of key design parameters. Genetic Algorithm (GA) is a random global search optimization method that simulates phenomena such as replication, crossover, and mutation that occur in natural selection and inheritance. Starting from any initial population, a group of individuals that are more suitable for the environment are generated through random selection, crossover, and mutation operations, so that the group evolves to better and better areas in the search space, so that generations continue to multiply and evolve, and finally converge to a group of individuals that are most adapted to the environment, thereby obtaining a high-quality solution to the problem. The optimization results are then evaluated, specifically by comparing the leaf-level functional requirements of the optimized structure and the initial structure through methods such as in vitro degradation experiments and numerical simulations of the degradation process. This embodiment includes the time when the bracket first breaks, the time when the bracket is completely degraded, and the uniformity of the degradation of the bracket, to verify the effectiveness and feasibility of the optimization results of the leaf-level functional requirements.

[0169] The optimized leaf-level functional requirements are compared with the user requirements. If the user requirements can be met, the optimized leaf-level design parameters are determined to be the optimal leaf-level design parameters. If the evaluation results do not meet the requirements, the leaf-level design parameters are adjusted again according to the evaluation results and optimized again until the expected user requirements are met.

[0170] Example 2

[0171] This embodiment takes a degradable magnesium alloy vena cava filter as an example to explain in detail the method for structural design and regulation of the degradation performance of a degradable implantable medical device according to the present invention. The specific process of this method is as follows:

[0172] (1) Based on the axiomatic design method, the key design parameters of biodegradable implantable medical devices are extracted.

[0173] i. Define user needs based on the clinical use purpose of the degradable implantable medical device and the anatomical constraints of the implantation location. The degradable vena cava filter in this embodiment is used to prevent deep vein thrombosis in the lower extremities. It is used for patients with deep vein thrombosis or who are bedridden for a long time, undergoing major surgery, trauma, serious illness or other high-risk factors, or who have ineffective anticoagulant treatment or are contraindicated for anticoagulation. The intervention site is the vena cava to intercept deep vein thrombi that may fall off and prevent them from flowing to the pulmonary artery and causing pulmonary embolism. After the various high-risk factors that may cause pulmonary embolism in the patient are eliminated, the degradable vena cava filter gradually undergoes biodegradation, and eventually the filter is completely degraded and metabolized and excreted from the body. Therefore, the degradable vena cava filter must meet the following requirements: a. intercept detached deep vein thrombosis; b. the filter does not shift significantly after intervention; c. the filter has little disturbance to normal blood flow after intervention, reduces the disturbance to the original blood flow pattern in the vena cava, and avoids thrombosis caused by the filter; d. the degradation cycle is similar to the high-risk period of pulmonary embolism, and the filter continues to maintain mechanical properties before passing the high-risk period to ensure that it can intercept thrombus; e. the degradation products do not accumulate and are non-toxic and harmless; f. good biocompatibility.

[0174] ii. According to the above user needs, determine the top-level performance requirements FR0 that the implantable medical device needs to meet. According to the characteristics of the implantable medical device to be evaluated, map the top-level performance requirements to the physical domain to form the top-level design parameters DP0 of the implantable medical device. Based on this definition:

[0175] Top-level performance requirement FR0: Have appropriate performance;

[0176] Top-level design parameter DP0: Overall design of the biodegradable vena cava filter.

[0177] iii. Decompose between the physical domain and the functional domain through Z-shaped mapping transformation to obtain the structural design parameters and material design parameters corresponding to the biomechanical performance requirements, material performance requirements, and biological performance requirements of the implantable medical device, until the leaf-level structural design parameters and material design parameters DPs that can be directly reflected in the mechanical, material science and biological test models are obtained, as well as various leaf-level performance measurement indicators FRs obtained through test result analysis.

[0178] The top-level performance requirement FR0 of the above-mentioned degradable vena cava filter is subdivided into primary functional indicators:

[0179] FR11: has good biomechanical properties; FR12: has appropriate degradation properties.

[0180] In the physical domain, DP0 can be decomposed into first-level design parameters:

[0181] DP11: Material Design; DP12: Structural Design.

