JP2011087866A - Cerebral infarction relapse calculation method for calculating date of relapse of embolism caused by blood clot formed on ruptured plaque and acute coronary syndrome relapse calculation method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a device and a method for calculating a date of relapse of embolism caused by a blood clot formed on a ruptured plaque. <P>SOLUTION: A cerebral infarction relapse calculator 1 for calculating includes: an input data acquisition instrument 2 for acquiring fluctuation coefficients of an input probability model and a calculation start instruction; a probability model acquisition instrument 3 for acquiring a probability model of the onset of a cerebral infraction after a transit cerebral ischemic attack due to the carotid artery lesion stored in a storage part 6 when the input data acquisition instrument 2 acquires the calculation start instruction; a parameter determination instrument 4 for determining a parameter of the probability model with reference to the fluctuation coefficients acquired by the input data acquisition instrument and to clinical data stored in the storage part 6; and a date of relapse calculator 5 for calculating a date of relapse of the onset of the cerebral infraction of a patient according to the probability model to which the parameter determined by the probability model acquisition instrument 3 is applied. <P>COPYRIGHT: (C)2011,JPO&INPIT

Description

  The present invention is a cerebral infarction recurrence calculation device for calculating the recurrence date of embolism caused by a thrombus formed on a broken plaque, an acute coronary syndrome recurrence calculation device, a cerebral infarction recurrence calculation method, an acute coronary syndrome recurrence calculation method, The present invention relates to a cerebral infarction recurrence calculation program, an acute coronary syndrome recurrence calculation program, and a computer-readable recording medium on which these programs are recorded.

  Thrombosis such as cerebral infarction and myocardial infarction is considered to occur when an arteriosclerotic plaque in a blood vessel breaks down and platelets adhere to it to form a thrombus. When a plaque breaks down, platelets bind to the exposed collagen via von Willebrand factor, and the platelets are activated, which in turn causes positive feedback to bind to other platelets and fibrin.

  In addition, tissue factor from tissue exposed in blood vessels activates the coagulation system and generates thrombin. When thrombin is generated, positive feedback also occurs in the coagulation system, the amount of thrombin increases rapidly, further activating platelets and forming fibrin. In this way, a thrombus is rapidly formed in the plaque rupture site. When the formed thrombus is covered with a broken plaque, the collagen and tissue factor signals disappear, the thrombus formation stops, the fibrinolytic system is activated, and the thrombus dissolves.

  In the process of thrombus formation and thrombolysis, when a part of the thrombus formed in the carotid artery is peeled off and transported to the brain to cause embolism, cerebral infarction or transient ischemic attack (TIA) ). Of these, transient cerebral ischemic attacks develop when the detached thrombus is small. A patient who has developed a transient cerebral ischemic attack due to a carotid artery lesion relapses with a predetermined probability.

  Similar to the carotid artery, acute coronary syndrome (acute myocardial infarction or unstable angina) is caused by the destruction of plaque in the coronary artery. Since the heart functions as a mass unlike the brain, even if a part of the thrombus is peeled off from the artery and obstructed as an embolus, myocardial infarction does not develop.

  However, since the coronary artery has a smaller blood vessel diameter than the carotid artery, when the size of the plaque failure is large, the artery may be blocked at once by a thrombus generated on the broken plaque. This condition is acute myocardial infarction. Moreover, when the magnitude of the plaque rupture is slightly small, it is considered that unstable angina develops when the blood clot is reduced although the thrombus generated on the ruptured plaque does not occlude the blood vessel. In addition, when the magnitude of the plaque rupture is smaller, no symptom occurs.

  Patent Document 1 discloses BNP or a BNP-related marker in a patient sample as a diagnostic marker for acute coronary syndrome. In Patent Document 1, these markers are used alone or in combination with other prognostic markers to predict morbidity and / or mortality after a short period in the entire range of acute coronary syndromes. ing.

  Furthermore, Patent Document 2 describes a method for diagnosing atherosclerotic cardiovascular disease by detecting the circulating level of a protein specifically produced in the blood vessel wall as a result of the atherosclerosis process. .

JP-A-2005-49351 Special table 2009-501318

  However, embolism due to plaque failure and thrombus formed on the failed plaque is considered a stochastic phenomenon. However, mathematical models for stochastic phenomena are useful, but no such models for thrombosis such as cerebral infarction or acute coronary syndrome have been proposed.

  Further, in the conventional techniques described in Patent Document 1 and Patent Document 2, analysis is performed based on a sample collected from a patient, and complicated processing is required until an analysis result is obtained.

  The present invention has been made in view of the above circumstances, and an object thereof is to provide an apparatus and a method for calculating the recurrence date of embolism caused by a thrombus formed on a broken plaque.

  The cerebral infarction recurrence calculation device of the present invention has an input data acquisition unit that acquires a coefficient of variation and a calculation start instruction of an input probability model, and is stored in a storage unit when the input data acquisition unit acquires a calculation start instruction. Refer to the probability model acquisition means to acquire the probability model of cerebral infarction after transient cerebral ischemic attack due to carotid artery lesion, the variation coefficient acquired by the input data acquisition means and the clinical data stored in the storage unit A parameter determination means for determining a parameter of the probability model; and a recurrence date calculation means for calculating a recurrence date of the onset of the cerebral infarction of the patient from the probability model to which the parameter determined by the parameter determination means is applied. Yes.

