Literature DB >> 20467804

Antral follice counts (AFC) predict ovarian response and pregnancy outcomes in oocyte donation cycles.

Alaina Vrontikis1, Peter L Chang, Peter Kovacs, Steven R Lindheim.   

Abstract

PURPOSE: Antral follicle count (AFC) is used as a marker of ovarian response. We assessed its value in predicting pregnancy outcomes in ovum donation cycles by retrospective review.
METHODS: Oocyte donors (n = 94) underwent ovarian hyperstimulation using rFSH and GnRH-antagonists. Recipients were synchronized using GnRH-agonist down-regulation followed by fixed dose of estrogen and progesterone following hCG. Outcomes measured included correlation of AFC to pregnancy outcomes and cycle characteristics in those with and without clinical and ongoing-delivered cycles.
RESULTS: AFC significantly correlated with clinical [Exp beta 1.12; 95% CI: 1.02-1.23, p < 0.05] and ongoing-delivered pregnancy [Exp beta 1.10; 95% CI: 1.01-1.20, p < 0.05]. Significantly greater AFC, total and M-2 oocytes, and cycles resulting in cryopreserved embryos were seen in clinical and ongoing-delivered cycles.
CONCLUSIONS: AFC predicts cycle stimulation responses and clinical outcomes and may serve as a guide for dosing protocols and in choosing to proceed with the most optimal cycle.

Mesh:

Substances:

Year:  2010        PMID: 20467804      PMCID: PMC2922700          DOI: 10.1007/s10815-010-9421-8

Source DB:  PubMed          Journal:  J Assist Reprod Genet        ISSN: 1058-0468            Impact factor:   3.412


  30 in total

1.  Basal follicle-stimulating hormone level is a better predictor of in vitro fertilization performance than age.

Authors:  J P Toner; C B Philput; G S Jones; S J Muasher
Journal:  Fertil Steril       Date:  1991-04       Impact factor: 7.329

2.  Day 3 serum inhibin-B is predictive of assisted reproductive technologies outcome.

Authors:  D B Seifer; G Lambert-Messerlian; J W Hogan; A C Gardiner; A S Blazar; C A Berk
Journal:  Fertil Steril       Date:  1997-01       Impact factor: 7.329

3.  Use of the antral follicle count to predict the outcome of assisted reproductive technologies.

Authors:  M Y Chang; C H Chiang; T T Hsieh; Y K Soong; K H Hsu
Journal:  Fertil Steril       Date:  1998-03       Impact factor: 7.329

4.  Inhibin, follicle-stimulating hormone, and age as predictors of ovarian response in in vitro fertilization cycles stimulated with gonadotropin-releasing hormone agonist-gonadotropin treatment.

Authors:  J Balasch; M Creus; F Fábregues; F Carmona; R Casamitjana; C Ascaso; J A Vanrell
Journal:  Am J Obstet Gynecol       Date:  1996-11       Impact factor: 8.661

5.  Pretreatment transvaginal ultrasound examination predicts ovarian responsiveness to gonadotrophins in in-vitro fertilization.

Authors:  C Tomas; S Nuojua-Huttunen; H Martikainen
Journal:  Hum Reprod       Date:  1997-02       Impact factor: 6.918

Review 6.  Prognostic assessment of ovarian reserve.

Authors:  R T Scott; G E Hofmann
Journal:  Fertil Steril       Date:  1995-01       Impact factor: 7.329

7.  Impact of repeated antral follicle counts on the prediction of poor ovarian response in women undergoing in vitro fertilization.

Authors:  László F J M M Bancsi; Frank J M Broekmans; Caspar W N Looman; J Dik F Habbema; Egbert R te Velde
Journal:  Fertil Steril       Date:  2004-01       Impact factor: 7.329

8.  Day 3 estradiol serum concentrations as prognosticators of ovarian stimulation response and pregnancy outcome in patients undergoing in vitro fertilization.

Authors:  F L Licciardi; H C Liu; Z Rosenwaks
Journal:  Fertil Steril       Date:  1995-11       Impact factor: 7.329

9.  Prognostic value of day 3 estradiol on in vitro fertilization outcome.

Authors:  D B Smotrich; E A Widra; P R Gindoff; M J Levy; J L Hall; R J Stillman
Journal:  Fertil Steril       Date:  1995-12       Impact factor: 7.329

10.  Combining cycle day 7 follicle count with the basal antral follicle count improves the prediction of ovarian response.

Authors:  Fatih Durmusoglu; Koray Elter; Pinar Yoruk; Mithat Erenus
Journal:  Fertil Steril       Date:  2004-04       Impact factor: 7.329

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  1 in total

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

Authors:  Liu Liu; Fujin Shen; Hua Liang; Zhe Yang; Jing Yang; Jiao Chen
Journal:  Diagnostics (Basel)       Date:  2022-02-14
  1 in total

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