Possibilities for predicting the effectiveness of assisted reproductive technology programs


Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription or Fee Access

Abstract

Continuous improvement of assisted reproductive technologies (ART) enhances the effectiveness of infertility treatment; however, its efficiency remains individual in each clinical case. Many factors can influence the successful implementation of ART programs. A competent assessment of the chances before starting treatment allows a couple to more easily accept the possible need for repeated IVF attempts, to correctly plan financial costs, and, in some cases, to choose an alternative solution to the problem. The objective of this review - to generalize the data available in the literature on the possibility of predicting the outcomes of ART programs. The review considers issues, such as the influence of age, ovarian reserve biomarkers, body mass index, hormonal background, lifestyle, and alternative markers on the efficiency of IVF treatment. It reflects complex clinical models and calculators for assessing the chances of a positive treatment outcome. Conclusion: There are a large number of criteria that potentially affect the effectiveness of ART programs; however, each of them has a low predictive value; in this connection it is important to create complex predictive models to improve overall accuracy. The creation of clinically significant models for predicting the efficiency of ART treatment will assist clinicians and patients to fundamentally and thoroughly plan treatment, to optimally use the ovarian reserve, and to promptly recommend fertility preservation programs, thereby increasing the chances of having healthy offspring.

Full Text

Restricted Access

About the authors

Anna E. Martynova

Academician V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia

Email: a_martynova@oparina4.ru
PhD, Researcher at the 1st Gynecological Department

