WO2020118790A1 - 评估受试者卵巢储备功能的系统和方法 - Google Patents

评估受试者卵巢储备功能的系统和方法 Download PDF

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WO2020118790A1
WO2020118790A1 PCT/CN2018/124766 CN2018124766W WO2020118790A1 WO 2020118790 A1 WO2020118790 A1 WO 2020118790A1 CN 2018124766 W CN2018124766 W CN 2018124766W WO 2020118790 A1 WO2020118790 A1 WO 2020118790A1
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ovarian
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fsh
probability
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李蓉
徐慧玉
乔杰
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北京大学第三医院
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
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    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/43—Detecting, measuring or recording for evaluating the reproductive systems
    • A61B5/4306—Detecting, measuring or recording for evaluating the reproductive systems for evaluating the female reproductive systems, e.g. gynaecological evaluations
    • A61B5/4318—Evaluation of the lower reproductive system
    • A61B5/4325—Evaluation of the lower reproductive system of the uterine cavities, e.g. uterus, fallopian tubes, ovaries
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/689—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to pregnancy or the gonads
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/74—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving hormones or other non-cytokine intercellular protein regulatory factors such as growth factors, including receptors to hormones and growth factors
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/74—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving hormones or other non-cytokine intercellular protein regulatory factors such as growth factors, including receptors to hormones and growth factors
    • G01N33/76—Human chorionic gonadotropin including luteinising hormone, follicle stimulating hormone, thyroid stimulating hormone or their receptors
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
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    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/08—Detecting organic movements or changes, e.g. tumours, cysts, swellings
    • A61B8/0866—Detecting organic movements or changes, e.g. tumours, cysts, swellings involving foetal diagnosis; pre-natal or peri-natal diagnosis of the baby
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/12—Diagnosis using ultrasonic, sonic or infrasonic waves in body cavities or body tracts, e.g. by using catheters
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/44—Constructional features of the ultrasonic, sonic or infrasonic diagnostic device
    • A61B8/4416—Constructional features of the ultrasonic, sonic or infrasonic diagnostic device related to combined acquisition of different diagnostic modalities, e.g. combination of ultrasound and X-ray acquisitions
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/52—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/5215—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving processing of medical diagnostic data
    • A61B8/5223—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving processing of medical diagnostic data for extracting a diagnostic or physiological parameter from medical diagnostic data

Definitions

  • the present invention relates to a system for evaluating the ovarian reserve function of a subject.
  • the system can be used to evaluate the subject's own ovarian reserve function to evaluate their fertility potential, and to assess the subject's Whether fertility potential has improved after treatment.
  • ovarian reserve The number of primordial follicles contained in the ovarian cortex is called ovarian reserve. It reflects the ability of the ovary to provide healthy and successfully conceived eggs, and is the most important evaluation index of female ovarian function. In general, the more primitive follicles, the better the quality and the higher the chance of conception.
  • ovarian reserve function can help women of childbearing age to understand their own fertility status in order to reasonably arrange their own birth planning. For women with a history of infertility, it can be used to predict the ovarian reactivity of women of childbearing age, providing a reference for the clinical diagnosis and treatment planning of infertility.
  • the main basis for diagnosing the decline of ovarian reserve function at home and abroad is the Bologna standard for the prediction of low ovarian response. Therefore, the index for evaluating ovarian reserve function is actually the index for predicting ovarian reactivity.
  • Age factor is an important factor to evaluate ovarian reserve.
  • a study on age and IVF success rate showed that women under 30 years old had an IVF success rate of about 26%, while when they were 37 years old and above, the IVF success rate was only 9%. .
  • Endocrine factor testing includes basic FSH level testing, AMH level testing and basic inhibit B level testing.
  • basic FSH detection FSH is follicle stimulating hormone, secreted by the pituitary gland, the main role is to promote follicle maturation.
  • Basic FSH is also called background FSH, 0-3 days from the beginning of the menstrual cycle, the initial stage of follicular growth, the granulosa cells in the follicle have not yet begun to secrete large amounts of estrogen, and the feedback regulation of the pituitary and ovary is at the initial stage.
  • the FSH concentration measured at this stage Known as basic FSH.
  • Basic FSH directly reflects the secretory function of the ovary and is the most commonly used index for clinical evaluation of ovarian reserve function.
  • the basic FSH of women with normal fertility is generally less than 10IU/L.
  • the basic FSH level is too high, reflecting poor ovarian secretion function.
  • FSH>12IU/L indicates hypothyroidism
  • FSH>40IU/L indicates ovarian failure, different laboratories should The values will vary.
  • the basic FSH will gradually increase with age. Studies have shown that the basic FSH levels in the ten years before menopause begin to rise, and this is the age at which the infertility rate begins to rise significantly.
  • AMH Anti-Mullerian Hormone level detection: AMH is one of the members of the transforming growth factor family, which was first secreted by the granulosa cells of the primary follicles, and the secretion of the pre-sinusoidal follicles and small follicles (follicle diameter less than 4mm) reached its peak. After that, the secretion volume gradually decreases. When the follicle diameter is greater than 8 mm, the secretion stops.
  • the AMH level can reflect the number of follicles recruited during the menstrual cycle, and the decrease in the number of follicles recruited suggests a decrease in ovarian reserve, so the AMH level can directly reflect the ovarian reserve.
  • the ability is currently the best internationally recognized serological indicator for predicting ovarian reactivity.
  • the higher the AMH level of women the higher the number of follicles recruited, the better the response to ovarian stimulation, and the higher the IVF success rate.
  • excessive serum AMH levels may be other diseases such as polycystic ovary syndrome, which should be excluded in conjunction with transvaginal ultrasound.
  • Inhibin B level detection Inhibin B is secreted by small follicular granulosa cells in the ovarian cortex, the secretion volume increases with the stimulation of GnRH and FSH, the secretion volume changes greatly during the menstrual cycle, and often cannot faithfully reflect the ovarian reserve capacity Therefore, Inhibin B level testing is not the current routine method for ovarian function evaluation.
  • Ultrasound examination of the ovary includes detection of sinus follicles, ovarian volume and ovarian stromal blood flow.
  • the number of sinus follicles refers to counting the total number of bilateral ovarian sinus follicles by transvaginal ultrasound exploration in the early follicular stage, which is a direct manifestation of ovarian reserve capacity.
  • the diameter of sinus follicles is 2-10mm or 3-8mm.
  • the decrease in the number of sinus follicles indicates poor responsiveness to ovarian stimulation and the decrease in pregnancy rate. Studies have shown that using sinus follicle numbers to predict IVF success rate is more effective than basic FSH testing.
  • Ovarian stromal blood flow and ovarian volume are not commonly used methods for predicting ovarian reactivity and evaluating ovarian reserve function.
  • AMH level detection and sinus follicle count are the best two internationally recognized indicators for predicting ovarian reactivity.
  • Basic FSH level detection is currently the most widely used evaluation index of ovarian reserve function in the world.
  • the age factor is also an important factor in evaluating ovarian reserve.
  • Basic level FSH screening is the most commonly used method for predicting ovarian reserve.
  • the threshold value of basic FSH level is set higher, the specificity for diagnosing ovarian dysfunction increases, but the sensitivity decreases.
  • Transvaginal ultrasound counting of sinus follicles has the characteristics of quickness, economy, and accuracy, but it is related to the doctor's experience and equipment, and there are human factors.
  • AMH is secreted by granulosa cells in the early follicular stage and can be the earliest and most direct indicator of the aging degree of granulosa cells. It is a potential research direction, but some disease conditions may interfere with the results of AMH, and its application alone is still not enough to accurately assess Ovarian reserve function. In short, any of the ovarian reserve indicators cannot be used alone to diagnose the decline in ovarian reserve function, which needs to be further clarified in conjunction with other methods.
  • judging the ovarian reserve function of the subject is a very important task for clinicians and the like.
  • the patient's ovarian reactivity can be predicted, an important clinical outcome in this ovulation induction process.
  • clinicians often used their own experience to make judgments based on age, body mass index, endocrine factor levels, and number of sinus follicles.
  • Our system can accurately assess the quality of the ovarian reserve function of the subjects who will receive treatment, so as to assist clinicians in the subsequent treatment to formulate more targeted treatment plans.
  • ovarian reactivity is related to the patient's basic conditions (age, basic FSH level, AMH level, and number of sinus follicles, etc.) and the dose of ovulatory drugs.
  • the inventor of the application first obtained the expected low probability of ovarian response based on the patient's basic situation, and then grouped the population according to whether the interaction between the expected low response probability and the dose of ovulatory drugs was meaningful, and divided the trend of low response into similar A group. From a statistical point of view, whether hierarchical analysis (ie grouping in this application) is needed is also based on whether the interaction is meaningful.
  • the present invention relates to the following:
  • a system for evaluating a subject's ovarian reserve function which includes:
  • a data collection module which is used to obtain data of the subject's age, anti-Mullerian hormone (AMH) level, follicle stimulating hormone (FSH) level, sinus follicle count (AFC) data; and
  • a module for calculating the ovarian reserve function which is used to calculate the above-mentioned information obtained in the data collection module to calculate the probability of ovarian low response (p) of the subject.
  • ovarian reactivity depends on ovarian reserve function, and ovarian reserve function is grouped according to the probability of low ovarian response.
  • a grouping module in which a default grouping parameter of the ovarian reserve function is pre-stored in the grouping module, and according to the grouping parameter, the calculated ovary low response probability p is grouped, thereby grouping the ovarian reserve level of the subject .
  • the subject’s age, subject’s anti-Mullerian hormone (AMH) level, subject’s follicle stimulating hormone (FSH) level, and subject’s sinus follicle count (AFC) are used.
  • the data is converted into a binary variable to calculate the probability (p) of the subject's ovarian hyporesponsiveness.
  • the receiver operating characteristic (ROC) curve is used to detect the boundaries between age, anti-Mullerian hormone (AMH) level, follicle stimulating hormone (FSH) level, and sinus follicle count (AFC) Point, and according to the cut-off value of the cut-off point, the age, anti-Mullerian hormone (AMH) level, follicle stimulating hormone (FSH) level, sinus follicle count (AFC) are converted into binary variables, so as to use the Binary variables were used to calculate the probability of subject's ovarian low response (p).
  • ROC receiver operating characteristic
  • the anti-Mullerian hormone (AMH) level refers to the concentration of anti-Mullerian hormone in the venous blood of a female subject during 2-4 days of menstruation
  • FSH follicle stimulating hormone
  • AFC sinus follicle count
  • the cut-off value of the age is 35 years old
  • the cut-off value of the anti-Mullerian hormone (AMH) level is 0.93 ng/ml
  • the cut-off value of the follicle stimulating hormone (FSH) level is 9.1 IU/
  • the value of L and the tangent point of the sinus follicle count (AFC) is 8.
  • the module for calculating ovarian reserve function pre-stored based on the subject's age, subject's anti-Mullerian hormone (AMH) level, and subject's follicle stimulating hormone (FSH) level based on the subject in the existing database 1.
