Platelet aggregation dynamic risk assessment method based on platelet control analyzer

Abstract

The present application relates to the technical field of medical data processing, in particular to a platelet aggregation dynamic risk assessment method based on a platelet control analyzer. The method first acquires platelet parameters and induced aggregation rates of platelets under different inducers; according to the volatility of the platelet parameters, the parameter abnormality degree of the platelet parameters is determined; for the induced aggregation rates under different inducers, according to the position of the maximum induced aggregation rate under different concentrations and the induced aggregation rates under different concentrations, the initial possibility of drug resistance abnormality is determined; combined with the parameter abnormality degree, the initial possibility is corrected to determine the corrected possibility of drug resistance abnormality; and according to the number of drug resistance abnormality and the corrected possibility, the drug resistance influence evaluation value is obtained. The present application improves the accuracy of platelet aggregation dynamic risk assessment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical data processing, in particular to a platelet aggregation dynamic risk assessment method based on a platelet control analyzer. BACKGROUND

[0002] Platelet aggregation is a key link of thrombosis and hemostasis, and its abnormality is closely related to cardiovascular diseases, anti-platelet drug efficacy monitoring and postoperative bleeding risk. Traditional evaluation methods rely on in vitro static detection, which has problems such as complex operation, long time consumption and insufficient standardization, and it is difficult to dynamically reflect the platelet function under the complex physiological environment in vivo. At present, the platelet function is mainly monitored based on various mainstream monitoring equipment. For example, a platelet control instrument, by placing the sample into the detection instrument and then analyzing and predicting the functional characteristics of platelets through an algorithm.

[0003] The existing method pays more attention to the maximum aggregation rate when assessing the dynamic risk of platelet aggregation based on a platelet analyzer. However, due to long-term regular use of therapeutic doses of aspirin, some patients may develop drug resistance such as aspirin resistance, i.e., they cannot effectively inhibit platelet function. This phenomenon can cause abnormalities in the aggregation rate of platelets, thereby affecting the accuracy of the dynamic risk assessment of platelet aggregation. SUMMARY

[0004] In order to solve the technical problem of low accuracy of platelet aggregation risk assessment, the purpose of the present application is to provide a platelet aggregation dynamic risk assessment method based on a platelet control analyzer, and the technical solution adopted is as follows:

[0005] In a first aspect, the present application provides a platelet aggregation dynamic risk assessment method based on a platelet control analyzer, which comprises:

[0006] Obtaining platelet parameters and induced aggregation rates of platelets under the induction of different inducers;

[0007] According to the volatility of the platelet parameters, determining the parameter abnormality degree of the platelet parameters;

[0008] For the induced aggregation rates under the induction of different inducers, according to the position of the maximum induced aggregation rate under different concentrations and the induced aggregation rates under different concentrations, determining the initial possibility of the occurrence of drug resistance abnormality;

[0009] Combining the parameter abnormality degree, correcting the initial possibility to determine the corrected possibility of the occurrence of drug resistance abnormality; and according to the number of times of the occurrence of drug resistance abnormality and the corrected possibility, obtaining a drug resistance impact assessment value.

[0010] Further, the determination of the parameter abnormality degree of the platelet parameters according to the volatility of the platelet parameters comprises:

[0011] The platelet parameters include: platelet count, mean platelet volume, and platelet hematocrit;

[0012] Any kind of platelet parameter in the monitoring process is taken as the platelet parameter to be measured, and the data mean of the platelet parameter to be measured is taken as the overall data value of the platelet parameter to be measured;

[0013] The average value of the absolute value of the slope corresponding to the platelet parameter to be measured at different time points in the fitting curve is taken as the fluctuation of the platelet parameter to be measured;

[0014] According to the overall data value and the fluctuation of all platelet parameters, the parameter abnormality degree of the platelet parameter is determined.

[0015] Further, according to the overall data value and the fluctuation of all platelet parameters, the parameter abnormality degree of the platelet parameter is determined.

[0016] For different kinds of platelet parameters, the platelet parameter closest to the preset ideal platelet parameter in the monitoring process is taken as the standard platelet parameter;

[0017] The standard deviation value is determined by comparing all platelet parameters in the monitoring process with the standard platelet parameter;

[0018] According to the overall trend of the fluctuation, the change trend is determined;

[0019] The parameter abnormality degree of the platelet parameter is determined in combination with the standard deviation value and the change trend.

[0020] Further, according to the position of the maximum induced aggregation rate under different concentrations and the induced aggregation rate under different concentrations, the initial possibility of drug resistance anomaly is determined for the induced aggregation rate under different inducers.

[0021] The induced aggregation rate under different inducers includes: the induced aggregation rate under arachidonic acid and the induced aggregation rate under adenosine diphosphate;

[0022] Any kind of inducer is taken as the target inducer, and any concentration is taken as the target concentration, the mean value of the induced aggregation rate of the target inducer under the target concentration is taken as the aggregation rate characteristic of the target inducer under the target concentration, and the aggregation rate characteristics of the target inducer are arranged in the order of corresponding concentration to construct an aggregation rate characteristic sequence;

[0023] acquire a concentration corresponding to a maximum aggregation rate feature of the target inducer, when the concentration is not in a preset aggregation rate occurrence interval, determine that a maximum aggregation rate occurrence position is abnormal; when the maximum aggregation rate occurrence position is abnormal, determine a position abnormality degree of the maximum aggregation rate according to the position of the maximum aggregation rate;

[0024] divide the aggregation rate feature sequence into multiple intervals as measurement intervals; compare the aggregation rate features of different measurement intervals to determine drug resistance abnormality occurrence;

[0025] when the drug resistance abnormality occurs, determine an aggregation abnormality degree of the aggregation rate according to a difference between the aggregation rate features of different measurement intervals;

[0026] determine an initial possibility of the drug resistance abnormality occurrence under the target inducer according to the position abnormality degree and the aggregation abnormality degree.