[0182] According to the above-mentioned first-level indicators, the indicators of degradable implantable medical devices can be further subdivided. In view of the fact that the degradable vena cava filter needs to have good biomechanical properties, the biomechanical properties that the degradable vena cava filter needs to have can be further subdivided into the following categories in combination with specific usage scenarios: (1) During the implantation process in the body, that is, during the compression and expansion process, it has good stress distribution to avoid stress concentration after implantation in the body and has good strength after implantation in the body; (2) It has good hemodynamic properties, reduces the blood flow velocity disturbance and vascular wall shear force changes caused by the implantation of the filter, reduces the blood flow reflux and stagnation downstream of the filter head, and avoids thrombosis caused by filter implantation; (3) It has good thrombus interception ability and improves the filter's ability to break up thrombi by regulating the filter's wall shear force. Therefore, the first-level functional requirement indicator FR11 biomechanical performance can be subdivided into second-level indicators:

[0183] FR111: sufficient strength; FR112: good hemodynamic performance;

[0184] FR113: has good thrombus interception ability.

[0185] The degradation behavior of the degradable vena cava filter during in vivo service includes the combined effects of various complex corrosion behaviors such as uniform corrosion, stress corrosion, random pitting, fatigue corrosion, etc. The ideal degradation cycle of the degradable vena cava filter matches the required effective thrombus interception time, maintains mechanical properties during the high-risk period of pulmonary embolism in patients, ensures that the mechanical strength is sufficient to intercept thrombi, and gradually degrades after the high-risk period until it is completely degraded and metabolized and excreted from the body. Therefore, the first-level functional requirement indicator FR12 degradation performance can be subdivided into secondary indicators:

[0186] FR121: Degradation cycle adapted to the high risk period of pulmonary embolism;

[0187] FR122: has good degradation mechanical properties.

[0188] In the physical domain, the design parameters that affect the above functional requirements in the design of degradable vena cava filters are further refined. For DP11, the main material properties are one of the important factors affecting the biomechanical properties, degradation behavior and biocompatibility of the filter, so the design of the main material is one of the design elements of the stent material; the degradable filter coating design is one of the important methods to alleviate degradation corrosion and improve biocompatibility, so the coating design is also one of the material design elements, and the secondary design parameters can be further refined:

[0189] DP111: filter body material design; DP112: filter coating material design.

[0190] For DP12, the structural design elements of the filter mainly include the design of the overall structure of the filter and the geometric structure of the filter mesh. The secondary design parameters of DP12 can be further refined as follows:

[0191] DP121: filter overall structure design; DP122: filter screen structure design.

[0192] For the above-mentioned secondary performance demand indicators for degradable implantable medical devices, specific test indicators in the corresponding mechanical tests, material tests and biological tests, i.e., tertiary indicators, can be determined. After the degradable vena cava filter is implanted in the body, it is necessary to maintain its mechanical properties for 1 to 2 months, during which time it is necessary to ensure that it has good strength to prevent serious adverse events such as breakage and falling off. Therefore, the secondary performance demand indicator FR111 can be subdivided into three levels of indicators, including:

[0193] FR1111: Maximum strength of compression test.

[0194] In view of the hemodynamic performance of the degradable vena cava filter, PIV fluid mechanics performance test is required to quantitatively observe the disturbance of the flow field and the influence of the shear force on the vascular wall after the filter is implanted. Studies have shown that too small WSS may induce inflammatory response and platelet deposition, and too large WSS may induce platelet activation. Therefore, the filter should have an appropriate range of wall shear force after implantation. It is necessary to reduce the difference between the peak and valley levels of the shear stress of the vascular wall after the filter intervention and the normal range of the human body. Therefore, the secondary performance requirement indicator FR112 hemodynamic performance can be subdivided into three levels of indicators including:

[0195] FR1121: Changes in vascular wall shear stress after filter intervention;

[0196] FR1122: Changes in blood flow field after filter intervention; FR1123: Turbulence phenomenon after filter intervention.

[0197] The biodegradable vena cava filter needs to intercept thrombi during the high-risk period of pulmonary embolism in patients, which is generally 1 to 2 months. During this period, the filter needs to intercept larger thrombi. Therefore, the secondary indicator FR113 thrombus interception ability can be divided into three levels of indicators, including: FR1131: in vitro thrombus interception rate.

