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A revised prediction model for natural conception

Reproductive BioMedicine Online

Editorial Comment

The ability to predict a couple's chances of spontaneous conception over a certain period would be an invaluable asset for reproductive counselling. The Hunault Model, which is freely available on the web ( has been a useful tool, despite its limited generalizability to other settings than the Netherlands, where it was developed and validated. Bensdorp and co-authors herald a revised version after 10 years of its development, which now include more predictive variables resulting in generalizability to a wider population. The model needs to be validated and perhaps fine-tuned to local conditions.


  • The Hunault model is the standard prediction model for natural conception.
  • This model can be revised by including additional predictors.
  • The revised model is applicable to a broader population.
  • The revised model needs to be externally validated.


One of the aims in reproductive medicine is to differentiate between couples that have favourable chances of conceiving naturally and those that do not. Since the development of the prediction model of Hunault, characteristics of the subfertile population have changed. The objective of this analysis was to assess whether additional predictors can refine the Hunault model and extend its applicability.

Consecutive subfertile couples with unexplained and mild male subfertility presenting in fertility clinics were asked to participate in a prospective cohort study. We constructed a multivariable prediction model with the predictors from the Hunault model and new potential predictors. The primary outcome, natural conception leading to an ongoing pregnancy, was observed in 1053 women of the 5184 included couples (20%). All predictors of the Hunault model were selected into the revised model plus an additional seven (woman's body mass index, cycle length, basal FSH levels, tubal status,history of previous pregnancies in the current relationship (ongoing pregnancies after natural conception, fertility treatment or miscarriages), semen volume, and semen morphology. Predictions from the revised model seem to concur better with observed pregnancy rates compared with the Hunault model; c-statistic of 0.71 (95% CI 0.69 to 0.73) compared with 0.59 (95% CI 0.57 to 0.61).

Keywords: Natural conception, Prediction model, Prognosis, Subfertility.


One of the aims in reproductive medicine is to differentiate between couples with favourable chances and those with unfavourable chances of natural conception, as treating couples with favourable chances implies overtreatment, causing unnecessary risks and costs. The tools to do this are prediction models.

In the past decade, several prediction models for natural conception have been developed (Leushuis et al., 2009). Currently, the established standard prediction model for natural conception is the Hunault model, also known as the synthesis model, which was based on the original data collected in three cohorts of subfertile couples between 1974 and 1995 (Collins et al, 1995, Eimers et al, 1994, and Snick et al, 1997). Of all the prediction models for natural conception, it is the one with the best calibration, and it has been externally validated (van der Steeg et al., 2007b). The synthesis Hunault model encompasses five predictors: female age, duration of subfertility, whether female subfertility is primary or secondary, sperm motility, and referral status. Data used for construction of the model was restricted to a subfertile population in whom female age was below 40 years, and in whom any tubal pathology and azoospermia had been excluded (Hunault et al., 2004).

Since the development and external validation of the synthesis Hunault model, characteristics of the subfertile population have changed. Obesity is increasing rapidly worldwide, and also affects women in their reproductive age (Haslam, James, 2005 and Talmor, Dunphy, 2015). Also, women tend to postpone their child wish, because of their career or other reasons (Mills et al., 2011). Consequently, ovarian reserve tests have been introduced as part of the fertility work-up. In addition, tests already incorporated into routine fertility work-up, such as semen analysis and tubal patency tests, are now reported in more detail than during the time the Hunault model was developed (Leushuis et al, 2010, van der Steeg et al, 2011, and Verhoeve et al, 2011).

It is, therefore, currently unclear whether the natural conception chances predicted by the Hunault model are reliable, either because possible predictors are not taken into account by the model, or because characteristics of a couple may differ substantially from the data that the model was developed upon. The aim of this study was to assess whether we could build an improved prediction model with additional predictors for the contemporary subfertile population, i.e. including couples with one-sided tubal pathology, severe male subfertility and maximum female age of 45 years.

