Skip to main content

Advertisement

SpringerLink
Log in

A novel mathematical model of true ovarian reserve assessment based on predicted probability of poor ovarian response: a retrospective cohort study

  • Assisted Reproduction Technologies
  • Published:
Journal of Assisted Reproduction and Genetics Aims and scope Submit manuscript

Abstract

Purpose

To establish a mathematical model for assessing the true ovarian reserve based on the predicted probability of poor ovarian response (POR).

Methods

In this retrospective cohort study, a total of 1523 GnRH-antagonist cycles in 2017 were firstly analyzed. The ovarian responses were calculated based on the number of retrieved oocytes. The continuous variables were converted into categorical variables according to cutoff values generated by the decision tree method. The optimal model was identified using forward stepwise multiple logistic regression with 5-fold cross-validation and further verified its performances using outer validation data.

Results

The predictors in our model were anti-Müllerian hormone (AMH), antral follicle counts (AFC), basal follicle-stimulating hormone (FSH), and age, in order of their significance, named AAFA model. The AUC, sensitivity, specificity, positive predictive value, and negative predictive value of AAFA model in inner validation and outer validation data were 0.861 and 0.850, 0.603 and 0.519, 0.917 and 0.930, 0.655 and 0.570, and 0.899 and 0.915. Ovarian reserve of 16 subgroups was further ranked according to the predicted probability of POR and further divided into 4 groups of A–D using clustering analysis. The incidence of POR in the four groups was 0.038 (0.030–0.046), 0.139 (0.101–0.177), 0.362 (0.308–0.415), and 0.571 (0.525–0.616), respectively. The order of ovarian reserve from adequate to poor followed the order of A to D.

Conclusion

We have established an easy applicable AAFA model for assessing true ovarian reserve and may have important implications in both infertile women and general reproductive women in Chinese or Asian population.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Qiao J, Wang ZB, Feng HL, Miao YL, Wang Q, Yu Y, et al. The root of reduced fertility in aged women and possible therapentic options: current status and future perspects. Mol Asp Med. 2014;38:54–85.

    Article  Google Scholar 

  2. Lass A. Assessment of ovarian reserve: is there still a role for ovarian biopsy in the light of new data? Hum Reprod. 2004;19:467–9.

    Article  Google Scholar 

  3. Broekmans FJ, Kwee J, Hendriks DJ, Mol BW, Lambalk CB. A systematic review of tests predicting ovarian reserve and IVF outcome. Hum Reprod Update. 2006;12:685–718.

    Article  CAS  Google Scholar 

  4. Wallace WH, Kelsey TW. Human ovarian reserve from conception to the menopause. PLoS One. 2010;5:e8772.

    Article  Google Scholar 

  5. Roudebush WE, Kivens WJ, Mattke JM. Biomarkers of ovarian reserve. Biomark Insights. 2008;3:259–68.

    Article  CAS  Google Scholar 

  6. Gynecologists TACoOa. Committee opinion no. 618: ovarian reserve testing. Obstet Gynecol. 2015;125:268–73.

    Article  Google Scholar 

  7. Medicine PCotASfR. Testing and interpreting measures of ovarian reserve: a committee opinion. Fertil Steril. 2015;103:e9–e17.

    Google Scholar 

  8. de Carvalho BR, Rosa e Silva AC, Rosa e Silva JC, dos Reis RM, Ferriani RA, Silva de Sa MF. Ovarian reserve evaluation: state of the art. J Assist Reprod Genet. 2008;25:311–22.

    Article  Google Scholar 

  9. Broer SL, van Disseldorp J, Broeze KA, Dolleman M, Opmeer BC, Bossuyt P, et al. Added value of ovarian reserve testing on patient characteristics in the prediction of ovarian response and ongoing pregnancy: an individual patient data approach. Hum Reprod Update. 2013;19:26–36.

    Article  Google Scholar 

  10. Durlinger AL, Gruijters MJ, Kramer P, Karels B, Ingraham HA, Nachtigal MW, et al. Anti-Mullerian hormone inhibits initiation of primordial follicle growth in the mouse ovary. Endocrinology. 2002;143:1076–84.

