A kit for predicting the course of progression of atrial fibrillation and recurrence after ablation

Abstract

The present application relates to the technical field of biological monitoring, in particular to a kit for predicting the progression of atrial fibrillation and the recurrence after ablation. The present application provides the use of biomarkers in the preparation of products for predicting the progression of atrial fibrillation or the recurrence after ablation, wherein the biomarkers include senescent CD8 + T cells. The present application discloses for the first time that senescent CD8 + T cells serve as biomarkers to predict the progression of atrial fibrillation or the recurrence risk after ablation. The present application develops a kit for predicting the progression of atrial fibrillation or the recurrence after ablation, which can quickly and accurately predict the high-risk patients of atrial fibrillation recurrence before surgery, and provide guidance for surgical selection and early clinical intervention. The present application only uses 1 mL of blood sample to predict the recurrence risk, which is convenient and less traumatic, and is easy for patients to accept.

Description

Technical Field

[0001] This application relates to the field of biomonitoring technology, and in particular to a kit for predicting the progression of atrial fibrillation and recurrence after ablation. Background Technology

[0002] With the increasing aging of the population and the rising incidence of other cardiovascular diseases, the incidence of atrial fibrillation (AF) and its related complications is showing a year-on-year upward trend. Because AF can cause hemodynamic changes and lead to cardiac structural remodeling, AF patients have a significantly higher risk of thrombosis, stroke, and sudden cardiac death than the general population. Compared with non-surgical methods such as drug-based heart rhythm control, reducing AF episodes, and preventing thrombosis, ablation surgery based on pulmonary vein isolation can significantly improve the prognosis and quality of life of AF patients. Despite significant advancements in techniques and instruments such as cryoablation and radiofrequency ablation, surgical treatment of AF still has a high recurrence rate, seriously affecting the long-term prognosis of patients.

[0003] Atrial fibrillation (AF) is one of the cardiovascular diseases most closely related to aging, and aging is also one of the most important risk factors for AF. Besides the significantly increased incidence of AF with age, the risk of related adverse events and disease progression in AF patients also increase significantly with age. Studies have shown that the aging of immune cells can promote the aging of other tissues and organs, while transplantation of young immune cells can delay individual aging. This suggests the important role of immunosenescence in individual aging and age-related diseases. Currently, CD4+ immune cells have been shown to undergo significant changes during aging. + T cells, CD8 + T cells and monocytes. In addition to common signs of cellular aging such as impaired proliferation, mitochondrial dysfunction, and telomere shortening, senescent T cells often exhibit changes in surface CD molecules, such as downregulation of the cell surface costimulatory molecule CD28 and upregulation of CD57. Previous studies have reported a potential relationship between senescent immune cells and some cardiovascular diseases: peripheral blood CD28... - CD4 + Increased T cell count is associated with increased risk of death from cardiovascular disease; high CD28 levels in peripheral blood - CD4 + T subsets can serve as independent predictors of disease progression in heart failure patients; senescent CD28 in unstable plaques - CD4 + A significant increase in T cell count is positively correlated with the risk of plaque rupture. However, research on the relationship between senescent immune cells and atrial fibrillation is relatively limited. Whether senescent immune cells are associated with atrial fibrillation recurrence, and whether changes in immune cells can be used to predict atrial fibrillation recurrence, remains unresolved. Summary of the Invention

[0004] In view of the shortcomings of the prior art described above, and in order to solve the technical problem that the relationship between senescent immune cells and atrial fibrillation recurrence is unknown in the prior art, the purpose of this application is to utilize senescent CD8 cells... + T cells serve as biomarkers to predict the progression of atrial fibrillation or the risk of postoperative recurrence.

[0005] To achieve the above and other related objectives, the first aspect of this application provides the use of biomarkers in the preparation of products for predicting the progression of atrial fibrillation or for predicting postoperative recurrence of atrial fibrillation, said biomarkers including aging CD8. + T cells.

[0006] A second aspect of this application provides a system for predicting the progression or postoperative recurrence of atrial fibrillation, the system comprising a data analysis module for analyzing test values ​​of biomarkers in a sample, the biomarkers including aging CD8+. + T cells.

[0007] A third aspect of this application provides a kit for predicting the progression or postoperative recurrence of atrial fibrillation, the kit comprising detection reagents for the biomarkers used in the foregoing purposes.

[0008] The fourth aspect of this application provides a method for predicting the progression or postoperative recurrence of atrial fibrillation for non-diagnostic and therapeutic purposes, comprising analyzing test values ​​of biomarkers in a sample, said biomarkers including aging CD8+. + T cells.

[0009] The fifth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the aforementioned prediction method.

[0010] A sixth aspect of this application provides a terminal including a processor and the aforementioned computer-readable storage medium, wherein the processor executes a computer program on the computer-readable storage medium to implement the steps of the aforementioned prediction method.

[0011] Compared with the prior art, the beneficial effects of this application are as follows:

[0012] 1. This application discloses for the first time the aging CD8 + T cells serve as biomarkers to predict disease progression or postoperative recurrence risk in patients with atrial fibrillation.

[0013] 2. This application develops a kit for predicting the progression of atrial fibrillation or postoperative recurrence. Using this kit, high-risk patients for atrial fibrillation recurrence can be quickly and accurately predicted before surgery, providing guidance for surgical selection and early clinical intervention.

[0014] 3. The kit of this application can predict the risk of recurrence using only 1 mL of blood sample. It is convenient, minimally invasive, and easily accepted by patients. Attached Figure Description

[0015] Figure 1 This is a flowchart of streaming data analysis.

[0016] Figure 2 For aging CD8 + T cells in all CD8 + The percentage of T cells.

[0017] Figure 3 Comparison of relapse-free survival among atrial fibrillation patients in different groups.