Materials and methods

Data for our analysis were collected in a prospective cohort study carried out across 38 hospitals in The Netherlands, between January 2002 and February 2004. These data have been used previously to validate the original Hunault model. The detailed study protocol has been described elsewhere (van der Steeg et al., 2007b). In short, the cohort consisted of consecutive subfertile couples who had completed their fertility work-up. Couples referred by a gynaecologist for fertility treatment were not included in this study. In the present analysis, previously published data, obtained from a study that was approved by the local ethics committee of each participating centre, was used.

Work-up of the woman

Work-up for women involved age, duration of subfertility, body mass index (BMI), information about cycle length, assessment of ovulation, basal FSH, patency of the Fallopian tubes and a detailed pregnancy history. Female age was calculated when the fertility work-up was completed, and couples with a female age of 45 years and above were excluded. Duration of subfertility was defined as the period between the moment the couple were attempting to conceive and the moment at which the infertility work-up was completed. If a previous pregnancy in the current relationship had not resulted in a live birth, the duration of subfertility was defined as the period between the renewed time of desired pregnancy and the moment at which the infertility work-up was completed. Subfertility was considered to be secondary if a woman had conceived in the current or in a prior partnership, regardless of the pregnancy outcome. Secondary subfertility was categorized according to the outcome, localization, origin and mode of conception of previous (clinical) pregnancy (van der Steeg et al., 2007b). The menstrual cycle was considered regular if the duration of the cycle was between 23 and 35 days. Ovulation was assessed by means of a basal body temperature chart, measurement of mid–luteal serum progesterone or by sonographic monitoring of the cycle. Women with an ovulation disorder, i.e. an irregular menstrual cycle or anovulation, were not included in the study. If there was a reason to suspect pathology i.e. endometrioma or fibroids, then an ultrasound was carried out. If confirmed, a couple was no longer eligible to participate in the study.

Basal FSH levels were measured at least once on cycle day two to four. Tubal pathology was assessed directly by hysterosalpingography or laparoscopy, or by a chlamydia antibody test (CAT). In case of a positive CAT, a hysterosalpingography or laparoscopy was subsequently carried out. Double-sided tubal pathology was an exclusion criterion.

Work-up of the man

Work-up for men consisted of a semen analysis, family history of subfertility and history of a previous pregnancy in the current or in a previous relationship. Semen analysis was carried out at least once and took into account sperm volume, semen concentration, morphology and motility. If semen analysis was carried out more than once, the average of the samples was used. Results were classified according to the World Health Organization (WHO) criteria (WHO, 1999). Couples with a total motile sperm count less than 1 × 106 were excluded from the study.


After completion of the fertility work-up, the probability of natural conception within 1 year, leading to live birth was calculated using the Hunault model (van der Steeg et al., 2007b). Twelve months was the time frame also used in the construction of the synthesis model of Hunault.

Couples were counselled for expectant management if the probability of natural conception within 1 year was higher than 30%; otherwise, fertility treatment was advised according to the national fertility guidelines (Dutch society of Obstetrics and Gynaecology, 2010). If couples declined expectant management, they started with intrauterine insemination with ovarian stimulation.

Follow-up started at completion of the fertility work-up, and ended after 12 months. The primary outcome was natural conception resulting in an ongoing pregnancy. Ongoing pregnancy was defined as the presence of fetal cardiac activity at transvaginal sonography at a gestational age of at least 12 weeks. Time to natural conception was measured in months. Because couples were only at risk of a natural conception until they initiated fertility treatment, time to pregnancy was considered censored whenever such treatment started.

Data analysis

Missing data of predictive variables were imputed, because deleting them would lead to a loss of statistical power in multivariable analysis and, more seriously, potentially biased results (Little, Rublin, 1987 and Schafer, 1997). We generated 10 imputed datasets, using the ‘aRegImpute’ imputation function in R.3.0.2 (Schafer and Graham, 2002). Models were fit on each of the 10 imputed datasets, and Rubin's rule was used to obtain aggregated estimates of coefficients and standard errors.