[0027] Further, the determination of the position abnormality degree of the maximum aggregation rate according to the position of the maximum aggregation rate includes:

[0028] acquire an endpoint closest to the preset aggregation rate occurrence interval as a standard position sequence number of the maximum aggregation rate; take an absolute value of a difference between a position sequence number of the maximum aggregation rate in the aggregation rate feature sequence and the standard position sequence number as a numerator, take a concentration quantity of the target inducer as a denominator, and take a corresponding ratio as the position abnormality degree.

[0029] Further, the division of the aggregation rate feature sequence into multiple intervals as measurement intervals includes:

[0030] the maximum aggregation rate occurrence interval is a standard concentration measurement interval, a left interval of the maximum aggregation rate occurrence interval is a low concentration measurement interval, and a right interval of the maximum aggregation rate occurrence interval is a high concentration measurement interval.

[0031] Further, the comparison of the aggregation rate features of different measurement intervals to determine the drug resistance abnormality occurrence includes:

[0032] calculate a difference between the aggregation rate features of the low concentration measurement interval and the standard concentration measurement interval as a first difference value;

[0033] calculate a difference between the aggregation rate features of the standard concentration measurement interval and the high concentration measurement interval as a second difference value;

[0034] when any one of the first difference value and the second difference value is greater than 0, determine that the drug resistance abnormality occurs.

[0035] Further, the determination of the initial possibility of the drug resistance abnormality occurrence under the target inducer according to the position abnormality degree and the aggregation abnormality degree includes:

[0036] The product value of the position abnormality degree and the aggregation abnormality degree is taken as an initial possibility of the drug resistance abnormality in the monitoring process.

[0037] Further, the initial possibility is corrected by combining the parameter abnormality degree to determine a corrected possibility of the drug resistance abnormality.

[0038] For any kind of induced aggregation rate induced by an inducing agent, the product value of the parameter abnormality degree and the initial possibility is taken as a single correction value of the drug resistance abnormality.

[0039] The mean value of the single correction values corresponding to the induced aggregation rates induced by all kinds of inducing agents is taken as the corrected possibility of the drug resistance abnormality.

[0040] Further, the drug resistance influence evaluation value is obtained according to the number of times of the drug resistance abnormality and the corrected possibility.

[0041] The proportion of the number of times of the drug resistance abnormality is taken as an influence adjustment value.

[0042] The mean value of the corrected possibility when the drug resistance abnormality occurs is taken as an initial evaluation value.

[0043] The initial evaluation value is adjusted by taking the influence adjustment value as a weight to obtain the drug resistance influence evaluation value.

[0044] In a second aspect, a platelet aggregation dynamic risk evaluation system based on a platelet control analyzer is provided, and the system includes the following modules.

[0045] An acquisition module is configured to acquire platelet parameters and induced aggregation rates of platelets under different inducing agents.

[0046] An abnormality analysis module is configured to determine a parameter abnormality degree of the platelet parameters according to fluctuation of the platelet parameters.

[0047] A resistance analysis module is configured to determine an initial possibility of a drug resistance abnormality for induced aggregation rates under different inducing agents according to positions of maximum induced aggregation rates under different concentrations and the induced aggregation rates under different concentrations.

[0048] An evaluation module is configured to correct the initial possibility by combining the parameter abnormality degree to determine a corrected possibility of the drug resistance abnormality, and obtain a drug resistance influence evaluation value according to the number of times of the drug resistance abnormality and the corrected possibility.

[0049] In a third aspect, an electronic device is provided, comprising a memory and a processor, the memory storing executable code, and the processor executing the executable code to implement the embodiments of any of the possible implementations of the first aspect.

[0050] In a fourth aspect, a computer program product is provided, comprising computer program code which, when run on a computer, causes the computer to perform the method of the first aspect or any of the possible implementations of the first aspect.

[0051] In a fifth aspect, a computer readable storage medium is provided, storing a computer program, which, when executed in a computer, causes the computer to perform the embodiments of any of the possible implementations of the first aspect.

[0052] The embodiments of the present application have at least the following beneficial effects:

[0053] The present application determines the abnormality degree of the basic parameters of the platelets of a patient by analyzing the fluctuation of the basic parameters of the platelets measured by a platelet control analyzer. Then, the initial possibility of the drug resistance abnormality is determined according to the abnormality of the position of the maximum aggregation rate of the platelets in each measurement process and the abnormality degree of the aggregation rate characteristics in the measurement process by analyzing the fluctuation of the time sequence fluctuation curve of the induced aggregation rate under the induction of different inducing agents in different test results, and the initial possibility of the drug resistance abnormality in each test process is obtained. The initial possibility of the drug resistance abnormality is corrected by the abnormality degree of the parameters, and the possibility of the aggregation rate of the platelets affected by the drug resistance is quantified, and the drug resistance influence evaluation value is obtained, thereby improving the accuracy of the dynamic risk assessment of the aggregation of the platelets. BRIEF DESCRIPTION OF DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, and the advantages thereof, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0055] Figure 1 The method flowchart of the platelet aggregation dynamic risk assessment method based on a platelet control analyzer provided by an embodiment of the present application;

[0056] Figure 2 The system block diagram of the platelet aggregation dynamic risk assessment system based on a platelet control analyzer provided by an embodiment of the present application;

[0057] Figure 3 A structural schematic diagram of a computer device provided by an embodiment of the present application;

[0058] Figure 4 A raw quantity of platelet quantity in a high fluctuation abnormal mode and a schematic diagram of curve fitting of the platelet quantity provided by an embodiment of the present application;

[0059] Figure 5 A raw quantity of platelet quantity in a high value abnormal mode and a schematic diagram of curve fitting of the platelet quantity provided by an embodiment of the present application;

