| Literature DB >> 32886340 |
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