Method and related device for noninvasive diagnosis of pulmonary arterial hypertension

Abstract

The invention discloses a method for noninvasive diagnosis of pulmonary arterial hypertension and a related device, and relates to the technical field of artery detection.The method comprises the steps that an echocardiogram result and right heart catheter pulmonary artery pressure data of a suspected pulmonary artery patient are obtained; the echocardiogram result is a tricuspid regurgitation spectrum; grading the quality of the tricuspid regurgitation spectrum based on a TR spectrum quality grading standard according to the regurgitation spectrum missing degree of the tricuspid regurgitation spectrum; according to the echocardiogram result and the right heart duct pulmonary artery pressure data, on the basis of the quality grading result of the tricuspid regurgitation spectrum, a personalized pulmonary artery hypertension diagnosis model suitable for different patients is constructed; according to the pulmonary arterial hypertension diagnosis model, on the basis of the echocardiogram result of the target pulmonary arterial patient, a pulmonary arterial hypertension diagnosis result of the target pulmonary arterial patient is obtained, and personalized accurate diagnosis can be carried out on different pulmonary arterial hypertension patients under the noninvasive condition.

Description

Technical Field

[0001] The present application relates to the technical field of arterial detection, and particularly to a method and related device for non-invasive diagnosis of pulmonary hypertension. Background Art

[0002] Right heart catheterization is the gold standard for diagnosing pulmonary hypertension. However, due to its invasiveness, it is not suitable for screening and follow-up. Echocardiography can estimate the pulmonary artery systolic pressure by measuring tricuspid regurgitation, and its accuracy has been verified, showing good consistency with right heart catheterization. However, not every pulmonary hypertension patient has tricuspid regurgitation. Even when there is regurgitation, when the regurgitation volume is small, the regurgitation spectrum may be unclear and the measurement may be inaccurate; this phenomenon is particularly common in early pulmonary hypertension patients. Therefore, early pulmonary hypertension patients are easily misdiagnosed. Summary of the Invention

[0003] The purpose of the present application is to provide a method and related device for non-invasive diagnosis of pulmonary hypertension, which can accurately diagnose early pulmonary hypertension patients without trauma.

[0004] To achieve the above purpose, the present application provides the following solutions:

[0005] In the first aspect, the present application provides a method for non-invasive diagnosis of pulmonary hypertension, including:

[0006] Obtaining the echocardiogram results and right heart catheter pulmonary artery pressure data of suspected pulmonary artery patients who have undergone echocardiography and right heart catheterization during a set time period and whose ultrasound images are clear; the echocardiogram includes cardiac structure parameters, right ventricular systolic function parameters, and right ventricular hemodynamic parameters; the echocardiogram result is the tricuspid regurgitation spectrum.

[0007] According to the degree of absence of the regurgitation spectrum of the tricuspid regurgitation spectrum, based on the TR spectrum quality grading standard, grading the quality of the tricuspid regurgitation spectrum; the grading results include the level where the peak cannot be recognized and the level where the peak can be recognized;

[0008] According to the echocardiogram results and the right heart catheter pulmonary artery pressure data, based on the grading results of the quality of the tricuspid regurgitation spectrum, constructing a pulmonary hypertension diagnosis model; the pulmonary hypertension diagnosis model is used to diagnose pulmonary hypertension for suspected pulmonary artery patients whose tricuspid regurgitation spectrum quality is at the level where the peak cannot be recognized based on the first calculation formula; the pulmonary hypertension diagnosis model is used to diagnose pulmonary hypertension for suspected pulmonary artery patients whose tricuspid regurgitation spectrum quality is at the level where the peak can be recognized based on the second calculation formula;

[0009] According to the pulmonary hypertension diagnosis model, based on the echocardiogram results of the target pulmonary artery patient, the pulmonary hypertension diagnosis result of the target pulmonary artery patient is obtained.

[0010] Optionally, the first calculation formula is:

[0011] A1 = -3.57 + 1.646 × Notch - 0.036 × TVE + 0.475 × RVESA + 0.251 × LAEDA;

[0012] Wherein, Notch is the mid-systolic spectral notch of the right ventricular outflow tract, TVE is the E wave velocity of the tricuspid valve, RVESA is the end-systolic area of the right ventricle, and LAEDA is the end-diastolic area of the left atrium. When A1 > 1.44, it is diagnosed that the patient has pulmonary hypertension; when A1 ≤ 1.44, it is diagnosed that the patient does not have pulmonary hypertension.

[0013] Optionally, the second calculation formula is:

[0014] A2 = -5.038 - 0.064 × TVE + 2.17 × TRV + 0.352 × RVESA;

[0015] Wherein, TVE is the E wave velocity of the tricuspid valve, TRV is the peak velocity of tricuspid regurgitation, and RVESA is the end-systolic area of the right ventricle. When A2 > 1.70, it is diagnosed that the patient has pulmonary hypertension; when A2 ≤ 1.70, it is diagnosed that the patient does not have pulmonary hypertension.

[0016] Optionally, according to the degree of absence of the regurgitation spectrum of the tricuspid regurgitation spectrum, based on the TR spectral quality grading standard, the quality of the tricuspid regurgitation spectrum is graded, specifically including:

[0017] When the degree of absence of the regurgitation spectrum of the tricuspid regurgitation spectrum is complete absence, the tricuspid regurgitation spectrum is set to grade 0;

[0018] When the degree of absence of the regurgitation spectrum of the tricuspid regurgitation spectrum is greater than or equal to 2 / 3 of the complete regurgitation spectrum, the tricuspid regurgitation spectrum is set to grade 1;

[0019] When the degree of absence of the regurgitation spectrum of the tricuspid regurgitation spectrum is greater than or equal to 1 / 3 of the complete regurgitation spectrum and less than 2 / 3 of the complete regurgitation spectrum, the tricuspid regurgitation spectrum is set to grade 2;

[0020] When the degree of absence of the regurgitation spectrum of the tricuspid regurgitation spectrum is less than 1 / 3 of the complete regurgitation spectrum, the tricuspid regurgitation spectrum is set to grade 3;

[0021] When the regurgitation spectrum of the tricuspid regurgitation spectrum is a complete regurgitation spectrum, the tricuspid regurgitation spectrum is set to grade 4.

[0022] Optionally, the levels where the peak cannot be recognized include level 0, level 1, and level 2.

[0023] Optionally, the levels where the peak can be recognized include level 3 and level 4.

[0024] In a second aspect, the present application provides a device for non-invasive diagnosis of pulmonary hypertension, including:

[0025] A data acquisition module, configured to acquire echocardiogram results and right heart catheter pulmonary artery pressure data of suspected pulmonary artery patients who have undergone echocardiogram and right heart catheter examination within a set time period and whose ultrasound images are clear; the echocardiogram includes cardiac structure parameters, right ventricular systolic function parameters, and right ventricular hemodynamic parameters; the echocardiogram result is the tricuspid regurgitation spectrum;

[0026] A grading module, configured to grade the quality of the tricuspid regurgitation spectrum according to the degree of absence of the regurgitation spectrum of the tricuspid regurgitation spectrum based on the TR spectrum quality grading standard; the grading results include levels where the peak cannot be recognized and levels where the peak can be recognized;

[0027] A model construction module, configured to construct a pulmonary hypertension diagnosis model according to the echocardiogram results and right heart catheter pulmonary artery pressure data based on the grading results of the quality of the tricuspid regurgitation spectrum; the pulmonary hypertension diagnosis model is used to diagnose pulmonary hypertension for suspected pulmonary artery patients whose tricuspid regurgitation spectrum quality is at a level where the peak cannot be recognized based on a first calculation formula; the pulmonary hypertension diagnosis model is used to diagnose pulmonary hypertension for suspected pulmonary artery patients whose tricuspid regurgitation spectrum quality is at a level where the peak can be recognized based on a second calculation formula;

[0028] A diagnosis module, configured to obtain a pulmonary hypertension diagnosis result of the target pulmonary artery patient according to the pulmonary hypertension diagnosis model based on the echocardiogram results of the target pulmonary artery patient.

[0029] In a third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement a method for non-invasive diagnosis of pulmonary hypertension according to any one of the above.

[0030] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements a method for non-invasive diagnosis of pulmonary hypertension according to any one of the above.

[0031] Fifth aspect, the present application provides a computer program product, including a computer program, which when executed by a processor implements a method for non-invasive diagnosis of pulmonary hypertension described in any one of the above.

[0032] According to the specific embodiments provided by the present application, the following technical effects are disclosed by the present application:

[0033] The present application provides a method and related device for non-invasive diagnosis of pulmonary hypertension. In this method, first, echocardiogram results and right heart catheter pulmonary artery pressure data of suspected pulmonary hypertension patients who have undergone echocardiogram and right heart catheter examination within a set time period and have clear ultrasonic images are obtained. These data are the basis for constructing a diagnostic model and can provide detailed information on the patient's heart structure and function, as well as a direct measurement value of pulmonary artery pressure. Then, cardiac structure parameters, right ventricular systolic function parameters, and right ventricular hemodynamic parameters are extracted from the echocardiogram, and a tricuspid regurgitation spectrum is obtained. The tricuspid regurgitation spectrum is important information for evaluating pulmonary artery pressure. Then, according to the degree of absence of the regurgitation spectrum of the tricuspid regurgitation spectrum, based on the TR spectrum quality grading standard, the quality of the tricuspid regurgitation spectrum is graded. The grading results include grades where the peak cannot be recognized and grades where the peak can be recognized, which helps to select appropriate calculation formulas for pulmonary hypertension diagnosis in the follow-up. Next, based on the echocardiogram, echocardiogram results, right heart catheter pulmonary artery pressure data, and the quality grading results of the tricuspid regurgitation spectrum, a pulmonary hypertension diagnostic model is constructed. This model can comprehensively consider various factors and improve the accuracy of diagnosis. For suspected pulmonary hypertension patients with a tricuspid regurgitation spectrum quality grade where the peak cannot be recognized, the first calculation formula is used for pulmonary hypertension diagnosis; for suspected pulmonary hypertension patients with a tricuspid regurgitation spectrum quality grade where the peak can be recognized, the second calculation formula is used for pulmonary hypertension diagnosis. Finally, according to the pulmonary hypertension diagnostic model, based on the echocardiogram results and right heart catheter pulmonary artery pressure data of the target pulmonary hypertension patient, an accurate pulmonary hypertension diagnosis result of the target pulmonary hypertension patient can be obtained. Description of the Drawings

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0035] Figure 1 It is an application environment diagram of a method for non-invasive diagnosis of pulmonary hypertension in an embodiment of the present application;

[0036] Figure 2Schematic flowchart of a method for non-invasive diagnosis of pulmonary hypertension provided by an embodiment of the present application;

[0037] Figure 3 Schematic diagram of the TR spectrum quality grading standard provided by an embodiment of the present application.

[0038] Figure 4 Schematic diagram of the functional modules of a device for non-invasive diagnosis of pulmonary hypertension provided by an embodiment of the present application;

[0039] Figure 5 Schematic diagram of the structure of a computer device provided by an embodiment of the present application. Detailed implementation manners

[0040] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0041] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below with reference to the accompanying drawings and specific implementation manners.

[0042] The method for non-invasive diagnosis of pulmonary hypertension provided by the embodiments of the present application can be applied to, for example Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set separately, integrated on the server 104, placed on the cloud or other servers. The terminal 102 can send the echocardiogram results to be processed and the right heart catheter pulmonary artery pressure data to the server 104. After receiving the echocardiogram results to be processed and the right heart catheter pulmonary artery pressure data, for the echocardiogram results to be processed and the right heart catheter pulmonary artery pressure data, the server 104 grades the quality of the tricuspid regurgitation spectrum according to the degree of absence of the regurgitation spectrum of the tricuspid regurgitation spectrum, based on the TR spectrum quality grading standard; the results of the grading include the grade where the peak cannot be recognized and the grade where the peak can be recognized; according to the echocardiogram results and the right heart catheter pulmonary artery pressure data, based on the grading results of the quality of the tricuspid regurgitation spectrum, a pulmonary hypertension diagnosis model is constructed; the pulmonary hypertension diagnosis model is used to diagnose pulmonary hypertension for patients with suspected pulmonary hypertension whose tricuspid regurgitation spectrum quality is at the grade where the peak cannot be recognized based on the first calculation formula; the pulmonary hypertension diagnosis model is used to diagnose pulmonary hypertension for patients with suspected pulmonary hypertension whose tricuspid regurgitation spectrum quality is at the grade where the peak can be recognized based on the second calculation formula; according to the pulmonary hypertension diagnosis model, based on the echocardiogram results of the target pulmonary hypertension patient, the pulmonary hypertension diagnosis result of the target pulmonary hypertension patient is obtained. The server 104 can feedback the obtained pulmonary hypertension diagnosis result of the target pulmonary hypertension patient to the terminal 102. In addition, in some embodiments, the method for non-invasive diagnosis of pulmonary hypertension can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly process the data of the echocardiogram results to be processed and the right heart catheter pulmonary artery pressure data, or the server 104 can obtain the echocardiogram results to be processed and the right heart catheter pulmonary artery pressure data from the data storage system and process the data of the echocardiogram results to be processed and the right heart catheter pulmonary artery pressure data.

[0043] Among them, the terminal 102 can be, but is not limited to, various desktop computers, laptop computers, smartphones, tablets, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0044] In an exemplary embodiment, such as Figure 2As shown, a method for non-invasive diagnosis of pulmonary hypertension is provided. This method is executed by a computer device, which can be specifically executed by a computer device such as a terminal or a server alone, or jointly executed by a terminal and a server. In the embodiments of the present application, taking the application of this method to Figure 1 server 104 in

[0045] Step 201: Obtain the echocardiogram results and right heart catheter pulmonary artery pressure data of suspected pulmonary artery patients who have undergone echocardiogram and right heart catheter examination within a set time period and have clear ultrasound images. The echocardiogram includes cardiac structure parameters, right ventricular systolic function parameters, and right ventricular hemodynamic parameters. The echocardiogram result is the tricuspid regurgitation spectrum.

[0046] Step 202: According to the degree of absence of the regurgitation spectrum of the tricuspid regurgitation spectrum, based on the TR spectrum quality grading standard, grade the quality of the tricuspid regurgitation spectrum. The grading results include the grade with the peak unidentifiable and the grade with the peak identifiable.

[0047] Step 203: According to the echocardiogram results and right heart catheter pulmonary artery pressure data, based on the grading results of the quality of the tricuspid regurgitation spectrum, construct a pulmonary hypertension diagnosis model. The pulmonary hypertension diagnosis model is used to diagnose pulmonary hypertension for suspected pulmonary artery patients with the grade of the quality of the tricuspid regurgitation spectrum being the grade with the peak unidentifiable based on the first calculation formula. The pulmonary hypertension diagnosis model is used to diagnose pulmonary hypertension for suspected pulmonary artery patients with the grade of the quality of the tricuspid regurgitation spectrum being the grade with the peak identifiable based on the second calculation formula.

[0048] Step 204: According to the pulmonary hypertension diagnosis model, based on the echocardiogram results of the target pulmonary artery patient, obtain the pulmonary hypertension diagnosis result of the target pulmonary artery patient.

[0049] In an exemplary embodiment, when executing step 201, it can be specifically as follows:

[0050] In this embodiment, suspected pulmonary artery patients who have undergone echocardiogram and right heart catheter examination within 7 days and have clear ultrasound images are included, and the parameters of the three aspects of cardiac structure, right ventricular systolic function, and right ventricular hemodynamics and the right heart catheter pulmonary artery pressure data are collected.

[0051] In an exemplary embodiment, when executing step 202, it can be specifically as follows:

[0052] As Figure 3 shown, according to the degree of absence of the regurgitation spectrum of the tricuspid regurgitation spectrum, based on the TR spectrum quality grading standard, grade the quality of the tricuspid regurgitation spectrum, specifically including:

[0053] When the degree of absence of the regurgitation spectrum of the tricuspid regurgitation spectrum is complete absence, set the tricuspid regurgitation spectrum to grade 0;

[0054] When the degree of absence of the regurgitation spectrum of the tricuspid regurgitation spectrum is greater than or equal to 2 / 3 of the complete regurgitation spectrum, set the tricuspid regurgitation spectrum to grade 1 (such as Figure 3 A in);

[0055] When the degree of absence of the regurgitation spectrum of the tricuspid regurgitation spectrum is greater than or equal to 1 / 3 of the complete regurgitation spectrum and less than 2 / 3 of the complete regurgitation spectrum, set the tricuspid regurgitation spectrum to grade 2 (such as Figure 3 B in);

[0056] When the degree of absence of the regurgitation spectrum of the tricuspid regurgitation spectrum is less than 1 / 3 of the complete regurgitation spectrum, set the tricuspid regurgitation spectrum to grade 3 (such as Figure 3 C in);

[0057] When the regurgitation spectrum of the tricuspid regurgitation spectrum is the complete regurgitation spectrum, set the tricuspid regurgitation spectrum to grade 4 (such as Figure 3 D in).

[0058] Among them, the grades with unidentifiable peaks include grade 0, grade 1 and grade 2. The grades with identifiable peaks include grade 3 and grade 4.

[0059] In an exemplary embodiment, when performing step 203,

[0060] The first calculation formula in the pulmonary hypertension diagnosis model is:

[0061] A1 = -3.57 + 1.646×Notch - 0.036×TVE + 0.475×RVESA + 0.251×LAEDA;

[0062] Among them, Notch is the mid-systolic spectral notch of the right ventricular outflow tract, TVE is the E peak of the tricuspid valve velocity, RVESA is the right ventricular end-systolic area, and LAEDA is the left atrial end-diastolic area. When A1 > 1.44, it is diagnosed that the patient has pulmonary hypertension; when A1 ≤ 1.44, it is diagnosed that the patient does not have pulmonary hypertension.

[0063] The second calculation formula is:

[0064] A2 = -5.038 - 0.064×TVE + 2.17×TRV + 0.352×RVESA;

[0065] Among them, TVE is the E peak of tricuspid valve velocity, TRV is the peak value of tricuspid regurgitation velocity, and RVESA is the right ventricular end-systolic area. When A2 > 1.70, it is diagnosed that the patient has pulmonary hypertension; when A2 ≤ 1.70, it is diagnosed that the patient does not have pulmonary hypertension.

[0066] Specifically, for patients with no or little tricuspid regurgitation, the spectral quality is grade 0-2, and a first calculation formula is constructed.

[0067] In this embodiment, ROC curve analysis is performed on the output result of the first calculation formula. On the training set, the AUC value reaches 0.903 (95% confidence interval is 0.862 to 0.944, and the P value is 0.524). When the score exceeds 1.44, it is considered the best cut-off value of the scoring model. The specificity of this model is 83%, the sensitivity is 83%, the positive predictive value is 97%, the negative predictive value is 46%, and the accuracy is 83%. The AUC values of the internal validation set and the external validation set are 0.814 and 0.868 respectively. (Specificity = true negative / (true negative + false positive); Sensitivity = true positive / (true positive + false negative); Positive predictive value = true positive / (true positive + false positive); Negative predictive value = true negative / (true negative + false negative); Accuracy = (true positive + true negative) / total number of predictions).

[0068] Specifically, for patients with clear tricuspid regurgitation spectrum (spectral quality is grade 3-4), a second calculation formula is constructed.

[0069] A2 = -5.038 - 0.064 × TVE + 2.17 × TRV + 0.352 × RVESA.

[0070] In this embodiment, ROC curve analysis is performed on the output result of the second calculation formula. The result is that the AUC of the training set is equal to 0.925 (95% IC 0.892 - 0.958, P = 0.532). A score greater than 1.70 is the best cut-off value of the scoring model. Its specificity is 91%, the sensitivity is 81%, the positive predictive value is 98%, the negative predictive value is 46%, and the accuracy is 82%. The AUC of the internal validation set is equal to 0.883. The AUC of the external data validation is equal to 0.917. (Specificity = true negative / (true negative + false positive); Sensitivity = true positive / (true positive + false negative); Positive predictive value = true positive / (true positive + false positive); Negative predictive value = true negative / (true negative + false negative); Accuracy = (true positive + true negative) / total number of predictions).

[0071] Based on the same inventive concept, an embodiment of the present application further provides a device for implementing the method for non-invasive diagnosis of pulmonary hypertension involved above. The solution provided by the device for solving the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more of the following device embodiments can refer to the limitations on the method for non-invasive diagnosis of pulmonary hypertension in the above text, and will not be repeated here.

[0072] In an exemplary embodiment, as Figure 4 shown, a device for non-invasive diagnosis of pulmonary hypertension is provided, including:

[0073] A data acquisition module 401, configured to acquire the echocardiogram results and right heart catheter pulmonary artery pressure data of a suspected pulmonary artery patient who has undergone echocardiography and right heart catheterization during a set time period and whose ultrasound images are clear; the echocardiogram includes cardiac structure parameters, right ventricular systolic function parameters, and right ventricular hemodynamic parameters; the echocardiogram result is the tricuspid regurgitation spectrum;

[0074] A grading module 402, configured to grade the quality of the tricuspid regurgitation spectrum according to the degree of absence of the regurgitation spectrum of the tricuspid regurgitation spectrum, based on the TR spectrum quality grading standard; the grading results include the level where the peak cannot be recognized and the level where the peak can be recognized;

[0075] A model construction module 403, configured to construct a pulmonary hypertension diagnosis model according to the echocardiogram results and right heart catheter pulmonary artery pressure data, based on the grading results of the quality of the tricuspid regurgitation spectrum; the pulmonary hypertension diagnosis model is used to diagnose pulmonary hypertension for a suspected pulmonary artery patient whose tricuspid regurgitation spectrum quality is at the level where the peak cannot be recognized, based on a first calculation formula; the pulmonary hypertension diagnosis model is used to diagnose pulmonary hypertension for a suspected pulmonary artery patient whose tricuspid regurgitation spectrum quality is at the level where the peak can be recognized, based on a second calculation formula;

[0076] A diagnosis module 404, configured to obtain the pulmonary hypertension diagnosis result of the target pulmonary artery patient according to the pulmonary hypertension diagnosis model, based on the echocardiogram results of the target pulmonary artery patient.

[0077] In an exemplary embodiment, a computer device is provided. The computer device can be a server or a terminal, and its internal structure diagram can be as Figure 5As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store diagnostic results. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. The computer program, when executed by the processor, implements a method for non-invasive diagnosis of pulmonary hypertension.

[0078] Those skilled in the art can understand that Figure 5 the structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0079] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0080] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0081] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0082] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0083] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random-access memories (ReRAM), magnetoresistive random-access memories (MRAM), ferroelectric random-access memories (FRAM), phase change memories (PCM), graphene memories, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0084] The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.

[0085] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0086] Specific examples are used in this article to elaborate on the principles and implementation manners of the present application. The descriptions of the above embodiments are only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A non-invasive method for diagnosing pulmonary hypertension, characterized in that, The method includes: Obtaining the echocardiogram results and right heart catheter pulmonary artery pressure data of suspected pulmonary artery patients who have undergone echocardiogram and right heart catheter examination within a set time period and have clear ultrasound images; the echocardiogram includes cardiac structure parameters, right ventricular systolic function parameters, and right ventricular hemodynamic parameters; the echocardiogram result is the tricuspid regurgitation spectrum. According to the degree of absence of the regurgitation spectrum of the tricuspid regurgitation spectrum, based on the TR spectrum quality grading standard, grade the quality of the tricuspid regurgitation spectrum; the grading results include a grade with an unidentifiable peak and a grade with an identifiable peak. According to the echocardiogram results and right heart catheter pulmonary artery pressure data, based on the grading results of the quality of the tricuspid regurgitation spectrum, construct a pulmonary hypertension diagnosis model; the pulmonary hypertension diagnosis model is used to diagnose pulmonary hypertension in suspected pulmonary artery patients with an unidentifiable peak quality of the tricuspid regurgitation spectrum based on a first calculation formula; the pulmonary hypertension diagnosis model is used to diagnose pulmonary hypertension in suspected pulmonary artery patients with an identifiable peak quality of the tricuspid regurgitation spectrum based on a second calculation formula. According to the pulmonary hypertension diagnosis model, based on the echocardiogram results of the target pulmonary artery patient, obtain the pulmonary hypertension diagnosis result of the target pulmonary artery patient.

2. The non-invasive method for diagnosing pulmonary hypertension according to claim 1, characterized in that, The first calculation formula is: A1 = -3.57 + 1.646×Notch - 0.036×TVE + 0.475×RVESA + 0.251×LAEDA; where Notch is the mid-systolic spectral notch of the right ventricular outflow tract, TVE is the E peak of the tricuspid valve velocity, RVESA is the right ventricular end-systolic area, and LAEDA is the left atrial end-diastolic area. When A1 > 1.44, it is diagnosed that the patient has pulmonary hypertension, and when A1 ≤ 1.44, it is diagnosed that the patient does not have pulmonary hypertension.

3. The non-invasive method for diagnosing pulmonary hypertension according to claim 2, wherein The second calculation formula is: A2 = -5.038 - 0.064×TVE + 2.17×TRV + 0.352×RVESA; where TVE is the E peak of the tricuspid valve velocity, TRV is the peak value of the tricuspid regurgitation velocity, and RVESA is the right ventricular end-systolic area. When A2 > 1.70, it is diagnosed that the patient has pulmonary hypertension, and when A2 ≤ 1.70, it is diagnosed that the patient does not have pulmonary hypertension.

4. The non-invasive method for diagnosing pulmonary hypertension according to claim 3, characterized in that, According to the degree of absence of the regurgitation spectrum of the tricuspid regurgitation spectrum, based on the TR spectrum quality grading standard, grade the quality of the tricuspid regurgitation spectrum, specifically including: When the degree of absence of the regurgitation spectrum of the tricuspid regurgitation spectrum is complete absence, set the tricuspid regurgitation spectrum to grade 0. When the degree of absence of the regurgitation spectrum of the tricuspid regurgitation spectrum is greater than or equal to 2 / 3 of the complete regurgitation spectrum, set the tricuspid regurgitation spectrum to grade 1. When the degree of absence of the regurgitation spectrum of the tricuspid regurgitation spectrum is greater than or equal to 1 / 3 of the complete regurgitation spectrum and less than 2 / 3 of the complete regurgitation spectrum, set the tricuspid regurgitation spectrum to grade 2. When the degree of absence of the regurgitation spectrum of the tricuspid regurgitation spectrum is less than 1 / 3 of the complete regurgitation spectrum, set the tricuspid regurgitation spectrum to grade 3. When the regurgitation spectrum of the tricuspid regurgitation spectrum is a complete regurgitation spectrum, the tricuspid regurgitation spectrum is set to grade 4.

5. A non-invasive method for diagnosing pulmonary hypertension according to claim 4, characterized in that, The grades with unidentifiable peak include grade 0, grade 1, and grade 2.

6. A non-invasive method for diagnosing pulmonary hypertension according to claim 5, characterized in that, The grades with identifiable peak include grade 3 and grade 4.

7. A non-invasive device for diagnosing pulmonary hypertension, characterized in that, The device includes: A data acquisition module, configured to acquire the echocardiogram results and right heart catheter pulmonary artery pressure data of suspected pulmonary artery patients who have undergone echocardiogram and right heart catheter examination within a set time period and whose ultrasound images are clear; the echocardiogram includes cardiac structure parameters, right ventricular systolic function parameters, and right ventricular hemodynamic parameters; the echocardiogram result is the tricuspid regurgitation spectrum. A grading module, configured to grade the quality of the tricuspid regurgitation spectrum based on the degree of absence of the regurgitation spectrum of the tricuspid regurgitation spectrum and the TR spectrum quality grading standard; the grading results include grades with unidentifiable peak and grades with identifiable peak. A model construction module, configured to construct a pulmonary hypertension diagnosis model based on the echocardiogram results and right heart catheter pulmonary artery pressure data and the grading results of the quality of the tricuspid regurgitation spectrum; the pulmonary hypertension diagnosis model is used to diagnose pulmonary hypertension for suspected pulmonary artery patients with the quality of the tricuspid regurgitation spectrum being grades with unidentifiable peak based on a first calculation formula; the pulmonary hypertension diagnosis model is used to diagnose pulmonary hypertension for suspected pulmonary artery patients with the quality of the tricuspid regurgitation spectrum being grades with identifiable peak based on a second calculation formula. A diagnosis module, configured to obtain the pulmonary hypertension diagnosis result of the target pulmonary artery patient based on the pulmonary hypertension diagnosis model and the echocardiogram results of the target pulmonary artery patient.

8. A computer device, comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement a non-invasive method for diagnosing pulmonary hypertension according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements a non-invasive method for diagnosing pulmonary hypertension according to any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements a non-invasive method for diagnosing pulmonary hypertension according to any one of claims 1-6.

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