ArticleAnti-Müllerian hormone-based prediction model for a live birth in assisted reproduction
Introduction
The prediction of assisted reproduction treatment outcome has been the focus of clinical research for many years, with a variety of prognostic models describing the probability of an ongoing pregnancy or a live birth following treatment. To date, models have predominantly been established using patient baseline characteristics and although these models have been heterogeneous in their performance they consistently demonstrate that certain patient characteristics are associated with IVF/intracytoplasmic sperm injection (ICSI) success, including female age (Bancsi et al., 2000, Bouckaert et al., 1994, Carrera-Rotllan et al., 2007, Commenges-Ducos et al., 1998, Hughes et al., 1998, Hunault et al., 2002, Hull et al., 1996, Lee et al., 2009, Lintsen et al., 2005, Minaretzis et al., 1998, Ottosen et al., 2007, Stolwijk et al., 1996, Stolwijk et al., 2000, Templeton et al., 1996, van Weert et al., 2008, Younis et al., 2009), duration of infertility (Haan et al., 1991, Lintsen et al., 2007, Younis et al., 2009), pregnancy history (Lintsen et al., 2007, Stolwijk et al., 2000, Templeton et al., 1996, van Weert et al., 2008), diagnostic category (Bancsi et al., 2000, Lintsen et al., 2007, van Weert et al., 2008) and body mass index (BMI; Ferlitsch et al., 2004, Verberg et al., 2008).
Alternative models have incorporated the characteristics of the intermediate results of the first treatment cycle thereby improving the accuracy of probability estimates for future cycles. The variables used in these models include the number of retrieved oocytes (Bouckaert et al., 1994, Hunault et al., 2002, Verberg et al., 2007), the fertilization rate and embryo number and quality (Hunault et al., 2002, Minaretzis et al., 1998, Ottosen et al., 2007, Verberg et al., 2007). Moreover it has been clearly demonstrated that in models predicting pregnancy based on intermediate results (at embryo transfer), the number of retrieved and fertilized oocytes have the highest prognostic value (Ferlitsch et al., 2004, Verberg et al., 2007). This suggests that any marker which can predict the number of retrieved oocytes prior to ovarian stimulation may be of value in initial baseline prognostic models (Bancsi et al., 2000, Carrera-Rotllan et al., 2007, Ferlitsch et al., 2004, Lee et al., 2009, Ottosen et al., 2007, Younis et al., 2009).
Recent studies have indicated that anti-Müllerian hormone (AMH) may constitute an important novel measure of ovarian reserve, with the current literature indicating that AMH is a superior marker for predicting ovarian response over either age of the patient, day-3 FSH, oestradiol or inhibin B levels, whereas the vast majority of studies have found AMH and antral follicle count to have similar predictive value (for review, see La Marca et al., 2009). Consistent with AMH being a strong correlate of oocyte yield, AMH has recently been proposed as a useful clinical marker for the prediction of both poor- and hyperresponses to ovarian stimulation (La Marca et al., 2009). In addition to reflecting the quantitative ovarian response, several authors have found a significant positive correlation between AMH concentrations and oocyte quality (Cupisti et al., 2007, Ebner et al., 2006, Hazout et al., 2004, Silberstein et al., 2006), fertilization rate (Lekamge, 2007) and embryo morphology (Silberstein et al., 2006). However, this relationship has not been confirmed by others (Lie Fong et al., 2008, Smeenk et al., 2007). Hence the possible prediction of qualitative aspects of assisted reproduction programmes by AMH measurement is largely controversial.
In one large study, AMH was shown to be associated with live birth independent of age after treatment (Nelson et al., 2007). More recently a further large cohort study demonstrated that serum AMH concentrations may predict live birth in women older than 34 (Lee et al., 2009). The aim of the present study was to assess whether serum AMH concentrations may be incorporated into a prediction model to enhance the prediction of a live birth in women undergoing their first IVF. In particular, the objective was to develop a simple multivariate score based on basal patients characteristics which was capable of predicting the outcome of the treatment cycle and to express this in a clean format which could be easily adopted into daily clinical practice.
Section snippets
Study population
This study analysed the database containing clinical and laboratory information on IVF treatment cycles carried out at the Mother–Infant Department of University Hospital, Modena between 2005 and 2008. These data were collected prospectively and recorded in the registered database in the fertility centre in Modena, Italy. Cycles were selected for analysis if all the following inclusion criteria were satisfied: (i) first IVF/ICSI attempt; (ii) a normal uterus and regular uterine cavity; (iii) no
Results
A total of 389 patients were selected on the basis of inclusion criteria. Eight cycles were cancelled because of wrong drug administration, hence 381 patients constituted the population included in the statistical analysis. Of 381 started cycles, 15 were cancelled during ovarian stimulation because of excessive ovarian response and 13 because of absent or insufficient ovarian response. Of the 353 patients who had an oocyte retrieval, three had no oocytes retrieved and three had no
Discussion
A number of factors have been reported as influencing the success of IVF either positively or negatively. A model which incorporates accurate estimates of the strength and independence of each factor in increasing or decreasing live birth rate would inevitably improve the advice underlying patient counselling on the basis of individualisation of likelihood of success. Furthermore, identification of patient characteristics which are directly linked with IVF outcome should enable
Acknowledgements
The authors thank Professor Richard Fleming for his constructive comments on a draft of the manuscript.
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Dr Antonio La Marca graduated in medicine in 1996 and completed his residency in obstetrics and gynaecology in 2001 and his PhD on biology of germinal cells in 2004 at University of Siena, Italy. Since then, he has worked at the Mother–Infant Department, University Hospital of Modena, Italy. His research areas are the physiology of ovarian function and its pharmacological manipulation. In recent years his research has been focused on anti-Müllerian hormone and ovarian reserve.