Age effect on in vitro fertilization pregnancy mediated by anti-Mullerian hormone (AMH) and modified by follicle stimulating hormone (FSH)

Hsieh, Han-Chih; Su, Jia-Ying; Wang, Shun-Ping; Huang, Yen‐Tsung

doi:10.1186/s12884-020-02875-2

Cited by 6 publications

(4 citation statements)

References 32 publications

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“…For the proposed PMORN, the feature of AFC (NMIV = 1.0), E 2 level on the HCG day (NMIV = 0.951), age (NMIV = −0.354), and AMH (NMIV = 0.314) have significant NMIV, meaning that they are the dominant features affecting the ovarian response, which is in accordance with the findings in [ 13 , 27 ]. The AFC has the most significant NMIV as compared to the other features, which matches with the findings in the early work [ 12 , 28 , 29 , 30 ] that (1) the AFC is highly correlated with the number of recruited follicles and the retrieved oocytes, and (2) the AFC is regarded as the best univariate predictor of ovarian reserve and determines the number of follicles that grows in response to Gn stimulation [ 13 ].…”

Section: Discussionsupporting

confidence: 87%

“…Results in our work suggest that we might need to re-evaluate the priority of the AMH and AFC on affecting the number of oocytes retrieved. In addition, we also find that the therapeutic regimen, days of Gn, dosage of Gn, bFSH, and infertility type have obvious influence on the ovarian response, as revealed in [ 27 , 34 ]. Regarding the infertility duration and infertility cause, their NMIV value is very small (−0.039 and 0.07), meaning they have very little effect on the COS outcome.…”

Section: Discussionmentioning

confidence: 93%

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Machine Learning-Based Modeling of Ovarian Response and the Quantitative Evaluation of Comprehensive Impact Features

et al. 2022

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Appropriate ovarian responses to the controlled ovarian stimulation strategy is the premise for a good outcome of the in vitro fertilization cycle. With the booming of artificial intelligence, machine learning is becoming a popular and promising approach for tailoring a controlled ovarian stimulation strategy. Nowadays, most machine learning-based tailoring strategies aim to generally classify the controlled ovarian stimulation outcome, lacking the capacity to precisely predict the outcome and evaluate the impact features. Based on a clinical cohort composed of 1365 women and two machine learning methods of artificial neural network and supporting vector regression, a regression prediction model of the number of oocytes retrieved is trained, validated, and selected. Given the proposed model, an index called the normalized mean impact value is defined and calculated to reflect the importance of each impact feature. The proposed models can estimate the number of oocytes retrieved with high precision, with the regression coefficient being 0.882% and 89.84% of the instances having the prediction number ≤ 5. Among the impact features, the antral follicle count has the highest importance, followed by the E2 level on the human chorionic gonadotropin day, the age, and the Anti-Müllerian hormone, with their normalized mean impact value > 0.3. Based on the proposed model, the prognostic results for ovarian response can be predicted, which enables scientific clinical decision support for the customized controlled ovarian stimulation strategies for women, and eventually helps yield better in vitro fertilization outcomes.

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Section: Discussionsupporting

confidence: 87%

Section: Discussionmentioning

confidence: 93%

Machine Learning-Based Modeling of Ovarian Response and the Quantitative Evaluation of Comprehensive Impact Features

et al. 2022

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“…Multinomial regression revealed an association between female age and diagnosis of female infertility [26]. For female IVF patients, follicle stimulating hormone (FSH) and anti-mu□llerian hormone (AMH) are the two most frequently utilized ovarian reserve tests that serve as important markers related to successful IVF outcomes [27]. It is suggested that AMH >0.8-1.0 ng/ml is indicative of normal ovarian reserve, while FSH >10 mIU/ml is indicative of diminished ovarian reserve [28].…”

Section: Discussionmentioning

confidence: 99%

“…-mu llerian hormone (AMH) are the two most frequently utilized ovarian reserve tests that serve as important markers related to successful IVF outcomes [27]. It is suggested that AMH >0.8-1.0 ng/ml is indicative of normal ovarian reserve, while FSH >10 mIU/ml is indicative of diminished ovarian reserve [28].…”

Section: Discussionmentioning

confidence: 99%

Effect of Female Age on Infertility Diagnostic Factors and In-vitro Fertilization Treatment Outcomes: A Single-center Retrospective Cohort Study

Tang

Liu

et al. 2021

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BackgroundInfertility has become an important issue in modern world because of the increasing number of infertile couples around the world. Advanced maternal age was considered to be a main factor of infertile problem. With second child policy published in China and more women in China intend to seek help for infertility problem, this study provided information of fertility diagnostic factors and IVF treatment outcomes of female IVF patients in different age groups, which can be a guidance for infertility diagnostic and treatment.MethodsA retrospective cohort study was conducted to IVF patient data collected by Jinjiang District Maternal and Child Health Hospital, Chengdu, China. Clinical and laboratory data of 45,743 fresh, autologous IVF cycles from January 2008 to September 2017 were included in the analysis. The diagnostic factors and treatment outcomes were analyzed for different age groups (age<35, n=30,708; age 35-41, n=11,921 and age≥42, n=3,114) as well as further stratified advanced age groups (age 42, n=933; age 43, n=744; age 44, n=556; age≥45, n=881). The characteristics including number of previous cycles, duration of infertility, BMI, basic FSH, basic AFC, AMH, retrieved oocyte number, fertilized oocyte number, transferred embryo number, baby number and economic cost were stratified according to patient age.ResultsThe basic characteristics of obesity rate, basic FSH and cancellation rate of IVF cycles in ≥42 years old group were significantly higher than other groups (p<0.01). Basic AFC, AMH, retrieved oocyte number, fertilized oocyte number and transferred embryo number in ≥42 years old group were significantly lower than other groups (p<0.01). Diagnostics characteristics and IVF-ET outcome declined significantly when maternal age increased (p<0.05). In the meanwhile, a preliminary analysis of cost per cycle showed that cost per child increased with patient’s age increase.ConclusionAlthough with increasing number of advanced maternal age IVF cycles, the age group of ≥42 years would intend to get unsatisfied outcome and higher cost per child. More guidance and considerations should be focused on encouraging earlier age treatment of infertility.Plain English summaryWith more and more women in the global range choose to get late pregnancy because of changes in society and economy, age has become an unavoidable factor in infertility diagnostic and treatment. Advanced age women may experience more infertility problems and negative IVF outcomes. A better understanding of the effect of maternal age on infertility would offer help to both diagnostic and treatment of IVF patient. This study conducted a single-center retrospective cohort analysis to female patients of different age groups and found that women with more advanced age (≥42) would be more easily to experience unsatisfied IVF outcome and higher economic cost to obtain one child. After the publication of second child policy in China in 2013, the number of advanced age patients increased, the necessity of special guidance for AMA patient may need to be taken into consideration.

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Individual and community factors associated with unintended pregnancies among women of advanced reproductive age in Nigeria

et al. 2021

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Age effect on in vitro fertilization pregnancy mediated by anti-Mullerian hormone (AMH) and modified by follicle stimulating hormone (FSH)

Cited by 6 publications

References 32 publications

Machine Learning-Based Modeling of Ovarian Response and the Quantitative Evaluation of Comprehensive Impact Features

Machine Learning-Based Modeling of Ovarian Response and the Quantitative Evaluation of Comprehensive Impact Features

Effect of Female Age on Infertility Diagnostic Factors and In-vitro Fertilization Treatment Outcomes: A Single-center Retrospective Cohort Study

Individual and community factors associated with unintended pregnancies among women of advanced reproductive age in Nigeria

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