[0060] Figure 6 A raw quantity of platelet quantity in a double abnormal mode and a schematic diagram of curve fitting of the platelet quantity provided by an embodiment of the present application;

[0061] Figure 7 A raw quantity of platelet quantity in a normal mode and a schematic diagram of curve fitting of the platelet quantity provided by an embodiment of the present application;

[0062] Figure 8 A broken line schematic diagram of an instantaneous slope corresponding to a PLT fluctuation curve in a high fluctuation abnormal mode provided by an embodiment of the present application;

[0063] Figure 9 A broken line schematic diagram of an instantaneous slope corresponding to a PLT fluctuation curve in a high value abnormal mode provided by an embodiment of the present application;

[0064] Figure 10 A broken line schematic diagram of an instantaneous slope corresponding to a PLT fluctuation curve in a double abnormal mode provided by an embodiment of the present application;

[0065] Figure 11 A broken line schematic diagram of an instantaneous slope corresponding to a PLT fluctuation curve in a normal mode provided by an embodiment of the present application;

[0066] Figure 12 A schematic diagram of parameter index statistics in a high fluctuation abnormal mode provided by an embodiment of the present application;

[0067] Figure 13 A schematic diagram of parameter index statistics in a high value abnormal mode provided by an embodiment of the present application;

[0068] Figure 14 A schematic diagram of parameter index statistics in a double abnormal mode provided by an embodiment of the present application;

[0069] Figure 15A schematic diagram of parameter index statistics in a normal mode provided by an embodiment of the present application;

[0070] Figure 16 A comparison schematic diagram of data values of parameter abnormality degrees in different abnormal modes provided by an embodiment of the present application. DETAILED DESCRIPTION

[0071] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined inventive purpose, the following describes in detail the specific implementation, structure, features and effects of the platelet aggregation dynamic risk assessment method based on a platelet control analyzer according to the present application, in combination with the accompanying drawings and preferred embodiments.

[0072] In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0073] In the description of embodiments of the present application, unless otherwise specified, " / " represents the meaning of or, for example, A / B can represent A or B: "and / or" in the text only describes the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent: A alone, A and B exist together, and B alone, in addition, in the description of embodiments of the present application, "multiple" means two or more than two.

[0074] Hereinafter, the terms "first", "second" are only used for description purposes, and cannot be understood as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more features.

[0075] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0076] The embodiments of the present application are described below in combination with the accompanying drawings. Those skilled in the art can know that with the development of technology and the appearance of new scenes, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0077] The specific scheme of the platelet aggregation dynamic risk assessment method based on a platelet control analyzer provided by the present application is specifically described below in combination with the accompanying drawings.

[0078] Please refer to Figure 1 which shows the step flowchart of the platelet aggregation dynamic risk assessment method based on a platelet control analyzer provided by an embodiment of the present application, which comprises the following steps:

[0079] Step S100, acquiring platelet parameters and induced aggregation rates of platelets under different inducers.

[0080] For patients with thrombotic diseases, it is necessary to dynamically monitor the platelet aggregation function after surgery to warn the risk of thrombosis.

[0081] After the blood sample of the patient is collected by the vacuum blood collection tube, the vacuum blood collection tube is placed on the platelet analyzer for measurement. During the measurement process, the instrument detects the changes of the basic parameters such as platelet count (PLT), mean platelet volume (MPV), and platelet hematocrit (PCT) in real time and generates real-time change curves, which are uploaded to the central database in real time through the Internet of Things module. The platelet count, mean platelet volume, and platelet hematocrit are collectively referred to as platelet parameters.

[0082] Then, by integrating the optical turbidimetry and impedance method, the arachidonic acid / adenosine diphosphate (AA / ADP) induced aggregation rate time sequence is dynamically captured through multi-concentration testing. Each concentration test will obtain the above time sequence. Among them, the induced aggregation rate under arachidonic acid induction and the induced aggregation rate under adenosine diphosphate induction, i.e. the induced aggregation rate under different inducer induction.

[0083] Among them, arachidonic acid AA and adenosine diphosphate ADP are two bioactive substances closely related to human physiological functions, which are used to evaluate drug resistance to abnormalities.

[0084] Step S200, determining the parameter abnormality degree of the platelet parameters according to the volatility of the platelet parameters.

[0085] First, taking the platelet count as an example, for example, the platelet control analyzer obtains M times of platelet counts of the patient during the monitoring process. The PLT fluctuation curve is obtained by curve fitting of the M platelet counts, and whether the platelet count of the patient is abnormal is determined by analyzing the change of the PLT fluctuation curve. Please refer to Figure 4 , Figure 4 The original number of platelet count in the high fluctuation abnormal mode and the schematic diagram of curve fitting of the platelet count; Figure 4 The abscissa in is the collection time, with seconds as the unit; each blue point corresponds to a platelet count (PLT value), which is the original data; the red broken line is the fitting curve obtained by curve fitting of the platelet count, which is denoted as the PLT waveform curve; it can be seen that the PLT fluctuation curve reflects the change of the platelet count, and whether the platelet count of the patient is abnormal can be judged by analyzing the PLT fluctuation curve.

[0086] Please refer to Figure 5 , Figure 5 is a schematic diagram of the original quantity of platelet quantity under the high value abnormal mode and the curve fitting of the platelet quantity; please refer to Figure 6 , Figure 6 is a schematic diagram of the original quantity of platelet quantity under the double abnormal mode and the curve fitting of the platelet quantity; please refer to Figure 7 , Figure 7 is a schematic diagram of the original quantity of platelet quantity under the normal mode and the curve fitting of the platelet quantity.

[0087] According to the PLT fluctuation curve of the patient, the fluctuation degree of the platelet quantity of the patient in the postoperative recovery process is determined. For the PLT fluctuation curve in a single monitoring process, the platelet quantity is normally horizontal or slightly oscillating. Therefore, the mean value of the M platelet quantities on the PLT fluctuation curve corresponding to the single monitoring process is represented as the overall data value of the platelet data in the single monitoring process. The mean value of the absolute value of the slope of all points on the PLT fluctuation curve is represented as the fluctuation of the PLT in the mth monitoring process.

