Literature DB >> 23925394

Individualized decision-making in IVF: calculating the chances of pregnancy.

L L van Loendersloot1, M van Wely, S Repping, P M M Bossuyt, F van der Veen.   

Abstract

STUDY QUESTION: Are we able to develop a model to calculate the chances of pregnancy prior to the start of the first IVF cycle as well as after one or more failed cycles? SUMMARY ANSWER: Our prediction model enables the accurate individualized calculation of the probability of an ongoing pregnancy with IVF. WHAT IS KNOWN ALREADY: To improve counselling, patient selection and clinical decision-making in IVF, a number of prediction models have been developed. These models are of limited use as they were developed before current clinical and laboratory protocols were established. STUDY DESIGN, SIZE, DURATION: This was a cohort study. The development set included 2621 cycles in 1326 couples who had been treated with IVF or ICSI between January 2001 and July 2009. The validation set included additional data from 515 cycles in 440 couples treated between August 2009 and April 2011. The outcome of interest was an ongoing pregnancy after transfer of fresh or frozen-thawed embryos from the same stimulated IVF cycle. If a couple became pregnant after an IVF/ICSI cycle, the follow-up was at a gestational age of at least 11 weeks. PARTICIPANTS/MATERIALS, SETTING,
METHODS: Women treated with IVF or ICSI between January 2001 and April 2011 in a university hospital. IVF/ICSI cycles were excluded in the case of oocyte or embryo donation, surgically retrieved spermatozoa, patients positive for human immunodeficiency virus, modified natural IVF and cycles cancelled owing to poor ovarian stimulation, ovarian hyperstimulation syndrome or other unexpected medical or non-medical reasons. MAIN RESULTS AND THE ROLE OF CHANCE: Thirteen variables were included in the final prediction model. For all cycles, these were female age, duration of subfertility, previous ongoing pregnancy, male subfertility, diminished ovarian reserve, endometriosis, basal FSH and number of failed IVF cycles. After the first cycle: fertilization, number of embryos, mean morphological score per Day 3 embryo, presence of 8-cell embryos on Day 3 and presence of morulae on Day 3 were also included. In validation, the model had moderate discriminative capacity (c-statistic 0.68, 95% confidence interval: 0.63-0.73) but calibrated well, with a range from 0.01 to 0.56 in calculated probabilities. LIMITATIONS, REASONS FOR CAUTION: In our study, the outcome of interest was ongoing pregnancy. Live birth may have been a more appropriate outcome, although only 1-2% of all ongoing pregnancies result in late miscarriage or stillbirth. The model was based on data from a single centre. WIDER IMPLICATIONS OF THE
FINDINGS: The IVF model presented here is the first to calculate the chances of an ongoing pregnancy with IVF, both for the first cycle and after any number of failed cycles. The generalizability of the model to other clinics has to be evaluated more extensively in future studies (geographical validation). Centres with higher or lower success rates could use the model, after recalibration, by adjusting the intercept to reflect the IVF success rates in their centre. STUDY FUNDING/COMPETING INTEREST(S): This project was funded by the NutsOhra foundation (Grant 1004-179). The NutsOhra foundation had no role in the development of our study, in the collection, analysis and interpretation of data; in writing of the manuscript, and in the decision to submit the manuscript for publication. There were no competing interests.

Entities:  

Keywords:  IVF/ICSI outcome; assisted reproduction; prediction model; pregnancy

Mesh:

Year:  2013        PMID: 23925394     DOI: 10.1093/humrep/det315

Source DB:  PubMed          Journal:  Hum Reprod        ISSN: 0268-1161            Impact factor:   6.918


  21 in total

1.  The role of serum AMH and FF AMH in predicting pregnancy outcome in the fresh cycle of IVF/ICSI: a meta-analysis.

Authors:  Lingnv Yao; Wei Zhang; Hong Li; Wenqin Lin
Journal:  Int J Clin Exp Med       Date:  2015-02-15

2.  A clinical counseling tool predicting supernumerary embryos after a fresh IVF cycle.

Authors:  Yetunde Ibrahim; Greg Stoddard; Erica Johnstone
Journal:  J Assist Reprod Genet       Date:  2020-03-09       Impact factor: 3.412

3.  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

4.  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

5.  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

6.  A predictive model for women's assisted fecundity before starting the first IVF/ICSI treatment cycle.

Authors:  Juan J Tarín; Eva Pascual; Miguel A García-Pérez; Raúl Gómez; Juan J Hidalgo-Mora; Antonio Cano
Journal:  J Assist Reprod Genet       Date:  2019-12-03       Impact factor: 3.412

7.  Soy Intake Modifies the Relation Between Urinary Bisphenol A Concentrations and Pregnancy Outcomes Among Women Undergoing Assisted Reproduction.

Authors:  Jorge E Chavarro; Lidia Mínguez-Alarcón; Yu-Han Chiu; Audrey J Gaskins; Irene Souter; Paige L Williams; Antonia M Calafat; Russ Hauser
Journal:  J Clin Endocrinol Metab       Date:  2016-01-27       Impact factor: 5.958

8.  Luteal phase ovarian stimulation following oocyte retrieval: is it helpful for poor responders?

Authors:  John Zhang
Journal:  Reprod Biol Endocrinol       Date:  2015-07-25       Impact factor: 5.211

Review 9.  Prediction models in in vitro fertilization; where are we? A mini review.

Authors:  Laura van Loendersloot; S Repping; P M M Bossuyt; F van der Veen; M van Wely
Journal:  J Adv Res       Date:  2013-05-09       Impact factor: 10.479

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

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.