  • a formula used to calculate the probability of low response of the ovary (p) of the subject by fitting a binary variable into which the data of the subject's sinus follicle count (AFC) is converted.
  • the formula is the following formula one:
  • p is the calculated parameter used to characterize the ovarian reserve function of the subject
  • i is any value selected from -1.786 to -0.499
  • a is any value selected from 0.063-1.342
  • b is Any value selected from -2.542 to -1.056
  • c is any value selected from 0.548 to 1.838
  • the grouping parameters pre-stored in the grouping module are:
  • the grouping module determines that the subject belongs to a good ovarian reserve function
  • the grouping module determines that the subject belongs to the ovarian reserve function and begins to decline;
  • the grouping module determines that the subject belongs to a significant decrease in ovarian reserve function
  • the grouping module determines that the subject belongs to poor ovarian reserve.
  • a method for evaluating a subject's ovarian reserve function comprising:
  • Data collection step in this step, the data of the subject's age, anti-Mullerian hormone (AMH) level, follicle stimulating hormone (FSH) level, sinus follicle count (AFC) are obtained; and
  • the step of calculating the ovarian reserve function In this step, the age, anti-Mullerian hormone (AMH) level, follicle stimulating hormone (FSH) level, and sinus follicle count (AFC) data obtained in the above data collection step are used. Calculate to calculate the parameter (p) of the subject's ovarian low response probability.
  • AMD anti-Mullerian hormone
  • FSH follicle stimulating hormone
  • AFC sinus follicle count
  • the calculated ovarian low response probability is grouped according to the default ovarian reserve function grouping parameter, thereby grouping the ovarian reserve levels of the subjects.
  • the subject In the step of calculating ovarian reserve function, the subject’s age, subject’s anti-Müllerian hormone (AMH) level, subject’s follicle stimulating hormone (FSH) level, subject’s sinus follicle count (AFC)
  • AMH anti-Müllerian hormone
  • FSH follicle stimulating hormone
  • AFC sinus follicle count
  • the receiver operating characteristic (ROC) curve is used to detect the age, anti-Mullerian hormone (AMH) level, follicle stimulating hormone (FSH) level, sinus follicle count (AFC) Cut-off value, and convert the age, anti-Mullerian hormone (AMH) level, follicle stimulating hormone (FSH) level, and sinus follicle count (AFC) into two according to the cut-off value of the cut-off point Categorical variables, so that the binary variability model is used to calculate the subject's ovarian low response probability (p).
  • ROC receiver operating characteristic
  • the anti-Mullerian hormone (AMH) level refers to the concentration of anti-Mullerian hormone in the venous blood serum of the female subject during 2-4 days of menstruation
  • FSH follicle stimulating hormone
  • the sinus follicle count (AFC) refers to the vaginal B-ultrasound count.
  • the diameter of the two ovaries of the female subject during 2-4 days of menstruation is 2- The number of all visible follicles at 8mm.
  • the cut-off value of the age is 35 years old
  • the cut-off value of the anti-Mullerian hormone (AMH) level is 0.93 ng/ml
  • the cut-off value of the follicle stimulating hormone (FSH) level is 9.1 IU/
  • the value of L and the tangent point of the sinus follicle count (AFC) is 8.
  • the pre-existing database based on the subject’s age, subject’s anti-Mullerian hormone (AMH) level, and subject’s follicle stimulating hormone (FSH) level are used. 1.
  • the subject’s sinus follicle count (AFC) data is converted into a binary variable fitted into a formula for calculating the probability of predicting the subject’s ovarian low response (p).
  • ovarian reserve function using the following formula 1, according to the age, anti-Mullerian hormone (AMH) level, follicle stimulating hormone (subjects obtained from the data collection step and converted into a binary variable) FSH) level, sinus follicle count (AFC) data to calculate the probability of subject's ovarian low response (p):
  • AMD anti-Mullerian hormone
  • FSH follicle stimulating hormone
  • AFC sinus follicle count
  • p is the calculated parameter used to characterize the ovarian reserve function of the subject
  • i is any value selected from -1.786 to -0.499
  • a is any value selected from 0.063-1.342
  • b is Any value selected from -2.542 to -1.056
  • c is any value selected from 0.548 to 1.838
  • the grouping basis pre-stored in the grouping module is:
  • the grouping module determines that the subject belongs to a good ovarian reserve function
  • the grouping module determines that the subject belongs to the ovarian reserve function and begins to decline;
  • the grouping module determines that the subject belongs to a significant decrease in ovarian reserve function
  • the grouping module determines that the subject belongs to poor ovarian reserve.
  • ovarian reserve function can help women of childbearing age to understand their own fertility status in order to reasonably arrange their own birth planning. For women with a history of infertility, it can be used to predict the ovarian reactivity of women of childbearing age, providing a reference for the clinical diagnosis and treatment planning of infertility.
  • the main basis for diagnosing the decline of ovarian reserve function at home and abroad is the diagnosis of low ovarian response by the Bologna standard. Therefore, the index for evaluating ovarian reserve function is actually the index for predicting ovarian reactivity.
  • the system for evaluating the ovarian reserve function of the subject can be used to calculate the probability of the subject's ovarian low response, so that the subject's ovarian response rate
  • the reserve levels are grouped.
  • a parameter (p) for predicting the ovarian low response probability 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 Group to determine the level of ovarian reserve function, so that the level of ovarian reserve can be evaluated.
  • ovarian reactivity is closely related to ovarian reserve.
  • Clinically, whether ovarian hyporesponsiveness is at high risk is commonly used to assess the decline in ovarian reserve function.
  • the order of ovarian reserve from high to low is the order of ovarian low response probability from low to high.
  • Ovarian reactivity is also closely related to the dosage of medication.
  • a grouping criterion is established and based on This grouping criterion evaluates the ovarian reserve level of the subjects.
  • the system and method of the present invention it is possible to accurately assess the quality of the ovarian reserve function of the subject who will receive treatment, which can assist the clinician in formulating a more targeted treatment plan in the subsequent treatment.
  • women of ordinary childbearing age especially those who want to have children, but are not sure when to have children, they can help them evaluate their ovarian reserve function and formulate a reasonable birth plan.
  • the inventors of the present invention used the four indexes of AFC, AMH level, age, FSH level of 2-4 days of menstruation and AFC count of vaginal B ultrasound of 2-4 days of menstruation to evaluate ovarian reserve function for the first time.
  • the system and method of the present invention can quickly and accurately assess the ovarian reserve level of the subject, which solves the problem in the existing technology that the ovarian reserve function is evaluated based on the doctor's experience and some simple ovarian reserve index cut-off values
  • the problem is that the repeatability is poor and the standards are not uniform.
  • Figure 6 regression analysis showing the effect of the interaction between the initial dose of Gn and the predicted probability of ovarian hyporesponsiveness on the occurrence of hypoovarian response.
  • ovarian reserve refers to: the number of original follicles contained in the ovarian cortex, called ovarian reserve. It reflects the ability of the ovary to provide healthy and successfully conceived eggs, and is the most important evaluation index of female ovarian function. In general, the more primitive follicles, the better the quality and the higher the chance of conception.
  • the number of original follicles cannot be evaluated non-invasively. It can only be evaluated by the number of follicles mobilized during each menstrual cycle. Too few follicles mobilized during the IVF-ET cycle (low ovarian response) suggest a decline in ovarian reserve.
  • age factor is the most important factor in evaluating ovarian reserve.
  • a study on age and IVF success rate shows that: women under 30 years old have an IVF success rate of about 26%, while when they are 37 years and older, the IVF success rate is only 9%.
  • the mechanism by which ovarian reserve capacity declines with age is as follows. (1) The number of follicles decreases, and the original follicles appear after embryonic sex differentiation. At this time, the number of follicles is the largest. The follicles begin to mature after puberty. With the completion of ovulation, a large number of follicles are recruited and the undischarged follicles shrink to form a corpus luteum.
  • the number of follicles continues to decrease with age: humans have the largest number of 20-week-old embryos, about 6 million follicles, the neonatal period is reduced to 700-200 million, the adolescence is about 40,000, and the menopause period is only more than 1,000 until Completely exhausted.
  • the quality of the egg decreases, and the quality of the embryo is mainly determined by the quality of the egg. Older ages can increase the probability of aneuploidy in egg cells, increase the risk of mitochondrial dysfunction, the disappearance of egg polarity, and epigenetic changes in egg cells.
  • Endocrine factors The hypothalamic-pituitary-ovarian axis regulates women's menstrual cycle and ovulation.
  • AMH and inhibit B are secreted by small follicles, which is a direct manifestation of ovarian reserve capacity. As the age increases, the ovarian reserve decreases and the number of follicles that can be recruited decreases, so the concentration of AMH and inhibitor B secreted by it also decreases. Inhibin B can regulate the secretion of pituitary FSH by negative feedback, and the decrease of inhibitor B causes the increase of FSH secretion in the luteal phase. The increased FSH in advance promotes the growth of new follicles and the secretion of E2, which ultimately shortens the menstrual cycle.
  • the menstrual cycle is a manifestation of ovarian reserve and fertility. As men get older, the menstrual cycle is shortened. The decrease of the menstrual cycle by 2-3 days is a sensitive indicator of aging of the reproductive system, suggesting that follicular growth starts early (FSH level increases), and the original follicular reserve decreases.
  • Endometriosis refers to a common female gynecological disease formed by active endometrial cells planted outside the endometrium. Endometrial cells should grow in the uterine cavity, but because the uterine cavity communicates with the pelvic cavity through the fallopian tube, the endometrial cells can enter the pelvic cavity and grow ectopically through the fallopian tube.
  • the main pathological changes of endometriosis are ectopic endometrial periodic bleeding and fibrosis of surrounding tissues, forming ectopic nodules. Dysmenorrhea, chronic pelvic pain, menstrual abnormalities and infertility are the main symptoms.
  • Fallopian tube infertility refers to the fact that the fallopian tube plays an important role in transporting sperm, picking up eggs, and transporting fertilized eggs to the uterine cavity. Impaired fallopian tubes or dysfunction becomes the main cause of female infertility. The cause of tubal obstruction or dysfunction is acute and chronic tubal inflammation.
  • unexplained infertility is defined as a couple who has normal test results such as ovulation test, fallopian tube patency and semen analysis test, but has a history of repeated conception failure.
  • Continuous variables In statistics, variables can be divided into continuous variables and categorical variables according to whether the values of the variables are continuous. Variables that can take values arbitrarily within a certain interval are called continuous variables, and their values are continuous. The two adjacent values can be infinitely divided, that is, they can take unlimited values. For example, the specifications of production parts, height, weight, and bust measured by human body are continuous variables, and their values can only be obtained by measurement or measurement. Conversely, those whose values can only be calculated in natural 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 units of measurement. The value of this variable is generally obtained by counting methods.
  • Categorical variables refer to variables such as geographic location and demographics. Their role is to group survey respondents. The description variable describes 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: unordered categorical variables and ordered categorical variables. Among them, unordered categorical variable (unordered categorical variable) refers to the degree and order difference between the classified categories or attributes. It can be divided into 1 two categories, such as gender (male, female), drug response (negative and positive), etc.; 2 multiple categories, such as blood type (O, A, B, AB), occupation (work, agriculture, Commerce, learning, soldiers, etc.).
