JP2006304833A - Diagnosis supporting system and computer program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a diagnosis supporting system and a computer program which can provide information concerning a patient's diabetes and information concerning a metabolic syndrome. <P>SOLUTION: The diagnosis supporting system 10 has function blocks of a physiological data input section 1, a diabetes risk analysis section 2, a metabolic syndrome risk analysis section 3, a diagnosis supporting information generation section 4, a biological model generation section 5, a condition simulation section 6 and a diagnosis supporting information output section 7. The risks of diabetes and metabolic syndrome are analyzed by the diabetes risk analysis section 2 and the metabolic syndrome risk analysis section 3. The diagnosis supporting information generation section 4 generates diagnosis supporting information from the results of analyses. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to a diagnosis support system that provides disease risk information of diabetes and metabolic syndrome by analyzing the risk of diabetes and metabolic syndrome in a subject, and a computer program for causing a computer to function as a diagnosis support system.

  Diabetes mellitus is a typical lifestyle-related disease, and is a disease in which the number of patients is rapidly increasing with the westernization of lifestyle habits. Such diabetes is divided into a number of patterns, and it is necessary to select a treatment pattern that matches each pathological pattern. It was difficult to choose. Therefore, a diagnostic support system has been developed that provides information for supporting the diagnosis of diabetes so that even non-diabetes practitioners, general physicians, etc. can diagnose diabetes optimally ( For example, see Patent Documents 1 to 3).

  On the other hand, factors that increase the risk of developing lifestyle-related diseases including diabetes include “visceral fat syndrome”, “syndrome X”, “death quartet”, “insulin resistance syndrome”, “multiple risk factor syndrome”, etc. Has been published by researchers. At present, these concepts are unified with the concept of “metabolic syndrome” which is also called metabolic syndrome. Metabolic syndrome is considered to be a syndrome that significantly increases the risk of developing arteriosclerotic diseases by combining risk factors for lifestyle-related diseases such as hypertension, hyperlipidemia, insulin resistance, and hyperuricemia.

Japanese Patent Laid-Open No. 10-332704 JP-A-11-296598 JP 2004-154341 A

  Such metabolic syndrome is a risk factor for many lifestyle-related diseases, and its correction is an important issue in health policy. For this purpose, it is necessary to accurately analyze the disease risk of the patient's metabolic syndrome. In addition, since metabolic syndrome is closely related to diabetes, in the diagnosis of diabetes, if there is disease information related to the patient's metabolic syndrome, the reliability of the diagnosis can be improved by taking this into account. On the other hand, in the diagnosis of metabolic syndrome, the reliability of the diagnosis can be increased by taking into account the disease information regarding the patient's diabetes. However, in the conventional diagnosis support system as described above, the information that can be provided by the system is only information related to diabetes such as the disease risk of diabetes, and information related to metabolic syndrome cannot be provided.

  The present invention has been made in view of such circumstances, and provides a diagnosis support system and a computer program capable of providing not only information related to diabetes of a subject (patient) but also information related to metabolic syndrome. With the goal.

  The diagnosis support system according to the present invention compares an input receiving unit that receives an input of physiological information indicating a physiological state of a subject, a physiological information received by the input receiving unit, and a preset criterion. The first risk analysis means for analyzing the disease risk of diabetes in the subject, the physiological information accepted by the input acceptance means, and a predetermined criterion are analyzed to analyze the disease risk of metabolic syndrome in the subject The second risk analysis means, an output means for outputting the analysis result by the first risk analysis means, and the analysis result by the second risk analysis means.

  With this configuration, it is possible to provide the disease risk of diabetes analyzed by the first risk analysis means and the disease risk of metabolic syndrome analyzed by the second risk analysis means, and diagnosis of diabetes or metabolic syndrome Therefore, the reliability of the diagnosis can be improved.

  In the above invention, the physiological information includes a waist diameter, a triglyceride value, an HDL cholesterol value, a blood pressure value, and a fasting blood glucose level, and the second risk analysis means includes the waist diameter, medium A structure for determining whether the sex fat level, the HDL cholesterol value, the blood pressure value, and the fasting blood glucose level each exceed a corresponding determination criterion, and analyzing a disease risk of metabolic syndrome in the subject based on the determination result; It is preferable to do. This makes it possible to accurately analyze the disease risk of metabolic syndrome.

  In the above invention, the second risk analysis means counts how many of at least a neutral fat value or HDL cholesterol value, a blood pressure value, and a fasting blood glucose level included in the physiological information exceed the criterion. Metabolic syndrome disease when the counting means and a determining means for determining whether or not the counting result by the counting means exceeds a predetermined number, and the determining means determines that the counting result exceeds the predetermined number It is preferably configured to analyze that the risk is high. Here, “exceeding the criterion” means that the value such as the waist diameter enters the abnormal region of the normal region and the abnormal region classified by the criterion, for example, determination If it is abnormal when it exceeds the standard, it means exceeding the criterion, and if it is abnormal when it is below the standard, it means below the criterion.

  In the above invention, it is preferable that the physiological information includes a blood pressure value, a test value related to hyperlipidemia, a test value related to visceral fat obesity, and a test value related to microalbuminuria.

  In the above invention, the physiological information is, for example, information indicating whether or not the subject falls under diabetes or impaired glucose tolerance, information indicating the degree of diabetes or impaired glucose tolerance (severe, moderate, mild, etc.) Further information relating to diabetes and / or impaired glucose tolerance, wherein the second risk analysis means includes first analysis means for determining whether or not the subject has diabetes or impaired glucose tolerance, and physiological information Second analysis means for determining whether or not the blood pressure value, the test value related to hyperlipidemia, the test value related to visceral fat obesity, and the test value related to microalbuminuria exceed the corresponding determination criteria, respectively. The disease risk of metabolic syndrome in the subject can be analyzed based on the determination result by the first analysis means and the determination result by the second analysis means. The second risk analysis means includes: first analysis means for determining whether or not the subject falls under diabetes based on an analysis result by the first risk analysis means; a blood pressure value included in physiological information; A second analysis means for determining whether a test value related to blood serum, a test value related to visceral fat obesity, and a test value related to microalbuminuria each exceed a corresponding determination criterion, respectively, according to the first analysis means Based on the determination result and the determination result by the second analysis means, it is possible to analyze the disease risk of metabolic syndrome in the subject.

  Further, in such a case, the second analysis means includes the blood pressure value included in the physiological information, the test value related to hyperlipidemia, the test value related to visceral fat obesity, and the test value related to microalbuminuria. The second risk analyzing means includes a determining means for determining whether or not a counting result obtained by the second analyzing means exceeds a predetermined number, wherein the first risk analyzing means is configured to count whether the determination criterion is exceeded. As a result of determination by the analysis means, when the subject falls under diabetes or impaired glucose tolerance, and the determination means determines that the counting result exceeds a predetermined number, the disease risk of metabolic syndrome is high It is preferably configured to analyze. This makes it possible to accurately analyze the disease risk of metabolic syndrome. Here, “exceeding the criterion” means that a value such as blood pressure enters the abnormal region of the normal region and the abnormal region classified by the criterion, for example, the criterion If the value exceeds the criterion, it means exceeding the criterion. If the factor is below the criterion, it means below the criterion.

  In the above invention, the physiological information further includes a test value relating to insulin resistance, and the second risk analysis means includes first analysis means for determining whether or not the subject falls under insulin resistance. Second analysis for determining whether blood pressure values included in physiological information, test values related to hyperlipidemia, test values related to visceral fat obesity, and test values related to microalbuminuria exceed the corresponding criteria Means for analyzing the disease risk of metabolic syndrome in the subject based on the determination result by the first analysis means and the determination result by the second analysis means, and The second risk analysis means determines whether or not the subject corresponds to insulin resistance based on an analysis result by the first risk analysis means. 1 Analytical means and whether or not the blood pressure value included in the physiological information, the test value related to hyperlipidemia, the test value related to visceral fat obesity, and the test value related to microalbuminuria exceed the corresponding criteria And a second analysis unit that analyzes the disease risk of metabolic syndrome in the subject based on the determination result by the first analysis unit and the determination result by the second analysis unit. is there.

  In such a case, the second analysis means determines whether some of the blood pressure value included in the physiological information, the test value related to hyperlipidemia, the test value related to visceral fat obesity, and the test value related to microalbuminuria are the determination criteria. The second risk analysis means includes a determination means for determining whether or not a counting result by the second analysis means exceeds a predetermined number, and the first analysis means As a result of determination by the method, if the subject falls under insulin resistance and the determination means determines that the counting result exceeds a predetermined number, the risk of metabolic syndrome disease is analyzed to be high It is preferable to be configured. This makes it possible to accurately analyze the disease risk of metabolic syndrome.