  According to this configuration, a survival curve similar to the survival curve obtained from patients participating in the NASCET test can be obtained by setting appropriate parameters in the probability model, as shown in the examples described later. Therefore, the recurrence date of the cerebral infarction of the patient after the transient cerebral ischemic attack can be accurately calculated without actually conducting a clinical test.

  The apparatus for calculating recurrence of acute coronary syndrome according to the present invention includes an input data acquisition unit that acquires a coefficient of variation of an input probability model and a calculation start instruction, and when the input data acquisition unit acquires a calculation start instruction, the storage unit stores Refer to the probability model acquisition means to acquire the probability model of recurrence of acute coronary syndrome after the onset of acute coronary syndrome due to the stored coronary artery lesion, the variation coefficient acquired by the input data acquisition means and the clinical data stored in the storage unit Parameter determination means for determining parameters of the probability model, and recurrence date calculation means for calculating the recurrence date of the onset of acute coronary syndrome of the patient from the probability model to which the parameters determined by the parameter determination means are applied. It is a feature.

  According to this configuration, as shown in the examples described later, by setting appropriate parameters in the probabilistic model, clinical trials in acute coronary syndromes such as the PARAGON-A trial (Circulation. 1998; 97: 2386-2395 A survival curve similar to that in (1) can be obtained. Therefore, the recurrence date of the onset of acute coronary syndrome of a patient after the onset of acute coronary syndrome can be accurately calculated without actually conducting a clinical test.

  Note that the above-described device is one embodiment of the present invention, and the device of the present invention may be any combination of the above components. The method, system, computer program, recording medium, etc. of the present invention have the same configuration.

  That is, a method for calculating recurrence of cerebral infarction and a method for calculating recurrence of acute coronary syndrome by performing each step corresponding to each of the above means by a computer or human power also fall within the scope of the present invention.

  In addition, the cerebral infarction recurrence calculation device and the acute coronary syndrome recurrence calculation device may be realized by a computer. In this case, the cerebral infarction recurrence calculation device and the acute coronary syndrome can be realized by operating the computer as the respective means. A cerebral infarction recurrence calculation program and an acute coronary syndrome recurrence calculation program for realizing the recurrence calculation device by a computer, and a computer-readable recording medium recording the program also fall within the scope of the present invention.

  According to the present invention, a survival curve similar to that in clinical trials can be obtained by setting appropriate parameters in the probability model, as shown in the examples described later. Therefore, it is possible to accurately calculate the recurrence date of the cerebral infarction of the patient after the transient cerebral ischemic attack and the recurrence date of the onset of the acute coronary syndrome of the patient after the onset of acute coronary syndrome without actually conducting a clinical trial. it can.

It is a block diagram which shows schematic structure of the cerebral infarction recurrence calculation apparatus which concerns on this invention. It is a figure which shows the data structure of the probability model of the cerebral infarction recurrence after a transient cerebral ischemic attack. It is a table figure which shows clinical data. It is a graph which shows the recurrence curve in an acute phase. It is a graph which shows the recurrence curve in a chronic phase. It is a flowchart for demonstrating the cerebral infarction recurrence calculation method using the said cerebral infarction recurrence calculation apparatus. It is a graph which shows the simulation result using a probability model in the said cerebral infarction recurrence calculation apparatus. It is a block diagram which shows schematic structure of the acute coronary syndrome recurrence calculation apparatus which concerns on this invention. It is a flowchart for demonstrating the acute coronary syndrome recurrence calculation method using the said acute coronary syndrome recurrence calculation apparatus. It is a graph for demonstrating the threshold value of the magnitude of plaque failure. It is a graph which shows the simulation result using a probability model in the said acute coronary syndrome recurrence calculation apparatus. It is a graph which shows the result of the PARAGON-A test which is a clinical test in an acute coronary syndrome.

[Cerebral infarction recurrence calculation device]
A cerebral infarction recurrence calculating apparatus 1 according to the present invention will be described with reference to FIGS. FIG. 1 is a block diagram showing a schematic configuration of a cerebral infarction recurrence calculating apparatus 1. The cerebral infarction recurrence calculation device 1 predicts the recurrence date of cerebral infarction after a transient cerebral ischemic attack in a patient generated by simulation using a probability model.

  As shown in FIG. 1, the cerebral infarction recurrence calculation device 1 includes an input data acquisition unit 2, a probability model acquisition unit 3, a parameter determination unit 4, and a recurrence date calculation unit 5. The cerebral infarction recurrence calculating device 1 is connected to an external storage unit 6.

  The storage unit 6 stores a probability model and clinical data of cerebral infarction recurrence after a transient ischemic attack due to a carotid artery lesion. This probabilistic model includes Equations 1 to 3 for calculating the recurrence date of cerebral infarction after a transient cerebral ischemic attack due to a carotid artery lesion, and Equation 4 for determining parameters of Equations 1 to 3 below. ~ 7 are included. The clinical data is data of clinical observational studies (cohort studies) or clinical trials. In the present embodiment, the storage unit 6 is provided outside the cerebral infarction recurrence calculation device 1, but may be provided inside the cerebral infarction recurrence calculation device 1.

  The input data acquisition unit 2 acquires a coefficient of variation of a probability model, a simulation start instruction, and the like input to an input unit (not shown) by a user. The input unit may be provided in the cerebral infarction recurrence calculating device 1 main body, or may be provided externally and connected to the cerebral infarction recurrence calculating device 1 by wire or wirelessly.

  The probability model acquisition unit 3 determines whether or not the input data acquisition unit 2 has acquired a simulation start instruction from the input unit, and stores it in the storage unit 6 when determining that the simulation start instruction has been acquired. A probabilistic model is acquired.

  The parameter determination unit 4 determines the parameters of the probability model acquired by the probability model acquisition unit 3 with reference to the variation coefficient of the probability model acquired by the input data acquisition unit 2 and the clinical data stored in the storage unit 6. To do. The parameter determination unit 4 includes a clinical data acquisition unit 41, a plaque failure frequency calculation unit 42, and a plaque failure size calculation unit 43.

  The clinical data acquisition unit 41 acquires clinical data stored in the storage unit 6. The plaque failure frequency calculating means 42 and the plaque failure size calculating means 43 refer to the variation coefficient acquired by the input data acquiring means 2 and the clinical data acquired by the clinical data acquiring means 41, and are plaque parameters that are parameters of the probability model. The failure frequency λ and the plaque failure magnitude ρ are respectively calculated.

  The recurrence date calculation means 5 calculates the recurrence date T of the patient's cerebral infarction from the probability model in which the parameter determined by the parameter determination means 4 is applied. The recurrence date calculation means 5 includes a transient cerebral ischemic attack determination means 51, an acute phase recurrence date calculation means 52, a new onset date calculation means 53, and a cerebral infarction recurrence date calculation means 54.

The transient cerebral ischemic attack determination means 51 determines the presence or absence of a transient cerebral ischemic attack from the probability model to which the parameter determined by the parameter determination means 4 is applied. The acute phase recurrence date calculation means 52 and the new onset date calculation means 53 indicate the acute recurrence date T ₁ and the new onset date T ₂ when the transient cerebral ischemic attack determination means 51 determines “Yes”, respectively. Is to be calculated. The cerebral infarction recurrence date calculation means 54 calculates the recurrence date T of cerebral infarction by obtaining the minimum value of the acute phase recurrence date T ₁ and the new onset date T ₂ .

[Probability model]
Next, the probability model stored in the storage unit 6 will be described with reference to FIG. FIG. 2 is a diagram illustrating a data structure of the probability model. This probability model calculates the recurrence date of cerebral infarction after a transient ischemic attack due to a carotid artery lesion, and is constructed based on the following assumptions (1) to (5).

(1) The magnitude ρ of atherosclerotic plaque failure follows a lognormal distribution, and the average value is proportional to the degree of arteriosclerosis.
(2) The surface area S of the thrombus generated on the ruptured atherosclerotic plaque is proportional to the magnitude ρ of the rupture of the atherosclerotic plaque.
(3) The probability ξ of cerebral infarction occurring from a thrombus is proportional to the surface area S of the thrombus.
(4) Atherosclerotic plaque failure is a Poisson process, and its parameters are proportional to the degree of arteriosclerosis.
(5) The degree of arteriosclerosis between patients follows a lognormal distribution.
In addition, the magnitude ρ of the plaque failure in (1) is not limited to being proportional to the degree of arteriosclerosis, it is only necessary to be correlated, and it can be similarly constructed even if the relational expression is changed.

Specifically, for the recurrence date T of cerebral infarction, the presence or absence of a transient cerebral ischemic attack is determined, and if “Yes”, the minimum value of the acute recurrence date T ₁ and the new onset date T ₂ is determined. Is calculated by Below, the method for calculating the presence or absence of a transient cerebral ischemic attack, the date of acute recurrence, and the date of new onset will be described.

(Presence or absence of transient ischemic attack)
A transient cerebral ischemic attack occurs when an arteriosclerotic plaque in a blood vessel breaks down and platelets adhere to it to form a thrombus. Here, the probability ξ of developing a transient cerebral ischemic attack, that is, the probability that a part of the thrombus is peeled off and embolism is proportional to the surface area S of the thrombus at that time. The surface area S of the thrombus is proportional to the magnitude ρ of plaque failure.

  Further, when a thrombus is formed on the plaque, plasmin is generated on the thrombus surface and the thrombolysis proceeds. Since this process is a phenomenon that occurs on the surface of the thrombus, the reduction rate of the thrombus is roughly proportional to the surface area S thereof.

  Considering these conditions, the probability ξ of developing a transient cerebral ischemic attack is expressed by the following formula 1 when t = 0 when the plaque breaks down and when t = ∞ is formalized when the plaque does not develop. It is obtained by the probability density function f (t) shown. Here, ρ represents the magnitude of plaque failure, and k represents the rate of thrombus reduction.

(Acute phase recurrence date)
The acute recurrence date T ₁ is the probability density function f _A shown in the following formula 2 when the probability in the above formula 1 when a transient cerebral ischemic attack occurs is χ, and the time of onset is t = 0. It is obtained by (t).

(New onset date)
The new onset date is a day when an individual who has not yet developed a transient cerebral ischemic attack newly develops a plaque rupture and develops a transient cerebral ischemic attack or cerebral infarction. That is, the date of new onset is related to the frequency λ of plaque failure.

  Plaque failure is more likely to occur in patients with strong arteriosclerosis, and the severity of the failure is considered to be a random phenomenon involving various conditions such as changes in blood flow and blood pressure. Therefore, it is assumed that the frequency of plaque failure is a Poisson process of the parameter λ. Further, since the magnitude ρ of the plaque failure is a positive value determined by various factors constituting the plaque and blood flow, it is assumed that it follows a lognormal distribution.

  Since there is no biological knowledge that suggests a relationship between the magnitude ρ of plaque failure and the time since the previous failure, it is assumed that the frequency λ of plaque failure and the size ρ are independent. To do. However, both λ and ρ reflect the degree of arteriosclerosis.

Considering these conditions, new onset date T ₂ are obtained by a probability represented by the following Equation 3 density function f _{N (t).} It is assumed that there is a relationship of λ = cE (ρ) and λ ′ = λE (ρ) / k = cE (ρ) ² / k. E (ρ) is an average value of the magnitude ρ of the plaque failure, and c is a proportional constant between the magnitude ρ of the plaque failure and the frequency λ.

[Determination of parameters]
Next, a method for determining the parameters of the probability model represented by the above formulas 1 to 3 from clinical data will be described. The parameters of the probabilistic model are (1) λ representing the frequency of plaque failure, (2) ρ representing the magnitude of plaque failure, and (3) the rate of thrombus reduction, that is, the rate of fibrinolysis of the thrombus k ( Or a time constant τ). As shown in FIG. 3, the clinical data includes patient i, the magnitude of plaque failure ρ _i , the frequency of plaque failure λi = cE (ρ _i ), and the rate constant k _{i of} fibrinolysis. Associated. However, k _i seems to take an almost constant value k among individuals.

  Since clinical data is considered to reflect the average of a large number of patients, the value that can be estimated from this is the average value among a large number of patients who have developed transient ischemic stroke or cerebral infarction. Therefore, an average value between patients who developed a transient cerebral ischemic attack or cerebral infarction is obtained for a predetermined value described later, and the parameters (1) to (3) of the probability model are determined from the average value.

First, E (χ | ω), which is an average value of the onset probability among patients who have developed a transient cerebral ischemic attack or cerebral infarction, is obtained. Assume that the onset probability of patient i who developed a transient ischemic attack or cerebral infarction due to a carotid artery lesion was χ. Further, it is assumed that the magnitude of plaque failure ρ _i of the patient follows a lognormal distribution of log _{ρ i to} N (u, σ _ρ ² ).

The onset probability after a plaque failure is ρ _i / k, and the probability that a plaque fails is proportional to λ _i = cE (ρ _i ). As described above, it is assumed that the magnitude ρ _i of the plaque failure and the frequency λ _i are independent. Therefore, the probability of developing a transient cerebral ischemic attack or cerebral infarction when patient i has an onset probability χ is proportional to E (ρ _i ), f _i (ρ _i ), h _ρi (χ). Here, f _{i (ρ i)} is the probability density function of the size [rho _i of plaque rupture in a patient _i, h ρi (χ) is the time of onset of when the magnitude of plaque rupture was [rho _i chi Is the probability density.

Also, the number of patients is large and i is considered to be continuous. The average of the plaque failure magnitude ρ _i of each patient i is u = E (ρ _i ), and the coefficient of variation v ₁ = (exp (σ _ρ ) ² −1) ^{1/2 of} the plaque failure magnitude ρ _i To do. Considering these conditions, if ω is the event that any patient develops a transient ischemic attack or cerebral infarction, the probability of onset among patients who develop a transient cerebral ischemic attack or cerebral infarction E (χ | ω), which is the average value of, is expressed by the following mathematical formula 4.

Next, E (λ ′ | ω), which is an average value of the parameter λ ′ on the date of new onset among patients who have developed a transient ischemic attack or cerebral infarction, is obtained. As described above, the magnitude ρ of the plaque failure is determined by the degree of arteriosclerosis. The degree of arteriosclerosis is determined by the product of various factors such as age, blood pressure, blood glucose level, and cholesterol. Therefore, the average u of the plaque failure magnitude ρ _i of each patient i is also assumed to be lognormal. Here, the variation coefficient of the average u and V _2, lambda in patients who developed transient ischemic attack or stroke 'is the average value of E (λ' | ω) is expressed by the following Equation 5 The

  Next, the parameters E (χ | ω) and E (λ ′ | ω) represented by the equations 4 and 5 and τ = 1 / k are estimated from actual clinical data.

  In clinical data, as long as the time t from the time of onset of a transient ischemic attack or cerebral infarction is sufficiently small, the probability of acute phase recurrence is significantly smaller than the probability of new onset. Therefore, as clinical data, when the time t is small, the recurrence curve data of the acute phase (t ≦ 30 days) shown in FIG. 4 is used, and when the time t is sufficiently large, the chronic phase (t ≧ 30) shown in FIG. 90 days) recurrence curve data is used. From this clinical data, parameters E (χ | ω), E (λ ′ | ω) and τ = 1 / k are calculated.

Next, a variation coefficient ν ₂ is set, and E (χ | ω) calculated from clinical data is applied to the following Equation 6 to obtain an average value E (u). Then, an average u of the plaque failure magnitudes ρ is obtained from E (u) according to a lognormal distribution.

  Next, E (u) calculated from the above equation 6 and E (λ ′ | ω) calculated from the clinical data are applied to the following equation 7, and the proportional constant c between the magnitude ρ of the plaque failure and the frequency λ Ask for. Then, using the calculated c and u, the plaque failure frequency λ = cu is obtained.

Next, a variation coefficient ν ₁ is set, and the magnitude ρ of the plaque failure is obtained from u and ν ₁ for each plaque failure.

[Calculation method of cerebral infarction recurrence]
Next, a method for calculating the recurrence date of cerebral infarction after a transient cerebral ischemic attack due to a carotid artery lesion using the cerebral infarction recurrence calculating device 1 will be described with reference to FIG. FIG. 6 is a flowchart for explaining a cerebral infarction recurrence date calculation method in the cerebral infarction recurrence calculation device 1.

  First, the input data acquisition unit 2 acquires input data, for example, a variation coefficient of a probability model, a simulation start instruction, and the like from the input unit (S1). Then, the probability model acquisition unit 3 determines whether or not the input data acquisition unit 2 has acquired a simulation start instruction (S2). If it is determined that the input has been acquired (YES in S2), the probability model acquisition unit 3 stores The stored probability models represented by the above mathematical expressions 1 to 7 are acquired (S3).

  If the probability model acquisition unit 3 determines that the input data acquisition unit 2 has not acquired a simulation start instruction (NO in S2), the process returns to S2.

Next, the parameter determination unit 4 acquires the clinical data stored in the storage unit 6 by the clinical data acquisition unit 41 (S4). The plaque failure frequency calculating means 42 and the plaque failure size calculating means 43 refer to the variation coefficients ν ₁ and ν _{2 of} the probability model acquired by the input data acquiring means ₂ and the clinical data acquired by the clinical data acquiring means 41. Then, the plaque failure frequency λ and the plaque failure magnitude ρ, which are parameters of the probability model, are respectively calculated by the method described above (S5).

  Next, the transient cerebral ischemic attack determination means 51 for the recurrence date calculation means 5 makes the transient cerebral imaginary from the above equation 1 with respect to the plaque failure magnitude ρ calculated by the plaque failure size calculation means 43. The presence or absence of onset of blood attack is determined (S6).

When the transient cerebral ischemic attack determination means 51 determines “Yes” (YES in S6), the acute recurrence date calculation means 52 sets the probability ξ to develop a transient cerebral ischemic attack as χ. Then, the acute phase recurrence date T ₁ is calculated by the above mathematical formula 2 (S 7). Further, the new onset date calculating means 53 calculates the new onset date T ₂ by the above formula 3 with an exponential distribution of λ ′ = cu ² / k (S8).

  If the transient cerebral ischemic attack determination means 51 determines “No” (NO in S6), the process returns to S2.

The cerebral infarction recurrence date calculation means 54 calculates the recurrence date T of cerebral infarction by obtaining the minimum value of the acute recurrence date T ₁ and the new onset date T ₂ (S9).

As described above, when the simulation was performed with the variation coefficient ν ₁ = ν ₂ = 0.3 in the probability model by the cerebral infarction recurrence calculation method, as shown in FIG. 7, it was obtained from patients participating in the NASCET test. A survival curve similar to the survival curve can be obtained. That is, by using the cerebral infarction recurrence calculating apparatus 1, the recurrence date T of the cerebral infarction of the patient after the transient cerebral ischemic attack can be accurately calculated without actually conducting a clinical test. The survival curve obtained from patients participating in the NASCET test refers to a survival curve obtained from a study in which patients who participated in the NASCET test were surveyed by telephone.

  Also, ideal conditions that reflect the patient's background factors and severity by performing two types of simulations on the “patient” simulated using this probabilistic model using the parameters of the efficacy of the active drug and placebo. The simulation result below can be obtained. In particular, the “patient” that occurs is randomly assigned to an active drug group and a placebo group, thereby simulating an actual clinical trial.

  For example, platelet activation may be inhibited as an actual drug, and the efficacy of an antiplatelet drug having antithrombotic activity may be included in the probability model as a parameter. When the efficacy of an antiplatelet drug is included in the parameters of the probability model, this corresponds to reducing the proportional constant between the surface area of the thrombus and the magnitude ρ of the plaque failure at the time of plaque failure.

  Further, the efficacy of statin, which is a therapeutic drug for hyperlipidemia, may be included in the probability model as a parameter. Statins have a plaque stabilizing effect and are thought to prevent plaque failure. Although the mechanism is not completely known, it is assumed here that the frequency λ and the magnitude ρ of plaque failure are reduced at the same rate.

  Furthermore, the effects of new drugs and treatments that may be developed in the future can be simulated by including the drug efficacy and effects as parameters.

[Acute coronary syndrome recurrence calculation device]
Next, the acute coronary syndrome recurrence calculating apparatus 100 according to the present invention will be described. In addition, about the component which has the same function as the cerebral infarction recurrence calculation apparatus 1, the same code | symbol is attached | subjected and description is abbreviate | omitted. The acute coronary syndrome recurrence calculating apparatus 100 predicts the recurrence date of acute coronary syndrome after the onset of acute coronary syndrome in a patient generated by simulation using a probability model.

  As shown in FIG. 8, the acute coronary syndrome recurrence calculation apparatus 100 includes input data acquisition means 2, probability model acquisition means 3, parameter determination means 104, and recurrence date calculation means 105. That is, the acute coronary syndrome recurrence calculation device 100 includes parameter determination means 104 and recurrence calculation means 105 instead of the parameter determination means 4 and recurrence calculation means 5 as compared with the cerebral infarction recurrence calculation device 1. It is different. In addition, since it is the same as that of the cerebral infarction recurrence calculation apparatus 1 about another structure, description is abbreviate | omitted here.

In addition, the storage unit 6 connected to the acute coronary syndrome recurrence calculating apparatus 100 stores a probability model of clinical recurrence of acute coronary syndrome after the onset of acute coronary syndrome due to coronary artery lesion, clinical data, and a threshold value ρ _A of the magnitude ρ of plaque failure. , Ρ _M are stored.

As shown in FIG. 9, the relationship between the magnitude ρ of the plaque failure and the threshold values ρ _A and ρ _M indicates that the myocardial infarction is represented when ρ _M <ρ, and ρ _A <ρ <ρ _M It is thought that unstable angina develops if

The parameter determination means 104 includes clinical data acquisition means 41, plaque failure frequency calculation means 42, plaque failure size calculation means 43, and onset determination means 106. The onset determination means 106 compares the plaque failure magnitude ρ determined by the plaque failure size calculation means 43 with the threshold values ρ _A and ρ _M stored in the storage unit 6, and determines acute coronary syndrome (myocardial infarction or Judge whether or not to develop stable angina. Then, the plaque failure frequency calculating means 42 calculates the plaque failure frequency λ, which is a parameter of the probability model, with reference to clinical data for the patient determined by the onset determining means 106 to develop acute coronary syndrome. To do. The other configuration of the parameter determination unit 104 is the same as that of the parameter determination unit 4 in the cerebral infarction recurrence calculation device 1.

The recurrence date calculation means 105 includes acute phase recurrence date calculation means 52, new onset date calculation means 53, and acute coronary syndrome recurrence date calculation means 107. Acute coronary syndrome recurrence day calculating means 107, like the stroke recurrence day calculating means 54 of the stroke recurrence calculation apparatus 1, by obtaining the minimum value of the acute relapse date T ₁ and the new day of onset T _2, acute The recurrence date T of coronary syndrome is calculated. The other configuration of the recurrence date calculation unit 105 is the same as the recurrence date calculation unit 5 in the cerebral infarction recurrence calculation device 1.

[Calculation method for acute coronary syndrome recurrence]
Next, a method for calculating the acute coronary syndrome recurrence date after the onset of acute coronary syndrome due to coronary artery lesion using the acute coronary syndrome recurrence calculating device 100 will be described with reference to FIG. FIG. 9 is a flowchart for explaining an acute coronary syndrome recurrence calculation method in the acute coronary syndrome recurrence calculation apparatus 100.

  First, the input data acquisition unit 2 acquires input data, for example, a variation coefficient of a probability model, a simulation start instruction, and the like from the input unit (S11). Then, the probability model acquisition unit 3 determines whether or not the input data acquisition unit 2 has acquired a simulation start instruction (S12). If it is determined that the acquisition has been acquired (YES in S12), the probability model acquisition unit 3 stores The stored probability models represented by the above mathematical expressions 1 to 7 are acquired (S13).

  When the probability model acquisition unit 3 determines that the input data acquisition unit 2 has not acquired a simulation start instruction (NO in S12), the process returns to S12 again.

Next, the parameter determination unit 4 acquires the clinical data stored in the storage unit 6 by the clinical data acquisition unit 41 (S14). Then, the plaque failure size calculation means 43 refers to the probability model parameters acquired by the input data acquisition means ₂ and the clinical data acquired by the clinical data acquisition means 41 with reference to the variation coefficients ν ₁ and ν _{2 of} the probability model. The magnitude ρ of the plaque failure is calculated (S15).

Next, the onset determination means 106 compares the plaque failure magnitude ρ calculated by the plaque failure size calculation means 43 with the threshold values ρ _A and ρ _M stored in the storage unit 6 (S16), and the acute coronary It is determined whether or not a syndrome (myocardial infarction or unstable angina) has developed (S17).

If the onset determination means 106 determines that an acute coronary syndrome has occurred (YES in S17), the plaque failure frequency calculation means 42 is determined by the onset determination means 106 to have developed an acute coronary syndrome. For each patient, the plaque failure frequency λ, which is a parameter of the probability model, is calculated with reference to the variation coefficients ν ₁ and ν ₂ of the probability model and clinical data (S18).

  In addition, when the onset determination means 106 determines that the acute coronary syndrome has not occurred (NO in S17), the process returns to S12 again.

Next, the acute phase recurrence date calculation unit 52 of the recurrence date calculation unit 105 calculates the acute phase recurrence date T ₁ by the above formula 2 with the probability ξ of developing acute coronary syndrome as χ (S19). Further, the new onset date calculating means 53 calculates the new onset date T ₂ by the above formula 3 with an exponential distribution of λ ′ = cu ² / k (S20).

Then, acute coronary syndrome recurrence day calculating means 107, by obtaining the minimum value of the acute relapse date T ₁ and the new day of onset T _2, calculating the recurrence day T acute coronary syndrome (S21).

As described above, according to the method for calculating the recurrence of acute coronary syndrome, each parameter in the probability model is set to k = 0.2, E (u) = 0.012, ν ₁ = ν ₂ = 0.2, c = 0.5. , Ρ _A = 0.027, ρ _M = 0.029, and simulations are performed, as shown in FIGS. 11 and 12, are close to the results of the PARAGON-A trial, which is a clinical trial in acute coronary syndromes A survival curve can be obtained. That is, by using the acute coronary syndrome recurrence calculating apparatus 100, the recurrence date T of the acute coronary syndrome of the patient after the onset of the acute coronary syndrome can be accurately calculated without actually conducting a clinical test.

  In addition, the “patient” simulated using this probability model may also include the efficacy of the active drug or placebo as a parameter, as in the case of the cerebral infarction recurrence calculation device 1.

  Finally, each block of the cerebral infarction recurrence calculation device 1 and the acute coronary syndrome recurrence calculation device 100, in particular, the input data acquisition means 2, the probability model acquisition means 3, the parameter determination means 4, 104, and the recurrence date calculation means 5 It may be configured by wear logic, or may be realized by software using a CPU as follows.

  That is, the cerebral infarction recurrence calculating device 1 and the acute coronary syndrome recurrence calculating device 100 are a CPU (central processing unit) that executes a command of a control program that realizes each function, a ROM (read only memory) that stores the program, A RAM (random access memory) for expanding the program, a storage device (recording medium) such as a memory for storing the program and various data, and the like are provided. An object of the present invention is to provide a recording medium in which a program code (execution format program, intermediate code program, source program) of a control program of the authentication device 1 which is software that realizes the above-described functions is recorded so as to be readable by a computer. This can also be achieved by supplying the authentication apparatus 1 and reading and executing the program code recorded on the recording medium by the computer (or CPU or MPU).

  Examples of the recording medium include a tape system such as a magnetic tape and a cassette tape, a magnetic disk such as a floppy (registered trademark) disk / hard disk, and an optical disk such as a CD-ROM / MO / MD / DVD / CD-R. Card system such as IC card, IC card (including memory card) / optical card, or semiconductor memory system such as mask ROM / EPROM / EEPROM / flash ROM.

  The cerebral infarction recurrence calculating device 1 and the acute coronary syndrome recurrence calculating device 100 may be configured to be connectable to a communication network, and the program code may be supplied via the communication network. The communication network is not particularly limited. For example, the Internet, intranet, extranet, LAN, ISDN, VAN, CATV communication network, virtual private network, telephone line network, mobile communication network, satellite communication. A net or the like is available. Also, the transmission medium constituting the communication network is not particularly limited. For example, even in the case of wired such as IEEE 1394, USB, power line carrier, cable TV line, telephone line, ADSL line, etc., infrared rays such as IrDA and remote control, Bluetooth ( (Registered trademark), 802.11 wireless, HDR, mobile phone network, satellite line, terrestrial digital network, and the like can also be used. The present invention can also be realized in the form of a computer data signal embedded in a carrier wave in which the program code is embodied by electronic transmission.

  In the above, this invention was demonstrated based on the Example. It is to be understood by those skilled in the art that this embodiment is merely an example, and that various modifications are possible and that such modifications are within the scope of the present invention.

DESCRIPTION OF SYMBOLS 1 Cerebral infarction recurrence calculation apparatus 2 Input data acquisition means 3 Probability model acquisition means 4, 104 Parameter determination means 41 Clinical data acquisition means 42 Plaque failure frequency calculation means 43 Plaque failure size calculation means 5, 105 Recurrence date calculation means 51 Transient Cerebral ischemic attack determination means 52 Acute phase recurrence date calculation means 53 New onset date calculation means 54 Cerebral infarction recurrence date calculation means 6 Storage unit 100 Acute coronary syndrome recurrence calculation device 107 Acute coronary syndrome recurrence date calculation means

Claims (15)

Input data acquisition means for acquiring a coefficient of variation of the input probability model and a calculation start instruction;
When the input data acquisition means acquires a calculation start instruction, a probability model acquisition means for acquiring a probability model of cerebral infarction after a transient cerebral ischemic attack due to a carotid artery lesion stored in the storage unit;
Parameter determining means for determining parameters of the probability model with reference to the coefficient of variation acquired by the input data acquiring means and the clinical data stored in the storage unit;
A cerebral infarction recurrence calculating device, comprising: a recurrence date calculating means for calculating a recurrence date of the onset of cerebral infarction of a patient from the probability model to which the parameter determined by the parameter determining means is applied. The cerebral infarction recurrence calculation device according to claim 1, wherein the probability model is constructed based on the following assumptions (1) to (5).
(1) The magnitude of atherosclerotic plaque failure follows a lognormal distribution, and the average value correlates with the degree of arteriosclerosis.
(2) The surface area of the thrombus generated on the failed arteriosclerotic plaque is proportional to the magnitude of the atherosclerotic plaque failure.
(3) The probability of developing cerebral infarction resulting from the thrombus is proportional to the surface area of the thrombus.
(4) The failure of the atherosclerotic plaque is a Poisson process, and its parameter is proportional to the degree of arteriosclerosis.
(5) The degree of arteriosclerosis between patients follows a lognormal distribution.   The cerebral infarction recurrence calculation apparatus according to claim 1 or 2, wherein the parameter includes an effect of a therapeutic agent having an antithrombotic action that inhibits platelet activation.   The cerebral infarction recurrence calculating apparatus according to claim 1 or 2, wherein the parameter includes an effect of a therapeutic agent having a plaque stabilizing action for preventing plaque breakdown. Input data acquisition means for acquiring a coefficient of variation of the input probability model and a calculation start instruction;
When the input data acquisition means acquires a calculation start instruction, a probability model acquisition means for acquiring a probability model of acute coronary syndrome recurrence after the onset of acute coronary syndrome due to coronary artery lesions stored in the storage unit;
Parameter determining means for determining parameters of the probability model with reference to the coefficient of variation acquired by the input data acquiring means and the clinical data stored in the storage unit;
An acute coronary syndrome recurrence calculating device, comprising: a recurrence date calculating means for calculating a recurrence date of the onset of acute coronary syndrome of a patient from the probability model to which the parameter determined by the parameter determining means is applied. 6. The acute coronary syndrome recurrence calculating apparatus according to claim 5, wherein the probability model is constructed based on the following assumptions (1) to (5).
(1) The magnitude of atherosclerotic plaque failure follows a lognormal distribution, and the average value correlates with the degree of arteriosclerosis.
(2) The surface area of the thrombus generated on the failed arteriosclerotic plaque is proportional to the magnitude of the atherosclerotic plaque failure.
(3) The probability of the onset of acute coronary syndrome resulting from the thrombus is proportional to the surface area of the thrombus.
(4) The failure of the atherosclerotic plaque is a Poisson process, and its parameter is proportional to the degree of arteriosclerosis.
(5) The degree of arteriosclerosis between patients follows a lognormal distribution.   7. The acute coronary syndrome recurrence calculating apparatus according to claim 5, wherein the parameter includes an effect of a therapeutic agent having an antithrombotic action that inhibits platelet activation.   The acute coronary syndrome recurrence calculating apparatus according to claim 5 or 6, wherein the parameter includes an effect of a therapeutic agent having a plaque stabilizing action for preventing plaque failure. The parameter determination means includes
A decision means to determine the magnitude of atherosclerotic plaque failure;
The magnitude of the atherosclerotic plaque failure determined by the determining means is compared with a predetermined threshold value stored in the storage unit, and if the size of the atherosclerotic plaque failure is equal to or greater than the threshold value, acute coronary syndrome And an onset determination means for determining that the
The recurrence calculation of acute coronary syndrome according to any one of claims 5 to 8, wherein the parameter of the probability model is determined for a patient determined to have developed acute coronary syndrome by the onset determination means. apparatus. An input data acquisition step of acquiring a variation coefficient of the input probability model and a calculation start instruction;
When obtaining a calculation start instruction in the input data acquisition step, a probability model acquisition step of acquiring a probability model of cerebral infarction after a transient cerebral ischemic attack due to a carotid artery lesion stored in the storage unit;
A parameter determination step of determining parameters of the probability model with reference to the coefficient of variation acquired in the input data acquisition step and the clinical data stored in the storage unit;
And a recurrence date calculating step of calculating a recurrence date of the onset of cerebral infarction of the patient from the probability model to which the parameter determined in the parameter determining step is applied. An input data acquisition step of acquiring a variation coefficient of the input probability model and a calculation start instruction;
When the calculation start instruction is acquired in the input data acquisition step, a probability model acquisition step of acquiring a probability model of acute coronary syndrome recurrence after the onset of acute coronary syndrome due to coronary artery lesion stored in the storage unit;
A parameter determination step of determining parameters of the probability model with reference to the coefficient of variation acquired in the input data acquisition step and the clinical data stored in the storage unit;
And a recurrence date calculating step of calculating a recurrence date of the onset of the patient's acute coronary syndrome from the probability model to which the parameter determined in the parameter determining step is applied.   The cerebral infarction recurrence calculation program for functioning a computer as said each means of the cerebral infarction recurrence calculation apparatus of any one of Claims 1-4.   The acute coronary syndrome recurrence calculation program for functioning a computer as said each means of the acute coronary syndrome recurrence calculation apparatus of any one of Claims 5-9.   The computer-readable recording medium which recorded the cerebral infarction recurrence calculation program of the cerebral infarction recurrence calculation apparatus of Claim 12.   The computer-readable recording medium which recorded the acute coronary syndrome recurrence calculation program of the acute coronary syndrome recurrence calculation apparatus of Claim 13.