For the construction of the new, revised model, variables of interest were the factors of the Hunault model and additional potential predictors: cycle length and regularity, dysmenorrhoea, female age, basal FSH levels, female BMI, a positive CAT or a history of chlamydia, one-sided tubal pathology, previous pelvic surgery in the female, whether a post-coital test was carried out, referral status, previous intrauterine ongoing pregnancy after natural conception or after fertility treatments, previous miscarriage, previous induced abortion (all in the current relationship), previous pregnancy in another relationship of the male, history of infertility in the family of the male, and the other semen parameters, (volume, concentration and morphology) and smoking of both partners. Also, we included the censoring indicator and follow-up time to pregnancy in the imputation process. The categorical variables were all dichotomous indicators, and continuous variables FSH, BMI, semen volume, concentration and percentage of normal forms were transformed to better accommodate non-linear associations (van der Steeg et al, 2007a, van der Steeg et al, 2008a, van der Steeg et al, 2008b, van der Steeg et al, 2011, and Verhoeve et al, 2011).

Most participating centres had used the WHO criteria to assess semen morphology, but, in 25% of patients, strict Kruger criteria were applied (Kruger et al., 1987). These Kruger morphology values were converted into WHO values, keeping the ordering of patients identical, assuming that the distribution of values would be similar.

After construction of the revised prediction model, we aimed to develop a parsimonious model by applying a stepwise backwards elimination procedure for all variables. As the incorrect exclusion of a factor would be more deleterious than the incorrect inclusion of too many factors, we used a non-stringent critical P-value of 0.30 as the criterion for elimination from the model (Steyerberg et al., 1999).

To assess the amount of overfitting of the new model, internal validation was conducted with bootstrapping on the first imputed dataset. Bootstrapping is a technique in which datasets of identical sample size are created from the original study group by repeated resampling with replacement to evaluate model performance in the population from which the study group is sampled. We bootstrapped 200 times. In each of these 200 new datasets, the same backward elimination was carried out to arrive at a multivariable Cox regression model. A shrinkage factor was calculated to adjust the model for overfitting, i.e. inbuilt optimism. For clinical practice, we constructed a score chart that encompasses all variables from the revised model. We determined the points in the score chart from the regression coefficients of the Cox model, which are on the log hazard ratio scale. We multiplied the coefficients to attain a number between zero (minimum score) and 30 (maximum score), and then rounded to whole numbers to produce the points.

To make a fair comparison between the revised model and the Hunault model, we needed a cohort with similar characteristics as the one Hunault was externally validated upon. Therefore, we used the same cohort that we described above for the construction of the refined model, and excluded couples with total motile spem count less than 3 × 106, female age over 40 years and one-sided tubal pathology. This ‘restricted cohort’, as we shall label it from here on, consists of 2807 couples. Discrimination and calibration were compared in both models in this restricted cohort. Predictions with the revised model were calculated by cross-validation at the centre level. This means that the probability of conceiving naturally for a couple in a particular centre was computed with a model constructed on data from all the other centres. This way, discrimination and calibration of the model could be evaluated using data not used for the construction of the model. To evaluate the discriminative performance of the prediction models, the concordance or c-statistic (identical to the area under the receiver operating characteristic curve) was calculated.

The gain in discrimination from the revised model was expressed both in terms of the difference in c-statistic and in terms of the Net Reclassification Index. This is a measure to express the differences between both models in the individual calculated chance of a pregnancy and the actual occurrence of a pregnancy. This is evaluated in those couples in whom predictions by both models are discordant, i.e. where the predicted probability of natural conception with the original model is low, and the revised model finds a high probability or vice versa.

Calibration of the models was conducted by assigning the calculated 1-year probabilities of natural conception leading to ongoing pregnancy for each included couple to one of 10 categories, based on deciles, and comparing the average probability in each category with the 1-year Kaplan–Meier estimate. We used a given cut-off on the predicted 1-year chance of pregnancy of 30%, classifying patients as high (≥30%) or low (<30%) chance, as it has been established that intrauterine insemination with ovarian stimulation is not effective in couples with natural conception chances greater than 30% (Steures et al., 2006).


