A prediction model for live birth and multiple births within the first three cycles of assisted reproductive technology

Effectiveness of recombinant human FSH: recombinant human LH combination treatment versus recombinant human FSH alone for assisted reproductive technology in women aged 35-40 years

RESEARCH QUESTION

According to real-world data, is recombinant human FSH (r-hFSH) combined with recombinant human LH (r-hLH) or r-hFSH alone more effective for women of advanced maternal age (AMA) in terms of live birth?

DESIGN

Non-interventional study comparing the effectiveness of r-hFSH and recombinant r-hLH (2:1 ratio) versus r-hFSH alone for ovarian stimulation during ART treatment in women aged 35-40 years, using real-world data from the Deutsches IVF-Register.

RESULTS

Overall clinical pregnancy (29.8%, 95% CI 28.2 to 31.6 versus 27.8%, 95% CI 26.5 to 29.2) and live birth (20.3%, 95% CI 18.7 to 21.8 versus 18.0%, 95% CI 16.6 to 19.4) rates were not significantly different between the combined r-hFSH and r-hLH group and the r-hFSH alone group (P = 0.269 and P = 0.092, respectively). Treatment effect was significantly higher for combined r-hFSH and r-hLH compared with r-hFSH alone for clinical pregnancy (33.1%, 95% CI 31.0 to 35.0 versus 28.5%, 95% CI 26.6 to 30.4; P = 0.001, not adjusted for multiplicity) and live birth (22.5%, 95% CI 20.5 to 24.2 versus 19.4%, 95% CI 17.6 to 20.9; P = 0.014, not adjusted for multiplicity) in a post-hoc analysis of women with five to 14 oocytes retrieved (used as a surrogate for normal ovarian reserve), highlighting the potential benefits of combined r-hFSH and r-hLH for ovarian stimulation in women aged 35-40 years with normal ovarian reserve.

CONCLUSIONS

Women of AMA with normal ovarian response benefit from treatment with combined r-hFSH and r-hLH in a 2:1 ratio versus r-hFSH alone in terms of live birth rate. The effectiveness of treatments is best assessed by RCTs; however, real-world data are valuable for examining the effectiveness of fertility treatment, especially among patient groups that are not well represented in clinical trials.

The details matter: personalized prediction of live birth after in vitro fertilization in women with polycystic ovary syndrome

OBJECTIVE

To derive and internally validate a clinical prediction model for live birth (LB) in women with polycystic ovary syndrome (PCOS) undergoing in vitro fertilization (IVF).

DESIGN

Retrospective cohort study.

SETTING

Four academic reproductive endocrinology clinics.

PATIENTS

A total of 207 women with PCOS confirmed using Rotterdam criteria undergoing their first fresh IVF cycle.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURE

The primary outcome was cumulative LB per IVF cycle start. This included any LB that resulted from either fresh embryo transfer or any subsequent frozen embryo transfer from embryos obtained at the index oocyte retrieval. A prediction model was derived using multivariable logistic regression. Covariates considered for inclusion in the prediction model included demographic characteristics, medical history, and prior fertility treatment. Predicted probabilities for LB were calculated using the prediction model which included the 90% shrinkage factor for each adjusted odds ratio.

RESULTS

The final model, on the basis of maximization of the area under the receiver operating characteristic curve, included age < 35 years, White race, presence of polycystic ovaries on ultrasound (polycystic ovary morphology), normal body mass index (<25 kg/m2), being metabolically healthy (no metabolic risk factors), and being a nonresponder to ovulation induction agents including letrozole and clomiphene citrate. The area under the receiver operating characteristic curve score for the model was 0.68 (95% confidence interval [CI]: 0.60, 0.77). Predicted probabilities of LB ranged from 8.1% (95% CI: 2.8, 21.5) for a woman who had no favorable predictors to 74.2% (95% CI: 59.5, 84.9) for a woman who had all favorable predictors.

CONCLUSION

Our study demonstrated that, in addition to anovulation, the underlying pathophysiology and associated comorbidities alter the likelihood of a successful pregnancy in women with PCOS undergoing IVF. Further validation of this model is needed before it can serve as a tool to personalize prediction estimates for the probability of LB in women with PCOS.

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.

Establishment and validation of a nomogram for subsequent first-cycle live births in patients diagnosed with recurrent implantation failure: a population-based analysis

Background

The inability of patients with recurrent implantation failure (RIF) to achieve pregnancy and a live birth after multiple high-quality embryo transfer treatments has been recognized as a major obstacle to successful application of artificial reproductive technologies. The objective of this study was to establish and validate a nomogram for prediction of subsequent first-cycle live births to guide clinical practice in patients diagnosed with RIF.

Methods

A total of 538 patients who underwent in vitro fertilization/intracytoplasmic sperm injection treatment and were first diagnosed with RIF at the Reproductive Center of the First Affiliated Hospital of Xinjiang Medical University between January 2017 and December 2020 were enrolled. The patients were randomly divided into a training cohort (n=408) and a validation set (n=175) in a ratio of 7:3. A nomogram model was constructed using the training set based on the results of univariate and multivariate logistic regression analyses and validated in the validation set.

Results

Age, body mass index, duration of RIF, endometrial thickness, type of embryo transferred, and number of previous biochemical pregnancies were included in the nomogram for prediction of subsequent first-cycle live births in patients diagnosed with RIF. Analysis of the area under the receiver-operating characteristic curve, calibration plots, and decision curve analysis showed that our predictive model for live births had excellent performance.

Conclusion

We have developed and validated a novel predictive model that estimates a woman's chances of having a live birth after a diagnosis of RIF and provides clinicians with a personalized clinical decision-making tool.

Does cleavage stage morphology increase the discriminatory power of prediction in blastocyst transfer outcome?

PURPOSE

To evaluate the contribution of the cleavage stage morphological parameters to the prediction of blastocyst transfer outcomes.

METHODS

A retrospective study was conducted on 8383 single-blastocyst transfer cycles including 2246 fresh and 6137 vitrified-warmed cycles. XGboost, LASSO, and GLM algorithms were employed to establish models for assessing the predictive value of the cleavage stage morphological parameters in transfer outcomes. Four models were developed using each algorithm: all-in model with or without day 3 morphology and embryo quality-only model with or without day 3 morphology.

RESULTS

The live birth rate was 48.04% in the overall cohort. The AUCs of the models with the algorithm of XGboost were 0.83, 0.82, 0.63, and 0.60; with LASSO were 0.66, 0.66, 0.61, and 0.60; and with GLM were 0.66, 0.66, 0.61, and 0.60 respectively. In models 1 and 2, female age, basal FSH, peak E2, endometrial thickness, and female BMI were the top five critical features for predicting live birth; In models 3 and 4, the most crucial factor was blastocyst formation on D5 rather than D6. In model 3, incorporating cleavage stage morphology, including early cleavage, D3 cell number, and fragmentation, was significantly associated with successful live birth. Additionally, the live birth rates for blastocysts derived from on-time, slow, and fast D3 embryos were 49.7%, 39.5%, and 52%, respectively.

CONCLUSIONS

The value of cleavage stage morphological parameters in predicting the live birth outcome of single blastocyst transfer is limited.

Pretreatment prediction for IVF outcomes: generalized applicable model or centre-specific model?

STUDY QUESTION

What was the performance of different pretreatment prediction models for IVF, which were developed based on UK/US population (McLernon 2016 model, Luke model, Dhillon model, and McLernon 2022 model), in wider populations?

SUMMARY ANSWER

For a patient in China, the published pretreatment prediction models based on the UK/US population provide similar discriminatory power with reasonable AUCs and underestimated predictions.

WHAT IS KNOWN ALREADY

Several pretreatment prediction models for IVF allow patients and clinicians to estimate the cumulative probability of live birth in a cycle before the treatment, but they are mostly based on the population of Europe or the USA, and their performance and applicability in the countries and regions beyond these regions are largely unknown.

STUDY DESIGN, SIZE, DURATION

A total of 26 382 Chinese patients underwent oocyte pick-up cycles between January 2013 and December 2020.

PARTICIPANTS/MATERIALS, SETTING, METHODS

UK/US model performance was externally validated according to the coefficients and intercepts they provided. Centre-specific models were established with XGboost, Lasso, and generalized linear model algorithms. Discriminatory power and calibration of the models were compared as the forms of the AUC of the Receiver Operator Characteristic and calibration curves.

MAIN RESULTS AND THE ROLE OF CHANCE

The AUCs for McLernon 2016 model, Luke model, Dhillon model, and McLernon 2022 model were 0.69 (95% CI 0.68-0.69), 0.67 (95% CI 0.67-0.68), 0.69 (95% CI 0.68-0.69), and 0.67 (95% CI 0.67-0.68), respectively. The centre-specific yielded an AUC of 0.71 (95% CI 0.71-0.72) with key predictors including age, duration of infertility, and endocrine parameters. All external models suggested underestimation. Among the external models, the rescaled McLernon 2022 model demonstrated the best calibration (Slope 1.12, intercept 0.06).

LIMITATIONS, REASONS FOR CAUTION

The study is limited by its single-centre design and may not be representative elsewhere. Only per-complete cycle validation was carried out to provide a similar framework to compare different models in the sample population. Newer predictors, such as AMH, were not used.

WIDER IMPLICATIONS OF THE FINDINGS

Existing pretreatment prediction models for IVF may be used to provide useful discriminatory power in populations different from those on which they were developed. However, models based on newer more relevant datasets may provide better calibrations.

