Investigation On The Current Status Of Fertility Decline In Women Of Childbearing Age In Liaoning Province And Establishment Of A Predictive Model For Ovarian Reserve And Pregnancy Outcome Of In Vitro Fertilization-embryo Transfer

Objective:With the rapid development of society,changes in lifestyle and environment are having a negative impact on women's reproductive health.There are about 2 million new infertile couples in the world every year~1.Infertility has become the third major disease that seriously affects human health after cardiovascular disease and cancer~2.The decline of reproductive capacity has become the main problem and severe challenge faced by the whole society.The decline of female reproductive capacity is mainly reflected in two aspects:the occurrence of infertility and the decline of ovarian reserve function.Infertility is a complexdisease,which is not only restricted by regional economic level,geographical and health conditions,but also affected by folk customs,diet structure and environmental pollution.The investigation on the prevalence rate of infertility in China began in the 1970s-1980s.In the past,the highest prevalence rate of infertility was24.3%and the lowest was 0.8%~3,which maybe related to the influence of the investigated region,nationality and economic level.At present,there is still a lack of large-scale epidemiological investigation on infertility in Liaoning Province and even in the whole country,only scattered statistical data in various regions.Therefore,it is necessary to conduct in-depth study on the prevalence of infertility and its related risk factors in our province,so as to provide theoretical basis for the prevention and treatment of infertility,and provide reliable data for the administrative departments to formulate primary prevention policies for infertility.Ovarian reserve refers to the number of primordial follicles in the ovarian cortexthat have the ability to develop into mature oocytes~4,which is the most accepted indicator to predict reproductive capacity.The decline of ovarian reserve function means the decline of reproductive capacity.The decrease of ovarian reserve function is the early stage of ovarian failure,which will further develop into premature ovarian failure.Through the accurate evaluation of ovarian function,predict the fertility potential and obtain the node of female fertility,it is helpful for women to make scientific fertility plan.At present,it is impossible to evaluate the condition of primordial follicles by non-invasive methods,and there is no unified standard for judging the decline of ovarian reserve function.There are many indicators to evaluate ovarian reserve function,but at present,no single marker can be used to predict ovarian reserve function with satisfactory sensitivity and specificity.Most scholars advocate that the combined application of indicators can improve the prediction ability."Prediction model"began to appear in our field of vision,it is often used to predict the future incidence of a disease.Specifically,it is to establish a statistical model based on the multiple causes of diseases,which can be used to predict the probability of certain outcome events in the future for people with certain characteristics.The prediction model can make the results obtained by epidemiological research better conform to clinical practice,and promote the prevention of disease three levels and reduce the incidence rate of diseases through high-risk screening and clinical evidence-based guidelines.In the past,the prediction models of ovarian reserve function established at home and abroad have the problems of less population and too many prediction variables,which reduces the range of predictable population in clinical application,increases the economic burden of patients and the frequency of treatment,and weakens the popularization of the prediction model in clinical application.Therefore,we need to build a stable predictive model with few predictive variables,high sensitivity and specificity to evaluate the real situation of ovarian reserve in women undergoing IVF-ET,so as to facilitate clinical consultation before ovarian stimulation and identify women with decreased ovarian reserve.And then applied to the majority of women of childbearing age,for women to evaluate their ovarian reserve,according to their ovarian reserve status to arrange fertility planning,is of great significance.In the process of assisted reproduction treatment,there are many factors that affect the outcome of clinical pregnancy and live birth.The main factors are:basic characteristics of the couple(age,length of infertility,cause of infertility,type of infertility,history of pregnancy and delivery,history of disease and surgery,body mass index,Basic sexhormone level,anti-Müllerian hormone level,number of antral follicles,etc.),ovarian stimulation(COH protocol,oocytes retrieved,estradiol level on HCG day,endometrial thickness on HCG day,etc),fertilization method(IVF,ICSI and IVF+ICSI)and embryo transfer related indicators(the stage of embryo transfer,the number of embryos transferred,etc).Therefore,we hope to screen out the influencing factors of the outcome of in vitro fertilization-embryo transfer treatment,and establish the prediction model of ovarian reserve function,and then establish the prediction model of pregnancy rate and live birth rate.Questions about patients receiving in vitro fertilization-embryo transfer treatment"I How many eggs can I get?Can I get pregnant?Can I give birth?"Give probabilistic answers to enable patients to have an objective and correct understanding of their own situation and the outcome of assisted pregnancy,reduce the patient's mental burden,and increase the patient's confidence in treatment and treatment.Compliance.Method:1.By using stratified cluster random sampling method,one city and one county were selected in the north,middle and south of Liaoning Province,which were Shenyang City and Zhangwu County,Jinzhou City and Panshan County,Dandong City and Suizhong County.Then,the number of streets or administrative villages and the sample size of the survey area are determined according to the proportion of population in each area.Randomly selected 20-49 years old women of childbearing age for cross-sectional survey of infertility.Face to face questionnaire survey was used to obtain the basic information of the population of childbearing age.According to the diagnostic criteria of infertility,that is,men and women living together have normal sexlife and have not taken contraceptive measures for at least 12 months,they are still unable to conceive,and the infertility patients of childbearing age in Liaoning area are screened out;the epidemiological distribution characteristics of infertility prevalence and related risk factors in Liaoning area are studied.2.Based on the population data of in vitro fertilization-embryo transfer,a mathematical model is established to predict ovarian reserve capacity on the basis of predicting low ovarian response.Set the outcome variable as the number of eggs obtained is less than 4.The predictive variables include age,BMI,AFC,AMH,FSH,LH,E2 levels and major infertility factors.Randomly select 60%of the study population data as the training set,and the remaining 40%as the validation set.Multi-factor logistic regression analysis was performed on the training set data to construct a prediction model of low ovarian response;the validation set was used as verification data to verify the performance of the model.It is evaluated from two aspects of discrimination and calibration,evaluating the ability of the model to correctly distinguish between patients and non-patients,and the degree of agreement between the average predicted probability of the evaluation model for the study population and the actual observed incidence probability.Finally,the established ovarian reserve prediction model was used to predict the occurrence of ovarian reserve hypofunction in women of childbearing age in Liaoning Province.3.Analyze the influencing factors of in vitro fertilization-embryo transfer pregnancy outcome and live birth outcome,and obtain statistically significant influencing factors through multivariate logistic regression analysis,and use them as covariates,whether to obtain clinical pregnancy and whether to obtain live birth as dependent variables to construct Prediction model for clinical pregnancy rate and live birth rate.Randomly select 60%of the study population data as the training set,and the remaining 40%as the validation set.The training set data is used to construct the prediction model of clinical pregnancy rate and live birth rate;the validation set is used as the verification data to verify the performance of the model.The evaluation is made from two aspects of discrimination and calibration.Results:1.The prevalence of infertility is 13.4%(95%CI,11.7%-15.1%),and the prevalence of primary infertility is 1.9%(95%CI,1.2%-2.6%),secondary The prevalence of infertility was 11.5%(95%CI,9.9%-13.1%).The results of multivariate analysis suggest that the older the age,the higher the risk of infertility.For every 1 year increase in age,the risk of infertility increases by 4.8%(OR=1.048,95%CI 1.024-1.072,P<0.001);Women with menarche age?14 years old,women with menarche age?13 years old have a higher risk of infertility,which is 67.2%higher than the control group(OR=1.672,95%CI 1.197-2.336,P=0.003);Relative to working women,unemployed/unemployed women increase the risk of infertility,and the risk increases by 124.2%(OR=2.242,95%CI 1.338-3.756,P=0.002);irregular menstruation increases women's infertility Compared with women with regular menstruation,the risk of infertility is increased by74%(OR=1.740,95%CI 1.078-2.807,P=0.023);women with normal menstrual flow have less menstrual flow and excessive menstrual flow Women have an increased risk of infertility,which increases by 138.7%and 165.3%respectively(OR=2.387,95%CI1.644-3.467,P<0.001;OR=2.653,95%CI 1.601-4.396,P<0.001);pregnancy The number of times and the number of births had statistically significant effects on the prevalence of infertility(OR=0.618,95%CI 0.484-0.791,P<0.001;OR=0.532,95%CI0.384-0.737,P<0.001).After removing confounding factors in the multivariate analysis,it was found that marriage age,dysmenorrhea,miscarriage,weight loss,whether there was decoration within sixmonths,BMI,whether the male partner had an occupation,and the male partner's education level were not between the infertility group and the non-infertility group Significant difference.Of the 200 infertile patients surveyed in the1494 study population,only 67 visited the hospital,accounting for 33.5%of the infertile patients.Nearly 60%of people go to county-level hospitals or hospitals below the county level.2.This study establishes a mathematical model to predict ovarian reserve capacity on the basis of predicting low ovarian response.The predictors were age,BMI,AMH,AFC and basal FSH.Obtain the prediction model of ovarian reserve function:P=exp(-3.23+0.065xAge+0.060xBMI-0.277xAFC-0.429xAMH+0.109xFSH)/[1+(-3.23+0.065xAge+0.060xBMI-0.277xAFC-0.429xAMH+0.109xFSH)]The AUC,sensitivity,and specificity of the prediction model were 90.9%,83.2%,and 85.5%,respectively.Using this model to predict that the average predicted probability of ovarian reserve decline in women of childbearing age in Liaoning Province is 16.07%.3.Obtain a clinical pregnancy rate prediction model that uses female age,b FSH,P on the dayof HCG injection,endometrial thickness on the dayof HCG injection,and embryo transfer as predictors.P=exp(1.669-0.069xAge-0.056xFSH-0.545xHCGdayP+0.063xHCGdayEm+0.807xtransplantation of 2 cleavage stage embryos+0.803xtransplantation of 1 blastocyst stage embryo)/[1+(1.669-0.069xAge-0.056xFSH-0.545xHCGdayP+0.063xHCGdayEm+0.807xtransplantation of 2 cleavage stage embryos+0.803xtransplantation of 1 blastocyst stage embryo)]A live birth rate prediction model with female age,basic E2,ovarian stimulation plan,P on the dayof HCG injection,endometrial thickness on the dayof HCG injection,and embryo transfer as predictors was obtained.P=exp(0.135-0.074xAge+0.003xbasic E2+1.110xextra-long-protocol+0.768xlong-protoco l+0.623xantagonist-protocol-0.544xHCGdayP+0.075xHCGdayEm+0.771xtransplantation of 2 embryos+0.750xtransplant 1 blastocyst)/[1+(0.135-0.074xAge+0.003xbasic E2+1.110xextra-long-protocol+0.768xlong-protocol+0.623xantagonist-protocol-0.544xHCGdayP+0.075xHCGdayEm+0.771xtransplantation of 2 embryos+0.750xtransplant 1 blastocyst)]Conclusion:1.The prevalence rate of infertility in Liaoning Province is 13.4%,which is in the middle level of China.The prevalence rate of primary infertility is 1.9%,and that of secondary infertility is 11.5%.2.Age,early menarche age,female unemployment/unemployment,irregular menstruation and abnormal menstrual volume were the risk factors of infertility;the number of pregnancies and childbirth times were the protective factors of infertility.Late marriage,unemployment/unemployment and weight loss within half a year maybe risk factors for infertility.In order to improve the quality of the population,we should promote marriage of the right age and childbearing of the right age.In addition,premature menarche maymean the risk of infertility,should be noted.If accompanied by unexplained irregular menstruation,menstrual volume is too little or too much,should be timely medical treatment,find out the cause.3.The medical treatment rate of infertility patients in our province is low.We call on the maternal and child health care department to strengthen the propaganda and education,and strengthen the diagnosis and treatment level of infertility in local hospitals,so as to improve the overall reproductive health level of our province.4.In this study,a mathematical model was established to predict ovarian reserve capacity on the basis of predicting ovarian low response.The predictive factors were age,BMI,AMH,AFC and FSH.The AUC,sensitivity and specificity of the prediction model were 90.9%,83.2%and 85.5%respectively.5.According to the preliminary prediction of ovarian reserve function of women of childbearing age in Liaoning Province,the average prediction probability of ovarian reserve function decline is 16.07%,which needs to further expand the epidemiological investigation population.6.The prediction model of ovarian reserve function constructed in this study is of great significance for women to evaluate their ovarian reserve and arrange their fertility plans according to their ovarian reserve status.However,it needs further improvement and optimization,and further verification in clinical practice.7.Female age,basic FSH,ovarian stimulation program,estrogen and progesterone on the dayof HCG injection,endometrial thickness on the dayof HCG injection,and embryo transfer are independent factors that affect the clinical pregnancy rate.Female age,basic E2,ovarian stimulation program,estrogen and progesterone on the dayof HCG injection,endometrial thickness on the dayof HCG injection,and embryo transfer are independent factors that affect the live birth rate.8.The clinical pregnancy rate prediction model and live birth rate prediction model we constructed have certain predictive value.The clinical pregnancy rate prediction model still has a certain difference between the average predicted probability and the actual occurrence probability of clinical pregnancy obtained in in vitro fertilization-embryo transfer patients,but the live birth rate prediction model is well calibrated and is an ideal live birth outcome prediction model.9.The above prediction model predicts ovarian reserve,pregnancy rate,and live birth rate for patients undergoing in vitro fertilization-embryo transfer,and evaluates the entire treatment process,so that patients have a clear understanding of their own conditions and an objective and correct understanding of the outcome of assisted pregnancy.Reduce the patient's psychological burden,while increasing the patient's confidence and compliance with treatment.