Risk prediction system for thrombocytopenic purpura and application

Abstract

The invention provides a risk prediction system for thrombotic thrombocytopenic purpura (TTP) and application. A short-term death risk prediction marker combination, a biomarker and a corresponding cut-off value of the biomarker are as follows: cardiac troponin I (cTnI) is greater than or equal to 353.1 pg / mL; lactic dehydrogenase (LDH) is greater than or equal to 992U / L; uREA (urea nitrogen) is greater than or equal to 10.9 mmol / L; indirect bilirubin (IBIL) is greater than or equal to 32.3 mu mol / L; a cTnI, LDH, UREA and IBIL combined scoring system can effectively predict the short-term death risk of the TTP patient, the patient with the score larger than or equal to 3 needs to be intervened in an intensified mode, and an evidence-based basis is provided for individualized treatment.

Description

Technical Field

[0001] The present invention relates to the field of biomedical detection technologies, and particularly to a risk prediction system and application for thrombotic thrombocytopenic purpura. Background Art

[0002] Thrombotic thrombocytopenic purpura (TTP) is a rare and potentially life-threatening thrombotic microangiopathy, and its main clinical features include severe thrombocytopenia, microangiopathic hemolytic anemia, and multi-organ ischemic injury. Since the clinical manifestations of TTP are diverse and overlap with other diseases, early diagnosis is challenging. Laboratory tests usually show thrombocytopenia, hemolytic anemia, elevated lactate dehydrogenase (LDH), and significantly reduced ADAMTS13 activity. TTP has a serious condition and a high fatality rate. Timely diagnosis helps to actively take measures to avoid unnecessary platelet transfusions that may even cause adverse consequences. In addition to timely diagnosis, assessing the risk of TTP death can also help determine which patients need more aggressive treatment and improve the prognosis.

[0003] The prior art cannot rapidly identify high-risk patients through routine laboratory indicators. Therefore, it is necessary to develop a risk prediction system for thrombotic thrombocytopenic purpura to provide important evidence-based basis for the early differential diagnosis of TTP, avoiding harmful platelet transfusions, and formulating individualized treatment strategies. Summary of the Invention

[0004] The object of the present invention is to provide a risk prediction system and application for thrombotic thrombocytopenic purpura, and to rapidly identify high-risk patients through routine laboratory indicators.

[0005] The present invention adopts the following technical solutions:

[0006] In the first aspect of the present invention, a combination of risk prediction markers for short-term death of patients with thrombotic thrombocytopenic purpura (TTP) is provided, and the marker combination includes:

[0007] Cardiac troponin I;

[0008] Lactate dehydrogenase;

[0009] Blood urea nitrogen;

[0010] Indirect bilirubin.

[0011] Furthermore, the cut-off values corresponding to the four biomarkers are:

[0012] Cardiac troponin I (cTnI) ≥ 353.1 pg / mL;

[0013] Lactate dehydrogenase (LDH) ≥ 992 U / L;

[0014] Urea nitrogen (UREA) ≥ 10.9 mmol / L;

[0015] Indirect bilirubin (IBIL) ≥ 32.3 μmol / L;

[0016] Among them, the marker combination is used to evaluate the 28-day death risk of patients. When at least three indicators are met, it is determined as high risk, that is, the death risk ≥ 60.9%.

[0017] In the second aspect of the present invention, a risk assessment model based on the marker combination is provided, and the risk score is calculated by the following formula:

[0018]

[0019] The risk score value is calculated according to the input levels of cTnI, LDH, UREA, and IBIL.

[0020] Furthermore, the short-term death risk of patients with thrombotic thrombocytopenic purpura (TTP) is evaluated according to the risk score as follows:

[0021] 0 - 1 point: low risk;

[0022] 2 points: medium risk;

[0023] 3 - 4 points: high risk.

[0024] In the third aspect of the present invention, a short-term death risk prediction system for patients with thrombotic thrombocytopenic purpura (TTP) is provided, characterized in that the system includes:

[0025] A processor and a memory, the memory is coupled to the processor, and the memory stores instructions that use the calculation steps of the model when executed by the processor.

[0026] In the fourth aspect of the present invention, an application of the marker combination or the risk prediction model of thrombotic thrombocytopenic purpura or the risk prediction system of thrombotic thrombocytopenic purpura in the preparation of a risk prediction product for thrombotic thrombocytopenic purpura is provided.

[0027] One or more technical solutions in the embodiments of the present invention have at least the following technical effects or advantages:

[0028] The risk prediction model of thrombotic thrombocytopenic purpura provided by the present invention and its construction method have the following advantages compared with the existing technical solutions:

[0029] 1. For the first time, myocardial injury markers (cTnI) and renal function indicators (UREA) were incorporated into the prognostic evaluation system of TTP. After combining myocardial injury markers (cTnI) and renal function indicators (UREA) with LDH and IBIL, the AUC of the model was increased to 0.776, which was significantly better than the existing methods (P<0.001).

[0030] 2. The 0-4 point risk scoring system constructed in this study showed that the mortality rate of patients with a score of ≥3 was as high as 60.9%-92.3%, which was significantly higher than that of the low-score group (21.3%). This model has multiple clinical significances: First, LDH>992U / L or IBIL>32.3μmol / L can be used as an early warning signal for occult organ damage. Even if the platelet count does not reach the critical value, the deterioration of the condition still needs to be vigilant; Second, high-risk patients need early intensive treatment, such as the combined use of rituximab or complement inhibitors, while low-risk patients can avoid excessive medical intervention; In addition, dynamic monitoring of the levels of LDH and IBIL helps to evaluate the treatment response, and continuous elevation indicates the need to adjust the plasma exchange frequency or explore second-line treatment options. Description of the Drawings

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to these drawings without creative efforts.

[0032] Figure 1 For the correlation analysis between the initial screening variables, notes and explanations: The more the color of the small square tends to red, the stronger the correlation between the two corresponding factors; the more the color tends to blue, the weaker the correlation between the two corresponding factors.

[0033] Figure 2 ROC curve of each differential index for the prognosis of TTP patients

[0034] Figure 3 Short-term mortality corresponding to the risk score at the time of TTP admission

[0035] Figure 4 Comparison results of the ROC curves between the four-index combined model and the existing methods. Among them, Figure A is the combined detection of IBIL and LDH (AUC is 0.669), and Figure B is the combined detection of four indicators (AUC is 0.776). Detailed Implementation Modes

[0036] The present invention will be specifically described below in conjunction with specific embodiments and examples, and the advantages and various effects of the present invention will be presented more clearly therefrom. Those skilled in the art should understand that these specific embodiments and examples are for illustrating the present invention rather than limiting the present invention.

[0037] Throughout the specification, unless otherwise specifically stated, the terms used herein should be understood as having the meanings as commonly used in the art. Therefore, unless otherwise defined, all technical and scientific terms used herein have the same meanings as those generally understood by those skilled in the art to which the present invention pertains. In case of any contradiction, this specification shall prevail.

[0038] Unless otherwise specifically stated, various raw materials, reagents, instruments, equipment, etc. used in the present invention can be obtained through market purchases or by existing methods.

[0039] The general idea of the present invention is as follows:

[0040] Method: Retrospective analysis was performed on the clinical data of 106 patients with TTP admitted to Tongji Hospital from June 2016 to February 2025. According to the 28-day survival status, they were divided into a death group (n = 45) and a survival group (n = 61). Prognosis-related indicators were screened through ROC curve and Logistic regression analysis, and a combined scoring system was established.

[0041] 1. First, the present invention screens and obtains markers through the following method:

[0042] Data source: 106 patients with TTP in Tongji Hospital from June 2016 to February 2025 were included, and those with incomplete ADAMTS13 activity data were excluded;

[0043] Statistical method: Variables were screened through baseline analysis and correlation analysis, and the OR value was calculated by multivariate Logistic regression. Finally, four independent risk factors, namely cTnI, LDH, UREA, and IBIL, were retained;

[0044] Cut-off value optimization: The ROC curve showed that the Youden index was the largest (0.394) when cTnI = 353.1 pg / mL, and the same was true for the other indicators (see Table 3 for details).

[0045] 2. Then, model validation was carried out

[0046] External validation: Validation was carried out in an independent cohort (n = 50). The AUC of the model was 0.792 (95% CI 0.689 - 0.895), and there was no statistical difference from the training set (P = 0.12);

[0047] Clinical decision-making example: When patient A was admitted to the hospital, cTnI = 420 pg / mL, LDH = 1020 U / L, UREA = 12.0 mmol / L, IBIL = 35 μmol / L, the score was 4 points, and the patient died within 28 days, which was consistent with the model prediction.

[0048] 3. Establish a risk assessment model based on the biomarker combination, and calculate the risk score through the following formula:

[0049]

[0050] The risk score value is calculated based on the input levels of cTnI, LDH, UREA, and IBIL.

[0051] Among them,

[0052] Risk classification:

[0053] 0 - 1 point: Low risk (21.3% mortality rate);

[0054] 2 points: Medium risk (39.1% mortality rate);

[0055] 3 - 4 points: High risk (≥60.9% mortality rate).

[0056] The results showed that: In the death group, cTnI (median 712.1 vs 164.5 pg / mL), LDH (1224 vs 990 U / L), UREA (10.9 vs 7.2 mmol / L), and IBIL (40.8 vs 27.9 μmol / L) were significantly increased (P < 0.05). ROC analysis showed that the optimal cut-off values of the four indicators were 353.1 pg / mL, 992 U / L, 10.9 mmol / L, and 32.3 μmol / L respectively, and the corresponding OR values of the death risk reached 4.778, 2.842, 5.527, and 2.995. A 0 - 4 point risk scoring model was constructed: the mortality rate for 0 - 1 point was 21.3% (10 / 47), for 2 points was 39.1% (9 / 23), for 3 points was 60.9% (14 / 23), and for 4 points was 92.3% (12 / 13). The OR value increased by 2.324 (95% CI 1.597 - 3.383) for each 1 - point increase in risk.

[0057] As can be seen from the above: The combined scoring system of cTnI, LDH, UREA, and IBIL can effectively predict the short-term death risk of TTP patients. Those with a score of ≥3 points need intensive intervention, providing an evidence-based basis for individualized treatment.

[0058] Next, the risk prediction model for thrombotic thrombocytopenic purpura of the present application and its construction method will be described in detail in combination with examples and experimental data.

[0059] Example 1: Screening of biomarker combination

[0060] 1 Data and Methods

[0061] 1.1 General Data

[0062] Clinical data of 106 patients with TTP diagnosed and treated in Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology from June 2016 to February 2025 were collected, including 45 males and 61 females, with an average age of (48.631±18.393) years. All patients underwent ADAMTS-13 activity detection. Inclusion criteria: meeting the diagnostic criteria of TTP ① plasma ADAMTS-13 < 10% or ② ADAMTS-13 < 20% and positive ADAMTS-13 inhibitor or gene detection. Exclusion criteria: ① patients with incomplete laboratory-related data; ② patients diagnosed in the outpatient department but not admitted to the hospital; ③ patients with hematological diseases. The follow-up until 28 days after admission was used as the observation window, and they were divided into a death group and a survival group according to whether they survived. This study was approved by the hospital ethics committee.

[0063] 1.2 Methods

[0064] This study was a case-control study. The patients were enrolled by continuous screening, and the general data, clinical symptoms, and laboratory results of the patients were analyzed. The data collected included the age, gender, clinical symptoms, medical history of all patients, and the results of the first laboratory index examinations that the patients could obtain, including white blood cells (WBC), neutrophils (NEU#), lymphocytes (LYM#), red blood cells (RBC), hemoglobin (HGB), platelets (PLT), activated partial thromboplastin time (APTT), prothrombin time (PT), thrombin time (TT), fibrinogen (FIB), D-dimer (D-D), troponin (CTnI), alanine aminotransferase (ALT), aspartate aminotransferase (AST), total protein (TP), albumin (ALB), indirect bilirubin (IBIL), alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT), lactate dehydrogenase (LDH), creatinine (CREA), urea nitrogen (UREA), uric acid (UA), potassium (K), sodium (NA), chloride (CL), calcium (CA), bicarbonate (HCO3), etc. The relevant indexes of blood routine were detected by the Japanese SYSMES hematology analyzer XE2100; the coagulation function was detected by the STAGO automatic coagulation analyzer and supporting reagents. CTnI was detected by the American Abbott i2000 automatic biochemical immunoassay analyzer, and biochemical indexes such as ALT, AST, TP, ALB, ALP, GGT, LDH, CREA, UREA, UA, K, NA, CL, CA, HCO3 were detected by the Roche C8000 biochemical analyzer.

[0065] 1.3 Statistical Methods

[0066] Data analysis was performed using SPSS 20.0 statistical software. The Shapiro-Wilk test was used to test the normality of the data. Normally distributed data were expressed as mean ± standard deviation, and skewed distributed data were expressed as median (quartiles) M(P25, P75). Independent sample t-tests were used to compare normally distributed data, Mann-Whitney U tests were used to compare skewed distributed data, and χ2 tests were used to compare the composition ratios between the two groups. The Receiver Operating Characteristic (ROC) curve was used to evaluate the efficacy of each detection index in predicting the short-term survival status of TTP, and the cut-off points of each significantly different index were found. Sensitivity, specificity, and the area under the curve (AUC) were calculated. P < 0.05 was considered statistically significant. Logistic regression was used to calculate the relative risk.

[0067] 2 Results

[0068] 2.1 Comparison of general data of the two groups of patients

[0069] There were no statistically significant differences in gender, admission body temperature, days from onset to admission between the two groups of patients (P > 0.05); while the age and the proportion of neurological symptoms in the survival group were lower than those in the death group, and the length of hospital stay was higher than that in the death group, with statistically significant differences (P < 0.05). See Table 1.

[0070] Table 1 General data of two groups of TTP patients

[0071]

[0072] 2.2 Comparison of laboratory-related indicators of the two groups of patients

[0073] There were no statistically significant differences in the comparison of WBC, NEU, LYM, NLR, RDW, RBC, HGB, PLT, APTT, PT, TT, FIB, D-D, AST, ALT, TP, ALB, ALP, GGT, UA, K, NA, CL, CA, HCO3, and ADAMTS13 activity between the two groups of patients (P > 0.05); compared with the survival group, cTnI, TB, DB, IBIL, LDH, UREA, and CREA in the death group were significantly increased, with statistically significant differences (P < 0.05). See Table 2.

[0074] Table 2 Laboratory indicators of two groups of TTP patients

[0075]

[0076]

[0077] 2.3 Correlation Analysis of Independent Variables

[0078] The above 7 independent variables with statistically significant differences were analyzed by Pearson correlation analysis. After removing TB, DB, and CREA with a correlation > 0.7, the correlations of the remaining 4 independent variables are as follows Figure 1 . At the same time, to reduce the impact of homoscedasticity on the model, a homogeneity of variance analysis was performed, and it was found that there were no variables with a Variance Inflation Factor (VIF) > 10, so no variables needed to be removed.

[0079] 2.4 Risk Assessment of Mortality for Differential Indicators

[0080] The ROC curve was used to find the optimal cut-off values of differential indicators including cTnI, LDH, UREA, and IBIL for predicting the short-term survival status of TTP patients, as shown in Figure 2 .

[0081] Table 3 AUC of ROC for Subjects

[0082]

[0083] As can be seen from Table 3, the cut-off values of cTnI, LDH, UREA, and IBIL are 353.1, 992, 10.9, and 32.3 respectively, and the areas under the curve are 0.715 (95% CI 0.632 - 0.816), 0.614 (95% CI 0.510 - 0.726), 0.695 (95% CI 0.587 - 0.782), and 0.608 (95% CI 0.480 - 0.730) respectively.

[0084] According to the cut-off values, the cases were divided into a low-level group and a high-level group respectively. The results of logistic regression are shown in Table 4.

[0085] Table 4 cTnI, LDH, UREA, and IBIL Predict the Short-Term Mortality Risk of TTP

[0086]

[0087] As can be seen from Table 4:

[0088] Compared with those at a low level, the OR values of the death risk for those with high levels of cTnI, LDH, UREA, and IBIL at admission were 4.778 (95% CI 2.086 - 10.944, P < 0.001), 2.842 (95% CI 1.239 - 6.515, P = 0.014), 5.527 (95% CI 2.207 - 13.837, P < 0.001), and 2.995 (95% CI 1.345 - 6.667, P = 0.007), respectively.

[0089] 2.5. Combining cTnI, LDH, UREA, and IBIL to predict the short-term death risk of TTP patients

[0090] The levels of the 4 indicators at admission were combined to construct a prognostic prediction score. One point was accumulated for each increased indicator and the cumulative score of the patient was calculated. The score range of the patient was 0 - 4 points. This study found that the short-term mortality rates of the groups with scores of 0 - 1, 2, 3, and 4 were 21.3% (10 / 47), 39.1% (9 / 23), 60.9% (14 / 23), and 92.3% (12 / 13) ( Figure 3 ).

[0091] The higher the patient score, that is, the more increased indicators, the higher the short-term mortality rate. The Logistic regression results with the score as the independent variable showed that for each one-point increase in risk, the OR value of the short-term death risk of TTP patients was 2.324 (95% CI 1.597 - 3.383, P < 0.001).

[0092] Example 2. Comparison between the four-index combined detection of the present application and existing methods

[0093] 1. The comparison results of the ROC curves of the four-index combined model and existing methods are as Figure 4 shown in and Table 5 below. Among them, Figure A is the combined detection of IBIL and LDH (AUC is 0.669), and Figure B is the combined detection of the four indicators (AUC is 0.776)

[0094] Currently, the prognosis assessment of TTP relies on the detection of ADAMTS13 activity, but its prediction sensitivity for short-term death is insufficient (AUC < 0.6). In the prior art, the AUC of the single-index model of LDH is only 0.614 (95% CI 0.510 - 0.726), and when IBIL and LDH are combined, the AUC is increased to 0.669, but still cannot meet the clinical needs. The present invention first discovers that the abnormal increase of cTnI and UREA can specifically reflect myocardial injury and renal failure. After combining them with LDH and IBIL, the AUC of the model is increased to 0.776, which is significantly better than the existing methods (P < 0.001).

[0095] 2. Comparison of Sensitivity and Specificity between the Four-Indicator Joint Model and Existing Methods

[0096] Table 5

[0097]

[0098] As can be seen from Table 5: The sensitivity of the four-indicator joint model of this application is increased by 6.7% compared with the combined detection of LDH + IBIL (71.1% vs 64.4%), and the specificity is increased by 14.7% (77.0% vs 62.3%).

[0099] Example 3. Risk Assessment Model Based on the Biomarker Combination

[0100] The risk assessment model based on the biomarker combination is characterized in that the risk score is calculated by the following formula:

[0101]

[0102] The risk score value is calculated according to the input levels of cTnI, LDH, UREA, and IBIL.

[0103] 0 - 1 point: Low risk (21.3% mortality rate);

[0104] 2 points: Medium risk (39.1% mortality rate);

[0105] 3 - 4 points: High risk (≥60.9% mortality rate)

[0106] Example 4. Risk Assessment System Based on the Biomarker Combination

[0107] The embodiment of the present invention provides a risk assessment based on the biomarker combination. The system includes:

[0108] A processor and a memory. The memory is coupled to the processor. The memory stores instructions that, when executed by the processor, use the steps described in Example 1 or Example 3.

[0109] Example 5. Computer-Readable Storage Medium

[0110] The embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the method in Example 1 and / or Example 2.

[0111] Of course, for a storage medium containing computer-executable instructions provided by the embodiment of the present invention, the computer-executable instructions are not limited to the method operations described above, and can also execute related operations in the methods provided by any embodiment of the present invention.

[0112] From the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software and necessary general hardware. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk, or optical disc of a computer, etc., including several instructions to enable an electronic device (which can be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention.

[0113] It should be noted that in the above embodiments, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of mutual distinction and do not limit the protection scope of the present invention.

[0114] Application Example 1: Predicting the prognosis of patients with clinical thrombotic thrombocytopenic purpura

[0115] I. Method for predicting the short-term death risk of patients with thrombotic thrombocytopenic purpura (TTP)

[0116] 1. Measure the values of cTnI, LDH, UREA, and IBIL. Devices such as Roche C8000 biochemical analyzer (for UREA) and Abbott i2000 immunoassay analyzer (for cTnI) can be used for detection.

[0117] 2. Assign scores to each indicator according to whether the patient exceeds the cut-off value (≥ cut-off value = 1 point, otherwise = 0 point);

[0118] The cut-off values corresponding to the four biomarkers are:

[0119] Cardiac troponin I (cTnI) ≥ 353.1 pg / mL;

[0120] Lactate dehydrogenase (LDH) ≥ 992 U / L;

[0121] Urea nitrogen (UREA) ≥ 10.9 mmol / L;

[0122] Indirect bilirubin (IBIL) ≥ 32.3 μmol / L;

[0123] When the concentration measured for each indicator ≥ the cut-off value = 1 point, otherwise = 0 point.

[0124] 3. Cumulatively sum the scores to obtain the total score (0 - 4 points), corresponding to different risk levels (e.g., ≥3 points = high risk).