STUDY FUNDING/COMPETING INTEREST(S)

This work was supported by the National Natural Science Foundation of China [grant number 22176159], the Xiamen Medical Advantage Subspecialty Construction Project [grant number 2018296], and the Special Fund for Clinical and Scientific Research of Chinese Medical Association [grant number 18010360765].

TRIAL REGISTRATION NUMBER

N/A.

External validation of models for predicting cumulative live birth over multiple complete cycles of IVF treatment

STUDY QUESTION

Can two prediction models developed using data from 1999 to 2009 accurately predict the cumulative probability of live birth per woman over multiple complete cycles of IVF in an updated UK cohort?

SUMMARY ANSWER

After being updated, the models were able to estimate individualized chances of cumulative live birth over multiple complete cycles of IVF with greater accuracy.

WHAT IS KNOWN ALREADY

The McLernon models were the first to predict cumulative live birth over multiple complete cycles of IVF. They were converted into an online calculator called OPIS (Outcome Prediction In Subfertility) which has 3000 users per month on average. A previous study externally validated the McLernon models using a Dutch prospective cohort containing data from 2011 to 2014. With changes in IVF practice over time, it is important that the McLernon models are externally validated on a more recent cohort of patients to ensure that predictions remain accurate.

STUDY DESIGN, SIZE, DURATION

A population-based cohort of 91 035 women undergoing IVF in the UK between January 2010 and December 2016 was used for external validation. Data on frozen embryo transfers associated with these complete IVF cycles conducted from 1 January 2017 to 31 December 2017 were also collected.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Data on IVF treatments were obtained from the Human Fertilisation and Embryology Authority (HFEA). The predictive performances of the McLernon models were evaluated in terms of discrimination and calibration. Discrimination was assessed using the c-statistic and calibration was assessed using calibration-in-the-large, calibration slope, and calibration plots. Where any model demonstrated poor calibration in the validation cohort, the models were updated using intercept recalibration, logistic recalibration, or model revision to improve model performance.

MAIN RESULTS AND THE ROLE OF CHANCE

Following exclusions, 91 035 women who underwent 144 734 complete cycles were included. The validation cohort had a similar distribution age profile to women in the development cohort. Live birth rates over all complete cycles of IVF per woman were higher in the validation cohort. After calibration assessment, both models required updating. The coefficients of the pre-treatment model were revised, and the updated model showed reasonable discrimination (c-statistic: 0.67, 95% CI: 0.66 to 0.68). After logistic recalibration, the post-treatment model showed good discrimination (c-statistic: 0.75, 95% CI: 0.74 to 0.76). As an example, in the updated pre-treatment model, a 32-year-old woman with 2 years of primary infertility has a 42% chance of having a live birth in the first complete ICSI cycle and a 77% chance over three complete cycles. In a couple with 2 years of primary male factor infertility where a 30-year-old woman has 15 oocytes collected in the first cycle, a single fresh blastocyst embryo transferred in the first cycle and spare embryos cryopreserved, the estimated chance of live birth provided by the post-treatment model is 46% in the first complete ICSI cycle and 81% over three complete cycles.

LIMITATIONS, REASONS FOR CAUTION

Two predictors from the original models, duration of infertility and previous pregnancy, which were not available in the recent HFEA dataset, were imputed using data from the older cohort used to develop the models. The HFEA dataset does not contain some other potentially important predictors, e.g. BMI, ethnicity, race, smoking and alcohol intake in women, as well as measures of ovarian reserve such as antral follicle count.

WIDER IMPLICATIONS OF THE FINDINGS

Both updated models show improved predictive ability and provide estimates which are more reflective of current practice and patient case mix. The updated OPIS tool can be used by clinicians to help shape couples' expectations by informing them of their individualized chances of live birth over a sequence of multiple complete cycles of IVF.

STUDY FUNDING/COMPETING INTEREST(S)

This study was supported by an Elphinstone scholarship scheme at the University of Aberdeen and Aberdeen Fertility Centre, University of Aberdeen. S.B. has a commitment of research funding from Merck. D.J.M. and M.B.R. declare support for the present manuscript from Elphinstone scholarship scheme at the University of Aberdeen and Assisted Reproduction Unit at Aberdeen Fertility Centre, University of Aberdeen. D.J.M. declares grants received by University of Aberdeen from NHS Grampian, The Meikle Foundation, and Chief Scientist Office in the past 3 years. D.J.M. declares receiving an honorarium for lectures from Merck. D.J.M. is Associate Editor of Human Reproduction Open and Statistical Advisor for Reproductive BioMed Online. S.B. declares royalties from Cambridge University Press for a book. S.B. declares receiving an honorarium for lectures from Merck, Organon, Ferring, Obstetric and Gynaecological Society of Singapore, and Taiwanese Society for Reproductive Medicine. S.B. has received support from Merck, ESHRE, and Ferring for attending meetings as speaker and is on the METAFOR and CAPRE Trials Data Monitoring Committee.

TRIAL REGISTRATION NUMBER

N/A.

Reproductive history does not compromise subsequent live birth and perinatal outcome following in-vitro fertilization: analysis of 25 329 first frozen-thawed embryo transfer cycles without preimplantation genetic testing for aneuploidy

OBJECTIVE

To investigate the effect of women's reproductive history on live-birth rate and perinatal outcome after first frozen-thawed embryo transfer (FET) without preimplantation genetic testing for aneuploidy.

METHODS

This was a retrospective cohort study of women who had undergone their first FET cycle between January 2014 and December 2020 at a university-affiliated fertility center. No transferred embryo underwent preimplantation genetic testing for aneuploidy. The women were categorized into five groups based on their reproductive history: no previous pregnancy; previous termination of pregnancy (TOP); previous pregnancy loss; previous ectopic pregnancy (EP); and previous live birth. The women with no previous pregnancy were considered as the reference group. The primary outcome was the live-birth rate and secondary endpoints included rates of positive pregnancy test, clinical pregnancy, pregnancy loss and EP as well as perinatal outcomes such as birth weight and preterm birth. Multivariable logistic regression analyses were used to control for a number of potential confounders, including age, body mass index, education level, duration and cause of infertility, insemination method, type of endometrial preparation, number of embryos transferred, embryo developmental stage, quality of the embryos transferred, year of treatment and endometrial thickness. Additionally, propensity score matching (PSM) was used to check the robustness of the main findings.

RESULTS

In total, 25 329 women were included in the final analysis. On univariate analysis, each reproductive-history type except for previous EP was significantly associated with worse pregnancy outcome following in-vitro fertilization (IVF), including rates of positive pregnancy test, clinical pregnancy, pregnancy loss and live birth, when compared with the group of women with no previous pregnancy. However, after correcting for several potential confounders, the differences in rates of live birth, pregnancy loss, positive pregnancy test and clinical pregnancy were no longer significant between the study and control groups on multivariable regression models, while the risk of EP after embryo transfer was elevated among women with a previous TOP or EP. There was no increased risk of adverse perinatal outcome associated with reproductive history compared with the control group. Notably, similar results were obtained from the PSM models, confirming the robustness of the main findings.

CONCLUSION

Relative to women without a previous pregnancy, those with a prior TOP, pregnancy loss, EP or live birth did not have compromised live-birth rate or perinatal outcomes following FET without preimplantation genetic testing for aneuploidy, with the exception of an increased risk of EP in those with prior TOP or EP. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

Effectiveness of recombinant human follicle-stimulating hormone (r-hFSH): recombinant human luteinizing hormone versus r-hFSH alone in assisted reproductive technology treatment cycles among women aged 35-40 years: A German database study

This non-interventional study compared the effectiveness of recombinant human follicle-stimulating hormone (r-hFSH) and recombinant human luteinizing hormone (r-hLH) (2:1 ratio) versus r-hFSH alone for ovarian stimulation (OS) during assisted reproductive technology treatment in women aged 35-40 years, using real-world data from the Deutsches IVF-Register (D·I·R). Numerically higher clinical pregnancy (29.8% [95% CI 28.2, 31.6] vs. 27.8% [26.5, 29.2]) and live birth (20.3% [18.7, 21.8] vs. 18.0% [16.6, 19.4]) rates were observed with r-hFSH:r-hLH versus r-hFSH alone. The treatment effect was consistently higher for r-hFSH:r-hLH compared with r-hFSH alone in terms of clinical pregnancy (relative risk [RR] 1.16 [1.05, 1.26]) and live birth (RR 1.16 [1.02, 1.31]) in a post-hoc analysis of women with 5-14 oocytes retrieved (used as a surrogate for normal ovarian reserve), highlighting the potential benefits of r-hFSH:r-hLH for OS in women aged 35-40 years with normal ovarian reserve.

Predicted probabilities of live birth following assisted reproductive technology using United States national surveillance data from 2016 to 2018

BACKGROUND

As the use of in vitro fertilization continues to increase in the United States, up-to-date models that estimate cumulative live birth rates after multiple oocyte retrievals and embryo transfers (fresh and frozen) are valuable for patients and clinicians weighing treatment options.

OBJECTIVE

This study aimed to develop models that generate predicted probabilities of live birth in individuals considering in vitro fertilization based on demographic and reproductive characteristics.

STUDY DESIGN

Our population-based cohort study used data from the National Assisted Reproductive Technology Surveillance System 2016 to 2018, including 196,916 women who underwent 207,766 autologous embryo transfer cycles and 25,831 women who underwent 36,909 donor oocyte transfer cycles. We used data on autologous in vitro fertilization cycles to develop models that estimate a patient's cumulative live birth rate after all embryo transfers (fresh and frozen) within 12 months after 1, 2, and 3 oocyte retrievals in new and returning patients. Among patients using donor oocytes, we estimated the cumulative live birth rate after their first, second, and third embryo transfers. Multinomial logistic regression models adjusted for age, prepregnancy body mass index (imputed for 18% of missing values), parity, gravidity, and infertility diagnoses were used to estimate the cumulative live birth rate.

