WO2021203450A1

WO2021203450A1 - System for assessing ovarian reserve function of subject

Info

Publication number: WO2021203450A1
Application number: PCT/CN2020/084465
Authority: WO
Inventors: 乔杰; 徐慧玉; 李蓉; 冯国双; 韩勇
Original assignee: 北京大学第三医院（北京大学第三临床医学院）
Priority date: 2020-04-07
Filing date: 2020-04-13
Publication date: 2021-10-14
Also published as: CN111524604A; CN111524604B

Abstract

An optimal system for assessing an ovarian reserve function of a subject, comprising: a data acquisition module for obtaining data about the age, the anti-mullerian hormone (AMH) level, and the follicle-stimulating hormone (FSH) level of a subject; and an ovarian reserve function calculation module for performing calculation of the information obtained in the data acquisition module, so as to calculate the probability (p) of a poor ovarian response of the subject.

Description

System for assessing the ovarian reserve function of subjects

Technical field

The present invention relates to an optimized system for evaluating the ovarian reserve function of a subject. The system can be used to evaluate the ovarian reserve function of the subject, to assess the reproductive potential of the subject, and to assess the subject’s ovarian reserve function. Whether the fertility potential improves after the corresponding treatment.

Background technique

The number of primitive follicles contained in the ovarian cortex is called ovarian reserve. It reflects the ability of the ovaries to provide healthy and successfully conceived eggs, and is the most important evaluation index for women's ovarian function. Generally speaking, the more the number of primordial follicles, the better the quality and the higher the chance of conception.

The evaluation of ovarian reserve function can help women of childbearing age understand their own fertility status so as to arrange their own birth plans reasonably. For women with a history of infertility, it can be used to predict the ovarian reactivity of women of childbearing age, and provide a reference for clinical diagnosis of infertility and the formulation of treatment plans. At present, the main basis for diagnosing the decline of ovarian reserve function at home and abroad is the prediction of low ovarian response by the Bologna standard. Therefore, the index for evaluating ovarian reserve is actually an index for predicting ovarian responsiveness.

Age factor is an important factor in evaluating ovarian reserve. The results of a study on age and IVF success rate show that the IVF success rate of women under 30 is about 26%, while the IVF success rate is only 9% when the age is 37 years and above. .

Ovarian ultrasound examination includes three aspects: the number of antral follicles, the volume of the ovaries and the blood flow of the ovarian stroma. The number of antral follicles refers to the total number of bilateral ovarian antral follicles counted by transvaginal ultrasonography in the early follicular phase, which is a direct manifestation of ovarian reserve capacity. The diameter of antral follicles is 2-10mm or 3-8mm. A decrease in the number of antral follicles indicates poor response to ovarian stimulation and a decrease in pregnancy rate. Studies have shown that the number of antral follicles is more effective than basic FSH testing to predict the success rate of IVF. Ovarian stromal blood flow and ovarian volume are currently not commonly used methods for predicting ovarian responsiveness and evaluating ovarian reserve.

In the field of reproductive medicine, the purpose of evaluating ovarian reserve is to predict ovarian responsiveness. At present, AMH level detection and antral follicle count (AFC) are the two best internationally recognized indicators for predicting ovarian responsiveness. Basic FSH level detection is currently the most widely used ovarian reserve evaluation index in the world. Age factor is also an important factor in evaluating ovarian reserve.

Antral follicle count (AFC) is the number of follicles with a diameter of less than 8mm in early Gn-dependent follicular growth. As we all know, the primordial follicle pool in the ovary is related to the number of growing antral follicles. Therefore, theoretically, AFC can reflect the accuracy of the remaining ovarian follicle pool as much as possible. However, to obtain good AFC results, a skilled transvaginal ultrasound (TVS) specialist is required to perform an ultrasound examination, which is time-consuming and resource-intensive. There is a lack of standards in AFC measurement. AFC will change with the menstrual cycle, the use of contraceptives, and the sensitivity and resolution of TVS equipment. All these existing confounding factors will make the reliable assessment of AFC more difficult.

Summary of the invention

In the previous patent application of the present inventor, a system for evaluating the ovarian reserve function of a subject was provided. ) Level, follicle stimulating hormone (FSH) level, antral follicle count (AFC) data; and a module for calculating ovarian reserve function, which is used to calculate the above-mentioned information obtained in the data collection module to calculate the subject The probability of a low response of the ovaries (p). In this system, receiver operating characteristic (ROC) curve is used to detect age, anti-Mullerian hormone (AMH) level, follicle stimulating hormone (FSH) level, antral follicle count (AFC) cut-off point, and based on this The cut point value of the cut-off point is used to convert age, anti-Mullerian hormone (AMH) level, follicle stimulating hormone (FSH) level, and antral follicle count (AFC) into binary variables, thereby using the binary variables as predictions Variable to calculate the subject’s probability of ovarian low response (p).

The above-mentioned system can effectively calculate the probability of a subject’s low response to the ovaries. Furthermore, the default ovarian reserve grouping parameters are pre-stored in the grouping module included in the system, and the ovarian reserve function calculated by the system is calculated based on the grouping parameters. The low response probability p is grouped, so that the ovarian reserve level of the subjects can be grouped.

The system for evaluating the ovarian reserve function of a subject developed by the inventor can calculate the probability of the subject's low ovarian response, and further group the subjects' ovarian reserve level according to the probability of the low ovarian response. Using this system, the parameter (p) used to predict the low response probability of the ovaries of the subject can be calculated, and the ovarian reserve function of the subject can be grouped according to the default ovarian reserve grouping parameters pre-stored in the system, In order to determine the level of its ovarian reserve function, and evaluate the level of ovarian reserve.

Although the above-mentioned system has been developed, the antral follicle count (AFC) requires the method of vaginal ultrasonography to count the total number of bilateral ovarian antral follicles, compared with age, and the AMH and FSH levels that can be obtained through blood draw It is difficult to obtain, cause certain harm to subjects, and difficult to sample. With the development of AMH kits in recent years, due to the complexity and cost of AFC testing and the differences between the personnel performing the testing, more and more It is recommended to use AMH instead of AFC to assess ovarian reserve. Therefore, the field needs to further develop new systems, hoping to use simpler and more convenient detection data to accurately predict the ovarian reserve function of the subject.

As mentioned above, judging the ovarian reserve function of a subject is a very important task for clinicians and others. By evaluating the ovarian reserve function, the patient's ovarian responsiveness can be predicted, which is an important clinical outcome in the process of ovulation induction therapy. In the past, clinicians often used their own experience to make judgments based on age, body mass index, endocrine factor levels, and the number of antral follicles, and there was a certain degree of subjectivity. Our system can accurately assess the quality of the ovarian reserve function of the subjects who will be treated, so as to assist clinicians in formulating more targeted treatment plans in the subsequent treatment.

In summary, it is known that the determinant of ovarian responsiveness is ovarian reserve. However, the inventor of the present application thinks backward and uses expected ovarian responsiveness to evaluate ovarian reserve. In addition, for patients receiving infertility treatment, from a clinical point of view, ovarian reactivity is not only related to the basic condition of the patient (age, basic FSH level, and AMH level), but also related to the dose of ovulation-stimulating drugs. The inventor of the present application First, obtain the expected low response probability of the ovarian according to the patient’s basic situation, and then group the subject’s ovarian reserve according to the default ovarian reserve grouping parameters pre-stored in the system to determine the level of their ovarian reserve, and Assess the level of ovarian reserve.

Specifically, the present invention relates to the following:

1. A system for evaluating the ovarian reserve function of a subject, comprising:

A data collection module, which is used to obtain data on the subject's age, anti-Mullerian hormone (AMH) level, and follicle stimulating hormone (FSH) level; and

The module for calculating the ovarian reserve function is used to calculate the above-mentioned information obtained in the data acquisition module, so as to calculate the probability (p) of the subject's ovarian low response.

2. The system according to item 1, further comprising:

A grouping module, in which the default ovarian reserve grouping parameters are prestored, and according to the grouping parameters, the calculated ovarian low response probability p is grouped, so as to group the subjects’ ovarian reserve levels .

3. The system according to item 1 or 2, wherein:

In the module of calculating ovarian reserve function, the data of the subject’s age, the subject’s anti-Mullerian hormone (AMH) level, and the subject’s follicle stimulating hormone (FSH) level are converted into multi-categorical variables to calculate The subject's ovarian low response probability (p).

4. The system according to item 3, wherein:

The anti-Mullerian hormone (AMH) level refers to the anti-Mullerian hormone concentration in the venous blood on any day of the menstrual cycle of the female subject, and the follicle-stimulating hormone (FSH) level refers to the female subject The concentration of follicle stimulating hormone in venous blood for 2-4 days of menstruation.

5. The system according to item 3 or 4, wherein:

In the module for calculating ovarian reserve function, the subject’s age is converted into three categorical variables,

The age of the subjects is divided into three groups, namely: the age of the subject is 30 years old and under, the age of the subject is greater than 30 years old and 40 years old and under, and the age of the subject is greater than 40 years old .

6. The system according to any one of items 3 to 5, wherein:

In the module for calculating ovarian reserve, the subject’s anti-Mullerian hormone (AMH) level is converted into five categorical variables,

The subjects’ anti-Mullerian hormone (AMH) levels are divided into five groups, namely: the subject’s anti-Mullerian hormone (AMH) level is less than 0.5ng/ml, and the subject’s anti-Muller hormone (AMH) level is less than 0.5ng/ml. The level of AMH is 0.5ng/ml and above and less than 1ng/ml, and the level of the subject’s anti-Muller Hormone (AMH) is 1ng/ml and above and less than 1.5ng/ml, the subject The anti-Mullerian hormone (AMH) level of 1.5ng/ml and above and less than 2ng/ml, and the subject's anti-Mullerian hormone (AMH) level is greater than or equal to 2ng/ml.

7. The system according to any one of items 3 to 6, wherein:

In the module for calculating ovarian reserve, the subject’s FSH (FSH) level is converted into four categorical variables,

That is to say, the subjects’ foam stimulating hormone (FSH) levels are divided into four groups, namely: the subject’s foam stimulating hormone (FSH) level is less than 6.5IU/L, and the subject’s foam stimulating hormone (FSH) level is 6.5 And IU/L above and less than 8.5IU/L, the subject’s foam stimulating hormone (FSH) level is 8.5IU/L and above and less than 10.5IU/L, and the subject’s foam stimulating hormone (FSH) level is 10.5IU/L and above.

8. The system according to any one of items 1 to 7, wherein:

In the module for calculating the ovarian reserve function, the subject’s age, the subject’s anti-Mullerian hormone (AMH) level, and the subject’s follicle stimulating hormone (FSH) are pre-stored based on the existing database. A formula for predicting the probability (p) of a subject’s low response to the ovaries, which is fitted with multi-categorical variables converted from level data.

9. The system according to item 8, wherein:

The formula is the following formula one:

p=1/(1+e^(-(a+b*age+c*FSH+d*AMH))) (Formula 1)

Where p is a calculated parameter used to characterize the subject's ovarian reserve function,

Among them, a, b, c and d are unitless parameters;

Among them, in the module for calculating the ovarian reserve function, b, c, and b are obtained based on the subject’s age, the subject’s anti-Mullerian hormone (AMH) level, and the subject’s foam stimulating hormone (FSH) level. The value of d is brought into formula 1 for calculation. In the calculation, the values of age, FSH, and AMH are 0 or 1.

10. The system according to item 9, wherein:

a is any value selected from -4.072 to -3.188, and a is preferably -3.630;

When the subject is 30 years old and below, age is 0,

When the age of the subject is more than 30 years old and 40 years old and below, age is 1, b is any value selected from 0.163 to 0.960, b is preferably 0.561, and

When the age of the subject is greater than 40 years old, age is 1, b is any value selected from 0.295 to 1.317, and b is preferably 0.806;

When the subject's foam stimulating hormone (FSH) level is less than 6.5IU/L, FSH is 0,

When the subject's foam stimulating hormone (FSH) level is 6.5 IU/L and above and less than 8.5 IU/L, FSH is 1, c is any value selected from 0.239 to 1.006, and c is preferably 0.622,

When the subject's foam stimulating hormone (FSH) level is 8.5 IU/L and above and less than 10.5 IU/L, FSH is 1, c is any value selected from 0.363 to 1.303, c is preferably 0.833, and

When the subject's FSH level is 10.5IU/L and above, FSH is 1, c is any value selected from 0.847 to 1.712, and c is preferably 1.279;

When the subject's anti-Mullerian hormone (AMH) level is 2ng/ml and above, AMH is 0;

When the subject's anti-Mullerian hormone (AMH) level is less than 0.5ng/ml, AMH is 1, d is any value selected from 2.708 to 3.701, and d is preferably 3.204,

When the subject's anti-Mullerian hormone (AMH) level is 0.5 ng/ml and above and less than 1 ng/ml, AMH is 1, d is any value selected from 1.985 to 2.887, and d is preferably 2.436 ,

When the subject's anti-Mullerian hormone (AMH) level is 1 ng/ml and above and less than 1.5 ng/ml, AMH is 1, d is any value selected from 1.153 to 2.070, and d is preferably 1.612,

When the subject's anti-Mullerian hormone (AMH) level is 1.5 ng/ml and above and less than 2 ng/ml, AMH is 1, d is any value selected from 0.230 to 1.356, and d is preferably 0.793.

11. The system according to any one of items 2 to 10, wherein:

The grouping basis pre-stored in the grouping module is:

When the calculated probability (p) of low ovarian response for predicting the subject is less than 10%, the grouping module determines that the subject has a good ovarian reserve function;

When 10%≤calculated to predict the subject’s low response probability (p)<25%, the grouping module determines that the subject has a good ovarian reserve function;

When 25%≤calculated to predict the subject’s low response probability (p)<50%, the grouping module determines that the subject has poor ovarian reserve function;

When the calculated probability (p) of low ovarian response for predicting the subject is ≥50%, the grouping module determines that the subject has poor ovarian reserve.

12. A method for assessing the ovarian reserve function of a subject, comprising:

A data acquisition step, which acquires data on the subject's age, anti-Mullerian hormone (AMH) level, and follicle stimulating hormone (FSH) level; and

The step of calculating the ovarian reserve function uses the above-mentioned information obtained in the data collection step to calculate, thereby calculating the probability (p) of the subject's ovarian low response.

13. The method according to item 12, further comprising:

In the grouping step, the pre-known ovarian reserve function grouping parameters are used in the grouping step, and the calculated low response probability p of the ovary is grouped according to the grouping parameters, so as to determine the ovarian reserve level of the subject Grouping.

14. The method according to item 12 or 13, wherein:

In the step of calculating the ovarian reserve function, the data of the subject’s age, the subject’s anti-Mullerian hormone (AMH) level, and the subject’s follicle stimulating hormone (FSH) level are converted into multi-category variables to calculate The subject's ovarian low response probability (p).

15. The method according to item 14, wherein:

16. The method according to item 14 or 15, wherein

In the step of calculating the ovarian reserve function, the subject’s age is converted into a three-category variable,

17. The method according to any one of items 14 to 16, wherein

In the step of calculating ovarian reserve, the subject’s anti-Mullerian hormone (AMH) level is converted into five categorical variables,

18. The method according to any one of items 14 to 17, wherein

In the step of calculating the ovarian reserve function, the subject’s FSH (FSH) level is converted into four categorical variables,

19. The method according to any one of items 11 to 18, wherein:

In the step of calculating the ovarian reserve function, based on the data of the subject’s age, the subject’s anti-Mullerian hormone (AMH) level, and the subject’s follicle stimulating hormone (FSH) level in the existing database A formula for predicting the probability (p) of a subject's low response to the ovary, which is fitted by the converted multi-categorical variables.

20. The method according to item 19, wherein

The formula is the following formula one:

p=1/(1+e^(-(a+b*age+c*FSH+d*AMH))) (Formula 1)

Among them, a, b, c and d are unitless parameters;

Among them, in the step of calculating the ovarian reserve function, b, c, and b are obtained based on the subject’s age, the subject’s anti-Mullerian hormone (AMH) level, and the subject’s foam stimulating hormone (FSH) level. The value of d is brought into formula 1 for calculation. In the calculation, the values of age, FSH, and AMH are 0 or 1.

21. The method according to item 20, wherein

a is any value selected from -4.072 to -3.188, and a is preferably -3.630;

When the subject is 30 years old and below, age is 0,

When the subject's anti-Mullerian hormone (AMH) level is less than 0.5 ng/ml, AMH is 1, d is any value selected from 2.708 to 3.701, and d is preferably 3.204,

When the subject's anti-Mullerian hormone (AMH) level is 0.5 ng/ml and above and less than 1 ng/ml, AMH is 1, d is any value selected from 1.985 to 2.887, and d is preferably 2.436,

22. The method according to any one of items 12 to 21, wherein

The grouping basis used in the grouping step is:

Invention effect

Decreased ovarian reserve is the main reason for the decline in female fertility, but ovarian reserve varies greatly among individuals. Some people face the risk of decreased ovarian reserve or even depletion of ovarian reserve at a young age. Therefore, timely assessment of ovarian reserve is very necessary. The evaluation of ovarian reserve function can help women of childbearing age understand their own fertility status so as to arrange their own birth plans reasonably. For women with a history of infertility, it can be used to predict the ovarian reactivity of women of childbearing age, and provide a reference for clinical diagnosis of infertility and the formulation of treatment plans. At present, the main basis for diagnosing decreased ovarian reserve function at home and abroad is the diagnosis of low ovarian response based on the Bologna standard. Therefore, the index for evaluating ovarian reserve is actually an index for predicting ovarian responsiveness.

Specifically, in the present invention, the system for assessing the ovarian reserve function of a subject can be used to calculate the probability of the subject’s ovarian low response, so as to determine the probability of the subject’s ovarian low response. Reserve levels are grouped. Using the system of the present invention, the parameter (p) used to predict the probability of low ovarian response of the subject can be calculated, and the ovarian reserve function of the subject can be calculated according to the default ovarian reserve function grouping parameters pre-stored in the system. Divide into groups to determine the level of their ovarian reserve function, so that the level of ovarian reserve can be evaluated.

The inventor of the present application realizes that ovarian responsiveness is closely related to ovarian reserve, and the worse the ovarian reserve function, the higher the risk of low ovarian response. It is commonly used clinically to evaluate the decline of ovarian reserve function whether the ovarian low response is high risk. The order of ovarian reserve from high to low is the order of the probability of low ovarian response from low to high.

The system and method of the present invention can accurately assess the ovarian reserve function of subjects who will receive treatment, and can assist clinicians to formulate more targeted treatment plans in subsequent treatments. For ordinary women of childbearing age, especially women of childbearing age who want to give birth but are not sure when to give birth, it can help them assess their ovarian reserve function and formulate a reasonable birth plan.

The inventors of the present invention used three indicators of serum AMH level on any day of the menstrual cycle, age, and serum FSH level of 2-4 days of menstruation to evaluate ovarian reserve function for the first time. Compared with the previous system, the antral follicle count (AFC) counted by the method of vaginal ultrasonography can no longer be used, but its accuracy can still reach the level of the previous system. In addition, because there is no need to detect the antral follicle count (AFC) ), which can reduce the cost of detection. In addition, there are many factors that affect AFC detection results. Compared with AFC, the accuracy and repeatability of FSH and AMH results are better. The three-index system of this application can achieve similar effects to the four-index system.

The system and method of the present invention can quickly and accurately assess the ovarian reserve level of the subject, which solves the problem of evaluating the ovarian reserve function in the prior art mainly based on the doctor’s experience and some simple cut-point values of the ovarian reserve index. The reproducibility is poor and the standards are not uniform.

Detailed ways

Specific embodiments of the present invention will be described in more detail below. However, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

It should be noted that certain words are used in the specification and claims to refer to specific components. Those skilled in the art should understand that they may use different terms to refer to the same component. This specification and claims do not use differences in terms as a way to distinguish components, but use differences in functions of components as a criterion for distinguishing. If "include" or "include" mentioned in the entire specification and claims is an open term, it should be interpreted as "include but not limited to". The following description of the specification is a preferred embodiment for implementing the present invention, but the description is based on the general principles of the specification and is not intended to limit the scope of the present invention. The protection scope of the present invention shall be subject to those defined by the appended claims.