  In the said invention, the test value regarding the said hyperlipidemia can be made into a neutral fat value or a HDL cholesterol value. Further, the test value relating to the visceral fat obesity can be a waist diameter, a waist / hip ratio, or a BMI. Further, the test value relating to the microalbuminuria can be set to μ-Alb or albumin / creatinine ratio.

  In the above invention, the first risk analysis means includes an insulin resistance risk analysis means for analyzing the degree of insulin resistance in the subject, and a glucose release enhancement risk analysis means for analyzing the degree of glucose release enhancement in the subject. It is preferable that the apparatus includes an insulin secretion decrease risk analysis means for analyzing the degree of insulin secretion decrease in the subject. Since the pathological condition of diabetes is roughly classified into insulin resistance, increased glucose release, and decreased insulin secretion, such a configuration requires the degree of each pathological condition. Therefore, by comprehensively judging the degree of these pathological conditions, it becomes possible to accurately analyze the disease risk of diabetes.

  In this case, the first risk analyzing means may further include a sugar toxicity risk analyzing means for analyzing the degree of sugar toxicity in the subject. Basically, the pathological condition of diabetes can be divided into the above three pathological conditions (insulin resistance, increased glucose release, and decreased insulin secretion). By combining the analysis results on glucose toxicity, the risk of diabetes can be further reduced. The analysis result can be further enhanced.

  In the above invention, a living body model that generates an output that mimics the pathology of diabetes in a living body in response to an input, and a living body when a subject is treated using the living body model are simulated, and after treatment It is preferable to further include a pathological condition predicting unit that predicts the pathological condition and a prediction result output unit that outputs a prediction result by the pathological condition predicting unit. Thereby, the effect of the treatment when the treatment is performed can be predicted, and even a non-specialist of diabetes can easily examine a correct treatment pattern.

  In the above invention, based on the analysis result by the first risk analysis means, diagnosis support information generating means for generating diagnosis support information for use in diagnosis by a doctor, and diagnosis generated by the diagnosis support information generating means It is preferable to further include a diagnosis support information output unit that outputs support information. As a result, even non-diabetes specialists can easily and accurately diagnose diabetes by using the diagnosis support information.

  A computer program according to the present invention is a computer program for causing a computer having an input device and an output device to function as a diagnosis support system used for support of diagnosis of diabetes and metabolic syndrome, and the computer includes the input device. The input accepting means for accepting the input of physiological information indicating the physiological state of the subject by comparing the physiological information accepted by the input accepting means with a predetermined criterion, thereby determining the disease risk of diabetes in the subject. A first risk analyzing means for analyzing, a second risk analyzing means for analyzing a disease risk of metabolic syndrome in the subject by comparing the physiological information received by the input receiving means with a predetermined criterion; The analysis result by the first risk analysis means and the second risk And an analysis result by analyzing means, characterized in that to function as output means for outputting to the output device.

  By doing so, it is possible to provide the disease risk of diabetes analyzed by the first risk analysis means and the disease risk of metabolic syndrome analyzed by the second risk analysis means. Diagnosis of diabetes or metabolic syndrome Therefore, the reliability of the diagnosis can be improved.

  According to the diagnosis support system and computer program according to the present invention, it is possible to provide a doctor or the like with a disease risk of diabetes and a disease risk of metabolic syndrome, and in the diagnosis of diabetes or metabolic syndrome, the reliability of the diagnosis can be improved. The present invention has an excellent effect, such as being able to be enhanced.

Hereinafter, a diagnosis support system and a computer program according to embodiments of the present invention will be specifically described with reference to the drawings.
(Embodiment 1)
FIG. 1 is a functional block diagram showing a conceptual configuration of a diagnosis support system according to Embodiment 1 of the present invention. As shown in FIG. 1, the diagnosis support system 10 of this embodiment includes a physiological data input unit 1, a diabetes disease risk analysis unit 2, a metabolic syndrome disease risk analysis unit 3, a diagnosis support information generation unit 4, and a biological model generation unit 5. , The biological model 5a, the biological model driving unit 5b, the pathological condition simulation unit 6, and the diagnostic support information output unit 7 are provided. Each of such functional blocks is realized by cooperation of the following hardware configuration and a computer program.

  FIG. 2 is a block diagram showing a hardware configuration of the diagnosis support system according to the first embodiment. The diagnosis support system 10 according to the first embodiment is configured by a computer 10a mainly composed of a main body 11, a display 12, and an input device 13. The main body 11 mainly includes a CPU 11a, a ROM 11b, a RAM 11c, a hard disk 11d, a reading device 11e, an input / output interface 11f, a communication interface 11g, and an image output interface 11h. The CPU 11a, ROM 11b, and RAM 11c. The hard disk 11d, the reading device 11e, the input / output interface 11f, and the image output interface 11h are connected by a bus 11i so that data communication is possible.

  The CPU 11a can execute computer programs stored in the ROM 11b and computer programs loaded in the RAM 11c. When the CPU 11a executes an application program 14a as described later, the above-described functional blocks are realized, and the computer 10a functions as the diagnosis support system 10.

  The ROM 11b is configured by a mask ROM, PROM, EPROM, EEPROM, or the like, and stores a computer program executed by the CPU 11a, data used for the same, and the like.

  The RAM 11c is configured by SRAM, DRAM, or the like. The RAM 11c is used for reading computer programs recorded in the ROM 11b and the hard disk 11d. Further, when these computer programs are executed, they are used as a work area of the CPU 11a.

  The hard disk 11d is installed with various computer programs to be executed by the CPU 11a, such as an operating system and application programs, and data used for executing the computer programs. An application program 14a described later is also installed in the hard disk 11d.

  The reading device 11e is configured by a flexible disk drive, a CD-ROM drive, a DVD-ROM drive, or the like, and can read a computer program or data recorded on the portable recording medium 14. The portable recording medium 14 stores an application program 14a for causing the computer to function as a diagnosis support system according to the present invention, and the computer 10a can transfer the application program 14a according to the present invention from the portable recording medium 14 to the portable recording medium 14. And the application program 14a can be installed in the hard disk 11d.

  The application program 14a is not only provided by the portable recording medium 14, but also from the external device communicatively connected to the computer 10a by an electric communication line (whether wired or wireless). It is also possible to provide through. For example, the application program 14a is stored in a hard disk of a server computer on the Internet. The computer 10a can access the server computer, download the computer program, and install it on the hard disk 11d. is there.

  The hard disk 11d is installed with an operating system that provides a graphical user interface environment such as Windows (registered trademark) manufactured and sold by Microsoft Corporation. In the following description, it is assumed that the application program 14a according to the first embodiment operates on the operating system.

  The input / output interface 11f includes, for example, a serial interface such as USB, IEEE1394, and RS-232C, a parallel interface such as SCSI, IDE, and IEEE1284, and an analog interface including a D / A converter and an A / D converter. ing. An input device 13 including a keyboard and a mouse is connected to the input / output interface 11f, and the user can input data to the computer 10a by using the input device 13.

  The image output interface 11h is connected to a display 12 constituted by an LCD, a CRT, or the like, and outputs a video signal corresponding to image data given from the CPU 11a to the display 12. The display 12 displays an image (screen) according to the input video signal.

  The physiological data input unit 1 shown in FIG. 1 shows numerical values (test values) of test results such as blood glucose levels, finding information obtained by doctors' interviews, and physiological states of other subjects (diagnostic subjects, patients). This is a part for inputting information to this system, and is configured by an input device 13, an input / output interface 11 f, and a computer program relating to reception of information input from the input device 13 as shown in FIG. In the first embodiment, the physiological data input unit 1 is configured by the input device 13. However, the present invention is not limited to this. For example, the input device 13 includes various input devices such as an OCR and a scanner. Alternatively, various information may be registered in a database or the like accessible by the computer 10a, and the information may be input by the computer 10a accessing the database. The input information is stored in the hard disk 11d so as to be used by the diabetes disease risk analysis unit 2, the metabolic syndrome disease risk analysis unit 3, and the like.

  Here, in this embodiment, the input physiological data includes at least the insulin value (μu / ml), blood glucose level (mg / dl), fasting time, and insulin value a 2 hours after meal in the oral glucose tolerance test ( μu / ml), blood glucose level b (mg / dl), HOMA-IR value (= a × b / 405), 24-hour urinary C peptide (μg), glycated protein index HbA1C, presence or absence of weight loss, BMI value, Examples include ΔIRI / ΔBS, whether urinary ketone bodies are positive, waist diameter, neutral fat value, HDL cholesterol value, blood pressure value, and the like. However, the present invention is not limited to these, and other inspection values may be input as necessary. Clinical findings include obesity or leanness, fasting or postprandial blood glucose levels, carbohydrate intake, and the like. Further, the HOMA-IR value may not be input as physiological data, but may be calculated by the CPU 11a using the fasting insulin value a and the blood glucose level b.