  • the ordered categorical variable (ordinal categorical variable) has a degree of difference between the categories. For example, urine glucose test results are classified by -, ⁇ , +, ++, and +++; curative effects are classified by cure, marked effect, improvement, and invalidity.
  • ordered categorical variables they should first be grouped in rank order, count the number of observation units in each group, and compile a frequency table of ordered variables (each rank). The data obtained is called rank data.
  • the types of variables are not static, and various types of variables can be converted according to the needs of the research purpose.
  • the amount of hemoglobin (g/L) is originally a numerical variable. If it is divided into two categories according to normal and low hemoglobin, it can be analyzed according to two categories of data; if it is based on severe anemia, moderate anemia, mild anemia, normal, hemoglobin When the increase is divided into five levels, it can be analyzed according to the level data. Sometimes the classification data can also be quantified. If the patient's nausea response can be expressed as 0, 1, 2, 3, then it can be analyzed according to numerical variable data (quantitative data).
  • the invention relates to a system for evaluating the ovarian reserve function of a subject, which includes:
  • Data collection module which is used to obtain the data of the subject's age, anti-Mullerian hormone (AMH) level, follicle stimulating hormone (FSH) level, sinus follicle count (AFC) data;
  • AMH anti-Mullerian hormone
  • FSH follicle stimulating hormone
  • AFC sinus follicle count
  • a module for calculating the ovarian reserve function which is used to calculate the above information acquired in the data collection module to calculate the parameter (p) of the subject's ovarian low response probability.
  • the invention also relates to a system for evaluating the ovarian reserve function of a subject, which includes:
  • a data collection module which is used to obtain data of the subject's age, anti-Mullerian hormone (AMH) level, follicle stimulating hormone (FSH) level, and sinus follicle count (AFC) data;
  • AMH anti-Mullerian hormone
  • FSH follicle stimulating hormone
  • AFC sinus follicle count
  • a module for calculating the ovarian reserve function which is used to calculate the above information obtained in the data collection module to calculate the parameter (p) of the subject's ovarian reserve function;
  • a grouping module in which a default grouping parameter of the ovarian reserve function is pre-stored in the grouping module, and according to the grouping parameter, the calculated ovary low response probability p is grouped, thereby grouping the ovarian reserve level of the subject .
  • the subject’s age, subject’s anti-Mullerian hormone (AMH) level, subject’s follicle stimulating hormone (FSH) level, and subject’s sinus follicle count (AFC) are used.
  • the above four continuous variable data is converted into a binary variable to calculate and evaluate the subject's ovarian low response probability (p).
  • Anti-Mullerian hormone is a hormone secreted by the granulosa cells of the small ovarian follicles.
  • the baby girl in the fetal period begins to manufacture AMH from 9 months old.
  • the more small follicles in the ovary the more AMH The higher the concentration; conversely, when the follicles are gradually consumed with age and various factors, the AMH concentration will also decrease, and the closer to menopause, the AMH will gradually become zero.
  • Follicle Stimulating Hormone is a hormone secreted by basophilic cells in the anterior pituitary gland. The component is glycoprotein and its main role is to promote follicular maturation. FSH can promote the proliferation and differentiation of cells in the granular layer of follicles and the growth of the entire ovary. And its effect on testicular seminiferous tubules can promote sperm formation. FSH is secreted in the human body by pulse, and the female changes with the menstrual cycle.
  • FSH in serum is of great significance for understanding the pituitary endocrine function, indirectly understanding the functional status of the ovary, assessing ovarian reserve and ovarian reactivity, and formulating the dosage of ovulation-promoting medications for the diagnosis and treatment of infertility and endocrine diseases.
  • Sinus follicle count refers to the number of all visible follicles with a diameter of 2-8 mm in two ovaries of 2-4 days of menstruation. AFC can measure and count follicles by ultrasound.
  • the level of anti-Mullerian hormone refers to the concentration of anti-Mullerian hormone in the venous blood serum sample of 2-4 days of menstruation of female subjects
  • the level of follicle stimulating hormone refers to the female
  • the sinus follicle count refers to the vaginal B-ultrasound count. The number of all visible follicles at 8mm.
  • ROC receiver operating characteristic
  • AMH anti-Mullerian hormone
  • FSH follicle stimulating hormone
  • AFC sinus follicle count
  • the cut-off value for age is 35 years old
  • the cut-off value for anti-Mullerian hormone (AMH) level is 0.93 ng/ml
  • the cut-off value for follicle stimulating hormone (FSH) level is 9.1 IU/L
  • the cut-off value of sinus follicle count (AFC) is 8.
  • the age of the subject based on the subject in the existing database the level of the subject's anti-Mullerian hormone (AMH), the level of the subject's follicle stimulating hormone (FSH),
  • the formula for calculating the parameter (p) used to characterize the subject's ovarian low response probability is fitted by a binary variable into which the subject's sinus follicle count (AFC) data is converted.
  • AFC sinus follicle count
  • the existing database refers to a database composed of subjects who are available for 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 more the sample size of the database The larger the better, for example, 100 subjects, 200 subjects, 300 subjects, preferably 400 subjects or more, and more preferably 500 subjects or more may be used. In a specific embodiment, an existing database of 561 samples is used.
  • inclusion criteria were: women aged 20 to 45 years old, body mass index (BMI) ⁇ 30, six consecutive menstrual cycles of 25 to 45 days, and bilateral ovaries assessed by vaginal ultrasound The shape is normal, and the number of previous IVF/ICSI-ET cycles is ⁇ 2.
  • Exclusion criteria are: hydrosalpinx, unilateral ovarian AFC>20, polycystic ovary syndrome, other untreated metabolic or endocrine diseases, previous surgery for the ovary or uterine cavity, intrauterine abnormalities, within 3 months of pregnancy, Smoking, using oral contraceptives or other hormones within the previous two months, previously undergoing radiotherapy or chemotherapy, receiving PGD (preimplantation genetic diagnosis)/PGS (preimplantation genetic screening) treatment genes Couple diagnosed.
  • the module for calculating the ovarian reserve function uses the following formula to calculate the parameter (p) for characterizing the ovarian reserve function of the subject according to the data acquired in the data collection module:
  • p is the calculated parameter used to characterize the ovarian reserve function of the subject
  • i is any value selected from -1.786 to -0.499
  • a is any value selected from 0.063-1.342
  • b is Any value selected from -2.542 to -1.056
  • c is any value selected from 0.548 to 1.838
  • the grouping basis pre-stored in the grouping module of the present invention (that is, the default ovarian reserve function grouping parameter) is to use the existing data and the initial dose (predictive variable) of exogenous ovulation induction (Gn) according to the regression analysis results
  • the interaction with the predicted probability of low ovarian response is the basis for grouping the establishment of ovarian reserve function in predicting whether low response (outcome variable) actually occurs. Because from the perspective of statistical analysis alone, if the interaction items are not statistically significant, it indicates that there is no need for hierarchical analysis, that is, there is no need for further grouping.
  • the grouping basis pre-stored in the grouping module is: when the calculated parameter (p) for characterizing the low probability of ovarian response of the subject is ⁇ 5%, the grouping module determines that the subject belongs to a good ovarian reserve function; When 5% ⁇ the calculated parameter (p) ⁇ 20% used to characterize the subject’s ovarian low response probability, the grouping module determines that the subject belongs to the ovarian reserve function and begins to decline; when 20% ⁇ the calculated The parameter (p) used to characterize the ovarian low response probability of the subject is ⁇ 50%, and the grouping module determines that the subject belongs to a significant decrease in ovarian reserve function; when the calculated is used to characterize the ovary of the subject The parameter (p) of low response probability is ⁇ 50%, and the grouping module determines that the subject belongs to poor ovarian reserve function.
  • ovarian reactivity is related to the ovulation drug dose in addition to the patient's basic condition.
  • ovarian reactivity is related to the ovulation drug dose in addition to the patient's basic condition.
  • the expected low ovarian response probability according to the patient's basic condition, and then according to the expected low response probability and the ovulation drug dose Whether the interaction between them is meaningful, group the people, and group the groups with similar trends of low response.
  • the present invention Using the subjects in the existing database to establish a grouping basis for the ovarian reserve function based on whether the interaction between the Gn initial dose of these subjects and the predicted probability of low ovarian response is meaningful, in the present invention, first try Different grouping criteria, combined with the initial dose of Gn and the distribution of the predicted low response rate, tried a variety of grouping methods according to the group spacing from small to large, and found that the grouping method in the present invention is the best through multiple fittings Combination, with the expected low response probability 5-20% group as a reference, the interaction of each group is meaningful.
  • the population is further divided into four groups based on the predicted relationship between the probability of low ovarian response and the dose of the ovulation-promoting drugs, with a view to dividing the ovarian reactivity into groups.
  • Ovarian reserve function is a determinant of ovarian responsiveness, and the inventors of the present application infer the ovarian reserve function based on the expected probability of low ovarian response. The lower the probability of low ovarian response, the better the ovarian reserve function. Therefore, as the probability of low ovarian response increases, the ovarian reserve function is divided into good ovarian reserve function, ovarian reserve function begins to decline, ovarian reserve function declines significantly, and ovarian reserve function Poor or poor.
  • the sample size is first estimated, the total sample size should be >553 people, and all couples try to be pregnant for at least 12 months.
  • a total of 561 couples were included in the three reproductive medicine centers according to the following inclusion and exclusion criteria for the study. That is, 561 couples who met the following inclusion and exclusion criteria were selected for follow-up studies.
  • the inclusion criteria were: women aged 20 to 45 years old, body mass index (BMI) ⁇ 30, six consecutive menstrual cycles of 25 to 45 days, and bilateral ovarian morphology assessed by vaginal ultrasound examination, previous IVF/ICSI- The number of ET cycles ⁇ 2.
  • Exclusion criteria are: hydrosalpinx, unilateral ovarian AFC>20, polycystic ovary syndrome, other untreated metabolic or endocrine diseases, previous surgery for the ovary or uterine cavity, intrauterine abnormalities, within 3 months of pregnancy, Smoking, using oral contraceptives or other hormones within the previous two months, previously undergoing radiotherapy or chemotherapy, receiving PGD (preimplantation genetic diagnosis)/PGS (preimplantation genetic screening) treatment genes Couple diagnosed.
  • COS Controlled ovarian stimulation
  • Gn human recombinant FSH treatment was given on the 2nd or 3rd day of the menstrual cycle.
  • the initial dose is based on age, BMI (Body Mass Index, which is the number obtained by dividing the weight in kilograms by the square of the height in meters, and is currently a commonly used international standard to measure the degree of body fat and thinness and whether it is healthy), menstruation 2 -4 days FSH and AFC levels to choose.
  • BMI Body Mass Index
  • menstruation 2 -4 days FSH and AFC levels to choose.
