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

Since the birth of Louise Brown in 1978, over five million babies have been born worldwide using in vitro fertilization (IVF) [1] . The number of in vitro fertilization cycles has increased rapidly; in 2006, 458,759 cycles were reported in 32 European countries, 99,199 cycles in the USA and 50,275 cycles in Australia and New Zealand [2–4] . The number of cycles is increasing each year even further. The increase in IVF cycles is not caused by a sudden epidemic of infertility, but by increased access to IVF, and by an expansion of the indications for IVF. Initially, IVF was performed in couples with bilateral tubal occlusion [5] . In 1992, intracytoplasmic sperm injection (ICSI) was first introduced and initiated in couples with severe male subfertility [6] . Later on, IVF/ICSI was also applied in couples without an absolute indication for IVF, such as unexplained subfertility, cervical hostility, failed ovulation induction, endometriosis, or unilateral tubal pathology [7,8] . The major difference between the original indication and the indications for which IVF is conducted nowadays is that the couples with bilateral tubal pathology or severe male subfertility have a zero chance of natural conception and completely depend on IVF/ICSI for a pregnancy, while couples with the newer indications are subfertile: they do have chances of natural conception, which may or may not be better than with IVF. Despite the lack of evidence that IVF is effective in couples without an absolute IVF indication, IVF is often considered as a last resort for all subfertile couples regardless of the etiology of their subfertility [7–12] . Contrary to the perception of many, IVF does not guarantee success; almost 38–49% of couples that start IVF will remain childless, even if they undergo six IVF cycles [13] . Subfertile couples should therefore be well informed about the chances of success with IVF before starting their first or before continuing with a new IVF cycle. Based on a couple’s specific probability, one should decide whether the chances of success with IVF justify the burden, risks, and costs of the treatment. The threshold at which probability to start or to continue treatment may differ between different stakeholders, such as insurance companies, the tax payer, and the patients. In optimal counseling on chances of a pregnancy after IVF, pregnancy prediction models may play a role, since doctors are not able to correctly predict pregnancy chances [14,15] . Predictions made by clinicians on the basis of clinical experience or “gut-feeling” have only slight to fair reproducibility, indicating that these predictions are likely to be inaccurate [15] . The efforts to develop prediction models for IVF reflect the need for such models in clinical practice. This need can be explained by the inability of diagnostic tests to detect factors that indicate subfertility with near 100% certainty in patients. Accurate diagnostic tests would allow treatment to focus on specific factors [16] . As IVF is currently used as an empirical treatment and not as a causal intervention for a specific disorder, there is a strong need to distinguish between couples with a good and a poor prognosis [16] . In the absence of randomized clinical trials, evaluating the effectiveness of IVF prediction models can be used to counsel couples. The development of a prediction model can be divided into three phases: model derivation, model validation, and impact analysis [16,17] ( Fig. 1 ). In the model derivation phase, predictors are identified, based on prior knowledge, and the weight of each predictor (regression coefficient) is calculated. In the model validation phase, the performance of the model, i.e. model’s ability to predict outcome is evaluated, and also the “generalizability” or “transportability” of the model is evaluated. The third and final phase consists of impact analysis. The impact analysis establishes whether the prediction model improves doctors’ decisions by evaluating the effect on patient outcome [16,17] . 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: model derivation, model validation, and impact analysis.

The third and final phase in the evaluation of models is impact analysis; it establishes whether the prediction model improves decisions, in terms of quality or cost-effectiveness of patient care [17,57,58] . This can be evaluated in one setting (phase 3a) or in varied settings (phase 3b). Different study designs to evaluate the impact of a prediction model are possible, such as comparing the outcomes between patients randomly assigned to receive management guided by the prediction model and patients managed without the prediction model (care-as-usual). A less valid alternative is to ask fertility specialists to document therapeutic management decisions before and after being “exposed” to a model’s predictions. None of the existing IVF prediction models has reached the impact analysis phase yet.

The authors have declared no conflict of interest .

As IVF can be stressful physically and emotionally and is not without health risks, subfertile couples should thus be well informed about the chances for success with IVF before each cycle. Unfortunately at this point, there are no randomized controlled clinical trials comparing IVF with natural conception. Thus, the only way to counsel couples properly is by model-based prognosis. Over 21 articles have reported on the development and/or validation of prediction models in IVF. Of these 21 articles, only two models had a good performance after external validation. Impact analyses have not yet been performed for any of these models. Future research should focus more on updating existing prediction models and adjust or recalibrate them to the local circumstances or setting rather than developing new prediction models. This way prediction models may strengthen evidence-based, individualized decision-making and can contribute to a rational use of scarce resources.