Pregnancy status at the end of follow-up was known for 6730 of the 7860 registered couples in the database. For the 17 variables of interest, 12% of the data were missing, mostly in the variables BMI, FSH and family history of the male.

After imputation, 5184 couples fulfilled the inclusion criteria. Baseline characteristics of these couples are shown in Table 1. Median female age was 32.5 years; the median duration of subfertility was 1.6 years. Sixty-five per cent of couples had not been pregnant before.

Table 1

Baseline characteristics.


(n = 5184)
Median 5–95th percentile
Female age (years) 32.5 24.9–39.4
Duration subfertility (years) 1.6 1.0–4.9
Female body mass index (kg/ m2) 22.9 18.7–33.6
Cycle length (days) 27.9 24.4–33.2
Basal FSH (day 2–4) U/l 7.0 3.4–11.9
Third line referral (n, %) 516(10)
Subfertility, primary (%) 65
Previous pregnancy current relationship (n,%)a
 Previous ongoing pregnancy 818(16)
 Previous miscarriage 596(11)
 Previous induced abortion 84(1.6)
 Previous pregnancy infertility treatment 146(2.8)
Previous pregnancy man previous partner(%) 568(11)
Semen TMC (per million) 48 2.4–283

a Women that conceived more than once with different pregnancy outcomes contribute to more than one row.

TMC, total motile sperm count.

Median time of follow-up of censored couples was 7.7 months, and 2125 couples initiated fertility treatment within 1 year. Natural conception leading to an ongoing pregnancy was observed in 1053 (20%) women. Estimated cumulative chances for ongoing pregnancy at 3, 6, 9 and 12 months were 9.9 %, 16%, 22% and 26%, respectively.

All predictors that are included in the Hunault model were also selected for the construction of the refined model (Table 2). Women's cycle length, BMI, basal FSH levels, tubal status and history of previous pregnancies in the current relationship which could be ongoing pregnancies after natural conception, fertility treatment, or miscarriages, semen volume and morphology, seemed to be additional predictors. A history of subfertility in the family of the man was of no additional prognostic value. Internal validation by bootstrapping led to an estimated shrinkage factor of 0.96, indicating negligible overfitting of 4% and good calibration (Table 2). Also, in internal validation, the model had a moderate discriminative capacity (c-statistic 0.73, 95% CI 0.71 to 0.74). A score chart with the selected variables that can be used in clinical practice is shown in Figure 1.

Table 2

Results of the Cox regression analyses.


(n = 5184)
Univariable analysis Multivariable analysis
Hazard ratio 95 % CI Hazard ratio 95% CI
Female age (per year <31) 0.97 0.94to1.00 0.96 0.94to0.99
Female age (per year ≥31) 0.94 0.91to0.96 0.97 0.93to1.02
Duration of subfertility 0.75 0.69to0.80 0.76 0.70to0.82
Female BMI per point > 29 kg/ m2 0.97 0.94to1.01 0.97 0.93to1.00
Cycle length (per day shorter) 0.96 0.93to0.98 0.97 0.94to1.00
FSH (per IU/l ≥ 8) 0.95 0.90to0.99 0.96 0.92to1.01
Tubapathology not diagnosed or negative CAT 2.62 2.26to3.04 2.93 2.53to3.41
Tubapathology one-sided 0.67 0.44to1.02 0.70 0.46to1.06
Third line of referral 0.44 0.33to0.59 0.56 0.41to0.75
Previous pregnancy current partnership
 Previous ongoing pregnancy 1.49 1.29to1.73 1.51 1.29to1.77
 Previous miscarriage 1.52 1.29to1.80 1.41 1.19to1.69
 Previous Induced abortion 1.47 1.01to2.14 1.49 1.01to2.20
 Previous pregnancy fertility treatment 0.81 0.50to1.30 0.73 0.45to1.19
Previous pregnancy male 0.61 0.48to0.78 0.75 0.59to0.95
Family history (male) of subfertilitya 1.11 0.81to1.54
Semen parameters
 Progressive, per 10% less 0.93 0.91to0.96 0.96 0.93to0.99
 Volume per ml, below 2 ml 0.83 0.67to1.03 0.86 0.69to1.06
 Concentration per 10% less than 40% 0.90 0.85to0.95 0.92 0.87to0.98
 Normal forms (per 10% below 20%) 0.87 0.77to0.99 0.87 0.76to1.00

a Variable was eliminated in the multivariable analysis.