[0198] The degradable vena cava filter must maintain mechanical strength for 2 to 3 months and maintain the ability to intercept thrombi. Therefore, the ideal degradable vena cava filter should be completely degraded 6 to 12 months after intervention. The degradation rate should not be too fast. Otherwise, when the patient is still at risk of thrombus detachment, the partial degradation of the filter mesh will cause the mechanical properties to decay too quickly, making it difficult to ensure the effectiveness of the filter. However, the degradation rate should not be too slow. After the patient has passed the high-risk period for pulmonary embolism, the filter still exists in the vena cava, which can cause disturbances to the blood flow and may cause abnormal hemodynamic parameters, leading to complications such as device-induced thrombosis. In addition, there may be a variety of long-term adverse reactions such as filter displacement, detachment, and penetration of the blood vessel wall. The degradation cycle of the degradable vena cava filter should match the high-risk period for pulmonary embolism, which can be quantitatively measured by the difference between the two.

[0199] FR121 has three levels of performance requirements:

[0200] FR1211: Degradation time of initial filter breakage; FR1212: Degradation time of complete filter breakage.

[0201] The good degradation uniformity of the degradable vena cava filter refers to the overall structure of the filter screen, the filter head structure, the filter fixing structure and the connection parts of the above parts, which can maintain a relatively consistent degradation rate. FR122 can be divided into three levels of indicators: FR1221; uniformity of overall degradation of the filter.

[0202] The main filter material can achieve sufficient mechanical strength and appropriate degradation rate by changing the material composition and microstructure. DP111 can be refined into three levels of design parameters:

[0203] DP1111: Main material composition; DP1112: Main material mechanical properties.

[0204] Surface modification or coating preparation on the surface, such as forming ceramic film, polymer film or composite film, also plays an important role in regulating the degradation rate. Currently, the commonly used degradable coating materials are mainly polymers, so the design elements include the chemical characteristics, microstructure and physical characteristic parameters of the coating materials. DP112 can be refined into three levels of design parameters:

[0205] DP1121: Chemical characteristics of coating materials; DP1122: Physical characteristics parameters of coatings.

[0206] The overall structural design elements of the filter include overall geometric dimension design and overall characteristic parameter design, so DP121 can be refined into three levels of design parameters:

[0207] DP1211: Overall geometric dimension design of the filter; DP1212: Overall characteristic parameter design of the filter.

[0208] The design elements of the filter geometry include its geometric dimensions and characteristic parameters. Therefore, DP122 can be refined into three levels of design parameters:

[0209] DP1221: Design of filter mesh geometry; DP1222: Design of filter mesh characteristic parameters.

[0210] The above three levels of performance requirements are further divided into four levels, which can directly correspond to various test indicators. The four levels of indicators corresponding to FR1111 are:

[0211] FR11111: Maximum load F at which the compression test structure fails.

[0212] The shear stress performance of the vascular wall after filter intervention can be characterized by the wall shear stress at each moment in the cardiac cycle. In a cardiac cycle, the WSS is usually the largest at the peak of the systolic period, which requires special attention, while the WSS in the diastolic period may be the smallest and also needs to be compared with the normal blood flow WSS. In addition, the time-averaged wall shear stress (TAWSS) is the average value of the absolute value of the wall shear stress in a cardiac cycle, which is used to evaluate the wall shear stress acting on the lumen wall during the cardiac cycle. The four-level indicators corresponding to FR1121 are divided into:

[0213] FR11211: Wall shear force WSS at each moment during the cardiac cycle.

[0214] FR11212: Time-averaged wall shear force TAWSS, calculated as:

[0215]

[0216] Where T is the duration of the cardiac cycle and WSS is the wall shear stress vector.

[0217] The changes in blood flow field disturbance after filter intervention are achieved by observing the changes in the blood flow field pattern and the changes in the direction of shear force. The oscillatory shear index (OSI) is a parameter that quantifies the change in the direction of wall shear stress. It is usually used to measure the directional change of WSS during the cardiac cycle and describe the disturbance of the flow field. It ranges from 0 to 0.5, where 0 means that the blood flow is unidirectional and there is no oscillation; 0.5 means that the blood flow is oscillatory and the instantaneous WSS direction deviates violently. Transverse wall shear force (transWSS) is the average magnitude of the WSS component perpendicular to the mean shear stress vector and the local surface normal. It is used to distinguish between uniaxial pulsating flow and multidirectional flow to capture the multidirectionality of the flow and reflect the degree of chaos in the flow field. The four-level indicators corresponding to FR1122 are:

[0218] FR11221: flow velocity v; FR11222: oscillatory shear index OSI, calculated as:

[0219]

[0220] where T is the duration of the cardiac cycle and WSS is the wall shear stress vector.

[0221] FR11223: Transverse wall shear force transWSS, calculated as:

[0222]

[0223] in represents the direction vector of the wall shear force, represents the normal direction of the artery surface, It represents the average direction of the wall shear force vector within a cardiac cycle, and the calculation formula is as follows:

[0224]

[0225] Turbulence intensity refers to the intensity of turbulent motion in a fluid. Turbulence intensity can be used to describe the size and number of vortices in a fluid. Turbulence intensity is usually expressed as the ratio of turbulent energy, that is, the ratio of the average turbulent energy to the average flow energy. The four-level indicators corresponding to FR1123 are:

[0226] FR11231: Turbulence intensity, calculated as:

[0227]

[0228] Where u′ represents the root mean square of the turbulent velocity fluctuation and U represents the Reynolds mean velocity.

[0229] The in vitro thrombus interception rate is obtained through an in vitro circulation experiment test. Artificial thrombi of different sizes are designed and released from the upstream to test the relationship between the filter interception rate and the volume of thrombi. Therefore, the four-level indicators corresponding to FR1131 are:

[0230] FR11311: Thrombus interception rate R, calculated as:

[0231]

[0232] Among them, n 总 is the total number of artificial thrombi released in the in vitro thrombus interception experiment, n 拦截 The number of thrombi intercepted by the filter.

[0233] The degradation time of the first break of the FR1211 filter corresponds to the four-level indicators:

[0234] FR12111: Time t1 when first cracks appear in in vitro degradation test.

[0235] The degradation time of FR1212 filter completely broken corresponds to the four-level index:

[0236] FR12121: Time t2 when first cracks appear in in vitro degradation test.

[0237] The overall degradation uniformity of the FR1221 filter can be divided into four levels of indicators:

[0238] FR12211: The overall mass loss rate M of the filter when it first breaks is calculated as:

[0239]

[0240] Among them, m 剩余 This is the overall residual mass of the filter, m 总 is the total mass of the filter when no degradation occurs.

[0241] The above eleven items are the fourth-level functional requirements of the degradable vena cava filter. So far, the leaf-level functional requirement index decomposition of the degradable vena cava filter has been completed.

[0242] In the physical domain, the third-level design parameters are decomposed and refined into the fourth-level design parameters, and the structural design of the biodegradable magnesium alloy vena cava filter is taken as an example to illustrate.

[0243] The composition parameters of the degradable magnesium alloy vena cava filter main material WE43 described in this embodiment mainly include metal magnesium content, metal yttrium content, metal zirconium content and rare metal content, so DP1111 can be refined into: DP11111: metal magnesium content; DP11112: metal yttrium content; DP11113: metal zirconium content; DP11114: rare metal content.

[0244] The mechanical properties of the main material are divided into several main parameters that characterize its mechanical properties, such as Young's modulus, Poisson's ratio, hardness, and tensile yield strength. Therefore, DP1112 can be refined into:

[0245] DP11121: Young's modulus; DP11122: Poisson's ratio; DP11123: Hardness; DP11124: Yield strength.

[0246] The degradation behavior of polymer coating materials is related to their molecular weight, monomer type, side branch groups, etc. Therefore, the design elements of the chemical characteristics of polymer coating materials include molecular weight distribution, stereoregularity of molecular groups, main chain molecular composition, etc. Therefore, DP1121 can be refined as follows:

[0247] DP11211: molecular weight of PLLA; DP11212: stereoregularity of PLLA molecular groups;

[0248] DP11213: Main chain molecular composition of PLLA.

[0249] The physical characteristic parameters of the coating mainly include the coating thickness, which regulates its degradation rate. The four-layer design parameters corresponding to DP1122 are: DP11221: thickness t.

[0250] The overall geometric design elements of the filter include the expansion diameter and the filter length. DP1211 is subdivided into:

[0251] DP12111: diameter d; DP12112: filter length l; DP12113: overall length L.

[0252] The overall characteristic parameter design of the filter includes the waveform design of the support part, the number of repeated units in the circumferential and axial distribution of the support part, and DP1212 is subdivided into:

[0253] DP12121: Waveform design of the support part; DP12122: Number of circumferential repeating units of the support part;

[0254] DP12123: Number of axial repeating units in the support part.

[0255] The geometric design of the filter screen mainly includes the thickness of the filter rod and the number of filter rods. DP1221 is divided into:

[0256] DP12211: filter rod width W; DP12212: filter rod thickness T; DP12213: number of filter rods N.

[0257] The filter characteristic parameter design includes the filter rod shape design, DP1222 is divided into:

[0258] DP12221: Filter rod cross-sectional shape.

[0259] The above 22 design parameters can be directly reflected and evaluated in the test model and test device, so they are used as the leaf-level design parameters DPs of the degradable magnesium alloy vena cava filter. So far, the FR-DP decomposition of the degradable vena cava filter is completed.

[0260] iv. The mapping relationship between the leaf-level functional requirement index FRs and the leaf-level design parameter DPs of the degradable vena cava filter is represented by the design matrix, and the design equations of each level are established to associate the performance index of each level with the design parameters. The specific form of the design matrix is ​​as follows:

[0261]

[0262]

[0263] Among them, FR i (i=1,2,…,n),DPj (j=1,2,…,m) are the functional requirements and design parameters at the same level. The element A in the design matrix ij Represents DP j Design parameters for FR i The degree of influence of functional requirement indicators.

[0264] v. Evaluate the degree to which the design parameters of implantable medical devices meet their functional requirements through numerical simulation, mechanical testing, material testing and biocompatibility testing. Before the experiment, it should be checked whether each test evaluation process meets the ethical constraints Cs in the functional domain, and the experiment should be conducted using experimental methods that meet the ethical constraints Cs.

[0265] Based on the test results, for quantitative leaf-level design parameters, such as length, width, thickness and other structural design parameters, sensitivity analysis is performed to determine the degree of influence on each leaf-level functional requirement indicator when these design parameters change. The sensitivity coefficient is used to measure the influence and compare it with the preset sensitivity coefficient benchmark. The design parameters with a sensitivity coefficient lower than the benchmark are eliminated. The sensitivity analysis process of the leaf-level functional requirement indicator FR12211 (the overall mass loss rate M when the filter is initially broken) of the degradable vena cava filter to the leaf-level structural design parameter DP12211 (filter rod width W) is explained as an example as follows:

[0266] a. Determine the sensitivity index of the functional requirement: Here, the sensitivity index of the functional requirement FR12211 can be defined as "keeping the remaining leaf-level structural design parameters unchanged, changing the width W of the degradable vena cava filter rod, and the rate of change ΔF of mass loss rate when the filter first breaks during degradation". b. Determine the rate of change of structural design parameters: Here, the rate of change ΔW of the width W of the degradable vena cava filter rod is set to ±10%. c. Determine the sensitivity coefficient benchmark: Here, the benchmark E0 of the sensitivity coefficient is set to ±1. d. Calculate the sensitivity coefficient: E=ΔF / ΔT, the larger the |E|, the higher the sensitivity coefficient, and the more sensitive the functional requirement FR12211 is to the structural design parameter DP12211. e. Compare the calculated sensitivity coefficient E with the benchmark E0: When the sensitivity coefficient E calculated based on the test results is within the range of [-1,1], it is considered that FR12211 is insensitive to changes in DP12211, and DP12211 is listed as a "non-critical design parameter" affecting FR12211; when the sensitivity coefficient is outside the range of [-1,1], it is considered that FR12211 is sensitive to changes in DP12211, and DP12211 is listed as a "critical design parameter" affecting FR12211.

[0267] For each leaf-level design parameter of a certain category, the error reduction ratio PRE of the correlation ratio E2 between the leaf-level design parameter and each leaf-level functional requirement index is calculated, and compared with the preset error reduction ratio benchmark, the design parameters with an impact lower than the benchmark are eliminated. The correlation ratio E2 is often used to measure the correlation between a certain category independent variable and a quantitative dependent variable. By calculating the PRE of the correlation ratio E2, the relative importance of the certain category design parameters can be measured. The meaning of PRE is that when the variable X and the dependent variable Y are assumed to have a correlation, the correlation ratio E12 obtained when predicting Y with the data of X can reduce the error compared with the correlation ratio E22 obtained when predicting Y without the data of X. The larger the PRE, the greater the influence of variable X on the dependent variable Y. Now, taking the calculation process of the error reduction ratio PRE of the correlation ratio E2 between the leaf-level structural design parameter DP12221 (filter rod cross-sectional shape) of the degradable vena cava filter and the leaf-level functional requirement index FR12211 (the overall mass loss rate M when the filter is initially broken) as an example, the specific explanation is as follows:

[0268] a. Calculate the relevant ratio E12 of FR12211 when the rod of the degradable vena cava filter is a rectangular cross-section. b. Calculate the relevant ratio E22 of FR12211 when the rod of the degradable vena cava filter is a circular cross-section. c. Set the benchmark PRE0 of the error reduction ratio: PRE0 is set as 0.1 here. c. Calculate the error reduction ratio PRE of the correlation ratio in the two cases and compare it with PRE0. If the error reduction ratio PRE of the correlation ratio E2 between DP12221 and FR12211 is less than 0.1, it is considered that the influence of DP12221 on FR12211 is negligible, and DP12221 is listed as a "non-critical design parameter" affecting FR12211; otherwise, DP12221 is listed as a "critical design parameter" affecting FR12211, so as to finally extract the key design parameters affecting the performance of the degradable vascular stent.

[0269] (2) According to the degradation mechanism of different degradable materials, a finite element corrosion model is established to provide a basis for quantitatively calculating the degradation performance of medical devices with different structures. For degradable materials with surface corrosion mechanisms such as magnesium alloys, a corrosion model based on continuum mechanics is established. Continuum damage mechanics establishes a model to describe the loss of mechanical strength of materials by defining a scalar field global damage variable D that can quantify the accumulation of material damage. This example considers two synergistic corrosion mechanisms in the degradation process of magnesium alloy vascular stents, namely uniform corrosion D U and stress corrosion D SC Uniform corrosion describes the mass loss of materials when exposed to a corrosive environment, causing uniform corrosion on the surface of the material. This corrosive environment is similar to ion corrosion, protein adsorption, blood flow, etc. in the body. Stress corrosion describes the phenomenon that corrosion is more severe in stress-concentrated areas mediated by stress.

[0270] The effective stress tensor σ during the degradation process of magnesium alloy is defined as Eq. (27), where σ is the effective stress, is the undamaged stress. When the material is not damaged, the global damage variable D is 0, and the effective stress tensor is equal to the Cauchy stress tensor. As the material damage increases, the D value increases and the mechanical properties of the material decrease.

[0271]

[0272] In this embodiment, the global damage parameter D is expressed as the uniform galvanic corrosion damage parameter D U and stress corrosion damage parameter D SC The linear superposition of is shown in equation (28).

[0273] D=D U +D SC (28)

[0274] The calculation formula for uniform galvanic corrosion parameters is equation (29).

[0275]

[0276] in, D U The derivative with respect to time; K U is the kinetics-related parameter of the uniform corrosion process; δU is the characteristic size of the uniform corrosion process, that is, the critical thickness of the corrosion layer; L e is the characteristic length of the finite element.

[0277] Since magnesium is degraded on the surface, the degradation rate is affected by the number of contact surfaces with the outside world (i.e., degradation surfaces). In order to consider the influence of degradation dimensions on the degradation process, the dimension influence coefficient λ is introduced. d ,λ d It increases with the increase of degradation surface, and its effect on degradation rate is shown in formula (30).