RESULTS

Among new and returning patients undergoing autologous in vitro fertilization, female age had the strongest association with cumulative live birth rate. Other factors associated with higher cumulative live birth rates were lower body mass index and parity or gravidity ≥1, although results were inconsistent. Infertility diagnoses of diminished ovarian reserve, uterine factor, and other reasons were associated with a lower cumulative live birth rate, whereas male factor, tubal factor, ovulatory disorders, and unexplained infertility were associated with a higher cumulative live birth rate. Based on our models, a new patient who is 35 years old, with a body mass index of 25 kg/m2, no previous pregnancy, and unexplained infertility diagnoses, has a 48%, 69%, and 80% cumulative live birth rate after the first, second, and third oocyte retrieval, respectively. Cumulative live birth rates are 29%, 48%, and 62%, respectively, if the patient had diminished ovarian reserve, and 25%, 41%, and 52%, respectively, if the patient was 40 years old (with unexplained infertility). Very few recipient characteristics were associated with cumulative live birth rate in donor oocyte patients.

CONCLUSION

Our models provided estimates of cumulative live birth rate based on demographic and reproductive characteristics to help inform patients and providers of a woman's probability of success after in vitro fertilization.

Quality of clinical prediction models in in vitro fertilisation: Which covariates are really important to predict cumulative live birth and which models are best?

The improvement in IVF cryopreservation techniques over the last 20 years has led to an increase in elective single embryo transfer, thus reducing multiple pregnancy rates. This strategy of successive transfers of fresh followed by frozen embryos has resulted in the acceptance of using cumulative live birth over complete cycles of IVF as a critical measure of success. Clinical prediction models are a useful way of estimating the cumulative chances of success for couples tailored to their individual clinical factors, which help them prepare for and plan future treatment. In this review, we describe several models that predict cumulative live birth and recommend which should be used by couples and/or their clinicians and when they should be used. We also discuss the most relevant predictors to consider when either developing new IVF prediction models or updating existing models.

Cost-effectiveness analysis of recombinant human follicle-stimulating hormone alfa(r-hFSH) and urinary highly purified menopausal gonadotropin (hMG) based on data from a large German registry

This was a retrospective real-world evidence analysis of the costs per live birth for reference recombinant human follicle-stimulating hormone alfa (r-hFSH-alfa) versus highly purified urinary human menopausal gonadotropin (hMG-HP), based on data from a German in vitro fertilization registry (RecDate). Pregnancy and live birth rates from the RecDate real-world evidence study over three complete assisted reproductive technology (ART) cycles using the same gonadotropin drug were used as clinical inputs. Costs related to ART treatment and to drugs were obtained from public sources. Treatment with r-hFSH-alfa resulted in higher adjusted cumulative live birth rates versus hMG-HP after one (25.3% vs. 22.3%), two (30.9% vs. 27.5%), and three (31.9% vs. 28.6%) ART cycles. Costs per live birth were lower with r-hFSH-alfa versus hMG-HP after one (€17,938 vs. €20,054), two (€18,251 vs. €20,437), and three (€18,473 vs. €20,680) ART cycles. r-hFSH-alfa was found to be a cost-effective strategy compared with hMG-HP over three cycles.

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.

Predicting cumulative live birth for couples beginning their second complete cycle of in vitro fertilization treatment

STUDY QUESTION

Can we develop an IVF prediction model to estimate individualized chances of a live birth over multiple complete cycles of IVF in couples embarking on their second complete cycle of treatment?

SUMMARY ANSWER

Yes, our prediction model can estimate individualized chances of cumulative live birth over three additional complete cycles of IVF.

WHAT IS KNOWN ALREADY

After the completion of a first complete cycle of IVF, couples who are unsuccessful may choose to undergo further treatment to have their first child, while those who have had a live birth may decide to have more children. Existing prediction models can estimate the overall chances of success in couples before commencing IVF but are unable to revise these chances on the basis of the couple’s response to a first treatment cycle in terms of the number of eggs retrieved and pregnancy outcome. This makes it difficult for couples to plan and prepare emotionally and financially for the next step in their treatment.

STUDY DESIGN, SIZE, DURATION

For model development, a population-based cohort was used of 49 314 women who started their second cycle of IVF including ICSI in the UK from 1999 to 2008 using their own oocytes and their partners’ sperm. External validation was performed on data from 39 442 women who underwent their second cycle from 2010 to 2016.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Data about all UK IVF treatments were obtained from the Human Fertilisation and Embryology Authority (HFEA) database. Using a discrete time logistic regression model, we predicted the cumulative probability of live birth from the second up to and including the fourth complete cycles of IVF. Inverse probability weighting was used to account for treatment discontinuation. Discrimination was assessed using c-statistic and calibration was assessed using calibration-in-the-large and calibration slope.

MAIN RESULTS AND THE ROLE OF CHANCE

Following exclusions, 49 314 women with 73 053 complete cycles were included. 12 408 (25.2%) had a live birth resulting from their second complete cycle. Cumulatively, 17 394 (35.3%) had a live birth over complete cycles two to four. The model showed moderate discriminative ability (c-statistic: 0.65, 95% CI: 0.64 to 0.65) and evidence of overprediction (calibration-in-the-large = −0.08) and overfitting (calibration slope 0.85, 95% CI: 0.81 to 0.88) in the validation cohort. However, after recalibration the fit was much improved. The recalibrated model identified the following key predictors of live birth: female age (38 versus 32 years—adjusted odds ratio: 0.59, 95% CI: 0.57 to 0.62), number of eggs retrieved in the first complete cycle (12 versus 4 eggs; 1.34, 1.30 to 1.37) and outcome of the first complete cycle (live birth versus no pregnancy; 1.78, 1.66 to 1.91; live birth versus pregnancy loss; 1.29, 1.23 to 1.36). As an example, a 32-year-old with 2 years of non-tubal infertility who had 12 eggs retrieved from her first stimulation and had a live birth during her first complete cycle has a 46% chance of having a further live birth from the second complete cycle of IVF and an 81% chance over a further three cycles.

LIMITATIONS, REASONS FOR CAUTION

The developed model was updated using validation data that was 6 to 12 years old. IVF practice continues to evolve over time, which may affect the accuracy of predictions from the model. We were unable to adjust for some potentially important predictors, e.g. BMI, smoking and alcohol intake in women, as well as measures of ovarian reserve such as antral follicle count. These were not available in the linked HFEA dataset.

WIDER IMPLICATIONS OF THE FINDINGS

By appropriately adjusting for couples who discontinue treatment, our novel prediction model will provide more realistic chances of live birth in couples starting a second complete cycle of IVF. Clinicians can use these predictions to inform discussion with couples who wish to plan ahead. This prediction tool will enable couples to prepare emotionally, financially and logistically for IVF treatment.

STUDY FUNDING/COMPETING INTEREST(S)

This study was supported by an Elphinstone scholarship scheme at the University of Aberdeen and Aberdeen Fertility Centre, University of Aberdeen. The authors have no conflict of interest.

TRIAL REGISTRATION NUMBER

N/A.

Internal validation and comparison of predictive models to determine success rate of infertility treatments: a retrospective study of 2485 cycles

Infertility is a significant health problem and assisted reproductive technologies to treat infertility. Despite all efforts, the success rate of these methods is still low. Also, each of these methods has side effects and costs. Therefore, accurate prediction of treatment success rate is a clinical challenge. This retrospective study aimed to internally validate and compare various machine learning models for predicting the clinical pregnancy rate (CPR) of infertility treatment. For this purpose, data from 1931 patients consisting of in vitro fertilization (IVF) or intra cytoplasmic sperm injection (ICSI) (733) and intra uterine insemination (IUI) (1196) treatments were included. Also, no egg or sperm donation data were used. The performance of machine learning algorithms to predict clinical pregnancy were expressed in terms of accuracy, recall, F-score, positive predictive value (PPV), brier score (BS), Matthew correlation coefficient (MCC), and receiver operating characteristic. The significance of the features with CPR and AUCs was evaluated by Student's t test and DeLong’s algorithm. Random forest (RF) model had the highest accuracy in the IVF/ICSI treatment. The sensitivity, F1 score, PPV, and MCC of the RF model were 0.76, 0.73, 0.80, and 0.5, respectively. These values for IUI treatment were 0.84, 0.80, 0.82, and 0.34, respectively. The BS was 0.13 and 0.15 for IVF/ICS and IUI, respectively. In addition, the estimated AUCs of the RF model for IVF/ICS and IUI were 0.73 and 0.7, respectively. Some essential features were obtained based on RF ranking for the two datasets, including age, follicle stimulation hormone, endometrial thickness, and infertility duration. The results showed a strong relationship between clinical pregnancy and a woman's age. Also, endometrial thickness and the number of follicles decreased with increasing female age in both treatments.

The (decision) tree of fertility: an innovative decision-making algorithm in assisted reproduction technique

PURPOSE

Several mathematical models have been developed to estimate individualized chances of assisted reproduction techniques (ART) success, although with limited clinical application. Our study aimed to develop a decisional algorithm able to predict pregnancy and live birth rates after controlled ovarian stimulation (COS) phase, helping the physician to decide whether to perform oocytes pick-up continuing the ongoing ART path.