In this application, the ovarian reserve refers to the number of primitive follicles contained in the ovarian cortex, which is called ovarian reserve. It reflects the ability of the ovaries to provide healthy and successfully conceived eggs, and is the most important evaluation index for women's ovarian function. Generally speaking, the more the number of primordial follicles, the better the quality and the higher the chance of conception.

However, the number of primordial follicles cannot be evaluated non-invasively. It can only be evaluated by the number of follicles mobilized in each menstrual cycle. Too few follicles mobilized in the IVF-ET cycle (low ovarian response), suggesting a decline in ovarian reserve.

The age factor is generally considered to be the most important factor in evaluating ovarian reserve. The results of a study on age and IVF success rate show that the IVF success rate of women under 30 is about 26%, while the IVF success rate is only when the age is 37 years and above. Is 9%.

The mechanism of the decline of ovarian reserve capacity with age is as follows. (1) The number of follicles decreases. Primitive follicles appear after embryonic sex differentiation. At this time, the number of follicles is the largest. After puberty, the follicles begin to develop and mature. With the completion of ovulation, a large number of follicles that are recruited but not discharged atrophy and disappear to form the corpus luteum. The number of follicles continues to decrease with age: 20-week-old embryos in humans are the most, about 6 million follicles, the neonatal period is reduced to 700,000-2 million, the puberty is about 40,000, and the menopause only has more than 1,000 until the beginning of the menopause. Completely depleted. (2) The quality of the egg decreases. The quality of the embryo is mainly determined by the quality of the egg. Older age can increase the probability of egg cell aneuploidy, increase the risk of mitochondrial dysfunction, loss of egg polarity, and egg cell epigenetic changes. (3) Endocrine factors. The hypothalamic-pituitary-ovarian axis regulates women's menstrual cycle and ovulation. Abnormal endocrine levels in this axis can lead to infertility. AMH and inhibin B are secreted by small follicles and are a direct manifestation of ovarian reserve capacity. With age, the ovarian reserve decreases, and the number of follicles that can be recruited decreases, so the concentration of AMH and inhibitor B secreted by them also decreases. Inhibin B can negatively regulate the secretion of FSH from the pituitary, and the decrease in the level of Inhibin B leads to an increase in FSH secretion in the luteal phase. The pre-increased FSH promotes the growth of new follicles and E2 secretion, and ultimately shortens the menstrual cycle. Serum FSH levels increase, inhibitor B levels decrease, and the sensitivity of follicles to FSH decreases, suggesting that the number of antral follicles that can be recruited decreases. The menstrual cycle is a manifestation of ovarian reserve and fertility. Older age shortens the menstrual cycle and reduces the menstrual cycle by 2-3 days. It is a sensitive indication of aging of the reproductive system, suggesting that follicle growth starts early (FSH level increases) and primordial follicle reserve decreases.

Continuous variables: In statistics, variables can be divided into continuous variables and categorical variables according to whether the variable value is continuous. Variables that can take values arbitrarily within a certain interval are called continuous variables, and their values are continuous. Two adjacent values can be divided infinitely, that is, an infinite number of values can be taken. For example, the specifications and dimensions of the production parts, the height, weight, and chest circumference of the human body are continuous variables, and the values can only be obtained by measurement or measurement. Conversely, those whose values can only be calculated in natural numbers or integer units are discrete variables. For example, the number of enterprises, the number of employees, the number of equipment, etc. can only be counted by the number of measurement units. The value of this variable is generally obtained by counting.

Categorical variables refer to variables such as geographic location, demographics, etc., whose function is to group survey respondents into groups. Descriptive variables describe the difference between a certain customer group and other customer groups. Most categorical variables are also descriptive variables. Categorical variables can be divided into two categories: disordered categorical variables and ordinal categorical variables. Among them, unordered categorical variable (unordered categorical variable) refers to the difference in degree and order between the sub-categories or attributes. It can be divided into ① two categories, such as gender (male, female), drug reaction (negative and positive), etc.; ② multiple categories, such as blood type (O, A, B, AB), occupation (work, agriculture, Business, learning, military), etc. However, there is a degree of difference between the categories of ordinal categorical variables. For example, urine glucose test results are classified according to -, ±, +, ++, +++; curative effects are classified according to cure, marked effect, improvement, and ineffectiveness. For ordinal categorical variables, you should first group them in hierarchical order, count the number of observation units in each group, and compile a frequency table of ordinal variables (each level). The data obtained is called hierarchical data.

Variable types are not static. According to the needs of research purposes, various types of variables can be transformed. For example, the amount of hemoglobin (g/L) is originally a numerical variable. If hemoglobin is divided into two categories according to normal and low hemoglobin, it can be analyzed according to the two classification data; if according to severe anemia, moderate anemia, mild anemia, normal, and hemoglobin When the increase is divided into five grades, it can be analyzed according to grade data. Sometimes the categorical data can also be quantified. For example, the patient’s nausea response can be expressed as 0, 1, 2, and 3, and then it can be analyzed by numerical variable data (quantitative data).

The present invention relates to a system for evaluating the ovarian reserve function of a subject, which includes:

A data acquisition module, which is used to acquire data on the subject’s age, anti-Mullerian hormone (AMH) level, and follicle stimulating hormone (FSH) level; and a module for calculating ovarian reserve function, which is used to collect data from the data acquisition module Calculate the above-mentioned information obtained in, so as to calculate the probability (p) of the subject's ovarian low response.

The present invention also relates to a system for evaluating the ovarian reserve function of a subject, which includes:

A module for calculating the ovarian reserve function, which is used to calculate the above-mentioned information obtained in the data acquisition module, so as to calculate the probability (p) of the subject's low ovarian response; and a grouping module, which is pre-stored in the grouping module There is a default grouping parameter of the ovarian reserve function, and according to the grouping parameter, the calculated low response probability p of the ovary is grouped, so as to group the ovarian reserve level of the subject.

Anti-Mullerian Hormone (AMH) is a hormone secreted by the granular cells of the small ovarian follicles. Female babies in the fetal period start to produce AMH from 9 months of defecation. The more small follicles in the ovaries, the more AMH’s The higher the concentration; on the contrary, when the follicles are gradually consumed with age and various factors, the AMH concentration will also decrease, and the closer to menopause, the AMH gradually tends to zero.

Follicle Stimulating Hormone (FSH) is a hormone secreted by basophils in the anterior pituitary gland. It is composed of glycoprotein and its main function is to promote the maturation of follicles. FSH can promote the proliferation and differentiation of follicular granulosa cells, and promote the growth of the entire ovary. And its action on the seminiferous tubules of the testis can promote sperm formation. FSH is secreted in pulses in the human body, and women change with the menstrual cycle. Determination of FSH in serum is of great significance for the diagnosis and treatment of infertility and endocrine diseases, such as understanding the pituitary endocrine function, indirectly understanding the functional status of the ovary, evaluating ovarian reserve and ovarian responsiveness, and formulating the dosage of ovulation-stimulating drugs.