BMI, body mass index. CAT, chlamydia antibody test.

Figure 1

Figure 1

Score chart for daily practice.


For the comparisons of the revised model to the Hunault mode, we used the restricted study cohort (n = 2807), the c-statistic was 0.71 (95% CI 0.69 to 0.73) for the revised model compared with 0.59 (95% CI 0.57 to 0.61) for the Hunault model. The calibration of the 1-year chances of an ongoing pregnancy from the Hunault model and the revised model, using the restricted cohort are shown in the Figure 2a and 2b. The revised prediction model has reasonable calibration, as does the Hunault model.

Figure 2

Figure 2

(a) Calibration curves of the Hunault model; (b) the revised model.

Calibration of the models was carried out by assigning the calculated 1-year probabilities of natural conception leading to ongoing pregnancy for each included couple to one of 10 categories, based on deciles, and comparing the average probability in each category to the 1-year Kaplan–Meier estimate.


We compared the fraction of couples with an unfavourable prognosis (<30%) that could benefit from treatment, and the fraction of couples with a good prognosis (≥30%), that could be counselled for expectant management in the restricted cohort as calculated with the new model with the same fraction as calculated with the Hunault model (Figure 3). For the couples in whom the Hunault model calculates a chance of less than 30% and the revised model calculates a chance of 30% or more, the Kaplan–Meier estimate of the 1-year chance of conceiving naturally is 32.3%. Where the calculated chances according to the Hunault model are 30% or more and the calculated chances according to the revised model are less than 30%, the Kaplan–Meier estimate is 19.1%. Therefore, the calculated chances of the revised model are in better agreement with the data. For a total of 1101 out of 2807 couples (39%), this will lead to reclassification, and for 707 out of 2807 couples (25%), this means that they would be classified as having a good prognosis according to the Hunault model, but according to the revised model, they would be classified has having unfavourable chances of natural conception in the following year. The Net Reclassification Index, using the 0.30 cut-off of, was 0.14. This implies, that using the revised model instead of the Hunault model, leads to a more accurate prediction; that is, a prediction that concurs better with observed pregnancy rates, in 7% of all subfertile couples.

Figure 3

Figure 3

Reclassification of the revised model versus the Hunault model based on 1-year predicted chances of ongoing pregnancy. Kaplan–Meier estimates of the real 1-year chance of ongoing pregnancy, per group. Reclassification was calculated in the cohort that the Hunault model was externally validated upon. Filled points, ongoing pregnant; unfilled points, not ongoing pregnant. KM, Kaplan–Meier.



In this study, we developed a model to predict natural conception chances for the subfertile population with female age up to 45 years and severe semen impairment. A total of 12 variables were included in the revised prediction model; female age, duration of subfertility, female BMI, cycle length, FSH levels, one-sided tubal pathology, referral status, a previous intrauterine pregnancy in the current relationship, after natural conception or after fertility treatment, leading to ongoing pregnancy or miscarriage, third line referral, and semen volume, morphology and motility.

The resulting model was moderate in discriminatory performance, but the predictions from the revised model that were calculated by cross-validation at the centre level, and thus in a different set, that was used to create the model, seem to concur better with the observed pregnancy rates compared with the Hunault model. This is reflected in the calibration, the most informative way of summarizing the performance of a model, which indicates whether the calculated probabilities agree with the observed probabilities.

A strength of this study is that we used unselected subfertile couples derived from 38 centres across the Netherlands, thereby increasing generalizability. We expressed all variables and their associations as continuous variables rather than dichotomizing them, and so used all available information.