References

  1. Babayev E., Duncan F.E. Age-associated changes in cumulus cells and follicular fluid: the local oocyte microenvironment as a determinant of gamete quality. Biol. Reprod. 2022; 106(2): 351-65. https://dx.doi.org/10.1093/biolre/ioab241.
  2. Амирова А.А., Назаренко Т.А., Мишиева Н.Г. Факторы, влияющие на исходы ЭКО. Проблемы репродукции. 2010; 1: 68-74.
  3. Паскарь С.С., Калугина A.C. Предикторы успешного лечения бесплодия с помощью ВРТ среди пациентов молодого репродуктивного возраста. В. кн.: Трансляционная медицина: Национальный медицинский инновационный форум: тезисы. Санкт-Петербург 16-18 мая 2019 года. СПб.; 2019: 62.
  4. Grondahl M.L., Christiansen S.L., Kesmodel U.S.I., Agerholm E., Lemmen J.G., Lundstrom P. Effect of women’s age on embryo morphology, cleavage rate and competence - a multicenter cohort study. PLoS One. 2017; 12: e0172456. https://dx.doi.org/10.1371/journal.pone.0172456.
  5. Stensen M.H., Tanbo T., St or eng R., By holm T., Fedorcsak P. Routine morphological scoring systems in assisted reproduction treatment fail to reflect age-related impairment of oocyte and embryo quality. Reprod. Biomed. Online. 2010; 21(1): 118-25. https://dx.doi.org/10.1016/j.rbmo.2010.03.018.
  6. Mazzilli R., Cimadomo D., Vaiarelli A., Capalbo A., Dovere L., Alviggi E. Effect of the male factor on the clinical outcome of intracytoplasmic sperm injection combined with preimplantation aneuploidy testing: observational longitudinal cohort study of 1,219 consecutive cycles. Fertil. Steril. 2017; 108(6): 961-72.e3. https://dx.doi.org/10.1016/j.fertnstert.2017.08.0339.
  7. Ubaldi F.M., Cimadomo D., Capalbo A., Vaiarelli A., Buffo L., Trabucco E. Preimplantation genetic diagnosis for aneuploidy testing in women older than 44 years: a multicenter experience. Fertil. Steril. 2017; 107(5): 1173-80. https://dx.doi.org/10.1016/j.fertnstert.2017.03.007.
  8. Franasiak J.M., Forman E.J., Hong K.H., Werner M.D., Upham K.M., Treff N.R. The nature of aneuploidy with increasing age of the female partner: a review of 15,169 consecutive trophectoderm biopsies evaluated with comprehensive chromosomal screening. Fertil. Steril. 2014; 101(3): 656-63.e1. https://dx.doi.org/10.1016/j.fertnstert.2013.11.00411.
  9. Capalbo A., Hoffmann E.R., Cimadomo D., Ubaldi M.F., Rienzi L. Human female meiosis revised: new insights into the mechanisms of chromosome segregation and aneuploidies from advanced genomics and time-lapse imaging. Hum. Reprod. Update. 2017; 23(6): 706-22. https://dx.doi.org/10.1093/humupd/dmx026.
  10. Бейк Е.П., Сыркашева А.Г., Долгушина Н.В. Эффективность программ вспомогательных репродуктивных технологий у пациенток позднего репродуктивного возраста. Гинекология. 2018; 20(1): 109-112.
  11. Gleicher N., Kushnir V.A., Darmon S., Albertini D.F., Bar ad D.H. Older women using their own eggs? Issue framed with two oldest reported IVF pregnancies and a live birth. Reprod. Biomed. Online. 2018; 37(2): 72-7. https://dx.doi.org/10.1016/j.rbmo.2018.05.010.
  12. Nelson S.M., Lawlor D.A. Predicting live birth, preterm delivery, and low birth weight in infants born from in vitro fertilisation: A prospective study of 144,018 treatment cycles. PLoS Med. 2011; 8(1): e1000386. https://dx.doi.org/10.1371/journal.pmed.1000386.
  13. Zhao D., Fan J., Wang P., Jiang X., Yao J., Li X. Age-specific definition of low anti-Mullerian hormone and associated pregnancy outcome in women undergoing IVF treatment. BMC Pregnancy Childbirth. 2021; 21(1): 186. https://dx.doi.org/10.1186/s12884-021-03649-0.
  14. Salama S., Sharaf M., Salem S.M., Rasheed M.A., Salama E., Elnahas T., Lotfy R. FSH versus AMH: age-related relevance to ICSI results. Middle East Fertil. Soc. J. 2021; 26(1): 27. https://dx.doi.org/10.1186/s43043-021-00071-6.
  15. Wang S., Zhang Y., Mensah V., Warren J. Huber 3rd, Huang Y.T., Alvero R. Discordant anti-mullerian hormone (AMH) and follicle stimulating hormone (FSH) among women undergoing in vitro fertilization (IVF): which one is the better predictor for live birth? J. Ovarian Res. 2018; 11(1): 60. https://dx.doi.org/10.1186/s13048-018-0430-z.
  16. Sadruddin S., Barnett B., Ku L., Havemann D., Mucowski S., Herrington R., Burggren W. Maternal serum concentration of anti-Mullerian hormone is a better predictor than basal follicle stimulating hormone of successful blastocysts development during IVF treatment PLoS One. 2020; 15(10): e0239779. https://dx.doi.org/10.1371/journal.pone.0239779.