METHODS

A single-center retrospective analysis of real-world data was carried out including all fresh ART cycles performed in 1998-2020. Baseline characteristics, ART parameters and biochemical/clinical pregnancies and live birth rates were collected. A seven-steps systematic approach for model development, combining linear regression analyses and decision trees (DT), was applied for biochemical, clinical pregnancy, and live birth rates.

RESULTS

Of fresh ART cycles, 12,275 were included. Linear regression analyses highlighted a relationship between number of ovarian follicles > 17 mm detected at ultrasound before pick-up (OF17), embryos number and fertilization rate, and biochemical and clinical pregnancy rates (p < 0.001), but not live birth rate. DT were created for biochemical pregnancy (statistical power-SP:80.8%), clinical pregnancy (SP:85.4%), and live birth (SP:87.2%). Thresholds for OF17 entered in all DT, while sperm motility entered the biochemical pregnancy's model, and female age entered the clinical pregnancy and live birth DT. In case of OF17 < 3, the chance of conceiving was < 6% for all DT.

CONCLUSION

A systematic approach allows to identify OF17, female age, and sperm motility as pre-retrieval predictors of ART outcome, possibly reducing the socio-economic burden of ART failure, allowing the clinician to perform or not the oocytes pick-up.

OUP accepted manuscript

STUDY QUESTION

What information and support should be offered to donors, intended parents and donor-conceived people, in general and in consideration of the availability of direct-to-consumer genetic testing and matching services?

SUMMARY ANSWER

For donors, intended parents and donor-conceived offspring, recommendations are made that cover information needs and informed consent, psychosocial implications and disclosure.

WHAT IS KNOWN ALREADY

Trends indicate that the use of donor-assisted conception is growing and guidance is needed to help these recipients/intended parents, the donors and offspring, navigate the rapidly changing environment in which donor-assisted conception takes place.

STUDY DESIGN, SIZE, DURATION

A working group (WG) collaborated on writing recommendations based, where available, on evidence collected from a literature search and expert opinion. Draft recommendations were published for stakeholder review and adapted where relevant based on the comments received.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Papers retrieved from PUBMED were included from 1 January 2014 up to 31 August 2020, focusing on studies published since direct-to-consumer genetic testing has become more widespread and accessible. The current paper is limited to reproductive donation performed in medically assisted reproduction (MAR) centres (and gamete banks): donation outside the medical context was not considered.

MAIN RESULTS AND THE ROLE OF CHANCE

In total, 32 recommendations were made for information provision and support to donors, 32 for intended parents and 27 for donor-conceived offspring requesting information/support.

LIMITATIONS, REASONS FOR CAUTION

The available evidence in the area of reproductive donation is limited and diverse with regards to the context and types of donation. General conclusions and recommendations are largely based on expert opinion and may need to be adapted in light of future research.

WIDER IMPLICATIONS OF THE FINDINGS

These recommendations provide guidance to MAR centres and gamete banks on good practice in information provision and support but should also be considered by regulatory bodies and policymakers at a national and international level to guide regulatory and legislative efforts towards the protection of donors and donor-conceived offspring.

STUDY FUNDING/COMPETING INTEREST(S)

The development of this good practice paper was funded by European Society of Human Reproduction and Embryology (ESHRE), covering expenses associated with the WG meetings, the literature searches and dissemination. The WG members did not receive any payment. The authors have no conflicts of interest to declare.

DISCLAIMER

This document represents the views of ESHRE, which are the result of consensus between the relevant ESHRE stakeholders and where relevant based on the scientific evidence available at the time of preparation.

The recommendations should be used for informational and educational purposes. They should not be interpreted as setting a standard of care, or be deemed inclusive of all proper methods of care nor exclusive of other methods of care reasonably directed to obtaining the same results. They do not replace the need for application of clinical judgement to each individual presentation, nor variations based on locality and facility type.

^†ESHRE pages content is not externally peer reviewed. The manuscript has been approved by the Executive Committee of ESHRE.

Nomogram for the cumulative live birth in women undergoing the first IVF cycle: Base on 26, 689 patients in China

OBJECTIVE

Predictive models of the cumulative live birth (CLB) in women undergoing in vitro fertilization (IVF) treatment are limited. The aim of this study was to develop and validate a nomogram for the CLB in women undergoing the first IVF cycle.

METHODS

Based on a cross-sectional study in assisted reproduction center of Northwest Women's and Children's Hospital, 26,689 Chinese patients who underwent IVF treatment was used to develop and validate a prediction model for the CLB. Among those participants, 70% were randomly assigned to the training set (18,601 patients), while the remaining 30% were assigned to the validation set (8,088 patients). A nomogram was constructed based on the results of the multivariate logistic regression analysis. The model performance was evaluated using the C statistic and the calibration performance was assessed by Hosmer-Lemeshow (HL) χ² statistics and calibration plots.

RESULTS

Multivariate logistic regression analyses revealed that female age, female body mass index (BMI), tubal factor infertility, male infertility, uterine factor infertility, unexplained infertility, antral follicle count (AFC) and basal serum follicle stimulating hormone (FSH) were significant factors for CLB in women undergoing the first IVF cycle. An area under the receiver operating characteristic curve (AUC) in the prediction model was 0.676 (95% CI 0.668 to 0.684) in the training group. The validation set showed possibly helpful discrimination with an AUC of 0.672 (95% CI 0.660 to 0.684). Additionally, the prediction model had a good calibration (HL χ² = 8.240, P=0.410).

CONCLUSIONS

We developed and validated a nomogram to predict CLB in women undergoing the first IVF cycle using a single center database in China. The validated nomogram to predict CLB could be a potential tool for IVF counselling.

Predicting personalized cumulative live birth following in vitro fertilization

OBJECTIVE

To develop in vitro fertilization (IVF) prediction models to estimate the individualized chance of cumulative live birth at two time points: pretreatment (i.e., before starting the first complete cycle of IVF) and posttreatment (i.e., before starting the second complete cycle of IVF in those couples whose first complete cycle was unsuccessful).

DESIGN

Population-based cohort study.

SETTING

National data from the Society for Assisted Reproductive Technology (SART) Clinic Outcome Reporting System.

PATIENT(S)

Based on 88,614 women who commenced IVF treatment using their own eggs and partner's sperm in SART member clinics.

INTERVENTION(S)

Not applicable.

MAIN OUTCOME MEASURE(S)

The pretreatment model estimated the cumulative chance of a live birth over a maximum of three complete cycles of IVF, whereas the posttreatment model did so over the second and third complete cycles. One complete cycle included all fresh and frozen embryo transfers resulting from one episode of ovarian stimulation. We considered the first live birth episode, including singletons and multiple births.

RESULT(S)

Pretreatment predictors included woman's age (35 years vs. 25 years, adjusted odds ratio 0.69, 95% confidence interval 0.66-0.73) and body mass index (35 kg/m² vs. 25 kg/m², adjusted odds ratio 0.75, 95% confidence interval 0.72-0.78). The posttreatment model additionally included the number of eggs from the first complete cycle (15 vs. 9 eggs, adjusted odds ratio 1.10, 95% confidence interval 1.03-1.18). According to the pretreatment model, a nulliparous woman aged 34 years with a body mass index of 23.3 kg/m², male partner infertility, and an antimüllerian hormone level of 3 ng/mL has a 61.7% chance of having a live birth over her first complete cycle of IVF (and a cumulative chance over three complete cycles of 88.8%). If a live birth is not achieved, according to the posttreatment model, her chance of having a live birth over the second complete cycle 1 year later (age 35 years, number of eggs 7) is 42.9%. The C-statistic for all models was between 0.71 and 0.73.

CONCLUSION(S)

The focus of previous IVF prediction models based on US data has been cumulative live birth excluding cycles involving frozen embryos. These novel prediction models provide clinically relevant estimates that could help clinicians and couples plan IVF treatment at different points in time.

Comparative effectiveness of recombinant human follicle-stimulating hormone alfa (r-hFSH-alfa) versus highly purified urinary human menopausal gonadotropin (hMG HP) in assisted reproductive technology (ART) treatments: a non-interventional study in Germany

BACKGROUND

This study compared the effectiveness of recombinant human follicle-stimulating hormone alfa (r-hFSH-alfa; GONAL-f^®) with urinary highly purified human menopausal gonadotropin (hMG HP; Menogon HP^®), during assisted reproductive technology (ART) treatments in Germany.

METHODS

Data were collected from 71 German fertility centres between 01 January 2007 and 31 December 2012, for women undergoing a first stimulation cycle of ART treatment with r-hFSH-alfa or hMG HP. Primary outcomes were live birth, ongoing pregnancy and clinical pregnancy, based on cumulative data (fresh and frozen-thawed embryo transfers), analysed per patient (pP), per complete cycle (pCC) and per first complete cycle (pFC). Secondary outcomes were pregnancy loss (analysed per clinical pregnancy), cancelled cycles (analysed pCC), total drug usage per oocyte retrieved and time-to-live birth (TTLB; per calendar week and per cycle).