The diabetes disease risk analysis unit 2 is a part that analyzes the disease risk related to diabetes of the subject based on the input physiological data. In the following examples, the etiology causing diabetes is classified into the following four.
(A) Peripheral insulin resistance (b) Increased glucose release from the liver (c) Glucose toxicity exposed to long-term hyperglycemia
(D) Decrease in insulin secretion The diabetes disease risk analysis unit 2 calculates an evaluation value for each of the four etiologies. The evaluation value means the degree of influence of the etiology on diabetes. Each evaluation value is calculated based on an analysis criterion as described later, and is output as a real value. Hereinafter, this evaluation value is referred to as a score. The analysis criterion is stored in advance in the hard disk 11d, and the score is calculated by sequentially comparing the input data with the criterion value based on the application program 14a. The output score is given to the diagnosis support information generation unit 4 and used to create support information such as a treatment method.

  The metabolic syndrome disease risk analysis unit 3 is a part that analyzes the disease risk related to the subject's metabolic syndrome based on the input physiological data. The metabolic syndrome disease risk analysis unit 3 calculates one evaluation value (score). Such a score is also calculated based on an analysis criterion as described later, and is output as a real value. The determination criteria used for the analysis regarding the metabolic syndrome are stored in the hard disk 11d in advance, and the score is calculated by sequentially comparing the input data and the determination reference value based on the application program 14a. The score output from the metabolic syndrome disease risk analysis unit 3 is also given to the diagnosis support information generation unit 4.

  The diagnosis support information generation unit 4 uses the analysis result in the diabetes disease risk analysis unit 2 and the metabolic syndrome disease risk analysis unit 3, the database storing the physiological data input from the physiological data input unit 1 and the know-how of the specialist. Thus, this is a part for generating diagnosis support information including a treatment method considered to be optimal based on a diagnosis judgment standard as will be described later. The database that stores the know-how of specialists consists of knowledge of drugs for diabetes, knowledge of exercise therapy, knowledge of diet therapy, etc., and is structured as a treatment policy according to the pattern of each disease state, clinical findings of patients, history of surgery, etc. It has been These pieces of information are stored in the hard disk 11d.

  Drug knowledge includes drug candidates that can be administered according to each pathology of diabetes, drug selection order and dose according to clinical findings, and types of drugs that cannot be administered depending on the patient's condition (indications for contraindications). . The administration ratio of the drug is given as a function of the intensity of the parameters such as the degree of influence of each etiology on diabetes in the patient and whether there is a disorder in an organ such as the kidney. For example, the drug administration policy of using AGI in combination with TZD has the greatest influence on the improvement of insulin resistance in the pathogenesis of diabetes in the patient, and there is no heart failure, no electrolyte abnormality, and digestion in the past. It is given by considering conditions such as the absence of tube surgery. Examples of exercise therapy include exercise intensity, amount of exercise, and recommended exercise type according to each disease state. Examples of dietary knowledge include allowable calorie intake according to each disease state, allowable intake of each nutrient, and the like.

  The biological model generation unit 5 is a part that generates a biological model 5a by estimating the biological parameter value unique to the subject based on the input physiological data and the analysis result by the diabetes disease risk analysis unit 2. The biological model 5a is a model in which an organ related to diabetes and a function of the organ are described as a mathematical model. For example, a secretory function of insulin in the pancreas, a glucose uptake and release function in the liver, and an insulin in peripheral tissues. The function of metabolism of the sugar used can be expressed. For example, the model can be expressed as a differential equation with the substance concentration as a variable. However, the present invention is not limited to these, and other organs, organs, and other functions may be included as necessary, and other expression methods may be used. This biological model 5a has a structure part common to each patient and biological parameters that are variables different for each patient. This biological parameter represents, for example, the degree of “sugar utilization”, “insulin secretion”, and “sugar release”, and different parameter values are determined for each subject. These parameter values are given to the pathological condition simulation unit 6 and used to predict the pathological condition of the subject's future diabetes. For example, “sugar utilization” refers to the consumption of sugar in each cell, including peripheral tissues such as muscle and adipose tissue, which is used to predict insulin resistance.

  The biological model driving unit 5b is a part for performing calculations for reproducing the behavior of the living body using the biological model 5a. For example, the behavior of the biological model may be calculated using MatLab (product of Masworks) or E-Cell (Keio University public software). Other calculation systems may also be used. When a mathematical model based on a differential equation as described above is used as the biological model 5a, numerical calculation software capable of calculating an arbitrary parameter value of the differential equation at an arbitrary time interval is used as the biological model driving unit 5b. it can.

  The biological model generation unit 5 estimates each parameter value of the biological model 5a such that the output of the biological model driving unit 5b matches the input physiological data and the analysis result given from the diabetes disease risk analysis unit 2. . As a method of estimating each parameter value, a known least square method, steepest descent method, genetic algorithm, simulated annealing, or the like can be used, but is not limited thereto, and other algorithms can be used as necessary. It may be used.

  The pathological condition simulation unit 6 is a part that predicts a pathological condition after performing a certain treatment for the patient using the patient-specific biological model generated by the biological model generation unit 5. For example, as a result of a simulation assuming that insulin was administered to a patient whose pre-treatment pathology was exposed to long-term hyperglycemia and had the greatest impact on the cause of glucose toxicity, an increase in "insulin secretion" and "sugar release" In the case where an increase in “sugar utilization” is not observed, the pathological condition after the treatment is predicted that the pathogenesis after the insulin treatment has the greatest influence, and a result indicating that is output.

  The diagnosis support information output unit 7 displays on the display 12 diagnosis support information such as the treatment method or metabolic syndrome disease risk generated by the diagnosis support information generation unit 4 and the disease state or treatment result predicted by the disease state simulation unit 6. It is a part to be made. Note that the present invention is not limited to display on the display 12, and for example, a configuration in which diagnosis support information is printed on a printing apparatus such as a printer may be used. As described above, the diagnosis support information is displayed on the display 12 or printed on a predetermined sheet, and is provided to users such as doctors and patients.

  Next, the operation of the diagnosis support system according to the first embodiment will be described. When the CPU 11a of the computer 10a executes the application program 14a, the computer 10a functions as the diagnosis support system 10 and operates as follows. FIG. 3 is a flowchart showing an operation flow of the diagnosis support system 10 according to the first embodiment of the present invention. First, the CPU 11a receives an input of physiological data of a certain patient as described above (step S1).

Next, the CPU 11a executes a diabetes disease risk analysis process (step S2).
In the diabetes disease risk analysis process, the following four determination processes are mainly performed.
Step S2-1: Determination of peripheral insulin resistance Step S2-2: Determination of increased glucose release from the liver Step S2-3: Determination of glucose toxicity exposed to long-term hyperglycemia Step S2-4: Determination of decrease in insulin secretion Detailed processing contents of the steps are shown in FIGS.

  When the determination process of each step is executed, the score is calculated by the CPU 11a for each step and temporarily stored in the RAM 11c or the hard disk 11d. For example, in step S 2-1, an evaluation value (score A) indicating how much insulin resistance has an effect as a cause of diabetes is obtained by executing peripheral insulin resistance determination processing. This score A is temporarily stored and used in later diagnosis support information generation processing (step S5).

  Similarly, in step S2-2, the CPU 11a calculates a score B, which is an evaluation value of glucose release enhancement, in step S2-3, the glucose toxicity score C, and in step S2-4, the insulin secretion decrease score. D is calculated by the CPU 11a. These scores indicate that the greater the numerical value, the greater the influence of the etiology.

  Next, the CPU 11a executes a metabolic syndrome disease risk analysis process (step S3). Here, an evaluation value (score E) indicating a disease risk of metabolic syndrome is calculated. The score E indicates that the higher the numerical value, the higher the risk of disease of the metabolic syndrome, that is, the more severe the symptoms of the metabolic syndrome, or the higher the probability of developing the metabolic syndrome. Details of the processing in step S3 are shown in FIG.

  Next, in step S4, the CPU 11a determines whether the process proceeds to the diagnosis support information generation process (step S5) or the biological model generation process (step S6). This determination may be performed by displaying a prompt for user input and inputting a predetermined key. Alternatively, at the time of input in step S1, information on which process the user performs may be input in advance. Further, the branch determination in step S4 is not essential, and step S6 and S7 may be executed sequentially after step S5 is executed. Alternatively, step S6 and step 7 are executed sequentially after step S6 and step 7 are executed. S5 may be executed.