    Article  CAS  Google Scholar 

  11. Eldar-Geva T, Ben-Chetrit A, Spitz IM, Rabinowitz R, Markowitz E, Mimoni T, et al. Dynamic assays of inhibin B, anti-Mullerian hormone and estradiol following FSH stimulation and ovarian ultrasonography as predictors of IVF outcome. Hum Reprod. 2005;20:3178–83.

    Article  CAS  Google Scholar 

  12. van Rooij IA, Broekmans FJ, Scheffer GJ, Looman CW, Habbema JD, de Jong FH, et al. Serum antimullerian hormone levels best reflect the reproductive decline with age in normal women with proven fertility: a longitudinal study. Fertil Steril. 2005;83:979–87.

    Article  Google Scholar 

  13. Yding Andersen C. Inhibin-B secretion and FSH isoform distribution may play an integral part of follicular selection in the natural menstrual cycle. Mol Hum Reprod. 2017;23:16–24.

    Article  CAS  Google Scholar 

  14. Santoro N. The menopausal transition. Am J Med. 2005;118(Suppl 12B):8–13.

    Article  Google Scholar 

  15. Ferraretti AP, La Marca A, Fauser BC, Tarlatzis B, Nargund G, Gianaroli L. ESHRE consensus on the definition of 'poor response' to ovarian stimulation for in vitro fertilization: the Bologna criteria. Hum Reprod. 2011;26:1616–24.

    Article  CAS  Google Scholar 

  16. Alviggi C, Andersen CY, Buehler K, Conforti A, De Placido G, Esteves SC, et al. A new more detailed stratification of low responders to ovarian stimulation: from a poor ovarian response to a low prognosis concept. Fertil Steril. 2016;105:1452–3.

    Article  Google Scholar 

  17. Johnson MKK. Applied predictive modeling, measuring performance in regresssion model. Berlin: Springer; 2013.

    Google Scholar 

  18. Xu H, Zeng L, Yang R, Feng Y, Li R, Qiao J. Retrospective cohort study: AMH is the best ovarian reserve markers in predicting ovarian response but has unfavorable value in predicting clinical pregnancy in GnRH antagonist protocol. Arch Gynecol Obstet. 2017;295:763–70.

    Article  CAS  Google Scholar 

  19. Qiao J, Wang ZB, Feng HL, Miao YL, Wang Q, Yu Y, et al. The root of reduced fertility in aged women and possible therapentic options: current status and future perspects. Mol Asp Med. 2013;38:54–85.

    Article  Google Scholar 

  20. Heidar Z, Bakhtiyari M, Mirzamoradi M, Zadehmodarres S, Sarfjoo FS, Mansournia MA. Prediction of different ovarian responses using anti-Mullerian hormone following a long agonist treatment protocol for IVF. J Endocrinol Investig. 2015;38:1007–15.

    Article  CAS  Google Scholar 

  21. La Marca A, Sunkara SK. Individualization of controlled ovarian stimulation in IVF using ovarian reserve markers: from theory to practice. Hum Reprod Update. 2014;20:124–40.

    Article  Google Scholar 

  22. Jayaprakasan K, Campbell B, Hopkisson J, Johnson I, Raine-Fenning N. A prospective, comparative analysis of anti-Mullerian hormone, inhibin-B, and three-dimensional ultrasound determinants of ovarian reserve in the prediction of poor response to controlled ovarian stimulation. Fertil Steril. 2010;93:855–64.

    Article  CAS  Google Scholar 

  23. Muttukrishna S, Suharjono H, McGarrigle H, Sathanandan M. Inhibin B and anti-Mullerian hormone: markers of ovarian response in IVF/ICSI patients? Bjog-an Int J Obstetrics Gynaecol. 2004;111:1248–53.

    Article  CAS  Google Scholar 

  24. Kotanidis L, Nikolettos K, Petousis S, Asimakopoulos B, Chatzimitrou E, Kolios G, et al. The use of serum anti-Mullerian hormone (AMH) levels and antral follicle count (AFC) to predict the number of oocytes collected and availability of embryos for cryopreservation in IVF. J Endocrinol Investig. 2016;39:1459–64.

    Article  CAS  Google Scholar 

  25. La Marca A, Argento C, Sighinolfi G, Grisendi V, Carbone M, D'Ippolito G, et al. Possibilities and limits of ovarian reserve testing in ART. Curr Pharm Biotechnol. 2012;13:398–408.