Although the inclusion of additional predictors in the revised model might be perceived as a drawback, most of them are easily available in any routine fertility work-up. A measurement of female BMI, involves a scale and a measuring tape at the most, and a more detailed history a previous intrauterine pregnancy requires no more than some additional questions. In the revised model, all semen parameters are of prognostic value and incorporated, instead of only semen motility as in the Hunault model. In contrast to the WHO criteria, in which semen variables are regarded as dichotomous variables, we expressed them as continuous variables, and therefore used all available information (van der Steeg et al., 2011).

A limitation of this study is that this new model was developed in the Netherlands. The Hunault model was originally developed from data collected from a Dutch and Canadian population, and has been validated in the Netherlands as well as in New Zealand. Ethnicity is a possible predictor in treatment outcome; therefore, it would be of value to assess the validity of the model in a population with different ethnicities. Validation in different settings, e.g. other parts of the world, especially those settings where access to care, cost of treatment and reimbursement are different would add to the robustness of the findings and increase the generalizability of the results.

Also, we have to be cautious in interpreting these results as, despite bootstrapping, we tested the revised model on the population it was built on. Also, we had 88% of the data points completed, whereas 12% was missing. We used multiple imputation to solve this problem.

A second limitation of our study is that data were collected between 2002 and 2004, more than a decade ago. In our cohort, basal FSH was measured as a marker for ovarian reserve, but not antral follicle count or anti-Müllerian hormone (Broer et al, 2014, Haadsma et al, 2008, and National Collaborating Centre for Women's and Children's Health, 2013). On the other hand, the biology of natural conception has not changed; therefore, we argue that our results robust.

We would have preferably used live birth as our primary outcome, instead of ongoing pregnancy rate leading to a live birth, but we did not have the required follow-up data available. Still, ongoing pregnancy leading to a live birth serves the purpose of the primary outcome well, as less than 2% of ongoing pregnancies do not lead to live birth, and we do not expect that our model would fundamentally change (Regan and Rai, 2000).

Ideally, we would create a model that calculates natural conception chances and live birth rates after treatment simultaneously (McLernon et al., 2014). For a clinician guiding a subfertile couple, the possibility to estimate their absolute increase in pregnancy chances after treatment, would assist individualized decision-making (McLernon et al., 2014). At present, these models do not exist. For the prediction of pregnancy after intrauterine insemination with ovarian stimulation, there is one prediction model, developed in a large prospective cohort of more than 1000 couples undergoing more than 4000 cycles (Steures et al., 2004). In an external validation study, the predictive ability of the model was confirmed (Custers et al., 2007). A review included 14 studies that reported on one or more variables associated with pregnancy after IVF (van Loendersloot et al., 2010). Of these models, only one had been had been externally validated (Smeenk et al, 2000, Templeton et al, 1996, and van Loendersloot et al, 2011). Results of this systematic review show that female age, duration of subfertility, basal FSH and number of oocytes are predictive for pregnancy chances after IVF (van Loendersloot et al., 2010). Three of these are also incorporated in the revised prediction model.

Prediction models should only be implemented in practice when their performance has been validated in new individuals or other settings (Altman, Royston, 2000, Laupacis et al, 1997, and McGinn et al, 2000). Although we have conducted per-clinic cross-validation to obtain independent predictions, this refined model still has to be validated externally and to be compared with the Hunault model in a different population. After validation, impact analysis should be conducted to compare the correspondence of the calculated chances with observed natural conception chances, and to measure its usefulness in a clinical setting. This is a critical step, as the use of a poor-quality prediction model could have a negative effect on clinical decisions, by introducing the illusion of objective improvement over clinical judgment (Leushuis et al., 2009).

In conclusion, an adequate calculation of the chances of natural conception helps in selecting those patients that have favourable chances of conceiving without medically assisted reproduction, and may prevent over-treatment. After prospective validation, the revised model could be useful in daily practice for counselling subfertile couples by calculating these chances.


