Predicting Outcome of Treating Anovulation

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Predicting Outcome of Treating Anovulation

Yvonne V. Louwers and Evert J.P. van Santbrink

Introduction

Treatment of anovulatory infertility aims at restoring normal ovarian physiology, that is, mono-follicular growth and mono-ovulation. First-line ovulation induction treatment with anti-estrogen clomiphene citrate (CC) has the advantage of a high response rate and low costs as well as minor side effects and complications. In case of clomiphene-resistant anovulation (CRA) or failure to conceive (CRF), second-line treatment to induce ovulation consists of daily administration of exogenous FSH. To enhance the ovarian sensitivity for FSH stimulation, laparoscopic electrocoagulation of the ovaries (LEO, Chapter 18) and the use of insulin sensitizers (i.e., Metformin, Chapter 17) are proposed. These modalities are utilized in patients after CRA and may be combined with the use of CC or FSH. Although effective, treatment with CC and FSH is complicated by the limited control of ovarian response due to large inter- and intrapatient variability. Development of prediction models taking into account individual patient characteristics may be a step forward in optimizing the decision-making process in the treatment of normogonadotropic anovulation, resulting in a more patient-tailored treatment.

Overview of Existing Evidence

Clomiphene Citrate

Over the last decades, several prediction models for success with ovulation induction have been proposed. In general, these prediction models used clinically easily accessible parameters.

In a prospective longitudinal single-center study, Imani et al. developed a model to predict the individual chances of live birth after CC administration using two distinct prediction models combined in a normo-gram. Univariate and multivariate analyses were used to identify these predictors. In this study population, a cumulative conception rate of 73% was reached within nine CC-induced ovulatory cycles. Body mass index and hyperandrogenemia were observed to be the predominant predictors for ovulation after CC treatment whereas age and cycle history dictated pregnancy chances in ovulatory women (1). These conclusions have been largely confirmed about 10 years later in an independent study population (2).

FSH

In addition, similar models have been considered for FSH low-dose ovulation induction. Age, duration of infertility, and insulin/glucose ratio were combined to predict live birth rate in clomiphene-resistant anovulatory women receiving FSH treatment (3). In this study, clomiphene-resistant anovulatory women receiving FSH achieved a cumulative 2-year live birth rate of 71%. Cox regression was used for univari-ate and multivariate analysis relating initial screening characteristics to the cumulative pregnancy rate, leading to singleton live birth. Subsequently, the latter prediction model was validated in an independent cohort of patients with polycystic ovary syndrome (4). Veltman et al. also included BMI resulting in a better predictive index of live birth: 60% at 12 months and 78% at 24 months.

Insulin Sensitizers (See Also Chapter 17)

The presumed central role of insulin resistance in hyperandrogenism in PCOS is the reason that insulin sensitizers were introduced in ovulation induction. By lowering insulin resistance, ovarian dysfunction may diminish and ovarian responsiveness to FSH improve. This effect might be more evident in overweight (BMI >28) and insulin-resistant PCOS patients. Clinically, Metformin is proving to be effective as an adjuvant to CC in CRA patients. In only a few small studies, it is suggested that Metformin cotreatment in gonadotropin induction of ovulation results in a decreased amount of FSH needed, a significantly shorter stimulation period, and more monofollicular cycles.

Laparoscopic Electrocautery of the Ovaries (LEO) or Laparoscopic Drilling of the Ovaries (LOD, See Also Chapter 18)

Patient characteristics reported to predict chances for ovulation and pregnancy after LEO in a WHO 2 infertility population, failing to ovulate or conceive after CC treatment, were hyperandrogenism (T and FAI) and BMI whereas elevated LH serum levels increased chances for pregnancy (5). These data could not be confirmed by a smaller prospective study in a group of patients with CRA (6). Only age at men-arche and LH/FSH ratio were significantly related to treatment response.

Genetic Factors

Also in the field of pharmacogenetics, efforts have been made to identify genetic factors that influence the outcome of ovulation induction treatment. PCOS patients carrying the Ser680 allele in the follicle stimulating hormone receptor gene were more often clomiphene citrate–resistant than noncarriers (7). Furthermore, genetic variants in the STK11 gene seem associated with ovulation induction outcome in PCOS (8).