[0018] Figure 4 For aging-based CD8 + A T-cell model for predicting atrial fibrillation recurrence.

[0019] Figure 5 Evaluation of atrial fibrillation recurrence prediction models.

[0020] Figure 6 CD57 as detected by flow cytometry in Example 3 + CD8 + T cell subsets.

[0021] Figure 7 This is the result of external validation of the atrial fibrillation recurrence prediction model in Example 3. Detailed Implementation

[0022] To make the inventive objectives, technical solutions, and beneficial effects of this application clearer, the following description, in conjunction with embodiments, further illustrates this application. It should be understood that the embodiments described are for illustrative purposes only and are not intended to limit the scope of the application. Unless otherwise specified, the experimental methods used in the following embodiments are conventional methods, and those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this description.

[0023] The inventors of this application, through extensive research and exploration, discovered a reagent kit for predicting the progression of atrial fibrillation and recurrence after ablation, and completed this application based on this discovery.

[0024] The first aspect of this application provides the use of biomarkers in the preparation of products for predicting the progression of atrial fibrillation or for predicting postoperative recurrence of atrial fibrillation, the biomarkers including aging CD8. + T cells. This application discloses for the first time the aging CD8 cells. + T cells serve as biomarkers to predict disease progression or postoperative recurrence risk in patients with atrial fibrillation.

[0025] In the uses provided in this application, aging CD8 + T cells and normal CD8 + Compared to T cells, these cells exhibit cellular senescence characteristics and / or alterations in surface CD molecules. Cellular senescence characteristics include impaired proliferation, mitochondrial dysfunction, and telomere shortening. Alterations in surface CD molecules include downregulation of the cell surface costimulatory molecule CD28 and / or upregulation of CD57.

[0026] In the uses provided in this application, based on the aging CD8 in the sample + T cells in all CD8 + The percentage of T cells was used to assess the risk of postoperative recurrence. Samples were taken from blood, specifically peripheral blood and / or left atrial blood. When aging CD8... + T cells in all CD8 + When the proportion of T cells is greater than a threshold, it is predicted that atrial fibrillation is more likely to recur after surgery; preferably, the threshold is 50.4%.

[0027] A second aspect of this application provides a system for predicting the progression or postoperative recurrence of atrial fibrillation. The system includes a data analysis module for analyzing test values ​​of biomarkers in a sample, including aging CD8+. + T cells.

[0028] In the system provided in this application, the sample is selected from blood, and more specifically, from peripheral blood and / or left atrial blood. Biomarker test values ​​include aging CD8+ in the sample. + T cells in all CD8 + The percentage of T cells. The data analysis module uses senescent CD8 cells... + T cells in all CD8 + The percentage of T cells is used to assess the risk of recurrence after atrial fibrillation surgery. In a specific embodiment of this application, when senescent CD8 cells... + T cells in all CD8 + When the percentage of T cells is >50.4%, atrial fibrillation is more likely to recur after surgery.

[0029] In the system provided in this application, the data analyzed by the data analysis module also includes one or more combinations of age, gender, body mass index, smoking, alcohol consumption, diabetes, coronary heart disease, heart failure, stroke or transient ischemic attack, atrial fibrillation type, and left atrial diameter.

[0030] In the system provided in this application, the data analysis module calculates the 10-month recurrence risk of the test subject in the following way: obtaining the individual scores of each data item and converting them into the 10-month recurrence risk of the test subject.

[0031] In the system provided in this application, the data analysis module obtains a calculation formula through Cox regression modeling. The calculation formula is as follows:

[0032]

[0033] Where h0(t) is the baseline risk function, Age represents the age index, which is the age of the subject in years;

[0034] Male represents the gender index. When the subject is female, the gender index is 0, and when the subject is male, the gender index is 1.

[0035] BMI stands for Body Mass Index. BMI = weight (kg) / height (m) 2 ;

[0036] Smoke represents the smoking index; when the subject has no history of smoking, Smoke is 0, and when the subject has a history of smoking, Smoke is 1.

[0037] Drink represents the alcohol consumption index. When the subject has no history of drinking, Drink is 0, and when the subject has a history of drinking, Drink is 1.

[0038] DM represents the diabetes index. When the subject has no history of diabetes, DM is 0, and when the subject has a history of diabetes, DM is 1.

[0039] HBp represents the hypertension index. When the subject has no history of hypertension, HBp is 0, and when the subject has a history of hypertension, HBp is 1.

[0040] CHD stands for Coronary Heart Disease Index. When the subject has no history of coronary heart disease, CHD is 0; when the subject has a history of coronary heart disease, CHD is 1.

[0041] HF represents the heart failure index. When the subject has no history of heart failure, HF is 0, and when the subject has a history of heart failure, HF is 1.

[0042] Stroke (TIA) represents the stroke and transient ischemic attack index. When the subject has no history of stroke or transient ischemic attack, Stroke (TIA) is 0, and when the subject has a history of stroke or transient ischemic attack, Stroke (TIA) is 1.

[0043] AF type represents the atrial fibrillation type index. When the subject has paroxysmal atrial fibrillation, AF type is 1, and when the subject has persistent atrial fibrillation, AF type is 2.

[0044] LAD stands for Left Atrial Diameter Index, which is the left atrial diameter of the object being measured, and its unit is mm.

[0045] Senescent CD8+T > 50.4% indicates senescent CD8+T. + T cell percentage index, when the CD8+ of the test subject is senescent. + T cells in all CD8 + When the percentage of T cells is >50.4%, Senescent CD8+ T >50.4% is defined as 1. When the percentage of CD8+ T cells in the tested subject is senescent, the percentage of CD8+ T cells is 1. + T cells in all CD8 + When the percentage of T cells is ≤50.4%, the percentage of Senescent CD8+ T cells >50.4% is 0.

