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Human Reproduction, Vol.32, No.8 pp. 1556–1559, 2017
Advanced Access publication on June 13, 2017 doi:10.1093/humrep/dex214
A prognosis-based approach
to infertility: understanding the role
of time
ESHRE Capri Workshop Group*,†
*Correspondence address: P.G. Crosignani, IRCCS Ca’ Granda Foundation Maggiore Policlinico Hospital, Via M. Fanti 6, 20122 Milano, Italy.
Submitted on January 30, 2017; resubmitted on May 9, 2017; accepted on May 19, 2017
ABSTRACT: The current definition of infertility acknowledges the importance of duration of pregnancy seeking but fails to recognize the
prevalent negative impact of female age. In fact, the diagnosis of unexplained infertility increases with women’s age because of our incapacity
to discern between age-related infertility and real unexplained infertility. Physicians’ response to the pressures of increased female age has
been to take prompt refuge in assisted reproduction despite the lack of robust evidence and the inherent risks and costs of these procedures.
Moreover, the prioritization of immediate health gains over those in the future, preference for accessing active treatment rapidly and
reluctance to wait for spontaneous pregnancy expose patients to additional risks of overtreatment. Solutions are not simple to find but an
alternative and innovative vision of infertility based on prognosis may be a valid solution. The availability of validated dynamic models based
on real-life data that could predict both natural and ART-mediated conceptions may be of benefit. They could facilitate patients’ counselling
and could optimize the chances of success without exposing patients to unnecessary, expensive and demanding treatments.
Key words: aging / subfertility / infertility / fecundity / age-related infertility / unexplained infertility
The dictionary definition of infertility implies a state of sterility. However,
it is the absence of conception over a specific time horizon of 12 months
despite regular sexual intercourses which underpins the International
Committee for Monitoring Assisted Reproductive Technology and the
World Health Organization definitions of infertility (Zegers-Hochschild
et al., 2009). The main purpose of defining infertility in this manner is to
identify a population of couples who have a poorer prognosis (rather
than absolute infertility) and merit further investigation and treatment.
Unlike most other medical conditions, the term ‘infertility’ (sometimes
called ‘subfertility’) represents a prognosis, rather than a diagnosis
(Dunson et al., 2004).
The current definition of infertility acknowledges the influence of
duration of pregnancy seeking as a prognostic factor but fails to accommodate the prevalent negative impact of increased female age on
fecundity (ESHRE Capri Workshop Group, 1996). To date, age has
not been part of the diagnostic criteria for infertility (Gurunath et al.,
2011). Given the profound impact of female age on prognosis
(Hunault et al., 2004), it would seem logical that the diagnosis of infertility should incorporate not just duration of pregnancy seeking but
also female age. Noteworthy, age-related fertility decline is a universal
pattern and age at last birth is extremely similar across historical ages
and cultures (Eijkemans et al., 2014).
Unexplained infertility
and age-related infertility
In at least one in four infertile couples (Evers, 2002; Practice Committee
of the ASRM, 2006) a cause cannot be identified. Consequently, couples
in this heterogeneous group are all grouped under a single heterogeneous
category of ‘unexplained’ infertility (NICE, 2013; Practice Committee of
the ASRM, 2015). While the general tendency in older women is to
expedite access to assisted reproduction, it may be worth disentangling
unexplained infertility from age-related infertility, as illustrated in a
recently published mathematical model of human conception according
to a woman’s age (Broer et al., 2011; Somigliana et al., 2016). This model
shows that the rate of false positive diagnosis of unexplained infertility
(i.e. infertility due to unknown causes but excluding age) after 2 years of
regular intercourse rises from 10% for women trying for a pregnancy
before 35 years to 80% for those initiating such attempts at age 40. In
The list of The ESHRE Capri Workshop Group contributors is given in the Appendix.
© The Author 2017. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology. All rights reserved.
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Time and infertility
fact, the reliability of a diagnosis of truly unexplained infertility (as
opposed to age-related infertility) falls dramatically with age. Accordingly,
epidemiological evidence showed that the incidence of unexplained infertility increases with women’s age (Maheshwari et al., 2008).
Theoretically, age-related infertility and unexplained infertility may
benefit from different therapeutic approaches. As ART does not
improve oocyte quality, one may expect ART to be of little efficacy in
women with age-related infertility (Van Voorhis, 2007). In contrast,
some forms of unexplained infertility could be overcome by IVF, such as
occult ovulation defects, subtle disorders of tubal function that cannot
be identified with the traditional tests of tubal patency, cervical abnormalities and male factor problems that cannot be identified by standard
semen analysis. However, firmly distinguishing between unexplained
infertility and age-related infertility in older women is currently impossible. Noteworthy, hormonal measurements of the ovarian reserve is of
scant utility in this context (Somigliana et al., 2016).
In fact, despite the weakness of the evidence and the lack of a robust
rational, there is a general agreement on the need to accelerate the diagnostic process in older women. Tests necessary to exclude any barriers
to natural conception and the possibility to opt for expectant approach
are generally deemed of scant interest because of the imperative to
access treatment early in these women, given their rapidly narrowing window of opportunity. NICE (2013) suggests that investigation and treatment should be considered after 6 months of trying for a baby in women
over 36 years of age in whom prognosis is relatively poor. However,
there is a real danger that this approach could lead to overtreatment.
The role of values and
preferences in informing
treatment choices
Freedom to choose between healthcare options and the ability to take
an active role in decision-making are emerging as important outcomes in
healthcare (Entwistle et al., 2012), suggesting that it is important to look
beyond the most obvious clinical endpoints of fertility treatment (pregnancy) to find relevant indicators of success. Immediate health benefits
are valued higher by patients than those realized at a future date. This has
led to the practice of discounting future health gains in clinical and monetary terms (Tinghög, 2012). Couples seeking help for infertility may actually have overriding preferences for a pregnancy which occurs sooner
rather than later. Approximately a third of Dutch couples with unexplained subfertility, many of whom had a realistic chance of a spontaneous pregnancy, found expectant management unacceptable (Kersten
et al., 2015), possibly because they associated early treatment with an
early pregnancy and/or valued treatment as a desirable endpoint in its
own right. A pregnancy leading to live birth at any point (over a given
time horizon) may have been the accepted currency of treatment success
in the past, but time to pregnancy is becoming the preferred outcome for
the couple.
Are we over-medicalizing
This emphasis on minimizing time to a desired pregnancy and the overwhelming belief in the power of active treatment makes IVF very
attractive for all couples including those with no obvious barrier to
conception. This is in marked contrast to the origins of IVF and ICSI,
which were developed to treat patients with ‘severe’ tubal factor infertility and ‘severe’ male factor infertility (Evers, 2016). The efficacy of
IVF for these indications is unquestionable. Thereafter, however, we
have found ourselves trapped in a ‘slippery slope’ situation where
there is a temptation to expand the use of ICSI for mild male factor
infertility and IVF for one-sided tubal pathology, unexplained infertility,
endometriosis, poor ovarian reserve and, most importantly, advanced
maternal age. In the UK, the number of IVF cycles increased from
35 450 to 60 473 between 2000 and 2011, and >50% of the increase
was accounted for by unexplained subfertility (including also agerelated infertility) (HFEA, 2012). Our response to the pressures of
increased female age and a diminishing reserve of time has been to
seek refuge in assisted reproduction. We have created a therapeutic
illusion on a grand scale (Casarett, 2016; Evers, 2016).
A prognosis-based approach
using clinical prediction models
to ensure timely treatment
We have established that in most couples infertility is not absolute but
time has a negative impact on prognosis (through both duration and
female age) which we are unable to reverse. We have also emphasized
the inappropriateness of a systematic leaning towards immediate use
of assisted reproduction and the potential clinical impact of hooking up
unexplained infertility and age-related infertility into a unique entity.
A possible way to tailor management strategies for couples that can
optimize their chances of success without leading to unnecessary,
expensive and demanding treatments could be to use prognostic models
based on clinical and laboratory parameters. These models should estimate chances of treatment-independent as well as treatment-related
chances of pregnancy so that couples and clinicians can make sensible
decisions about when to initiate treatments. They should be pragmatic
and thus developed based on real-life clinical data. This approach could
consent to overcome some yet obscure aspects of our discipline, such
as in particular the above-mentioned elusive difference between unexplained infertility and age-related infertility. The main challenges in making prediction models that can estimate the benefit of treatment of no
treatment are (i) uniform data collection of both no treatment period
and treatment periods and (ii) random allocation of treatment/correcting for non random allocation in observational data.
Existing fertility prediction models are able to compute pregnancy
outcomes within a fixed time horizon. For example, the validated
Hunault model that has entered clinical practice (Hunault et al., 2004;
Leushuis et al., 2009; van Loendersloot et al., 2010) estimates the
chance of spontaneous pregnancy leading to live birth over a period of 1
year in couples attending a fertility clinic for the first time. It can be used
to decide whether a couple should receive fertility treatment straightaway or undergo a period of expectant management. However, it is a
static model that does not adapt to time. It does not provide any individualized decision support regarding when to initiate active treatment in
a couple who have been advised expectant management. Since a couple’s prognosis decreases over time, it would be beneficial to have an
individualized and dynamic prediction model that could estimate this
changing prognosis at different points in time. Van Eekelen et al. (2017)
recently developed such a dynamic model with promising results but
external validation is still required.
On the other hand, in order to make decisions regarding timing and
type of treatment, we should also be aware that the impact of treatment
may not be identical in all women with similar chances of spontaneous
pregnancy (McLernon et al., 2014). There are current intense efforts in
developping universal models for the prediction of ART success, with
most recent models being more and more refined. However, none has
yet been incorporated within routine clinical practice (Leushuis et al.,
2009; van Loendersloot et al., 2010; McLernon et al., 2016).
Ideally, we need a model which can provide estimates of the change in
the absolute benefit of treatment versus no treatment over time such as
a dynamic prediction model with landmarking. This involves fitting a series of separate regression models from each consecutive month (landmark) since registration at the fertility clinic, in order to predict the
chances of pregnancy leading to live birth over, say, a follow-up window
of 1 year (van Houwelingen and Putter, 2012). In a couple with a good
prognosis who has been initially advised expectant management, this
approach could be used to decide when, in the future, the absolute benefit of treatment is likely to trump their chance of spontaneous pregnancy
such that treatment should begin (McLernon et al., 2014).
It could be argued that a prognosis-based approach may not be consistent with a patient-centered approach as it ignores patients’ potential preferences for active versus expectant treatment. However,
patient-centred care is not about patients choosing treatments based
on their intuitive beliefs. Instead, it implies focus on the patient as a
person (rather than the disease) and shared control of treatment decisions, which are best made in the presence of clear and accurate prognostic information.
Time is an essential but intricate aspect of infertility care. It influences
diagnosis, prognosis and success of treatments. Handling the faceted
and sometimes contrasting aspects of time for infertile couples in everyday clinical practice is sometimes demanding. This situation may actually
favour irrational and potentially harmful clinical attitudes. On the other
hand, keeping decision-making simple is also important and patients’
values and wishes deserve utmost consideration. Physicians have currently to frequently deal with this intricate scenario. Developing and validating comprehensive dynamic models for a tailored and rational use
of assisted reproduction may be of benefit in this context. These models could optimize resource allocation, prevent risks and improve the
quality of shared clinical decision-making.
The secretarial assistance of Mrs Simonetta Vassallo is gratefully
Authors’ roles
All lecturers and chairs contributed to the preparation of the final
ESHRE Capri Workshop Group
The meeting was organized by the European Society of Human
Reproduction and Embryology with an unrestricted educational grant
from Institut Biochimique S.A. (Switzerland).
Conflict of interest
None declared.
A meeting was organized by ESHRE [September 1–2, 2016] to discuss
human infertility. The lecturers included: D.F. Albertini (Director,
Division of Laboratories, Center For Human Reproduction, New York,
USA); R. Anderson (Head of Section, Obstetrics and Gynaecology,
University of Edinburgh, Edinburgh, UK); S. Bhattacharya (Professor of
Reproductive Medicine, Head of Division of Applied Health Sciences
and Director Institute of Applied Health Sciences, School of Medicine
and Dentistry, University of Aberdeen, Aberdeen Maternity Hospital,
Foresterhill, Aberdeen, UK); J.L.H. Evers (Department of Obstetrics
and Gynecology, Maastricht University Medical Centre, Maastricht, The
Netherlands); D.J. Mclernon (CSO Postdoctoral Fellow, Medical
Statistics Team, Division of Applied Health Sciences, University of
Aberdeen, Aberdeen, UK); S. Repping (Universiteit van Amsterdam,
Academisch Medisch Centrum, Centrum voor Voortplantingsgeneeskunde,
Amsterdam, The Netherlands); E. Somigliana (Clinica Ostetrica
e Ginecologica, IRCCS Ca’ Granda Foundation, Maggiore Policlinico
Hospital, Milano, Italy). The chairs included: D.T. Baird (Centre for
Reproductive Biology, University of Edinburgh, UK); P.G. Crosignani
(IRCCS Ca’ Granda Foundation, Maggiore Policlinico Hospital, Milano,
Italy); K. Diedrich (Klinik für Frauenheilkunde und Geburtshilfe,
Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Germany); R.
G. Farquharson (Liverpool Women’s Hospital, Department of OB/
GYN, Liverpool, UK); K. Lundin (Reproductive Medicine, Sahlgrenska
University Hospital, Gothenburg, Sweden); J.S. Tapanainen (Department
of Obstetrics and Gynaecology, University of Helsinki, Helsinki University
Hospital, Helsinki, Finland); A. Van Steirteghem (Centre for Reproductive
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