Predictors of success after in vitro fertilization

The last few decades have witnessed a rise in the global uptake of in vitro fertilization (IVF) treatment. To ensure optimal use of this technology, it is important for patients and clinicians to have access to tools that can provide accurate estimates of treatment success and understand the contribution of key clinical and laboratory parameters that influence the chance of conception after IVF treatment. The focus of this review was to identify key predictors of IVF treatment success and assess their impact in terms of live birth rates. We have identified 11 predictors that consistently feature in currently available prediction models, including age, duration of infertility, ethnicity, body mass index, antral follicle count, previous pregnancy history, cause of infertility, sperm parameters, number of oocytes collected, morphology of transferred embryos, and day of embryo transfer.

The reproductive potential of vitrified-warmed euploid embryos declines following repeated uterine transfers

BACKGROUND

Recurrent implantation failure (RIF) represents a vague clinical condition with an unclear diagnostic challenge that lacks solid scientific underpinning. Although euploid embryos have demonstrated consistent implantation capabilities across various age groups, a unanimous agreement regarding the advantages of preimplantation genetic testing for aneuploidy (PGT-A) in managing RIF is absent. The ongoing discussion about whether chromosomal aneuploidy in embryos significantly contributes to recurrent implantation failure remains unsettled. Despite active discussions in recent times, a universally accepted characterization of recurrent implantation failure remains elusive. We aimed in this study to measure the reproductive performance of vitrified-warmed euploid embryos transferred to the uterus in successive cycles.

METHODS

This observational cohort study included women (n = 387) with an anatomically normal uterus who underwent oocyte retrieval for PGT-A treatment with at least one biopsied blastocyst, between January 2017 and December 2021 at a university-affiliated public fertility center. The procedures involved in this study included ICSI, blastocyst culture, trophectoderm biopsy and comprehensive 24-chromosome analysis of preimplantation embryos using Next Generation Sequencing (NGS). Women, who failed a vitrified-warmed euploid embryo transfer, had successive blastocyst transfer cycles (FET) for a total of three using remaining cryopreserved euploid blastocysts from the same oocyte retrieval cycle. The primary endpoints were sustained implantation rate (SIR) and live birth rate (LBR) per vitrified-warmed single euploid embryo. The secondary endpoints were mean euploidy rate (m-ER) per cohort of biopsied blastocysts from each patient, as well as pregnancy and miscarriage rates.

RESULTS

The mean age of the patient population was 33.4 years (95% CI 32.8-33.9). A total of 1,641 embryos derived from the first oocyte retrieval cycle were biopsied and screened. We found no associations between the m-ER and the number of previous failed IVF cycles among different ranges of maternal age at oocyte retrieval (P = 0.45). Pairwise comparisons showed a significant decrease in the sustained implantation rate (44.7% vs. 30%; P = 0.01) and the livebirth rate per single euploid blastocyst (37.1% vs. 25%; P = 0.02) between the 1st and 3rd FET. The cumulative SIR and LBR after up to three successive single embryo transfers were 77.1% and 68.8%, respectively. We found that the live birth rate of the first vitrified-warmed euploid blastocyst transferred decreased significantly with the increasing number of previously failed IVF attempts by categories (45.3% vs. 35.8% vs. 27.6%; P = 0.04). A comparable decrease in sustained implantation rate was also observed but did not reach statistical significance (50% vs. 44.2 vs. 37.9%; P = NS). Using a logistic regression model, we confirmed the presence of a negative association between the number of previous IVF failed attempts and the live birth rate per embryo transfer cycle (OR = 0.76; 95% CI 0.62-0.94; P = 0.01).

CONCLUSIONS

These findings are vital for enhancing patient counseling and refining management strategies for individuals facing recurrent implantation failure. By tailoring interventions based on age and ovarian reserve, healthcare professionals can offer more personalized guidance, potentially improving the overall success rates and patient experiences in fertility treatments.

TRIAL REGISTRATION NUMBER

N/A.

Should we adopt a prognosis-based approach to unexplained infertility?

The treatment of unexplained infertility is a contentious topic that continues to attract a great deal of interest amongst clinicians, patients and policy makers. The inability to identify an underlying pathology makes it difficult to devise effective treatments for this condition. Couples with unexplained infertility can conceive on their own and any proposed intervention needs to offer a better chance of having a baby. Over the years, several prognostic and prediction models based on routinely collected clinical data have been developed, but these are not widely used by clinicians and patients. In this opinion paper, we propose a prognosis-based approach such that a decision to access treatment is based on the estimated chances of natural and treatment-related conception, which, in the same couple, can change over time. This approach avoids treating all couples as a homogeneous group and minimizes unnecessary treatment whilst ensuring access to those who need it early.

Adaptive data-driven models to best predict the likelihood of live birth as the IVF cycle moves on and for each embryo transfer

PURPOSE

To dynamically assess the evolution of live birth predictive factors' impact throughout the in vitro fertilization (IVF) process, for each fresh and subsequent frozen embryo transfers.

METHODS

In this multicentric study, data from 13,574 fresh IVF cycles and 6,770 subsequent frozen embryo transfers were retrospectively analyzed. Fifty-seven descriptive parameters were included and split into four categories: (1) demographic (couple's baseline characteristics), (2) ovarian stimulation, (3) laboratory data, and (4) embryo transfer (fresh and frozen). All these parameters were used to develop four successive predictive models with the outcome being a live birth event.

RESULTS

Eight parameters were predictive of live birth in the first step after the first consultation, 9 in the second step after the stimulation, 11 in the third step with laboratory data, and 13 in the 4th step at the transfer stage. The predictive performance of the models increased at each step. Certain parameters remained predictive in all 4 models while others were predictive only in the first models and no longer in the subsequent ones when including new parameters. Moreover, some parameters were predictive in fresh transfers but not in frozen transfers.

CONCLUSION

This work evaluates the chances of live birth for each embryo transfer individually and not the cumulative outcome after multiple IVF attempts. The different predictive models allow to determine which parameters should be taken into account or not at each step of an IVF cycle, and especially at the time of each embryo transfer, fresh or frozen.

An algorithm to personalise the diagnosis of recurrent implantation failure based on theoretical cumulative implantation rate

Recurrent implantation failure (RIF) is an imprecisely defined disorder lacking a robust scientific basis. The incomplete understanding of RIF provides significant diagnostic and therapeutic challenges, and a better understanding of the underlying issues is necessary to improve outcomes. We propose a novel concept termed 'Theoretical Cumulative Implantation Rate', the calculation of which is based on objective data, to define whether a patient should be diagnosed with RIF. An updated definition to assist with patient counselling and planning research studies, which is more precise and standardised, is well overdue.

Gamete-level immunogenetic incompatibility in humans-towards deeper understanding of fertilization and infertility?

Human leukocyte antigen (HLA) immune genes play an important role in partner selection, but it has remained unclear if nonrandom pairing with respect to parental HLA genes could occur at the level of the gametes. We tested this possibility by investigating whether the sperm fertilization competence in humans is dependent on HLA genotype combination of the partners. We conducted a full-factorial experiment, in which the sperm physiological preparation for fertilization among multiple males was studied in the presence of follicular fluid (oocyte surrounding bioactive liquid) of several females. All the studied sperm pre-fertilization physiological parameters (motility, hyperactivation, acrosome reaction, and viability) were strongly dependent on male-female combination. In other words, follicular fluids (women) that induce strong sperm physiological response in some males often induce much weaker response in the other(s). Sperm physiological responses were stronger in HLA-dissimilar male-female pairs than in HLA-similar combinations, but none of the measured sperm traits were associated with genome-wide similarity. Together, these findings shed new light on the evolutionary and immunological mechanisms of fertilization. Furthermore, our results raise an intriguing possibility that against currently prevailing WHO's definition, infertility may not represent exclusively a pathological condition, but may also result from immunogenetic incompatibility of the gametes.

Predicting the chances of having a baby with or without treatment at different time points in couples with unexplained subfertility

STUDY QUESTION

Can we develop a prediction model that can estimate the chances of conception leading to live birth with and without treatment at different points in time in couples with unexplained subfertility?

SUMMARY ANSWER

Yes, a dynamic model was developed that predicted the probability of conceiving under expectant management and following active treatments (in vitro fertilisation (IVF), intrauterine insemination with ovarian stimulation (IUI + SO), clomiphene) at different points in time since diagnosis.

WHAT IS KNOWN ALREADY

Couples with no identified cause for their subfertility continue to have a realistic chance of conceiving naturally, which makes it difficult for clinicians to decide when to intervene. Previous fertility prediction models have attempted to address this by separately estimating either the chances of natural conception or the chances of conception following certain treatments. These models only make predictions at a single point in time and are therefore inadequate for informing continued decision-making at subsequent consultations.

STUDY DESIGN, SIZE, DURATION

A population-based study of 1316 couples with unexplained subfertility attending a regional clinic between 1998 and 2011.

PARTICIPANTS/MATERIALS, SETTING, METHODS

A dynamic prediction model was developed that estimates the chances of conception within 6 months from the point when a diagnosis of unexplained subfertility was made. These predictions were recomputed each month to provide a dynamic assessment of the individualised chances of conception while taking account of treatment status in each month. Conception must have led to live birth and treatments included clomiphene, IUI + SO, and IVF. Predictions for natural conception were externally validated using a prospective cohort from The Netherlands.

MAIN RESULTS AND THE ROLE OF CHANCE

A total of 554 (42%) couples started fertility treatment within 2 years of their first fertility consultation. The natural conception leading to live birth rate was 0.24 natural conceptions per couple per year. Active treatment had a higher chance of conception compared to those who remained under expectant management. This association ranged from weak with clomiphene to strong with IVF [clomiphene, hazard ratio (HR) = 1.42 (95% confidence interval, 1.05 to 1.91); IUI + SO, HR = 2.90 (2.06 to 4.08); IVF, HR = 5.09 (4.04 to 6.40)]. Female age and duration of subfertility were significant predictors, without clear interaction with the relative effect of treatment.

LIMITATIONS, REASONS FOR CAUTION

We were unable to adjust for other potentially important predictors, e.g. measures of ovarian reserve, which were not available in the linked Grampian dataset that may have made predictions more specific. This study was conducted using single centre data meaning that it may not be generalizable to other centres. However, the model performed as well as previous models in reproductive medicine when externally validated using the Dutch cohort.

WIDER IMPLICATIONS OF THE FINDINGS

For the first time, it is possible to estimate the chances of conception following expectant management and different fertility treatments over time in couples with unexplained subfertility. This information will help inform couples and their clinicians of their likely chances of success, which may help manage expectations, not only at diagnostic workup completion but also throughout their fertility journey.

STUDY FUNDING/COMPETING INTEREST(S)

This work was supported by a Chief Scientist Office postdoctoral training fellowship in health services research and health of the public research (ref PDF/12/06). B.W.M. is supported by an NHMRC Practitioner Fellowship (GNT1082548). B.W.M. reports consultancy for ObsEva, Merck, and Guerbet. None of the other authors declare any conflicts of interest.

Is IUI with ovarian stimulation effective in couples with unexplained subfertility?

STUDY QUESTION

Does starting IUI with ovarian stimulation (IUI-OS) within 1.5 years after completion of the fertility workup increase ongoing pregnancy rates compared to expectant management in couples with unexplained subfertility?

SUMMARY ANSWER

IUI-OS is associated with higher chances of ongoing pregnancy compared to expectant management in unexplained subfertile couples, specifically those with poor prognoses of natural conception, i.e. <15% over 6 months or <25% over 1 year.

WHAT IS KNOWN ALREADY

IUI-OS is often the first-line treatment for couples with unexplained subfertility. Two randomized controlled trials compared IUI-OS to expectant management using different thresholds for the prognosis of natural conception as inclusion criteria and found conflicting results. A cohort of couples with unexplained subfertility exposed to expectant management and IUI-OS offers an opportunity to determine the chances of conception after both strategies and to evaluate whether the effect of IUI-OS depends on a couple's prognosis of natural conception.

STUDY DESIGN, SIZE, DURATION

A prospective cohort study on couples with unexplained or mild male subfertility who could start IUI-OS at any point after completion of the fertility workup, recruited in seven Dutch centres between January 2002 and February 2004. Decisions regarding treatment were subject to local protocols, the judgement of the clinician and the wishes of the couple. Couples with bilateral tubal occlusion, anovulation or a total motile sperm count <1 × 106 were excluded. Follow up was censored at the start of IVF, after the last IUI cycle or at last contact and truncated at a maximum of 1.5 years after the fertility workup.

PARTICIPANTS/MATERIALS, SETTING, METHODS

The endpoint was time to conception leading to an ongoing pregnancy. We used the sequential Cox approach comparing in each month ongoing pregnancy rates over the next 6 months of couples who started IUI-OS to couples who did not. We calculated the prognosis of natural conception for individual couples, updated this over consecutive failed cycles and evaluated whether prognosis modified the effect of starting IUI-OS. We corrected for known predictors of conception using inverse probability weighting.

MAIN RESULTS AND THE ROLE OF CHANCE

Data from 1896 couples were available. There were 800 couples whom had at least one IUI-OS cycle within 1.5 years post fertility workup of whom 142 couples conceived (rate: 0.50 per couple per year, median follow up 4 months). The median period between fertility workup completion and starting IUI-OS was 6.5 months. Out of 1096 untreated couples, 386 conceived naturally (rate: 0.31 per couple per year, median follow up 7 months). Starting IUI-OS was associated with a higher chance of ongoing pregnancy by a pooled, overall hazard ratio of 1.96 (95% CI: 1.47-2.62) compared to expectant management. The effect of treatment was modified by a couple's prognosis of achieving natural conception (P = 0.01), with poorer prognoses or additional failed natural cycles being associated with a stronger effect of treatment. The predicted 6-month ongoing pregnancy rate for a couple with a prognosis of 25% at completion of the fertility workup over the next six cycles (~40% over 1 year) was 25% (95% CI: 21-28%) for expectant management and 24% (95% CI: 9-36%) when starting IUI-OS directly. For a couple with a prognosis of 15% (25% over 1 year), these predicted rates were 17% (95% CI: 15-19%) for expectant management and 24% (95% CI: 15-32%) for starting IUI-OS.

LIMITATIONS, REASONS FOR CAUTION

The effect estimates are based on a prospective cohort followed up for 1.5 years after completion of the fertility workup. Although we balanced the known predictors of conception between treated and untreated couples using inverse probability weighting, observational data may be subject to residual confounding. The results need to be confirmed in external datasets.

WIDER IMPLICATIONS OF THE FINDINGS

These results explain the discrepancies between previous trials that compared IUI-OS to expectant management, but further studies are required to establish the threshold at which IUI-OS is (cost-)effective.

STUDY FUNDING/COMPETING INTEREST(S)

This study was facilitated by (Grant 945/12/002) from ZonMW, The Netherlands Organization for Health Research and Development, The Hague, The Netherlands. B.W.M. is supported by a NHMRC Practitioner Fellowship (GNT1082548). B.W.M. reports consultancy for ObsEva, Merck and Guerbet. S.B. reports acting as Editor-in-Chief of HROpen. The other authors have no conflicts of interest.

A prognosis-based approach to infertility: understanding the role of time

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.

Decision support tools in low back pain

Information from individual classification systems or clinical prediction rules that aim to facilitate stratified care in low back pain is important but often not comprehensive enough to be used to support clinical decision-making. The development and implementation of a clinically useful decision support tool (DST) that considering all key features is a challenging enterprise, requiring a multidisciplinary approach. Key features are inclusion of all relevant treatment options, patient characteristics, and benefits and harms and presentation as an accessible and easy to use toolkit. To be of clinical value, a DST should (1) be based on large numbers of high-quality data, allowing robust estimation of benefits and harms; (2) be presented using visually attractive and easy-to-use software; (3) be externally validated with a clinical beneficial impact established; and (4) include a procedure for regular updating and monitoring. As an illustration, we describe the development; presentation; and plans for further validation, implementation, and updating of the Nijmegen Decision Tool for Chronic Low Back Pain (NDT-CLBP).

Natural conception: repeated predictions over time

STUDY QUESTION

How can we predict chances of natural conception at various time points in couples diagnosed with unexplained subfertility?

SUMMARY ANSWER

We developed a dynamic prediction model that can make repeated predictions over time for couples with unexplained subfertility that underwent a fertility workup at a fertility clinic.

WHAT IS KNOWN ALREADY

The most frequently used prediction model for natural conception (the 'Hunault model') estimates the probability of natural conception only once per couple, that is, after completion of the fertility workup. This model cannot be used for a second or third time for couples who wish to know their renewed chances after a certain period of expectant management.

STUDY DESIGN, SIZE, DURATION

A prospective cohort studying the long-term follow-up of subfertile couples included 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 × 10⁶ were excluded.

PARTICIPANTS/MATERIALS, SETTING, METHODS

The primary endpoint was time to natural conception, leading to an ongoing pregnancy. Follow-up time was censored at the start of treatment or at the last date of contact. In developing the new dynamic prediction model, we used the same predictors as the Hunault model, i.e. female age, duration of subfertility, female subfertility being primary or secondary, sperm motility and referral status. The performance of the model was evaluated in terms of calibration and discrimination. Additionally, we assessed the utility of the model in terms of the variability of the calculated predictions.

MAIN RESULTS AND THE ROLE OF CHANCE

Of the 4999 couples in the cohort, 1053 (21%) women reached a natural conception leading to an ongoing pregnancy within a mean follow-up of 8 months (5th and 95th percentile: 1-21). Our newly developed dynamic prediction model estimated the median probability of conceiving in the first year after the completion of the fertility workup at 27%. For couples not yet pregnant after half a year, after one year and after one and a half years of expectant management, the median probability of conceiving over the next year was estimated at 20, 15 and 13%, respectively. The model performed fair in an internal validation. The prediction ranges were sufficiently broad to aid in counselling couples for at least two years after their fertility workup.

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 allows reassessment of natural conception chances after various periods of unsuccessful expectant management. This gives valuable information to counsel couples with unexplained subfertility that are seen for a fertility workup.

STUDY FUNDING/COMPETING INTERESTS

This study was facilitated by grant 945/12/002 from ZonMW, The Netherlands Organization for Health Research and Development, The Hague, The Netherlands. No competing interests.

Predicting the chances of a live birth after one or more complete cycles of in vitro fertilisation: population based study of linked cycle data from 113 873 women

OBJECTIVE

 To develop a prediction model to estimate the chances of a live birth over multiple complete cycles of in vitro fertilisation (IVF) based on a couple's specific characteristics and treatment information.

DESIGN

 Population based cohort study.

SETTING

 All licensed IVF clinics in the UK. National data from the Human Fertilisation and Embryology Authority register.

PARTICIPANTS

 All 253 417 women who started IVF (including intracytoplasmic sperm injection) treatment in the UK from 1999 to 2008 using their own eggs and partner's sperm.

MAIN OUTCOME MEASURE

 Two clinical prediction models were developed to estimate the individualised cumulative chance of a first live birth over a maximum of six complete cycles of IVF-one model using information available before starting treatment and the other based on additional information collected during the first IVF attempt. A complete cycle is defined as all fresh and frozen-thawed embryo transfers arising from one episode of ovarian stimulation.

RESULTS

 After exclusions, 113 873 women with 184 269 complete cycles were included, of whom 33 154 (29.1%) had a live birth after their first complete cycle and 48 925 (43.0%) after six complete cycles. Key pretreatment predictors of live birth were the woman's age (31 v 37 years; adjusted odds ratio 1.66, 95% confidence interval 1.62 to 1.71) and duration of infertility (3 v 6 years; 1.09, 1.08 to 1.10). Post-treatment predictors included number of eggs collected (13 v 5 eggs; 1.29, 1.27 to 1.32), cryopreservation of embryos (1.91, 1.86 to 1.96), the woman's age (1.53, 1.49 to 1.58), and stage of embryos transferred (eg, double blastocyst v double cleavage; 1.79, 1.67 to 1.91). Pretreatment, a 30 year old woman with two years of unexplained primary infertility has a 46% chance of having a live birth from the first complete cycle of IVF and a 79% chance over three complete cycles. If she then has five eggs collected in her first complete cycle followed by a single cleavage stage embryo transfer (with no embryos left for freezing) her chances change to 28% and 56%, respectively.

CONCLUSIONS

 This study provides an individualised estimate of a couple's cumulative chances of having a baby over a complete package of IVF both before treatment and after the first fresh embryo transfer. This novel resource may help couples plan their treatment and prepare emotionally and financially for their IVF journey.

[Types of immune response in advanced suppurative peritonitis]

AIM

to assess types of immune response in patients with advanced suppurative peritonitis and course of disease.

MATERIAL AND METHODS

We examined 79 patients with acute surgical abdominal diseases and injuries complicated by advanced suppurative peritonitis. Blood immunological parameters were estimated using flowing cytometry and enzyme immunoassay.

RESULTS

It was concluded that functional parameters of immune system are very various in patients with advanced suppurative peritonitis. Cluster analysis defined 4 immune types which are determined by different state of congenital and acquired immunity. Immunodeficient and unreactive immune types are unfavorable. Immune types with activation of congenital and acquired immunity are the most favourable. This stratification personifies diagnosis and treatment of immune disorders in patients with advanced suppurative peritonitis.

Do the causes of infertility play a direct role in the aetiology of preterm birth?

BACKGROUND

It is well established that singletons born of assisted reproductive technology are at higher risk of preterm birth and other adverse outcomes. What remains unclear is whether the increased risk is attributable to the effects of the treatment alone or whether the underlying causes of infertility also play a role. The aim of this study was to examine whether any of the six categories of causes of infertility were associated with a direct effect on preterm birth using causal mediation analysis.

METHODS

We assembled a hospital-based cohort of births delivered at a large tertiary care hospital in Montreal, Canada between 2001 and 2007. Causes of infertility were ascertained through a clinical database and medical chart abstraction. We employed marginal structural models (MSM) to estimate the controlled direct effect of each cause of infertility on preterm birth compared with couples without the cause under examination.

RESULTS

The final study cohort comprised 18,598 singleton and twin pregnancies, including 1689 in couples with ascertained infertility. MSM results suggested no significant direct effect for any of the six categories of causes. However, power was limited in smaller subgroup analyses, and a possible direct effect for uterine abnormalities (e.g. fibroids and malformations) could not be ruled out.

CONCLUSION

In this cohort, most of the increased risk of preterm birth appeared to be explained by maternal characteristics (such as age, body mass index, and education) and by assisted reproduction. If these findings are corroborated, physicians should consider these risks when counselling patients.