[0046] The data analysis module calculates h(t) by substituting each individual data point into the formula, representing the relapse risk of the subject. Specific information about the data analysis module is as follows: Figure 4 As shown, the formula allows for a detailed assessment of various biological indicators of the subjects and provides an accurate risk of recurrence. Visualizing the formula using the nomogram function of the "rms" R package yields a nomogram, which clearly shows the 10-month recurrence-free rate. This allows for rapid and accurate prediction of high-risk patients for atrial fibrillation recurrence before surgery, providing guidance for surgical selection and early clinical intervention.

[0047] A third aspect of this application provides a kit for predicting the progression or recurrence of atrial fibrillation after surgery, the kit including detection reagents for the biomarkers used in the aforementioned purposes.

[0048] The kit provided in this application also includes lymphocyte separation reagents and / or flow cytometry staining reagents. In a specific embodiment of this application, the lymphocyte separation reagents include conventional reagents such as EDTA-containing anticoagulant tubes, human lymphocyte separation tubes, Duchenne phosphate-buffered saline (DPBS), and RPMI 1640 medium. The flow cytometry staining reagents include conventional reagents such as flow cytometry tubes, flow cytometry staining buffer, cell viability dyes, and Fc receptor blockers.

[0049] The kit provided in this application includes antibodies as detection reagents. Specifically, the antibodies include anti-CD3 antibodies, anti-CD8 antibodies, and anti-CD57 antibodies. CD3 cells are a common surface marker for T cells, while CD57 cells are a marker for recognizing senescent T cells. The anti-CD3, anti-CD8, and anti-CD57 antibodies are all fluorescent anti-human monoclonal flow cytometry antibodies. The fluorescent dyes used to label the anti-human monoclonal flow cytometry antibodies include conventional dyes such as FITC, APC, and Cy5.5.

[0050] The kit provided in this application also includes an atrial fibrillation recurrence prediction tool card. The tool card includes individual scoring criteria for each data point and their conversion relationship to the 10-month recurrence risk. Biomarkers include aging-related CD8+. + T cells in all CD8 + The percentage of T cells. In some implementations, the data also includes one or more combinations of age, sex, body mass index, smoking, alcohol consumption, diabetes, coronary artery disease, heart failure, stroke or transient ischemic attack, atrial fibrillation type, and left atrial diameter. For specific conversion relationships, see [link to relevant documentation]. Figure 4 ,according to Figure 4 The tool assigns individual scores to each indicator, which are then summed to obtain a total score. Based on the correlation between the total score and the 10-month recurrence-free rate in the tool card, the 10-month recurrence-free rate for the tested individual is obtained. The scale markings on the line segment represent the predicted value of the 10-month recurrence-free rate. A higher 10-month recurrence-free rate indicates a greater likelihood that the patient will not experience atrial fibrillation recurrence, making them more clinically suitable for assessing surgical suitability. This allows for rapid and accurate preoperative prediction of high-risk patients for atrial fibrillation recurrence, providing guidance for surgical selection and early clinical intervention.

[0051] In one specific embodiment of this application, the kit includes the following substances:

[0052]

[0053]

[0054] The fourth aspect of this application provides a method for predicting the progression or postoperative recurrence of atrial fibrillation for non-diagnostic and therapeutic purposes, including analyzing test values ​​of biomarkers in a sample, the biomarkers including aging CD8. + T cells. For non-diagnostic or therapeutic purposes, such as statistical analysis.

[0055] In the prediction method provided in this application, the sample is selected from blood, and more specifically, from peripheral blood and / or left atrial blood. The biomarker test values ​​include aging CD8+ in the sample. + T cells in all CD8 + The percentage of T cells. Analysis of sample biomarker test values ​​included: CD8+ via aging. + T cells in all CD8 + The percentage of T cells is used to assess the risk of recurrence after atrial fibrillation surgery. Furthermore, when aging CD8 cells... + T cells in all CD8 + When the percentage of T cells is >50.4%, atrial fibrillation is more likely to recur after surgery.

[0056] The prediction method provided in this application also includes the following data analyzed: age, sex, body mass index, smoking, alcohol consumption, diabetes, coronary heart disease, heart failure, stroke or transient ischemic attack, atrial fibrillation type, and left atrial diameter, or a combination of one or more of these factors.

[0057] In the prediction method provided in this application, the analysis of the test values ​​of the sample includes calculating the 10-month recurrence risk of the test subject based on the test values ​​of each data point. In a specific embodiment of this application, the 10-month recurrence risk is calculated in the following way: obtaining the individual scores of each data point, and converting them into the 10-month recurrence risk of the test subject.

[0058] In the prediction method provided in this application, the analysis of the test values ​​of the sample includes obtaining a calculation formula through Cox regression modeling. The calculation formula is as follows:

[0059]

[0060] Where h0(t) is the baseline risk function, Age represents the age index, which is the age of the subject in years;

[0061] Male represents the gender index. When the subject is female, the gender index is 0, and when the subject is male, the gender index is 1.

[0062] BMI stands for Body Mass Index. BMI = weight (kg) / height (m) 2 ;

[0063] Smoke represents the smoking index; when the subject has no history of smoking, Smoke is 0, and when the subject has a history of smoking, Smoke is 1.

[0064] Drink represents the alcohol consumption index. When the subject has no history of drinking, Drink is 0, and when the subject has a history of drinking, Drink is 1.

[0065] DM represents the diabetes index. When the subject has no history of diabetes, DM is 0, and when the subject has a history of diabetes, DM is 1.

[0066] HBp represents the hypertension index. When the subject has no history of hypertension, HBp is 0, and when the subject has a history of hypertension, HBp is 1.

[0067] CHD stands for Coronary Heart Disease Index. When the subject has no history of coronary heart disease, CHD is 0; when the subject has a history of coronary heart disease, CHD is 1.