  • the initial Gn dose was adjusted according to ultrasound observation and serum E 2 level.
  • GnRH antagonist treatment begins on the 5th-7th day of stimulation when the growing follicle is 10-12mm in diameter.
  • the follicle stimulating hormone (FSH) level at 2-4 days of menstruation refers to the level of follicle stimulating hormone obtained by detecting the venous blood serum samples of female subjects on the second to fourth days of menstruation.
  • the estrogen level (E 2 ) on 2-4 days of menstruation refers to the estrogen level measured on the venous blood serum samples of female subjects from the second day to the fourth day of menstruation
  • the AMH level on 2-4 days of menstruation is Refers to the anti-Mullerian hormone level and the LH level of 2-4 days of menstruation measured by the venous blood serum samples of female subjects from the second day to the fourth day of menstruation. Serum samples of venous blood from four-day female subjects were tested for luteinizing hormone levels.
  • Serum samples were extracted and frozen at -80°C. The sealed samples are stored at room temperature (15 to 30°C) for no more than 24 hours. Sample collection is done at the hospital where the patient is seen, and then immunoassays are performed to avoid multiple freeze-thaw cycles.
  • FSH, LH, E 2 and AMH measurements were all performed by Access UniCel DxI 800 chemiluminescence system (Beckman Coulter Inc). The quality control of FSH, LH and E 2 was provided by Bio-RAD Laboratories (Lyphochek Immunoassay Plus Control, trilevel) with product number 370 and lot number 40300. AMH quality control is provided by the Beckman AMH kit. For AMH, FSH and LH, the coefficient of variation of high, middle and low three levels is controlled to be less than 5%, and for estradiol, the coefficient of variation of high, middle and low three levels is controlled to be less than 10%.
  • the variables used for prediction include age, BMI, cause of infertility, AFC number of vaginal B-ultrasound counts for 2-4 days of menstruation, FSH levels for 2-4 days of menstruation, and AMH levels for 2-4 days of menstruation , LH level for 2-4 days of menstruation and E 2 level for 2-4 days of menstruation, the set outcome variable is hypoovarian response.
  • ovarian hyporesponsiveness is defined as less than 5 (ie 0-4) oocytes obtained on the day of egg harvest. The difference between the two groups is tested by t test, Wilcoxon test or chi-square test, and the appropriate method is adopted according to the difference of data.
  • a total of 561 couples participating in the GnRH antagonist cycle met the above inclusion and exclusion criteria, collected the data of the 561 couples, and input the collected data into SAS software and R software for analysis , And obtain the data in Tables 1 and 2 below.
  • Table 1 shows the basic and clinical characteristics of the aforementioned 561 couples related to the oocytes removed.
  • age, AMH, FSH, and AFC are converted into binary variables by using ROC curve tangent values.
  • the ROC curve is used to determine the cut-off point of age, AMH, FSH, and AFC, and the tangent point value of the cut-off point is determined.
  • the results are shown in Figures 1 to 4, respectively, according to the results of Figures 1 to 4.
  • the cut-off values for age, AMH, FSH, and AFC are 35 years old, 0.93, 9.1, and 8, respectively.
  • the ROC curve is drawn. As shown in FIG. 5, the area under the ROC curve is 0.883. According to the results in FIG. 5, it can be seen that the model performs well in evaluating ovarian reserve function.
  • the specific method of the above interaction is such that the predicted low response probability calculated using Equation 1 and the initial dose of Gn and the interaction between the two are taken as X, and whether the low response is taken as Y, multiple logistic regression is performed to achieve a fixed
  • the four main factors that affect ovarian hyporesponsiveness are the binary variables AFC, AMH, FSH, and age (ie, the predicted probability of low response) used in the present invention to see whether the change in dose affects ovarian hyporesponsiveness.

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Abstract

一种用于评估受试者卵巢储备功能的系统和方法。该用于评估受试者卵巢储备功能的系统包括：数据采集模块，其用于获取受试者的年龄、抗缪勒氏管激素（AMH）水平、卵泡刺激素（FSH）水平、窦卵泡计数（AFC）的数据；以及计算卵巢储备功能的模块，用于将数据采集模块中获取的信息进行计算从而计算出受试者的卵巢低反应的概率（p）。进一步，根据预测的卵巢低反应概率和促排卵药用药剂量之间交互作用的关系将人群分成四组，以期将卵巢反应性相近的分成一组。

Description

[根据细则37.2由ISA制定的发明名称]　评估受试者卵巢储备功能的系统和方法 技术领域
本发明涉及一种用于评估受试者卵巢储备功能的系统，利用该系统可以评估受试者其自身的卵巢储备功能的情况，以评估其生育潜能，以及评估受试者在经过了相应的治疗之后生育潜能是否改善。
背景技术
卵巢皮质内含有的原始卵泡数，称为卵巢储备。它反映卵巢提供健康可成功受孕卵子的能力，是女性卵巢功能的最重要的评价指标。一般来说，原始卵泡数量越多质量也越好，受孕几率也越高。
卵巢储备功能评估可以帮助育龄妇女了解自己的生育力现状，以便合理安排自己的生育计划。对于有不孕病史的妇女来说它可以用来预测育龄妇女的卵巢反应性，为不孕的临床诊断和治疗计划的制定提供参考。目前国际国内诊断卵巢储备功能下降的主要依据即博洛尼亚标准关于卵巢低反应的预测。因此评价卵巢储备功能的指标实际上也就是预测卵巢反应性的指标。
年龄因素是评价卵巢储备的重要因素，一项关于年龄与IVF成功率的研究结果显示：30岁以下妇女IVF成功率约26％，而当年龄在37岁及以上时IVF成功率仅为9％。
内分泌因子检查包括基础FSH水平检测、AMH水平检测和基础inhibin B水平检测等。其中，(1)基础FSH检测：FSH即卵泡刺激素，由垂体分泌，主要作用是促进卵泡成熟。基础FSH也称背景FSH，月经周期开始的0-3天，卵泡生长初始阶段，卵泡内的颗粒细胞尚未开始大量分泌雌激素，垂体和卵巢的反馈调节处于初始阶段，这一阶段测量的FSH浓度，称为基础FSH。基础FSH直接反映了卵巢的分泌功能，是临床评价卵巢储备功能的最常用的指标。具有正常生育能力的妇女基础FSH一般小于10IU/L，基础FSH水平过高，反映卵巢分泌功能不良，FSH>12IU/L提示卵巢功能减退，FSH>40IU/L提示卵巢功能衰竭，不同实验室该数值会有所不同。青春期开始后，基础FSH会随年龄逐步升高。研究显示绝经前十年基础FSH水平即开始升高，而此时正是不孕率开始显著上升的年龄。多项研究结果显示，基础E2水平随卵巢功能减退而下降，基础LH水平随年龄而增加，但基础FSH的变化更早且更显著。另外，FSH:LH比率也可以预测卵巢储备功能。(2)AMH(Anti-Mullerian Hormone)水平检测：AMH是转化生长因子家族成员之一， 最早由初级卵泡的颗粒细胞分泌，窦前卵泡及小卵泡阶段(卵泡直径小于4mm)分泌量达到高峰，之后分泌量逐渐降低，当卵泡直径大于8mm时停止分泌，因此AMH水平可以反映月经周期被募集的卵泡数的多少，而被募集的卵泡数减少提示卵巢储备降低，故AMH水平可以直接反映卵巢储备的能力，是目前国际公认的最好的预测卵巢反应性的血清学指标。一般来说妇女AMH水平越高被募集的卵泡数越多，对卵巢刺激的反应性越好，IVF成功率也越高。但血清AMH水平过高可能是多囊卵巢综合征等其他疾病，应结合经阴道超声检查予以排除。(3)Inhibin B水平检测，Inhibin B是由卵巢皮质中小卵泡颗粒细胞分泌的，其分泌量随GnRH及FSH刺激而增加，在月经周期中分泌量变化很大，往往不能忠实地反映卵巢储备能力，因此Inhibin B水平检测并不是目前卵巢功能评估的常规方法。
卵巢超声检查包括检测窦卵泡数、卵巢体积和卵巢基质血流三方面。窦卵泡数是指早卵泡期通过经阴道超声学探查的方法计数双侧卵巢窦卵泡的总数，是卵巢储备能力的直接体现。窦卵泡直径在2-10mm或3-8mm，窦卵泡数减少提示对卵巢刺激的反应性差，妊娠率下降，研究表明，用窦卵泡数预测IVF成功率比基础FSH检测更有效。卵巢基质血流与卵巢体积现在不是预测卵巢反应性以及评估卵巢储备功能的常用方法。
在生殖医学领域，评估卵巢储备的目的是用来预测卵巢反应性。目前，AMH水平检测和窦卵泡计数(AFC)是国际公认的最好的两个预测卵巢反应性的指标。基础FSH水平检测是目前国际上应用最广泛的卵巢储备功能评估指标。年龄因素也是评价卵巢储备的重要因素。
发明内容
基础水平FSH筛查是最常用的预测卵巢储备的方法，当基础FSH水平临界值设定较高时，诊断卵巢功能低下的特异性增加，但敏感性下降。经阴道超声计数窦卵泡数具有快捷、经济、准确的特点，但与医者的经验和设备有关，存在人为因素。AMH由早卵泡期颗粒细胞分泌，能够最早、最直接反映颗粒细胞衰老程度的指标，是一个有潜力的研究方向，但一些疾病情况可能对AMH的结果造成干扰，其单独应用仍不足以准确评估卵巢储备功能。总之，任何一项卵巢储备指标都不能单独用于诊断卵巢储备功能下降，需结合其他方法进一步明确。
如上所述，判断受试者的卵巢储备功能对于临床医生等来说是一个非常重要的工作。通过评估卵巢储备功能，可以预测患者的卵巢反应性，这一促排卵治疗过程中重要的临床结局。以往临床医生常结合自己的经验，根据年龄、体重指数、内分泌因子水平和窦卵泡数等进行判断，存在一定的主观性。我们的系统对于将要接受治疗的受试者，可以 准确地评估出其卵巢储备功能的好坏，以便在随后的治疗中辅助临床医生制定出更为有针对性的治疗方案。
综上所述，已知卵巢反应性的决定因素是卵巢储备功能，但本申请的发明人反向思维，用预期的卵巢反应性来评估卵巢储备功能。另外，针对接受不孕治疗的患者而言，从临床角度，卵巢反应性除了与患者的基本情况(年龄、基础FSH水平、AMH水平以及窦卵泡数等)有关还与促排卵药剂量有关，本申请的发明人首先根据患者基本情况得到预期的卵巢低反应概率，再根据预期低反应概率与促排卵药剂量之间的交互作用是否有意义，对人群进行分组，把低反应发生趋势相近的分成一组。从统计学角度讲，是否需要分层分析(即本申请中的分组)也是按照交互作用是否有意义来进行的。
具体来说，本发明涉及如下内容：
1.一种用于评估受试者卵巢储备功能的系统，其包括：
数据采集模块，其用于获取受试者的年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平、窦卵泡计数(AFC)的数据；以及
计算卵巢储备功能的模块，其用于将数据采集模块中的获取的上述信息进行计算，从而计算出受试者的卵巢低反应概率(p)。
在本发明中，已知卵巢反应性取决于卵巢储备功能，根据卵巢低反应概率对卵巢储备功能进行分组。
2.根据项1所述的系统，其还包括：
分组模块，在所述分组模块中预存有默认的卵巢储备功能分组参数，并且依据该分组参数，对所述计算得到的卵巢低反应概率p进行分组，从而对受试者的卵巢储备水平进行分组。
3.根据项1或2所述的系统，其中，
在计算卵巢储备功能的模块中，利用将受试者年龄、受试者抗缪勒氏管激素(AMH)水平、受试者卵泡刺激素(FSH)水平、受试者窦卵泡计数(AFC)的数据转换成的二分类变量来计算受试者的卵巢低反应的概率(p)。
4.根据项3所述的系统，其中，
在计算卵巢储备功能的模块中，利用受试者工作特征(ROC)曲线来检测年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平、窦卵泡计数(AFC)的分界点，并根据该分界点的切点值来将年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平、窦卵泡计数(AFC)转换成二分类变量，从而利用所述二分类变量来计算受试者的卵巢低反应概率(p)。
5.根据项4所述的系统，其中，
所述抗缪勒氏管激素(AMH)水平是指女性受试者月经2-4天的静脉血中的抗缪勒氏管激素浓度，所述卵泡刺激素(FSH)水平是指女性受试者月经2-4天的静脉血中的卵泡刺激素浓度，所述窦卵泡计数(AFC)是指阴道B超计数女性受试者月经2-4天时的两个卵巢中直径为2-8mm的所有可见卵泡的个数。
6.根据项4或5所述的系统，其中，
所述年龄的切点值为35岁，所述抗缪勒氏管激素(AMH)水平的切点值为0.93ng/ml，所述卵泡刺激素(FSH)水平的切点值为9.1IU/L，以及所述窦卵泡计数(AFC)的切点值为8。
7.根据项1～6中任一项所述的系统，其中，
在计算卵巢储备功能的模块中，预先存储有基于现有数据库中受试者的受试者年龄、受试者抗缪勒氏管激素(AMH)水平、受试者卵泡刺激素(FSH)水平、受试者窦卵泡计数(AFC)的数据转换成的二分类变量拟合而成的用于计算受试者的卵巢低反应概率(p)的公式。
8.根据项7所述的系统，其中，
所述公式为如下公式一：
Figure PCTCN2018124766-appb-000001
其中，p为计算出的用于表征所述受试者的卵巢储备功能的参数，i为选自-1.786～-0.499中的任意数值，a为选自0.063-1.342中的任意数值，b为选自-2.542～-1.056中的任意数值，c为选自0.548～1.838中的任意数值，d为选自-2.133～-0.51中的任意数值，其中优选i＝-1.143，优选a＝0.703，优选b＝-1.799，优选c＝1.193，优选d＝-1.322。
9.根据项2～8中任一项所述的系统，其中，
在所述分组模块中预存的分组参数为：
当计算出的用于预测受试者的卵巢低反应概率(p)<5％时，分组模块确定该受试者属于卵巢储备功能良好；
当5％≤计算出的用于预测受试者的卵巢低反应概率(p)<20％，分组模块确定该受试者属于卵巢储备功能开始下降；
当20％≤计算出的用于预测受试者的卵巢低反应概率(p)<50％，分组模块确定该受试者属于卵巢储备功能下降明显；
当计算出的用于预测受试者的卵巢低反应概率(p)≥50％，分组模块确定该受试者属于卵巢储备功能差。
10.一种用于评估受试者卵巢储备功能的方法，其包括：
采集数据步骤，在该步骤中，获取受试者的年龄、抗缪勒氏管激素(AMH)水平、卵 泡刺激素(FSH)水平、窦卵泡计数(AFC)的数据；以及
计算卵巢储备功能的步骤，在该步骤中，利用在上述采集数据步骤中获取的年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平、窦卵泡计数(AFC)数据进行计算，从而计算出受试者的卵巢低反应概率的参数(p)。
11.根据项10所述的方法，其中，
分组步骤，在所述分组步骤中依据默认的卵巢储备功能分组参数对所述计算得到的卵巢低反应概率进行分组，从而对受试者的卵巢储备水平进行分组。
12.根据项10或11所述的方法，其中，
在计算卵巢储备功能的步骤中，将受试者年龄、受试者抗缪勒氏管激素(AMH)水平、受试者卵泡刺激素(FSH)水平、受试者窦卵泡计数(AFC)的数据转换成二分类变量，并利用所述转换成的二分类变量来计算受试者的卵巢低反应概率(p)。
13.根据项12所述的方法，其中，
在计算卵巢低反应概率的步骤中，利用受试者工作特征(ROC)曲线来检测年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平、窦卵泡计数(AFC)的分界点(cut-off value)，并根据该分界点的切点值来将年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平、窦卵泡计数(AFC)转换成二分类变量，从而利用所述二分类变量模型来计算受试者的卵巢低反应概率(p)。
14.根据项13所述的方法，其中，
所述抗缪勒氏管激素(AMH)水平是指女性受试者月经2-4天的静脉血血清中的抗缪勒氏管激素浓度，所述卵泡刺激素(FSH)水平是指女性受试者月经2-4天的静脉血血清中的卵泡刺激素浓度，所述窦卵泡计数(AFC)是指阴道B超计数女性受试者月经2-4天时的两个卵巢中直径为2-8mm的所有可见卵泡的个数。
15.根据项13或14所述的方法，其中，
所述年龄的切点值为35岁，所述抗缪勒氏管激素(AMH)水平的切点值为0.93ng/ml，所述卵泡刺激素(FSH)水平的切点值为9.1IU/L，以及所述窦卵泡计数(AFC)的切点值为8。
16.根据项10～15中任一项所述的方法，其中，
在计算卵巢储备功能的步骤中，利用预先的基于现有数据库中受试者的受试者年龄、受试者抗缪勒氏管激素(AMH)水平、受试者卵泡刺激素(FSH)水平、受试者窦卵泡计数(AFC)的数据转换成的二分类变量拟合而成的用于计算用于预测所述受试者的卵巢低反应概率(p)的公式来进行计算。
17.根据项10～16中任一项所述的方法，其中，
在计算卵巢储备功能的步骤中，利用如下公式一，根据数据采集步骤中获取的并已 经转化为二分类变量的受试者的年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平、窦卵泡计数(AFC)的数据来计算受试者的卵巢低反应概率(p)：
Figure PCTCN2018124766-appb-000002
其中，p为计算出的用于表征所述受试者的卵巢储备功能的参数，i为选自-1.786～-0.499中的任意数值，a为选自0.063-1.342中的任意数值，b为选自-2.542～-1.056中的任意数值，c为选自0.548～1.838中的任意数值，d为选自-2.133～-0.51中的任意数值，其中优选i＝-1.143，优选a＝0.703，优选b＝-1.799，优选c＝1.193，优选d＝-1.322。
18.根据项11～17中任一项所述的方法，其中，
在所述分组模块中预存的分组依据为：
当计算出的用于预测受试者的卵巢低反应概率(p)<5％时，分组模块确定该受试者属于卵巢储备功能良好；
当5％≤计算出的用于预测受试者的卵巢低反应概率(p)<20％，分组模块确定该受试者属于卵巢储备功能开始下降；
当20％≤计算出的用于预测受试者的卵巢低反应概率(p)<50％，分组模块确定该受试者属于卵巢储备功能下降明显；
当计算出的用于预测受试者的卵巢低反应概率(p)≥50％，分组模块确定该受试者属于卵巢储备功能差。
发明效果
卵巢储备功能评估可以帮助育龄妇女了解自己的生育力现状，以便合理安排自己的生育计划。对于有不孕病史的妇女来说它可以用来预测育龄妇女的卵巢反应性，为不孕的临床诊断和治疗计划的制定提供参考。目前国际国内诊断卵巢储备功能下降的主要依据即博洛尼亚标准关于卵巢低反应的诊断。因此评价卵巢储备功能的指标实际上也就是预测卵巢反应性的指标。
具体来说，在本发明中首先可以利用本发明的用于评估受试者卵巢储备功能的系统来计算受试者的卵巢低反应概率，从而依据该卵巢低反应的概率对受试者的卵巢储备水平进行分组。利用本发明的系统，可以计算出用于预测所述受试者的卵巢低反应概率的参数(p)，并依据系统预存的默认的卵巢储备功能分组参数，对该受试者的卵巢储备功能进行分组，从而判断其卵巢储备功能所处的水平，从而可以对卵巢储备水平进行评估。
本申请的发明人意识到卵巢反应性与卵巢储备密切相关，卵巢储备功能越差发生卵巢低反应的风险也越高，临床常用是否卵巢低反应高风险来评估卵巢储备功能下降。卵 巢储备由高到低的顺序即卵巢低反应概率由低到高的顺序。卵巢反应性还与用药剂量密切相关。根据预测的卵巢低反应概率(预测变量)与外源性促排卵药(Gn)起始剂量(预测变量)的交互作用对卵巢低反应这一结局变量是否有意义，从而建立分组标准，并依据该分组标准，对受试者的卵巢储备水平进行评估。
利用本发明的系统和方法，针对将要接收治疗的受试者，能够准确地评估出其卵巢储备功能的好坏，可以在随后的治疗中辅助临床医生制定出更为有针对性的治疗方案。针对普通育龄妇女，尤其是想要生育，但是不确定何时生育的育龄妇女，可以帮助其评估自己的卵巢储备功能，制定合理的生育计划。
本发明的发明人首次应用月经2-4天AMH水平、年龄、月经2-4天FSH水平及月经2-4天阴道B超计数的AFC四个指标的对卵巢储备功能进行评估。
利用本发明的系统和方法可以快速并准确地评估受试者的卵巢储备水平，解决了现有技术中主要根据医生经验和一些简单的根据卵巢储备指标切点值来进行评估卵巢储备功能所带来的可重复性差，标准不统一的问题。
附图说明
通过阅读下文优选的具体实施方式中的详细描述，本申请各种其他的优点和益处对于本领域普通技术人员将变得清楚明了。说明书附图仅用于示出优选实施方式的目的，而并不认为是对本申请的限制。显而易见地，下面描述的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。而且在整个附图中，用相同的附图标记表示相同的部件。
图1针对年龄变量的ROC曲线分析。
图2针对月经2-4天AMH水平变量的ROC曲线分析。
图3针对月经2-4天血清FSH水平变量的ROC曲线分析。
图4针对月经2-4天阴道B超AFC水平变量的ROC曲线分析。
图5评估卵巢储备功能的模型的ROC曲线分析。
图6回归分析，展示Gn起始剂量与卵巢低反应的预测概率之间的交互对卵巢低反应发生的影响。
具体实施方式
下面将参照附图更详细地描述本发明的具体实施例。虽然附图中显示了本发明的具体实施例，然而应当理解，可以以各种形式实现本发明而不应被这里阐述的实施例所限制。相反，提供这些实施例是为了能够更透彻地理解本发明，并且能够将本发明的范围 完整的传达给本领域的技术人员。
需要说明的是，在说明书及权利要求当中使用了某些词汇来指称特定组件。本领域技术人员应可以理解，技术人员可能会用不同名词来称呼同一个组件。本说明书及权利要求并不以名词的差异来作为区分组件的方式，而是以组件在功能上的差异来作为区分的准则。如在通篇说明书及权利要求当中所提及的“包含”或“包括”为一开放式用语，故应解释成“包含但不限定于”。说明书后续描述为实施本发明的较佳实施方式，然所述描述乃以说明书的一般原则为目的，并非用以限定本发明的范围。本发明的保护范围当视所附权利要求所界定者为准。
在本申请涉及卵巢储备是指：卵巢皮质内含有的原始卵泡数，称为卵巢储备。它反映卵巢提供健康可成功受孕卵子的能力，是女性卵巢功能的最重要的评价指标。一般来说，原始卵泡数量越多质量也越好，受孕几率也越高。
但是原始卵泡数没办法进行无创的评估，只能通过每个月经周期动员的卵泡数进行评估，IVF-ET周期动员的卵泡过少(卵巢低反应)，提示卵巢储备功能下降。
通常认为年龄因素是评价卵巢储备的最重要因素，一项关于年龄与IVF成功率的研究结果显示：30岁以下妇女IVF成功率约26％，而当年龄在37岁及以上时IVF成功率仅为9％。
卵巢储备能力随年龄增长而下降的机制如下。(一)卵泡数量减少，原始卵泡出现于胚胎性别分化以后，此时卵泡数最多，青春期后卵泡开始发育成熟，随着排卵的完成大量被募集而未排出的卵泡萎缩消失形成黄体。卵泡数随着年龄增加而不断减少：人类中20周龄胚胎最多，约为600万个卵泡，新生儿期减少至70-200万，青春期约4万，绝经期开始时仅余千余，直至完全耗竭。(二)卵子质量下降，胚胎质量主要由卵子质量决定，大龄可致卵细胞非整倍体几率增加、线粒体功能异常风险增加、卵子极性消失和卵细胞表观遗传学改变。(三)内分泌因素，下丘脑-垂体-卵巢轴调节妇女月经周期和排卵，该轴内分泌水平异常会导致不孕。AMH和inhibin B由小卵泡分泌，是卵巢储备能力的直接体现。随着年龄的增长卵巢储备降低，可募集的卵泡数减少，因此其分泌的AMH和inhibin B浓度也随之下降。Inhibin B可负反馈调节垂体FSH分泌，inhibin B水平下降导致黄体期FSH分泌增加。提前增加的FSH促进新卵泡的生长和E2分泌，最终缩短了月经周期。血清FSH水平增加，inhibin B水平下降，卵泡对FSH敏感度下降，提示可被募集的窦状卵泡数减少。月经周期是卵巢储备和生育力的体现，大龄致月经周期缩短，月经周期减少2-3天是生殖系统衰老的敏感指征，提示卵泡生长提前启动(FSH水平升高)，原始卵泡储备下降。
在本申请中涉及的几种不孕的因素定义如下。子宫内膜异位症是指有活性的内膜细 胞种植在子宫内膜以外的位置而形成的一种女性常见妇科疾病。内膜细胞本该生长在子宫腔内，但由于子宫腔通过输卵管与盆腔相通，因此使得内膜细胞可经由输卵管进入盆腔异位生长。子宫内膜异位症的主要病理变化为异位内膜周期性出血及其周围组织纤维化，形成异位结节，痛经、慢性盆腔痛、月经异常和不孕是其主要症状。病变可以波及所有的盆腔组织和器官，以卵巢、子宫直肠陷凹、宫骶韧带等部位最常见，也可发生于腹腔、胸腔、四肢等处。输卵管性不孕是指，由于输卵管具有运送精子、拾取卵子及把受精卵运送到子宫腔的重要作用，输卵管不通或功能障碍成为女性不孕症的主要原因。造成输卵管不通或功能障碍的原因是急、慢性输卵管炎症。此外，不明原因不孕症被定义为经排卵测试，输卵管通畅和精液分析的测试等标准检测结果显示正常的，但具有反复受孕失败的历史的夫妇。
连续变量：在统计学中，变量按变量值是否连续可分为连续变量与分类变量两种。在一定区间内可以任意取值的变量叫连续变量，其数值是连续不断的，相邻两个数值可作无限分割，即可取无限个数值。例如，生产零件的规格尺寸，人体测量的身高、体重、胸围等为连续变量，其数值只能用测量或计量的方法取得。反之，其数值只能用自然数或整数单位计算的则为离散变量。例如，企业个数，职工人数，设备台数等，只能按计量单位数计数，这种变量的数值一般用计数方法取得。
分类变量是指地理位置、人口统计等方面的变量，其作用是将调查响应者分群。描述变量是描述某一个客户群与其他客户群的区别。大部分分类变量也就是描述变量。分类变量可以分为无序分类变量和有序分类变量两大类。其中，无序分类变量(unordered categorical variable)是指所分类别或属性之间无程度和顺序的差别。其又可分为①二项分类，如性别(男、女)，药物反应(阴性和阳性)等；②多项分类，如血型(O、A、B、AB)，职业(工、农、商、学、兵)等。而有序分类变量(ordinal categorical variable)各类别之间有程度的差别。如尿糖化验结果按－、±、+、++、+++分类；疗效按治愈、显效、好转、无效分类。对于有序分类变量，应先按等级顺序分组，清点各组的观察单位个数，编制有序变量(各等级)的频数表，所得资料称为等级资料。
变量类型不是一成不变的，根据研究目的的需要，各类变量之间可以进行转化。例如血红蛋白量(g/L)原属数值变量，若按血红蛋白正常与偏低分为两类时，可按二项分类资料分析；若按重度贫血、中度贫血、轻度贫血、正常、血红蛋白增高分为五个等级时，可按等级资料分析。有时亦可将分类资料数量化，如可将病人的恶心反应以0、1、2、3表示，则可按数值变量资料(定量资料)分析。
本发明涉及一种用于评估受试者卵巢储备功能的系统，其包括：
数据采集模块，其用于获取受试者的年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激 素(FSH)水平、窦卵泡计数(AFC)的数据；
计算卵巢储备功能的模块，其用于将数据采集模块中的获取的上述信息进行计算，从而计算出受试者的卵巢低反应概率的参数(p)。
本发明还涉及一种用于评估受试者卵巢储备功能的系统，其包括：
数据采集模块，其用于获取受试者的年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平、窦卵泡计数(AFC)的数据；
计算卵巢储备功能的模块，其用于将数据采集模块中的获取的上述信息进行计算，从而计算出受试者的卵巢储备功能的参数(p)；以及
分组模块，在所述分组模块中预存有默认的卵巢储备功能分组参数，并且依据该分组参数，对所述计算得到的卵巢低反应概率p进行分组，从而对受试者的卵巢储备水平进行分组。
在计算卵巢储备功能的模块中，利用将受试者年龄、受试者抗缪勒氏管激素(AMH)水平、受试者卵泡刺激素(FSH)水平、受试者窦卵泡计数(AFC)上述四个连续变量数据转换成的二分类变量来计算并评估受试者的卵巢低反应概率(p)。
抗缪勒氏管激素(AMH)是一种由卵巢小卵泡的颗粒层细胞所分泌的荷尔蒙，胎儿时期的女宝宝从9个月大便开始制造AMH，卵巢内的小卵泡数量越多，AMH的浓度便越高；反之，当卵泡随着年龄及各种因素逐渐消耗，AMH浓度也会随之降低，越接近更年期，AMH便渐趋于0。
卵泡刺激素(FSH)是垂体前叶嗜碱性细胞分泌的一种激素，成分为糖蛋白，主要作用为促进卵泡成熟。FSH可促进卵泡颗粒层细胞增生分化，并促进整个卵巢长大。而其作用于睾丸曲细精管则可促进精子形成。FSH在人体内呈脉冲式分泌，女性随月经周期而改变。测定血清中FSH对了解垂体内分泌功能，间接了解卵巢的功能状态、评估卵巢储备及卵巢反应性、制定促排卵用药剂量等不孕和内分泌疾病的诊断治疗都有重要的意义。
窦卵泡计数(AFC)是指月经2-4天两个卵巢中直径为2-8mm的所有可见卵泡的个数。AFC可以通过超声波对卵泡测量和计数。
在本发明中抗缪勒氏管激素(AMH)水平是指女性受试者月经2-4天的静脉血血清样本中的抗缪勒氏管激素浓度，卵泡刺激素(FSH)水平是指女性受试者月经2-4天的静脉血血清样本中的卵泡刺激素浓度，窦卵泡计数(AFC)是指阴道B超计数女性受试者月经2-4天时的两个卵巢中直径为2-8mm的所有可见卵泡的个数。
在计算卵巢储备功能的模块中，首先利用受试者工作特征(ROC)曲线来检测年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平、窦卵泡计数(AFC)的分界点，将连续变量变成二分类变量，带入分类变量模型，计算得到卵巢低反应概率，并根据分组原则 对卵巢储备功能进行分组，得到受试者的卵巢储备功能情况。