[0088] Because the platelet parameter includes not only the platelet quantity, but also the mean platelet volume and the platelet hematocrit.

[0089] With any kind of platelet parameter in any monitoring process as the to-be-detected platelet parameter, the data mean value of the to-be-detected platelet parameter is obtained as the overall data value of the to-be-detected platelet parameter. Here, the overall data value of the to-be-detected platelet parameter is calculated because the platelet parameter also normally has slight oscillation, but these slight oscillations do not represent that the to-be-detected platelet parameter is abnormal, so in order to eliminate the influence of the slight oscillation of the to-be-detected platelet parameter on abnormal monitoring, the overall data value of the to-be-detected platelet parameter is used as a new to-be-detected platelet parameter in the subsequent steps.

[0090] The mean value of the absolute value of the slope corresponding to the to-be-detected platelet parameter on the fitting curve at different time points in the monitoring process is obtained as the fluctuation of the to-be-detected platelet parameter.

[0091] After obtaining the PLT fluctuation curve capable of representing whether the platelet parameter is abnormal, further analysis needs to be performed on the PLT fluctuation curve to obtain a value capable of specifically representing the fluctuation of the platelet parameter of the patient. Since the slope can represent the inclination of the line segment in the plane rectangular coordinate system, when the inclination is greater, it is known that the size change between the platelet parameters at the corresponding two collection time points is greater, and the fluctuation of the corresponding platelet parameter is greater. Therefore, in the embodiment of the present application, the mean value of the absolute value of the slope of the fitting curve is used as the fluctuation of the to-be-detected platelet parameter. Please refer to Figure 8 , Figure 8Fig. 2 is a schematic diagram of a broken line of instantaneous slope corresponding to a PLT fluctuation curve in a high fluctuation abnormal mode. Please refer to Fig. 1. Figure 9 , Figure 9 Fig. 3 is a schematic diagram of a broken line of instantaneous slope corresponding to a PLT fluctuation curve in a high value abnormal mode. Please refer to Fig. 1. Figure 10 , Figure 10 Fig. 4 is a schematic diagram of a broken line of instantaneous slope corresponding to a PLT fluctuation curve in a double abnormal mode. Please refer to Fig. 1. Figure 11 , Figure 11 Fig. 5 is a schematic diagram of a broken line of instantaneous slope corresponding to a PLT fluctuation curve in a normal mode.

[0092] After the overall data value and fluctuation of all the platelet parameters are determined, the parameter abnormality degree of the platelet parameters is determined according to the overall data value and fluctuation of all the platelet parameters, and specifically:

[0093] In the target monitoring process, the platelet parameter closest to the preset ideal platelet parameter is obtained as the standard platelet parameter for different kinds of platelet parameters. It should be noted that the preset ideal platelet parameter is set by the implementer according to the actual situation and the actual data value of the platelet parameter, and the preset ideal platelet parameter corresponding to different platelet parameters is different, for example, the preset ideal platelet parameter corresponding to the platelet count is different from the preset ideal platelet parameter corresponding to the average platelet volume. In the embodiment of the present application, the value range of the preset ideal platelet parameter is 100×10 9 / L~300×10 9 / L, which is a general standard in the existing industry, and more specifically, in the embodiment of the present application, the preset ideal platelet parameter is 200×10 9 / L. Since the fluctuation of the platelet parameters of the patient is usually more frequent, when the platelet parameters temporarily fluctuate due to short-term factors such as mild infection or drug effects, etc., the appropriate ideal value of the platelet parameter can have a larger buffer space when the platelet temporarily fluctuates, and therefore 200×10 9 / L is used as the preset ideal platelet parameter, which can simultaneously consider the upward and downward buffer space.

[0094] The specific method of obtaining the platelet parameter closest to the preset ideal platelet parameter in the target monitoring process as the standard platelet parameter is to calculate the absolute value of the difference between the platelet parameter in the target monitoring process and the preset ideal platelet parameter, and the platelet parameter corresponding to the minimum value of the absolute value is used as the standard platelet parameter.

[0095] For any kind of platelet parameter, compare all platelet parameters in the target monitoring process and the standard platelet parameters to determine the standard difference value. Specifically, calculate the average value of the absolute value of the difference between all platelet parameters in the target monitoring process and the standard platelet parameters as the standard difference value. It should be noted that the standard difference value corresponding to different types of platelet parameters is different, and each type of platelet parameter has its own corresponding standard difference value. The smaller the standard difference value, the more normal the change of the patient's platelet parameters. The standard difference value of the platelet parameter represents the difference between the platelet parameter and the standard platelet parameter. When the difference between the platelet parameter and the standard platelet parameter is greater, the probability of abnormal platelet parameters in the current analysis is greater. When the difference between the platelet parameter and the standard platelet parameter is smaller, the corresponding platelet parameter is closer to the standard difference, and the platelet parameter of the corresponding patient is more normal, and the probability of its occurrence is smaller.

[0096] Please refer to Figure 12 , Figure 12 For the schematic diagram of parameter index statistics under high fluctuation abnormal mode, Figure 12 The overall data value, volatility, standard parameter value, standard difference value and parameter abnormality degree data value are given respectively; please refer to Figure 13 , Figure 13 For the schematic diagram of parameter index statistics under high value abnormal mode; please refer to Figure 14 , Figure 14 For the schematic diagram of parameter index statistics under double abnormal mode; please refer to Figure 15 , Figure 15 For the schematic diagram of parameter index statistics under normal mode;

[0097] According to the overall trend of the volatility, determine the change trend, specifically: for any kind of platelet parameter, calculate the mean value of the volatility at different times as the change trend of the current kind of platelet parameter. The smaller the change trend value, the more stable the platelet parameter of the patient, and the less likely the corresponding platelet parameter of the patient to fluctuate. Here, the mean value of the volatility at different times is calculated in order to realize the analysis of multiple monitoring processes, and to avoid analyzing the volatility in a single monitoring process, which may lead to the situation that the complete change trend or correlation of the data cannot be reflected.