[0068] HF represents the heart failure index. When the subject has no history of heart failure, HF is 0, and when the subject has a history of heart failure, HF is 1.

[0069] Stroke (TIA) represents the stroke and transient ischemic attack index. When the subject has no history of stroke or transient ischemic attack, Stroke (TIA) is 0, and when the subject has a history of stroke or transient ischemic attack, Stroke (TIA) is 1.

[0070] AF type represents the atrial fibrillation type index. When the subject has paroxysmal atrial fibrillation, AF type is 1, and when the subject has persistent atrial fibrillation, AF type is 2.

[0071] LAD stands for Left Atrial Diameter Index, which is the left atrial diameter of the object being measured, and its unit is mm.

[0072] Senescent CD8+T > 50.4% indicates senescent CD8+T. + T cell percentage index, when the CD8+ of the test subject is senescent. + T cells in all CD8 + When the percentage of T cells is >50.4%, Senescent CD8+ T >50.4% is defined as 1. When the percentage of CD8+ T cells in the tested subject is senescent, the percentage of CD8+ T cells is 1. + T cells in all CD8 + When the percentage of T cells is ≤50.4%, the percentage of Senescent CD8+ T cells >50.4% is 0.

[0073] The analysis of the sample data includes obtaining h(t) by substituting each individual data point into the calculation formula, which represents the recurrence risk of the subject.

[0074] In the prediction method provided in this application, aging CD8 + T-cell levels are obtained by taking fresh, anticoagulated whole blood from the patient, separating lymphocytes, incubating them with anti-CD3, anti-CD8, and anti-CD57 antibodies, and then detecting them using flow cytometry.

[0075] In a specific embodiment of this application, the prediction method specifically includes the following steps:

[0076] 1. Isolation of lymphocytes

[0077] Fresh anticoagulated whole blood was collected using EDTA-containing anticoagulated blood collection tubes. Lymphocytes were extracted using human lymphocyte separation tubes. 1 mL of whole blood was added to each tube and centrifuged (20°C, 800g, 30 min). Lymphocytes were aspirated from the plasma layer and washed 1-2 times with RPMI 1640 medium (20°C, 350g, 10 min). The lymphocytes were resuspended in DPBS for later use. This application utilizes only 1 mL of blood sample to predict recurrence risk, is convenient and minimally invasive, and is easily accepted by patients.

[0078] 2. Flow cytometry staining

[0079] Resuspend lymphocytes in 1 mL of DPBS into flow cytometry tubes, add 1 μL of cell viability / deactivation dye to each tube, incubate at room temperature in the dark for 5 minutes, and centrifuge (20°C, 350g, 5 minutes). Wash once with 1 mL of pre-chilled flow cytometry staining buffer, then resuspend in 100 μL of pre-chilled flow cytometry staining buffer. Add 1 μL of Fc receptor blocker to each tube, mix well, and incubate at room temperature in the dark for 10 minutes. Add the desired flow cytometry staining antibody to each tube: APC Mouse Anti-Human CD3, PerCP-Cy TM 5.5 μL each of Mouse Anti-Human CD8 and FITC Mouse Anti-human CD57 were incubated at room temperature in the dark for 20 minutes. The mixture was then centrifuged (20°C, 350g, 5 minutes), resuspended in 300 μL of flow cytometry staining buffer, and analyzed by flow cytometry.

[0080] 3. Streaming data analysis

[0081] Data analysis was performed using flow cytometry software. Live cells were circled using FSC-A and FSC-H, single cells were circled using FSC-A and FSC-H, and lymphocytes were circled using FSC-A and SSC-A. Subsequently, T cells and CD8+ cells were circled. + T cells and senescent CD8 + T cells (CD57) + CD8 + T), to obtain the proportion of each subpopulation of cells.

[0082] 4. Predicting Atrial Fibrillation Recurrence

[0083] Acquire aging CD8 + After determining the T-cell percentage, atrial fibrillation recurrence prediction tool cards (such as...) were used. Figure 4 Predicting atrial fibrillation recurrence. This involves collecting the patient's personal information, including age, sex, body mass index, smoking history, alcohol consumption, history of diabetes, hypertension, coronary artery disease, heart failure, stroke / transient ischemic attack, atrial fibrillation type, left atrial diameter, and CD8 count (indicating aging). + By mapping information such as the percentage of T cells to the top axis, a score can be obtained for each indicator. By summing up all the individual scores, a total score can be obtained, which can then predict the patient's median relapse-free survival time.

[0084] The predictive method proposed in this application can quickly and accurately predict high-risk patients for atrial fibrillation recurrence before surgery, providing guidance for surgical selection and early clinical intervention.

[0085] The fifth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the aforementioned prediction method.

[0086] Embodiments of the present invention can be implemented using hardware, software, or a combination of both. The software portion can be implemented using the logic of the aforementioned prediction method; the software portion can be stored in memory and executed by a suitable instruction execution system, such as a microprocessor or dedicated hardware. Those skilled in the art will understand that the above-described devices and methods can be implemented using computer-executable instructions and / or included in processor control code, for example, such code provided on a carrier medium such as a disk, CD, or DVD-ROM, a memory, or a data carrier such as an optical or electronic signal carrier.

[0087] It is understood that memory can be volatile or non-volatile, or both. Non-volatile memory can be read-only memory (ROM) or programmable read-only memory (PROM), used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM) and synchronous static random access memory (SSRAM). The memory described in the embodiments of this invention is intended to include, but is not limited to, these and any other suitable categories of memory. The memory of this invention is used to store various types of data to support the operation of the controller. Examples of this data include: any executable program for operation on the controller, such as an operating system and applications; the operating system contains various system programs, such as the framework layer, core library layer, driver layer, etc., for implementing various basic services and handling hardware-based tasks. Applications can include various applications, such as media players, browsers, etc., for implementing various application services.

[0088] A sixth aspect of this application provides a terminal including a processor and the aforementioned computer-readable storage medium, wherein the processor executes a computer program on the computer-readable storage medium to implement the steps of the aforementioned prediction method.

[0089] The processor in this application may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above prediction method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the accessory optimization method provided in the embodiments of this invention can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor.

[0090] The present application will be further illustrated by the following examples, but these examples do not limit the scope of the present application.

[0091] Example 1

[0092] method

[0093] 1. Research Subjects

[0094] Patients with atrial fibrillation who underwent radiofrequency ablation at the Department of Cardiology, Ruijin Hospital, affiliated with Shanghai Jiao Tong University School of Medicine, from March 2021 to August 2021 were included. Inclusion criteria: (1) patients with a clinically confirmed diagnosis of atrial fibrillation; (2) patients aged ≥18 years; (3) patients who signed a written informed consent form. Exclusion criteria: (1) patients with severe heart failure (left ventricular ejection fraction ≤35%); (2) patients with a left atrial diameter ≥55 mm as indicated by echocardiography; (3) pregnant women; (4) patients with a family history of arrhythmias; (5) patients with acute infection or high-sensitivity C-reactive protein ≥10 mg / L; (6) patients with allergic diseases or autoimmune diseases; (7) patients with tumors or who have received radiotherapy / chemotherapy; (8) patients who have taken or used growth hormone, dehydroepiandrosterone, spermidine, or NAD+. + Enhancers or lithium preparations. Diagnostic criteria for atrial fibrillation follow the latest clinical guidelines. All patients signed informed consent forms before the procedure and discontinued antiarrhythmic medications for at least five half-lives.

[0095] 2. Electrophysiological examination and ablation

[0096] All patients received standard anticoagulation therapy. After transesophageal echocardiography confirmed the absence of atrial thrombi, each patient underwent electrophysiological studies while awake. Electrophysiological studies were performed using a saline infusion catheter (NaviStar). TM RMT ThermoCool TMBiosenseWebster Inc.'s 3D mapping system (CARTO) TM Biosense Webster or EnSite TM The system (St. Jude Medical) and the robotic magnetic navigation system (Stereotaxis Inc., St. Louis, MO) were used for 3D mapping and ablation. Additional fragmentation potential ablation or linear ablation was performed as needed.

[0097] 3. Follow-up

[0098] All enrolled patients were scheduled for regular outpatient follow-up. The primary diagnostic method for atrial fibrillation recurrence was 24-hour Holter monitoring. Furthermore, if patients experienced symptoms that might indicate atrial fibrillation recurrence, they were advised to undergo an electrocardiogram at the nearest hospital. Atrial fibrillation recurrence was defined as: (1) atrial fibrillation recurrence lasting >30 seconds; (2) atrial flutter or atrial tachycardia; (3) use of Class I or Class III antiarrhythmic drugs 3 months post-procedure; (4) reablation. A single recurrence during the follow-up period after the blank period (3 months post-procedure) was defined as atrial fibrillation recurrence.

[0099] 4. Flow cytometry

[0100] 4.1 Isolation of lymphocytes

[0101] Collect fresh, anticoagulated whole blood from the patient's peripheral blood and left atrium, avoiding freezing and prolonged refrigeration during collection. Add 1 mL of whole blood to each tube and centrifuge (20°C, 800g, 30 minutes, acceleration and deceleration settings 3, Eppendorf Centrifuge 5810R). Aspirate lymphocytes from the plasma layer and wash 1-2 times with RPMI 1640 medium (20°C, 350g, 10 minutes). Resuspend the lymphocytes in DPBS for later use.

[0102] 4.2 Flow Cytometry Staining

[0103] Resuspend lymphocytes in 1 mL of DPBS into flow cytometry tubes, add 1 μL of cell viability dye (DAPI staining solution) to each tube, incubate at room temperature in the dark for 5 minutes, and centrifuge (20°C, 350g, 5 minutes). Wash once with 1 mL of pre-chilled flow cytometry staining buffer (PBS solution containing 3% fetal bovine serum), then resuspend in 100 μL of pre-chilled flow cytometry staining buffer. Add 1 μL of Fc receptor blocker to each tube, mix well, and incubate at room temperature in the dark for 10 minutes. Add the flow cytometry antibodies to each tube: APC Mouse Anti-Human CD3, PerCP-Cy TM5.5 μL each of Mouse Anti-Human CD8 and FITC Mouse Anti-Human CD57 were incubated at room temperature in the dark for 20 minutes. The mixture was then centrifuged (20°C, 350g, 5 minutes), resuspended in 300 μL of flow cytometry staining buffer, and transferred to an LSR Tortessa staining solution. TM The X-20 flow cytometer (BD Biosciences) was used for analysis.

[0104] 4.3 Streaming Data Analysis

[0105] Data analysis was performed using FlowJo (BD Biosciences). Live cells were circled using FSC-A and FSC-H markers; single cells were circled using FSC-A and FSC-H markers; and lymphocytes were circled using FSC-A and SSC-A markers. T cells and CD8+ cells were then circled. + T cells and senescent CD8 + T cells (CD57) + CD8 + T), to obtain the proportion of each subpopulation of cells. The results are as follows: Figure 1 .

[0106] 5. Statistical Analysis

[0107] Statistical analysis and visualization were performed using R. Quantitative data were expressed as mean ± standard deviation, and categorical data were expressed as number and percentage. Continuous variables were analyzed using t-tests or rank-sum tests, and categorical variables were analyzed using chi-square tests. 2 The recurrence-free survival time was assessed using either the test or Fisher's exact probability method. The Kaplan-Meier curve was used to assess recurrence-free survival, and the Log-rank test was used to compare the recurrence-free survival rates between groups. The index of concordance (C-index) was used to evaluate the predictive accuracy of the model, and the bootstrap method (1000 samples) was used to construct a calibration curve to assess the model's calibration. A two-tailed p-value < 0.05 was considered statistically significant.

[0108] Example 2

[0109] result

[0110] 1. Patient's baseline information

[0111] A total of 71 patients with atrial fibrillation were included. The mean age was 65.28 ± 9.60 years, of whom 49.3% were male. Paroxysmal atrial fibrillation accounted for 69.0% and persistent atrial fibrillation accounted for 31.0%. Baseline data such as personal history, past medical history, and left atrial diameter are shown in Table 1.

[0112] Table 1 Baseline data of patients with atrial fibrillation included in the study.

[0113]

[0114] TIA = Transient ischemic attack of the brain.

[0115] 2. Comparison of immune cell subsets

[0116] Tables 2 and 3 show the differences in peripheral blood and left atrial blood between patients with recurrent and non-recurrent atrial fibrillation after radiofrequency ablation, as detected by flow cytometry. It is evident that aging CD8+ cells are present in patients with recurrent atrial fibrillation. + T cells were significantly higher in both peripheral blood and left atrial blood than in non-relapsed patients. Figure 2 Demonstrates CD8 senescence as detected by flow cytometry + Representative results and statistical analysis of the proportion of T cells in peripheral blood and left atrial blood of patients with recurrent and non-recurrent atrial fibrillation.

[0117] Table 2 Comparison of peripheral blood immune cell subsets in patients with recurrent and non-recurrent atrial fibrillation

[0118]

[0119] Table 3 Comparison of various immune cell subsets in left atrial blood in patients with recurrent and non-recurrent atrial fibrillation.

[0120]

[0121] 3. Aging CD8 + T-cell prediction of atrial fibrillation recurrence

[0122] To clarify the aging CD8 + Can T cells predict recurrence after radiofrequency ablation in patients with atrial fibrillation? This application used Kaplan-Meier curves to compare the recurrence rates in different groups of patients. Figure 3 It can be seen that patients with persistent atrial fibrillation are more prone to recurrence than those with paroxysmal atrial fibrillation (p = 0.24); the recurrence rate is higher in patients with a left atrial diameter > 44 mm than in those with a left atrial diameter ≤ 44 mm (p = 0.032), while aging CD8 + Patients with T > 50.4% had a higher rate of CD8+ than those who were older. + Patients with T≤50.4% had a significantly increased relapse rate (p<0.0001), indicating that aging CD8 + T cells can better distinguish patients with atrial fibrillation prone to recurrence than atrial fibrillation type and left atrial diameter.

[0123] Furthermore, this application constructs a CD8-based aging-related system. + The atrial fibrillation recurrence prediction model based on T-cell percentage is derived from Cox regression modeling. The calculation formula is as follows:

[0124]

[0125] Where Age represents the age index, which is the age of the subject being tested, and its unit is years;

[0126] Male represents the gender index. When the subject is female, the gender index is 0, and when the subject is male, the gender index is 1.

[0127] BMI stands for Body Mass Index. BMI = weight (kg) / height (m) 2 ;

[0128] Smoke represents the smoking index; when the subject has no history of smoking, Smoke is 0, and when the subject has a history of smoking, Smoke is 1.

[0129] Drink represents the alcohol consumption index. When the subject has no history of drinking, Drink is 0, and when the subject has a history of drinking, Drink is 1.

[0130] DM represents the diabetes index. When the subject has no history of diabetes, DM is 0, and when the subject has a history of diabetes, DM is 1.

[0131] HBp represents the hypertension index. When the subject has no history of hypertension, HBp is 0, and when the subject has a history of hypertension, HBp is 1.

[0132] CHD stands for Coronary Heart Disease Index. When the subject has no history of coronary heart disease, CHD is 0; when the subject has a history of coronary heart disease, CHD is 1.

[0133] HF represents the heart failure index. When the subject has no history of heart failure, HF is 0, and when the subject has a history of heart failure, HF is 1.

[0134] Stroke (TIA) represents the stroke and transient ischemic attack index. When the subject has no history of stroke or transient ischemic attack, Stroke (TIA) is 0, and when the subject has a history of stroke or transient ischemic attack, Stroke (TIA) is 1.

[0135] AF type represents the atrial fibrillation type index. When the subject has paroxysmal atrial fibrillation, AF type is 1, and when the subject has persistent atrial fibrillation, AF type is 2.

[0136] LAD stands for Left Atrial Diameter Index, which is the left atrial diameter of the object being measured, and its unit is mm.

[0137] Senescent CD8+T > 50.4% indicates senescent CD8+T. + T cell percentage index, when the CD8+ of the test subject is senescent.+ T cells in all CD8 + When the percentage of T cells is >50.4%, Senescent CD8+ T >50.4% is defined as 1. When the percentage of CD8+ T cells in the tested subject is senescent, the percentage of CD8+ T cells is 1. + T cells in all CD8 + When the percentage of T cells is ≤50.4%, the percentage of Senescent CD8+ T cells >50.4% is 0.

[0138] The atrial fibrillation recurrence prediction model calculates h(t) by substituting individual data points into a formula, representing the recurrence risk of the subject. This is achieved using the nomogram function of the "rms" R package. Figure 4 The visualization tool card shown displays the patient's personal information such as age, gender, body mass index, smoking, and alcohol consumption; past medical history such as diabetes, hypertension, coronary heart disease, heart failure, stroke / transient ischemic attack; atrial fibrillation type; left atrial diameter; and aging CD8 count. + By mapping information such as the percentage of T cells to the top axis, a score can be obtained for each indicator. After summing all the individual scores, a total score is obtained, which allows a direct view of the 10-month relapse-free rate and predicts the patient's 10-month relapse risk.

