Literature DB >> 32886340

Can Ratios Between Prognostic Factors Predict the Clinical Pregnancy Rate in an IVF/ICSI Program with a GnRH Agonist-FSH/hMG Protocol? An Assessment of 2421 Embryo Transfers, and a Review of the Literature.

Philippe Merviel1,2, Michel Menard3, Rosalie Cabry4, Florence Scheffler4, Emmanuelle Lourdel4, Marie-Thérèse Le Martelot3, Sylvie Roche3, Jean-Jacques Chabaud3, Henri Copin4, Hortense Drapier3, Moncef Benkhalifa4, Damien Beauvillard3.   

Abstract

None of the models developed in in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) is sufficiently good predictors of pregnancy. The aim of this study was to determine whether ratios between prognostic factors could predict the clinical pregnancy rate in IVF/ICSI. We analyzed IVF/ICSI cycles (based on long GnRH agonist-FSH protocols) at two ART centers (the second to validate externally the data). The ratios studied were (i) the total FSH dose divided by the serum estradiol level on the hCG trigger day, (ii) the total FSH dose divided by the number of mature oocytes, (iii) the serum estradiol level on the trigger day divided by the number of mature oocytes, (iv) the serum estradiol level on the trigger day divided by the endometrial thickness on the trigger day, (v) the serum estradiol level on the trigger day divided by the number of mature oocytes and then by the number of grade 1 or 2 embryos obtained, and (vi) the serum estradiol level on the trigger day divided by the endometrial thickness on the trigger day and then by the number of grade 1 or 2 embryos obtained. The analysis covered 2421 IVF/ICSI cycles with an embryo transfer, leading to 753 clinical pregnancies (31.1% per transfer). Four ratios were significantly predictive in both centers; their discriminant power remained moderate (area under the receiver operating characteristic curve between 0.574 and 0.610). In contrast, the models' calibration was excellent (coefficients: 0.943-0.978; p < 0.001). Our ratios were no better than existing models in IVF/ICSI programs. In fact, a strongly discriminant predictive model will be probably never be obtained, given the many factors that influence the occurrence of a pregnancy.

Entities:  

Keywords:  IVF/ICSI; predictive factor; predictive model; pregnancy; validation

Year:  2020        PMID: 32886340     DOI: 10.1007/s43032-020-00307-2

Source DB:  PubMed          Journal:  Reprod Sci        ISSN: 1933-7191            Impact factor:   3.060


  93 in total

1.  Detrimental effects of high-dose gonadotropin on outcome of IVF: making a case for gentle ovarian stimulation strategies.

Authors:  Peter Kovacs; Attila Sajgo; Steven G Kaali; Lubna Pal
Journal:  Reprod Sci       Date:  2012-02-28       Impact factor: 3.060

Review 2.  A comparison of goodness-of-fit tests for the logistic regression model.

Authors:  D W Hosmer; T Hosmer; S Le Cessie; S Lemeshow
Journal:  Stat Med       Date:  1997-05-15       Impact factor: 2.373

3.  Embryo score is a better predictor of pregnancy than the number of transferred embryos or female age.

Authors:  P Terriou; C Sapin; C Giorgetti; E Hans; J L Spach; R Roulier
Journal:  Fertil Steril       Date:  2001-03       Impact factor: 7.329

4.  Gonadotropin dose is negatively correlated with live birth rate: analysis of more than 650,000 assisted reproductive technology cycles.

Authors:  Valerie L Baker; Morton B Brown; Barbara Luke; George W Smith; James J Ireland
Journal:  Fertil Steril       Date:  2015-08-18       Impact factor: 7.329

5.  High FSH dosing is associated with reduced live birth rate in fresh but not subsequent frozen embryo transfers.

Authors:  Erika M Munch; Amy E Sparks; M Bridget Zimmerman; Bradley J Van Voorhis; Eyup Hakan Duran
Journal:  Hum Reprod       Date:  2017-07-01       Impact factor: 6.918

6.  ART in Europe, 2014: results generated from European registries by ESHRE: The European IVF-monitoring Consortium (EIM) for the European Society of Human Reproduction and Embryology (ESHRE).

Authors:  Ch De Geyter; C Calhaz-Jorge; M S Kupka; C Wyns; E Mocanu; T Motrenko; G Scaravelli; J Smeenk; S Vidakovic; V Goossens
Journal:  Hum Reprod       Date:  2018-09-01       Impact factor: 6.918

Review 7.  Prediction models in reproductive medicine: a critical appraisal.

Authors:  Esther Leushuis; Jan Willem van der Steeg; Pieternel Steures; Patrick M M Bossuyt; Marinus J C Eijkemans; Fulco van der Veen; Ben W J Mol; Peter G A Hompes
Journal:  Hum Reprod Update       Date:  2009-05-12       Impact factor: 15.610

8.  A systematic review of the quality of clinical prediction models in in vitro fertilisation.

Authors:  M B Ratna; S Bhattacharya; B Abdulrahim; D J McLernon
Journal:  Hum Reprod       Date:  2020-01-01       Impact factor: 6.918

9.  Cumulative live birth rate and assisted reproduction: impact of female age and transfer day.

Authors:  M I Abuzeid; O Bolonduro; J La Chance; T Abozaid; M Urich; K Ullah; T Ali; M Ashraf; I Khan
Journal:  Facts Views Vis Obgyn       Date:  2014

10.  FSH dose to stimulate different patient' ages: when less is more.

Authors:  Edson Borges; Bianca F Zanetti; Amanda S Setti; Daniela Paf Braga; Rita de Cássia S Figueira; Assumpto Iaconelli
Journal:  JBRA Assist Reprod       Date:  2017-12-01
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