  If the diagnosis support information generation process is not performed in step S4 (NO in step S4), the CPU 11a advances the process to step S6 and executes the biological model generation process. In the biological model generation process (step S6), the function value is calculated mainly based on the inputted physiological data and the like, and the biological parameter value as described above is obtained. After step S6, a pathological condition simulation process (step S7) is executed, and the CPU 11a predicts the pathological condition of the patient after treatment using the obtained biological parameter value. The prediction of the pathological condition is, for example, by inputting information such as administration of a specific amount of a certain drug, the CPU 11a calculates each function value representing the ecological parameter value, and the increase or decrease in each of the biological parameter values indicates the patient's condition. This is done by predicting physiological data and using the result to execute step S5. Thereafter, the process proceeds to step S8, and information useful for diagnosis obtained in steps S2, S3, S6 and S7 is displayed on the display 12 (or printed by a printer).

In step S4, when the diagnosis information generation process is performed (YES in step S4), the CPU 11a advances the process to step S5 and executes the diagnosis support information generation process. In the diagnosis support information generation process (step S5), one of the following four processes is executed by the CPU 11a depending on the magnitude relationship of the four scores (A, B, C, D) obtained in step S2. .
Step S5-1: If determined to be peripheral insulin resistance Step S5-2: If determined to be increased glucose release from the liver Step S5-3: Determined to be glucose toxicity exposed to long-term hyperglycemia Case Step S5-4: When it is determined that insulin secretion is reduced In these four processes, the CPU 11a performs diagnosis support information including a treatment policy, a drug to be administered, and the like based on predetermined diagnosis criteria for each etiology. Is generated.

  Moreover, in step S5, CPU11a determines a disease risk to a metabolic syndrome using the score (E) calculated | required by step S3 (step S5-5). Detailed processing contents of steps S5-1 to S5-5 are shown in FIGS. After step S5, the CPU 11a displays the diagnosis support information on the display 12 (or prints with a printer) (step S8). The above is the overall flow of the diagnosis support system of the present invention.

  Next, each determination process in step S2 will be described. FIG. 4 is a flowchart showing a processing procedure of peripheral insulin resistance determination processing (S2-1), and FIG. 5 is a flowchart showing a processing procedure of glucose release enhancement determination processing (S2-2) from the liver. FIG. 6 is a flowchart showing the processing procedure of the glucose toxicity determination process (S2-3) exposed to long-term hyperglycemia, and FIG. 7 shows the processing procedure of the insulin secretion decrease determination process (S2-4). It is a flowchart to show. At the end of each process of steps S2-1, S2-2, S2-3, and S2-4, scores A, B, C, and D are calculated, respectively.

  Each score (A, B, C, D) is obtained as a total value (SC) of numerical values predetermined in the determination process. However, in order to express the score as a percentage (%), a value obtained by dividing the calculated score value SC by the total score value SA when it is determined that the score corresponding to the worst case is the highest. It may be used as a score. That is, score (A, B, C, D) = (SC / SA) × 100 (%). If there is a test K that is not implemented in the analysis criteria, the total score is obtained by subtracting the score value (SB) that should have been obtained from the test K from the worst-case total score value SA. The value SA is set (SA = SA−SB). Thus, by expressing it in%, it is possible to obtain a result that does not depend on the type or number of tests performed.

  First, in the determination process (S2-1) in FIG. 4, the CPU 11a inputs the “fasting insulin value”, “2 hour postprandial blood glucose level”, “HOMA-IR”, “insulin OGTT peak value”, “24 Each “time urinary C-peptide” is compared with a predetermined criterion value. More specifically, the CPU 11a prepares a variable A for calculating the score A, sets the initial value to zero, and gives a predetermined score when “YES” is determined in each determination of steps S101 to S115. The variable A is added.

  For example, in step S101, if the inputted “fasting insulin value” is 10 or more, “YES”, so the CPU 11a adds 1.5 to the variable A. For example, if the “HOMA-IR” inspection is not performed, “NO” is determined in the step S107, and the CPU 11a advances the processing to the next determination (step S110) without adding the numerical value. After executing each determination from step S101 to step S115, the CPU 11a calculates and stores the score A using the value of the variable A in step S116. As the score A, the numerical value of the variable A may be adopted as it is, but as described above, the total score value SA (=) when the variable A is traced along the route that becomes the maximum score value of the entire step S2-1. It is preferable to adopt (variable A / SA) × 100 divided by 23 points) and expressed in%.

  Similarly, in step S2-2 of FIG. 5, the CPU 11a adds a numerical value for each determination to the variable B having an initial value of zero. After steps S121 to S133, the CPU 11a starts from the variable B in step S134. Score B is calculated. In step S2-3 of FIG. 6, the CPU 11a adds a numerical value to the variable C having an initial value of zero for each determination in steps S141 to S152, and calculates a score C from the variable C in step S153. To do. Furthermore, in step S2-4 in FIG. 7, the CPU 11a adds a numerical value for each determination in steps S161 to S169 to the variable D having an initial value of zero, and calculates a score D from the variable D in step S170. To do. As described above, the process of step S2 is completed, and scores A, B, C, and D are calculated.

  As described above, four evaluation values (scores) are obtained by the four determination processes (FIGS. 4 to 7) of the diabetes disease risk analysis process (step S2), and the magnitudes of these scores should be compared. Thus, it is possible to determine which etiology has the greatest influence. Further, the CPU 11a performs a process of classifying the four score values according to a combination of absolute magnitudes, whereby the patient's pathological condition can be classified as one of the five diabetic pathologies as described above. . For example, if three score values of “insulin resistance”, “glycotoxicity”, and “insulin secretion” are mapped in a three-dimensional space as shown in FIG. The pathological condition can be classified accordingly. In addition, as shown in FIG. 17, when the three score values of “insulin resistance”, “hepatic glucose release enhancement”, and “insulin secretion decrease” are mapped in a three-dimensional space, they are plotted at different positions depending on clinical features, and the main etiology The pathological condition can be classified according to

  FIG. 15 is a chart showing a specific example for each patient of each etiology for the input data and the score calculated in step S2. For example, in the determination of insulin resistance, a patient whose score A is the highest value of 0.85 indicates that the pathogenesis of insulin resistance has the greatest influence, and is determined to be an “insulin resistant patient”. The

  Next, the content of the metabolic syndrome disease risk analysis process (step S3) will be described. The factors that make up the metabolic syndrome are not independent of each other, but are divided into the main factor that causes it and the subordinate factors that are caused by the factor, and these factors are related to each other. Yes. Therefore, in order to analyze the disease risk of metabolic syndrome from patient findings, it is necessary to analyze disease risk by combining multiple laboratory test values and findings, not just using specific laboratory test values. Is done. From this point of view, the following standards have been published as ATPIII (Adult Treatment Panel III 2001) by the US Cholesterol Education Program (NCEP).

According to the ATPIII diagnostic criteria, it is determined that the disease risk of metabolic syndrome is high when three or more of the following conditions are satisfied.
(1) The built-in obesity waist diameter is 102 cm or more for men and 88 cm or more for women.
(2) Neutral fat Neutral fat value is 150 mg / dL or more.
(3) HDL cholesterol The HDL cholesterol level is less than 40 mg / dL for men and less than 55 mg / dL for women.
(4) Blood pressure The blood pressure value is 130 mmHg or more in the systole and 85 mmHg or more in the diastole.
(5) Fasting blood glucose level Fasting blood glucose level is 110 mg / dL or more.
In the metabolic syndrome disease risk analysis process described below, it is possible to analyze a disease risk using such a diagnostic criterion. As a result, it is possible to accurately determine the disease risk of metabolic syndrome that meets the criteria. In addition, since the above diagnostic criteria were prepared for Americans, for example, if the patient is Japanese, instead of the above-mentioned diagnostic criteria for waist diameter, the following diagnostics for Japanese Standards (announced at the 102nd Annual Meeting of the Japan Society of Internal Medicine, April 8, 2005) can be used.

It is determined that the disease risk of metabolic syndrome is high when the following condition (1) is satisfied and at least two of the conditions (2) to (4) are satisfied.
(1) The waist diameter is 85 cm or more for men and 90 cm or more for women.
(2) Either or both of hypertriglycerideemia having a triglyceride level of 150 mg / dL or higher and low HDL cholesterolemia having an HDL cholesterol level of less than 40 mg / dL.
(3) The blood pressure value is 130 mmHg or more in the systole and 85 mmHg or more in the diastole or both.
(4) Fasting blood glucose level is 110 mg / dL or more.
Thus, by adopting diagnostic criteria suitable for the subject (patient), the disease risk can be accurately determined.

  FIG. 8 is a flowchart showing the processing procedure of the metabolic syndrome disease risk analysis process (step S3) according to the first embodiment. Here, the case where the disease risk analysis of a metabolic syndrome is performed using the diagnostic criteria for Japanese mentioned above is demonstrated. First, the CPU 11a compares the waist diameter included in the physiological data received in step S1 with a predetermined reference value (step S181), and if the waist diameter exceeds the reference value (YES in step S181), the initial value is set. 1 is added to the variable E set to the value zero (step S182), and the process proceeds to step S183. On the other hand, when the waist diameter does not exceed the reference value in step S181 (NO in step S181), the CPU 11a returns the process.

  In step S183, the CPU 11a compares the neutral fat value included in the physiological data with a predetermined reference value. As a result of the comparison in step S183, if the neutral fat value exceeds the reference value (YES in step S183), the CPU 11a adds 1 to the variable E (step S184), and proceeds to step S187. . If the neutral fat value does not exceed the reference value in step S183 (NO in step S183), the CPU 11a advances the process to step S185.

  In step S185, the CPU 11a compares the HDL cholesterol value included in the physiological data with a predetermined reference value. As a result of the comparison in step S185, if the HDL cholesterol value falls below the reference value (YES in step S185), the CPU 11a adds 1 to the variable E (step S186), and proceeds to step S187. Even in the case where the HDL cholesterol value exceeds the reference value in step S185 (NO in step S185), the CPU 11a advances the process to step S187.

  In step S187, the CPU 11a compares the blood pressure value included in the physiological data with a predetermined reference value. If the blood pressure value exceeds the reference value as a result of the comparison in step S187 (YES in step S187), the CPU 11a adds 1 to the variable E (step S188), and the process proceeds to step S189. Even when the blood pressure value does not exceed the reference value in step S187 (NO in step S187), the CPU 11a advances the process to step S189.

  In step S189, the CPU 11a compares the fasting blood glucose level included in the physiological data with a predetermined reference value. As a result of the comparison in step S189, when the fasting blood glucose level exceeds the reference value (YES in step S189), the CPU 11a adds 1 to the variable E (step S190), and the process returns. If the fasting blood glucose level does not exceed the reference value in step S189 (NO in step S189), the CPU 11a returns the process. In the metabolic syndrome disease risk analysis process 3 as described above, the numerical value of the variable E is directly adopted as the score E.

  Next, processing contents of the diagnosis support information generation process (step 5) will be described. FIG. 9 is a flowchart showing the procedure of the diagnosis support information generation process (step S5). First, the CPU 11a reads out each score (A, B, C, D, E) obtained in step S2 and step S3, and compares four scores A, B, C, D among them to obtain four scores A. , B, C, D are searched for (step S51). Then, the process of determining the etiology treatment policy corresponding to the maximum score is executed, and the process returns. In step S52, when the score A is larger than the other scores and is the maximum (YES in step S52), the CPU 11a advances the process to step S5-1 and executes the “peripheral insulin resistance treatment policy determination process”.

  Similarly, when the score B is the maximum in step S53 (YES in step S53), the CPU 11a advances the process to step S5-2, and executes the “treatment policy determination process for enhancing glucose release from the liver”. When the score C is the maximum in step S54 (YES in step S54), the CPU 11a advances the process to step S5-3 and executes “a treatment policy determination process for glycotoxicity exposed to long-term hyperglycemia”. When the score D is the maximum in step S55 (YES in step S55), the CPU 11a advances the process to step S5-4, and executes the “treatment policy determination process for a decrease in insulin secretion”. In addition, when there is a score of the same score, it means that a plurality of etiologies have an influence at the same time. All you have to do is to output all the results. Moreover, after performing the above-mentioned process, CPU11a moves a process to step S5-5, and performs the "metabolic syndrome disease risk determination process".

  FIG. 10 is a flowchart showing the processing procedure of “peripheral insulin resistance treatment policy determination processing” in step S5-1. First, in step S201, the CPU 11a determines whether “condition 1: no heart failure” and “condition 2: no electrolyte abnormality” are satisfied. Here, “the absence of heart failure” is determined based on information already input to a database or the like among the input data. Similarly, “there is no electrolyte abnormality” is determined based on information already input in a database or the like.

  If it is determined in Step S201 that Condition 1 and Condition 2 are satisfied (YES in Step S201), the CPU 11a advances the process to Step S204 and treats the treatment as “first selection of TZD rather than insulin injection”. Select a policy. If “condition 1 and condition 2” are not satisfied in step S201 (NO in step S201), the CPU 11a proceeds to step S202 and determines “presence / absence of past gastrointestinal surgery” from the input data. . If there is no history of surgery in step S202 (NO in step S202), the CPU 11a advances the process to step S203 and determines “whether or not correction of hyperglycemia should be urgently performed”. Determination of correction of hyperglycemia can be made based on information already input to a database or the like among the input data. That is, if the data “Hurry to correct hyperglycemia” is input to the database or the like, the CPU 11a determines that correction of hyperglycemia should be urgently performed.

  If there is a history of surgery in step S202 (“Yes” in step S202), the CPU 11a proceeds to step S205 and adopts a treatment policy corresponding to this step. When there is no history of surgery (“none” in step S202), the CPU 11a determines whether the result of determination in step S203 is step S206 (in the case of YES in step S203) or step S207 (NO in step S203). If) treatment policy is adopted.

  FIG. 11 is a flowchart illustrating a processing procedure of “determination processing of a treatment policy for enhancing glucose release from the liver” in step S5-2. First, in step S211, the CPU 11a determines whether or not “condition 1: creatinine <1.40” and “condition 2: no liver damage” are established. “Condition 2: No hepatic disorder” can be determined from information already entered in a database or the like.

  When “condition 1 and condition 2” are not satisfied (NO in step S211), the CPU 11a advances the process to step S212, and determines whether or not the operation is within a few days after the operation. To determine whether the operation is in progress or within a few days after the operation, information already entered in a database relating to the input patient may be referred to. In addition, several days is calculated as a value of a function derived from the inputted physiological data of the patient. Based on such determination, the CPU 11a selects one of the treatment policies shown in S213 to S215, respectively.

  FIG. 12 is a flowchart showing a processing procedure of “determination processing of a treatment policy for glycotoxicity exposed to long-term hyperglycemia” in step S5-3. First, in step S221, the CPU 11a determines whether or not “the degree of sugar toxicity is light”. It can be judged from the sugar toxicity score C calculated in step S2-3 that the degree of glycotoxicity is light. For example, the CPU 11a determines that the degree of sugar toxicity is light when the sugar toxicity score C is the smallest of the four etiology scores A, B, C, and D. When it is determined that the degree is not light (NO in step S221), the CPU 11a adopts the diet therapy in step S226. When the degree is light (YES in step 221), the CPU 11a sequentially determines the blood glucose lowering state from step S222 to step S225.

  When it is determined that blood sugar has been lowered by any of SU, AGI, BG, and TZD (YES in steps S222, S223, S224, and S225), CPU 11a adopts the treatment policy of step S228 and either When the blood sugar is not lowered (NO in steps S222, S223, S224, and S225), the treatment policy of step S227 is adopted. Here, the determination as to whether or not the blood glucose has decreased can be made based on information already input to a database or the like. For example, when there is a history of administration of a certain drug and the blood glucose level of the subsequent input data has decreased, it is determined that the blood glucose has decreased.

  FIG. 13 is a flowchart showing the processing procedure of “decision processing of treatment policy for decrease in insulin secretion” in step S5-4. First, in step S231, the CPU 11a determines whether or not “the urine and blood ketone bodies have a high value”. Here, the high value means that, for example, the urinary ketone body of the input data is about 2+ or more. If it is determined that the ketone body has a high value (YES in step S231), the CPU 11a proceeds to step S232, and determines whether or not the “24-hour urinary C peptide” of the input data is smaller than 30 μg. To do. When “24-hour urinary C-peptide” is smaller than 30 μg (YES in step S232), it is determined as “type I diabetes”, and thus CPU 11a adopts the treatment policy in step S236.

  When determining “NO” in step S231 or S232, the CPU 11a advances the process to step S233 to determine “whether or not insulin secretion has been depleted in the course of treatment”. Here, whether or not it is depleted can be determined by the fasting insulin value or the 24-hour urinary C peptide. For example, when the 24-hour urinary C peptide is less than 50 μg, it is determined that insulin secretion has been depleted.