Discussion

Interestingly, following CC treatment, approximately 70%–80% of the women gained ovulations whereas only 40%–50% of them will conceive. Different predictive parameters are likely underlying this large discrepancy between successful regaining ovulations and actual pregnancy rate, insinuating that anovulatory is a complex multifactorial phenomenon. Taking this into account, it seems obvious that one treatment regimen will be not be suitable for all patients’ anovulatory subfertility. Current prediction models for ovulation induction outcome have been developed in single center prospective follow-up studies. Large multicenter randomized controlled trials are lacking in identifying predictors of ovulation induction outcome. The performance of these prediction models is often evaluated with a receiver operating characteristic (ROC) curve. As a measure of model performance, usually the area under the ROC curve (AUC), also known as c-statistic, is used. However, in the field of reproductive medicine, the value for this c-statistic is low because it only expresses discrimination and is, as such, not a good measure of the extent to which predictive models can be used in clinical practice (9). Therefore, we need to realize that in reproductive medicine, prognostic models that perfectly predict pregnancy in anovulatory subfertility most likely do not exist. However, this does not mean that these prediction models can be supportive in terms of clinical decision making (see further Table 27.3).

Conclusions

Body mass index and hyperandrogenemia seem to be the predominant predictors for ovulation after CC treatment whereas age and cycle history dictate pregnancy chances in ovulatory women. Apart from these clinical and endocrine parameters, the genetically determined sensitivity of the FSH receptor might be an important factor too in predicting treatment outcome. This implies that genetic data might have a role in further optimizing the existing patient-tailored strategies in ovulation induction treatment in anovulatory women. Obviously, it is a major challenge to combine clinical, endocrine, and genetic factors to reliably predict outcome of ovulation induction and, even more importantly, healthy live birth.

TABLE 27.3

Predictors for Success (+) or Failure (–) of Ovulation Induction Treatment

TABLE 27.1

Level of Evidence of Statements

TABLE 27.2

Grade of Strength for Recommendations

REFERENCES

1. Imani B, Eijkemans MJ, te Velde ER, Habbema JD, Fauser BC. A nomogram to predict the probability of live birth after clomiphene citrate induction of ovulation in normogonadotropic oligoamenorrheic infertility. Fertil Steril. 2002; 77:91–7.

2. Rausch ME, Legro RS, Barnhart HX, Schlaff WD, Carr BR, Diamond MP et al. Predictors of pregnancy in women with polycystic ovary syndrome. J Clin Endocrinol Metab. 2009; 94:3458–66.

3. Eijkemans MJ, Imani B, Mulders AG, Habbema JD, Fauser BC. High singleton live birth rate following classical ovulation induction in normogonadotrophic anovulatory infertility (WHO 2). Hum Reprod. 2003; 18:2357–62.

4. Veltman-Verhulst SM, Fauser BC, Eijkemans MJ. High singleton live birth rate confirmed after ovulation induction in women with anovulatory polycystic ovary syndrome: Validation of a prediction model for clinical practice. Fertil Steril. 2012; 98:761–8, e761.

5. Amer SA, Li TC, Ledger WL. Ovulation induction using laparoscopic ovarian drilling in women with polycystic ovarian syndrome: Predictors of success. Hum Reprod. 2004; 19:1719–24.

6. van Wely M, Bayram N, van der Veen F, Bossuyt PM. Predictors for treatment failure after laparoscopic electrocautery of the ovaries in women with clomiphene citrate resistant polycystic ovary syndrome. Hum Reprod. 2005; 20:900–5.

7. Simoni M, Tempfer CB, Destenaves B, Fauser BC. Functional genetic polymorphisms and female reproductive disorders: Part 1: Polycystic ovary syndrome and ovarian response. Hum Reprod Update. 2008; 14:459–84.

8. Legro RS, Barnhart HX, Schlaff WD, Carr BR, Diamond MP, Carson SA et al. Ovulatory response to treatment of polycystic ovary syndrome is associated with a polymorphism in the STK11 gene. J Clin Endocrinol Metab. 2008; 93:792–800.

9. Coppus SF, van der Veen F, Opmeer BC, Mol BW, Bossuyt PM. Evaluating prediction models in reproductive medicine. Hum Reprod. 2009; 24:1774–8.

10. Moll E, Korevaar JC, Bossuyt PM, van der Veen F. Does adding metformin to clomifene citrate lead to higher pregnancy rates in a subset of women with polycystic ovary syndrome? Hum Reprod. 2008; 23:1830–4

11. Palomba S, Orio F Jr, Nardo LG, Falbo A, Russo T, Corea D et al. Metformin administration versus laparoscopic ovarian diathermy in clomiphene citrate-resistant women with polycystic ovary syndrome: A prospective parallel randomized double-blind placebo-controlled trial. J Clin Endocrinol Metab. 2004; 89:4801–9.

12. Creanga AA, Bradley HM, McCormick C, Witkop CT. Use of metformin in polycystic ovary syndrome: A meta-analysis. Obstet Gynecol. 2008; 111:959–68.

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