In the present invention, the level of anti-Mullerian hormone (AMH) refers to the concentration of anti-Mullerian hormone in venous blood serum samples on any day of the menstrual cycle of the subject, and the level of follicle stimulating hormone (FSH) refers to the female subject Follicle stimulating hormone concentration in venous blood serum samples from 2-4 days of menstruation.

In the module for calculating the ovarian reserve function, the inventor of the present application has conducted in-depth research and converted the age of the subjects into three classification variables, that is, the age is divided into three groups, namely: the age of the subject is under 30 years old, The age of the subject is greater than 30 years old and under 40 years old, and the age of the subject is greater than 40 years old.

In the module for calculating ovarian reserve function, the inventor of the present application has conducted in-depth research and converted the subject’s anti-Mullerian hormone (AMH) level into a five-category variable, namely the anti-Mullerian hormone (AMH) level Divided into five groups: the subject's anti-Mullerian hormone (AMH) level is less than 0.5ng/ml, and the subject's anti-Mullerian hormone (AMH) level is above 0.5ng/ml and less than 1ng/ml, the subject's anti-Mullerian hormone (AMH) level is above 1ng/ml and less than 1.5ng/ml, and the subject's anti-Mullerian hormone (AMH) level is above 1.5ng/ml And less than 2ng/ml, and the subject's anti-Mullerian hormone (AMH) level is greater than 2ng/ml;

In the module for calculating ovarian reserve function, the inventor of the present application has conducted in-depth research and converted the subjects’ FSH levels into four categorical variables, that is, FSH levels are divided into four groups, respectively As follows: the subject's foam stimulating hormone (FSH) level is less than 6.5IU/L, the subject's foam stimulating hormone (FSH) level is above 6.5IU/L and less than 8.5IU/L, the subject's foam stimulating hormone (FSH) level is above 8.5IU/L and less than 10.5IU/L, and the subject’s foam stimulating hormone (FSH) level is above 10.5IU/L.

In the module for calculating ovarian reserve function, the applicant of this application has carefully studied, as described above, the age of the subject is divided into three categorical variables, the anti-Mullerian hormone (AMH) level is divided into five categorical variables, and the follicles Stimulant hormone (FSH) levels are divided into four categorical variables, so as to realize that continuous variables can be transformed into different multi-categorical variables, brought into the categorical variable model, calculated to obtain the low response probability of the ovary, and according to the grouping principle summarized by the inventor of this application The ovarian reserve function is divided into groups to obtain the ovarian reserve function of the subjects.

By transforming the above three variables into different multi-classification variables, data analysis using such multi-classification variables can more accurately predict the ovarian reserve function of the subject, and the model has better stability. In this application, three indicators of age, anti-Mullerian hormone (AMH) level, and follicle-stimulating hormone (FSH) level are used to construct a system for predicting ovarian reserve, which can replace the original use of age and anti-Mullerian hormone. Hormone (AMH) level, follicle stimulating hormone (FSH) level, and antral follicle count (AFC) are four indicators to construct a system for predicting ovarian reserve. Although the prediction effect of the original system of four indicators is very good, if the use of antral follicle count (AFC) data can be avoided, it will further improve the operability of the system and reduce the cost of the entire system. In addition, the original four-variable model has strong correlations, and there is overlap between the functions of the indicators. Therefore, the inventor of this application made a careful design in this application, deleting the antral follicle count (AFC) index, which is more difficult to obtain, and classifying other indexes in a more detailed manner to replace the previous binary classification variables. After a lot of attempts and continuous improvement of the system, based on the classification basis described in this application, instead of adopting the method of converting all into two categorical variables, but turning age into three categorical variables, at the same time, the classification criteria are optimized. , The level of anti-Mullerian hormone (AMH) was transformed into five categorical variables, and the classification criteria were optimized, the level of follicle stimulating hormone (FSH) was transformed into four categorical variables, and the classification criteria were optimized. Thus, the original four-index system is replaced with three indicators, and the same good forecasting effect is also achieved.

Using the system of this application to accurately assess the ovarian reserve function of the subject can help clinicians formulate a more effective plan, and more accurately assess whether the subject has received the treatment for a period of time. It can effectively improve the ovarian reserve function of the subject.

In the module for calculating the ovarian reserve function, the subject’s age, the subject’s anti-Mullerian hormone (AMH) level, and the subject’s follicle stimulating hormone (FSH) are pre-stored based on the existing database. A formula for predicting the probability (p) of a subject’s low response to the ovaries, which is fitted with multi-categorical variables converted from level data. And according to the grouping criteria, the subjects' ovarian reserve function was divided into groups.

In the present invention, an existing database refers to a database composed of subjects who are receiving treatment or who have previously received treatment and meet the following inclusion and exclusion criteria. There is no agreement on the sample size of the database. Of course, the sample size of the database is larger. The larger the better, for example, 100 subjects, 200 subjects, 300 subjects may be used, preferably 400 subjects or more, and more preferably 500 subjects or more. In a specific embodiment, an existing database composed of 1523 samples is used. In a specific embodiment, an existing database composed of 3,273 samples is used.

The above inclusion and exclusion criteria are as follows. The inclusion criteria are: women between 20 and 45 years old, body mass index (BMI) ≤ 30, and six consecutive menstrual cycles of 25 to 45 days. Both ovaries are assessed by vaginal ultrasound. The morphology is normal, and the number of previous IVF/ICSI-ET cycles is ≤2. Exclusion criteria are: hydrosalpinx, unilateral ovary AFC>20, polycystic ovary syndrome, other untreated metabolic or endocrine diseases, previous surgery for ovaries or uterine cavity, intrauterine abnormalities, within 3 months of pregnancy, Smoking, using oral contraceptives or other hormones in the previous two months, having previously undergone radiotherapy or chemotherapy, receiving genes for PGD (preimplantation embryo genetic diagnosis)/PGS (preimplantation genetic screening) treatment Diagnosed couple.

When selecting database samples, subjects who can be included in the database must meet the above inclusion and exclusion criteria at the same time.