  17. Dai X., Wang Y., Yang H., Gao T., Yu C., Cao F. et al. AMH has no role in predicting oocyte quality in women with advanced age undergoing IVF/ICSI cycles. Sci. Rep. 2020; 10(1): 19750. https://dx.doi.org/10.1038/s41598-020-76543-y.
  18. Tal R., Tal O., Seifer B.J., Seifer D.B. Antimullerian hormone as predictor of implantation and clinical pregnancy after assisted conception: a systematic review and meta-analysis. Fertil. Steril. 2015; 103(1): 119-30.e3. https://dx.doi.org/10.1016/j.fertnstert.2014.09.041.
  19. Frattarelli J.L., LauriaCostab D.F., Miller B.T., Bergh P.A., Scott R.T. Basal antral follicle number and mean ovarian diameter predict cycle cancellation and ovarian responsiveness in assisted reproductive technology cycles. Fertil. Steril. 2000; 74(3): 512-7. https://dx.doi.org/10.1016/S0015-0282(00)00708-1.
  20. Nahum R., Shifren J.L., Chang Y., Leykin L., Isaacson K., Toth T.L. Antral follicle assessment as a tool for predicting outcome in IVF-is it a better predictor than age and FSH? J. Assist. Reprod. Genet. 2001; 18(3): 151-5. https://dx.doi.org/10.1023/A:1009424407082.
  21. Durmusoglu F., Elter K., Yoruk P., Erenus M. Combining cycle day 7 follicle count with the basal antral follicle count improves the prediction of ovarian response. Fertil; Steril. 2004; 81(4): 1073-8. https://dx.doi.org/10.1016/j.fertnstert.2003.08.044.
  22. Klinkert E.R., Broekmans F.J., Looman C.W., Habbema J.D., te Velde E.R. The antral follicle count is a better marker than basal follicle-stimulating hormone for the selection of older patients with acceptable pregnancy prospects after in vitro fertilization. Fertil. Steril. 2005; 83(3): 811-4. https://dx.doi.org/10.1016/j.fertnstert.2004.11.005.
  23. Wun H., Li R. Role of baseline antral follicle count and anti-Mullerian hormone in prediction of cumulative live birth in the first in vitro fertilisation cycle: a retrospective cohort analysis. PLoS One. 2013; 8(4): e61095. https://dx.doi.org/10.1371/journal.pone.0061095.
  24. Mutlu M.F., Erdem M., Erdem A., Yildiz S., Mutlu I., Arisoy O., Oktem M. Antral follicle count determines poor ovarian response better than anti-Mullerian hormone but age is the only predictor for live birth in in vitro fertilization cycles J. Assist. Reprod. Genet. 2013; 30(5): 657-65. https://dx.doi.org/10.1007/s10815-013-9975-3.
  25. Cortes-Vazquez A., Goitia-Landeros G.A., Regalado M.A., Leon-Hernandez S.R., Cortes-Algara A.L., Bandala C. et al. Prediction of ovarian response in IVF/ICSI cycles. JBRA Assist. Reprod. 2021; 25(3): 422-7. https://dx.doi.org/10.5935/1518-0557.20210003.
  26. Fedorcsak P., Dale P.O., Storeng R., Ertzeid G., Bjercke S., Oldereid N. et al. Impact of overweight and underweight on assisted reproduction treatment. Hum. Reprod. 2004; 19(11): 2523-8. https://dx.doi.org/10.1093/humrep/deh485.
  27. Yang Z., Zhao X., Hu X., Ou X., Yin T., Yang J., Wu G. Body mass index showed no impact on the outcome of in vitro fertilization in progestin-primed ovarian stimulation protocol. Evid Based Complement. Alternat. Med. 2021; 2021: 9979972. https://dx.doi.org/10.1155/2021/9979972.
  28. Goldman R.H., Farland L.V., Thomas A.M., Zera C.A., Ginsburg E.S. The combined impact of maternal age and body mass index on cumulative live birth following in vitro fertilization. Am. J. Obstet. Gynecol. 2019; 221(6): 617.e1-617. e13. https://dx.doi.org/10.1016/j.ajog.2019.05.043.
  29. Паскарь С.С., Калугина А.С. Скрининговая оценка уровней тиреотропного гормона и пролактина и их влияние на исходы программ вспомогательных репродуктивных технологий у женщин молодого репродуктивного возраста. Проблемы репродукции. 2020; 26(6): 70-6. https://dx.doi.org/10.17116/repro20202606170.
  30. Zhang D., Yuan X., Zhen J., Sun Z., Deng C., Yu Q. Mildly higher serum prolactin levels are directly proportional to cumulative pregnancy outcomes in in-vitro fertilization/intracytoplasmic sperm injection cycles. Front. Endocrinol. (Lausanne). 2020; 11: 584. https://dx.doi.org/10.3389/fendo.2020.00584.
  31. Hornstein M.D. Lifestyle and IVF outcomes. Reprod. Sci. 2016; 23(12): 1626-9. https://dx.doi.org/10.1177/1933719116667226.
  32. Waylen A.L., Metwally M., Jones G.L., Wilkinson A.J., Ledger W.L. Effects of cigarette smoking upon clinical outcomes of assisted reproduction: a metaanalysis. Hum. Reprod. 2009; 15(1): 31-44. https://dx.doi.org/10.1093/humupd/dmn046.
  33. Fuentes A., Munoz A., Barnhart K., Arguello B., Diaz M., Pommer R. Recent cigarette smoking and assisted reproductive technologies outcome. Fertil. Steril. 2010; 93(1): 89-95. https://dx.doi.org/10.1016/j.fertnstert.2008.09.073.
