Literature DB >> 28108009

Which factors are most predictive for live birth after in vitro fertilization and intracytoplasmic sperm injection (IVF/ICSI) treatments? Analysis of 100 prospectively recorded variables in 8,400 IVF/ICSI single-embryo transfers.

Katarina Kebbon Vaegter1, Tatevik Ghukasyan Lakic2, Matts Olovsson3, Lars Berglund2, Thomas Brodin4, Jan Holte5.   

Abstract

OBJECTIVE: To construct a prediction model for live birth after in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) treatment and single-embryo transfer (SET) after 2 days of embryo culture.
DESIGN: Prospective observational cohort study.
SETTING: University-affiliated private infertility center. PATIENT(S): SET in 8,451 IVF/ICSI treatments in 5,699 unselected consecutive couples during 1999-2014. INTERVENTION(S): A total of 100 basal patient characteristics and treatment data were analyzed for associations with live birth after IVF/ICSI (adjusted for repeated treatments) and subsequently combined for prediction model construction. MAIN OUTCOME MEASURE(S): Live birth rate (LBR) and performance of live birth prediction model. RESULT(S): Embryo score, treatment history, ovarian sensitivity index (OSI; number of oocytes/total dose of FSH administered), female age, infertility cause, endometrial thickness, and female height were all independent predictors of live birth. A prediction model (training data set; n = 5,722) based on these variables showed moderate discrimination, but predicted LBR with high accuracy in subgroups of patients, with LBR estimates ranging from <10% to >40%. Outcomes were similar in an internal validation data set (n = 2,460). CONCLUSION(S): Based on 100 variables prospectively recorded during a 15-year period, a model for live birth prediction after strict SET was constructed and showed excellent calibration in internal validation. For the first time, female height qualified as a predictor of live birth after IVF/ICSI.
Copyright © 2016 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  IVF; live birth rate; multiple pregnancy; prediction model; single-embryo transfer

Mesh:

Year:  2017        PMID: 28108009     DOI: 10.1016/j.fertnstert.2016.12.005

Source DB:  PubMed          Journal:  Fertil Steril        ISSN: 0015-0282            Impact factor:   7.329


  46 in total

1.  Methodological approaches to analyzing IVF data with multiple cycles.

Authors:  Jennifer Yland; Carmen Messerlian; Lidia Mínguez-Alarcón; Jennifer B Ford; Russ Hauser; Paige L Williams
Journal:  Hum Reprod       Date:  2019-03-01       Impact factor: 6.918

2.  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.

Authors:  Philippe Merviel; Michel Menard; Rosalie Cabry; Florence Scheffler; Emmanuelle Lourdel; Marie-Thérèse Le Martelot; Sylvie Roche; Jean-Jacques Chabaud; Henri Copin; Hortense Drapier; Moncef Benkhalifa; Damien Beauvillard
Journal:  Reprod Sci       Date:  2020-09-04       Impact factor: 3.060

3.  Prediction of live birth and cumulative live birth rates in freeze-all-IVF treatment of a general population.

Authors:  Kemal Ozgur; Hasan Bulut; Murat Berkkanoglu; Levent Donmez; Kevin Coetzee
Journal:  J Assist Reprod Genet       Date:  2019-02-21       Impact factor: 3.412

Review 4.  The Role of Endometrial Stem/Progenitor Cells in Recurrent Reproductive Failure.

Authors:  Hannan Al-Lamee; Christopher J Hill; Florence Turner; Thuan Phan; Andrew J Drakeley; Dharani K Hapangama; Nicola Tempest
Journal:  J Pers Med       Date:  2022-05-11

5.  Predicting the probability of a live birth after a freeze-all based in vitro fertilization-embryo transfer (IVF-ET) treatment strategy.

Authors:  Hong Chen; Zi-Li Sun; Miao-Xin Chen; Yang Yang; Xiao-Ming Teng; Yun Wang; Yuan-Yuan Wu
Journal:  Transl Pediatr       Date:  2022-06

6.  Predicting clinical pregnancy using clinical features and machine learning algorithms in in vitro fertilization.

Authors:  Cheng-Wei Wang; Chao-Yang Kuo; Chi-Huang Chen; Yu-Hui Hsieh; Emily Chia-Yu Su
Journal:  PLoS One       Date:  2022-06-08       Impact factor: 3.752

7.  Effect of the time for embryo transfer from oocyte retrieval on clinical outcomes in freeze-all cycles: a retrospective cohort study.

Authors:  Shiping Chen; Yachao Yao; Yang Luo; Yuling Mao; Hanyan Liu; Hongzi Du; Xiangjin Kang; Lei Li
Journal:  Arch Gynecol Obstet       Date:  2020-01-04       Impact factor: 2.493

8.  Chromatin Protamination and Catsper Expression in Spermatozoa Predict Clinical Outcomes after Assisted Reproduction Programs.

Authors:  S Marchiani; L Tamburrino; F Benini; L Fanfani; R Dolce; G Rastrelli; M Maggi; S Pellegrini; E Baldi
Journal:  Sci Rep       Date:  2017-11-09       Impact factor: 4.379

9.  Key performance indicators score (KPIs-score) based on clinical and laboratorial parameters can establish benchmarks for internal quality control in an ART program.

Authors:  José G Franco; Claudia G Petersen; Ana L Mauri; Laura D Vagnini; Adriana Renzi; Bruna Petersen; M C Mattila; Vanessa A Comar; Juliana Ricci; Felipe Dieamant; João Batista A Oliveira; Ricardo L R Baruffi
Journal:  JBRA Assist Reprod       Date:  2017-06-01

10.  Multivariate analysis of the factors associated with live births during in vitro fertilisation in Southeast Asia: a cross-sectional study of 104,015 in vitro fertilisation records in Taiwan.

Authors:  Hsi-Cheng Yu; Wen-May Rei; Shu-Ti Chiou; Chung-Yeh Deng
Journal:  J Assist Reprod Genet       Date:  2021-06-01       Impact factor: 3.357

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