    Article  Google Scholar 

  26. Medicine TPCotASfR. Testing and interpreting measures of ovarian reserve: a committee opinion. Fertil Steril. 2012;98:1407–15.

    Article  Google Scholar 

  27. Riggs R, Kimble T, Oehninger S, Bocca S, Zhao Y, Leader B, et al. Anti-Mullerian hormone serum levels predict response to controlled ovarian hyperstimulation but not embryo quality or pregnancy outcome in oocyte donation. Fertil Steril. 2011;95:410–2.

    Article  CAS  Google Scholar 

  28. Nardo LG, Gelbaya TA, Wilkinson H, Roberts SA, Yates A, Pemberton P, et al. Circulating basal anti-Mullerian hormone levels as predictor of ovarian response in women undergoing ovarian stimulation for in vitro fertilization. Fertil Steril. 2009;92:1586–93.

    Article  CAS  Google Scholar 

Download references

Funding

This study was supported by the National Key Research and Development Program of China (Grant No. 2018YFC1002100, 2016YFC1000302, 2016YFC1000201); the capital health research and development of special project (Grant No. 2018-1-4091); National Natural Science Foundation of China (Grant No. 81771650); and Major National R&D Projects of China (Grant No. 2017ZX09304012-012).

Author information

Author notes

  1. Huiyu Xu, Guoshuang Feng and Haiyan Wang contributed equally to this work.

Authors and Affiliations

  1. Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, 49 Huayuan North Road, Haidian District, Beijing, 100191, People’s Republic of China

    Huiyu Xu, Haiyan Wang, Rui Yang, Ying Song, Lixue Chen, Li Shi, Meng Qian Zhang, Rong Li & Jie Qiao

  2. National Clinical Research Center for Obstetrics and Gynecology, Beijing, 100191, People’s Republic of China

    Huiyu Xu, Haiyan Wang, Rui Yang, Ying Song, Lixue Chen, Li Shi, Meng Qian Zhang, Rong Li & Jie Qiao

  3. Key Laboratory of Assisted Reproduction (Peking University), Ministry of Education, Beijing, 100191, People’s Republic of China

    Huiyu Xu, Haiyan Wang, Rui Yang, Ying Song, Lixue Chen, Li Shi, Meng Qian Zhang, Rong Li & Jie Qiao

  4. Beijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing, 100191, People’s Republic of China

    Huiyu Xu, Haiyan Wang, Rui Yang, Ying Song, Lixue Chen, Li Shi, Meng Qian Zhang, Rong Li & Jie Qiao

  5. Center for Clinical Epidemiology & Evidence-Based Medicine, Beijing Children’s Hospital, National Center for Children’s Health, Capital Medical University, Beijing, China

    Guoshuang Feng

  6. Clinical Research Institute, Zhejiang Provincial People’s Hospital, Hangzhou, 310014, Zhejiang Province, China

    Yong Han

Authors
  1. Huiyu Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guoshuang Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Haiyan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yong Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rui Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ying Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Lixue Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Li Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Meng Qian Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Rong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Jie Qiao
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Huiyu Xu: data collection and manuscript writing. Guoshang Feng: statistical analysis and manuscript writing. Haiyan Wang: data collection and manuscript writing. Yong Han: editing of this manuscript. Rui Yang: data collection and clinical consultation. Ying song: data collection and clinical consultation. Lixue Chen: data collection. Li Shi: data collection. Mengqian Zhang: data collection. Rong Li: resources, study design, supervision, and finally manuscript approval. Jie Qiao: resources, study design

Corresponding author

Correspondence to Rong Li.

Ethics declarations

The dataset used in this study contains de-identified data; thus, the informed consent by the patients was waived and the institutional review board approval was exempted, which conform to the Helsinki declaration.

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

ESM 1

(RTF 8194 kb)

ESM 2

(RTF 8312 kb)

ESM 3

(RTF 8214 kb)

ESM 4

(XLSX 137 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, H., Feng, G., Wang, H. et al. A novel mathematical model of true ovarian reserve assessment based on predicted probability of poor ovarian response: a retrospective cohort study. J Assist Reprod Genet 37, 963–972 (2020). https://doi.org/10.1007/s10815-020-01700-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10815-020-01700-1

Keywords

Search

Navigation