  When the CPU 11a determines that the insulin secretion has been exhausted (YES in step S233), the CPU 11a proceeds to step S237, determines whether or not the gastrointestinal tract has been operated, and adopts a treatment policy corresponding to this determination result. (Steps S238 and S239). On the other hand, when it is determined in step S233 that no depletion has occurred (NO in step S233), the CPU 11a advances the process to step S234. In step S234, the CPU 11a determines whether or not the remaining pancreatic reserve is preserved to some extent. If it is determined that the remaining pancreatic reserve is preserved to some extent (YES in step S234), CPU 11a advances the process to step S240; otherwise (NO in step S234), the process advances to step S235. .

Whether the residual pancreatic reserve is preserved to some extent
(1) Fasting insulin ≧ 5 μg / ml
(2) 24-hour urinary C peptide ≧ 50 μg
This is determined by whether or not either of the above holds. That is, if (1) or (2) is established, the CPU 11a determines that the remaining pancreatic reserve capacity is preserved to some extent.

  In step S240, the CPU 11a determines whether or not it gradually stops responding to SU, and adopts each treatment policy based on the determination result (steps S241 and S242). For example, information already input to a database or the like may be used to determine whether or not the reaction to the SU gradually stops. At this time, if the blood glucose level after SU administration is not lower than that before administration, it is determined that the reaction to SU has stopped.

  Further, in step S235, the CPU 11a determines whether or not the skin has been thinned before the operation, and adopts each treatment policy based on the determination result (steps S243 and S244). Whether or not the thinness has continued is determined by, for example, information already entered in a database or the like, and the thinness continues when BMI (= weight × 10000 / (height × height)) <18 continues multiple times. It is judged that

  FIG. 14 is a flowchart illustrating a processing procedure of “metabolic syndrome disease risk determination processing” in step S5-5. The CPU 11a determines whether or not the score E calculated in step S3 is a predetermined reference value (for example, 3) or more (step S241). If the score E is greater than or equal to the reference value (YES in step S241), the CPU 11a adopts “high disease risk of metabolic syndrome” as diagnosis support information (step S242), and returns the process. On the other hand, if the score E is less than the reference value in step S241 (NO in step S241), “the disease risk of metabolic syndrome is a normal level” is used as diagnosis support information (step S243), and the process returns. To do.

  The diagnosis support information generated by the diagnosis support information generation process S5 as described above is output (screen display) in step S8. Such diagnosis support information includes information on a disease risk centered on a cardiovascular event that increases by having a metabolic syndrome as described above. Nakamura et al., Magnitude of Sustained, Magnitude of Sustained Multiple Risk Factors for Ischemic Heart Disease in Japanese Employees-A Case-Control Study-), Japanese Circulation Journal (Japan), Japan Circulation Society, 2001, 65, 1 , P. As disclosed in 11-17, in Japanese, a diabetic patient further has metabolic syndrome when visceral fat obesity is present and hyperlipidemia or hypertension coexists. However, it is known to greatly increase the risk for macrovascular disorders, the most serious complication of diabetes.

  For this reason, when a doctor considers whether a subject is only diabetes, is diabetic, and is also a metabolic syndrome, it can perform a more suitable diagnosis with respect to a subject, determination of a treatment policy, etc. In addition to simply outputting the metabolic syndrome disease risk as part of the diagnosis support information, for example, the disease risk of diabetes may be weighted according to the magnitude of the score E, and the metabolic syndrome disease risk is high. Is not simply information indicating whether it is normal level or not, for example, information indicating the disease risk of metabolic syndrome in a multivalued manner by the magnitude of score E (for example, disease risk is very high, high, medium, low, etc.) May be displayed. A large number of clinical epidemiological data and EBM data have already been published and can be used as a reference.

The above is the detailed processing procedure of diagnostic support information generation processing, but it is not limited to this, and it is necessary in consideration of the patient's situation, doctor's own criteria, papers such as diabetes and metabolic syndrome research results, etc. The criteria may be added, deleted, or changed according to A dedicated tool for adding or changing judgment criteria may be used so that a user of this system or a specialist doctor can easily add judgment criteria.
(Embodiment 2)
Next, a diagnosis support system according to Embodiment 2 of the present invention will be described. As shown in FIG. 2, in the diagnosis support system 20 according to the second embodiment, an application program 24a that causes the computer 10a to operate as follows is installed in the hard disk 11d. Since the other configuration of the diagnosis support system according to the second embodiment is the same as the configuration of the diagnosis support system 10 according to the first embodiment, the same components are denoted by the same reference numerals and description thereof is omitted. .

  Next, the operation of the diagnosis support system 20 according to the second embodiment will be described. When the CPU 11a of the computer 10a executes the application program 24a, the computer 10a functions as the diagnosis support system 20 and operates as follows. The diagnosis support system 20 according to the second embodiment is the same as the diagnosis support system 10 according to the first embodiment, except for the metabolic syndrome disease risk analysis process (S23) and the diagnosis support information generation process (step S25). Therefore, the description of the same part is omitted.

  FIG. 18 is a flowchart showing a processing procedure of metabolic syndrome disease risk analysis processing (step S23) according to the second embodiment. In the second embodiment, the physiological data input in step S1 includes the presence / absence of diabetes, presence / absence of impaired glucose tolerance, blood pressure value, triglyceride value, HDL cholesterol value, waist / hip ratio, BMI, μ -Alb, albumin / creatinine ratio included. In addition, in the metabolic syndrome disease risk analysis process, two evaluation values (scores F and G) indicating the disease risk of metabolic syndrome are calculated.

According to the diagnostic criteria established by WHO, the disease risk of metabolic syndrome when one of the following conditions (1) and (2) and two or more of conditions (3) to (5) are met Is determined to be high.
(1) Glucose tolerance abnormality Glucose tolerance abnormality.
(2) Diabetes mellitus.
(3) Blood pressure The blood pressure value is 140 mmHg or more in the systole and 90 mmHg or more in the diastole.
(4) The hyperlipidemic serum neutral fat amount is 150 mg / dL or more, or the HDL cholesterol level is less than 35 mg / dL for men and less than 39 mg / dL for women.
(5) Visceral fat obesity waist / hip ratio is 0.90 or more for men, 0.85 or more for women, or BMI is 30 or more.
n (6) Microalbuminuria μ-Alb is 20 μg / min or more, or the albumin / creatinine ratio is 30 mg / g or more.
In the metabolic syndrome disease risk analysis process S23 according to the second embodiment, the disease risk is analyzed using such diagnostic criteria.

  In the metabolic syndrome disease risk analysis process according to the second embodiment, first, the CPU 11a sets variables F and G to 0 (step S281). Next, the CPU 11a determines whether or not the patient has diabetes from the presence or absence of diabetes included in the physiological data (step S282). If the patient is diabetic (YES in step S282), the CPU 11a adds 1 to the variable F (step S283), and the process proceeds to step S286. If not diabetic in step S282 (NO in step S282), the CPU 11a advances the process to step S284.

  In step S284, the CPU 11a determines whether or not the patient has impaired glucose tolerance from the presence or absence of impaired glucose tolerance included in the physiological data. If the glucose tolerance is abnormal (YES in step S284), the CPU 11a adds 1 to the variable F (step S285), and advances the process to step S286. Even when the glucose tolerance is not abnormal in step S284 (NO in step S284), the CPU 11a advances the process to step S286.

  In step S286, the CPU 11a compares the blood pressure value included in the physiological data with a predetermined reference value. As a result of the comparison in step S286, if the blood pressure value exceeds the reference value (YES in step S286), the CPU 11a adds 1 to the variable G (step S287), and the process proceeds to step S288. Even when the blood pressure value does not exceed the reference value in step S286 (NO in step S286), the CPU 11a advances the process to step S288.

  In step S288, the CPU 11a compares the test values (for example, neutral fat level and HDL cholesterol level) related to hyperlipidemia included in the physiological data with predetermined reference values, and the patient has hyperlipidemia. It is determined whether or not. As a result, if the condition corresponds to hyperlipidemia (YES in step S288), CPU 11a adds 1 to variable G (step S289), and proceeds to step S290. Even in the case where hyperlipidemia is not met in step S288 (NO in step S288), the CPU 11a advances the process to step S290.

  In step S290, the CPU 11a compares a test value (eg, waist / hip ratio, BMI) regarding visceral fat obesity included in the physiological data with a predetermined reference value to determine whether or not the patient has visceral fat obesity. Determine. As a result, when it corresponds to visceral fat obesity (YES in step S290), the CPU 11a adds 1 to the variable G (step S291), and proceeds to step S292. Even when the visceral fat obesity does not correspond in step S290 (NO in step S290), the CPU 11a advances the process to step S292.