[0278]

[0279] Stress corrosion damage factor The calculation formula is as shown in formula (31).

[0280]

[0281]

[0282] in, is the equivalent stress, and this embodiment adopts the first principal stress; σ th is the stress threshold for stress corrosion, which is set to 50% of the yield stress of the magnesium alloy; δ SCis the characteristic dimension of the stress corrosion process; R and S are related to the kinetic energy of stress corrosion and are functions of the corrosion environment conditions. Since the pH of the corrosion environment is constant, they are constants. In this embodiment, they are set to 2 and The material degradation model is implemented through the VUMAT subroutine of the custom material constitutive relationship of the ABAQUS software. During the explicit dynamics solution, when the global damage parameter D of a unit is greater than 0.9, the unit is considered to have completed degradation and is deleted. After that, the corrosion layer is automatically updated, the global damage parameter of each unit is calculated, and the stress and strain states are continuously updated in a cycle.

[0283] (3) Further taking the clinical requirements for degradation performance as the optimization target, the optimization algorithm is used to regulate the degradation performance of degradable implantable medical devices. This embodiment optimizes the structure of a degradable magnesium alloy vena cava filter, performs parametric modeling based on the structure of the filter, selects the key design parameters of the filter DP12211 filter rod width W; DP12212 filter rod thickness T; DP12213 filter rod number N as three design parameters, and the design intervals of the design parameters are: 0.2mm≤W≤0.5mm, 0.2mm≤T≤0.5mm, 4≤N≤8. This embodiment uses the Latin Hypercube Sampling (LHS) method to sample the design variables, obtain a data set, and perform training and testing. LHS is a method for obtaining an approximate random distribution from a multivariate parameter distribution, which belongs to stratified sampling and is one of the most widely used sampling methods. This method ensures the comprehensiveness and uniformity of the sample projection in the probability space from the perspective of spatial coverage. The key is to stratify each design variable with equal probability and then sample from it.

[0284] This embodiment uses the degradation cycle as the optimization target and adopts the Kriging proxy model structure optimization algorithm to perform structural optimization. The Kriging model is the most widely used method in multi-objective optimization. It is a method for functional modeling of random processes, so it is called a "random process model". The function expression is:

[0285]

[0286] in, represents the i-th sampling point, with m variables; is an approximate function applicable to n sampling points; β is the regression coefficient; z(X i ) has a mean of 0 and a variance of σ 2 The spatial correlation of the random function is given by:

[0287]

[0288] Among them, R(θ,Xi ,X j ) is the Gaussian correlation function related to θ, θ represents the spatial correlation between two samples and can be estimated by maximizing the likelihood of the sampling points, as shown in Equation (34).

[0289]

[0290]

[0291] A linear combination of the sample Y response values ​​can be used to estimate the value of the new point X * The response value

[0292]

[0293] Minimizing the mean squared error (MSE) of this predictor via an unbiased estimate yields:

[0294]

[0295] in,

[0296] r(X * )=[R(θ,X1,X * ),…,R(θ,X n ,X * )] (40)

[0297] After the mathematical model is established, the genetic algorithm is also used to find the optimal solution to obtain the optimal solution of key design parameters.

[0298] Through in vitro degradation experiments and numerical simulation of the degradation process, the leaf-level functional requirement indicators of the optimized structure and the initial structure are compared. This embodiment includes the time when the stent first breaks, the time when the stent is completely degraded, and the uniformity of stent degradation, so as to verify the effectiveness of the leaf-level functional requirement optimization results.

[0299] The optimized leaf-level functional requirements are compared with the user requirements. If the user requirements can be met, the optimized leaf-level design parameters are determined to be the optimal leaf-level design parameters. If the evaluation results do not meet the requirements, the leaf-level design parameters are adjusted again according to the evaluation results and optimized again until the expected user requirements are met.

[0300] The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention, and the protection scope of the patent of the present invention shall be subject to the claims.