[0098] In combination with the standard difference value and the change trend, the parameter abnormality degree of the platelet parameter is determined, specifically: for any kind of platelet parameter, the product of the normalization value of the standard difference value and the change trend is taken as the single abnormality degree of the current kind of platelet parameter; then the single abnormality degrees of all kinds of platelet parameters can be obtained to judge the abnormality probability of different kinds of platelet parameters; and the mean value of the single abnormality degrees of all kinds of platelet parameters is taken as the parameter abnormality degree of the platelet parameter. The greater the parameter abnormality degree is, the greater the abnormality probability of the platelet parameter is. The standard difference value represents the difference between the platelet parameter and the standard platelet parameter, and the change trend represents the fluctuation stability of the PLT fluctuation curve, so in combination with the standard difference value and the change trend, the change of the platelet parameter in the multiple monitoring process can be more accurately analyzed, and the error monitoring that the platelet parameter is in the normal range but presents abnormal fluctuation compared with other monitoring data can be avoided. Please refer to Figure 16 , Figure 16 is a comparison diagram of data values of the parameter abnormality degrees in different abnormal modes.

[0099] In step S300, for the induced aggregation rates under different inducers, the initial possibility of drug resistance abnormality is determined according to the positions of the maximum induced aggregation rates under different concentrations and the induced aggregation rates under different concentrations.

[0100] Further, whether the patient presents the drug resistance abnormality is judged.

[0101] In the embodiment of the present application, the induced aggregation rates under different inducers include the induced aggregation rate under arachidonic acid and the induced aggregation rate under adenosine diphosphate.

[0102] Taking any kind of inducer as a target inducer and any concentration as a target concentration, the mean value of the induced aggregation rate of the target inducer under the target concentration is calculated as the aggregation rate feature of the target inducer under the target concentration.

[0103] It should be noted that in the subsequent steps, arachidonic acid is referred to as AA inducer, and adenosine diphosphate is referred to as ADP inducer.

[0104] Since with the increase of the concentration of the inducer, when the concentration of the AA inducer is too high, negative feedback inhibition will occur, and the aggregation rate will first increase and then decrease. Whether the aggregation abnormality occurs is determined by analyzing the change of the time sequence of the induced aggregation rate corresponding to the AA inducer.

[0105] First, in order of concentration from low to high, the mean of all aggregation rates of the time sequence of the aggregation rate corresponding to each AA inducer is obtained as the aggregation rate feature. That is, to calculate the mean of the target aggregation rate at each concentration as the aggregation rate feature, the aggregation rate features arranged in order of the corresponding concentration are used to construct the aggregation rate feature sequence.

[0106] The inducer 1.6 mmol / L is a standard concentration known to reliably induce platelet aggregation and not to mask the inhibitory effect of aspirin. In healthy people, a concentration of 0.7 mmol / L can induce maximum aggregation. Since postoperative patients usually have corresponding conditions, the concentration at which maximum aggregation occurs will be greater than 0.7 mmol / L. Therefore, the concentration between 0.7 mmol / L and 1.6 mmol / L is taken as the maximum aggregation occurrence interval.

[0107] The concentration corresponding to the maximum aggregation rate feature of the target inducer is obtained. When the concentration is not in the preset aggregation rate occurrence interval, it is determined that the maximum aggregation rate occurrence position is abnormal. Specifically, the maximum value in the aggregation rate features corresponding to the multiple concentrations of the target inducer is calculated, denoted as . Then the concentration value AAC corresponding to is determined. If the concentration value AAC is in the maximum aggregation rate occurrence interval, it indicates that the maximum aggregation rate occurrence position is normal. If the concentration value AAC does not appear in the maximum aggregation rate occurrence interval, it indicates that the maximum aggregation rate occurrence position is abnormal. When the maximum aggregation rate occurrence position is normal, the corresponding position abnormality degree is set to 0.

[0108] When the maximum aggregation rate occurrence position is abnormal, the position abnormality degree of the maximum aggregation rate is determined according to the position of the maximum aggregation rate. Specifically, the endpoint closest to the maximum aggregation rate in the preset aggregation rate occurrence interval is obtained as the standard position sequence number of the maximum aggregation rate. The absolute value of the difference between the position sequence number of the maximum aggregation rate in the aggregation rate feature sequence and the standard position sequence number is taken as the numerator, the number of concentrations of the target inducer is taken as the denominator, and the corresponding ratio is taken as the position abnormality degree.

[0109] Regarding the standard position sequence number of the maximum aggregation rate, when the concentration value AAC corresponding to the maximum aggregation rate appears on the left side of the maximum aggregation rate occurrence interval, the standard position corresponding to the maximum aggregation rate is set to the left half of the maximum aggregation rate occurrence interval, i.e. the position sequence number corresponding to 0.7 mmol / L. Conversely, when the concentration value AAC corresponding to the maximum aggregation rate appears on the right half of the maximum aggregation rate occurrence interval, the standard position corresponding to the maximum aggregation rate is set to the right endpoint of the maximum aggregation rate occurrence interval, i.e. the position sequence number corresponding to 1.6 mmol / L.

[0110] In some embodiments of the present application, the formula for calculating the degree of position abnormality in the mth monitoring process is: ; wherein, is the position number of the maximum aggregation rate in the aggregation rate characteristic sequence; is the standard position number; and N is the number of concentrations of the target inducer, i.e., the number of elements in the aggregation rate characteristic sequence. When the patient has aspirin resistance, taking aspirin does not effectively inhibit platelet aggregation. Therefore, for the mth test, the change in the aggregation rate at N concentrations is analyzed to determine whether there is aspirin resistance.

[0111] For the aggregation rate characteristic sequence of N aggregation rate characteristics, the aggregation rate characteristic sequence is divided into multiple intervals according to the interval in which the maximum aggregation rate occurs. The interval in which the maximum aggregation rate occurs is the middle measurement interval, the interval to the left of the interval in which the maximum aggregation rate occurs is the front measurement interval, and the interval to the right of the interval in which the maximum aggregation rate occurs is the rear measurement interval. The front measurement interval is the low concentration measurement interval, the middle measurement interval is the standard concentration measurement interval, and the rear measurement interval is the high concentration measurement interval.

[0112] Since the aggregation rate in the standard concentration measurement interval is usually smaller than the aggregation rate in the low concentration measurement interval and the aggregation rate in the high concentration measurement interval, if the aggregation rate in the standard concentration measurement interval or the aggregation rate in the high concentration measurement interval is greater than the aggregation rate in the low concentration measurement interval, it indicates that there may be a situation of aspirin resistance. The mean value of the aggregation rate characteristics at different concentrations in the measurement interval is calculated as the aggregation rate characteristic of the measurement interval.

[0113] The aggregation rate characteristics of different measurement intervals are compared to determine whether a drug resistance abnormality occurs.

[0114] The difference between the aggregation rate characteristics of the low concentration measurement interval and the standard concentration measurement interval is calculated as the first difference value, and the difference between the aggregation rate characteristics of the standard concentration measurement interval and the high concentration measurement interval is calculated as the second difference value. When either the first difference value or the second difference value is greater than 0, it is determined that a drug resistance abnormality occurs.

[0115]

[0116] ​If the difference is greater than 0, it indicates that the aggregation rate characteristic of the standard concentration measurement interval is abnormal, and it is determined that the drug resistance anomaly occurs. Conversely, since the aggregation rate of the high concentration measurement interval is usually less than that of the standard concentration measurement interval, the difference between the aggregation rate characteristics of the standard concentration measurement interval and the high concentration measurement interval is calculated, and it is determined that the drug resistance anomaly occurs. If the difference is greater than 0, it indicates that the aggregation rate characteristic of the high concentration measurement interval is abnormal, and vice versa, that is, the patient does not have a drug resistance anomaly during the test.

[0117] When the drug resistance anomaly occurs, the aggregation anomaly degree of the aggregation rate is determined according to the difference between the aggregation rate characteristics of different measurement intervals. Specifically, the average value of the difference between the aggregation rate characteristics of the previous measurement interval and the next measurement interval in the adjacent two measurement intervals is calculated as the aggregation anomaly degree of the aggregation rate, and the aggregation anomaly degree is updated as a normalized data value.

[0118] Further, the higher the abnormality degree of the position where the maximum aggregation rate occurs during the test and the higher the abnormality degree of the aggregation rate characteristic, the more abnormal the maximum aggregation rate of the platelets under different concentrations during the test and the more abnormal the change of the aggregation rate, and further, the greater the possibility of the occurrence of aspirin resistance during the monitoring process.

[0119] According to the position anomaly degree and the aggregation anomaly degree, the initial possibility of the occurrence of the drug resistance anomaly under the target inducer is determined. Specifically, the product value of the position anomaly degree and the aggregation anomaly degree is taken as the initial possibility of the occurrence of the drug resistance anomaly under the target inducer during the monitoring process.

[0120] In step S400, the initial possibility is corrected in combination with the parameter anomaly degree to determine the corrected possibility of the occurrence of the drug resistance anomaly, and the drug resistance influence evaluation value is obtained according to the number of times of the occurrence of the drug resistance anomaly and the corrected possibility.

[0121] Since the basic parameters of the platelets also affect the aggregation ability, for example, when the PLT is too low, the aggregation ability will decrease due to the decrease of physical contact. Therefore, the abnormal degree of the basic parameters of the platelets of the patient is combined to determine whether the drug resistance anomaly occurs during the monitoring process.

[0122] For the induced aggregation rate under any inducer, the product value of the parameter anomaly degree and the initial possibility is taken as the single correction value of the occurrence of the drug resistance anomaly.

[0123] The above is based on the analysis under the AA inducer, and the corrected possibility of the occurrence of aspirin resistance during the monitoring process under the ADP inducer can be calculated according to the above method.

[0124] The average of the single correction values corresponding to the induced aggregation rates under the induction of all kinds of inducers is taken as the correction possibility of the drug resistance anomaly.

[0125] Since the C times of monitoring are performed simultaneously, if the patient has aspirin resistance, the patient is likely to have a drug resistance anomaly during the C times of monitoring.

[0126] First, the number of times of drug resistance anomalies of the patient in the C times of monitoring is analyzed. The more times of drug resistance anomalies of the patient, and the greater the possibility of drug resistance anomaly, indicate that the influence of drug resistance on the patient needs to be considered when the platelet aggregation risk of the patient is evaluated.

[0127] According to the number of times of drug resistance anomalies and the correction possibility, an evaluation value of the influence of drug resistance is obtained. Specifically, the proportion of the number of times of drug resistance anomalies is taken as an influence adjustment value, the average of the correction possibilities of the drug resistance anomalies is taken as an initial evaluation value, and the initial evaluation value is adjusted by taking the influence adjustment value as a weight to obtain the evaluation value of the influence of drug resistance.

[0128] In some embodiments, the calculation formula of the evaluation value of the influence of drug resistance is: K is the number of times of drug resistance anomalies, and C is the total number of monitoring times. P k is the initial possibility of the kth time of drug resistance anomaly.

[0129] Then, the platelet control analyzer calculates the platelet basic parameters and the platelet aggregation rate and other parameter values of the patient according to a preset algorithm. Then, the parameter values and the evaluation value of the influence of drug resistance that need to be considered are displayed on the panel of the platelet control analyzer.

[0130] Please refer to Figure 2 which shows the system block diagram of the platelet aggregation dynamic risk evaluation system based on the platelet control analyzer provided by an embodiment of the present application, and the system comprises:

[0131] An acquisition module is configured to acquire platelet parameters and induced aggregation rates of platelets under the induction of different inducers.

[0132] An anomaly analysis module is configured to determine the parameter anomaly degree of the platelet parameters according to the fluctuation of the platelet parameters.

[0133] A resistance analysis module is configured to determine the initial possibility of drug resistance anomaly according to the position of the maximum induced aggregation rate under different concentrations and the induced aggregation rate under different concentrations for the induced aggregation rates under the induction of different inducers.

[0134] The evaluation module is used for correcting the initial possibility in combination with the parameter abnormality degree to determine a corrected possibility of the drug resistance abnormality, and obtaining a drug resistance influence evaluation value according to the number of times of the drug resistance abnormality and the corrected possibility.

[0135] Optionally, the transmission medium can be a wired link such as, but not limited to, a coaxial cable, an optical fiber, a digital subscriber line, etc., or a wireless link such as, but not limited to, Wireless Fidelity (WIFI), Bluetooth, a mobile device network, etc.

[0136] It should be noted that: the device provided in the above embodiment is only exemplified by the division of the above functional modules, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above.

[0137] Figure 3 is a structural schematic diagram of a computer device provided by an embodiment of the present application. As shown in the example, Figure 3 the computer device 500 includes a memory 510, a processor 520, and a computer program 530 stored in the memory 510 and running on the processor 520, wherein when the processor 520 executes the computer program 530, the computer device can execute any of the above-mentioned platelet aggregation dynamic risk assessment methods based on a platelet control analyzer.

[0138] In addition, an embodiment of the present application also protects a device, which can include a memory and a processor, wherein the memory stores executable program code, and the processor is used to call and execute the executable program code to execute the platelet aggregation dynamic risk assessment method based on a platelet control analyzer provided by an embodiment of the present application.

[0139] The embodiment of the present application can divide the device into functional modules according to the above method examples, for example, each functional module can be corresponding, or two or more functions can be integrated in one processing module, and the above integrated module can be realized in the form of hardware. It should be noted that the division of the modules in the present embodiment is illustrative, and is only a logical function division, and another division mode can be used in actual implementation.

[0140] In the case of dividing each module corresponding to each function, the device can further include a signal uploading module, a determination module, and an adjustment module, etc. It should be noted that all related contents of each step involved in the above method embodiment can be referred to the function description of the corresponding functional module, and will not be repeated here.

[0141] It should be understood that the device provided by the embodiments of the present application is used to execute the platelet aggregation dynamic risk assessment method based on the platelet control analyzer, and thus the same effects as the implementation method can be achieved.

[0142] In the case of using the integrated unit, the device can include a processing module and a storage module. When the device is applied to equipment, the processing module can be used to control and manage the actions of the equipment. The storage module can be used to support the equipment to execute mutual program codes and the like. The processing module can be a processor or a controller, which can realize or execute various exemplary logical blocks, modules and circuits described in combination with the disclosure. The processor can also be a combination of computing functions, such as a combination of one or more microprocessors, a combination of a digital signal processing (DSP) and a microprocessor, and the like. The storage module can be a memory.

[0143] In addition, the device provided by the embodiments of the present application can be a chip, an assembly or a module. The chip can include a processor and a memory connected thereto. The memory is used to store instructions. When the processor calls and executes the instructions, the chip can execute the platelet aggregation dynamic risk assessment method based on the platelet control analyzer provided by the above embodiments.

[0144] The embodiments of the present application also provide a computer readable storage medium, which stores computer program codes. When the computer program codes run on a computer, the computer executes the related method steps to realize the platelet aggregation dynamic risk assessment method based on the platelet control analyzer provided by the above embodiments.

[0145] The embodiments of the present application also provide a computer program product. When the computer program product runs on a computer, the computer executes the related steps to realize the platelet aggregation dynamic risk assessment method based on the platelet control analyzer provided by the above embodiments.

[0146] The device, the computer readable storage medium, the computer program product or the chip provided by the embodiments of the present application are used to execute the corresponding method provided above, and thus the beneficial effects can be referred to the beneficial effects of the corresponding method provided above, which will not be described here. Through the description of the above embodiments, those skilled in the art can understand that, for the convenience and brevity of description, only the division of the above functional modules is taken as an example for illustration, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In the embodiments provided by the present application, it should be understood that the disclosed device and method can be implemented in other ways.

[0147] The device embodiments described above are merely illustrative, for example, the division of modules or units is only a logical function division, and actual implementation can have another division manner, for example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed each other can be indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0148] It should also be noted that the terms "comprising", "containing", or any other variant thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal device including a list of elements does not only include those elements, but also includes other elements not explicitly listed, or further includes elements inherent in such a process, method, article, or terminal device. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article, or terminal device including the element.

[0149] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are possible or can be advantageous.

[0150] Each embodiment in the specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other, and each embodiment mainly describes the difference from other embodiments.

[0151] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A method for assessing the risk of platelet aggregation dynamics based on a platelet control analyzer, characterized by, The method comprises the following steps: Obtaining platelet parameters and induced aggregation rates of platelets under induction of different inducers; According to the fluctuation of the platelet parameters, determining the parameter abnormality degree of the platelet parameters; For the induced aggregation rates under the induction of different inducers, according to the position of the maximum induced aggregation rate under different concentrations and the induced aggregation rates under different concentrations, determining the initial possibility of the occurrence of drug resistance abnormality; Combining the parameter abnormality degree, correcting the initial possibility, determining the corrected possibility of the occurrence of drug resistance abnormality; and obtaining the drug resistance influence evaluation value according to the number of times of the occurrence of drug resistance abnormality and the corrected possibility; Wherein, the method for obtaining the initial possibility is: the induced aggregation rates under the induction of different inducers include: the induced aggregation rate under the induction of arachidonic acid and the induced aggregation rate under the induction of adenosine diphosphate; taking any kind of inducer as a target inducer and any concentration as a target concentration, calculating the mean value of the induced aggregation rate of the target inducer under the target concentration as the aggregation rate characteristic of the target inducer under the target concentration; for the target inducer, arranging the aggregation rate characteristics in the order of corresponding concentration size to construct an aggregation rate characteristic sequence; obtaining the concentration corresponding to the maximum aggregation rate characteristic of the target inducer, and determining that the position of the maximum aggregation rate is abnormal when the concentration is not in the preset aggregation rate occurrence interval; when the position of the maximum aggregation rate is abnormal, determining the position abnormality degree of the maximum aggregation rate according to the position of the maximum aggregation rate; dividing the aggregation rate characteristic sequence into multiple intervals as measurement intervals; comparing the aggregation rate characteristics of different measurement intervals to determine the occurrence of drug resistance abnormality; when the drug resistance abnormality occurs, determining the aggregation abnormality degree of the aggregation rate according to the difference between the aggregation rate characteristics of different measurement intervals; and determining the initial possibility of the occurrence of drug resistance abnormality of the target inducer according to the position abnormality degree and the aggregation abnormality degree.

2. The platelet aggregation dynamics risk assessment method based on a platelet control analyzer according to claim 1, characterized in that, The method comprises the following steps: The platelet parameters include: platelet count, mean platelet volume and platelet hematocrit; Taking any kind of platelet parameter in any monitoring process as a to-be-measured platelet parameter, obtaining the data mean value of the to-be-measured platelet parameter as the overall data value of the to-be-measured platelet parameter; Obtaining the average value of the absolute value of the slope corresponding to the to-be-measured platelet parameter on the fitting curve at different time points in the monitoring process as the fluctuation of the to-be-measured platelet parameter; According to the overall data value and the fluctuation of all platelet parameters, determining the parameter abnormality degree of the platelet parameters.

3. The platelet aggregation dynamics risk assessment method based on a platelet control analyzer according to claim 2, characterized in that, The method comprises the following steps: For different types of platelet parameters, obtaining the platelet parameter closest to the preset ideal platelet parameter in the monitoring process as a standard platelet parameter; Comparing all platelet parameters in the monitoring process with the standard platelet parameter to determine a standard deviation value; According to the overall trend of the fluctuation, determining a change trend; Combining the standard deviation value and the change trend, determining the parameter abnormality degree of the platelet parameters.

4. The platelet aggregation dynamics risk assessment method based on a platelet control analyzer according to claim 1, wherein, The position of the maximum aggregation rate is determined according to the position anomaly degree of the maximum aggregation rate, and the position anomaly degree of the maximum aggregation rate comprises: An endpoint closest to the maximum aggregation rate and a preset aggregation rate appearance interval is obtained as a standard position sequence number of the maximum aggregation rate; an absolute value of a difference between a position sequence number of the maximum aggregation rate in the aggregation rate feature sequence and the standard position sequence number is taken as a numerator, a concentration number of the target inducer is taken as a denominator, and a corresponding ratio is taken as a position anomaly degree.

5. The platelet aggregation dynamics risk assessment method based on a platelet control analyzer according to claim 1, wherein, The aggregation rate feature sequence is divided into multiple intervals as measurement intervals, and the division comprises: The maximum aggregation rate appearance interval is taken as a standard concentration measurement interval, a left interval of the maximum aggregation rate appearance interval is taken as a low concentration measurement interval, and a right interval of the maximum aggregation rate appearance interval is taken as a high concentration measurement interval.

6. The platelet aggregation dynamics risk assessment method based on a platelet control analyzer according to claim 1, wherein, The aggregation rate features of different measurement intervals are compared to determine whether a drug resistance anomaly occurs, and the comparison comprises: A difference between the aggregation rate features of the low concentration measurement interval and the standard concentration measurement interval is calculated as a first difference value; A difference between the aggregation rate features of the standard concentration measurement interval and the high concentration measurement interval is calculated as a second difference value; When any one of the first difference value and the second difference value is greater than 0, it is determined that the drug resistance anomaly occurs.

7. The platelet aggregation dynamics risk assessment method based on a platelet control analyzer according to claim 1, wherein, The initial possibility of the drug resistance anomaly occurring under the target inducer is determined according to the position anomaly degree and the aggregation anomaly degree, and the determination comprises: A product value of the position anomaly degree and the aggregation anomaly degree is taken as the initial possibility of the drug resistance anomaly occurring under the target inducer in the monitoring process.

8. The platelet aggregation dynamics risk assessment method based on a platelet control analyzer according to claim 1, wherein, The initial possibility is corrected in combination with the parameter anomaly degree to determine a corrected possibility of the drug resistance anomaly, and the correction comprises: For an induced aggregation rate under any kind of inducer induction, a product value of the parameter anomaly degree and the initial possibility is taken as a single correction value of the drug resistance anomaly; A mean value of single correction values corresponding to induced aggregation rates under all kinds of inducer inductions is taken as the corrected possibility of the drug resistance anomaly.

9. The platelet aggregation dynamics risk assessment method based on a platelet control analyzer according to claim 1, wherein, The drug resistance influence evaluation value is obtained according to a number of times of the drug resistance anomaly and the corrected possibility, and the obtaining comprises: A proportion of the number of times of the drug resistance anomaly is calculated as an influence adjustment value; A mean value of the corrected possibility when the drug resistance anomaly occurs is calculated as an initial evaluation value; The initial evaluation value is adjusted by taking the influence adjustment value as a weight to obtain the drug resistance influence evaluation value.

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