RESULTS

Twenty-eight thousand six hundred forty-one women initiated a first treatment cycle (r-hFSH-alfa: 17,725 [61.9%]; hMG HP: 10,916 [38.1%]). After adjustment for confounding variables, treatment with r-hFSH-alfa versus hMG HP was associated with a significantly higher probability of live birth (hazard ratio [HR]-pP [95% confidence interval (CI)]: 1.10 [1.04, 1.16]; HR-pCC [95% CI]: 1.13 [1.08, 1.19]; relative risk [RR]-pFC [95% CI]: 1.09 [1.05, 1.15], ongoing pregnancy (HR-pP [95% CI]: 1.10 [1.04, 1.16]; HR-pCC [95% CI]: 1.13 [1.08, 1.19]; RR-pFC [95% CI]: 1.10 [1.05, 1.15]) and clinical pregnancy (HR-pP [95% CI]: 1.10 [1.05, 1.14]; HR-pCC [95% CI]: 1.14 [1.10, 1.19]; RR-pFC [95% CI]: 1.10 [1.06, 1.14]). Women treated with r-hFSH-alfa versus hMG HP had no statistically significant difference in pregnancy loss (HR [95% CI]: 1.07 [0.98, 1.17], were less likely to have a cycle cancellation (HR [95% CI]: 0.91 [0.84, 0.99]) and had no statistically significant difference in TTLB when measured in weeks (HR [95% CI]: 1.02 [0.97, 1.07]; p = 0.548); however, r-hFSH-alfa was associated with a significantly shorter TTLB when measured in cycles versus hMG HP (HR [95% CI]: 1.07 [1.02, 1.13]; p = 0.003). There was an average of 47% less drug used per oocyte retrieved with r-hFSH-alfa versus hMG HP.

CONCLUSIONS

This large (> 28,000 women), real-world study demonstrated significantly higher rates of cumulative live birth, cumulative ongoing pregnancy and cumulative clinical pregnancy with r-hFSH-alfa versus hMG HP.

'Genes versus children': if the goal is parenthood, are we using the optimal approach?

First medical contact for couples trying for a child will usually emphasise the array of assistance available to ‘help them have their own child’, usually with options involving ART, after diagnosis. For many poorer prognosis couples, this means repetitive unsuccessful cycles of invasive and stressful treatment. What is sometimes lost at this stage is a reflection on the likelihood of success of different options, which may lead patients to focus on hoping for their own ‘genetic’ progeny, but failing to consider the alternative and potentially more successful other options, including donation and adoption, for achieving parenthood of a child. Factors not only such as female age but also advanced requirements such as preimplantation genetic testing or even mitochondrial replacement therapies all have reduced chances of success but further tend to reinforce the importance of a genetic link. The financial, physical and psychosocial burden associated with cumulative failure also lead to a higher probability of dropout and consequently an even higher probability of remaining in involuntary childlessness. We advocate formulation of a detailed roadmap for discussion of parenthood, with reference explanation to genetics and epigenetics, which gives due consideration to the psychological effects from the beginning to end of the treatment process, alongside a balanced consideration of the likelihood of treatment success and discussion of other options. Only when we provide patients with the service of a clear and transparent discussion of these matters, we will really realise the true potential of our field, which may then be better considered as assisted families.

A systematic review of the quality of clinical prediction models in in vitro fertilisation

STUDY QUESTION

What are the best-quality clinical prediction models in IVF (including ICSI) treatment to inform clinicians and their patients of their chance of success?

SUMMARY ANSWER

The review recommends the McLernon post-treatment model for predicting the cumulative chance of live birth over and up to six complete cycles of IVF.

WHAT IS KNOWN ALREADY

Prediction models in IVF have not found widespread use in routine clinical practice. This could be due to their limited predictive accuracy and clinical utility. A previous systematic review of IVF prediction models, published a decade ago and which has never been updated, did not assess the methodological quality of existing models nor provided recommendations for the best-quality models for use in clinical practice.

STUDY DESIGN, SIZE, DURATION

The electronic databases OVID MEDLINE, OVID EMBASE and Cochrane library were searched systematically for primary articles published from 1978 to January 2019 using search terms on the development and/or validation (internal and external) of models in predicting pregnancy or live birth. No language or any other restrictions were applied.

PARTICIPANTS/MATERIALS, SETTING, METHODS

The PRISMA flowchart was used for the inclusion of studies after screening. All studies reporting on the development and/or validation of IVF prediction models were included. Articles reporting on women who had any treatment elements involving donor eggs or sperm and surrogacy were excluded. The CHARMS checklist was used to extract and critically appraise the methodological quality of the included articles. We evaluated models' performance by assessing their c-statistics and plots of calibration in studies and assessed correct reporting by calculating the percentage of the TRIPOD 22 checklist items met in each study.

MAIN RESULTS AND THE ROLE OF CHANCE

We identified 33 publications reporting on 35 prediction models. Seventeen articles had been published since the last systematic review. The quality of models has improved over time with regard to clinical relevance, methodological rigour and utility. The percentage of TRIPOD score for all included studies ranged from 29 to 95%, and the c-statistics of all externally validated studies ranged between 0.55 and 0.77. Most of the models predicted the chance of pregnancy/live birth for a single fresh cycle. Six models aimed to predict the chance of pregnancy/live birth per individual treatment cycle, and three predicted more clinically relevant outcomes such as cumulative pregnancy/live birth. The McLernon (pre- and post-treatment) models predict the cumulative chance of live birth over multiple complete cycles of IVF per woman where a complete cycle includes all fresh and frozen embryo transfers from the same episode of ovarian stimulation. McLernon models were developed using national UK data and had the highest TRIPOD score, and the post-treatment model performed best on external validation.

LIMITATIONS, REASONS FOR CAUTION

To assess the reporting quality of all included studies, we used the TRIPOD checklist, but many of the earlier IVF prediction models were developed and validated before the formal TRIPOD reporting was published in 2015. It should also be noted that two of the authors of this systematic review are authors of the McLernon model article. However, we feel we have conducted our review and made our recommendations using a fair and transparent systematic approach.

WIDER IMPLICATIONS OF THE FINDINGS

This study provides a comprehensive picture of the evolving quality of IVF prediction models. Clinicians should use the most appropriate model to suit their patients' needs. We recommend the McLernon post-treatment model as a counselling tool to inform couples of their predicted chance of success over and up to six complete cycles. However, it requires further external validation to assess applicability in countries with different IVF practices and policies.

STUDY FUNDING/COMPETING INTEREST(S)

The study was funded by the Elphinstone Scholarship Scheme and the Assisted Reproduction Unit, University of Aberdeen. Both D.J.M. and S.B. are authors of the McLernon model article and S.B. is Editor in Chief of Human Reproduction Open. They have completed and submitted the ICMJE forms for Disclosure of potential Conflicts of Interest. The other co-authors have no conflicts of interest to declare.

REGISTRATION NUMBER

N/A.

Single Embryo Transfer Versus Double Embryo Transfer: A Cost-Effectiveness Analysis in a Non-IVF Insurance Mandated System

INTRODUCTION

Because of increased morbidity seen in multiple gestations, the American Society of Reproductive Medicine recommends transfer of blastocysts one at a time for most patients. While cost-effectiveness models have compared single embryo transfer (SET) versus double embryo transfer (DET), few incorporate maternal and neonatal morbidity, and none have been performed in U.S. Military facilities. The purpose of this study was to determine the cost effectiveness of sequential SET versus DET in a U.S. Military treatment facility.

MATERIALS AND METHODS

A cost-effectiveness model was created based on 250 patients between the ages of 20-44 who previously underwent in vitro fertilization (IVF) at our facility. The model consisted of patients pursuing either SET or DET with two total embryos. Cycle outcomes were determined using the published SARTCORS success calculator. Neonatal and obstetrical outcomes were simulated based on singleton and twin IVF pregnancies. Neonatal and obstetrical cost estimates were based on internal data as well.

RESULTS

If 250 model patients pursue SET, 140 live births would occur, with total cost of $5.7 million, and cost per delivery of $40,500. If the model patients pursued DET, 117 live births would occur, with total cost of $9.2 million and a cost per delivery of $77.700. DET would lead to more total infants (207 vs. 143 in SET cohort). Personal costs are higher in SET versus DET cohorts ($23,036 vs. $20,535).

CONCLUSIONS

SET in a system with no infertility coverage saves approximately $3.5 million per 250 patients. Higher personal costs as seen with SET may incentivize patients to seek DET. The total savings should encourage alteration to practice patterns with the U.S Military Healthcare System.

Individualized embryo selection strategy developed by stacking machine learning model for better in vitro fertilization outcomes: an application study

BACKGROUND

To minimize the rate of in vitro fertilization (IVF)- associated multiple-embryo gestation, significant efforts have been made. Previous studies related to machine learning in IVF mainly focused on selecting the top-quality embryos to improve outcomes, however, in patients with sub-optimal prognosis or with medium- or inferior-quality embryos, the selection between SET and DET could be perplexing.

METHODS

This was an application study including 9211 patients with 10,076 embryos treated during 2016 to 2018, in Tongji Hospital, Wuhan, China. A hierarchical model was established using the machine learning system XGBoost, to learn embryo implantation potential and the impact of double embryos transfer (DET) simultaneously. The performance of the model was evaluated with the AUC of the ROC curve. Multiple regression analyses were also conducted on the 19 selected features to demonstrate the differences between feature importance for prediction and statistical relationship with outcomes.

RESULTS

For a single embryo transfer (SET) pregnancy, the following variables remained significant: age, attempts at IVF, estradiol level on hCG day, and endometrial thickness. For DET pregnancy, age, attempts at IVF, endometrial thickness, and the newly added P1 + P2 remained significant. For DET twin risk, age, attempts at IVF, 2PN/ MII, and P1 × P2 remained significant. The algorithm was repeated 30 times, and averaged AUC of 0.7945, 0.8385, and 0.7229 were achieved for SET pregnancy, DET pregnancy, and DET twin risk, respectively. The trend of predictive and observed rates both in pregnancy and twin risk was basically identical. XGBoost outperformed the other two algorithms: logistic regression and classification and regression tree.