Claims

1. A method for structural design and regulation of the degradation performance of a degradable implantable medical device, characterized in that: include: (1) Extracting key design parameters of biodegradable implantable medical devices based on axiomatic design method; (2) Establishing corrosion models based on the degradation mechanisms of different degradable materials and quantitatively calculating the degradation kinetic parameters of different structures, including degradation rate, degradation cycle and degradation uniformity; (3) Taking the requirements of clinical degradation performance as the optimization target and the key design parameters as the geometric design parameters, the structural optimization algorithm is used to regulate the degradation performance of degradable implantable medical devices.

2. The method for structural design and regulation of degradation performance of degradable implantable medical devices according to claim 1, characterized in that: In step (1), the step of extracting key design parameters includes: i. Define the user domain of implantable medical devices based on the clinical purpose and anatomical constraints of the implant location; ii. Mapping the user domain to the functional domain to form top-level performance requirements and ethical constraints, and mapping the top-level performance requirements FR0 to the physical domain to form top-level design parameters for implantable medical devices; iii. Perform a Z-shaped mapping transformation between the functional domain and the physical domain, decompose the various performance requirements of the implantable medical device, and obtain the leaf-level functional requirement indicators and leaf-level design parameters of the various performance of the implantable medical device; iv. The mapping relationship between the leaf-level functional requirement indicators and the leaf-level design parameters of the implantable medical device is expressed through the design matrix, and the design equations of each level are established to associate the performance indicators of each level with the design parameters; v. Conduct ethical constraint experiments, and combine the experimental results to evaluate the degree to which the leaf-level design parameters of implantable medical devices achieve the corresponding leaf-level functional requirement indicators through sensitivity coefficients and / or error reduction ratios, and determine the key leaf-level design parameters.

3. The method for structural design and regulation of degradation performance of degradable implantable medical devices according to claim 2, characterized in that: In step v, the ethical constraint experiment includes numerical simulation experiment, mechanical experiment, degradation experiment, material experiment and biocompatibility experiment.

4. The method for structurally designing and regulating the degradation performance of a degradable implantable medical device according to any one of claims 1 to 3, characterized in that: The corrosion model in step (2) includes a corrosion model based on continuum mechanics and a phenomenological corrosion model.

5. The method for structural design and regulation of degradation performance of degradable implantable medical devices according to claim 4, characterized in that: The corrosion model based on continuum mechanics targets degradable materials with surface corrosion mechanisms and transforms the geometric discontinuity of the material degradation process into a reduction in the macroscopic mechanical properties of the material by defining a scalar field damage variable that can quantify the accumulation of material damage.

6. The method for structural design and regulation of degradation performance of degradable implantable medical devices according to claim 5, characterized in that: The phenomenological corrosion model is aimed at degradable materials with bulk corrosion mechanism. Through in vitro mechanical loading, in vitro degradation experiments under gradient stress are carried out to establish a mathematical model between mass loss rate, stress and degradation time during material degradation, and the fitted degradation model is verified using experimental data.

7. The method for structural design and regulation of degradation performance of degradable implantable medical devices according to claim 6, characterized in that: The corrosion model realizes the updating of material properties and the deletion of units during the degradation process through the subroutine VUMAT of the user-defined material constitutive relationship in the ABAQUS software.

8. The method for structurally designing and regulating the degradation performance of a degradable implantable medical device according to any one of claims 1 to 7, characterized in that: The structural optimization method in step (3) comprises the following steps: a. Based on the corrosion model, establish the functional relationship between key blade design parameters and degradation kinetic parameter targets; b. Taking the functional relationship as the optimization target, the key leaf-level design parameters are optimized in combination with the structural optimization algorithm to obtain the optimized leaf-level design parameters.

9. The method for structural design and regulation of degradation performance of degradable implantable medical devices according to claim 8, characterized in that: The structural optimization method in step (3) further includes: Step c. Evaluate the degradation performance of the implantable medical device with optimized leaf-level design parameters. If the evaluation results meet the expected requirements, determine the optimized leaf-level design parameters as the optimal leaf-level design parameters. If the evaluation results do not meet the requirements, adjust the leaf-level design parameters again according to the evaluation results and re-optimize until the expected degradation performance is achieved.

10. The method for structurally designing and regulating the degradation performance of a degradable implantable medical device according to claim 8 or 9, characterized in that: In step a, a machine learning method is used to obtain known information using a corrosion model to establish the functional relationship.

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