The authors thank all participating hospitals for their contribution to this study.

Appendix. Supplementary material

The following is the supplementary data to this article:

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Appendix S1

CECERM study group.



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Alexandra Bensdorp attended medical school at the University of Groningen, the Netherlands. She worked as an MD in the field of obstetrics and gynaecology, and as a fertility doctor. She is currently studying for her PhD at the Centre of Reproductive Medicine, Department of Obstetrics and Gynaecology at the Academic Medical Center in Amsterdam. Her research interests include (prediction of) natural conception and intrauterine insemination.


Key message


The Hunault model is the standard prediction model for natural conception. This model can be revised by including additional predictors. The revised model is applicable to a broader population. The revised model needs to be externally validated.

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1 Medisch Centrum Alkmaar, Alkmaar

2 Twenteborg Ziekenhuis, Almelo

3 Meander Medisch Centrum, Amersfoort

4 Academisch Medisch Centrum, Amsterdam

5 BovenIJ Ziekenhuis, Amsterdam

6 St. Lucas Adreas Ziekenhuis, Amsterdam

7 Onze Lieve Vrouwe Gasthuis, Amsterdam

8 Slotervaart Ziekenhuis, Amsterdam

9 Vrije Universiteit Medisch Centrum, Amsterdam

10 Gelre Ziekenhuis, Apeldoorn

11 Wilhelmina Ziekenhuis, Assen

12 Rode Kruis Ziekenhuis, Beverwijk

13 Ziekenhuis Gooi-Noord, Blaricum

14 Amphia Ziekenhuis Breda, Breda

15 Medisch Centrum Haaglanden, lcoatie Westeinde, Den Haag

16 Ziekenhuis Deventer, Deventer

17 A. Schweitzer Ziekenhuis, Dordrecht

18 Scheper Ziekenhuis, Emmen

19 Medisch Spectrum Twente, Enschede

20 St. Anna Ziekenhuis, Geldrop

21 Atrium Medisch Centrum, Heerlen

22 Elkerliek Ziekenhuis, Helmond

23 Jeroen Bosch Ziekenhuis, ‘s Hertogenbosch

24 Ziekenhuis Hilversum, Hilversum

25 Spaarne Ziekenhuis, Hoofddorp

26 Westfries Gasthuis, Hoorn

27 Academisch Ziekenhuis Maastricht, Maastricht

28 St. Antonius Ziekenhuis, Nieuwegein

29 UMC St. Radboud, Nijmegen

30 Waterland Ziekenhuis, Purmerend

31 St. Franciscus Gasthuis, Rotterdam

32 TweeSteden Ziekenhuis, Tilburg/Waalwijk

33 UMC Utrecht, Utrecht

34 Ziekenhuis Bernhoven, Veghel

35 Máxima Medisch Centrum, Veldhoven

36 Vie Curi Medisch Centrum, Venlo/Venray

37 Zaans Medisch Centrum, Zaandam

38 Isala Klinieken, Zwolle

a Centre for Reproductive Medicine, Academic Medical Centre, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands

b Centre for Reproductive Medicine, Jeroen Bosch Hospital, Henri Dunantstraat 1, 5223 GZ ‘s-Hertogenbosch, The Netherlands

c Centre for Reproductive Medicine, Sint Elisabeth Hospital, Hilvarenbeekseweg 60, 5022 GC Tilburg, The Netherlands

d Department of Public Health, Erasmus MC, University Medical Centre Rotterdam, Dr Molewaterplein 50-60, 3015 GE Rotterdam, The Netherlands

e Department of Obstetrics and Gynecology, Vrije Universiteit Medical Centre, De Boelelaan 1118, 1081 HZ Amsterdam, The Netherlands

f Department of Clinical Epidemiology and Biostatistics, Academic Medical Centre, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands

g The Robinson Institute, School of Paediatrics and Reproductive Health, University of Adelaide, Australia

h Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Universiteitsweg 100, 3584 CG Utrecht, The Netherlands

* Corresponding author.