CONCLUSION

Artificial intelligence based on determinant-weighting analysis could offer an individualized embryo selection strategy for any given patient, and predict clinical pregnancy rate and twin risk, therefore optimizing clinical outcomes.

Long-term outcome of patients undergoing in-vitro fertilisation in France: The outcome study

The Outcome study examines the fate of 4083 patients beginning IVF in 41 IVF centres, between January 2010 and December 2013. Cumulative live birth rate per patient (CLBR), the best reflection of IVF efficacy, is rarely presented in publications as it requires long-term follow-up, including all successive cycles, and pregnancies outcome. Analysis of international publications shows an average CLBR of 41.6 % and a drop-out rate of 49.5 %, both greatly varying by country and IVF centres. Because of the frequency with which patients change centre (8%), the Outcome study distinguishes patients with a past history of IVF in another centre (CLBR=47.2 %) and patients undergoing their first true cycle (CLBR=56.4 %). Survival techniques by Competing Risk, intended to take account of drop-out and lost to follow-up, assessed the overall CLBR as being 65.4 %. Differences in performance between centres are considerable for both CLBR (32-64%) and Performance Index, taking account of the number of cycles required to achieve a pregnancy (2-5). Multiple variance logistic regression analysis shows that the indicators influencing performance are age, parity, number of oocytes, smoking habit and overweight. These indicators are independent each other and are influencing performance in a high significant way. After adjusting for these indicators, the differences between centres are reduced but remain large and very significant. No centre appears to have specific expertise in the management of patients with adverse indicators. The Outcome study therefore confirms that the large differences in performance between centres are not explained by a difference in the treated population.

Can Ratios Between Prognostic Factors Predict the Clinical Pregnancy Rate in an IVF/ICSI Program with a GnRH Agonist-FSH/hMG Protocol? An Assessment of 2421 Embryo Transfers, and a Review of the Literature

None of the models developed in in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) is sufficiently good predictors of pregnancy. The aim of this study was to determine whether ratios between prognostic factors could predict the clinical pregnancy rate in IVF/ICSI. We analyzed IVF/ICSI cycles (based on long GnRH agonist-FSH protocols) at two ART centers (the second to validate externally the data). The ratios studied were (i) the total FSH dose divided by the serum estradiol level on the hCG trigger day, (ii) the total FSH dose divided by the number of mature oocytes, (iii) the serum estradiol level on the trigger day divided by the number of mature oocytes, (iv) the serum estradiol level on the trigger day divided by the endometrial thickness on the trigger day, (v) the serum estradiol level on the trigger day divided by the number of mature oocytes and then by the number of grade 1 or 2 embryos obtained, and (vi) the serum estradiol level on the trigger day divided by the endometrial thickness on the trigger day and then by the number of grade 1 or 2 embryos obtained. The analysis covered 2421 IVF/ICSI cycles with an embryo transfer, leading to 753 clinical pregnancies (31.1% per transfer). Four ratios were significantly predictive in both centers; their discriminant power remained moderate (area under the receiver operating characteristic curve between 0.574 and 0.610). In contrast, the models' calibration was excellent (coefficients: 0.943-0.978; p < 0.001). Our ratios were no better than existing models in IVF/ICSI programs. In fact, a strongly discriminant predictive model will be probably never be obtained, given the many factors that influence the occurrence of a pregnancy.

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.

The Success Rate and Factors Affecting the Outcome of Assisted Reproductive Treatment in Subfertile Men

BACKGROUND

This study was conducted to evaluate the success rate of male infertility treatment and the factors affecting its outcome.

METHODS

In a historical cohort study, from Mar 2013 to Mar 2014, 323 couples with male factor were investigated. Couples had treated with IUI or/and ICSI were included randomly. Assisted reproduction technology (ART) outcome (treatment success) was defined as a live birth. Age, duration of infertility, type of infertility, treatment history and clinical examination results were investigated. The logistic regression and survival analysis were applied.

RESULTS

The average of men age, duration of infertility and BMI were 33.5, 4.7 (yr) and 26.6 (kg/m²) respectively. 87.9% of men have primary infertility and average duration of treatment was 14.1(month). Previous treatment, type of infertility, treatment method, man's BMI, normality of sperm and sperm head were important variable that affecting outcome. The rate of live birth in the first attempt was 29.7%, and 44.9% of the couples succeeded to give live birth after several treatment cycles. Couples who had no previous history of treatment were 8.5 times more successful in live birth. The Cox analysis showed that "BMI of man" and percentage of "Sperm with normal head" are predictors that had a significant effect on live birth.

CONCLUSION

Live birth in the first treatment cycles was influenced by four variables but two other variable were affecting several treatment cycles outcome. The chances of successful treatment were higher with taking into account the length of time and having live birth was determined as 78% for five years of continuous treatment.

Cumulative probabilities of live birth across multiple complete IVF/ICSI cycles: a call for attention

PURPOSE

To call attention to the fact that cumulative live birth (LB) proportions exhibit an inverted pattern to that displayed by each individual oocyte retrieval cycle (ORC-specific LB proportions) as well as when grouping together all the ORCs undergone by a woman (TNORC-specific LB proportions).

METHODS

A retrospective study of 1433 infertile women that had a LB using autologous fresh or frozen embryos and/or dropped out of IVF/ICSI treatment after completing a maximum number of three treatment cycles. Generalized Estimating Equations (GEE) and standard and landmark Kaplan-Meier survival analyses were applied.

RESULTS

A standard Kaplan-Meier analysis indicated that cumulative LB proportions rose as number of ORCs increased (0.320, 0.484, and 0.550 at ORC 1, 2, and 3, respectively). In contrast, landmark ORC-specific LB proportions showed an inverted pattern (0.320, 0.242, and 0.127 at ORC 1, 2, and 3, respectively). GEE models revealed that women's clinical outcomes decreased as TNORCs increased. In particular, compared to women that experienced just one ORC, women that underwent two and three ORCs displayed higher incidences of cycle cancellations before either oocyte retrieval or embryo transfer, and clinical pregnancy losses, and lower odds of LB.

CONCLUSION

Infertile women should be informed that cumulative LB probabilities exhibit an inverted pattern to that displayed by each individual ORC as well as when grouping together all the ORCs undergone by a woman.

Contribution of cryopreservation to the cumulative live birth rate: a large multicentric cycle-based data analysis from the Italian National Registry

PURPOSE

To estimate the contribution of cryopreservation to the cumulative live birth rate (CLBR) after law modification in Italy in the era of vitrification and freeze-all.

METHODS

The Italian National Registry performed a cycle-based data collection. Nine Italian IVF clinics were involved incorporating a total of 10,260 fresh cycles performed between January 2015 and April 2016 resulting in 9273 oocyte retrievals and 3266 subsequent warming cycles from the same oocyte retrievals performed up to December 2016. Mean female age was 37 ± 4.3 years. Primary outcome measure was CLBR per oocyte retrieval. Confounding factors were tested in multivariate regression analysis, and the relative impact of cryopreservation to the CLBR in different patient categories was calculated.

RESULTS

CLBR per oocyte retrieval was 32.6%, 26.5%, 18.7%, 13.0%, and 5.5% for women younger than 36, aged 36-39, 40-41, and older than 41 years, respectively. The total relative contribution of oocyte/embryo cryopreservation was 40.6% (95% CI 38.41-42.75). An association between maternal age, number of oocytes retrieved, fertilization rate, cryopreservation, and cumulative live birth was shown. When adjusted for confounders, a 2.3-fold increase was observed in the chance of live birth when cryopreservation was performed (OR 2.3; 95% CI 1.99-2.56). In high responder patients (> 15 oocytes retrieved) where freeze-all was applied in 67.6% of cycles to avoid the risk of hyper stimulation syndrome, the relative contribution of vitrification to the CLBR was 80.6%.

CONCLUSIONS

Cryopreservation is essential in IVF and should always be available to patients to optimize success rates. Multicentric, cycle-based data analyses are crucial to provide infertile couples, clinicians, and regulatory bodies with accurate information on IVF effectiveness including fresh and cryopreserved cycles.

How much does the uterus matter? Perinatal outcomes are improved when donor oocyte embryos are transferred to gestational carriers compared to intended parent recipients

OBJECTIVE

To assess the reproductive and neonatal outcomes of cycles in which donor oocyte embryos were transferred to gestational carriers compared to intended parent recipients.

DESIGN

Retrospective cohort study.

SETTING

Not applicable.

PATIENT(S)

Intended parent recipients and gestational carriers receiving donor oocyte embryos in 2014 in the United States.

INTERVENTIONS(S)

None.

MAIN OUTCOMES MEASURE(S)

Clinical pregnancy, live birth, miscarriage, plurality, prematurity, and birth weight from pregnancies conceived with donor oocyte embryos transferred to either a gestational carrier or an intended parent recipient.

RESULT(S)

The mean ages of intended parent recipients (N=18,317) and gestational carriers (N=1,927) were 41.6 and 31.6 years, respectively. Compared to an intended parent recipient, patients using a gestational carrier had significantly higher odds of a clinical pregnancy (65.2% vs. 56.3%, adjusted odds ratio (aOR) 1.33, 95% confidence interval (CI) 1.17-1.51) and live birth (57.1% vs. 46.4%, aOR 1.37, 95% CI 1.21-1.55) using fresh or frozen donor-oocyte embryos. Of the singletons born (n=716 using a gestational carrier and n=5,632 in intended parent recipients), the incidence of prematurity was significantly lower in gestational carriers compared to intended parent recipients (17.5% vs. 25.4%, aOR 0.78, 95% CI 0.61-0.99). The incidence of low birthweight among singletons was significantly reduced in gestational carrier cycles (6.4% vs. 12.1%, aOR 0.62, 95% CI 0.44-0.89).

CONCLUSION

Intended parent recipients had decreased pregnancy rates and poorer neonatal outcomes compared to a gestational carrier. This suggests that a history of infertility adversely affects the uterine microenvironment, independent of the oocyte.

A Novel Predictive Model to Estimate the Number of Mature Oocytes Required for Obtaining at Least One Euploid Blastocyst for Transfer in Couples Undergoing in vitro Fertilization/Intracytoplasmic Sperm Injection: The ART Calculator

The POSEIDON group (Patient-Oriented Strategies Encompassing IndividualizeD Oocyte Number) has introduced "the ability to retrieve the number of oocytes needed to achieve at least one euploid embryo for transfer" as an intermediate marker of successful outcome in IVF/ICSI cycles. This study aimed to develop a novel calculator to predict the POSEIDON marker. We analyzed clinical and embryonic data of infertile couples who underwent IVF/ICSI with the intention to have trophectoderm biopsy for preimplantation genetic testing for aneuploidy. We used the negative binomial distribution to model the number of euploid blastocysts and the adaptive LASSO (Least Absolute Shrinkage and Selection Operator) method for variable selection. The fitted model selected female age, sperm source used for ICSI, and the number of mature (metaphase II) oocytes as predictors (p < 0.0001). Female age was the most important factor for predicting the probability of a blastocyst being euploid given each mature oocyte (loglikelihood of age [adjusted for sperm source]: 30.9; df = 2; p < 0.0001). The final predictive model was developed using logistic regression analysis, and internally validated by the holdout method. The predictive ability of the model was assessed by the ROC curve, which resulted in an area under the curve of 0.716. Using the final model and mathematical equations, we calculated the individualized probability of blastocyst euploidy per mature retrieved oocyte and the minimum number of mature oocytes required to obtain ≥1 euploid blastocyst-with their 95% confidence interval [CI]-for different probabilities of success. The estimated predicted probabilities of a mature oocyte turn into a euploid blastocyst decreased progressively with female age and was negatively modulated overall by use of testicular sperm across age (p < 0.001). A calculator was developed to make two types of predictions automatically, one using pretreatment information to estimate the minimum number of mature oocytes to achieve ≥1 euploid blastocyst, and another based on the actual number of mature oocytes collected/accumulated to estimate the chances of having a euploid blastocyst using that oocyte cohort for IVF/ICSI. The new ART calculator may assist in clinical counseling and individualized treatment planning regarding the number of oocytes required for at least one euploid blastocyst in IVF/ICSI procedures.

Prediction of implantation after blastocyst transfer in in vitro fertilization: a machine-learning perspective

OBJECTIVE

To develop a random forest model (RFM) to predict implantation potential of a transferred embryo and compare it with a multivariate logistic regression model (MvLRM), based on data from a large cohort including in vitro fertilization (IVF) patients treated with the use of single-embryo transfer (SET) of blastocyst-stage embryos.

DESIGN

Retrospective study of a 2-year single-center cohort of women undergoing IVF or intracytoplasmatic sperm injection (ICSI).

SETTING

Academic hospital.

PATIENT(S)

Data from 1,052 women who underwent fresh SET in IVF or ICSI cycles were included.

INTERVENTION(S)

None.

MAIN OUTCOME MEASURE(S)

The performance of both RFM and MvLRM to predict pregnancy was quantified in terms of the area under the receiver operating characteristic (ROC) curve (AUC), classification accuracy, specificity, and sensitivity.

RESULT(S)

ROC analysis resulted in an AUC of 0.74 ± 0.03 for the proposed RFM and 0.66 ± 0.05 for the MvLRM for the prediction of ongoing pregnancies of ≥11 weeks. This RFM approach and the MvLRM yielded, respectively, sensitivities of 0.84 ± 0.07 and 0.66 ± 0.08 and specificities of 0.48 ± 0.07 and 0.58 ± 0.08.

CONCLUSION(S)

The performance to predict ongoing implantation will significantly improve with the use of an RFM approach compared with MvLRM.

Validation of ART Calculator for Predicting the Number of Metaphase II Oocytes Required for Obtaining at Least One Euploid Blastocyst for Transfer in Couples Undergoing in vitro Fertilization/Intracytoplasmic Sperm Injection

This multicenter study evaluated the reliability of the recently published ART calculator for predicting the minimum number of metaphase II (MII) oocytes (MIImin) to obtain at least one euploid blastocyst in patients undergoing in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI). We used clinical and embryonic retrospective data of 1,464 consecutive infertile couples who underwent IVF/ICSI with the intention to have preimplantation genetic testing for aneuploidy. The validation procedure followed a stepwise approach. Firstly, we assessed the distribution of euploid blastocysts per patient and found that it followed a negative binomial distribution. Secondly, we used generalized linear models and applied the Lasso procedure-including MII oocytes to adjust the data-to select the factors predicting the response variable "euploid blastocyst." Third, a logistic regression model-fit to the binomial response euploid (yes/no) for each MII oocyte-was built using the relevant factors. The observational unit was the "woman" whereas the response was the pair (m, n), where n is the number of retrieved MII oocytes and m the corresponding number of euploid blastocysts. The model was internally validated by randomly splitting the data into training and validation sets. The R-squares (~0.25) and the area under the ROC curve (~0.70) did not differ between the training and validation datasets. Fourth, mathematical equations and the calculated probabilities generated by the validation model were used to determine the MIImin required for obtaining at least one euploid blastocyst according to different success probabilities. Lastly, we compared the fittings generated by the validation model and the ART calculator and assessed the predictive value of the latter using the validation dataset. The fittings were sufficiently close for both the estimated probabilities of blastocyst euploid per MII oocyte (r = 0.91) and MIImin (r = 0.88). The ART calculator positive predictive values, i.e., the frequency of patients with at least one euploid blastocyst among those who achieved the estimated MIImin, were 84.8%, 87.5%, and 90.0% for 70%, 80%, and 90% predicted probabilities of success, respectively. The ART calculator effectively predicts the MIImin needed to achieve at least one euploid blastocyst in individual patients undergoing IVF/ICSI. The prediction tool might be used for counseling and planning IVF/ICSI treatments.

Making IVF more effective through the evolution of prediction models: is prognosis the missing piece of the puzzle?

Assisted reproductive technology has evolved tremendously since the emergence of in vitro fertilization (IVF). In the course of the recent decade, there have been significant efforts in order to minimize multiple gestations, while improving percentages of singleton pregnancies and offering individualized services in IVF, in line with the trend of personalized medicine. Patients as well as clinicians and the entire IVF team benefit majorly from 'knowing what to expect' from an IVF cycle. Hereby, the question that has emerged is to what extent prognosis could facilitate toward the achievement of the above goal. In the current review, we present prediction models based on patients' characteristics and IVF data, as well as models based on embryo morphology and biomarkers during culture shaping a complication free and cost-effective personalized treatment. The starting point for the implementation of prediction models was initiated by the aspiration of moving toward optimal practice. Thus, prediction models could serve as useful tools that could safely set the expectations involved during this journey guiding and making IVF treatment more effective. The aim and scope of this review is to thoroughly present the evolution and contribution of prediction models toward an efficient IVF treatment.

ABBREVIATIONS

IVF: In vitro fertilization; ART: assisted reproduction techniques; BMI: body mass index; OHSS: ovarian hyperstimulation syndrome; eSET: elective single embryo transfer; ESHRE: European Society of Human Reproduction and Embryology; mtDNA: mitochondrial DNA; nDNA: nuclear DNA; ICSI: intracytoplasmic sperm injection; MBR: multiple birth rates; LBR: live birth rates; SART: Society for Assisted Reproductive Technology Clinic Outcome Reporting System; AFC: antral follicle count; GnRH: gonadotrophin releasing hormone; FSH: follicle stimulating hormone; LH: luteinizing hormone; AMH: anti-Müllerian hormone; DHEA: dehydroepiandrosterone; PCOS: polycystic ovarian syndrome; NPCOS: non-polycystic ovarian syndrome; CE: cost-effectiveness; CC: clomiphene citrate; ORT: ovarian reserve test; EU: embryo-uterus; DET: double embryo transfer; CES: Cumulative Embryo Score; GES: Graduated Embryo Score; CSS: Combined Scoring System; MSEQ: Mean Score of Embryo Quality; IMC: integrated morphology cleavage; EFNB2: ephrin-B2; CAMK1D: calcium/calmodulin-dependent protein kinase 1D; GSTA4: glutathione S-transferase alpha 4; GSR: glutathione reductase; PGR: progesterone receptor; AMHR2: anti-Müllerian hormone receptor 2; LIF: leukemia inhibitory factor; sHLA-G: soluble human leukocyte antigen G.

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.

Factors predicting double embryo implantation following double embryo transfer in assisted reproductive technology: implications for elective single embryo transfer

PURPOSE

The aim of this study was to identify factors associated with double embryo implantation following double embryo transfer (DET) during assisted reproductive technology (ART) procedures and to evaluate the implications of findings in selecting candidates for elective single embryo transfer (eSET).