The module for calculating the ovarian reserve function uses the following formula to calculate the parameter (p) used to characterize the ovarian reserve function of the subject according to the data obtained in the data acquisition module:

p=1/(1+e^(-(a+b*age+c*FSH+d*AMH))) (Formula 1)

Among them, a, b, c and d are unitless parameters;

Further, a is any value selected from -4.072 to -3.188, and a is preferably -3.630; when the age of the subject is 30 years old and below, age is 0, and when the age of the subject is greater than 30 Years old and under 40 years old, age is 1, b is any value selected from 0.163 to 0.960, b is preferably 0.561, and when the age of the subject is greater than 40 years old, age is 1, and b is selected from 0.295 Any value in ~1.317, b is preferably 0.806; when the subject's FSH level is less than 6.5IU/L, FSH is 0, and when the subject's FSH level is 6.5IU /L and above and less than 8.5IU/L, FSH is 1, c is any value selected from 0.239 to 1.006, and c is preferably 0.622, when the subject's FSH level is 8.5IU/L And above and less than 10.5IU/L, FSH is 1, c is any value selected from 0.363 to 1.303, c is preferably 0.833, and when the subject’s foam stimulating hormone (FSH) level is 10.5IU/L and In the above, FSH is 1, c is any value selected from 0.847 to 1.712, and c is preferably 1.279. When the subject's anti-Mullerian hormone (AMH) level is 2ng/ml and above, AMH is 0; when the subject's anti-Mullerian hormone (AMH) level is less than 0.5ng/ml, AMH Is 1, d is any value selected from 2.708 to 3.701, and d is preferably 3.204. When the subject's anti-Mullerian hormone (AMH) level is 0.5ng/ml and above and less than 1ng/ml, AMH Is 1, d is any value selected from 1.985 to 2.887, d is preferably 2.436, when the subject's anti-Mullerian hormone (AMH) level is 1ng/ml and above and less than 1.5ng/ml, AMH Is 1, d is any value selected from 1.153 to 2.070, and d is preferably 1.612. When the subject's anti-Mullerian hormone (AMH) level is 1.5ng/ml and above and less than 2ng/ml, AMH Is 1, d is any value selected from 0.230 to 1.356, and d is preferably 0.793.

The grouping module of this application has pre-stored the basis for evaluating and grouping the ovarian reserve function. When the calculated parameter (p) used to characterize the low response probability of the subject's ovary is less than 10%, the grouping module determines that the subject has good ovarian reserve function; when 10% ≤ calculated for characterization The parameter (p) of the subject’s ovarian low response probability is <25%, and the grouping module determines that the subject has a good ovarian reserve function; when 20%≤calculated, it is used to characterize the subject’s ovaries The parameter of low response probability (p)<50%, the grouping module determines that the subject has poor ovarian reserve function; when the calculated parameter (p) ≥50 to characterize the subject’s low response probability %, the grouping module determines that the subject has poor ovarian reserve.

In another specific embodiment of the present application, the present application also relates to a method for assessing the ovarian reserve function of a subject. The method includes a data collection step, which obtains the subject’s age and anti-Mullerian hormone. ( p). In addition, the method further includes: a grouping step. In the grouping step, a pre-known ovarian reserve function grouping parameter is used, and the calculated low response probability p of the ovary is grouped according to the grouping parameter, so as to group the patients. The subjects’ ovarian reserve levels were divided into groups.

As mentioned above, the specific contents of the steps performed in the method of the present application are data on the subject’s age, subject’s anti-Mullerian hormone (AMH) level, and subject’s follicle stimulating hormone (FSH) level. The methods of obtaining, grouping and processing can all refer to the steps performed by the modules of the system involved in the application described above.

Example

In the embodiment, the sample size is estimated first, the total sample size should be >553 people, and all couples are trying to get pregnant for at least 12 months.

According to the following inclusion and exclusion criteria, a total of 561 couples from the three reproductive medicine centers were included for the study, that is, 561 couples meeting the following inclusion and exclusion criteria were selected for the follow-up study.

The inclusion criteria are: women between 20 and 45 years old, body mass index (BMI) ≤ 30, six consecutive menstrual cycles of 25 to 45 days, normal bilateral ovaries assessed by vaginal ultrasound examination, previous IVF/ICSI- The number of ET cycles is less than or equal to 2.

Exclusion criteria are: hydrosalpinx, unilateral ovary AFC>20, polycystic ovary syndrome, other untreated metabolic or endocrine diseases, previous surgery for ovaries or uterine cavity, intrauterine abnormalities, within 3 months of pregnancy, Smoking, using oral contraceptives or other hormones in the previous two months, having previously undergone radiotherapy or chemotherapy, receiving genes for PGD (preimplantation embryo genetic diagnosis)/PGS (preimplantation genetic screening) treatment Diagnosed couple.

Controlled ovarian stimulation (COS) treatment

Gn (ie, human recombinant FSH) treatment was started on the 2nd or 3rd day of the menstrual cycle. The starting dose is based on age, BMI (the body mass index, which is a figure obtained by dividing the weight in kilograms by the square of the height in meters, which is a commonly used international standard to measure the degree of body weight and health), menstruation 2 -4 days FSH and AFC level to choose. During the period of ovulation induction, the starting dose of Gn was _{adjusted according to ultrasound observation and serum E 2} level. GnRH antagonist treatment starts on the 5-7th day of stimulation, when the growing follicles are 10-12mm in diameter. When at least 2 dominant follicles (diameter≥18mm) are visible by ultrasound, 5000-10000IU of hCG is given to initiate the final oocyte maturation. Egg retrieval was performed 36 hours after hCG administration. Transfer 1-3 embryos or cryopreserve embryos. Then a progesterone support for the luteal phase is provided.

In the examples of this application, the applicant who used this application received subjects treated with the above GnRH antagonist between 2017 and 2018, and in 2017, the data of 1523 subjects met the above criteria. Included in this example, in 2018, the data of 3273 subjects that met the above criteria were included in this example. Used to build the system involved in this application.

Obtain samples and endocrine assays

For 4796 subjects as described above, venous blood samples were drawn and immediately reversed five times to promote complete blood coagulation, and the serum was collected by centrifugation and used for endocrine evaluation. The subject's follicle stimulating hormone (FSH) level is measured on the second day of the subject's menstrual cycle, and the subject's anti-Mullerian hormone (AMH) level is measured on any day of the subject's menstrual cycle. The FSH measurement of serum was performed using Siemens Immulite 2000 immunoassay system (Siemens Medical Diagnostics Co., Ltd., Shanghai, China). The quality control of FSH determination is provided by Bio-RAD laboratory (Lyphochek Immunoassay Plus Control, Trilevel, catalog number 370, batch number 40340). An ultra-sensitive two-point ELISA kit (Ansh Labs, USA) was used to detect the serum AMH concentration of the subjects.

In this embodiment, the level of follicle stimulating hormone (FSH) at 2-4 days of menstruation refers to the level of follicle stimulating hormone obtained by detecting venous blood serum samples of female subjects on the second to fourth days of menstruation. The level of AMH on any day of the menstrual cycle refers to the level of anti-Mullerian hormone obtained by detecting venous blood serum samples of female subjects on any day of the menstrual cycle. The data of the system used to build the model is shown in Table 1 below.