  34. Zitzmann M., Rolf C., Nordhoff V. Male smokers have a decreased success rate for in vitro fertilization and intracytoplasmic sperm injection. Fertil. Steril. 2003; 79(Suppl. 3): 1550-4. https://dx.doi.org/10.1016/s0015-0282(03)00339-x.
  35. Benedict M.D., Missmer S.A., Vahratian A. Secondhand tobacco smoke exposure is associated with increased risk of failed implantation and reduced IVF success. Hum. Reprod. 2001; 26(9): 2525-31. https://dx.doi.org/10.1093/humrep/der226.
  36. Rossi B.V., Berry K.F., Hornstein M.D., Cramer D.W., Ehrlich S., Missmer S.A. Effect of alcohol consumption on in vitro fertilization. Obstet. Gynecol. 2011; 117(1): 136-42. https://dx.doi.org/10.1097/AOG.0b013e31820090e1.
  37. Klonoff-Cohen H., Bleha J., Lam-Kruglick P. A prospective study of the effects of female and male caffeine on the reproductive endpoints of IVF and gamete intra-fallopian transfer. Hum. Reprod. 2002; 17(7): 1746-54. https://dx.doi.org/10.1093/humrep/17.7.1746.
  38. Al-Saleh I., El-Doush I., Grisellhi B., Coskun S. The effect of caffeine consumption on the success rate of pregnancy as well various performance parameters of in-vitro fertilization treatment. Med. Sci. Monit. 2010; 16(12): CR598-605.
  39. Twigt J.M., Bolhuis M.E., Steegers E.A. The preconception diet is associated with the chance of ongoing pregnancy in women undergoing IVF/ICSI treatment. Hum. Reprod. 2012; 27(8): 2526-31. https://dx.doi.org/10.1093/humrep/des157.
  40. Vujkovic M., de Vries J.H., Lindemans J. The preconception mediterranean dietary pattern in couples undergoing in vitro fertilization/intracytoplasmic sperm injection treatment increases the chance of pregnancy. Fertil. Steril. 2010; 94(6): 2096-101. https://dx.doi.org/10.1016/j.fertnstert.2009.12.079.
  41. Scaravelli G., Zaca C., Levi Setti P.E., Livi C., Ubaldi F.M., Villani M.T. et al. Fertilization rate as a novel indicator for cumulative live birth rate: a multicenter retrospective cohort study of 9,394 complete in vitro fertilization cycles. Fertil. Steril. 2021; 116(3): 766-73. https://dx.doi.org/10.1016/j.fertnstert.2021.04.006.
  42. Datta A.K., Campbell S., Felix N., Harbhajan J.S., Nargund G. Oocyte or embryo number needed to optimize live birth and cumulative live birth rates in mild stimulation IVF cycles. Reprod. Biomed. Online. 2021; 43(2): 223-32. https://dx.doi.org/10.1016/j.rbmo.2021.02.010.
  43. Carosso A.R., van Eekelen R., Revelli A., Canosa S., Mercaldo N., Benedetto C., Gennarelli G. Women in advanced reproductive age: are the follicular output rate, the follicle-oocyte index and the ovarian sensitivity index predictors of live birth in an IVF cycle? J. Clin. Med. 2022; 11(3): 859. https://dx.doi.org/10.3390/jcm11030859.
  44. Прорубщикова М.Ю., Сутурина Л.В., Колесникова Л.И. Микроокружение ооцита: обзор литературы. Бюллетень восточно-сибирского научного центра сибирского отделения Российской академии медицинских наук. 2011; 5: 220-3.
  45. Шестакова М.А., Морозова Д.С., Рабаданова А.К., Созарукова М.М. Оценка антиоксидантного статуса фолликулярной жидкости у пациенток с бесплодием, проходящих процедуру экстракорпорального оплодотворения. Журнал акушерства и женских болезней. 2017; 66(5): 168
  46. Рабаданова А.К., Шалина Р.И., Гугушвили Н.А. Гемодинамика матки и состояние овариального резерва в оценке эффективности экстракорпорального оплодотворения. Вестник Российского государственного медицинского университета. 2018; 2: 52-9.
  47. Зазулина Я. А., Самыкина О. В., Минни гулов а Г. М., Мельников В. А. Возможности прогнозирования успешного исхода программы экстракорпорального оплодотворения и переноса эмбриона. Фундаментальные исследования. 2015; 1,ч.5: 941-5.
  48. Humaidan P. Future perspectives of POSEIDON stratifcation for clinical practice and research. Front. Endocrinol. (Lausanne). 2019; 10: 439. https://dx.doi.org/10.3389/fendo.2019.00439.
  49. McLernon D.J., Raja E.A., Toner J.P., Baker V.L., Doody K.J., Seifer D.B. et al. Predicting personalized cumulative live birth following in vitro fertilization. Fertil. Steril. 2022; 117(2): 326-38. https://dx.doi.org/10.1016/j.fertnstert.2021.09.015.
  50. Ratna M.B., Bhattacharya S., Abdulrahim B., McLernon D.J. A systematic review of the quality of clinical prediction models in in vitro fertilization. Hum. Reprod. 2020; 35(1): 100-16. https://dx.doi.org/10.1093/humrep/dez258.

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies