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Medically assisted reproduction in the context of time
Scholten, I.
Publication date
2015
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Final published version
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Scholten, I. (2015). Medically assisted reproduction in the context of time.
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Medically
Assisted
Reproduction
in the context
of time
Irma Scholten
Medically
Assisted Reproduction in the context of time Irma Scholten
Medically
Assisted
Reproduction
in the context
of time
Uitnodiging
voor het bijwonen van de
openbare verdediging van het
proefschrift
Woensdag 2 september 2015
om 12.00u in de
Agnietenkapel
Oudezijds Voorburgwal 231
te Amsterdam
Receptie ter plaatse
na afloop van de promotie
Paranimfen
Raissa Tjon-Kon-Fat
06-41675164
R.I.Tjonkonfat@amc.nl
Bart Voskamp
06-14836444
B.Voskamp@amc.nl
Irma Scholten
door
209433-os-Scholten.indd 1 27-07-15 13:00Medically assisted reproduction
in the context of time
209433-L-bw-Scholten 209433-L-bw-Scholten 209433-L-bw-Scholten 209433-L-bw-Scholten
Cover design: Irma Scholten
Layout: Ilse Stronks, persoonlijkproefschrift.nl Printing: Ipskamp Drukkers, Enschede, the Netherlands
ISBN: ...
© 2015, I. Scholten
All rights reserved. No parts of this publication may be reproduced in any form without permission of the author.
The printing of this thesis was supported by Stichting gynaecologische endocrinologie en kunstmatige voortplanting, Amsterdam
Medically assisted reproduction
in the context of time
ACADEMISCH PROEFSCHRIFT ter verkrijging van de graad van doctor
aan de Universiteit van Amsterdam op gezag van de Rector Magnificus
prof. dr. D.C. van den Boom
ten overstaan van een door het College voor Promoties ingestelde commissie, in het openbaar te verdedigen in de Agnietenkapel
op woensdag 2 september 2015, te 12.00 uur door
Irma Scholten
209433-L-bw-Scholten 209433-L-bw-Scholten 209433-L-bw-Scholten 209433-L-bw-Scholten
PROMOTIECOMMISSIE
Promotores: Prof. dr. B.W.J. MolProf. dr. F. van der Veen Copromotores: Dr. J. Gianotten
Dr. P.G.A. Hompes Overige leden: Prof. dr. J.G.P. Tijssen
Prof. dr. S. Repping Dr. M. van Wely Prof. dr. C.B. Lambalk Prof. dr. J.L.H. Evers Prof. dr. F.M. Helmerhorst
209433-L-bw-Scholten 209433-L-bw-Scholten 209433-L-bw-Scholten 209433-L-bw-Scholten
CONTENTS
Chapter 1 Introduction 9
Chapter 2 Natural conception: the importance of repeated predictions
In progress
19
Chapter 3 Reporting multiple cycles in trials on medically assisted reproduction
Submitted
37
Chapter 4 The effectiveness of intrauterine insemination: a matched cohort study
Submitted
51
Chapter 5 Long term outcome in subfertile couples with isolated cervical factor
European Journal of Obstetrics & Gynecology and Reproductive Biology 2013;170:429-433
63
Chapter 6 Long-term follow-up of couples initially randomized between immobilization and immediate mobilization subsequent to intrauterine insemination
Reproductive BioMedicine Online 2014;29:125-130
75
Chapter 7 The impact of assisted reproductive technology on the incidence of multiple gestation infants: a population perspective
Fertility and Sterility 2015 Jan; 103(1): 179-83
87
Chapter 8 General discussion and implications for future research 101
Chapter 9 Summary 109
Chapter 10 Nederlandse samenvatting 113
Appendices Portfolio 119
Dankwoord 125
209433-L-bw-Scholten 209433-L-bw-Scholten 209433-L-bw-Scholten 209433-L-bw-Scholten
209433-L-bw-Scholten 209433-L-bw-Scholten 209433-L-bw-Scholten 209433-L-bw-Scholten 10 Chapter 1
Subfertility, defined as failure to conceive after 12 months of unprotected intercourse, is a major health problem that affects up to 1 in 10 couples (1,2). The inability to conceive carries a high psychological burden and couples may exceed their own physical and mental boundaries to achieve the desired pregnancy. Subfertility manifests itself as an acute and unanticipated life crisis. It creates overwhelming stress and tests coping strategies as it is unanticipated, may be unexplained and lasts for an undetermined length of time. When subfertility becomes infertility, a chronic life crisis may result (3).
The introduction of ovulation induction, intrauterine insemination with or without ovarian stimulation, and in vitro fertilization with or without assisted fertilization, has provided millions of couples with hope.
Over the past decades, these treatments have been applied in ever increasing numbers (4,5). Although millions of babies have been born as a result of these treatments, their extended use is increasingly questioned (6). To prevent overtreatment, we here re-consider medically assisted reproduction in the context of time.
Time is by definition the most important commodity we lose in life but in subfertile couples it is what it is all about. Subfertile couples may experience three different timeframes. First, there is a period of tapping the natural conception potential. Second, if not successful, a period of fertility treatment cycles may follow and finally, if treatment has failed, a third period follows in which there are still pregnancy chances left, although diminished. Couples are then forced to balance between hoping for a miracle to happen and preparing on a further live without a child. In this introduction, these three timeframes and their according pregnancy chances are discussed.
Pre-treatment phase
A woman is considered to be of ‘reproductive age’ from 15 years of age until she is 45. Increasing age has a negative influence on fecundity as the quality of oocytes and therewith pregnancy chances decline over time (7). Yet, most women generally use the first part of their 30 reproductive years focusing on nót becoming pregnant and it is only after stopping contraceptives that their truly reproductive years start.
Every month an ovulation takes place and thus, there is a chance for conception. As fertility is not absolute, it may take several attempts to come to a pregnancy (8). After the decision ‘to try for a baby’, the majority of couples conceive within the first six months of unprotected intercourse (9). After one year, 80-90% of couples have conceived. Although the remaining couples now fit the definition of subfertility, natural conception still occurs in these couples. 50% of them will conceive in the next year, followed by another 14% in the third year of starting unprotected intercourse (10). Subfertility is
Introduction
1
therefore not synonymous with needing treatment. Tailored expectant management can be applied in couples with an intermediate or good prognosis for natural conception within the next year, reducing the burden, costs and risks associated with fertility treatment without compromising birth rates (11,12). Currently, couples with a chance on natural conception of 30% or more are considered to have a favorable prognosis and treatment is postponed (13). Truly infertile couples, those with double-sided tubal factor and severe male infertility, will not benefit from expectant management and can be selected through fertility work up (13). In the remaining couples, prediction models can be used to determine natural conception chances which then are the basis to decide on expectant management or medical assisted reproduction.
Treatment phase
If this first period of trying to use the natural fertility potential has failed, a second period has arrived in which Medically Assisted Reproduction (MAR) can be applied. The term medically assisted reproduction has recently been introduced, and is defined as reproduction brought about through ovulation induction, ovarian stimulation, ovulation triggering, intrauterine, intracervical, and intravaginal insemination and artificial reproductive techniques, consisting of In Vitro Fertilisation (IVF) and Intracytoplasmic Sperm Injection (ICSI) (2).