[0139] Figure 5 The evaluation results of the atrial fibrillation recurrence prediction model show a C-index score of 0.877, indicating good model accuracy. Using the Bootstrap method to perform 1000 random samplings, the obtained calibration curve closely approximates the diagonal, demonstrating excellent model calibration.

[0140] Example 3

[0141] The method is the same as in Example 2.

[0142] result

[0143] 1. Patient's baseline information

[0144] A total of 77 patients with atrial fibrillation who underwent radiofrequency ablation at the Department of Cardiology, Ruijin Hospital, affiliated with Shanghai Jiao Tong University School of Medicine, from September 2021 to February 2022 were included as an external validation cohort. The mean age was 63.64 ± 10.92 years, of whom 57.1% were male. Paroxysmal atrial fibrillation accounted for 54.5%, and persistent atrial fibrillation accounted for 45.5%. Baseline data are shown in Table 4.

[0145] Table 4 Baseline data of patients with atrial fibrillation included in the study.

[0146]

[0147] TIA = Transient ischemic attack of the brain.

[0148] 2. Comparison of immune cell subsets

[0149] Figure 6 CD57 was detected using flow cytometry. + CD8 + A comparison of T-cell subsets in peripheral blood of patients with recurrent and non-recurrent atrial fibrillation after radiofrequency ablation. It is evident that aging CD8+ cells are present in patients with recurrent atrial fibrillation. + T cell counts were significantly higher in patients with relapsed disease than in those without (p<0.0001).

[0150] 3. Aging CD8 + T-cell prediction of atrial fibrillation recurrence

[0151] Figure 7 The accuracy of the constructed atrial fibrillation recurrence prediction model in predicting atrial fibrillation recurrence 8 months after radiofrequency ablation in the validation cohort is demonstrated. The area under the ROC curve reaches 0.832, indicating the model's stability.

[0152] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this invention should still be covered by the claims of this application.

Claims

1. The use of biomarkers in the preparation of products for predicting recurrence of atrial fibrillation after surgery, characterized in that, The biomarkers include peripheral blood CD57. + CD8 + T cells; the surgical method in the product for predicting recurrence after atrial fibrillation surgery is selected from radiofrequency ablation.

2. The use as described in claim 1, characterized in that, Based on CD57 in peripheral blood + CD8 + T cells in all CD8 + The percentage of T cells is used to assess the risk of recurrence after atrial fibrillation surgery.

3. The use as described in claim 2, characterized in that, When CD57 in peripheral blood + CD8 + T cells in all CD8 + When the proportion of T cells is greater than a threshold, it is predicted that atrial fibrillation is more likely to recur after surgery.

4. The use as described in claim 3, characterized in that, The threshold is 50.4%.

5. A system for predicting recurrence after atrial fibrillation surgery, characterized in that, The system includes a data analysis module for analyzing the test values ​​of peripheral blood biomarkers, including peripheral blood CD57. + CD8 + T cells; the surgical method for postoperative recurrence is selected from radiofrequency ablation.

6. The system as described in claim 5, characterized in that, The biomarker test values ​​include CD57 in peripheral blood. + CD8 + T cells in all CD8 + The percentage of T cells.

7. The system as described in claim 6, characterized in that, The data analysis module uses peripheral blood CD57 + CD8 + T cells in all CD8 + The percentage of T cells is used to assess the risk of recurrence after atrial fibrillation surgery.

8. The system as described in claim 7, characterized in that, When CD57 in peripheral blood + CD8 + T cells in all CD8 + When the proportion of T cells is greater than a threshold, it is predicted that atrial fibrillation is more likely to recur after surgery.

9. The system as described in claim 8, characterized in that, The threshold is 50.4%.

10. The system as described in claim 5, characterized in that, The data analyzed by the data analysis module also includes one or more combinations of age, gender, body mass index, smoking, alcohol consumption, diabetes, coronary heart disease, heart failure, stroke or transient ischemic attack, atrial fibrillation type, and left atrial diameter.

11. The system as claimed in claim 10, characterized in that, The data analysis module calculates the 10-month recurrence risk of the test subject in the following way: obtain the individual scores of each data item, and convert them into the 10-month recurrence risk of the test subject.

12. The system as claimed in claim 11, characterized in that, The data analysis module obtains the calculation formula through Cox regression modeling.

13. The system as described in claim 12, characterized in that, The calculation formula is as follows: ； Where h0(t) is the baseline risk function, Age represents the age index, which is the age of the subject in years; Male represents the gender index. When the subject is female, the gender index is 0, and when the subject is male, the gender index is 1. BMI stands for Body Mass Index. BMI = weight (kg) / height (m) 2 ; Smoke represents the smoking index; when the subject has no history of smoking, Smoke is 0, and when the subject has a history of smoking, Smoke is 1. Drink represents the alcohol consumption index. When the subject has no history of drinking, Drink is 0, and when the subject has a history of drinking, Drink is 1. DM represents the diabetes index. When the subject has no history of diabetes, DM is 0, and when the subject has a history of diabetes, DM is 1. HBp represents the hypertension index. When the subject has no history of hypertension, HBp is 0, and when the subject has a history of hypertension, HBp is 1. CHD stands for Coronary Heart Disease Index. When the subject has no history of coronary heart disease, CHD is 0; when the subject has a history of coronary heart disease, CHD is 1. HF represents the heart failure index. When the subject has no history of heart failure, HF is 0, and when the subject has a history of heart failure, HF is 1. Stroke (TIA) represents the stroke and transient ischemic attack index. When the subject has no history of stroke or transient ischemic attack, Stroke (TIA) is 0, and when the subject has a history of stroke or transient ischemic attack, Stroke (TIA) is 1. AF type represents the atrial fibrillation type index. When the subject has paroxysmal atrial fibrillation, AF type is 1, and when the subject has persistent atrial fibrillation, AF type is 2. LAD stands for Left Atrial Diameter Index, which is the left atrial diameter of the object being measured, and its unit is mm. Senescent CD8+T > 50.4% indicates high CD57 levels in peripheral blood. + CD8 + T cell percentage index, when CD57 in the peripheral blood of the subject is high. + CD8 + T cells in all CD8 + When the proportion of T cells is greater than the threshold, Senescent CD8+ T > 50.4% is defined as 1. When the peripheral blood of the test subject contains CD57... + CD8 + T cells in all CD8 + When the percentage of T cells is ≤ a threshold, SenescentCD8+T > 50.4% is 0; the threshold is 50.4%. The data analysis module obtains h(t), which is the recurrence risk of the subject, by substituting each data point into the calculation formula.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, this program implements steps of a method for predicting recurrence after atrial fibrillation surgery, the prediction method including analyzing test values ​​of peripheral blood biomarkers, the biomarkers including CD57 in peripheral blood. + CD8 + T cells; the surgical method for postoperative recurrence is selected from radiofrequency ablation.