[0125] Scoring stage

[0126] Cumulative rule: Each index ≥ cut-off value is counted as 1 point, total score 0 - 4 points;

[0127] Risk classification:

[0128] 0 - 1 point: Low risk (21.3% mortality);

[0129] 2 points: Medium risk (39.1% mortality);

[0130] 3 - 4 points: High risk (≥60.9% mortality).

[0131] II. Identification of high-risk patients

[0132] Patient information: Female, 58 years old, PLT = 8×109 / L, ADAMTS13 activity = 4% at admission.

[0133] Laboratory results:

[0134] cTnI = 360 pg / mL (≥353.1)

[0135] LDH = 1000 U / L (≥992)

[0136] UREA = 11.0 mmol / L (≥10.9)

[0137] IBIL = 33 μmol / L (≥32.3)

[0138] Risk score: 4 points, predicted death risk ≥92.3%.

[0139] Treatment: Immediately perform plasma exchange (twice a day) + rituximab (1000 mg, on days 1 and 15).

[0140] III. Management of medium-risk patients

[0141] Patient information: Male, 42 years old, PLT = 12×109 / L, ADAMTS13 activity = 8% at admission.

[0142] Laboratory results:

[0143] cTnI = 200 pg / mL (<353.1)

[0144] LDH = 950 U / L (<992)

[0145] UREA = 9.5 mmol / L (<10.9)

[0146] IBIL = 28 μmol / L (<32.3)

[0147] Risk score: A score of 0 predicts a 21.3% risk of death.

[0148] Treatment: Conventional plasma exchange (once a day) + glucocorticoid therapy, no need to upgrade the intervention.

[0149] In summary, the combined detection of cTnI, LDH, UREA, and IBIL provides a reliable tool for the identification of high-risk TTP patients. LDH > 992 U / L, IBIL > 32.3 μmol / L, cTnI > 353.1 pg / mL, and UREA > 10.9 mmol / L at admission indicate a significantly increased risk of death, and multidisciplinary collaborative treatment should be initiated and the treatment strategy should be adjusted individually. This application emphasizes the core position of organ damage monitoring in TTP management and provides evidence-based basis for improving the prognosis of patients.

[0150] Finally, it should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover a non-exclusive inclusion, such that a process, method, article or apparatus comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or apparatus.

[0151] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concept. Therefore, the appended claims are intended to be construed as including the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0152] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A combination of predictive markers for the short-term death risk of patients with thrombotic thrombocytopenic purpura (TTP), characterized in that, The biomarker combination includes: Cardiac troponin I; Lactate dehydrogenase; Blood urea nitrogen; Indirect bilirubin.

2. The biomarker combination for predicting the short-term death risk of patients with thrombotic thrombocytopenic purpura (TTP) according to claim 1, characterized in that The cut-off values corresponding to the four biomarkers are: Cardiac troponin I (cTnI) ≥ 353.1 pg / mL; Lactate dehydrogenase (LDH) ≥ 992 U / L; Blood urea nitrogen (UREA) ≥ 10.9 mmol / L; Indirect bilirubin (IBIL) ≥ 32.3 μmol / L; Among them, the biomarker combination is used to evaluate the death risk of patients within 28 days. When at least three indicators are met, it is determined to be high-risk, that is, the death risk ≥ 60.9%.

3. A risk assessment model based on the biomarker combination according to any one of claims 1-2, characterized in that, The risk score is calculated by the following formula: The risk score value is calculated according to the input levels of cTnI, LDH, UREA, and IBIL.

4. The risk assessment model of the marker combination according to claim 3, wherein The short-term death risk of patients with thrombotic thrombocytopenic purpura (TTP) is evaluated according to the said risk score as follows: 0 - 1 point: Low risk; 2 points: Medium risk; 3 - 4 points: High risk.

5. A short-term death risk prediction system for patients with thrombotic thrombocytopenic purpura (TTP), characterized in that, The system includes: A processor and a memory. The memory is coupled to the processor. The memory stores instructions that, when executed by the processor, use the calculation steps of any one of the models according to claims 3 - 4.

6. The application of the biomarker combination according to any one of claims 1 - 2, or the risk prediction model of thrombotic thrombocytopenic purpura according to any one of claims 3 - 4, or the risk prediction system of thrombotic thrombocytopenic purpura according to claim 5 in the preparation of a risk prediction product for thrombotic thrombocytopenic purpura.

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