Table 1 Clinical and biochemical data of subjects treated with GnRH antagonists

To	2017(n＝1523)2017 (n = 1523)	2018(n＝3273)2018 (n=3273)
平均年龄(岁)Average age (years)	33.4±5.333.4±5.3	32.7±4.832.7±4.8
平均FSH(IU/L)Average FSH (IU/L)	7.5±3.37.5±3.3	7.2±3.17.2±3.1
平均AMH(ng/ml)Average AMH (ng/ml)	2.2(1.1-4.0)2.2(1.1-4.0)	2.7(1.2-4.8)2.7(1.2-4.8)

System model construction

In this example, the 4796 subjects mentioned above had poor ovarian response and fewer than 5 oocytes (specifically 0, 1, 2, 3, or 4) as the outcome variable, and predicted The variables are age, FSH level and AMH level. Among them, in this embodiment, the prediction model is constructed using 2017 data, that is, the data of 1523 subjects is used to initially construct the model system of this application, and the data of 2018 is used, that is, the data of 3273 subjects. To verify the effect of the system model.

The specific steps are to use the JMP Pro 14.2 software, first apply multi-factor logistic regression in the modeling data to build a predictive model of poor ovarian response, and verify the effect of the model in the verification data. The area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) measurements provided in the software are used to evaluate the performance of the established predictive model.

First of all, multi-factor logistic regression was performed on the modeling data, that is, the data of 1523 subjects, and whether the ovarian response was low was used as the outcome variable, and AMH, FSH, and age were used as independent variables. Correlation, therefore, the three continuous variables are converted into categorical variables, and the grouping criteria for the three parameters age, FSH level and AMH level are shown in Table 2.

Table 2 Grouping basis

According to the grouping confirmed in Table 2, the subject's age, AMH and FSH have been converted into multi-categorical variables. The ages of the subjects are divided into three groups, namely: the age of the subject is under 30, the age of the subject is greater than 30 and under 40, and the age of the subject is greater than 40. The subjects’ anti-Mullerian hormone (AMH) levels were divided into five groups: the subjects’ anti-Mullerian hormone (AMH) levels were less than 0.5ng/ml, and the subjects’ anti-Muller hormone (AMH) levels were less than 0.5ng/ml. The anti-Mullerian hormone (AMH) level is above 0.5ng/ml and less than 1ng/ml, and the subject’s anti-Mullerian hormone (AMH) level is above 1ng/ml and less than 1.5ng/ml. The Mullerian Hormone (AMH) level is above 1.5ng/ml and less than 2ng/ml, and the subject's Anti-Mullerian Hormone (AMH) level is greater than 2ng/ml. The subjects’ FSH levels were divided into four groups: the subjects’ FSH levels were less than 6.5IU/L, and the subjects’ FSH levels were 6.5 IU/L or more and less than 8.5IU/L, the subject's foam stimulating hormone (FSH) level is 8.5IU/L or more and less than 10.5IU/L, and the subject's foam stimulating hormone (FSH) level is 10.5IU /L above, so that age, AMH and FSH are converted into multi-categorical variables based on the above criteria.

At the same time, the following formula was fitted with the data of the above training group and the parameters involved in the formula were confirmed, as shown in Table 3:

p=1/(1+e^(-(a+b*age+c*FSH+d*AMH))) (Formula 1)

table 3

As shown in formula 1, p is a calculated parameter used to characterize the subject's ovarian reserve function, where a, b, c, and d are unitless parameters; among them, in the module for calculating ovarian reserve function, Obtain the values of b, c, and d based on the subject's age, the subject's anti-Mullerian hormone (AMH) level, and the subject's foam stimulating hormone (FSH) level to bring them into formula 1 for calculation In the calculation, age, FSH, and AMH take the values 0 or 1.

As shown in Table 3, the parameters involved in one formula are: a is any value selected from -4.072 to -3.188, a is preferably -3.630; when the age of the subject is 30 years old and below, age is 0 When the age of the subject is more than 30 years old and 40 years old and below, age is 1, b is any value selected from 0.163 to 0.960, b is preferably 0.561, and when the age of the subject is greater than 40 years old , Age is 1, b is any value selected from 0.295 to 1.317, b is preferably 0.806; when the subject's foam stimulating hormone (FSH) level is less than 6.5IU/L, FSH is 0, when the subject's When the foam stimulating hormone (FSH) level is 6.5IU/L and above and less than 8.5IU/L, FSH is 1, c is any value selected from 0.239 to 1.006, and c is preferably 0.622. When the subject’s foam stimulates When the FSH level is 8.5IU/L and above and less than 10.5IU/L, FSH is 1, c is any value selected from 0.363 to 1.303, c is preferably 0.833, and when the subject’s foam stimulating hormone When the (FSH) level is 10.5 IU/L and above, FSH is 1, c is any value selected from 0.847 to 1.712, and c is preferably 1.279. When the subject's anti-Mullerian hormone (AMH) level is 2ng/ml and above, AMH is 0; when the subject's anti-Mullerian hormone (AMH) level is less than 0.5ng/ml, AMH Is 1, d is any value selected from 2.708 to 3.701, and d is preferably 3.204. When the subject's anti-Mullerian hormone (AMH) level is 0.5ng/ml and above and less than 1ng/ml, AMH Is 1, d is any value selected from 1.985 to 2.887, d is preferably 2.436, when the subject's anti-Mullerian hormone (AMH) level is 1ng/ml and above and less than 1.5ng/ml, AMH Is 1, d is any value selected from 1.153 to 2.070, and d is preferably 1.612. When the subject's anti-Mullerian hormone (AMH) level is 1.5ng/ml and above and less than 2ng/ml, AMH Is 1, d is any value selected from 0.230 to 1.356, and d is preferably 0.793.

Then use the data of 3273 subjects in 2018 to verify the data using the above grouping basis and formula. Through the above verification, it is confirmed that the acquisition of the model constructed as described above can well predict the ovarian reserve function of the subject.

In order to verify the accuracy of the system, we used the evaluation function of the JMP Pro 14.2 software to evaluate the accuracy of the evaluation of this application system and the previous application system (CN201811516206.4) for the same population. The results are shown in Table 4 below. It can be seen that the system constructed in this embodiment and the online application system can reach the same evaluation level.

Table 4

Therefore, according to the above formula one, the subject can be calculated based on the age of a subject, the concentration of anti-Mullerian hormone on any day of the menstrual cycle, and the concentration of follicle stimulating hormone in the venous blood of 2-4 days of menstruation. The patient’s ovarian response rate is low.

The population is grouped according to the calculated parameters of the low response probability of the ovary. The grouping method adopts the grouping standard confirmed by the applicant before (see CN201811516206.4), that is, when the calculated value is used to characterize the subject’s ovaries When the parameter of low response probability (p)<10%, the grouping module determines that the subject has good ovarian reserve function; when 10%≤the calculated parameter (p) used to characterize the subject’s low response probability )<25%, the grouping module determines that the subject has good ovarian reserve; when 25%≤the calculated parameter (p) to characterize the subject’s low response probability (p)<50%, the grouping module It is determined that the subject has poor ovarian reserve; when the calculated parameter (p) used to characterize the subject’s low response probability is ≥50%, the grouping module determines that the subject has poor ovarian reserve .

