Literature DB >> 24242632

Comparison of two models predicting IVF success; the effect of time trends on model performance.

E R te Velde1, D Nieboer, A M Lintsen, D D M Braat, M J C Eijkemans, J D F Habbema, Y Vergouwe.   

Abstract

STUDY QUESTION: How well does the recently developed UK model predicting the success rate of IVF treatment (the 2011 Nelson model) perform in comparison with a UK model developed in the early 1990s (the Templeton model)? SUMMARY ANSWER: Both models showed similar performance, after correction for the increasing success rate over time of IVF. WHAT IS KNOWN ALREADY: For counselling couples undergoing IVF treatment it is of paramount importance to be able to predict success. Several prediction models for the chance of success after IVF treatment have been developed. So far, the Templeton model has been recommended as the best approach after having been validated in several independent patient data sets. The Nelson model, developed in 2011 and characterized by the largest development sample containing the most recently treated couples, may well perform better. STUDY DESIGN, SIZE, DURATION: We tested both models in couples that were included in a national cohort study carried out in the Netherlands between the beginning of January 2002 and the end of December 2004. PARTICIPANTS/MATERIALS, SETTING,
METHODS: We analysed the IVF cycles of Dutch couples with primary infertility (n = 5176). The chance of success was calculated using the two UK models that had been developed using the information collected in the Human Fertilisation and Embryology Authority database. Women were treated in 1991-1994 (Templeton) or 2003-2007 (Nelson). The outcome of success for both UK models is the occurrence of a live birth after IVF but the outcome in the Dutch data is an ongoing pregnancy. In order to make the outcomes compatible, we used a factor to convert the chance of live birth to ongoing pregnancy and use the overall terms 'success or no success after IVF'. The discriminative ability and the calibration of both models were assessed, the latter before and after adjustment for time trends in IVF success rates. MAIN RESULTS AND THE ROLE OF CHANCE: The two models showed a similarly limited degree of discriminative ability on the tested data (area under the receiver operating characteristic curve 0.597 for the Templeton model and 0.590 for the Nelson model). The Templeton model underestimated the success rate (observed 21% versus predicted 14%); the Nelson model overestimated the success rate (observed 21% versus predicted 29%). When the models were adjusted for the changing success rates over time, the calibration of both models considerably improved (Templeton observed 21% versus predicted 20%; Nelson observed 21% versus predicted 24%). LIMITATIONS, REASONS FOR CAUTION: We could only test the models in couples with primary infertility because detailed information on secondary infertile couples was lacking in the Dutch data. This shortcoming may have negatively influenced the performance of the Nelson model. WIDER IMPLICATIONS OF THE
FINDINGS: The changes in success rates over time should be taken into account when assessing prediction models for estimating the success rate of IVF treatment. In patients with primary infertility, the choice to use the Templeton or Nelson model is arbitrary.

Entities:  

Keywords:  IVF; comparative performance; external test data; external validation; prediction models

Mesh:

Year:  2013        PMID: 24242632     DOI: 10.1093/humrep/det393

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


  8 in total

1.  To what extent does Anti-Mullerian Hormone contribute to a better prediction of live birth after IVF?

Authors:  Catherine Rongieres; Carolina Colella; Philippe Lehert
Journal:  J Assist Reprod Genet       Date:  2014-11-05       Impact factor: 3.412

2.  External validation and calibration of IVFpredict: a national prospective cohort study of 130,960 in vitro fertilisation cycles.

Authors:  Andrew D A C Smith; Kate Tilling; Debbie A Lawlor; Scott M Nelson
Journal:  PLoS One       Date:  2015-04-08       Impact factor: 3.240

3.  Predicting the chances of a live birth after one or more complete cycles of in vitro fertilisation: population based study of linked cycle data from 113 873 women.

Authors:  David J McLernon; Ewout W Steyerberg; Egbert R Te Velde; Amanda J Lee; Siladitya Bhattacharya
Journal:  BMJ       Date:  2016-11-16

4.  Can we predict the IVF/ICSI live birth rate?

Authors:  José Luis Metello; Claudia Tomás; Pedro Ferreira
Journal:  JBRA Assist Reprod       Date:  2019-10-14

5.  Calibration: the Achilles heel of predictive analytics.

Authors:  Ben Van Calster; David J McLernon; Maarten van Smeden; Laure Wynants; Ewout W Steyerberg
Journal:  BMC Med       Date:  2019-12-16       Impact factor: 8.775

6.  Predicting the chance on live birth per cycle at each step of the IVF journey: external validation and update of the van Loendersloot multivariable prognostic model.

Authors:  Johanna Devroe; Karen Peeraer; Geert Verbeke; Carl Spiessens; Joris Vriens; Eline Dancet
Journal:  BMJ Open       Date:  2020-10-08       Impact factor: 2.692

7.  Nomogram for the cumulative live birth in women undergoing the first IVF cycle: Base on 26, 689 patients in China.

Authors:  Pengfei Qu; Lijuan Chen; Doudou Zhao; Wenhao Shi; Juanzi Shi
Journal:  Front Endocrinol (Lausanne)       Date:  2022-08-25       Impact factor: 6.055

8.  Definition by FSH, AMH and embryo numbers of good-, intermediate- and poor-prognosis patients suggests previously unknown IVF outcome-determining factor associated with AMH.

Authors:  Norbert Gleicher; Vitaly A Kushnir; Aritro Sen; Sarah K Darmon; Andrea Weghofer; Yan-Guang Wu; Qi Wang; Lin Zhang; David F Albertini; David H Barad
Journal:  J Transl Med       Date:  2016-06-10       Impact factor: 5.531

  8 in total

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