15. The computer-readable storage medium as claimed in claim 14, characterized in that, The biomarker test values ​​include CD57 in peripheral blood. + CD8 + T cells in all CD8 + The percentage of T cells.

16. The computer-readable storage medium as claimed in claim 15, characterized in that, The analysis of peripheral blood biomarkers includes the following test values: CD57 in peripheral blood. + CD8 + T cells in all CD8 + The percentage of T cells is used to assess the risk of recurrence after atrial fibrillation surgery.

17. The computer-readable storage medium as claimed in claim 16, characterized in that, When CD57 in peripheral blood + CD8 + T cells in all CD8 + When the proportion of T cells is greater than a threshold, it is predicted that atrial fibrillation is more likely to recur after surgery.

18. The computer-readable storage medium as claimed in claim 17, characterized in that, The threshold is 50.4%.

19. The computer-readable storage medium as claimed in claim 14, characterized in that, The data analyzed also includes one or more of the following: age, sex, body mass index, smoking, alcohol consumption, diabetes, coronary heart disease, heart failure, stroke or transient ischemic attack, atrial fibrillation type, and left atrial diameter.

20. The computer-readable storage medium as claimed in claim 19, characterized in that, The analysis of peripheral blood sample test values ​​includes calculating the 10-month recurrence risk of the subjects based on the test values ​​of each data point.

21. The computer-readable storage medium as claimed in claim 20, characterized in that, The 10-month recurrence risk is calculated as follows: obtain the individual scores of each data point, and convert them into the 10-month recurrence risk of the subject.

22. The computer-readable storage medium as claimed in claim 21, characterized in that, The test values ​​of the peripheral blood samples analyzed include those calculated using a Cox regression model, and the calculation formula is as follows: ； Where h0(t) is the baseline risk function, Age represents the age index, which is the age of the subject in years; Male represents the gender index. When the subject is female, the gender index is 0, and when the subject is male, the gender index is 1. BMI stands for Body Mass Index. BMI = weight (kg) / height (m) 2 ; Smoke represents the smoking index; when the subject has no history of smoking, Smoke is 0, and when the subject has a history of smoking, Smoke is 1. Drink represents the alcohol consumption index. When the subject has no history of drinking, Drink is 0, and when the subject has a history of drinking, Drink is 1. DM represents the diabetes index. When the subject has no history of diabetes, DM is 0, and when the subject has a history of diabetes, DM is 1. HBp represents the hypertension index. When the subject has no history of hypertension, HBp is 0, and when the subject has a history of hypertension, HBp is 1. CHD stands for Coronary Heart Disease Index. When the subject has no history of coronary heart disease, CHD is 0; when the subject has a history of coronary heart disease, CHD is 1. HF represents the heart failure index. When the subject has no history of heart failure, HF is 0, and when the subject has a history of heart failure, HF is 1. Stroke (TIA) represents the stroke and transient ischemic attack index. When the subject has no history of stroke or transient ischemic attack, Stroke (TIA) is 0, and when the subject has a history of stroke or transient ischemic attack, Stroke (TIA) is 1. AF type represents the atrial fibrillation type index. When the subject has paroxysmal atrial fibrillation, AF type is 1, and when the subject has persistent atrial fibrillation, AF type is 2. LAD stands for Left Atrial Diameter Index, which is the left atrial diameter of the object being measured, and its unit is mm. Senescent CD8+T > 50.4% indicates high CD57 levels in peripheral blood. + CD8 + T cell percentage index, when CD57 in the peripheral blood of the subject is high. + CD8 + T cells in all CD8 + When the proportion of T cells is greater than the threshold, Senescent CD8+ T > 50.4% is defined as 1. When the peripheral blood of the test subject contains CD57... + CD8 + T cells in all CD8 + When the percentage of T cells is ≤ a threshold, Senescent CD8+T > 50.4% is 0; the threshold is 50.4%. The analysis of peripheral blood sample test values ​​involves substituting each individual data point into the calculation formula to obtain h(t), which represents the recurrence risk of the subject.

23. The computer-readable storage medium as claimed in claim 14, characterized in that, CD57 in peripheral blood + CD8 + T-cell assay values ​​are obtained by taking fresh, anticoagulated whole blood from the patient's peripheral blood, separating lymphocytes, incubating them with anti-CD3, anti-CD8, and anti-CD57 antibodies, and then detecting them by flow cytometry.

24. A terminal comprising a processor and a computer-readable storage medium as described in any one of claims 14-23, characterized in that, The processor executes a computer program on the computer-readable storage medium.

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