METHODS

Factors predicting double embryo implantation, defined as embryo transfers with two or more heartbeats on 6-week ultrasound following DET, were assessed using the US National ART Surveillance System data from 2000 to 2012 (n = 1,793,067 fresh, autologous transfers). Adjusted risk ratios (aRRs) were estimated after stratifying by prognosis. Favorable prognosis was defined as first-time ART with supernumerary embryo(s) cryopreserved. Average prognosis was defined as first-time ART without supernumerary embryo(s) cryopreserved, prior unsuccessful ART with supernumerary embryo(s) cryopreserved, or prior ART with previous birth(s) conceived with ART or naturally. Rates and factors associated with double embryo implantation were compared with single embryo implantation following DET among both prognosis groups.

RESULTS

Double embryo implantation was positively associated with blastocyst (versus cleavage) transfer in favorable (aRR = 1.58 (1.51-1.65)) and average (aRR = 1.67 (1.60-1.75)) prognosis groups and negatively associated with age >35 years in both prognosis groups. For average prognosis patients, double embryo implantation was associated with retrieving >10 oocytes (aRR = 1.22 (1.18-1.24)).

CONCLUSIONS

Regardless of prognosis, patients aged <35 years with blastocyst-stage embryos and average prognosis patients from whom >10 oocytes were retrieved may be good candidates for eSET. Physicians may consider using these data to counsel patients on eSET, which would reduce multiple gestations and associated complications.

Promoting the use of elective single embryo transfer in clinical practice

Background

The transfer of multiple embryos after in vitro fertilization (IVF) increases the risk of twins and higher-order births. Multiple births are associated with significant health risks and maternal and neonatal complications, as well as physical, emotional, and financial stresses that can strain families and increase the incidence of depression and anxiety disorders in parents. Elective single embryo transfer (eSET) is among the most effective methods to reduce the risk of multiple births with IVF.

Main body

Current societal guidelines recommend eSET for patients <35 years of age with a good prognosis, yet even this approach is not widely applied. Many patients and clinicians have been reluctant to adopt eSET due to studies reporting higher live birth rates with the transfer of two or more embryos rather than eSET. Additional barriers to eSET include risk of treatment dropout after embryo transfer failure, patient preference for twins, a lack of knowledge about the risks and complications associated with multiple births, and the high costs of multiple IVF cycles. This review provides a comprehensive summary of strategies to increase the rate of eSET, including personalized counseling, access to educational information regarding the risks of multiple pregnancies and births, financial incentives, and tools to help predict the chances of IVF success. The use of comprehensive chromosomal screening to improve embryo selection has been shown to improve eSET outcomes and may increase acceptance of eSET.

Conclusions

eSET is an effective method for reducing multiple pregnancies resulting from IVF. Although several factors may impede the adoption of eSET, there are a number of strategies and tools that may encourage the more widespread adoption of eSET in clinical practice.

Models Predicting Success of Infertility Treatment: A Systematic Review

BACKGROUND

Infertile couples are faced with problems that affect their marital life. Infertility treatment is expensive and time consuming and occasionally isn't simply possible. Prediction models for infertility treatment have been proposed and prediction of treatment success is a new field in infertility treatment. Because prediction of treatment success is a new need for infertile couples, this paper reviewed previous studies for catching a general concept in applicability of the models.

METHODS

This study was conducted as a systematic review at Avicenna Research Institute in 2015. Six data bases were searched based on WHO definitions and MESH key words. Papers about prediction models in infertility were evaluated.

RESULTS

Eighty one papers were eligible for the study. Papers covered years after 1986 and studies were designed retrospectively and prospectively. IVF prediction models have more shares in papers. Most common predictors were age, duration of infertility, ovarian and tubal problems.

CONCLUSION

Prediction model can be clinically applied if the model can be statistically evaluated and has a good validation for treatment success. To achieve better results, the physician and the couples' needs estimation for treatment success rate were based on history, the examination and clinical tests. Models must be checked for theoretical approach and appropriate validation. The privileges for applying the prediction models are the decrease in the cost and time, avoiding painful treatment of patients, assessment of treatment approach for physicians and decision making for health managers. The selection of the approach for designing and using these models is inevitable.

Prognostic indicators of assisted reproduction technology outcomes of cycles with ultralow serum antimüllerian hormone: a multivariate analysis of over 5,000 autologous cycles from the Society for Assisted Reproductive Technology Clinic Outcome Reporting System database for 2012-2013

OBJECTIVE

To assess cycle outcomes when antimüllerian hormone (AMH) is ultralow (≤0.16 ng/mL) and to determine which parameters contribute to the probability of cycle cancellation and/or outcome.

DESIGN

Retrospective analysis.

SETTING

Not applicable.

PATIENT(S)

5,087 (7.3%) fresh and 243 (1.5%) thawed cycles with ultralow AMH values.

INTERVENTION(S)

Linear and logistic regression, comparison with age-matched cycles with normal AMH concentrations.

MAIN OUTCOME MEASURE(S)

Cancellation rate; number of retrieved oocytes, embryos, transferred embryos, and cryopreserved embryos; clinical pregnancy, live-birth, and multiple birth rates.

RESULT(S)

The total cancellation rate per cycle start for fresh cycles was 54%. Of these, 38.6% of the cycles were canceled before retrieval, and 3.3% of cycles obtained no oocytes at time of retrieval. Of all retrieval attempts, 50.7% had three oocytes or fewer retrieved, and 25.1% had no embryo transfer. The live-birth rates were 9.5% per cycle start. Cycles with ultralow AMH levels compared with age-matched normal AMH cycles demonstrated more than a fivefold greater pre-retrieval cancellation rate, a twofold less live-birth rate per cycle and a 4.5-fold less embryo cryopreservation rate.

CONCLUSION(S)

Refusing treatment solely on the basis of ultralow AMH levels is not advisable, but patients should be counseled appropriately about the prognostic factors for cancellation and outcomes.

Antimüllerian hormone levels and antral follicle count as prognostic indicators in a personalized prediction model of live birth

OBJECTIVE

To compare antimüllerian hormone (AMH) and antral follicle count (AFC) separately and in combination with clinical characteristics for the prediction of live birth after controlled ovarian stimulation.

DESIGN

Retrospective development and temporal external validation of prediction model.

SETTING

Outpatient IVF clinic.

PATIENT(S)

We applied the boosted tree method to develop three prediction models incorporating clinical characteristics plus AMH or AFC or the combination on 2,124 linked IVF cycles from 2006 to 2010 and temporally externally validated predicted live-birth probabilities with an independent data set comprising 1,121 cycles from 2011 to 2012.

INTERVENTION(S)

None.

MAIN OUTCOME MEASURE(S)

Predictive power (posterior log of odds ratio compared to age, or PLORA), reclassification, receiver operator characteristic analysis, calibration, dynamic range.

RESULT(S)

Predictive power, was highest for the AMH model (PLORA = 29.1), followed by the AMH-AFC model (PLORA = 28.3) and AFC model (PLORA = 22.5). The prediction errors were 1% to <5% in each prognostic tier for all three models, except for the predicted live-birth probabilities of <10% in the AFC model, where the prediction error was 8%. The improvement in predictive power was highest for the AMH model: 76.2% improvement over age alone relative to 59% improvement for AFC and 73.3% for the combined model. Receiver operating characteristic analysis demonstrated that the AMH and the combined model had comparable discrimination (area under the curve = 0.716) and similar prediction error for high and low strata of live-birth prediction, with an improvement of 6.3% over age alone.

CONCLUSION(S)

The validated prediction model confirmed that AMH when combined with clinical characteristics can accurately identify the likelihood of live birth with a low prediction error. AFC provided no added predictive value beyond AMH.

Application of a validated prediction model for in vitro fertilization: comparison of live birth rates and multiple birth rates with 1 embryo transferred over 2 cycles vs 2 embryos in 1 cycle

OBJECTIVE

The purpose of this study was to use a validated prediction model to examine whether single embryo transfer (SET) over 2 cycles results in live birth rates (LBR) comparable with 2 embryos transferred (DET) in 1 cycle and reduces the probability of a multiple birth (ie, multiple birth rate [MBR]).

STUDY DESIGN

Prediction models of LBR and MBR for a woman considering assisted reproductive technology developed from linked cycles from the Society for Assisted Reproductive Technology Clinic Outcome Reporting System for 2006-2012 were used to compare SET over 2 cycles with DET in 1 cycle. The prediction model was based on a woman's age, body mass index (BMI), gravidity, previous full-term births, infertility diagnoses, embryo state, number of embryos transferred, and number of cycles.

RESULTS

To demonstrate the effect of the number of embryos transferred (1 or 2), the LBRs and MBRs were estimated for women with a single infertility diagnosis (male factor, ovulation disorders, diminished ovarian reserve, and unexplained); nulligravid; BMI of 20, 25, 30, and 35 kg/m2; and ages 25, 35, and 40 years old by cycle (first or second). The cumulative LBR over 2 cycles with SET was similar to or better than the LBR with DET in a single cycle (for example, for women with the diagnosis of ovulation disorders: 35 years old; BMI, 30 kg/m2; 54.4% vs 46.5%; and for women who are 40 years old: BMI, 30 kg/m(2); 31.3% vs 28.9%). The MBR with DET in 1 cycle was 32.8% for women 35 years old and 20.9% for women 40 years old; with SET, the cumulative MBR was 2.7% and 1.6%, respectively.

CONCLUSION

The application of this validated predictive model demonstrated that the cumulative LBR is as good as or better with SET over 2 cycles than with DET in 1 cycle, while greatly reducing the probability of a multiple birth.