A myocardial infarction quantitative early warning method and system based on blood ceramide markers

Abstract

The present application relates to the technical field of health informatics, in particular to a myocardial infarction quantitative early warning method and system based on blood ceramide markers, comprising the following steps: obtaining blood ceramide concentration and clinical indicators, labeling change trend, comparing abnormal samples, screening myocardial infarction data, calculating correlation coefficient, extracting meteorological data, comparing coincidence degree, screening intervention data, mapping curve to judge difference, extracting abnormal fluctuation to calculate deviation period, and generating myocardial infarction early warning structure index set. In the present application, by accurately monitoring the ceramide concentration fluctuation and carrying out multi-dimensional clinical data correlation analysis, the cardiovascular risk early warning ability is effectively improved, important clinical information is avoided to be missed, the comprehensiveness of data processing and risk assessment is improved, the prediction is more accurate, the individualization characteristics of myocardial infarction risk assessment are enhanced, it is ensured that the early warning system can adapt to the needs of different patients, more personalized medical decision basis is provided, and the accuracy and practicality of cardiovascular event prevention and control are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of health informatics, and in particular to a myocardial infarction quantification and early warning method and system based on blood ceramide markers. BACKGROUND

[0002] The technical field of health informatics involves the use of information technology to address the management, analysis and processing of health data, particularly in the collection, storage, transmission and analysis of medical data. In this field, core issues include data collection and processing, electronic health record systems, medical big data analysis, the application of artificial intelligence and machine learning technology in medicine, and risk assessment and early warning systems based on biomarkers. With the advancement of technology, health informatics has gradually covered multiple levels from individual health monitoring to global public health management, and plays an important role in precision medicine and individualized medicine.

[0003] Among them, the traditional myocardial infarction quantification and early warning method based on blood ceramide markers refers to detecting the concentration changes of ceramides in blood to assess risk indicators related to cardiovascular diseases. This method uses mass spectrometry and chemiluminescence technology to quantitatively detect ceramides, thereby providing data support for cardiovascular mortality risk assessment. Traditional methods mostly rely on the analysis of ceramides and their metabolites, using changes in vascular endothelial damage markers to predict the occurrence of cardiovascular events. The core content of this technology includes accurately detecting ceramide levels and combining relevant biomarkers to quantitatively assess cardiovascular risk through a specific scoring system. Traditional methods mostly use mass spectrometry for sample analysis and combine statistical methods to establish a risk assessment model, thereby achieving early warning of cardiovascular death.

[0004] The cardiovascular risk assessment methods in the prior art mostly rely on single analysis of ceramide concentration and vascular endothelial damage markers, ignoring potential influencing factors such as patient clinical background, environmental factors and intervention measures, resulting in a lack of individualization and comprehensiveness in risk assessment results, and often failing to accurately identify early cardiovascular risk in actual application. In particular, in acute myocardial infarction patients, due to the lack of dynamic tracking of ceramide concentration fluctuations and identification of interference factors, the early warning capability is insufficient, and accurate intervention signals cannot be provided to the clinic in a timely manner, thereby affecting early diagnosis and treatment decisions. SUMMARY

[0005] The purpose of the present application is to solve the shortcomings in the prior art and to provide a myocardial infarction quantification and early warning method and system based on blood ceramide markers.

[0006] In order to achieve the above-mentioned purpose, the present application adopts the following technical solution: A myocardial infarction quantification and early warning method based on blood ceramide markers, comprising the following steps:

[0007] S1: Obtain ceramide concentration data in blood samples, extract clinical indicators of natural population, stable coronary heart disease patients, angina patients and acute myocardial infarction patients, analyze ceramide concentration trend, and generate ceramide concentration fluctuation list;

[0008] S2: Based on the ceramide concentration fluctuation list, screen the acute myocardial infarction sample, combine the coronary stenosis, electrocardiogram and myocardial enzyme indicators, identify the correlation coefficient of ceramide and clinical indicators, analyze the deviation amplitude, and generate the ceramide and clinical indicators correlation label group;

[0009] S3: Call the ceramide and clinical indicators correlation label group, extract the fluctuation sample number, identify the synchronous temperature, humidity and wind speed data, compare the weather interference period and the ceramide concentration fluctuation time period, record the number of coincidence time periods, and generate the ceramide concentration affected sample list;

[0010] S4: Based on the ceramide concentration affected sample list, screen the sample data not affected by interference, extract the low-dose oral aspirin intervention record and follow-up data, map the intervention time node and the ceramide concentration change curve, and obtain the heart infarction risk warning fluctuation group.

[0011] As a further scheme of the present application, the ceramide concentration fluctuation list includes sample number, concentration change trend label, time fluctuation identification, sample classification, the ceramide and clinical indicators correlation label group includes correlation level, deviation type, reference comparison result, sample correlation number, the ceramide concentration affected sample list includes interference weather type, influence time section, coincidence period number, affected sample number, and the heart infarction risk warning fluctuation group includes effect distribution abnormal number, intervention time node record, concentration change deviation, and follow-up data matching degree.

[0012] As a further scheme of the present application, the ceramide concentration fluctuation list acquisition step specifically comprises:

[0013] S111: Obtain ceramide concentration data in blood samples, extract clinical indicators of natural population, stable coronary heart disease patients, angina patients and acute myocardial infarction patients, match ceramide concentration detection time and sample collection time, compare detection time range and reference time range, and generate sample concentration record period;

[0014] S112: Based on the sample concentration record period and reference time interval, judge the period coincidence, extract the coincidence time and the reference total time ratio, screen the sample number with the coincidence ratio lower than the reference value, and combine the concentration change trend label number to obtain the sample concentration coverage deviation rate;

[0015] S113: According to the sample concentration coverage deviation rate, the sample number is judged for deviation state, the sample number with the concentration synchronization threshold is identified, the sample number, the concentration coverage information and the deviation rate value are integrated, and the ceramide concentration fluctuation list is generated.

[0016] As a further scheme of the present application, the acquisition step of the ceramide and clinical index association label group is specifically:

[0017] S211: Based on the ceramide concentration fluctuation list, sample data of acute myocardial infarction patients, coronary stenosis degree, electrocardiogram and myocardial enzyme index table are identified, the correlation coefficient of ceramide concentration and clinical index is extracted, the change amplitude of the correlation coefficient deviating from the reference value is calculated, and the correlation deviation value is obtained;

[0018] S212: The correlation deviation value is called, combined with sample distribution parameters, deviation set and adjustment frequency, sample deviation data is uniformly collected, correlation deviation degree is identified according to sample number, sample correlation dispersion value is calculated, deviation direction is judged combined with adjustment frequency, and ceramide and clinical index association label group is generated.

[0019] As a further scheme of the present application, the acquisition step of the ceramide concentration affected sample list is specifically:

[0020] S311: The ceramide and clinical index association label group is called, the fluctuation deviation sample number is screened, the concentration fluctuation time period is extracted according to the sample association table, the concentration fluctuation time period is processed according to time dimension, the concentration fluctuation time period index table is identified, and the fluctuation sample concentration time period set is obtained;

[0021] S312: According to the fluctuation sample concentration time period set, temperature, humidity and wind speed data of the area where the sample is located at the same period are collected, the meteorological influence table is identified, daily meteorological disturbance is judged according to interference threshold, and is matched with fluctuation sample concentration period day by day, the meteorological disturbance intervention intensity value of sample is calculated, whether the sample exists concentration monitoring abnormal association is judged, and the ceramide concentration affected sample list is generated.

[0022] As a further scheme of the present application, the acquisition step of the heart infarction risk early warning fluctuation group is specifically:

[0023] S411: Based on the ceramide concentration affected sample list, unmarked sample data is screened, low-dose oral aspirin intervention record and follow-up data are extracted, intervention time node and follow-up time range are recorded, and un-affected sample data set is obtained.

[0024] S412: Call the meteorological interference-free sample data set, match the low-dose oral aspirin intervention time node with the ceramide concentration change curve, extract the intervention time node concentration change amplitude according to the sample number, count the concentration change trend in the intervention time node and the follow-up time range, and generate an intervention effect matching data set;

[0025] S413: According to the intervention effect matching data set, judge the matching degree between the intervention effect and the concentration change trend, identify the effect fluctuation sample and evaluate the concentration deviation degree, analyze the concentration fluctuation degree of the sample, mark the sample whose fluctuation degree exceeds the set reference value as an abnormal sample, and obtain a myocardial infarction risk warning fluctuation group.

[0026] As a further scheme of the present application, the matching degree between the intervention effect and the concentration change trend is judged according to the continuous follow-up data after the low-dose oral aspirin intervention time node, the change trend direction and the change amplitude of the ceramide concentration in the follow-up time range are analyzed, and when the ceramide concentration presents a continuous downward trend and the downward amplitude reaches or exceeds a preset effective downward threshold, it is judged as matching.

[0027] The identification of the effect fluctuation sample and the evaluation of the concentration deviation degree refer to the calculation of the absolute deviation value between the ceramide concentration at the follow-up time point in the follow-up time range and the preset steady-state ceramide concentration target value.

[0028] As a further scheme of the present application, the method further comprises a S5 step:

[0029] S5: Call the myocardial infarction risk warning fluctuation group, extract the abnormal fluctuation sample group, calculate the deviation value of the ceramide concentration change and the clinical index in the sample, record the time distribution of the concentration change period and the clinical index deviation period, and generate a myocardial infarction warning structure index set.

[0030] The myocardial infarction warning structure index set includes concentration deviation value, index deviation level, period deviation identifier, and warning efficiency index.

[0031] As a further scheme of the present application, the acquisition step of the myocardial infarction warning structure index set is specifically:

[0032] S511: Call the sample number and the corresponding concentration change time interval in the myocardial infarction risk warning fluctuation group, calculate the concentration change amplitude of adjacent follow-up periods, screen samples exceeding the reference value, record the time interval and the concentration change amplitude, and obtain a concentration fluctuation abnormality identifier set.

[0033] S512: Based on the ceramide concentration deviation value and the clinical index deviation value corresponding to the samples in the concentration fluctuation abnormality identifier set, identify the sample deviation value sequence, extract the abnormal distribution interval and compare with the critical value, record the deviation direction and the sample number, and form a warning deviation index group.

[0034] S513: According to the sample number in the early warning deviation index group, the ceramide concentration change and the clinical index deviation time period are extracted, the difference value between the concentration change period and the deviation period is calculated, the time synchronization deviation distribution table is identified, the difference is marked according to the early warning benchmark sorting, and the myocardial infarction early warning structure index set is generated.

[0035] The myocardial infarction quantitative early warning system based on blood ceramide markers is used for executing the myocardial infarction quantitative early warning method based on blood ceramide markers, and the system comprises:

[0036] The concentration state extraction module obtains ceramide concentration data and clinical index information in a blood sample, extracts sample numbers and concentration detection times, compares concentration change trends and clinical index states, marks samples with inconsistent times, and generates a ceramide concentration fluctuation list;

[0037] The index correlation classification module is based on the ceramide concentration fluctuation list, locates acute myocardial infarction patient samples, extracts sample numbers, clinical indexes and benchmark times, calculates ceramide concentration and clinical index deviation values, classifies and marks deviation types, and generates a ceramide and clinical index correlation label group;

[0038] The weather interference identification module is based on the ceramide and clinical index correlation label group, screens fluctuation lag samples, locates corresponding sample areas, extracts temperature, humidity and wind speed interference periods, determines coincidence with concentration fluctuation time periods, screens interference frequent samples, and generates a ceramide concentration affected by environment sample list;

[0039] The intervention effect diagnosis module is based on the ceramide concentration affected by environment sample list, eliminates interference sample data, extracts low-dose oral aspirin intervention records and follow-up data, matches intervention time nodes and concentration change curves, calculates intervention effect and concentration change trend ratio, identifies samples with concentration fluctuation but low intervention effect, and obtains a myocardial infarction risk early warning fluctuation group;

[0040] The early warning index analysis module is based on the myocardial infarction risk early warning fluctuation group, locates the concentration deviation record of the sample in the early warning model, extracts the concentration change and clinical index deviation ratio, compares the difference between the concentration change period and the deviation period, maps the sample concentration use and the early warning state, and forms a myocardial infarction early warning structure index set.

[0041] Compared with the prior art, the advantages and positive effects of the present application are that:

[0042] In the present application, by introducing the accurate monitoring of ceramide concentration fluctuation and the multidimensional correlation analysis of clinical data, the early warning ability of cardiovascular risk is effectively improved, especially in the early identification of acute myocardial infarction, solving the limitations of traditional methods relying on single biomarker evaluation, avoiding missing important clinical information, improving the comprehensiveness of data processing and risk assessment, combining meteorological factors for interference screening, providing more comprehensive and accurate prediction for cardiovascular events, and through the introduction of low-dose intervention effect analysis, enhancing the individualized characteristics of myocardial infarction risk assessment, so that the early warning system can adapt to the specific circumstances of different patients, providing more personalized medical decision-making basis, enhancing the precision and practicality of cardiovascular event prevention and control. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 The workflow diagram of the present application is shown in the figure;

[0044] Figure 2 The acquisition flowchart of ceramide concentration fluctuation list in the present application is shown in the figure;

[0045] Figure 3 The acquisition flowchart of ceramide and clinical index correlation label set in the present application is shown in the figure;

[0046] Figure 4 The acquisition flowchart of ceramide concentration affected by environment sample list in the present application is shown in the figure;

[0047] Figure 5 The acquisition flowchart of myocardial infarction risk early warning fluctuation group in the present application is shown in the figure;

[0048] Figure 6 The acquisition flowchart of myocardial infarction early warning structure index set in the present application is shown in the figure. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application is further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0050] In the description of the present application, it should be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and therefore cannot be understood as indicating or implying that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In addition, in the description of the present application, the meaning of "multiple" is two or more, unless otherwise specifically limited.

[0051] Embodiment one:

[0052] Please refer to Figure 1 The application provides a technical solution: a myocardial infarction quantitative early warning method based on blood ceramide markers, including the following steps:

[0053] S1: Obtain ceramide concentration data in blood samples, extract clinical index information of natural population, stable coronary heart disease patients, angina patients and acute myocardial infarction patients, label ceramide concentration trend and compare with clinical index, identify abnormal fluctuation sample number, and generate ceramide concentration fluctuation list;

[0054] S2: Based on the ceramide concentration fluctuation list, screen the sample data of acute myocardial infarction patients, combine the coronary stenosis degree, electrocardiogram and myocardial enzyme index table, extract the correlation coefficient of ceramide concentration and clinical index, analyze the change amplitude of the correlation coefficient deviating from the reference value, and generate a ceramide and clinical index correlation label group;

[0055] S3: Call the ceramide and clinical index correlation label group, extract the fluctuation sample number, identify the same period temperature, humidity and wind speed data, compare the meteorological interference period with the ceramide concentration fluctuation period, record the number of coincidence time periods, and generate a ceramide concentration affected by the environment sample list;

[0056] S4: Based on the ceramide concentration affected by the environment sample list, screen the sample data not affected by the interference, extract the low-dose oral aspirin intervention record and follow-up data, map the intervention time node and ceramide concentration change curve, judge whether the intervention effect exists significant difference, and get a myocardial infarction risk early warning fluctuation group;

[0057] S5: Call the myocardial infarction risk early warning fluctuation group, extract the abnormal fluctuation sample group, calculate the ceramide concentration change and clinical index deviation value in the sample, record the time distribution of concentration change period and clinical index deviation period, and generate a myocardial infarction early warning structure index set.

[0058] The ceramide concentration fluctuation list includes sample number, concentration trend label, time fluctuation identification, sample classification, the ceramide and clinical index correlation label group includes correlation level, deviation type, reference comparison result, sample correlation number, the ceramide concentration affected by the environment sample list includes interference weather type, influence time section, number of overlapping periods, affected sample number, the myocardial infarction risk early warning fluctuation group includes effect distribution abnormal number, intervention time node record, concentration change deviation, follow-up data matching degree, and the myocardial infarction early warning structure index set includes concentration deviation value, index deviation level, period deviation identification and early warning efficiency index.

[0059] Please refer toFigure 2 The acquiring step of the ceramide concentration fluctuation list is specifically:

[0060] S111: Acquire ceramide concentration data in the blood sample, extract the clinical index information of the natural population, stable coronary heart disease patients, angina patients and acute myocardial infarction patients, match the ceramide concentration detection time and the sample collection time, compare the detection time range and the reference time range, and generate a sample concentration record period;

[0061] Acquire ceramide concentration data in the blood sample. In the patient diagnosis and follow-up process, first collect venous blood. Patient A completed blood collection at 09:45 on March 15. The blood sample was then sent to the laboratory. The liquid chromatography-tandem mass spectrometry (LC-MS / MS) method was used to detect the concentration of multiple ceramides in the serum. The C16:0 ceramide concentration was 325 nmol / L, the C18:0 ceramide concentration was 280 nmol / L, and the C24:1 ceramide concentration was 550 nmol / L. At the same time, the clinical index information of the patient was retrieved from the electronic medical record system. For the natural population, blood pressure, blood lipid, blood glucose and other routine physical examination data were collected. For stable coronary heart disease patients, coronary CTA results and cardiac function indicators were collected. For angina patients, angina attack frequency and nitroglycerin dosage were collected. For acute myocardial infarction patients, coronary stenosis degree was collected, for example, left main stenosis degree was 90%. Electrocardiogram characteristics, for example, ST segment elevation amplitude was 0.2 mV. Myocardial enzyme level, for example, troponin I peak value was 200 ng / mL. Match the ceramide concentration detection time and the sample collection time. For each blood sample, record the collection time point and the ceramide concentration detection report issuance time point. The collection time of sample A was 09:45 on March 15, and the ceramide concentration detection report time was 10:30 on March 15. Time stamp comparison was performed on the collection time and the detection time, and the time difference between the two was calculated to be 45 minutes. Compare the detection time range with the reference time range. The preset reference time range threshold is 60 minutes, which is set based on the clinical laboratory standard operating procedure to ensure sample stability before analysis and detection result accuracy. Samples with a time difference exceeding this threshold have distorted results due to sample degradation. 45 minutes is less than 60 minutes. It is determined that the detection time range of sample A meets the reference requirements. A sample concentration record period is generated. The period records the effectiveness of the ceramide concentration data of sample A within the specified time window, i.e. the ceramide concentration data is valid within the period from 09:45 on March 15 to 10:30 on March 15.

[0062] S112: Perform period coincidence judgment based on the sample concentration record period and the reference time interval, extract the coincidence time and the reference total time ratio, screen sample numbers with a coincidence ratio below the reference value, and combine the concentration change trend to mark the number of samples, and obtain the sample concentration coverage deviation rate;

[0063] The period coincidence judgment is performed based on the sample concentration recording period and the reference time interval. The effective recording period of sample A is from 09:45 to 10:30 on March 15, with a duration of 45 minutes. The preset reference time interval is from 00:00 to 24:00 every day, with a total duration of 1440 minutes. The recording period of sample A is compared with the reference time interval, and the coincident time and the reference total duration ratio are extracted. The coincident time is 45 minutes, and the coincidence ratio is calculated as 45 minutes divided by 1440 minutes, which is 0.03125. The sample number with a coincidence ratio lower than the reference value is screened, and the coincidence ratio reference value is set to 0.05. This reference value is determined according to the lower limit of the daily fluctuation data coverage rate of the normal individual serum ceramide concentration in long-term clinical observation. A ratio lower than this ratio indicates that the sample collection is discontinuous or the monitoring data is insufficient to reflect the true fluctuation. The calculated ratio 0.03125 is less than the reference value 0.05, and the number of sample A is screened. The number of concentration change trends is marked. For the screened sample A, the C16:0 ceramide concentration increases from 325 nmol / L to 330 nmol / L and then decreases to 328 nmol / L within the 45-minute recording period, and the C18:0 ceramide concentration is marked twice. The C18:0 ceramide concentration is stable at 281 nmol / L, and the ceramide total concentration change trend is marked three times. The sample concentration coverage deviation rate is obtained, which combines the insufficient time coverage and the frequency of concentration change. The concentration coverage deviation rate of sample A is 0.03125, accompanied by three concentration change marks.

[0064] S113: According to the sample concentration coverage deviation rate, the deviation state of the sample number is determined, the sample number with a concentration synchronization threshold higher than the deviation rate is identified, the sample number, concentration coverage information and deviation rate value are integrated, and a ceramide concentration fluctuation list is generated.

[0065] According to the sample concentration coverage deviation rate, the preset concentration synchronization threshold is 0.15, which is based on a large amount of patient clinical data analysis. When the deviation rate is higher than this value, the ceramide concentration fluctuation behavior and the synchronicity of clinical events appear significant difference. The sample number of the sample A is identified as the deviation state sample, and the coverage deviation rate 0.03125 of the sample A is compared with the concentration synchronization threshold 0.15. 0.03125 is less than 0.15, and the sample A is not identified as a deviation state sample. It is assumed that there is a sample B, and the coverage deviation rate of the sample B is 0.18, which is higher than 0.15. The number of the sample B will be identified as a deviation state sample. The sample number, concentration coverage information and deviation rate value are integrated. For the sample B identified as the deviation state, the number, specific concentration coverage information, its recording period is 09:00 to 09:30 on March 16, the number of concentration change trend labels is 5, and the deviation rate value 0.18 are collected. The ceramide concentration fluctuation list is generated. The list contains the complete deviation record of the sample identified as the deviation state due to insufficient monitoring coverage or abnormal frequent concentration fluctuation.

[0066] Please refer to Figure 3 The acquisition step of the ceramide and clinical index association label group is specifically:

[0067] S211: Based on the ceramide concentration fluctuation list, the sample data of the acute myocardial infarction patient is identified with the coronary stenosis degree, electrocardiogram and myocardial enzyme index table. The correlation coefficient of ceramide concentration and clinical index is extracted, the change amplitude of the correlation coefficient deviation from the reference value is calculated, and the correlation deviation value is obtained.

[0068] Based on the list of ceramide concentration fluctuations, patient sample numbers with a confirmed diagnosis of acute myocardial infarction (AMI) were selected, and sample number AMI001 was identified. For sample AMI001, coronary angiography results at the time of onset were extracted, showing a 90% stenosis of the left anterior descending artery. Electrocardiogram results showed ST segment elevation amplitude of 0.2 mV in leads II, III, and aVF. Cardiac enzyme test results showed a peak troponin I level of 200 ng / mL. Ceramide concentration data at this time point was also obtained, with a C16:0 ceramide concentration of 450 nmol / L. The correlation coefficient between ceramide concentration and clinical indicators was extracted. For the AMI patient population, Spearman correlation coefficients between ceramide concentration and various clinical indicators were calculated through regression analysis of historical data. Statistical analysis of ceramide concentration and troponin I data from 200 AMI patients showed a correlation coefficient of 0.75 between the two, and a correlation coefficient with ST segment elevation amplitude. The correlation coefficient between ceramide and troponin I was 0.68, and the correlation coefficient with the degree of coronary artery stenosis was 0.72. The deviation of the correlation coefficient from the baseline value was calculated. The baseline correlation coefficients were preset as follows: the baseline correlation coefficient between ceramide and troponin I was 0.65, the baseline correlation coefficient with ST segment elevation amplitude was 0.60, and the baseline correlation coefficient with the degree of coronary artery stenosis was 0.62. The baseline values ​​were obtained by statistically analyzing the correlation between ceramide and clinical indicators in a large number of non-AMI patients and AMI recovery patients, and taking the average value as the reference value under stable conditions. The absolute difference between each actual correlation coefficient and the corresponding baseline value was calculated. The deviation of the correlation coefficient between ceramide and troponin I was 0.10, the deviation of the correlation coefficient with ST segment elevation amplitude was 0.08, and the deviation of the correlation coefficient with the degree of coronary artery stenosis was 0.10. The correlation deviation value was obtained. For the sample AMI001, the correlation deviation value included 0.10 for troponin I, 0.08 for ST segment elevation, and 0.10 for coronary artery stenosis.

[0069] S212: Call the correlation deviation value, combine it with sample distribution parameters, deviation set, and adjustment frequency to uniformly collect sample deviation data, identify the correlation deviation degree by sample number, and use the formula:

[0070] ;

[0071] Calculate the dispersion value of sample association, combine it with the adjustment frequency to determine the direction of deviation, and generate a label group that associates ceramide with clinical indicators;

[0072] in, Represents the dispersion value of sample correlation. Representing the The sample at the th The deviation parameter value in the class deviation set. Representing the The average deviation parameter value of a sample across all deviation sets. Representing the The sample at the th The correction amount of the sample distribution parameter corresponding to the class deviation set. Representing the Adjustment frequency for each sample This represents the total number of categories that deviate from the set;

[0073] The correlation deviation value is retrieved using the formula: Calculate the dispersion value of sample correlation, where The value represents the dispersion of sample correlation, which is used to quantify the degree to which the correlation between the concentration of ceramide in a single sample and its clinical indicators deviates from the average level of the population. The larger the value, the more significant the deviation. Representing the The sample at the th The deviation parameter values ​​in the deviation set are the actual deviation values ​​of a specific sample in relation to a certain clinical indicator, such as calculated values ​​of 0.10, 0.08, etc. Representing the The average deviation parameter value of the nth sample across all deviation sets is for the nth sample. The arithmetic mean of the correlation deviations of all different clinical indicators for each sample. Representing the The sample at the th The correction amount of the sample distribution parameter in the deviation set is used to apply different weights to the sample according to the distribution characteristics of the sample on a specific deviation type. For example, in the diagnosis of AMI, the weight of troponin I is higher than that of electrocardiogram and coronary artery stenosis, so its correction amount will be higher. The correction amount is determined based on the clinical expert experience and historical data analysis. For example, when the diagnostic specificity and sensitivity of a certain indicator are high, its correction amount is also increased accordingly to ensure a more comprehensive consideration of the deviation. Representing the The adjustment frequency for each sample reflects the number of times the sample data has been calibrated or verified. The higher the value, the higher the denominator term. The larger, the more A relatively smaller value indicates that the more adjustments the sample has undergone, the more stable its current deviation is considered or has been corrected to some extent in the calculation. The total number of categories representing the deviation set refers to the total number of types of deviations related to the clinical indicators considered, such as deviations related to the degree of coronary artery stenosis, deviations related to electrocardiogram, and deviations related to myocardial enzymes.

[0074] The calculation logic of this formula is as follows: first, calculate the deviation parameter value of a single sample in a specific deviation set. the difference of the average deviation parameter value of the sample in all deviation sets , which reflects the fluctuation of the sample in a specific deviation type relative to its average deviation level; then, multiply this fluctuation by the corresponding sample distribution parameter correction , different weights are applied to different types of deviation, ensuring that each deviation contributes reasonably to the total deviation; then, sum the weighted fluctuations of all deviation sets to obtain the total weighted fluctuation of the sample in all deviation types; finally, divide the total weighted fluctuation by the adjustment frequency , add 1, take the absolute value and square root to obtain the final sample relevance deviation . The introduction of square root and absolute value makes the value always non-negative, and has a more obvious amplification effect on larger deviation values, used to measure the degree of deviation rather than the direction. The adjustment frequency as the denominator, adding 1 is to avoid the denominator being zero, while ensuring that the larger the value, the smaller the value, reflecting that after multiple adjustments, the deviation of the sample is suppressed;

[0075] The benefit of the formula is that by introducing the term, it ensures that the calculated deviation reflects the relative deviation of the individual sample from its own average level, rather than the absolute deviation, thus more accurately capturing the fluctuation characteristics within the sample, while the correction amount is weighted according to the importance of different clinical indicators, improving the sensitivity to key indicator deviations, and the term reduces the weight of samples that have been reviewed or corrected multiple times, enhancing the robustness of the model. Taking sample AMI001 as an example, set (coronary stenosis relevance deviation, ECG relevance deviation, myocardial enzyme relevance deviation), its deviation parameter value (coronary stenosis), (ECG), (myocardial enzyme), calculate , set the sample distribution parameter correction (coronary stenosis), (ECG), (myocardial enzyme), this correction is set according to the clinical weight of each indicator in AMI diagnosis, for example, troponin I as the gold standard for AMI diagnosis, its correction is the highest, set the adjustment frequency of sample AMI001 , substitute into the formula to calculate:

[0076] ;

[0077] Combine the adjustment frequency to determine the deviation direction, if is positive, indicating that the item deviates above the average level, and if it is negative, it is below the average level. For example, the sample AMI001 has a coronary stenosis correlation deviation of is positive, indicating that the item deviates above the average level, and if it is negative, it is below the average level. For example, the sample AMI001 has a coronary stenosis correlation deviation of

[0078] Please refer to Figure 4 The specific steps for obtaining the sample list affected by the concentration of ceramide under the influence of the environment are as follows:

[0079] S311: Call the ceramide and clinical indicator association label set to screen the fluctuation deviation sample number, extract the concentration fluctuation period according to the sample association table, process the concentration fluctuation period according to the time dimension, identify the concentration fluctuation period index table, and obtain the fluctuation sample concentration period set;

[0080] Call the ceramide and clinical indicator association label set to screen out sample numbers with a correlation deviation degree higher than a certain threshold. This threshold is set to 0.03. This threshold is set to the average deviation of the normal population plus twice the standard deviation based on a large amount of historical data analysis and combined with the experience of clinicians, to ensure that the selected samples have significant deviations. The correlation deviation of sample AMI001 is 0.01987, which is less than 0.03, and it is not selected as a fluctuation deviation sample. Assume that there is a sample AMI002 with a correlation deviation of 0.035, which is higher than 0.03. The number of AMI002 will be selected as a fluctuation deviation sample. According to the sample association table, extract the concentration fluctuation period. For the selected fluctuation deviation sample number AMI002, query the pre-established sample association table, which associates sample numbers with generated sample concentration record periods, and extract the corresponding ceramide concentration fluctuation period. The concentration fluctuation period of sample AMI002 is from 3 / 20 11:00 to 3 / 20 11:45. Process the concentration fluctuation period according to the time dimension, standardize the extracted concentration fluctuation period, and mark the fluctuation period of sample AMI002 as the 11th hour of the day for subsequent time matching with weather data. Identify the concentration fluctuation period index table to establish an index table that records the number of each fluctuation deviation sample and its corresponding specific fluctuation period. The index table contains the following entries: AMI002: [2024-03-20 11:00, 2024-03-20 11:45], and obtain the fluctuation sample concentration period set, which contains detailed concentration monitoring time windows for all identified fluctuation deviation samples, for example, the time period information of AMI002 is included in the set.

[0081] S312: Based on the set of fluctuating sample concentration periods, collect temperature, humidity, and wind speed data for the area where the samples are located during the same period, identify the meteorological impact table, determine daily meteorological interference based on the interference threshold, and match it with the fluctuating sample concentration periods day by day using the formula:

[0082] ;

[0083] Calculate the meteorological interference intensity value of the sample, determine whether there is an abnormal correlation in concentration monitoring of the sample, and generate a list of samples whose ceramide concentration is affected by the environment;

[0084] in, The meteorological disturbance intervention intensity value representing the sample. This represents the number of time periods for which meteorological data is collected within a single day. Representing the Temperature values ​​of the sample area for each time period. The daily average value representing the temperature of the sample area. Representing the Humidity values ​​of the sample area for each time period. The daily average value representing the humidity of the sample area. Representing the Wind speed values ​​for the sample area during each time period;

[0085] Based on the time period set of fluctuating sample concentrations, the meteorological disturbance intervention intensity value of the samples is calculated using a formula, where... The meteorological interference intensity value representing the sample is used to quantify the potential interference of environmental meteorological factors on the monitoring of ceramide concentration. The larger the Q value, the more significant the meteorological interference. This represents the number of time periods for which meteorological data is collected within a single day. For example, if data is collected once per hour, there are 24 time periods in a day. ; Representing the Temperature values ​​of a sample area for a given time period, for example, the real-time temperature of a certain hour, in degrees Celsius (°C). The daily average value representing the temperature of the sample area is the arithmetic mean of the temperature over the 24 hours of the day, expressed in degrees Celsius (°C). Representing the Humidity values ​​of a sample area for a given time period, for example, real-time humidity for a given hour, expressed as a percentage (%). The daily average value representing the humidity of the sample area is the arithmetic mean of the humidity over the 24 hours of the day, expressed as a percentage (%). Representing the The wind speed values ​​of the sample area for a given time period, for example, the real-time wind speed for a given hour, in meters per second (m / s).

[0086] The formula's calculation logic is that the numerator includes the absolute value of temperature deviation and the percentage of humidity deviation. It quantifies the fluctuation range of temperature relative to the daily average temperature over a certain period of time. The fluctuation range of humidity relative to the daily average humidity was quantified and normalized to the range of 0-1 for comparison with temperature fluctuations. Humidity was normalized by dividing by 100 to ensure that temperature and humidity are comparable in magnitude. The sum of the fluctuations reflects the potential cumulative effect of temperature and humidity changes on ceramide concentration. (Denominator term) The influence of wind speed was considered, with 1 added to avoid a denominator of zero. This also indicates that higher wind speeds have a stronger dilution or diffusion effect on ceramide concentration changes, thus reducing the direct impact of temperature and humidity fluctuations on concentration. The overall meteorological interference intensity for the day was obtained by summing this ratio across all data collection periods within a single day and taking the average. This quantifies the overall interference effect of environmental factors on sample concentration.

[0087] The advantage of this formula lies in its ability to provide a concise and comprehensive meteorological disturbance intensity index by quantifying the combined effects of temperature, humidity, and wind speed fluctuations on ceramide concentration. In particular, by normalizing humidity changes and introducing wind speed as a suppression factor, the model can more accurately assess the interactions of complex environmental factors. Taking sample AMI002 as an example, its concentration fluctuation period was from 11:00 to 11:45 on March 20th. Meteorological data for the entire day of March 20th was collected for its location. Assuming... Average daily temperature ℃, average daily humidity The following are meteorological data for some periods:

[0088] Table 1: Example of meteorological data for March 20:

[0089]

[0090] Table 1 shows the meteorological data for a hospital area in Shanghai on March 20th, covering certain time periods. Calculations were performed for each time period. For example, consider time periods 10, 11, and 12:

[0091] Time slot 10 (09:00): ;

[0092] Time slot 11 (10:00): ;

[0093] Time slot 12 (11:00): ;

[0094] Assuming that after summing over all 24 time periods The sum is 4.5, then , according to the interference threshold value to judge daily weather interference, set the weather interference threshold value as 0.10, this threshold value is through the analysis of a large number of normal population ceramide concentration data under different weather conditions, combined with the clinical medical expert opinion, when Q value is higher than 0.10, ceramide concentration is significantly affected by environmental meteorological factors, its stability is damaged, judge whether the sample exists concentration monitoring abnormal correlation, compare the calculated Q value 0.1875 with the weather interference threshold value 0.10, since 0.1875 is greater than 0.10, determine that sample AMI002 exists concentration monitoring abnormal correlation, that is, its ceramide concentration fluctuation is disturbed by meteorological factors, generate the sample list of ceramide concentration affected by environment, this list contains sample number AMI002, and its weather interference intervention intensity Q value is 0.1875, indicating that its ceramide concentration fluctuation is significantly affected by environmental meteorological factors.

[0095] Please refer to Figure 5 , the obtaining steps of the myocardial infarction risk early warning fluctuation group are specifically:

[0096] S411: based on the sample list of ceramide concentration affected by environment, screen the unmarked sample data, extract the low-dose oral aspirin intervention record and follow-up data, record the intervention time node and follow-up time range, and obtain the sample data set not affected by weather interference;

[0097] Based on the sample list of ceramide concentration affected by environment, select the sample not determined to be affected by weather interference, and its weather interference intervention intensity is lower than or equal to the weather interference threshold value 0.10. The weather interference intervention intensity of sample P001 is 0.07, which is lower than 0.10. Therefore, it is screened as a sample data not affected by weather interference. Extract the low-dose oral aspirin intervention record and follow-up data. For the screened sample P001, all low-dose oral aspirin medication records are retrieved from its electronic medical record system, including the first medication date, medication date, dose adjustment record, and at the same time, the ceramide concentration follow-up data of the patient during the use of aspirin is extracted, such as monthly serum ceramide concentration detection results, platelet aggregation rate, C-reactive protein level and other related clinical indicators. Record the intervention time node and follow-up time range, and determine the specific date and time of sample P001 starting to take low-dose aspirin. January 1, 08:00 is taken as the intervention time node. After recording this intervention time node, the follow-up time range of continuous monitoring of ceramide concentration is from January 1 to June 30, covering a follow-up period of 6 months. Obtain the sample data set not affected by weather interference. This data set only contains samples whose ceramide concentration fluctuation is not significantly affected by environmental factors and has complete aspirin intervention and follow-up records. The intervention and follow-up data set of sample P001 is included in this data set.

[0098] S412: Call the weather interference-free sample data set, match the low-dose oral aspirin intervention time node with the ceramide concentration change curve, extract the concentration change amplitude at the intervention time node according to the sample number, count the concentration change trend in the intervention time node and the follow-up time range, and generate an intervention effect matching data set;

[0099] For sample P001 in the weather interference-free sample data set, its low-dose oral aspirin intervention time node (January 1, 08:00) is time-aligned with its historical ceramide concentration change curve. Before intervention, the average concentration of C16:0 ceramide in sample P001 was 280 nmol / L. After intervention, the concentration was found to be decreasing. The concentration change amplitude at the intervention time node was extracted according to the sample number. The change amplitude of each sample at a specific time point after the intervention time node was calculated. At the 3rd month after intervention (April 1), the change amplitude of the baseline ceramide concentration before intervention was 250 nmol / L. On April 1, the C16:0 ceramide concentration of sample P001 was 250 nmol / L, and the change amplitude was 250 nmol / L minus 280 nmol / L, resulting in -30 nmol / L. The concentration change trend in the intervention time node and the follow-up time range was counted. The overall trend of ceramide concentration change in each sample during the entire follow-up time range was analyzed. The slope was determined by linear regression analysis. If the slope is negative, it indicates a downward trend. The C16:0 ceramide concentration of sample P001 from January 1 to June 30 showed a continuous downward trend, with a linear regression slope of -8.5 nmol / L / month. The intervention effect matching data set was generated. This data set contains the intervention time point, the ceramide concentration change amplitude after intervention, and the concentration change trend during the follow-up period for each sample. The record of sample P001 is: intervention time January 1, 3-month change amplitude -30 nmol / L, follow-up period concentration trend continuously decreasing.

[0100] S413: According to the intervention effect matching data set, judge the matching degree between the intervention effect and the concentration change trend, identify the effect fluctuation samples and evaluate the concentration deviation degree, analyze the concentration fluctuation degree of the sample, mark the sample whose fluctuation degree exceeds the set reference value as an abnormal sample, and obtain a heart attack risk warning fluctuation group;

[0101] The matching degree between the intervention effect and the concentration change trend is determined according to the continuous follow-up data after the low-dose oral aspirin intervention time node. The change trend direction and amplitude of ceramide concentration in the follow-up time range are analyzed. When the ceramide concentration shows a continuous downward trend and the downward amplitude reaches or exceeds the preset effective downward threshold, it is judged as matching.

[0102] Identifying the effect fluctuation sample and evaluating the concentration deviation degree refers to calculating the absolute deviation value between the ceramide concentration at the follow-up time point in the follow-up time range and the preset steady-state ceramide concentration target value;

[0103] According to the intervention effect matching data set, according to the continuous follow-up data after the low-dose oral aspirin intervention time node, the change trend direction and change amplitude of ceramide concentration in the follow-up time range are analyzed, and an effective decline threshold is set: the ceramide concentration decreases by 10% or more than the baseline value within 3 consecutive months, which is based on the results of multiple clinical studies and Meta analysis, indicating that the risk reduction of cardiovascular events is closely related to the significant decrease of ceramide concentration. The ceramide concentration of sample P001 decreased from 280 nmol / L to 240 nmol / L 3 months after the intervention, a decrease of 40 nmol / L, a decrease of 40 divided by 280 and then multiplied by 100%, about 14.3%, more than 10%, and the linear regression shows a continuous downward trend, which is judged to be matched. Identify the effect fluctuation sample and evaluate the concentration deviation degree, calculate the absolute deviation value between the ceramide concentration at the follow-up time point in the follow-up time range and the preset steady-state ceramide concentration target value, and the steady-state ceramide concentration target value is set to 180 nmol / L. This target value is the ideal healthy level determined by long-term monitoring of serum ceramide concentration in a large number of healthy adults, combined with the target value of blood lipid management in the treatment guidelines for cardiovascular diseases. The ceramide concentration of sample P001 at a certain time point during follow-up is 250 nmol / L, and the absolute deviation value is |250-180|=70 nmol / L. Analyze the concentration fluctuation degree of the sample, calculate the standard deviation of the ceramide concentration value of each sample in the follow-up period to quantify the fluctuation degree. The C16:0 ceramide concentration detection value sequence of sample P001 in the 6-month follow-up period is: 280, 275, 260, 250, 245, 240 nmol / L, and the standard deviation is about 15.1 nmol / L. Mark the samples with fluctuation degree exceeding the set reference value as abnormal samples. Set the fluctuation degree reference value: the standard deviation of ceramide concentration exceeds 10 nmol / L. This reference value is obtained by analyzing the long-term dynamic monitoring data of serum ceramide concentration in 500 healthy people. The standard deviation in the normal physiological fluctuation range does not exceed 8 nmol / L, and 10 nmol / L is used as the threshold value, indicating that the concentration fluctuation has exceeded the normal physiological or acceptable treatment response range. The fluctuation standard deviation of sample P001 is 15.1 nmol / L, which exceeds 10 nmol / L, and is marked as an abnormal sample. Get the heart attack risk warning fluctuation group, which includes abnormal samples with significant ceramide concentration fluctuations after aspirin intervention or not reaching the expected stable decline target.

[0104] Please refer to Figure 6 , the steps of obtaining the heart attack warning structure index set are:

[0105] S511: Call the sample number and the corresponding concentration change time interval in the myocardial infarction risk early warning fluctuation group, calculate the concentration change amplitude in the adjacent follow-up period, screen out samples exceeding the reference value, record the time interval and the concentration change amplitude, and obtain the concentration fluctuation abnormality identification set;

[0106] For the abnormal sample P001 in the myocardial infarction risk early warning fluctuation group, the follow-up time interval is from January 1 to June 30, and the monitoring points are the beginning of each month. The ceramide concentration data are as follows: 280 nmol / L on January 1, 275 nmol / L on February 1, 260 nmol / L on March 1, 250 nmol / L on April 1, 245 nmol / L on May 1, and 240 nmol / L on June 1. The ceramide concentration change amplitude in the adjacent follow-up period is calculated, for example, the change amplitude from February 1 to March 1 is 260 nmol / L minus 275 nmol / L, resulting in -15 nmol / L. The samples exceeding the reference value are screened out, and the concentration change amplitude reference value is set: the absolute change amplitude exceeds 20 nmol / L. This reference value is determined according to the degree of attention to rapid changes in ceramide concentration in clinical practice. Fluctuations exceeding this amplitude in a short period of time indicate potential aggravation of cardiovascular risk. The calculated monthly change amplitude is compared with the reference value of 20 nmol / L. The change amplitude from February 1 to March 1 is -15 nmol / L, and its absolute value is 15 nmol / L, which does not exceed the reference value. Assuming that there is an adjacent period, the ceramide concentration suddenly decreases from 250 nmol / L to 220 nmol / L, and the change amplitude is -30 nmol / L, and its absolute value is 30 nmol / L, which exceeds the reference value of 20 nmol / L. Therefore, the sample is screened out in this period. The time interval of the occurrence of the dramatic concentration fluctuation is recorded, for example, from March 1 to April 1, and the specific concentration change amplitude is recorded, for example, -30 nmol / L. The concentration fluctuation abnormality identification set is obtained, which includes the sample number of the sample that appears dramatic ceramide concentration fluctuation, the specific time interval of the fluctuation, and the corresponding change amplitude.

[0107] S512: Based on the ceramide concentration deviation value and the clinical index deviation value of the samples in the concentration fluctuation abnormality identification set, identify the sample deviation value sequence, extract the abnormal distribution interval and compare the critical value, record the deviation direction and the sample number, and form the early warning deviation index group;

[0108] Taking the Q001 sample in the concentration fluctuation anomaly identification set as an example, the ceramide concentration change range of Q001 is-30 nmol / L from March 1 to April 1, the C16:0 ceramide concentration on March 15 is 260 nmol / L, which deviates from the target value of 180 nmol / L by 80 nmol / L, and the deviation is 70 nmol / L on March 30. Combined with the calculated clinical index deviation, the troponin I deviation on March 15 is 0.12, and the ST segment deviation is 0.10. Organize the deviation values in chronological order to obtain the ceramide concentration deviation sequence [80, 70] and the clinical index deviation sequence [0.12, 0.10]. Compared with the critical value (ceramide concentration 50 nmol / L, clinical index 0.08), it is found that the above values are all above the threshold. Therefore, it is determined that the ceramide concentration continuously abnormal interval is from March 15 to March 30, and the clinical index continuously abnormal interval is from March 15 to the present. Combined with the deviation direction, the ceramide concentration is continuously higher than the target, and the troponin I and ST segment are both higher than the baseline. Finally, the warning deviation index group of sample Q001 is formed, which provides a basis for identifying the risk of myocardial infarction.

[0109] S513: According to the sample number in the warning deviation index group, extract the ceramide concentration change and clinical index deviation time period, calculate the difference value between the concentration change period and the deviation period, identify the time synchronization deviation distribution table, and generate the myocardial infarction warning structure index set according to the warning benchmark.

[0110] Taking sample Q001 in the sample number in the warning deviation index group as an example, the ceramide concentration significantly changes from March 1 to April 1, and the troponin I in the clinical index is continuously higher than the threshold from March 15 to April 15. Take the center points of the time periods for comparison. The ceramide concentration change center point is March 16, and the clinical index deviation center point is March 30. The difference between the two is 14 days. Thus, the time synchronization deviation distribution table is constructed, and the difference values of each sample are recorded. Combined with clinical experience, the warning benchmark is set: a time difference of less than 7 days is considered high synchronization, which indicates a higher sensitivity of myocardial infarction warning. The comparison result shows that the difference value of Q001 is 14 days, which does not reach the high synchronization threshold. Assuming that the difference value of sample R002 is 5 days, it meets the high synchronization standard and improves the warning priority. Finally, the myocardial infarction warning structure index set is generated by comprehensively considering the sample number, deviation direction, time difference value, and synchronization annotation, which provides standardized data support for risk classification and early intervention.

[0111] The myocardial infarction quantification warning system based on blood ceramide markers is used to execute the myocardial infarction quantification warning method based on blood ceramide markers, and the system comprises:

[0112] The concentration state extraction module obtains ceramide concentration data and clinical index information in a blood sample, extracts sample number and concentration detection time, compares concentration change trend and clinical index state, marks samples with inconsistent time, and generates a ceramide concentration fluctuation list;

[0113] The index correlation classification module locates acute myocardial infarction patient samples based on the ceramide concentration fluctuation list, extracts sample number, clinical index and reference time, calculates ceramide concentration and clinical index deviation value, classifies and labels deviation type, and generates a ceramide and clinical index correlation label group;

[0114] The weather interference identification module screens fluctuation lag samples based on the ceramide and clinical index correlation label group, locates corresponding sample areas, extracts temperature, humidity and wind speed interference period, determines coincidence with concentration fluctuation time period, screens interference frequent samples, and generates a ceramide concentration affected by environment sample list;

[0115] The intervention effect diagnosis module eliminates interference sample data based on the ceramide concentration affected by environment sample list, extracts low-dose oral aspirin intervention records and follow-up data, matches intervention time nodes and concentration change curves, calculates intervention effect and concentration change trend ratio, identifies samples with concentration fluctuation but low intervention effect, and obtains a myocardial infarction risk early warning fluctuation group.

[0116] The early warning index analysis module locates sample concentration deviation records in the early warning model based on the myocardial infarction risk early warning fluctuation group, extracts concentration change and clinical index deviation ratio, compares concentration change period and deviation period difference, maps sample concentration use and early warning state, and forms a myocardial infarction early warning structure index set.

[0117] The above is only a preferred embodiment of the present application, and does not limit the form of the present application, any skilled person in the art can use the disclosed technical content to make changes or modifications as equivalent embodiments applied to other fields, but any simple modification, equivalent change and modification made according to the technical essence of the present application to the above embodiments without departing from the technical solution content of the present application, still belongs to the protection scope of the technical solution of the present application.

Claims

1. A quantitative early warning method for myocardial infarction based on blood ceramide biomarkers, characterized in that, Includes the following steps: S1: Obtain ceramide concentration data in blood samples, extract clinical indicators from natural populations, patients with stable coronary heart disease, patients with angina pectoris, and patients with acute myocardial infarction, analyze the trend of ceramide concentration changes, and generate a list of ceramide concentration fluctuations. S2: Based on the list of ceramide concentration fluctuations, screen acute myocardial infarction samples, combine coronary artery stenosis, electrocardiogram and myocardial enzyme indicators, identify the correlation coefficient between ceramide and clinical indicators, analyze the deviation range, and generate a label group for the association between ceramide and clinical indicators. S3: Call the ceramide and clinical indicator association tag group, extract the fluctuation sample number, identify the temperature, humidity and wind speed data of the same period, compare the meteorological interference period with the ceramide concentration fluctuation period, record the number of overlapping period periods, and generate a sample list of ceramide concentration affected by the environment. S4: Based on the list of samples whose ceramide concentration is affected by the environment, filter the sample data that is not disturbed, extract the low-dose oral aspirin intervention records and follow-up data, map the intervention time nodes and the ceramide concentration change curve, and obtain the myocardial infarction risk warning fluctuation group; S5: Call the myocardial infarction risk warning fluctuation group, extract the abnormal fluctuation sample group, calculate the deviation of ceramide concentration change and clinical indicators in the sample, record the time distribution of concentration change cycle and clinical indicator deviation cycle, and generate a myocardial infarction warning structure indicator set. The myocardial infarction early warning structure index set includes concentration deviation value, index deviation level, period deviation identifier, and early warning efficiency index.

2. The method for quantitative early warning of myocardial infarction based on blood ceramide biomarkers according to claim 1, characterized in that, The list of ceramide concentration fluctuations includes sample number, concentration change trend label, time fluctuation identifier, and sample classification. The ceramide and clinical indicator association label group includes association level, deviation type, benchmark comparison result, and sample association number. The list of samples whose ceramide concentration is affected by the environment includes interfering meteorological type, affected time period, number of overlapping time periods, and affected sample number. The myocardial infarction risk warning fluctuation group includes abnormal effect distribution number, intervention time node record, concentration change deviation, and follow-up data matching degree.

3. The method for quantitative early warning of myocardial infarction based on blood ceramide biomarkers according to claim 1, characterized in that, The specific steps for obtaining the ceramide concentration fluctuation list are as follows: S111: Obtain ceramide concentration data in blood samples, extract clinical indicator information from natural populations, patients with stable coronary heart disease, patients with angina pectoris, and patients with acute myocardial infarction, match ceramide concentration detection time with sample collection time, compare the detection time range with the baseline time range, and generate sample concentration recording time periods; S112: Based on the sample concentration recording period and the benchmark time interval, determine the overlap of the time periods, extract the ratio of the overlap time to the benchmark total duration, filter the sample numbers with an overlap ratio lower than the benchmark value, and combine the number of labels with the concentration change trend to obtain the sample concentration coverage deviation rate. S113: Based on the sample concentration coverage deviation rate, determine the deviation status of the sample number, identify the sample number whose deviation rate is higher than the concentration synchronization threshold, integrate the sample number, concentration coverage information and deviation rate value, and generate a list of ceramide concentration fluctuations.

4. The method for quantitative early warning of myocardial infarction based on blood ceramide biomarkers according to claim 3, characterized in that, The specific steps for obtaining the ceramide-clinical indicator association tag group are as follows: S211: Based on the ceramide concentration fluctuation list, identify the sample data of patients with acute myocardial infarction and the coronary artery stenosis degree, electrocardiogram and myocardial enzyme index table, extract the correlation coefficient between ceramide concentration and clinical indicators, calculate the change range of the correlation coefficient from the benchmark value, and obtain the correlation deviation value. S212: Call the aforementioned correlation deviation value, combine it with sample distribution parameters, deviation set and adjustment frequency, uniformly collect sample deviation data, identify the correlation deviation degree according to sample number, calculate the sample correlation dispersion value, combine the adjustment frequency to determine the deviation direction, and generate a label group for the association between ceramide and clinical indicators.

5. The method for quantitative early warning of myocardial infarction based on blood ceramide biomarkers according to claim 4, characterized in that, The specific steps for obtaining the sample list of ceramide concentrations affected by the environment are as follows: S311: Call the ceramide and clinical indicator association tag group, filter the sample number of fluctuation deviation, extract the concentration fluctuation time period according to the sample association table, process the concentration fluctuation time period according to the time dimension, identify the concentration fluctuation time period index table, and obtain the set of fluctuation sample concentration time periods. S312: Based on the set of fluctuating sample concentration periods, collect temperature, humidity and wind speed data of the area where the samples are located during the same period, identify the meteorological impact table, determine the daily meteorological interference based on the interference threshold, match it with the fluctuating sample concentration periods day by day, calculate the meteorological interference intervention intensity value of the samples, determine whether there is an abnormal correlation in concentration monitoring of the samples, and generate a list of samples whose ceramide concentration is affected by the environment.

6. The method for quantitative early warning of myocardial infarction based on blood ceramide biomarkers according to claim 5, characterized in that, The specific steps for obtaining the myocardial infarction risk warning fluctuation group are as follows: S411: Based on the list of samples whose ceramide concentration is affected by the environment, filter unlabeled sample data, extract low-dose oral aspirin intervention records and follow-up data, record intervention time nodes and follow-up time ranges, and obtain a sample dataset that is not affected by meteorological interference. S412: Call the dataset of samples that are not affected by meteorological disturbances, match the low-dose oral aspirin intervention time points with the ceramide concentration change curve, extract the concentration change amplitude of the intervention time points according to the sample number, statistically analyze the concentration change trend within the intervention time point and follow-up time range, and generate an intervention effect matching dataset. S413: Based on the intervention effect matching dataset, determine the degree of matching between the intervention effect and the concentration change trend, identify the effect fluctuation samples and assess the degree of concentration deviation, analyze the concentration fluctuation of the samples, and mark the samples with fluctuation exceeding the set benchmark value as abnormal samples to obtain the myocardial infarction risk warning fluctuation group.

7. The method for quantitative early warning of myocardial infarction based on blood ceramide biomarkers according to claim 6, characterized in that, The determination of the matching degree between the intervention effect and the concentration change trend is based on the continuous follow-up data after the low-dose oral aspirin intervention time point. The analysis of the trend direction and magnitude of the change in ceramide concentration within the follow-up time range is performed. When the ceramide concentration shows a continuous downward trend and the magnitude of the decrease reaches or exceeds the preset effective decrease threshold, it is judged as a match. The identification of fluctuating samples and evaluation of concentration deviation refers to the calculation of the absolute deviation between the ceramide concentration at follow-up time points within the follow-up time range and the preset steady-state ceramide concentration target value.

8. The method for quantitative early warning of myocardial infarction based on blood ceramide biomarkers according to claim 1, characterized in that, The specific steps for obtaining the myocardial infarction early warning structure index set are as follows: S511: Call the sample number and corresponding concentration change time interval in the myocardial infarction risk warning fluctuation group, calculate the concentration change amplitude of adjacent follow-up periods, screen samples that exceed the benchmark value, record the time interval and concentration change amplitude, and obtain the concentration fluctuation abnormality identifier set. S512: Based on the ceramide concentration deviation and clinical indicator deviation corresponding to the samples in the concentration fluctuation abnormality identifier set, identify the sample deviation sequence, extract the abnormal distribution interval and compare it with the critical value, record the deviation direction and sample number, and form a warning deviation indicator group. S513: Based on the sample number in the warning deviation index group, extract the time period of ceramide concentration change and clinical indicator deviation, calculate the difference between the concentration change cycle and the deviation cycle, identify the time synchronization deviation distribution table, and generate a myocardial infarction warning structure index set according to the warning benchmark sorting and labeling difference.

9. A quantitative early warning system for myocardial infarction based on blood ceramide biomarkers, characterized in that, The system is used to implement the myocardial infarction quantitative early warning method based on blood ceramide biomarkers as described in any one of claims 1-8, and the system comprises: The concentration status extraction module acquires ceramide concentration data and clinical indicator information in blood samples, extracts sample numbers and concentration detection time, compares concentration change trends with clinical indicator status, marks samples with inconsistent times, and generates a list of ceramide concentration fluctuations. Based on the list of ceramide concentration fluctuations, the indicator association classification module locates acute myocardial infarction patient samples, extracts sample numbers, clinical indicators and baseline time, calculates the deviation values ​​between ceramide concentration and clinical indicators, classifies and labels the deviation types, and generates ceramide and clinical indicator association label groups. The meteorological interference identification module, based on the ceramide and clinical indicator association tag group, filters samples with fluctuation lag, locates the corresponding sample area, extracts the interference time periods of temperature, humidity and wind speed, determines the overlap with the concentration fluctuation time period, filters samples with frequent interference, and generates a list of samples whose ceramide concentration is affected by the environment. The intervention effect diagnosis module, based on the list of samples whose ceramide concentration is affected by the environment, removes interfering sample data, extracts low-dose oral aspirin intervention records and follow-up data, matches intervention time points with concentration change curves, calculates the ratio of intervention effect to concentration change trend, identifies samples with fluctuating concentration but inefficient intervention effect, and obtains the myocardial infarction risk warning fluctuation group. The early warning indicator analysis module, based on the aforementioned myocardial infarction risk early warning fluctuation group, locates the concentration deviation records of samples in the early warning model, extracts the ratio of concentration change to clinical indicator deviation, compares the difference between the concentration change cycle and the deviation cycle, maps the sample concentration usage with the early warning status, and forms a set of myocardial infarction early warning structural indicators.

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