Although the embodiments of the present invention are described above, the present invention is not limited to the above-mentioned specific embodiments and application fields, and the above-mentioned specific embodiments are only illustrative, instructive, and not restrictive. Under the enlightenment of this specification and without departing from the scope of protection of the claims of the present invention, those of ordinary skill in the art can also make many forms, which all belong to the protection of the present invention.

Claims

A system for evaluating the ovarian reserve function of a subject, which includes:

A data collection module, which is used to obtain data on the subject's age, anti-Mullerian hormone (AMH) level, and follicle stimulating hormone (FSH) level; and

The module for calculating the ovarian reserve function is used to calculate the above-mentioned information obtained in the data acquisition module, so as to calculate the probability (p) of the subject's ovarian low response.
The system according to claim 1, further comprising:

A grouping module, in which the default ovarian reserve grouping parameters are prestored, and according to the grouping parameters, the calculated ovarian low response probability p is grouped, so as to group the subjects’ ovarian reserve levels .
The system according to claim 1 or 2, wherein:

In the module of calculating ovarian reserve function, the data of the subject’s age, the subject’s anti-Mullerian hormone (AMH) level, and the subject’s follicle stimulating hormone (FSH) level are converted into multi-categorical variables to calculate The subject's ovarian low response probability (p).
The system according to claim 3, wherein:

The anti-Mullerian hormone (AMH) level refers to the concentration of anti-Mullerian hormone in the venous blood on any day of the menstrual cycle of the female subject, and the follicle stimulating hormone (FSH) level refers to the female subject The concentration of follicle stimulating hormone in venous blood for 2-4 days of menstruation.
The system according to claim 3 or 4, wherein:

In the module for calculating ovarian reserve function, the subject’s age is converted into three categorical variables,

The age of the subjects is divided into three groups, namely: the age of the subject is 30 years old and under, the age of the subject is greater than 30 years old and 40 years old and under, and the age of the subject is greater than 40 years old .
The system according to any one of claims 3 to 5, wherein:

In the module for calculating ovarian reserve, the subject’s anti-Mullerian hormone (AMH) level is converted into five categorical variables,

The subjects’ anti-Mullerian hormone (AMH) levels are divided into five groups: the subject’s anti-Mullerian hormone (AMH) level is less than 0.5ng/ml, and the subject’s anti-Muller hormone (AMH) level is less than 0.5ng/ml. The level of AMH is 0.5ng/ml and above and less than 1ng/ml, and the level of the subject’s anti-Muller Hormone (AMH) is 1ng/ml and above and less than 1.5ng/ml, the subject The anti-Mullerian hormone (AMH) level of 1.5ng/ml and above and less than 2ng/ml, and the subject's anti-Mullerian hormone (AMH) level is greater than or equal to 2ng/ml.
The system according to any one of claims 3 to 6, wherein:

In the module for calculating ovarian reserve, the subject’s FSH (FSH) level is converted into four categorical variables,

That is to say, the subjects’ foam stimulating hormone (FSH) levels are divided into four groups, namely: the subject’s foam stimulating hormone (FSH) level is less than 6.5IU/L, and the subject’s foam stimulating hormone (FSH) level is 6.5 And IU/L above and less than 8.5IU/L, the subject’s foam stimulating hormone (FSH) level is 8.5IU/L and above and less than 10.5IU/L, and the subject’s foam stimulating hormone (FSH) level is 10.5IU/L and above.
The system according to any one of claims 1-7, wherein:

In the module for calculating the ovarian reserve function, the subject’s age, the subject’s anti-Mullerian hormone (AMH) level, and the subject’s follicle stimulating hormone (FSH) are pre-stored based on the existing database. A formula for predicting the probability (p) of a subject’s low response to the ovaries, which is fitted with multi-categorical variables converted from level data.
The system according to claim 8, wherein:

The formula is the following formula one:

p=1/(1+e^(-(a+b*age+c*FSH+d*AMH))) (Formula 1)

Where p is a calculated parameter used to characterize the subject's ovarian reserve function,

Among them, a, b, c and d are unitless parameters;

Among them, in the module for calculating the ovarian reserve function, b, c, and b are obtained based on the subject’s age, the subject’s anti-Mullerian hormone (AMH) level, and the subject’s foam stimulating hormone (FSH) level. The value of d is brought into formula 1 for calculation. In the calculation, the values of age, FSH, and AMH are 0 or 1.
The system according to claim 9, wherein:

a is any value selected from -4.072 to -3.188, and a is preferably -3.630;

When the subject is 30 years old and below, age is 0,

When the age of the subject is more than 30 years old and 40 years old and below, age is 1, b is any value selected from 0.163 to 0.960, b is preferably 0.561, and

When the age of the subject is greater than 40 years old, age is 1, b is any value selected from 0.295 to 1.317, and b is preferably 0.806;

When the subject's foam stimulating hormone (FSH) level is less than 6.5IU/L, FSH is 0,

When the subject's foam stimulating hormone (FSH) level is 6.5 IU/L and above and less than 8.5 IU/L, FSH is 1, c is any value selected from 0.239 to 1.006, and c is preferably 0.622,

When the subject's foam stimulating hormone (FSH) level is 8.5 IU/L and above and less than 10.5 IU/L, FSH is 1, c is any value selected from 0.363 to 1.303, c is preferably 0.833, and

When the subject's FSH level is 10.5IU/L and above, FSH is 1, c is any value selected from 0.847 to 1.712, and c is preferably 1.279;

When the subject's anti-Mullerian hormone (AMH) level is 2ng/ml and above, AMH is 0;

When the subject's anti-Mullerian hormone (AMH) level is less than 0.5ng/ml, AMH is 1, d is any value selected from 2.708 to 3.701, and d is preferably 3.204,

When the subject's anti-Mullerian hormone (AMH) level is 0.5 ng/ml and above and less than 1 ng/ml, AMH is 1, d is any value selected from 1.985 to 2.887, and d is preferably 2.436,

When the subject's anti-Mullerian hormone (AMH) level is 1 ng/ml and above and less than 1.5 ng/ml, AMH is 1, d is any value selected from 1.153 to 2.070, and d is preferably 1.612,

When the subject's anti-Mullerian hormone (AMH) level is 1.5 ng/ml and above and less than 2 ng/ml, AMH is 1, d is any value selected from 0.230 to 1.356, and d is preferably 0.793.
The system according to any one of claims 2-10, wherein:

The grouping basis pre-stored in the grouping module is:

When the calculated probability (p) of low ovarian response for predicting the subject is less than 10%, the grouping module determines that the subject has a good ovarian reserve function;

When 10%≤calculated to predict the subject’s low response probability (p)<25%, the grouping module determines that the subject has a good ovarian reserve function;

When 25%≤calculated to predict the subject’s low response probability (p)<50%, the grouping module determines that the subject has poor ovarian reserve function;

When the calculated probability (p) of low ovarian response for predicting the subject is ≥50%, the grouping module determines that the subject has poor ovarian reserve.