  In step S292, the CPU 11a compares the test value (for example, μ-Alb, albumin / creatinine ratio) regarding microalbuminuria included in the physiological data with a predetermined reference value, and determines whether or not the patient has microalbuminuria. Is determined. If the result is microalbuminuria (YES in step S292), the CPU 11a adds 1 to the variable G (step S293), and the process returns. Even if the microalbuminuria does not correspond in step S292 (NO in step S292), the CPU 11a returns the process.

  As shown in FIG. 3, in the diagnosis support information generation process (step S25) according to the second embodiment, the peripheral insulin resistance treatment policy determination process (step S5-1), the glucose release enhancement treatment from the liver Policy decision process (step S5-2), treatment process decision process for glycotoxicity exposed to long-term hyperglycemia (step S503), treatment process decision process for reduction of insulin secretion (step S5-4), and A disease risk determination process (step S5-25) is performed on the metabolic syndrome. Here, processes (steps S5-1 to S5-4) other than the disease risk determination process (step S5-25) in the metabolic syndrome are the same as those described in the first embodiment, and thus the description thereof is omitted. . FIG. 19 is a flowchart showing a processing procedure of “metabolic syndrome disease risk determination processing” according to the second exemplary embodiment. In step S341, the CPU 11a determines whether or not the score F calculated in step S23 is a predetermined reference value (for example, 1) or more. When the score F is greater than or equal to the reference value (YES in step S341), the CPU 11a determines whether or not the score G calculated in step S23 is greater than or equal to a predetermined reference value (for example, 2) (step S342). . When the score G is equal to or greater than the reference value (YES in step S342), the CPU 11a adopts “high disease risk of metabolic syndrome” as diagnosis support information (step S343), and returns the process. On the other hand, if the score E is less than the reference value in step S341 (NO in step S341), or if the score G is less than the reference value in step S342 (NO in step S342), the diagnosis support information "Metallic syndrome disease risk is normal level" is adopted (step S344), and the process returns.

  In addition, in the analysis of the risk of metabolic syndrome as described above, the diagnostic criteria established by the WHO as described above may be used. In the case of a person, more accurate diagnosis support information can be provided by using a diagnosis standard suitable for the Japanese. In addition, the above-mentioned diagnostic criteria are examples, and therefore, the diagnostic criteria for diabetes or metabolic syndrome used in the diagnostic support system according to the present invention are not limited to those described above, and other criteria may be used as long as they are diagnostic criteria for diabetes or metabolic syndrome. Needless to say, the diagnostic criteria may be applied.

In the second embodiment, the configuration in which the presence / absence of diabetes is received from the outside as physiological data and the disease risk of metabolic syndrome is analyzed using this data is described. However, the present invention is not limited to this. The structure which determines whether it is diabetes in -1 to S5-4, and performs a metabolic syndrome disease risk analysis using this determination result may be sufficient. The determination as to whether or not the patient has diabetes is, for example, whether or not at least one of the scores A to D is greater than a predetermined value, or whether the sum of the scores A to D is greater than a predetermined value. Or the like. In this case, it is necessary to execute the processes of steps S5-1 to S5-4 prior to the metabolic syndrome disease risk analysis process (step S33).
(Embodiment 3)
Next, a diagnosis support system according to Embodiment 3 of the present invention will be described. As shown in FIG. 2, in the diagnosis support system 30 according to the third embodiment, an application program 34a that causes the computer 10a to operate as follows is installed in the hard disk 11d. Since the other configuration of the diagnosis support system according to the third embodiment is the same as the configuration of the diagnosis support system 10 according to the first embodiment, the same components are denoted by the same reference numerals and the description thereof is omitted. .

  Next, the operation of the diagnosis support system 30 according to the third embodiment will be described. When the CPU 11a of the computer 10a executes the application program 34a, the computer 10a functions as the diagnosis support system 30 and operates as follows. The diagnosis support system 30 according to the third embodiment performs the same operation as the diagnosis support system 20 according to the second embodiment except for the metabolic syndrome disease risk analysis process (S33) (see FIG. 3). Explanation of similar parts is omitted.

  FIG. 20 is a flowchart showing a processing procedure of metabolic syndrome disease risk analysis processing (step S33) according to the third embodiment. In the third embodiment, the physiological data input in step S1 includes the presence / absence of insulin resistance in place of the presence / absence of diabetes and the presence / absence of abnormal glucose tolerance. In the metabolic syndrome disease risk analysis process according to the third embodiment, after the CPU 11a sets the variables F and G to 0 (step S281), the patient determines whether or not the insulin resistance is included in the physiological data. It is determined whether or not it is sex (step S382). If it is insulin resistance (YES in step S382), the CPU 11a adds 1 to the variable F (step S383), and proceeds to step S286. Even if it is not insulin resistance in step S382 (NO in step S382), the CPU 11a advances the process to step S286. Since the other processes of the metabolic syndrome disease risk analysis process (step S33) in the third embodiment are the same as the metabolic syndrome disease risk analysis process (step S23) described in the second embodiment, the description thereof is omitted. .

  Thus, in this Embodiment 3, the disease risk analysis of a metabolic syndrome is performed using the presence or absence of insulin resistance. Glucose metabolism abnormalities in metabolic syndrome are considered to be caused by insulin resistance. Therefore, high-accuracy risk analysis should be performed by using the presence or absence of insulin resistance, which is a significant risk factor for such metabolic syndrome. Can do.

  In the third embodiment, the configuration in which insulin resistance is received from the outside as physiological data and the disease risk of metabolic syndrome is analyzed by this is described. However, the present invention is not limited to this, and step S5-1 is performed. It may be configured to determine whether or not it is insulin resistant and to perform metabolic syndrome disease risk analysis using this determination result. The determination of whether or not insulin resistance can be made, for example, by determining whether or not the score A is greater than a predetermined value. In this case, it is necessary to execute the processes of steps S5-1 to S5-4 prior to the metabolic syndrome disease risk analysis process (step S33).

  As described above, in the diagnosis support systems 10, 20, and 30 according to the first to third embodiments, after the treatment policy for diabetes and the disease risk analysis result of the metabolic syndrome are determined, the treatment determined in step S8. Diagnosis support information including the policy and metabolic syndrome disease risk analysis results will be provided to doctors and the like. For one patient, it is possible to obtain the analysis result (score) obtained by performing the same examination and analysis for each re-examination and re-examination, and to provide diagnosis support information. It is possible to grasp the change in the state objectively and accurately and perform treatment based on an appropriate judgment according to the progress state.

  For example, by looking at changes in the score, you can not only know the changes in the pathology of classified diabetes and the pathology of the metabolic syndrome, but also check how much the pathology has changed from the quantified score value This makes it possible to make more appropriate judgments and treatments. In addition, by using this diagnosis support system, it is possible to obtain a pathological analysis and treatment policy based on uniform judgment criteria prepared in advance without relying on experience and subjectivity with many uncertainties. Even those who are not specialists in metabolic syndrome can make judgments and treatments equivalent to or close to those of specialists.

  Moreover, since the disease risk about the metabolic syndrome closely related to diabetes can be provided to the user, a doctor can determine a treatment policy and a prevention policy for diabetes based on this information. For example, when a non-diabetes specialist uses the systems 10 and 20 for a patient and obtains diagnostic support information that diabetes has not yet developed in the patient but the metabolic syndrome has a high disease risk. The patient can be diagnosed with a metabolic syndrome treatment and a diabetes prevention program.

  The diagnosis support system and computer program according to the present invention can provide doctors and the like with the disease risk of diabetes and the disease risk of metabolic syndrome, and increase the reliability of the diagnosis in the diagnosis of diabetes or metabolic syndrome. A diagnostic support system that provides information on the risk of diabetes and metabolic syndrome by analyzing the risk of diabetes and metabolic syndrome in the subject, and a computer program for causing the computer to function as a diagnostic support system Useful as.

It is a functional block diagram which shows the notional structure of the diagnosis assistance system which concerns on Embodiment 1 of this invention. It is a block diagram which shows the hardware constitutions of the diagnosis assistance system which concerns on Embodiment 1 of this invention. It is a flowchart which shows the flow of operation | movement of the diagnosis assistance system which concerns on Embodiment 1 of this invention. It is a flowchart which shows the process sequence of a peripheral insulin resistance determination process. It is a flowchart which shows the process sequence of the glucose discharge | release acceleration | stimulation determination process from a liver. It is a flowchart which shows the process sequence of the glucose toxicity determination process exposed to the long-term hyperglycemia. It is a flowchart which shows the process sequence of the fall determination process of insulin secretion. It is a flowchart which shows the process sequence of the metabolic syndrome disease risk analysis process which concerns on Embodiment 1 of this invention. It is a flowchart which shows the process sequence of the diagnostic assistance information generation process which concerns on Embodiment 1 of this invention. It is a flowchart which shows the process sequence of the determination process of the peripheral insulin resistance treatment policy. It is a flowchart which shows the process sequence of the determination process of the treatment policy of glucose discharge | release enhancement from a liver. It is a flowchart which shows the process sequence of the determination process of the treatment policy of the glycotoxicity exposed to the long-term hyperglycemia. It is a flowchart which shows the process sequence of the decision process of the treatment policy of the fall of insulin secretion. It is a flowchart which shows the process sequence of the disease risk determination process of the metabolic syndrome which concerns on Embodiment 1 of this invention. It is a graph which shows the specific example of the physiological data and score for every patient of each etiology. It is a three-dimensional graph which shows the example which classified the pathological condition of diabetes three-dimensionally. It is a three-dimensional graph which shows the example which classified the pathological condition of diabetes three-dimensionally. It is a flowchart which shows the process sequence of the metabolic syndrome disease risk analysis process which concerns on Embodiment 2 of this invention. It is a flowchart which shows the process sequence of the disease risk determination process of the metabolic syndrome which concerns on Embodiment 2 of this invention. It is a flowchart which shows the process sequence of the metabolic syndrome disease risk analysis process which concerns on Embodiment 3 of this invention.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Physiological data input part 2 Diabetes disease risk analysis part 3 Metabolic syndrome disease risk analysis part 4 Diagnosis support information generation part 5 Biological model generation part 5a Biological model 5b Biological model drive part 6 Disease state simulation part 7 Diagnosis support information output part 10,20 , 30 Diagnosis support system 10a Computer 11 Main body 11a CPU
11b ROM
11c RAM
11d Hard disk 11e Reading device 11f Input / output interface 11g Communication interface 11h Image output interface 11i Bus 12 Display 13 Input device 14 Portable recording medium 14a, 24a, 34a Application program

Claims (18)

Input receiving means for receiving input of physiological information indicating the physiological state of the subject person;
A first risk analyzing means for analyzing the risk of diabetes in the subject by comparing the physiological information received by the input receiving means with a predetermined criterion;
A second risk analysis means for comparing the physiological information received by the input reception means with a predetermined criterion and analyzing a disease risk of metabolic syndrome in the subject;
A diagnosis support system comprising: an output means for outputting an analysis result by the first risk analysis means and an analysis result by the second risk analysis means. The physiological information includes waist diameter, neutral fat level, HDL cholesterol level, blood pressure level, and fasting blood glucose level,
The second risk analysis means determines whether or not the waist diameter, the triglyceride value, the HDL cholesterol value, the blood pressure value, and the fasting blood glucose level included in the physiological information respectively exceed the corresponding determination criteria. The diagnosis support system according to claim 1, wherein a disease risk of metabolic syndrome in a subject is analyzed based on the result.   The second risk analysis means includes a counting means for counting how many of at least a neutral fat value or HDL cholesterol value, a blood pressure value, and a fasting blood glucose level included in the physiological information exceed the determination criterion; And determining means for determining whether or not the counting result by the counting means exceeds a predetermined number, and when the determining means determines that the counting result exceeds the predetermined number, analysis is made that the disease risk of metabolic syndrome is high The diagnosis support system according to claim 3, wherein the diagnosis support system is configured to.   The diagnosis support system according to claim 1, wherein the physiological information includes a blood pressure value, a test value related to hyperlipidemia, a test value related to central obesity, and a test value related to microalbuminuria. The physiological information further includes information on diabetes and / or impaired glucose tolerance,
The second risk analysis means includes
First analysis means for determining whether the subject falls under diabetes or glucose intolerance;
Second analysis means for determining whether the blood pressure value included in the physiological information, the test value related to hyperlipidemia, the test value related to central obesity, and the test value related to microalbuminuria individually exceed the corresponding determination criteria And
The diagnosis support system according to claim 4, wherein a disease risk of metabolic syndrome in the subject is analyzed based on a determination result by the first analysis unit and a determination result by the second analysis unit. The second risk analysis means includes
First analysis means for determining whether or not the subject has diabetes based on an analysis result by the first risk analysis means;
Second analysis means for determining whether the blood pressure value included in the physiological information, the test value related to hyperlipidemia, the test value related to central obesity, and the test value related to microalbuminuria individually exceed the corresponding determination criteria And
The diagnosis support system according to claim 4, wherein a disease risk of metabolic syndrome in the subject is analyzed based on a determination result by the first analysis unit and a determination result by the second analysis unit. The second analysis means counts how many of the blood pressure value, the test value related to hyperlipidemia, the test value related to central obesity, and the test value related to microalbuminuria included in the physiological information exceed the criterion. Is configured to
The second risk analysis means includes a determination means for determining whether or not a counting result by the second analysis means exceeds a predetermined number,
As a result of discrimination by the first analysis means, when the subject falls under diabetes or abnormal glucose tolerance, and the discrimination result determines that the count result exceeds a predetermined number, the disease risk of metabolic syndrome The diagnosis support system according to claim 5 or 6, wherein the diagnosis support system is configured to analyze that the value is high. The physiological information further includes a test value related to insulin resistance,
The second risk analysis means includes
First analysis means for determining whether or not the subject is insulin resistant;
Second analysis means for determining whether the blood pressure value included in the physiological information, the test value related to hyperlipidemia, the test value related to central obesity, and the test value related to microalbuminuria individually exceed the corresponding determination criteria And
The diagnosis support system according to claim 4, wherein a disease risk of metabolic syndrome in the subject is analyzed based on a determination result by the first analysis unit and a determination result by the second analysis unit. The second risk analysis means includes
First analysis means for determining whether or not the subject corresponds to insulin resistance based on an analysis result by the first risk analysis means;
Second analysis means for determining whether the blood pressure value included in the physiological information, the test value related to hyperlipidemia, the test value related to central obesity, and the test value related to microalbuminuria individually exceed the corresponding determination criteria And
The diagnosis support system according to claim 4, wherein a disease risk of metabolic syndrome in the subject is analyzed based on a determination result by the first analysis unit and a determination result by the second analysis unit. The second analysis means counts how many of the blood pressure value, the test value related to hyperlipidemia, the test value related to central obesity, and the test value related to microalbuminuria included in the physiological information exceed the criterion. Is configured to
The second risk analysis means includes a determination means for determining whether or not a counting result by the second analysis means exceeds a predetermined number,
As a result of the determination by the first analysis means, the subject falls under insulin resistance, and the disease risk of metabolic syndrome is high when the determination means determines that the counting result exceeds a predetermined number The diagnosis support system according to claim 8 or 9, wherein the diagnosis support system is configured to analyze the following.   The diagnostic support system according to any one of claims 4 to 10, wherein the test value related to hyperlipidemia is a neutral fat value or an HDL cholesterol value.   The diagnostic support system according to claim 4, wherein the test value related to central obesity is a waist diameter, a waist / hip ratio, or BMI.   The diagnostic support system according to any one of claims 4 to 12, wherein the test value relating to the microalbuminuria is μ-Alb or albumin / creatinine ratio. The first risk analysis means includes
An insulin resistance analysis means for analyzing the degree of insulin resistance in the subject;
Glucose release enhancement analysis means for analyzing the extent of glucose release enhancement in the subject,
The diagnosis support system according to any one of claims 1 to 13, further comprising insulin secretion decrease analysis means for analyzing the degree of insulin secretion decrease in the subject.   The diagnosis support system according to claim 14, wherein the first risk analysis means further includes a sugar toxicity analysis means for analyzing the degree of sugar toxicity in the subject. A biological model that generates an output that mimics the pathology of diabetes in a living organ with respect to the input;
A pathological condition predicting means for simulating a living organ when a subject is treated using the biological model and predicting a pathological condition after the treatment;
The diagnosis support system according to any one of claims 1 to 15, further comprising: a prediction result output unit that outputs a prediction result by the disease state prediction unit. Diagnosis support information generating means for generating diagnosis support information for use in diagnosis by a doctor based on the analysis result by the first risk analysis means;
The diagnosis support system according to any one of claims 1 to 16, further comprising diagnosis support information output means for outputting diagnosis support information generated by the diagnosis support information generation means. A computer program for causing a computer having an input device and an output device to function as a diagnosis support system used to support diagnosis of diabetes and metabolic syndrome,
Input accepting means for accepting input of physiological information indicating the physiological state of the subject by the input device;
A first risk analyzing means for analyzing the risk of diabetes in the subject by comparing the physiological information received by the input receiving means with a predetermined criterion;
A second risk analysis means for comparing the physiological information received by the input reception means with a predetermined criterion and analyzing a disease risk of metabolic syndrome in the subject;
A computer program for causing an output unit to output an analysis result by the first risk analysis unit and an analysis result by the second risk analysis unit.

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