虽然在现有技术中，有研究者尝试采用某些上述参数来进行分析，但是通过利用受试者工作特征(ROC)曲线来检测年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平、窦卵泡计数(AFC)的分界点，并根据该分界点的切点值来将年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平、窦卵泡计数(AFC)转换成二分类变量，从而利用全部的二分类变量来带入上述公式来计算受试者发生卵巢低反应的概率是本发明的发明人意想不到的发现，通过将上述4个变量变换成二分类变量，利用这样的二分类变量来进行数据分析可以更为准确地预测受试者的卵巢储备功能，且模型稳定性更好。通过准确地评估受试者的卵巢储备功能，能够帮助临床医生制定更为有效的方案，以及更为准确地评估受试者在接收了一段时间的治疗之后，该治疗方案是否能够有效地改善了受试者的卵巢储备功能。
在本发明中，年龄的切点值为35岁，抗缪勒氏管激素(AMH)水平的切点值为0.93ng/ml，卵泡刺激素(FSH)水平的切点值为9.1IU/L，以及窦卵泡计数(AFC)的切点值为8。
在计算卵巢储备功能的模块中预先存储有基于现有数据库中受试者的受试者年龄、受试者抗缪勒氏管激素(AMH)水平、受试者卵泡刺激素(FSH)水平、受试者窦卵泡计数(AFC)的数据转换成的二分类变量拟合而成的用于计算用于表征所述受试者的卵巢低反应概率的参数(p)的公式。并根据分组标准对受试者卵巢储备功能情况进行分组。
在本发明中，现有数据库是指能够获取的正在接受治疗或以前接受治疗满足下述纳入和排除标准的受试者组成的数据库，对于数据库的样本量没有任何约定，当然数据库的样本量越大越好，例如可以是利用100个受试者，200个受试者，300个受试者，优选为400个受试者以上，更优选为500个受试者以上。在一个具体的实施例中，采用的561个样本组成的现有数据库。
上述纳入和排除标准分别为，纳入标准为：年龄在20ゲ45岁之间的女性，体重指数(BMI)≤30，连续六个月经周期为25至45天，通过阴道超声检查评估双侧卵巢形态正常，既往IVF/ICSI-ET周期数≤2。排除标准为：输卵管积水，单侧卵巢AFC>20，多囊卵巢综合征，其他未经治疗的代谢或内分泌疾病，针对卵巢或宫腔的既往手术，宫内异常，妊娠3个月以内，吸烟，在之前的两个月内使用口服避孕药或其它激素，之前经历过放疗或化疗，接受PGD(植入前胚胎遗传学诊断)/PGS(胚胎植入前遗传学筛查)治疗的基因诊断的夫妇。
在选择数据库的样本时，能够纳入数据库使用的受试者需要同时满足上述纳入和排除标准。
计算卵巢储备功能的模块利用如下公式来根据数据采集模块中获取的数据来计算用于表征所述受试者的卵巢储备功能的参数(p)：
Figure PCTCN2018124766-appb-000003
其中，p为计算出的用于表征所述受试者的卵巢储备功能的参数，i为选自-1.786～-0.499中的任意数值，a为选自0.063-1.342中的任意数值，b为选自-2.542～-1.056中的任意数值，c为选自0.548～1.838中的任意数值，d为选自-2.133～-0.51中的任意数值，其中最优选i＝-1.143，最优选a＝0.703，最优选b＝-1.799，最优选c＝1.193，最优选d＝-1.322。
进一步，在本发明的分组模块中预存的分组依据(即默认的卵巢储备功能分组参数)为利用现有数据，根据回归分析结果即外源性促排卵药(Gn)起始剂量(预测变量)与预测的卵巢低反应概率(预测变量)之间的交互作用在预测是否实际发生低反应(结局变量)是否有意义而对卵巢储备功能建立的分组依据。因为单从统计分析角度看，如果交互项没有统计学意义，提示没必要分层分析，即没必要进一步分组。在分组模块中预存的分组依据为：当计算出的用于表征所述受试者的卵巢低反应概率的参数(p)<5％时，分组模块确定该受试者属于卵巢储备功能良好；当5％≤计算出的用于表征所述受试者的卵巢低反应概率的参数(p)<20％，分组模块确定该受试者属于卵巢储备功能开始下降；当20％≤计算出的用于表征所述受试者的卵巢低反应概率的参数(p)<50％，分组模块确定该受试者属于卵巢储备功能下降明显；当计算出的用于表征所述受试者的卵巢低反应概率的参数(p)≥50％，分组模块确定该受试者属于卵巢储备功能差。
也就是说，在本发明中，已知卵巢反应性的决定因素是卵巢储备功能，本申请的发明人反向思维，用预期的卵巢反应性来评估卵巢储备功能。另外，从临床角度，卵巢反应性除了与患者的基本情况有关还与促排卵药剂量有关，我们首先根据患者基本情况得到预期的卵巢低反应概率，再根据预期低反应概率与促排卵药剂量之间的交互作用是否有意义，对人群进行分组，把低反应发生趋势相近的分成一组。利用现有数据库中的受试者根据这些受试者的Gn起始剂量与预测的卵巢低反应概率之间的交互作用是否有意义而对卵巢储备功能建立分组依据，在本发明中，首先尝试不同的分组标准，结合Gn起始剂量与预测的低反应发生率分布情况，按照分组间距从小到大的方式试了多种分组方式，通过多次拟合发现本发明中的分组方式为最佳组合，以预期低反应概率5-20％组为参照，各组交互作用是有意义的。
在本发明中，进一步依据预测的卵巢低反应概率和促排卵药用药剂量之间交互作用的关系将人群分成四组，以期将卵巢反应性相近的分成一组。卵巢储备功能是 卵巢反应性的决定因素，本申请的发明人根据预期的卵巢低反应概率反推卵巢储备功能。卵巢低反应概率越低，卵巢储备功能越好，因此，随着卵巢低反应概率的增加，卵巢储备功能依次分成卵巢储备功能良好、卵巢储备功能开始下降，卵巢储备功能下降明显，以及卵巢储备功能差或较差。
实施例
在实施例中，首先进行样本量估算，总样本量应>553人，所有夫妇均努力尝试怀孕至少12个月。
按照下述纳入和排除标准来三个生殖医学中心共纳入561对夫妇进行该研究，即选择了561对满足下述纳入和排除标准的夫妇用于后续的研究。
纳入标准为：年龄在20ゲ45岁之间的女性，体重指数(BMI)≤30，连续六个月经周期为25至45天，通过阴道超声检查评估双侧卵巢形态正常，既往IVF/ICSI-ET周期数≤2。
排除标准为：输卵管积水，单侧卵巢AFC>20，多囊卵巢综合征，其他未经治疗的代谢或内分泌疾病，针对卵巢或宫腔的既往手术，宫内异常，妊娠3个月以内，吸烟，在之前的两个月内使用口服避孕药或其它激素，之前经历过放疗或化疗，接受PGD(植入前胚胎遗传学诊断)/PGS(胚胎植入前遗传学筛查)治疗的基因诊断的夫妇。
控制性卵巢刺激(COS)治疗
在月经周期的第2天或第3天开始给予Gn(即人重组FSH)治疗。起始剂量根据年龄、BMI(即身体质量指数，是用体重公斤数除以身高米数平方得出的数字，是目前国际上常用的衡量人体胖瘦程度以及是否健康的一个标准)、月经2-4天FSH和AFC水平来选择。在促排卵期间，Gn起始剂量根据超声观察和血清E 2水平来调整。GnRH拮抗剂治疗开始于刺激第5-7天，生长的卵泡直径为10-12mm时。当通过超声可见至少2个优势卵泡(直径≥18mm)时，给予5000-10000IU的hCG以引发最终的卵母细胞成熟。hCG给药36小时后进行取卵。移植1-3个胚胎或进行胚胎冷冻保存。然后提供了黄体期黄体酮支持物。
窦卵泡计数测量和内分泌测定
在COS周期期间，所有受试者在月经周期第2-4天进行经阴道超声扫描，通过测量在两个卵巢中直径为2-8mm的所有可见卵泡来进行窦卵泡计数(AFC)。在同一天，为了进行月经2-4天血清AMH，月经2-4天血清FSH，月经2-4天血清LH(促黄体生成素)和 月经2-4天血清E 2(雌二醇)检测，而进行静脉血液采样。
在本实施例中，月经2-4天时的卵泡刺激素(FSH)水平是指对处于经期第二天～第四天的女性受试者的静脉血血清样本进行检测得到的卵泡刺激素水平。月经2-4天雌激素水平(E 2)是指对处于经期第二天～第四天的女性受试者的静脉血血清样本进行检测得到的雌激素水平，月经2-4天AMH水平是指对处于经期第二天～第四天的女性受试者的静脉血血清样本进行检测得到的抗缪勒氏管激素水平、月经2-4天LH水平是指对处于经期第二天～第四天的女性受试者的静脉血血清样本进行检测得到的促黄体生成素水平。
提取血清样品，并在-80℃冷冻。密封的样品被储存在室温(15至30℃)下不超过24小时。样品采集在患者就诊的医院完成，然后进行免疫测定，以避免多次冻融循环。FSH，LH，E 2和AMH测量均由Access UniCel DxI 800化学发光体系(Beckman Coulter Inc)来进行。FSH，LH和E 2的质量控制是由Bio-RAD Laboratories(Lyphochek Immunoassay Plus Control，trilevel)提供，产品号为370，批号为40300。AMH的质量控制由Beckman AMH试剂盒提供。对于AMH、FSH和LH，高中低三水平变异系数控制在小于5％，对于雌二醇，高中低三水平变异系数控制在小于10％。
分析方案
在实施例的分析中，用于预测的变量包括年龄、BMI、不孕原因、月经2-4天阴道B超计数的AFC个数、月经2-4天FSH水平、月经2-4天AMH水平、月经2-4天LH水平以及月经2-4天E 2水平，设定的结果变量是卵巢低反应。其中，卵巢低反应定义为获卵日得到低于5个(即0-4个)卵母细胞。两组之间的差异通过t检验，Wilcoxon检验或卡方检验进行检验，根据数据的不同采取合适的方法。
首先，使用二元逻辑回归模型来选择与卵巢低反应相关的重要因素。为了让模型有更好的适应性，即变换了数据(人群)后仍然可能有较好的实际意义，我们使用ROC(受试者工作特征)曲线的方法对预测卵巢低反应的相关四个连续变量指标根据分界点(cut-off point)，进行变量变化，将连续变量变成了二分类变量，下文中还将详细地描述该过程。然后使用逻辑回归利用二分类变量来重新建立预测模型，并计算卵巢低反应的预测概率。
如上所述，在本实施例中，共有561对参加GnRH拮抗剂周期夫妇符合上述纳入和排除标准，收集了这561对夫妇的数据，将收集到的数据输入SAS软件和R软件中，进行分析，并获得下表1和2的数据。表1显示了上述561对夫妇与取出的卵母细胞相关的基本和临床特征。
表1.与取出的卵母细胞结果相关的患者临床和生物数据
Figure PCTCN2018124766-appb-000004
Figure PCTCN2018124766-appb-000005
中位数，四分位数
根据表1中的结果可以看出，BMI、月经2-4天LH水平和月经2-4天E 2变量没有显著差异。另一方面，年龄、不孕原因(对不同不孕原因分类进行计数，应用卡方检验，计算是否有统计学意义p<0.001，说明不孕原因对是否为低反应有统计学意义、月经2-4天血清AMH和FSH水平、AFC水平、Gn起始剂量和总剂量与获得的卵母细胞具有重要的相关性，p值列于表1中。
利用上述软件对以上数据进行多因素回归分析，以确定在校正了相关因素后，哪些因素是影响卵巢低反应的独立预测指标，结果如表2所示。根据表2的结果显示年龄、月经2-4天血清AMH水平、月经血清2-4天FSH水平和月经2-4天AFC个数与卵巢低反应概率显著相关，其各自的p值分别为0.0056、0.0044、0.0195和0.0049。
表2.多因素分析以用于确定影响卵巢低反应的独立预测指标
Figure PCTCN2018124766-appb-000006
Figure PCTCN2018124766-appb-000007
进一步为了提供更实际的含义，通过使用ROC曲线的切点值将年龄、AMH、FSH和AFC转换成二分类变量。具体来说，采用ROC曲线确定年龄、AMH、FSH、AFC的分界点，并分别确定该分界点的切点值，其结果分别如图1到图4所示，根据图1到图4的结果，找到年龄、AMH、FSH、AFC的切点值分别为35岁、0.93、9.1、8。可以确认四个指标的结果分别依次如下年龄，AMH，FSH和AFC的切点值分别为35岁，0.93ng/ml，9.1IU/L和8，由此将年龄分为<＝35和>35，AMH分为<＝0.93和>0.93，FSH分为<＝9.1和>9.1，AFC分为<＝8和>8，从而依据上述标准将年龄、AMH、FSH和AFC转换成二分类变量。
对如上所述新产生的四个二分类变量，将其视为X，对于是否为卵巢低反应视为Y，做预测是否卵巢低反应的多因素分析，建立的模型参数估计值如下表3所示。
表3 利用多因素分析建立模型的参数值
Figure PCTCN2018124766-appb-000008
Figure PCTCN2018124766-appb-000009
根据表3的数据最终建立的模型logistic预测模型为：
Figure PCTCN2018124766-appb-000010
根据表3的数据可以确定，i的范围为-1.786～-0.499，最优选i＝-1.143，
a的范围为0.063-1.342，最优选a＝0.703，b的范围为-2.542～-1.056，最优选b＝-1.799，c的范围为0.548～1.838，最优选c＝1.193，d的范围为-2.133～-0.51，最优选d＝-1.322。
进一步，按照预测模型，画出ROC曲线，如图5所示，其ROC曲线下的面积为0.883，根据图5的结果可以看出该模型在评估卵巢储备功能方面表现出色。
由此，根据上述公式一可以基于对某一受试者的年龄、月经2-4天的静脉血中的抗缪勒氏管激素浓度，月经2-4天的静脉血中的卵泡刺激素浓度，月经2-4天时的两个卵巢中直径为2-8mm的所有可见卵泡的个数来计算这个受试者的卵巢低反应概率。
根据计算出的卵巢低反应概率的参数对人群进行分组，在本发明中尝试了不同的分组方法，并随后通过交互作用进行检验发现只有按照预测低反应概率<5％,5％≤低反应概率<20％；20％≤低反应概率<50％；50％≤低反应概率分成四组的时候，预测的低反应概率与Gn起始剂量的交互作用才有意义(p<0.05)。
上述交互作用的具体做法是这样的，将利用公式一计算的预测的低反应概率、Gn起始剂量以及两者的交互作用作为X，是否低反应作为Y，进行多元逻辑回归，从而实现固定了影响卵巢低反应的四个主要因素即本发明中使用的二分类变量AFC，AMH，FSH以及年龄(即预测的低反应概率)看剂量变化对是否发生卵巢低反应的影响。交互作用结果显示，将人群分为低反应概率<5％,5％≤低反应概率<20％；20％≤低反应概率<50％；50％≤低反应概率这四组时，交互作用模型是有意义的P<0.05，如交互作用图，即图6可见，按照以上四组建立分组标准后，进行交互作用具有意义，表明应该进行这样的分层(即分组)，因为这四组的低反应概率发生趋势各不相同。
尽管以上结合附图对本发明的实施方案进行了描述，但本发明并不局限于上述的具体实施方案和应用领域，上述的具体实施方案仅仅是示意性的、指导性的，而不是限制性的。本领域的普通技术人员在本说明书的启示下和在不脱离本发明权利要求所保护的范围的情况下，还可以做出很多种的形式，这些均属于本发明保护之列。

Claims (18)

  1. 一种用于评估受试者卵巢储备功能的系统，其包括：
    数据采集模块，其用于获取受试者的年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平、窦卵泡计数(AFC)的数据；以及
    计算卵巢储备功能的模块，其用于将数据采集模块中的获取的上述信息进行计算，从而计算出受试者的卵巢低反应概率(p)。
    在本发明中，已知卵巢反应性取决于卵巢储备功能，根据卵巢低反应概率对卵巢储备功能进行分组。
  2. 根据权利要求1所述的系统，其还包括：
    分组模块，在所述分组模块中预存有默认的卵巢储备功能分组参数，并且依据该分组参数，对所述计算得到的卵巢低反应概率p进行分组，从而对受试者的卵巢储备水平进行分组。
  3. 根据权利要求1或2所述的系统，其中，
    在计算卵巢储备功能的模块中，利用将受试者年龄、受试者抗缪勒氏管激素(AMH)水平、受试者卵泡刺激素(FSH)水平、受试者窦卵泡计数(AFC)的数据转换成的二分类变量来计算受试者的卵巢低反应的概率(p)。
  4. 根据权利要求3所述的系统，其中，
    在计算卵巢储备功能的模块中，利用受试者工作特征(ROC)曲线来检测年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平、窦卵泡计数(AFC)的分界点，并根据该分界点的切点值来将年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平、窦卵泡计数(AFC)转换成二分类变量，从而利用所述二分类变量来计算受试者的卵巢低反应概率(p)。
  5. 根据权利要求4所述的系统，其中，
    所述抗缪勒氏管激素(AMH)水平是指女性受试者月经2-4天的静脉血中的抗缪勒氏管激素浓度，所述卵泡刺激素(FSH)水平是指女性受试者月经2-4天的静脉血中的卵泡刺激素浓度，所述窦卵泡计数(AFC)是指阴道B超计数女性受试者月经2-4天时的两个卵巢中直径为2-8mm的所有可见卵泡的个数。
  6. 根据权利要求4或5所述的系统，其中，
    所述年龄的切点值为35岁，所述抗缪勒氏管激素(AMH)水平的切点值为0.93ng/ml， 所述卵泡刺激素(FSH)水平的切点值为9.1IU/L，以及所述窦卵泡计数(AFC)的切点值为8。
  7. 根据权利要求1～6中任一项所述的系统，其中，
    在计算卵巢储备功能的模块中，预先存储有基于现有数据库中受试者的受试者年龄、受试者抗缪勒氏管激素(AMH)水平、受试者卵泡刺激素(FSH)水平、受试者窦卵泡计数(AFC)的数据转换成的二分类变量拟合而成的用于计算受试者的卵巢低反应概率(p)的公式。
  8. 根据权利要求7所述的系统，其中，
    所述公式为如下公式一：
    Figure PCTCN2018124766-appb-100001
    其中，p为计算出的用于表征所述受试者的卵巢储备功能的参数，i为选自-1.786～-0.499中的任意数值，a为选自0.063-1.342中的任意数值，b为选自-2.542～-1.056中的任意数值，c为选自0.548～1.838中的任意数值，d为选自-2.133～-0.51中的任意数值，其中优选i＝-1.143，优选a＝0.703，优选b＝-1.799，优选c＝1.193，优选d＝-1.322。
  9. 根据权利要求2～8中任一项所述的系统，其中，
    在所述分组模块中预存的分组参数为：
    当计算出的用于预测受试者的卵巢低反应概率(p)<5％时，分组模块确定该受试者属于卵巢储备功能良好；
    当5％≤计算出的用于预测受试者的卵巢低反应概率(p)<20％，分组模块确定该受试者属于卵巢储备功能开始下降；
    当20％≤计算出的用于预测受试者的卵巢低反应概率(p)<50％，分组模块确定该受试者属于卵巢储备功能下降明显；
    当计算出的用于预测受试者的卵巢低反应概率(p)≥50％，分组模块确定该受试者属于卵巢储备功能差。
  10. 一种用于评估受试者卵巢储备功能的方法，其包括：
    采集数据步骤，在该步骤中，获取受试者的年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平、窦卵泡计数(AFC)的数据；以及
    计算卵巢储备功能的步骤，在该步骤中，利用在上述采集数据步骤中获取的年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平、窦卵泡计数(AFC)数据进行计算，从而计算出受试者的卵巢低反应概率的参数(p)。
  11. 根据权利要求10所述的方法，其中，
    分组步骤，在所述分组步骤中依据默认的卵巢储备功能分组参数对所述计算得到的卵巢低反应概率进行分组，从而对受试者的卵巢储备水平进行分组。
  12. 根据权利要求10或11所述的方法，其中，
    在计算卵巢储备功能的步骤中，将受试者年龄、受试者抗缪勒氏管激素(AMH)水平、受试者卵泡刺激素(FSH)水平、受试者窦卵泡计数(AFC)的数据转换成二分类变量，并利用所述转换成的二分类变量来计算受试者的卵巢低反应概率(p)。
  13. 根据权利要求12所述的方法，其中，
    在计算卵巢低反应概率的步骤中，利用受试者工作特征(ROC)曲线来检测年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平、窦卵泡计数(AFC)的分界点(cut-off value)，并根据该分界点的切点值来将年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平、窦卵泡计数(AFC)转换成二分类变量，从而利用所述二分类变量模型来计算受试者的卵巢低反应概率(p)。
  14. 根据权利要求13所述的方法，其中，
    所述抗缪勒氏管激素(AMH)水平是指女性受试者月经2-4天的静脉血血清中的抗缪勒氏管激素浓度，所述卵泡刺激素(FSH)水平是指女性受试者月经2-4天的静脉血血清中的卵泡刺激素浓度，所述窦卵泡计数(AFC)是指阴道B超计数女性受试者月经2-4天时的两个卵巢中直径为2-8mm的所有可见卵泡的个数。
  15. 根据权利要求13或14所述的方法，其中，
    所述年龄的切点值为35岁，所述抗缪勒氏管激素(AMH)水平的切点值为0.93ng/ml，所述卵泡刺激素(FSH)水平的切点值为9.1IU/L，以及所述窦卵泡计数(AFC)的切点值为8。
  16. 根据权利要求10～15中任一项所述的方法，其中，
    在计算卵巢储备功能的步骤中，利用预先的基于现有数据库中受试者的受试者年龄、受试者抗缪勒氏管激素(AMH)水平、受试者卵泡刺激素(FSH)水平、受试者窦卵泡计数(AFC)的数据转换成的二分类变量拟合而成的用于计算用于预测所述受试者的卵巢低反应概率(p)的公式来进行计算。
  17. 根据权利要求10～16中任一项所述的方法，其中，
    在计算卵巢储备功能的步骤中，利用如下公式一，根据数据采集步骤中获取的并已经转化为二分类变量的受试者的年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平、窦卵泡计数(AFC)的数据来计算受试者的卵巢低反应概率(p)：
    Figure PCTCN2018124766-appb-100002
    其中，p为计算出的用于表征所述受试者的卵巢储备功能的参数，i为选自-1.786～-0.499中的任意数值，a为选自0.063-1.342中的任意数值，b为选自-2.542～-1.056中的任意数值，c为选自0.548～1.838中的任意数值，d为选自-2.133～-0.51中的任意数值，其中优选i＝-1.143，优选a＝0.703，优选b＝-1.799，优选c＝1.193，优选d＝-1.322。
  18. 根据权利要求11～17中任一项所述的方法，其中，
    在所述分组模块中预存的分组依据为：
    当计算出的用于预测受试者的卵巢低反应概率(p)<5％时，分组模块确定该受试者属于卵巢储备功能良好；
    当5％≤计算出的用于预测受试者的卵巢低反应概率(p)<20％，分组模块确定该受试者属于卵巢储备功能开始下降；
    当20％≤计算出的用于预测受试者的卵巢低反应概率(p)<50％，分组模块确定该受试者属于卵巢储备功能下降明显；
    当计算出的用于预测受试者的卵巢低反应概率(p)≥50％，分组模块确定该受试者属于卵巢储备功能差。
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