In the UK, the NICE guideline ‘fertility’ advises to start treatment with IVF after two years of unsuccessful unprotected intercourse (14). In the Netherlands, IUI is started as first line therapy if the predicted chance for natural conception is below 30%. Further treatment with three cycles of IVF is advised when six cycles of IUI have failed (13). The differences in these recommendations might be explained by a lack of evidence on the effectiveness of medically assisted reproduction in couples with unexplained subfertility.
In the past decades, several randomized clinical trials were performed to assess the effectiveness of IUI, all in different patient groups with different treatment protocols. The Cochrane review on this topic concludes that there is evidence that IUI with mild stimulation increases the live birth rate compared to IUI alone. Yet, a statement on the effectiveness of IUI compared to no intervention could not be given (15). One trial compared live birth rate after IVF to expectant management in couples with non tubal infertility, in which couples with unexplained subfertility formed only a minute subgroup (n=51) and was thus not designed and powered to find an answer on the effectiveness of IVF in these couples (16). A recently published trial compared IUI with mild stimulation to immediate IVF in couples with unexplained subfertility and a poor prognosis. Treatment with IVF was found not to be superior to treatment with IUI (17).
In view of the available evidence, the question – even in 2015- remains in whom to start medically assisted reproduction and when to start.
209433-L-bw-Scholten 209433-L-bw-Scholten 209433-L-bw-Scholten 209433-L-bw-Scholten 12 Chapter 1
Also in this second time period of treatment, one must realize that medically assisted reproduction is characterized by repeated treatment cycles. Just as for natural conception, in medically assisted reproduction cumulative pregnancy rates rise with additional cycles (9,18). One treatment cycle can therefore not be seen as independent and effectiveness can only be assessed when multiple cycles and -in some instances- even multiple treatments are reported (19). In this respect, the cumulative live birth over a given period of time instead of per cycle success has been proposed as the primary outcome of trials (20). It was already acknowledged years ago that randomization should be performed at the first cycle and the allocated treatment continued thereafter, since a per cycle analysis or a cross-over design may lead to overestimation of treatment success (21). In addition, multiple cycle follow up reflects how a treatment works in daily practice. Couples might switch treatments between cycles, natural conception can occur or couples can drop out of treatment (22). Single cycle analysis does not account for the above and thus estimates of effectiveness from the first treatment cycle are not representative for the further course of treatment. Extrapolating a single cycle analysis can lead to judging a treatment as more effective than it actually is, which can lead to overtreatment and unnecessary costs (21).
Post-treatment phase
After unsuccessful treatment, subfertile couples enter the next period , in which there is still some fertility potential left. Cohort studies show natural conception rates of 18-24% in couples in the years after finishing treatment (23,24), although this figure might be biased by couples becoming pregnant in between treatment cycles and therefore stopping treatment. This phenomenon of residual fertility potential after ending a randomized clinical trial is often overlooked in the assessment of effectiveness of interventions since the time horizon included in RCTs does usually not exceed six months and follow up after the trial has ended is rarely performed.
Nevertheless, in the end, despite all the effort, there will be couples who do not conceive. The effect of remaining childless reaches beyond a woman’s reproductive years. Twenty years after finishing unsuccessful fertility treatment, childlessness is still an important theme in the life of these women. The effects are on both a personal and on interpersonal and social levels. When leaving behind their fertile years, these women are still confronted with the child that is missing. While their peers are becoming grandparents, they move from childlessness to grandchildlessness (25).
BACKGROUND OF THE RESEARCH IN THIS THESIS
In this thesis, we focus on reproduction in the context of time. To do so, we divided time in three time frames: pre-treatment phase, treatment phase and post-treatment phase.
Introduction
1
The pre-treatment phase includes the time in which couples use their natural conception chances. The difficulty lies in determining the moment a couple should switch to the next phase. The Hunault model is nowadays used in the Netherlands to decide whether expectant management should be applied (13,26). This model is designed to be used once: after finishing fertility work-up. In clinical practice doctors are tempted to use it again after a period of expectant management to decide on further treatment. In doing so, they do not realize that the couples that are not pregnant after a period of expectant management are a selection of the initial group included after fertility work up. This selection has a poorer prognosis than the initial group, as time has elapsed without them conceiving, while the couples that left the group are now pregnant (8). Using the Hunault model again after a period of expectant management to decide on further treatment or not would overestimate their chances. The first aim of this thesis was to develop a model that can be used repeatedly in the same couple, using the patient characteristics that are also incorporated in the Hunault model.
In the treatment phase, multiple cycles of treatment are applied to hopefully increase pregnancy chances. At the start of this thesis, it was unclear if trials on medically assisted reproduction were designed to include several treatment cycles to assess the effectiveness of an intervention as practiced in real life. The second aim of this thesis was to assess whether trials on medically assisted reproduction report multiple cycles and which factors are associated with this.
In counselling couples on the effectiveness of a particular treatment, the pretreatment and post treatment phases are important as well. The moment a couple starts their primary treatment, a pre-treatment phase preceded this pre-treatment. Amongst others, it’s duration determines the prognosis of the couple when entering the treatment phase (11,26). After initial treatment, other treatments or a period without treatment may be given. Collecting all this information gives a more succinct and precise view on the effectiveness of a treatment in real life, where we treat our patients over some cycles, where couples drop out of treatment, and where couples decide to pause for a while. The recently published NICE guideline on fertility advices not to routinely offer IUI to subfertile couples, but to proceed immediately to treatment with in vitro fertilization (IVF) after a period of expectant management. The authors base their recommendation for this strategy on the lack of evidence for the effectiveness of IUI in couples with unexplained subfertility and mild male subfertility (14). However, the evidence underpinning this recommendation is very scarce indeed. In view of this, the third aim of this thesis was to compare the three year ongoing pregnancy rates after IUI to no treatment in a cohort of couples with unexplained subfertility. With this design, the overall effectiveness of starting treatment with IUI is tested, rather than the effectiveness in a few months. The fourth aim of this thesis was to perform a three year follow up of a previous trial comparing ongoing pregnancy rate of couples randomized between immediate IUI or expectant management
209433-L-bw-Scholten 209433-L-bw-Scholten 209433-L-bw-Scholten 209433-L-bw-Scholten 14 Chapter 1
in couples with an isolated cervical factor. This trial found a relative risk of 1.6 (95% CI 0.91-2.6), which showed no evidence that immediate IUI is successful in couples with isolated cervical factor (27). Yet, the point estimate points to a possible beneficial effect. The three year outcome might provide additional information to decide whether immediate IUI should be advised in couples with isolated cervical factor.
The fifth aim of this thesis was to perform a three year follow up of a previous trial comparing ongoing pregnancy rates after immediate mobilisation subsequent to IUI with fifteen minutes of immobilisation subsequent to IUI. This initial study found a relative risk of 1.5 (95% CI 1.1 -2.2) favouring immobilisation (28).
One of the drawbacks of MAR is that it bears the risk of multiple pregnancies, which are known to be high risk pregnancies (29). In ART, this risk can be diminished by applying Single Embryo Transfer (SET) (30,31). Yet, a high uptake of ART, even when applying SET, might have consequences on the outcome of the offspring (32–35). The sixth aim of these thesis was to untangle the effects of both uptake of SET and utilization of ART on the amount of multiple pregnancies as this emphasizes the importance of applying ART to the right couple at the proper time.
Introduction
1
OUTLINE OF THIS THESIS
Chapter 2 presents a new prediction model to predict natural conception. A previously conducted cohort study including almost 5000 couples after fertility work-up was used to create a model that can be repeatedly used in the same couples.
Chapter 3 provides a review of a systematic collected sample of 223 RCTs on medically assisted reproduction. We assessed whether these trials report on multiple cycles and which factors are associated with this.
Chapter 4 presents the results of a retrospective matched cohort study in which we compared treatment with IUI versus no treatment in 72 couples with poor prognosis on natural conception. We report on the three year outcome of these couples.
Chapter 5 reports on the three year outcome of a previously published RCT comparing immediate treatment with IUI to six months expectant management in 99 couples with isolated cervical factor subfertility.
Chapter 6 reports on the three year outcome of a previously published RCT comparing immediate mobilisation to fifteen minute of immobilisation after IUI in 391 couples who had received up to three cycles.
Chapter 7 addresses the problem of multiple pregnancy after ART from a broader point of view. We incorporated population statistics with ART statistics and assessed the contribution of ART multiple pregnancies on all multiple pregnancies in seven countries with differences in utilisation of ART and usage of single embryo transfer.
Chapter 8 provides discussion on the findings presented in this thesis, with recommendations for further research.
209433-L-bw-Scholten 209433-L-bw-Scholten 209433-L-bw-Scholten 209433-L-bw-Scholten 16 Chapter 1
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2. Zegers-Hochschild F, Adamson GD, de Mouzon J, Ishihara O, Mansour R, Nygren KG, et al. International
Committee for Monitoring Assisted Reproductive Technology (ICMART) and the World Health Organization (WHO) revised glossary of ART terminology, 2009. Fertil Steril. Elsevier Ltd; 2009 Nov;92(5):1520–4.
3. Whiteford LM, Gonzalez L. Stigma: The hidden burden of infertility. Soc Sci Med. 1995;40(1):27–36.
4. Nyboe Andersen A, Gianaroli L, Nygren KG. Assisted reproductive technology in Europe, 2000. Results generated
from European registers by ESHRE. Hum Reprod. 2004 Mar;19(3):490–503.
5. Kupka MS, Ferraretti AP, de Mouzon J, Erb K, D’Hooghe T, Castilla JA, et al. Assisted reproductive technology in
Europe, 2010: results generated from European registers by ESHRE†. Hum Reprod. 2014 Jul 27;0(0):1–15.
6. Kamphuis EI, Bhattacharya S, van der Veen F, Mol BWJ, Templeton A. Are we overusing IVF? BMJ. 2014
Jan;348(January):g252.
7. Broekmans FJ, Soules MR, Fauser BC. Ovarian aging: Mechanisms and clinical consequences. Endocr Rev.
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8. Evers JLH. Female subfertility. Lancet. 2002;360(9327):151–9.
9. Gnoth C, Godehardt D, Godehardt E, Frank-Herrmann P, Freundl G. Time to pregnancy: results of the German
prospective study and impact on the management of infertility. Hum Reprod. 2003 Sep 1;18(9):1959–66. 10. Te Velde ER, Eijkemans R, Habbema HD. Variation in couple fecundity and time to pregnancy, an essential
concept in human reproduction. Lancet. 2000 Jun 3;355(9219):1928–9.
11. Van den Boogaard NM. Tailored Expectant Management in Reproductive Medicine. Vrije Universiteit, Amsterdam; 2013.
12. Steures P, van der Steeg JW, Hompes PGA, Habbema JDF, Eijkemans MJC, Broekmans FJ, et al. Intrauterine insemination with controlled ovarian hyperstimulation versus expectant management for couples with unexplained subfertility and an intermediate prognosis: a randomised clinical trial. Lancet. 2006 Jul 15;368(9531):216–21. 13. Netwerkrichtlijn Subfertiliteit. 2011.
14. National Institute for Health and Clinical Excellence. Assessment and treatment for people with fertility problems. 2013.
15. Veltman-Verhulst SM, Cohlen BJ, Hughes E, Heineman MJ. Intra-uterine insemination for unexplained subfertility. Cochrane database Syst Rev. 2012 Jan;9(9):CD001838.
16. Hughes EG, Beecroft ML, Wilkie V, Burville L, Claman P, Tummon I, et al. A multicentre randomized controlled trial of expectant management versus IVF in women with Fallopian tube patency. Hum Reprod. 2004 May;19(5):1105–9.
17. Bensdorp AJ, Tjon-Kon-Fat RI, Bossuyt PMM, Koks CAM, Oosterhuis GJE, Hoek A, et al. Prevention of multiple pregnancies in couples with unexplained or mild male subfertility: randomised controlled trial of in vitro fertilisation with single embryo transfer or in vitro fertilisation in modified natural cycle compared with intrauterine inse. BMJ. 2015 Jan;350:g7771.
18. Malizia BA, Hacker MR, Penzias AS. Cumulative live-birth rates after in vitro fertilization. N Engl J Med. 2009 Jan 15;360(3):236–43.
19. Daya S. Pitfalls in the design and analysis of efficacy trials in subfertility: Associate editor’s commentary: on the article “Common statistical errors in the design and analysis of subfertility trials” by A.Vail and E.Gardner. Hum Reprod. 2003 May 1;18(5):1005–9.
Introduction
1
20. Eijkemans MJC, Heijnen EMEW, de Klerk C, Habbema JDF, Fauser BCJM. Comparison of different treatment strategies in IVF with cumulative live birth over a given period of time as the primary end-point: methodological considerations on a randomized controlled non-inferiority trial. Hum Reprod. 2006 Feb;21(2):344–51.
21. Daya S. Life table (survival) analysis to generate cumulative pregnancy rates in assisted reproduction: are we overestimating our success rates? Hum Reprod. 2005 May;20(5):1135–43.
22. Custers IM, van Rumste MME, van der Steeg JW, van Wely M, Hompes PGA, Bossuyt P, et al. Long-term outcome in couples with unexplained subfertility and an intermediate prognosis initially randomized between expectant management and immediate treatment. Hum Reprod. 2012 Feb;27(2):444–50.
23. Cahill DJ, Meadowcroft J, Akande VA, Corrigan E. Likelihood of natural conception following treatment by IVF. J Assist Reprod Genet. 2005 Dec;22(11-12):401–5.
24. Troude P, Bailly E, Guibert J, Bouyer J, de la Rochebrochard E. Spontaneous pregnancies among couples previously treated by in vitro fertilization. Fertil Steril. 2012 Jul;98(1):63–8.
25. Wirtberg I, Möller A., Hogström L, Tronstad SE, Lalos a. Life 20 years after unsuccessful infertility treatment. Hum Reprod. 2007;22(2):598–604.
26. Hunault CC, Habbema JDF, Eijkemans MJC, Collins JA, Evers JLH, te Velde ER. Two new prediction rules for spontaneous pregnancy leading to live birth among subfertile couples, based on the synthesis of three previous models. Hum Reprod. 2004 Sep;19(9):2019–26.
27. Steures P, van der Steeg JW, Hompes PGA, Bossuyt PMM, Habbema JDF, Eijkemans MJC, et al. Effectiveness of intrauterine insemination in subfertile couples with an isolated cervical factor: a randomized clinical trial. Fertil Steril. 2007 Dec;88(6):1692–6.
28. Custers IM, Flierman PA, Maas P, Cox T, Van Dessel TJHM, Gerards MH, et al. Immobilisation versus immediate mobilisation after intrauterine insemination: randomised controlled trial. BMJ. 2009 Jan;339:b4080.
29. Pinborg A. IVF/ICSI twin pregnancies: risks and prevention. Hum Reprod Update. 11(6):575–93.
30. Maheshwari A, Griffiths S, Bhattacharya S. Global variations in the uptake of single embryo transfer. Hum Reprod Update. 2011;17(1):107–20.
31. Gerris J, Van Royen E. Avoiding multiple pregnancies in ART: a plea for single embryo transfer. Hum Reprod. 2000 Sep;15(9):1884–8.
32. Pandey S, Shetty A, Hamilton M, Bhattacharya S, Maheshwari A. Obstetric and perinatal outcomes in singleton pregnancies resulting from IVF/ICSI: a systematic review and meta-analysis. Hum Reprod Update. 18(5):485–503. 33. Pinborg A, Wennerholm UB, Romundstad LB, Loft A, Aittomaki K, Söderström-Anttila V, et al. Why do singletons conceived after assisted reproduction technology have adverse perinatal outcome? Systematic review and meta-analysis. Hum Reprod Update. 2013;19(2):87–104.
34. Ceelen M, van Weissenbruch MM, Vermeiden JPW, van Leeuwen FE, Delemarre-van de Waal HA. Growth and development of children born after in vitro fertilization. Fertil Steril. 2008 Nov;90(5):1662–73.
35. Davies MJ, Moore VM, Willson KJ, Van Essen P, Priest K, Scott H, et al. Reproductive technologies and the risk of birth defects. N Engl J Med. 2012 May 10;366(19):1803–13.
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2
Natural conception:
The importance of repeated
predictions
In progress
Nan van Geloven Irma Scholten Raissa I. Tjon-Kon-Fat Fulco van der Veen Jan-Willem van der Steeg Pieternel Steures Peter G.A. Hompes Ben W.J. Mol Marinus J. Eijkemans Egbert R. Te Velde
209433-L-bw-Scholten 209433-L-bw-Scholten 209433-L-bw-Scholten 209433-L-bw-Scholten 20 Chapter 2
ABSTRACT
Study questionHow can we predict natural pregnancy chances repeatedly at different time points in the same couple? Summary answer
We developed a dynamic prediction model that can make repeated predictions over time for the same couple.
What is known already
The most frequently used prediction model for natural conception (the “Hunault model”) is able to estimate the probability of a natural conception only once per couple, after the completion of the fertility work-up. The model cannot be used for a second or third time in the same couple.
Study design, size, duration
We studied couples who participated in a prospective cohort study of subfertile couples in 38 centres in The Netherlands between January 2002 and February 2004. Couples with bilateral tubal occlusion, anovulation, or a total motile sperm count <1 x 106 were excluded.
Participants/materials, setting, methods
The primary endpoint was a natural ongoing pregnancy. Time to pregnancy was censored when treatment was started, or at the last date of contact during follow up.
A new dynamic prediction model was developed using the same predictors as in the Hunault model: female age, duration of subfertility, subfertility being primary or secondary, sperm motility and referral status. The performance of the repeated predictions from the model was evaluated in terms of calibration, discrimination and relative error reduction.
Main results and the role of chance
Of the 4,996 couples in the cohort, 1,086 (22%) women reached a naturally conceived ongoing pregnancy within a mean follow up of 10 months (range 1-70 months). The pregnancy prognosis in the first year after completion of the fertility workup was 26%. If pregnancy did not occur in this first year, the chance of conceiving in the next year was 13%. The yearly chance lowered to 8% after 2 years of unsuccessful expectant management. Discrimination and calibration of the repeated predictions was fair up to 3 years after the fertility workup. The relative error reduction interpretable as the amount of variation explained by the predictors lowered from 24% in the first year to 5% in the third year. Limitations, reasons for caution
The dynamic prediction model needs to be validated in an external population. Wider implications of the findings
This dynamic prediction model enables to re-assess the natural pregnancy chances after different periods of unsuccessful expectant management. This gives valuable information for treatment decisions in couples with unexplained unfulfilled child wish.
Natural conception: the importance of repeated predictions
2
INTRODUCTION
Approximately 10% of all couples who want to have a child do not conceive within the first year of trying (1–3). In about 50% of these couples the subfertility is more or less unexplained because no major underlying cause is found (4). The prognosis for these couples is still quite good. Demographic studies have shown that 30-60% are expected to conceive within the second year of trying (2,5). There is huge variation in the pregnancy chances of these couples; some couples may take much longer or never conceive (6,7).
A pressing question for unexplained subfertile couples is whether they should continue their attempts of conceiving naturally or start treatment with medically assisted reproduction (MAR). The recent NICE guideline on Fertility suggests that in vitro fertilisation (IVF) should be offered to couples with unexplained subfertility after two years of unfulfilled child wish (8). This general recommendation does not match the need of a personalized approach in which a couples’ prognosis determines whether or not treatment adds value (9). Couples prefer to be informed about their personal chances (10,11). Several prediction models have been developed that can provide patient-specific predictions, of which the synthesis model developed by Hunault has reached the phase of clinical implementation (12,13). This model can be used to estimate the probability of a natural pregnancy within the first year after a couple seeks medical advice in a fertility clinic after absolute causes of subfertility such as anovulation, azoospemia and bilateral tubal pathology have been excluded. It is based on female age, duration of subfertility, female subfertility being primary or secondary, sperm motility and whether the couple has been referred to the fertility center by a general practitioner or gynaecologist.
A major shortcoming is that the Hunault model can only be used once, i.e. after the results of the fertility work-up are known. When couples who agreed on a period of expectant management do not conceive and return to the fertility clinic, the need to assess the natural pregnancy chance becomes even more compelling as these couples often perceive the additional unsuccessful period as evidence that further waiting is senseless. Calculation of the chances a second time for the same couple by simply updating the couples’ characteristics (raise female age and duration of child wish) and then using Hunault’s model a second time as if they came for the first time, results in erroneous estimates. Such predictions are systematically too optimistic because the couple with a longer period of unsuccessful natural attempts belongs to a selection of the population with lower fertility potential and the Hunault model was not developed for such a negatively selected subgroup. To make such temporal updated predictions correctly, we need a dynamic prediction model. Unlike ‘one time only’ models that can only predict pregnancy chances at one fixed moment in time, a dynamic prediction model can reassess pregnancy chances repeatedly for the same couple at different time points in the future (14,15).
209433-L-bw-Scholten 209433-L-bw-Scholten 209433-L-bw-Scholten 209433-L-bw-Scholten 22 Chapter 2
Such a dynamic prediction model is currently not available. A mathematical model that has proven to match the dynamics of pregnancy chances over time in the general population and has been used in the demographic literature is the beta-geometric model (16,17). Here we use the beta-geometric model to make dynamic predictions of natural pregnancy for couples with unexplained subfertility that repeatedly seek counselling about whether still to continue attempting natural conception or to embark on MAR.
METHODS
The data for our analysis were collected in a prospective cohort study performed in 38 hospitals in The Netherlands, between January 2002 and February 2004. The study, of which the detailed study protocol has been described elsewhere (18), was designed to validate the Hunault model. The information obtained during the fertility work-up of consecutive couples was recorded and after completion of the workup couples were followed until natural conception leading to an ongoing pregnancy occurred. Ongoing pregnancy was defined as the presence of foetal cardiac activity at transvaginal sonography at a gestational age of at least 12 weeks. If no pregnancy occurred, time to pregnancy was censored when treatment started, or at the last date of contact during follow up. For the current analysis we selected couples with regular cycle (cycle length between 23 and 35 days), at least one patent tube (women with two sided occlusion on hysterosalpingography, diagnostic laparoscopy or on transvaginal hydrolaparoscopy were excluded) and sufficient sperm quality (total motile sperm count > 1 x 106). Patients in whom tubal status was only assessed by a Chlamydia
antibody titre test during the fertility workup were retained in the analysis, regardless of the outcome. Missing data and data description
Missing patient factors were imputed using a single imputation technique. Although in general a multiple imputation approach might better capture the uncertainty associated with unmeasured data values, we here chose to adhere to the approach used in previous analysis of the same data set. Numerical variables are described as mean with 5th and 95th percentile and categorical variables as
frequency with percentage. The beta-geometric model
The beta-geometric model focusses on the pregnancy probability per menstrual cycle. This probability differs considerably between couples, but we assumed that for one couple, the probability remains stable during the follow up time of the study. This means that there are couples with higher and lower pregnancy chances per cycle, who cannot be identified at the start of follow up. After several cycles, the
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couples with the higher pregnancy chances are more likely to have become pregnant. The remaining couples are likely to have lower chances. This selection process is modelled by assuming that the per cycle pregnancy probability varies among couples according to a beta distribution, leading to a beta-geometric distribution of the number of cycles to pregnancy (17). The model can be characterized by two parameters: the mean and the variance of the probability of pregnancy in the first cycle after completion of the basic fertility workup. The distribution of the pregnancy probabilities in subsequent cycles follows from these two parameters and the assumed selection process.
As the model focusses on the probability per cycle, we discretized the time to pregnancy into number of cycles, by dividing time to pregnancy by the average cycle length. We used rounding to the first following whole cycle number for women who conceived and rounding to the nearest whole cycle number for censored observations. In the reporting of results we assume that a year consists of 13 menstrual cycles, which matches the average cycle length in our cohort (28 days). A period of 6 menstrual cycles is denoted as half a year.
Overall model fit of the beta-geometric model without covariates was assessed visually by comparing the cumulative predictions from the model to Kaplan-Meier estimates on the discretized data. This was first done for the cumulative predictions in the full cohort over maximum three years of follow up, and thereafter, to assess the dynamic fit, for cumulative within one year predictions in couples not yet pregnant after half a year, one and two years of expectant management.
The dynamic prediction model
We incorporated the five known predictors of pregnancy, i.e., female age at completion of workup, duration of subfertility at completion of workup, female subfertility being primary or secondary, percentage of motile sperm and referral status, into the beta-geometric model by expressing the logit of the mean pregnancy probability as a linear function of the covariates. This results in covariate effects interpretable as odds ratios. The predictions following from the model were expressed in a prediction formula in which the duration of expectant management and the prediction window, i.e. the time period ahead over which you want to predict the pregnancy chance, can both be chosen. Also, to facilitate the estimation of prognosis without complex computation, a nomogram was created for the one year pregnancy chances after completion of the workup, after half a year, after one year and after two years of expectant management.
Model validation
We evaluated the performance of the predictions from the dynamic prediction model on four fixed time intervals: chances of pregnancy within one year after completion of the fertility workup, after half a year, after one year and after two years of unsuccessful expectant management.
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The overall predictive performance was measured by the Brier prediction error score, i.e. the squared difference between the predictions and the observed pregnancy outcomes (14). To obtain a score that is comparable over the different evaluated time intervals, the Brier score was expressed relatively to the Brier score of the null model: the beta-geometric model without covariates that assigns to all couples an equal prediction per time interval. This relative Brier score is referred to as the relative error reduction and can be interpreted as the amount of variation that is explained by the predictors, ranging from 0% when the model is no better than assigning all patients an equal probability per time point to 100% when the model predicts perfectly (14). This measure naturally combines the two most important aspects of model evaluation in reproductive medicine: calibration and variability in predicted probabilities (19). A well calibrated model will give a good Brier score. However, if the predictions have low variability, the model will not show much gain over the Brier score of the null model. The relative error reduction in that case will be moderate.
We also assessed calibration and discrimination of the model separately. The degree of agreement between observed and predicted pregnancy rates, i.e. calibration, was assessed visually by comparing the mean predicted one year pregnancy chances with the observed fraction of pregnancies at one year estimated by the Kaplan-Meier method. Again for optimizing comparability of the evaluation on the four different time intervals, this was done in risk groups of fixed size (about 200 couples per risk group). The visual assessment was additionally quantified as the mean distance per risk group between the predicted chances and the observed rate. The ability of the dynamic prediction model to distinguish between women who do and women who do not conceive, i.e. discrimination, was assessed by calculating Harrel’s c-index (20). Models are typically considered reasonable when the c-index is higher than 0.7 and strong when the c-index exceeds 0.8 (21).
Lastly, we assessed the robustness of the parametric assumptions of our model in a sensitivity analysis. To this end, we compared the performance of the first year predictions from the parametric beta-geometric to predictions obtained from an alternatively fitted semi-parametric Cox prediction model. The parameters of the beta-geometric models were estimated by optimizing the log-likelihood of the observed data, using the ‘BFGS’ method of the general optimization procedure optim in the R environment for statistical computing (R Development Core Team (2011), R Foundation for Statistical Computing, Vienna, Austria).
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RESULTS
Data from 4,996 couples matching our inclusion criteria were available. Mean female age was 32.5 (24.9-39.4) years, the mean duration of subfertility was 1.9 (1.0-4.7) years, both evaluated at the time of completing the fertility work-up. There were 3,205 (62%) women who had not been pregnant before, while 555 (11%) were referred to the fertility clinic by a gynaecologist. Mean cycle length was 28.2 (24.4-33.2) days and mean semen total motile count was 84 (2.4-280) per million.
Pregnancy outcomes and the beta-geometric model
An ongoing pregnancy after natural conception occurred in 1,086 couples after a mean follow-up of 10 months. The cumulative ongoing pregnancy chances after the completion of fertility workup on are depicted in figure 1, panel A. Cumulative chances for ongoing pregnancy according to the Kaplan-Meier estimates within one, two and three years were 26%, 35% and 40% respectively. The beta-geometric model without covariates estimated a mean pregnancy probability in the first menstrual cycle of 3.6%, decreasing over time to 1.4% per cycle after one year of unsuccessful expectant management and to 0.6% per cycle after three years. The cumulative chances according to this beta-geometric model have been added to figure 1, panel A and show that the model fits the data well.
For couples not yet pregnant after half a year, one year and two years of expectant management, the probability of conceiving in the coming year is estimated at 18%, 13% and 8% respectively (Figure 1, panel B). The dotted line again represents the estimates from the beta-geometric model without covariates and shows that the model matches the data well.
The dynamic prediction model
Incorporating the five known predictors of pregnancy into the beta-geometric model led to the prediction formula in the Appendix. This formula enables to calculate the individual probability of natural pregnancy after any number of unsuccessful cycles since the completion of the fertility workup for a chosen number of future cycles. Figure 2 shows the prediction nomogram for four fixed time intervals: the probability to conceive within one year after completion of the workup, after half a year, after a year and after two years of unsuccessful expectant management. To help demonstrate the utility of the nomogram the pregnancy predictions for an example couple are shown in Figure 3. Internal validation of the dynamic prediction model
The effect of the patient characteristics in the beta-geometric model are depicted in Table 1 as odds ratios together with 95% confidence intervals. The calibration plots and performance indices for the four evaluated time intervals are presented in Figure 4. Calibration of the model seems acceptable over the full three year follow up by visual inspection: the absolute difference between the mean
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Figure 1. Cumulative pregnancy rates after completion of fertility workup (panel A) and updated cumulative one year
pregnancy rates after half a year, one year and two years of unsuccessful expectant management (panel B)
predictions and the observed fractions of pregnancies, i.e. distance between the dots and the 45 degree reference line, was on average 3 percent points in risk groups of 200 couples. The relative error reduction by the model was 24% in the first year after the workup, decreasing to 5% after two years of expectant management. As the population becomes more homogeneous at later time points, this decrease is understandable: although at the correct level, the predictions do not vary much anymore at the later time points and for that reason are less useful than the earlier predictions (19). The discriminative ability of the model was moderate to reasonable, ranging over time from a c-index of 0.67 in the first year to a c-index of 0.73 in the third year. This suggests that the model does
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a bit better job in distinguishing couples with relatively lower prognosis from couples with relatively higher prognosis at later time points, presumably due to a bigger impact at the later time points of the subgroup of patients with nearly zero chances who are relatively easily distinguishable.
The sensitivity analysis showed that the first year predictions from the beta-geometric model were highly comparable to predictions from an alternatively fitted Cox model. The per couple predictions differed at maximum 2 percent points and the performance indices coincided.
Figure 2. Nomogram of the dynamic prediction rule.
Upper panel: Each of the five predictors has a certain weight expressed as points. For example, female age varies from 0 points at age 20 to 75 points at age 44 and duration varies from 0 at 1 year duration to 100 at 5 year duration. Add up all points of the predictors; the more points the lower the chance of a natural pregnancy.
Lower panel: The sum of all points can be used to obtain the pregnancy chance of an individual couple. The lines represent the chances within one year after completion of the workup, after half a year, after a year and after 2 years of unsuccessful expectant management
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Figure 3. Example estimation of the pregnancy chances for a couple with female age 26, 2 years duration of subfertility
(both at completion of workup), secondary subfertility, 50% motile sperm referred to the fertility center by a gynaecologist. The upper panel of the nomogram shows that the weights of the five predictors add up to a sum of 94 points. In the lower panel one can read that the chance of a pregnancy within one year is 21% after completion of the workup, 17% after half a year, 14% after a year and 10% after two years of unsuccessful expectant management (EM).
Table 1. Estimated effects of patient factors in the beta geometric model
OR 95% CI
Female age
per year below 31 0.97 (0.94-1.00)
per year above 31 0.92 (0.90-0.95)
Duration child wish/year 0.62 (0.58-0.67)
Subfertility
secondary ref
primary 0.71 (0.63-0.81)
Semen per % motile sperm 1.008 (1.005-1.011)
Referral
GP or other specialism ref
gynaecologist 0.48 (0.36-0.63) CI=confidence interval, OR=odds ratio, GP=general practitioner
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0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0. 0 0. 2 0. 4 0. 6after fertility workup (n=4996)
predicted probability beta-geometric model
obs
er
ved f
rac
tion
rel err reduc 24% av abs diff= 0.03 c-index 0.67 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0. 0 0. 2 0. 4 0. 6
after 6 months expectant management (n=2815)
predicted probability beta-geometric model
obs
er
ved f
rac
tion
rel err reduc 15% av abs diff= 0.03 c-index 0.68 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0. 0 0. 2 0. 4 0. 6
after one year expectant management (n=1354)
predicted probability beta-geometric model
obs
er
ved f
rac
tion
rel err reduc 10% av abs diff= 0.03 c-index 0.72 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0. 0 0. 2 0. 4 0. 6
after two years expectant management (n=443)
predicted probability beta-geometric model
obs
er
ved f
rac
tion
rel err reduc 5% av abs diff= 0.01 c-index 0.73
Figure 4. Calibration of the predictions of the dynamic prediction rule: predicted versus observed one year pregnancy
rates at four time points. In case zero pregnancies were observed in a certain risk group, no confidence interval could be calculated. Rel err reduc = relative error reduction
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DISCUSSION
The newly developed dynamic prediction model is able to estimate the chance of a natural pregnancy repeatedly for the same couple and in addition can predict chances for any chosen time period ahead. In an internal validation, the calibration of the repeated predictions seemed acceptable during the whole three year follow up period. Although seemingly on the correct level, the predictions at the later time points may be less useful than the more early predictions of the model due to lower variability (19). Discrimination was moderate to reasonable, as is to be expected for these kind of models (19). It is well known that apparent model performance in an internal validation overestimates the performance in external data. The generalizability of the developed dynamic model needs to be confirmed in an external dataset before implementation in clinical practice can be advised.
The dynamic prediction model can be used several times for calculating predictions for the same couple. Such predictions cannot be made with currently available models like the Hunault model. For example when a couple referred by a general practitioner with one year primary subfertility, total motile sperm 50% and female age 28 at completion of the fertility workup that was advised expectant management based on a 40% pregnancy chance in the first year after the workup, returns to the clinic after that year and still is not pregnant, erroneously reusing Hunault’s model would suggest a remaining chance of 34% in the second year, which may be a reason to continue the expectant policy. When using the new dynamic prediction model for this couple, the prediction for the second year would more realistically be estimated at only 28%, which may be a reason to consider starting treatment with MAR.
In the development of the dynamic model several choices were made that merit some discussion. We subdivided the time axis into three periods: the duration of subfertility until completion of the diagnostic workup, the time span of expectant management since the diagnostic workup and the prediction window. The model was targeted at calculating the pregnancy chance over the latter period (prediction window) given the duration of the first two periods. This matched the way our dataset was collected: inclusion at fertility workup and prospective pregnancy follow up from completion of the workup on. This implies that our model cannot be used before completion of the diagnostic workup in a fertility clinic, whilst couples probably already want to know their prediction in an earlier phase. For this a model would be needed that can calculate pregnancy chances for any duration of subfertility, irrespective of whether or not couples seek or get medical advice in a clinic. This would either require new data collected in an earlier phase or strong assumptions about the selection of patients that reach the milestone of completion of the workup compared to patients not starting or not finishing the workup. The potential of such an approach should be explored in future research. The exact moment of completion of the workup can vary largely depending on the local clinic’s diagnostic protocol: part of the clinics in our dataset considered the workup completed quite
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rapidly after assessing tuba pathology with a Chlamydia antibody titer test, others took more time making more elaborate investigation with for instance a diagnostic laparoscopy. This variation seems inevitable when collecting a large multicenter dataset.
A technical modelling choice was the use of the parametric beta-geometric model while other semi-parametric methods such as (sliding) landmark approaches could also have been used to develop dynamic predictions (14,22). Our choice was prompted by the desire to make one fixed prediction formula that is applicable to several time periods, which is not possible when using semi-parametric approaches. The beta-geometric model has proven to match fecundity data well in the general population and thus seemed a good candidate for our analyses. We must realize however that we here apply the beta-geometric model in a different setting, especially on a much more homogeneous subfertile population compared the general population. We observed that the beta distribution of pregnancy chances estimated in our subfertile population peaked at the lower range where chances are close to zero. Possibly this right skewed shape is caused by a relatively large subgroup of patients with absolute infertility (zero pregnancy chances) (7). Our model did not explicitly account for such a potential infertile subgroup. A third argument for our choice of the beta-geometric model was the decreasing numbers at risk at later time points. The selection process estimated by our method is mainly based on the selection observed in the cycles where patient numbers are highest, this seems more robust than strict land marking methods that only use those patients still at risk at later time points in the estimates for those periods.
Another potential limitation of our model is that we did not explicitly account for reproductive ageing. In our model we assume that the age at completion of workup matches with a constant pregnancy chance in the next three years. The influence of ageing could be studied more extensively by for instance assuming a fixed age at which menopause occurs (23). Neglecting ageing effects in an analysis has been shown to lead to limited (one or two cycles) overestimation of the number of elapsed cycles before the remaining subgroup of patients has a certain low pregnancy profile (5). We therefore do not expect this to have largely impacted our results.
Dynamic predictions can give valuable input to individualized treatment decisions. When and over which future time horizon to predict can be maximally tailored to the couple’s situation. This tailoring is known to help patients recognize that predictions raised by a doctor actually apply to their situation and can add to an evidence based shared decision process.
Ultimately, for making treatment decisions we not only need an individualized prediction of a couple’s natural pregnancy prognosis, but also of their chances after treatment. So far, no study has been able to assess both of these within the same patient population. In the absence of such studies, the only alternative is to calculate natural pregnancy chances and pregnancy chances after treatment by using
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separate models that have been developed in different patient cohorts. Our dynamic model for natural conception can optimize the comparability of such separately obtained predictions. The prediction can be rendered at the exact moment one considers starting treatment and for a number of menstrual cycles ahead matching the time period necessary for the alternatively considered treatment.
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and outcome of infertility. Br Med J (Clin Res Ed). 1985 Dec 14;291(6510):1693–7.
2. Gnoth C, Godehardt D, Godehardt E, Frank-Herrmann P, Freundl G. Time to pregnancy: results of the German
prospective study and impact on the management of infertility. Hum Reprod. 2003 Sep 1;18(9):1959–66.
3. Wang X, Chen C, Wang L, Chen D, Guang W, French J. Conception, early pregnancy loss, and time to clinical
pregnancy: a population-based prospective study. Fertil Steril. 2003 Mar;79(3):577–84.
4. Brandes M, Hamilton CJCM, de Bruin JP, Nelen WLDM, Kremer JAM. The relative contribution of IVF to the
total ongoing pregnancy rate in a subfertile cohort. Hum Reprod. 2010 Jan;25(1):118–26.
5. Sozou PD, Hartshorne GM. Time to Pregnancy: A Computational Method for Using the Duration of
Non-Conception for Predicting Non-Conception. PLoS One. 2012;7(10).
6. Te Velde ER, Eijkemans R, Habbema HD. Variation in couple fecundity and time to pregnancy, an essential
concept in human reproduction. Lancet. 2000 Jun 3;355(9219):1928–9.
7. Van Geloven N, Van Der Veen F, Bossuyt PMM, Hompes PG, Zwinderman AH, Mol BW. Can we distinguish
between infertility and subfertility when predicting natural conception in couples with an unfulfilled child wish? Hum Reprod. 2013;28(3):658–65.
8. National Institute for Health and Clinical Excellence. Assessment and treatment for people with fertility problems.
2013.
9. Van den Boogaard NM, Oude Rengerink K, Steures P, Bossuyt PM, Hompes PGA, van der Veen F, et al. Tailored
expectant management: risk factors for non-adherence. Hum Reprod. 2011 Jul;26(7):1784–9.
10. Dancet EA, D’Hooghe TM, Van Der Veen F, Bossuyt P, Sermeus W, Mol BW, et al. “patient-centered fertility treatment”: What is required? Fertil Steril. 2014;101(4):924–6.
11. Dancet EA, Van Empel IWH, Rober P, Nelen WLDM, Kremer JAM, Dhooghe TM. Patient-centred infertility care: A qualitative study to listen to the patients voice. Hum Reprod. 2011;26(4):827–33.
12. Leushuis E, van der Steeg JW, Steures P, Bossuyt PMM, Eijkemans MJC, van der Veen F, et al. Prediction models in reproductive medicine: a critical appraisal. Hum Reprod Update. 2009;15(5):537–52.
13. Hunault CC, Habbema JDF, Eijkemans MJC, Collins JA, Evers JLH, te Velde ER. Two new prediction rules for spontaneous pregnancy leading to live birth among subfertile couples, based on the synthesis of three previous models. Hum Reprod. 2004 Sep;19(9):2019–26.
14. Van Houwelingen H, Putter H. Dynamic prediction in Clinical Survival Analysis. Monographs on statistics and applied probability. Chapman and Hall; 2012.
15. McLernon DJ, Te Velde ER, Steyerberg EW, Mol BWJ, Bhattacharya S. Clinical prediction models to inform individualized decision-making in subfertile couples: a stratified medicine approach. Hum Reprod. 2014 Sep;29(9):1851–8.
16. Bongaarts J. A method for the estimation of fecundability. Demography. 1975;12(4):645–60.
17. Weinberg CR, Gladen BC. The beta-geometric distribution applied to comparative fecundability studies. Biometrics. 1986 Sep;42(3):547–60.
18. Van der Steeg JW, Steures P, Eijkemans MJC, Habbema JDF, Hompes PGA, Broekmans FJ, et al. Pregnancy is predictable: a large-scale prospective external validation of the prediction of spontaneous pregnancy in subfertile couples. Hum Reprod. 2007 Feb;22(2):536–42.
19. Coppus SFPJ, van der Veen F, Opmeer BC, Mol BWJ, Bossuyt PMM. Evaluating prediction models in reproductive medicine. Hum Reprod. 2009 Aug;24(8):1774–8.
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20. Harrell FE, Lee KL, Mark DB. Multivariable prognostic models: Issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15(4):361–87.
21. Hosmer D, Lemeshow S. Applied Logistic Regression. New York: John Wiley & Sons; 1989.
22. Van Houwelingen H. Dynamic prediction by landmarking in event history analysis. Scand J Stat. 2007;34:70–85. 23. Eijkemans MJC, Van Poppel F, Habbema DF, Smith KR, Leridon H, Te Velde ER. Too old to have children?
Lessons from natural fertility populations. Hum Reprod. 2014;29(6):1304–12.
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APPENDIx PREDICTION FORMULA
After m unsuccessful expectant management cycles since the completion of fertility workup, the predicted cumulative probability P of natural conception within the next j cycles is:
With
μ = mean pregnancy chance in first cycle
PI = prognostic index = –1.64 – 0.03 * female age years below 31 –0.08 * female age years above 31
–0.47 * duration of child wish –0.34 * primary subfertility +0.008 * percentage of motile sperm –0.74 * referred by gynaecologist
The ∏ sign in the formula indicates replicated multiplication for i’s within the range m+1 to m+j. We will illustrate the formula with two examples.
Example 1 For a couple referred to the fertility clinic by their general practitioner with primary subfertility, VMC 20, duration of subfertility 1.5 years, female age 30 at completion of the fertility workup, the probability of conceiving naturally within the next 3 cycles is calculated as follows:
As no cycles have passed yet since the fertility workup, m=0. We want to calculate the probability for the next 3 cycles, so j=3. The multiplication then has to be done for i=1, i=2 and i=3:
Example 2 If the couple does not conceive within these three months, and returns to the fertility clinic, the probability for another 3 months can be calculated as follows. The parameter remains the same (note that age and duration should not be adjusted!). The m is now 3 and the j is also 3, so the multiplication has to be done for i=4, i=5 and i=6:
𝑃𝑃 = 1 − ∏𝑖𝑖=𝑚𝑚+𝑗𝑗1 − 𝜇𝜇 + 𝑖𝑖 ∗ 0.061 + 𝑖𝑖 ∗ 0.06 𝑖𝑖=𝑚𝑚+1 = 1 + 𝑒𝑒𝑒𝑒𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝑃𝑃𝑃𝑃 = −1.64 − 0.03 ∗ 30 − 0.08 ∗ 0 − 0.47 ∗ 1.5 − 0.34 ∗ 1 + 0.008 ∗ 20 − 0.74 ∗ 0 = −3.4 𝜇𝜇 =1 + 𝑒𝑒𝑒𝑒−3.4−3.4= 0.03 𝑃𝑃 = 1 − (1 − 0.03 + 1 ∗ 0.061 + 1 ∗ 0.06 ) ∗ (1 − 0.03 + 2 ∗ 0.061 + 2 ∗ 0.06 ) ∗ (1 − 0.03 + 3 ∗ 0.061 + 3 ∗ 0.06 ) = 1 − 0.972 ∗ 0.973 ∗ 0.975 = 0.08 = 8% 𝑃𝑃 = 1 − (1 − 0.03 + 4 ∗ 0.061 + 4 ∗ 0.06 ) ∗ (1 − 0.03 + 4 ∗ 0.061 + 4 ∗ 0.06 ) ∗ (1 − 0.03 + 4 ∗ 0.061 + 4 ∗ 0.06 ) = 1 − 0.976 ∗ 0.977 ∗ 0.978 = 0.07 = 7%
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Reporting multiple cycles in
trials on medically assisted
reproduction
Submitted
Irma Scholten Miriam Braakhekke Jacqueline Limpens Peter G.A. Hompes Fulco van der Veen Ben W.J. Mol Judith Gianotten
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ABSTRACT
Trials assessing effectiveness in medically assisted reproduction (MAR) should preferably be designed to study the desired effect over multiple cycles, as this reflects clinical practice and captures the relevant perspective for the couple. The aim of this study was to assess to what extent multiple cycles are reported in MAR trials and to identify any trial characteristics associated with reporting multiple cycles. We collected a sample of RCTs on MAR published in the periods 1999/2000, 2004/2005 and 2009/2010 in 11 pre-specified peer-reviewed journals. 223 trials -172 on in vitro fertilization (IVF), 32 on intrauterine insemination (IUI) and 19 on ovulation induction (OI)- were included. Of all 223 RCTs, 41 (18%) reported on multiple cycles. Reporting of multiple cycles was significantly more common in trials on IUI (n=18, 56%) and OI (n=12, 63%) compared to trials on IVF (n= 11, 7%, p<0.01).
Trials on IVF that used live birth as primary outcome reported significantly more often on multiple cycles (OR 11.7 (1.8-73)). Trials designed to compare protocol variations reported less often multiple cycles (OR 0.06 (0.005-0.65)). In trials on IUI and OI, characteristics influencing the reporting of multiple cycles could not be identified.
Our analysis shows that the majority of RCTs on MAR, especially those on IVF, do not report cumulative pregnancy rates over a longer time horizon. Since infertile couples usually undergo multiple cycles, the clinical significance of these trials is limited.
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INTRODUCTION
Approximately 10% of couples who wish to conceive fail to do so within one year of unprotected intercourse (1). These couples may choose to enter fertility care and, if indicated, receive Medically Assisted Reproduction (MAR). Decisions on adequate treatment for subfertile couples should be based on sound knowledge, which is ideally generated by randomized clinical trials (RCTs). RCTs are, in case of equipoise, widely accepted as the most robust method to evaluate effectiveness of an intervention (2,3).
Just as for natural conception, in medically assisted reproduction cumulative pregnancy rates rise with additional cycles (1,4). One treatment cycle can therefore not be seen as independent and effectiveness can only be assessed when multiple cycles and -in some instances- even multiple treatments are reported (5). In this respect, the cumulative live birth over a given period of time instead of per cycle success has been proposed as the primary outcome of trials (6). To capture overall chances on a live birth, RCTs on MAR should reflect this (1,4).
This issue has been emphasized by a recent editorial in the BMJ that advised studies on MAR with pregnancy or live birth rates as the outcome of interest to report cumulative rates with a follow-up period of at least one year (7). This would greatly enhance the clinical significance of trials.
It is unclear to what extent this approach is actually used in studies on MAR. Therefore, we systematically analysed a representative sample of RCTs published in the past decade, and assessed whether a multiple cycle approach was used in these RCTs, and which trial characteristics were associated with reporting multiple cycles.
METHODS
To create a representative database with RCTs on MAR, we conducted a systematic Medline search to RCTs published in the years 1999/2000, 2004/2005 or 2009/2010. By choosing five-year intervals, we were able to describe changes over time. We chose six journals in reproductive medicine and obstetrics and gynaecology with a high impact factor (Human Reproduction, Fertility and Sterility, Reproductive BioMedicine Online, British Journal of Obstetrics and Gynaecology, American Journal of Obstetrics and Gynecology, Obstetrics and Gynecology), and five high ranked general journals (New England Journal of Medicine, the Lancet, Journal of the American Medical Association, British Medical Journal and Plos Medicine).
Search methods
An information specialist (JL) identified RCTs on MAR by electronically searching OVID MEDLINE for the selected journals and the chosen publication years in combination with two broad search filters:
