Article

A case study of the applicability of a prediction model for the selection of patients undergoing in vitro fertilization for single embryo transfer in another center

Erasmus Universiteit Rotterdam, Rotterdam, South Holland, Netherlands
Fertility and sterility (Impact Factor: 4.3). 07/2007; 87(6):1314-21. DOI: 10.1016/j.fertnstert.2006.11.052
Source: PubMed

ABSTRACT To evaluate the application in a different fertility clinic of a prediction model for selecting IVF patients for elective single embryo transfer.
Retrospective analysis of a large database obtained from a tertiary infertility center.
University medical center.
The model, derived at the "development center" was applied in 494 consecutive first IVF cycles carried out at the "application center."
After adjustment of embryo scoring system to be compatible with that used by the prediction model, it was applied to the development center data. A score chart for predicting the probability of singleton or twin pregnancy was constructed.
The area under the receiver operator curve (ROC) was determined to measure the ability of the model to discriminate between ongoing pregnancy and twin pregnancy. Calibration plots were made to assess agreement between predicted and observed pregnancy rates (PR).
The areas under the ROC for predicting ongoing pregnancy and twin pregnancy were 0.63 and 0.66, respectively. Insertion of a correction factor equivalent to the difference in odds ratios for ongoing PR between the two centers was required to improve the calibration of the model.
After adaptation, the model performed well in the application center.

0 Followers
 · 
57 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: To evaluate our elective single embryo transfer policy performed at 48/72 h and define predictive factors of pregnancy after frozen/thawed embryo transfer.
    Gynécologie Obstétrique & Fertilité 03/2015; 43(4). DOI:10.1016/j.gyobfe.2015.02.007 · 0.58 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Significant progress has been made in several fields of medicine towards personalizing treatment recommendations based on individual patient genotype. As the number of clinical and genetic biomarkers available to physicians has increased, predictive models able to integrate the contributions of multiple variables simultaneously have become valuable tools for medical decision making. Leveraging genotype information and multivariate predictive models holds the promise of bringing greater efficiency to, and reducing the costs of, fertility treatments. This work reviews the advances that have been made in genetic biomarker discovery and predictive modelling for fertility treatment outcomes. We also discuss some of the limitations of these studies for translation to clinical diagnostics and the challenges that remain. Personalized medicine holds the promise of allowing doctors to create ‘bespoke’ treatment recommendations for each patient based on multiple clinical variables such as age and hormone concentrations combined with the patient’s genetic sequence information. A number of challenges remain for the field of reproductive medicine to make the research discoveries necessary to usher in this new era of personalized fertility care. Here, we discuss some of these challenges and make recommendations for overcoming them.
    Reproductive biomedicine online 09/2013; DOI:10.1016/j.rbmo.2013.09.010 · 2.98 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Since the introduction of in vitro fertilization (IVF) in 1978, over five million babies have been born worldwide using IVF. Contrary to the perception of many, IVF does not guarantee success. Almost 50% of couples that start IVF will remain childless, even if they undergo multiple IVF cycles. The decision to start or pursue with IVF is challenging due to the high cost, the burden of the treatment, and the uncertain outcome. In optimal counseling on chances of a pregnancy with IVF, prediction models may play a role, since doctors are not able to correctly predict pregnancy chances. There are three phases of prediction model development: model derivation, model validation, and impact analysis. This review provides an overview on predictive factors in IVF, the available prediction models in IVF and provides key principles that can be used to critically appraise the literature on prediction models in IVF. We will address these points by the three phases of model development.
    Journal of Advanced Research 05/2014; 5(3):295–301. DOI:10.1016/j.jare.2013.05.002