Predicting the chance of live birth for women undergoing IVF: a novel pretreatment counselling tool

Patient-Centric In Vitro Fertilization Prognostic Counseling Using Machine Learning for the Pragmatist

Although in vitro fertilization (IVF) has become an extremely effective treatment option for infertility, there is significant underutilization of IVF by patients who could benefit from such treatment. In order for patients to choose to consider IVF treatment when appropriate, it is critical for them to be provided with an accurate, understandable IVF prognosis. Machine learning (ML) can meet the challenge of personalized prognostication based on data available prior to treatment. The development, validation, and deployment of ML prognostic models and related patient counseling report delivery require specialized human and platform expertise. This review article takes a pragmatic approach to review relevant reports of IVF prognostic models and draws from extensive experience meeting patients' and providers' needs with the development of data and model pipelines to implement validated ML models at scale, at the point-of-care. Requirements of using ML-based IVF prognostics at point-of-care will be considered alongside clinical ML implementation factors critical for success. Finally, we discuss health, social, and economic objectives that may be achieved by leveraging combined human expertise and ML prognostics to expand fertility care access and advance health and social good.

Nomogram for predicting live birth in ovulatory women undergoing frozen-thawed embryo transfer

BACKGROUND

Study objectives included the development of a practical nomogram for predicting live birth following frozen-thawed embryo transfers in ovulatory women.

METHODS

Totally, 2884 patients with regular menstrual cycles in our center were retrospectively enrolled. In an 8:2 ratio, we randomly assigned patients to training and validation cohorts. Then we identified risk factors by multivariate logistic regression and constructed nomogram. Finally, receiver operating characteristic curve analysis, calibration curve and decision curve analysis were performed to assess the calibration and discriminative ability of the nomogram.

RESULTS

We identified five variables which were related to live birth, including age, anti-Müllerian hormone (AMH), protocol of frozen-thawed embryo transfer (FET), stage of embryos and amount of high-quality embryos. We then constructed nomograms that predict the probabilities of live birth by using those five parameters. Receiver operating characteristic curve analysis (ROC) showed that the area under the curve (AUC) for live birth was 0.666 (95% CI: 0.644-0.688) in the training cohort. The AUC in the subsequent validation cohorts was 0.669 (95% CI, 0.625-0.713). The clinical practicability of this nomogram was demonstrated through calibration curve analysis and decision curve analysis.

CONCLUSIONS

Our nomogram provides a visual and simple tool in predicting live birth in ovulatory women who received FET. It could also provide advice and guidance for physicians and patients on decision-making during the FET procedure.

Clinical data-based modeling of IVF live birth outcome and its application

BACKGROUND

The low live birth rate and difficult decision-making of the in vitro fertilization (IVF) treatment regimen bring great trouble to patients and clinicians. Based on the retrospective clinical data of patients undergoing the IVF cycle, this study aims to establish classification models for predicting live birth outcome (LBO) with machine learning methods.

METHODS

The historical data of a total of 1405 patients undergoing IVF cycle were first collected and then analyzed by univariate and multivariate analysis. The statistically significant factors were identified and taken as input to build the artificial neural network (ANN) model and supporting vector machine (SVM) model for predicting the LBO. By comparing the model performance, the one with better results was selected as the final prediction model and applied in real clinical applications.

RESULTS

Univariate and multivariate analysis shows that 7 factors were closely related to the LBO (with P < 0.05): Age, ovarian sensitivity index (OSI), controlled ovarian stimulation (COS) treatment regimen, Gn starting dose, endometrial thickness on human chorionic gonadotrophin (HCG) day, Progesterone (P) value on HCG day, and embryo transfer strategy. By taking the 7 factors as input, the ANN-based and SVM-based LBO models were established, yielding good prediction performance. Compared with the ANN model, the SVM model performs much better and was selected as the final model for the LBO prediction. In real clinical applications, the proposed ANN-based LBO model can predict the LBO with good performance and recommend the embryo transfer strategy of potential good LBO.

CONCLUSIONS

The proposed model involving all essential IVF treatment factors can accurately predict LBO. It can provide objective and scientific assistance to clinicians for customizing the IVF treatment strategy like the embryo transfer strategy.

Predicting personalized cumulative live birth rate after a complete in vitro fertilization cycle: an analysis of 32,306 treatment cycles in China

BACKGROUND

The cumulative live birth rate (CLBR) has been regarded as a key measure of in vitro fertilization (IVF) success after a complete treatment cycle. Women undergoing IVF face great psychological pressure and financial burden. A predictive model to estimate CLBR is needed in clinical practice for patient counselling and shaping expectations.

METHODS

This retrospective study included 32,306 complete cycles derived from 29,023 couples undergoing IVF treatment from 2014 to 2020 at a university-affiliated fertility center in China. Three predictive models of CLBR were developed based on three phases of a complete cycle: pre-treatment, post-stimulation, and post-treatment. The non-linear relationship was treated with restricted cubic splines. Subjects from 2014 to 2018 were randomly divided into a training set and a test set at a ratio of 7:3 for model derivation and internal validation, while subjects from 2019 to 2020 were used for temporal validation.

RESULTS

Predictors of pre-treatment model included female age (non-linear relationship), antral follicle count (non-linear relationship), body mass index, number of previous IVF attempts, number of previous embryo transfer failure, type of infertility, tubal factor, male factor, and scarred uterus. Predictors of post-stimulation model included female age (non-linear relationship), number of oocytes retrieved (non-linear relationship), number of previous IVF attempts, number of previous embryo transfer failure, type of infertility, scarred uterus, stimulation protocol, as well as endometrial thickness, progesterone and luteinizing hormone on trigger day. Predictors of post-treatment model included female age (non-linear relationship), number of oocytes retrieved (non-linear relationship), cumulative Day-3 embryos live-birth capacity (non-linear relationship), number of previous IVF attempts, scarred uterus, stimulation protocol, as well as endometrial thickness, progesterone and luteinizing hormone on trigger day. The C index of the three models were 0.7559, 0.7744, and 0.8270, respectively. All models were well calibrated (p = 0.687, p = 0.468, p = 0.549). In internal validation, the C index of the three models were 0.7422, 0.7722, 0.8234, respectively; and the calibration P values were all greater than 0.05. In temporal validation, the C index were 0.7430, 0.7722, 0.8234 respectively; however, the calibration P values were less than 0.05.

CONCLUSIONS

This study provides three IVF models to predict CLBR according to information from different treatment stage, and these models have been converted into an online calculator ( https://h5.eheren.com/hcyc/pc/index.html#/home ). Internal validation and temporal validation verified the good discrimination of the predictive models. However, temporal validation suggested low accuracy of the predictive models, which might be attributed to time-associated amelioration of IVF practice.

Trends in acupuncture for infertility: a scoping review with bibliometric and visual analysis

Background

Unexplained recurrent implantation failure and the high cost of assisted reproductive techniques for those experiencing infertility have increasingly resulted in the use of acupuncture. However, the trends and research status of acupuncture on infertility resulting in natural conception have not been systematically summarized. This scoping review and knowledge graph analysis aimed to summarize existing clinical studies on acupuncture for infertility that resulted in natural conception.

Methods

Seven databases, namely, PubMed, Embase, the Cochrane Library, CNKI, VIP, Wanfang Data, and SinoMed, were searched up to August 2023 (updated on 1 April). Two authors independently identified related clinical studies and systematic reviews, and extracted data from included studies on acupuncture for infertility; any discrepancies were resolved by discussion or judged by a third author. A meta-analysis was conducted based on randomized controlled trials (RCTs), and data were synthesized using risk ratios with 95% confidence intervals.

Results

Of the 310 articles meeting the inclusion criteria, 274 were primary studies, 7 were systematic reviews, and 29 were case reports. Reported adverse events included mild ovarian irritation and early signs of miscarriage. Out of the 274 primary studies, there were 40 (14.60%) cases of male infertility and 234 (85.40%) cases of female infertility. Current research highlights on acupuncture for infertility focused on female infertility caused by polycystic ovary syndrome, ovulation disorder, and luteinized unruptured follicle syndrome (LUFS), while acupuncture for male infertility was a hotspot in the early research stage. The meta-analysis also suggested that acupuncture was more effective than human chorionic gonadotropin (HCG) [RR = 1.89, 95% CI (1.47, 2.42), 11 RCTs, 662 participants]. Acupuncture combined with HCG was comparable to HCG [RR = 2.33, 95% CI (1.53, 3.55), four RCTs, 259 participants]. Compared with no treatment, acupuncture resulted in a higher pregnancy rate [RR = 22.12, 95% CI (1.39, 353.09), one RCT, 47 participants]. There was no statistical difference between acupuncture combined with HCG plus letrozole and HCG plus letrozole [RR = 1.56, 95% CI (0.84, 2.89), one RCT, 84 participants].

Conclusion

Current research highlights on acupuncture for infertility resulting in natural conception focused on female infertility caused by polycystic ovary syndrome, ovulation disorder, and LUFS, while studies on male infertility and female infertility caused by blockage in the fallopian tube, thin endometrium, and other factors were insufficient. Well-designed confirmatory clinical studies are still needed as the research hypotheses of most studies were unclear.

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.

A Systematic Review of Genetics and Reproductive Health Outcomes: Asian Perspective

In the last four decades, advances in assisted reproductive technology (ART) have offered hope to individuals with fertility problems to conceive. However, a closer examination of the clinical outcomes of ART shows a stark contrast in Asian women compared to Caucasians, with majority of studies reporting lower reproductive success among Asian women. We performed a systematic review to elucidate the genes associated with ART clinical outcomes, with a focus on Asian ethnicities. We completed a database search to identify all studies associated with reproductive outcomes in women of different ethnic backgrounds. Following PRISMA, 128 studies were analyzed. Pathway analysis of gene sets was done using Cytoscapev3.4.0. We observed that age at menarche (AAM) was correlated with the timing of the first pregnancy, with Hawaiians having the lowest age (22.2 years) and Japanese the highest age (25.0 years). LIN28 mutations were associated with AAM and prevalent in both Chinese and American populations. FMR1 was most associated with ovarian reserve. Network analysis highlighted a close association between FMR1, FSHR, ESR1, BMP15, and INHA, through biological functions affecting menstrual cycle and hypothalamic-pituitary axis and therefore ovarian follicle development. Leveraging these findings, we propose the development of a personalized, ethnic-specific biomarker panel which would enhance patient stratification to address every woman's unique reproductive potential.

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.

Predicting the ovarian response: towards a determinant model and implications for practice

OBJECTIVE

To improve the reliability of prediction models for ovarian response to stimulation in ART.

DESIGN

A multicenter retrospective cohort study.

SETTING

Twelve reproductive centers.

PATIENTS

A total of 25,854 controlled ovarian stimulations between 2005 and 2016, including cycles cancelled for inadequate response, were included.

INTERVENTION(S)

None.

MAIN OUTCOME MEASURE(S)

Precision of the prediction of the number of oocytes at ovarian pickup and of cancellation rate for poor ovarian response.

RESULTS

Both AMH and antral follicle count exhibit a non-linear effect on the oocyte yield, with a linear relationship after log-transformation. After adjustment for age, BMI, and center, ovarian response observed in a previous stimulation was found to be the best predictor, followed by AMH and AFC. The zero-inflated binomial negative model showed that predictors of cycle cancellation and number of oocytes at retrieval were different, and assimilating cancellation to zero oocyte greatly reduces the determination of the model. Our model was characterized by the best ever reached determination (R2=0.505 for non-naïve women, 0.313 for all the women) and provided evidence of a very strong difference among centers. The results can be easily converted in the prediction of response levels (poor-medium-good-high). Finally, in case of partial report of the above predictors, we show that the univariate prediction based on the best predictor provides a good approximation.

CONCLUSION(S)

A substantial improvement of the ovarian response prediction is possible in modelling the possible cancellation decision, followed by the oocyte retrieval itself, according to an appropriate model based on previous stimulation and non-linear effects of AMH and AFC.

Using feature optimization and LightGBM algorithm to predict the clinical pregnancy outcomes after in vitro fertilization

Background

According to a recent report by the WHO, approximately 17.5\% (about one-sixth) of the global adult population is affected by infertility. Consequently, researchers worldwide have proposed various machine learning models to improve the prediction of clinical pregnancy outcomes during IVF cycles. The objective of this study is to develop a machine learning(ML) model that predicts the outcomes of pregnancies following in vitro fertilization (IVF) and assists in clinical treatment.

Methods

This study conducted a retrospective analysis on provincial reproductive centers in China from March 2020 to March 2021, utilizing 13 selected features. The algorithms used included XGBoost, LightGBM, KNN, Naïve Bayes, Random Forest, and Decision Tree. The results were evaluated using performance metrics such as precision, recall, F1-score, accuracy and AUC, employing five-fold cross-validation repeated five times.

Results

Among the models, LightGBM achieved the best performance, with an accuracy of 92.31%, recall of 87.80%, F1-score of 90.00\%, and an AUC of 90.41%. The model identified the estrogen concentration at the HCG injection(etwo), endometrium thickness (mm) on HCG day(EM TNK), years of infertility(Years), and body mass index(BMI) as the most important features.

Conclusion

This study successfully demonstrates the LightGBM model has the best predictive effect on pregnancy outcomes during IVF cycles. Additionally, etwo was found to be the most significant predictor for successful IVF compared to other variables. This machine learning approach has the potential to assist fertility specialists in providing counseling and adjusting treatment strategies for patients.

A call for better validation of opioid overdose risk algorithms

Clinical decision support (CDS) systems powered by predictive models have the potential to improve the accuracy and efficiency of clinical decision-making. However, without sufficient validation, these systems have the potential to mislead clinicians and harm patients. This is especially true for CDS systems used by opioid prescribers and dispensers, where a flawed prediction can directly harm patients. To prevent these harms, regulators and researchers have proposed guidance for validating predictive models and CDS systems. However, this guidance is not universally followed and is not required by law. We call on CDS developers, deployers, and users to hold these systems to higher standards of clinical and technical validation. We provide a case study on two CDS systems deployed on a national scale in the United States for predicting a patient's risk of adverse opioid-related events: the Stratification Tool for Opioid Risk Mitigation (STORM), used by the Veterans Health Administration, and NarxCare, a commercial system.

Does a public online IVF prediction tool help set patient expectations? A mixed methods evaluation study

STUDY QUESTION

Does a public online IVF success prediction calculator based on real-world data help set patient expectations?

SUMMARY ANSWER

The YourIVFSuccess Estimator aided consumer expectations of IVF success: one quarter (24%) of participants were unsure of their estimated IVF success before using the tool; one half changed their prediction of success after using the tool and one quarter (26%) had their expectations of IVF success confirmed.

WHAT IS KNOWN ALREADY

Several web-based IVF prediction tools exist worldwide but have not been evaluated for their impact on patient expectations, nor for patient perceptions of usefulness and trustworthiness.

STUDY DESIGN, SIZE, DURATION

This is a pre-post evaluation of a convenience sample of 780 online users of the Australian YourIVFSuccess Estimatorhttps://yourivfsuccess.com.au/ between 1 July and 31 November 2021.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Participants were eligible if they were over 18 years of age, Australian residents, and considering IVF for themselves or their partner. Participants filled in online surveys before and after using the YourIVFSuccess Estimator.

MAIN RESULTS AND THE ROLE OF CHANCE

The response rate of participants who completed both surveys and the YourIVFSuccess Estimator was 56% (n = 439). The YourIVFSuccess Estimator aided consumer expectations of IVF success: one quarter (24%) of participants were unsure of their estimated IVF success before using the tool; one half changed their prediction of success after using the tool (20% increased, 30% decreased), bringing their predictions in line with the YourIVFSuccess Estimator, and one quarter (26%) had their IVF success expectations confirmed. One in five participants claimed they would change the timing of IVF treatment. The majority of participants found the tool to be at least moderately trustworthy (91%), applicable (82%), and helpful (80%), and would recommend it to others (60%). The main reasons given for the positive responses were that the tool is independent (government funded, academic) and based on real-world data. Those who did not find it applicable or helpful were more likely to have had a worse-than-expected prediction, or to have experienced non-medical infertility (e.g. single women, LGBTQIA+), noting that at the time of evaluation the Estimator did not accommodate these patient groups.

LIMITATIONS, REASONS FOR CAUTION

Those who dropped out between the pre- and post-surveys tended to have a lower education status or have been born outside of Australia or New Zealand, therefore there may be issues with generalizability.

WIDER IMPLICATIONS OF THE FINDINGS

With consumers demanding increasing levels of transparency and participation in decisions around their medical care, public-facing IVF predictor tools based on real-world data are useful for aligning expectations about IVF success rates. Given differences in patient characteristics and IVF practices internationally, national data sources should be used to inform country-specific IVF prediction tools.

STUDY FUNDING/COMPETING INTEREST(S)

The YourIVFSuccess website and evaluation of the YourIVFSuccess Estimator are supported by the Medical Research Future Fund (MRFF) Emerging Priorities and Consumer Driven Research initiative: EPCD000007. BKB, ND, and OF have no conflicts to declare. DM holds a clinical role at Virtus Health. His role did not influence the analysis plan or interpretation of results in this study. GMC is an employee of the UNSW Sydney, and Director of the UNSW NPESU. UNSW receives research funding on behalf of Prof Chambers from the MRFF to develop and manage the Your IVF Success website. Grant ID: MRFF Emerging Priorities and Consumer Driven Research initiative: EPCD000007.

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.

ESHRE good practice recommendations on recurrent implantation failure

STUDY QUESTION

How should recurrent implantation failure (RIF) in patients undergoing ART be defined and managed?

SUMMARY ANSWER

This is the first ESHRE good practice recommendations paper providing a definition for RIF together with recommendations on how to investigate causes and contributing factors, and how to improve the chances of a pregnancy.

WHAT IS KNOWN ALREADY

RIF is a challenge in the ART clinic, with a multitude of investigations and interventions offered and applied in clinical practice, often without biological rationale or with unequivocal evidence of benefit.

STUDY DESIGN SIZE DURATION

This document was developed according to a predefined methodology for ESHRE good practice recommendations. Recommendations are supported by data from the literature, if available, and the results of a previously published survey on clinical practice in RIF and the expertise of the working group. A literature search was performed in PubMed and Cochrane focussing on 'recurrent reproductive failure', 'recurrent implantation failure', and 'repeated implantation failure'.

PARTICIPANTS/MATERIALS SETTING METHODS

The ESHRE Working Group on Recurrent Implantation Failure included eight members representing the ESHRE Special Interest Groups for Implantation and Early Pregnancy, Reproductive Endocrinology, and Embryology, with an independent chair and an expert in statistics. The recommendations for clinical practice were formulated based on the expert opinion of the working group, while taking into consideration the published data and results of the survey on uptake in clinical practice. The draft document was then open to ESHRE members for online peer review and was revised in light of the comments received.

MAIN RESULTS AND THE ROLE OF CHANCE

The working group recommends considering RIF as a secondary phenomenon of ART, as it can only be observed in patients undergoing IVF, and that the following description of RIF be adopted: 'RIF describes the scenario in which the transfer of embryos considered to be viable has failed to result in a positive pregnancy test sufficiently often in a specific patient to warrant consideration of further investigations and/or interventions'. It was agreed that the recommended threshold for the cumulative predicted chance of implantation to identify RIF for the purposes of initiating further investigation is 60%. When a couple have not had a successful implantation by a certain number of embryo transfers and the cumulative predicted chance of implantation associated with that number is greater than 60%, then they should be counselled on further investigation and/or treatment options. This term defines clinical RIF for which further actions should be considered. Nineteen recommendations were formulated on investigations when RIF is suspected, and 13 on interventions. Recommendations were colour-coded based on whether the investigations/interventions were recommended (green), to be considered (orange), or not recommended, i.e. not to be offered routinely (red).

LIMITATIONS REASONS FOR CAUTION

While awaiting the results of further studies and trials, the ESHRE Working Group on Recurrent Implantation Failure recommends identifying RIF based on the chance of successful implantation for the individual patient or couple and to restrict investigations and treatments to those supported by a clear rationale and data indicating their likely benefit.

WIDER IMPLICATIONS OF THE FINDINGS

This article provides not only good practice advice but also highlights the investigations and interventions that need further research. This research, when well-conducted, will be key to making progress in the clinical management of RIF.

STUDY FUNDING/COMPETING INTERESTS

The meetings and technical support for this project were funded by ESHRE. N.M. declared consulting fees from ArtPRED (The Netherlands) and Freya Biosciences (Denmark); Honoraria for lectures from Gedeon Richter, Merck, Abbott, and IBSA; being co-founder of Verso Biosense. He is Co-Chief Editor of Reproductive Biomedicine Online (RBMO). D.C. declared being an Associate Editor of Human Reproduction Update, and declared honoraria for lectures from Merck, Organon, IBSA, and Fairtility; support for attending meetings from Cooper Surgical, Fujifilm Irvine Scientific. G.G. declared that he or his institution received financial or non-financial support for research, lectures, workshops, advisory roles, or travelling from Ferring, Merck, Gedeon-Richter, PregLem, Abbott, Vifor, Organon, MSD, Coopersurgical, ObsEVA, and ReprodWissen. He is an Editor of the journals Archives of Obstetrics and Gynecology and Reproductive Biomedicine Online, and Editor in Chief of Journal Gynäkologische Endokrinologie. He is involved in guideline developments and quality control on national and international level. G.L. declared he or his institution received honoraria for lectures from Merck, Ferring, Vianex/Organon, and MSD. He is an Associate Editor of Human Reproduction Update, immediate past Coordinator of Special Interest Group for Reproductive Endocrinology of ESHRE and has been involved in Guideline Development Groups of ESHRE and national fertility authorities. D.J.M. declared being an Associate Editor for Human Reproduction Open and statistical Advisor for Reproductive Biomedicine Online. B.T. declared being shareholder of Reprognostics and she or her institution received financial or non-financial support for research, clinical trials, lectures, workshops, advisory roles or travelling from support for attending meetings from Ferring, MSD, Exeltis, Merck Serono, Bayer, Teva, Theramex and Novartis, Astropharm, Ferring. The other authors had nothing to disclose.

DISCLAIMER

This Good Practice Recommendations (GPR) document represents the views of ESHRE, which are the result of consensus between the relevant ESHRE stakeholders and are based on the scientific evidence available at the time of preparation. ESHRE GPRs should be used for information and educational purposes. They should not be interpreted as setting a standard of care or be deemed inclusive of all proper methods of care, or be 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, or variations based on locality and facility type. Furthermore, ESHRE GPRs do not constitute or imply the endorsement, or favouring, of any of the included technologies by ESHRE.

Nomogram for Predicting Live Birth after the First Fresh Embryo Transfer in Patients with PCOS Undergoing IVF/ICSI Treatment with the GnRH-Ant Protocol

Polycystic ovary syndrome (PCOS) is the leading cause of anovulatory infertility. A better understanding of factors associated with pregnancy outcomes and successful prediction of live birth after IVF/ICSI are important to guide clinical practice. This was a retrospective cohort study investigating live birth after the first fresh embryo transfer using the GnRH-ant protocol in patients with PCOS between 2017 and 2021 at the Reproductive Center of Peking University Third Hospital. A total of 1018 patients with PCOS were qualified for inclusion in this study. BMI, AMH level, initial FSH dosage, serum LH and progesterone levels on the hCG trigger day, and endometrial thickness were all independent predictors of live birth. However, age and infertility duration were not significant predictors. We developed a prediction model based on these variables. The predictive ability of the model was demonstrated well, with areas under the curve of 0.711 (95% CI, 0.672-0.751) and 0.713 (95% CI, 0.650-0.776) in the training cohort and validation cohort, respectively. Additionally, the calibration plot showed good agreement between the prediction and the observation (p = 0.270). The novel nomogram could be helpful for clinicians and patients in clinical decision-making and outcome evaluation.

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.

Uterine factors modify the association between embryo transfer depth and clinical pregnancy

The embryo transfer depth may affect the chance of pregnancy. However, embryo dislodging caused by uterine contraction may occur after the transfer. The aim of the retrospective study was to investigate whether the factors associated with uterine contractilities, such as endometrial thickness and progesterone elevation, affect the association between transfer depth and implantation. A total of 7849 fresh transfer cycles on conventional stimulation in a single in vitro fertilization (IVF) center during the period 2013–2015 was reviewed. Patients were categorized according to quartiles of embryo transfer depth (≤ 9 mm, n = 1735, 9.1–11 mm, n = 2557, 11.1–14 mm, n = 1933, ≥ 1.4 mm, n = 1624, respectively). Adjusted for confounding factors, the adjusted odds ratio (aOR) (95% confidence interval, CI) for clinical pregnancy was 0.90 (0.79–1.02), 0.86 (0.74–0.99), and 0.70 (0.60–0.82) respectively in quartiles 2 through 4, comparing with quartile 1. However, the aORs were significantly increased when the endometrial thickness was < 8 mm. In comparison with that in the cycles with a normal endometrial thickness (8–11 mm), the aORs comparing quartiles 2 through 4 with quartile 1 in the cycles with an endometrial thickness < 8 mm increased from 0.78 (95% CI 0.65–0.93), 0.79 (95% CI 0.65–0.97), and 0.64 (95% CI 0.51–0.81) to 1.73 (95% CI 1.21–2.47), 1.04 (95% CI 0.69–1.56), and 1.45 (95% CI 0.91–2.31), respectively. In the cycles with elevated progesterone and blastocyst stage transfer, the aORs comparing quartiles 4 with quartile 1 decreased from 0.73 (95% CI 0.62–0.87) and 0.74 (95% CI 0.63–0.87) to 0.58 (95% CI 0.40–0.84) and 0.42 (95% CI 0.25–0.73) than those in the cycles without. However, only blastocyst transfer showed a significant interaction with transfer depth (p = 0.043). Our data suggested that endometrial thickness and blastocyst transfer significantly affect the association between embryo transfer depth and clinical pregnancy.

Complementary and alternative medicine use in infertility: A review of infertile women's needs

The use of complementary and alternative medicine (CAM) is common in infertile women in different countries. The purpose of the current study was to review the infertile women's needs in relation to CAM use. This narrative review was conducted through searching English databases including Scopus, PubMed, Embase, Web of Science, Cochrane library as well as Persian databases consisted of SID, and Magiran. The used keywords included "CAM/therapy, needs, and Infertility." All studies published in English peer-reviewed journals from conception to October 2020, which examined the infertile women's needs in the field of CAM use were included in the review. In the process of data extraction, two researchers screened the title, abstract, and full text of the articles. Out of the 2166 articles reviewed, 29 articles including six qualitative and mixed methods studies, four review, and 19 quantitative studies met the inclusion criteria. The results showed that infertile women have different needs in six domains consisted of educational and informational needs, the need for psychological counseling, supportive needs, the need for CAM use counseling, the need to treatment consistent with women's culture and demands, and the need to the integration of CAM with conventional medicine. Awareness of infertile women's needs toward CAM use can help health policymakers and planners in designing and implementing counseling services in accordance with the demands and culture of infertile couples. It also helps to develop a coherent program to integrate the use of CAM in the classical infertility treatment.

Predicting the probability of a live birth after a freeze-all based in vitro fertilization-embryo transfer (IVF-ET) treatment strategy

BACKGROUND

The predictors for live birth rate (LBR) following one episode of in vitro fertilization (IVF) cycle for patients using a "freeze-all" strategy are not entirely clear.

METHODS

A retrospective cohort study utilizing a prediction model was developed to assess the relationship to the LBR. Women undergoing IVF with a freeze-all strategy were screened. Univariate models were first fitted for female age at oocytes retrieval/frozen-thawed embryo transfer (FET), body mass index (BMI), duration and etiology of infertility, previous IVF failures, total dose and duration of gonadotrophin, ovarian sensitivity index (OSI), number of oocytes collected, method of fertilization, number of embryos created, number and stage of embryos frozen, type and number of FET cycles, endometrial thickness (EMT)/pattern, hormone level on transplantation day, storage duration, number of embryos thawed and damaged thawed embryos, number and stage of embryos transferred and number of different quality embryos transferred. Variables with P<0.05 in the univariate model were selected for further analysis of the final multivariate discrete-time logistic regression model.

RESULTS

A total of 7,602 women undergoing one ovarian stimulation resulted in 9,964 FETs, of whom 3,066 (40.33%) had a live-birth after their first FET and 3,929 (51.68%) after total FETs. The EMT and woman's age at oocyte retrieval were the most important predictors. In the first FET, the LBR of women with an EMT ≤8 mm [27.40%; 95% confidence interval (CI): (21.60-33.81%)] was significantly lower than that of women with EMT between 9 and 11 mm [36.51%; 95% CI: (34.25-38.81%)] and thicker than 12 mm [44.23%; 95% CI: (42.22-46.25%)] (P<0.05). The optimistic and conservative cumulative LBRs of women younger than 31 years [87.5%; 95% CI: (86.32-88.61%) and 63.04%; 95% CI: (61.36-64.69%)] were significantly decreased in women aged 31-35, 36-40 and >40 (P<0.001).

CONCLUSIONS

Our study provides an effective prediction model for a woman's chance of having a baby after a "freeze-all" policy. The use of EMT and female age as tools to identify LBR are shown to be justified, and repeated FETs cannot reverse the age-dependent decline in fertility.

Influence of ambient air pollution on successful pregnancy with frozen embryo transfer: A machine learning prediction model

Numerous air pollutants have been reported to influence the outcomes of in vitro fertilization (IVF). However, whether air pollution affects implantation in frozen embryo transfer (FET) process is under debate. We aimed to find the association between ambient air pollution and implantation potential of FET and test the value of adding air pollution data to a random forest model (RFM) predicting intrauterine pregnancy. Using a retrospective study of a 4-year single-center design，we analyzed 3698 cycles of women living in Shanghai who underwent FET between 2015 and 2018. To estimate patients' individual exposure to air pollution, we computed averages of daily concentrations of six air pollutants including PM_2.5, PM₁₀, SO₂, CO, NO₂, and O₃ measured at 9 monitoring stations in Shanghai for the exposure period (one month before FET). Moreover, A predictive model of 15 variables was established using RFM. Air pollutants levels of patients with or without intrauterine pregnancy were compared. Our results indicated that for exposure periods before FET, NO₂ were negatively associated with intrauterine pregnancy (OR: 0.906, CI: 0.816-0.989). AUROC increased from 0.712 to 0.771 as air pollutants features were added. Overall, our findings demonstrate that exposure to NO₂ before transfer has an adverse effect on clinical pregnancy. The performance to predict intrauterine pregnancy will improve with the use of air pollution data in RFM.

Evaluation of the Risk of Birth Defects Related to the Use of Assisted Reproductive Technology: An Updated Systematic Review

Fertility problems constitute a serious medical, social, and demographic problem. With this review, we aim to critically appraise and evaluate the existing literature surrounding the risk of birth defects in offspring conceived using techniques based on assisted reproductive technology (ART). Based on searches of the literature in PubMed and ScienceDirect, we obtained a total of 2,003,275 works related to the topic. Ultimately, 11 original papers published in the last 10 years qualified for inclusion in the study. Based on five studies included in this analysis, it was shown that ART significantly increases the risk of congenital malformations in associated newborns. Due to the specifics of given studies, as well as potential confounding risk factors, this influence cannot be ignored. Therefore, considering the information contained in the articles included in this systematic review, it was determined that the risk of birth defects is not directly related to the use of ART itself but also depends on the age of partners, causes of infertility, comorbidities, and the number of fetuses during a pregnancy, as well as many other factors not covered in the literature. It is thus necessary to impress upon infertile couples who wish to have offspring that the use of ART is not risk-free but that the benefits outweigh the risks. Further education in this field, as well as social understanding, is also required.

Reliability of AMH and AFC measurements and their correlation: a large multicenter study

PURPOSE

Anti-Müllerian hormone (AMH) and antral follicle count (AFC) are correlated with the ovarian response, but their reliability and reproducibility are questionable. This large multicenter study describes their distribution, inter-cycle and inter-center variability, and their correlation.

METHODS

A total of 25,854 IVF cycles among 15,219 patients were selected in 12 ART centers. Statistical distribution of AMH and AFC was studied by using the Kolmogorov-Smirnov test and Shapiro goodness of fit test. The reproducibility of AFC and AMH was measured using a mixed model regressing the logarithmic transformation of AFC with age.

RESULTS

The distribution of AMH and AFC was characterized by a wide dispersion of values, twice more important for AFC, and a logarithmic distribution. The faster decline in AMH than in AFC with age suggests that their correlation changes with age. AMH and AFC showed a very low proportion of concordance in the range of expected poor responders according to Bologna cutoffs. The heterogeneity for AMH and AFC across centers was small, but much larger across patients within each center. Concerning the patients with several successive cycles, the reproducibility for AMH seemed much better than for AFC. Comparing respective performances of AMH and AFC for the prediction of ovarian response depended on the local conditions for measuring these indicators and on the reproducibility of results improved over time.

CONCLUSION

Distribution of AMH and AFC was characterized by the wide dispersion of values, and a logarithmic distribution. Establishing cutoffs or a direct relationship AMH/AFC without considering age seems hazardous. Correlation between AMH and AFC was very poor in the range of poor responders.

A Personalized Management Approach of OHSS: Development of a Multiphase Prediction Model and Smartphone-Based App

OBJECTIVE

This study aimed to develop multiphase big-data-based prediction models of ovarian hyperstimulation syndrome (OHSS) and a smartphone app for risk calculation and patients' self-monitoring.

METHODS

Multiphase prediction models were developed from a retrospective cohort database of 21,566 women from January 2017 to December 2020 with controlled ovarian stimulation (COS). There were 17,445 women included in the final data analysis. Women were randomly assigned to either training cohort (n = 12,211) or validation cohort (n = 5,234). Their baseline clinical characteristics, COS-related characteristics, and embryo information were evaluated. The prediction models were divided into four phases: 1) prior to COS, 2) on the day of ovulation trigger, 3) after oocyte retrieval, and 4) prior to embryo transfer. The multiphase prediction models were built with stepwise regression and confirmed with LASSO regression. Internal validations were performed using the validation cohort and were assessed by discrimination and calibration, as well as clinical decision curves. A smartphone-based app "OHSS monitor" was constructed as part of the built-in app of the IVF-aid platform. The app had three modules, risk prediction module, symptom monitoring module, and treatment monitoring module.

RESULTS

The multiphase prediction models were developed with acceptable distinguishing ability to identify OHSS at-risk patients. The C-statistics of the first, second, third, and fourth phases in the training cohort were 0.628 (95% CI 0.598-0.658), 0.715 (95% CI 0.688-0.742), 0.792 (95% CI 0.770-0.815), and 0.814 (95% CI 0.793-0.834), respectively. The calibration plot showed the agreement of predictive and observed risks of OHSS, especially at the third- and fourth-phase prediction models in both training and validation cohorts. The net clinical benefits of the multiphase prediction models were also confirmed with a clinical decision curve. A smartphone-based app was constructed as a risk calculator based on the multiphase prediction models, and also as a self-monitoring tool for patients at risk.

CONCLUSIONS

We have built multiphase prediction models based on big data and constructed a user-friendly smartphone-based app for the personalized management of women at risk of moderate/severe OHSS. The multiphase prediction models and user-friendly app can be readily used in clinical practice for clinical decision-support and self-management of patients.

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.

Nomogram incorporating ultrasonic markers of endometrial receptivity to determine the embryo-endometrial synchrony after in vitro fertilization

BACKGROUND

A successful pregnancy using in vitro fertilization and embryo transfer (IVF-ET) requires a receptive endometrium, good-quality embryos, and a synchronized embryo-endometrial dialogue. Although embryo quality and endometrial receptivity (ER) have been fully assessed to exclude substandard conditions, the probability of successful ET is relatively low. Currently, embryo-endometrial synchrony is considered to be a possible explanation, because delayed, advanced, or narrowed window of implantation (WOI) may lead to ET failure.

OBJECTIVE

This study aims to establish a nomogram incorporating a series of ultrasonic ER markers on the day before implantation to assess the embryo-endometrial synchrony, which may contribute to the improvement of clinical pregnancy outcomes.

METHODS

Totally 583 women with 1135 complete IVF cycles were retrospectively analyzed. Among them, 357 women with 698 cycles and 226 women with 437 cycles were assigned to the training and validation cohorts, respectively. Ultrasonic ER markers obtained on the day before implantation were collected for analyses. In the training cohort, the screened correlates of clinical pregnancy failure were utilized to develop a nomogram for determining whether an infertile woman is suitable for the ET next day. This model was validated both in the training and validation cohorts.

RESULTS

Spiral artery (SA) resistance index (RI), vascularisation index (VI), and flow index (FI) were independently associated with the ET failure (all P < 0.05). They were served as the components of the developed nomogram to visualize the likelihood of implantation failure in IVF-ET. This model was validated to present good discrimination and calibration, and obtained clinical net benefits both in the training and validation cohorts.

CONCLUSION

We developed a nomogram that included SA-RI, VI, and FI on the day before implantation. It may assist physicians to identify patients with displaced WOI, thus avoiding meaningless ET prior to implantation.

Risk Assessment of the Increased Occurrence of Congenital Cardiac and Non-Cardiac Defects in Fetuses with a Normal Karyotype after Assisted Fertilization in Comparison to Natural Fertilization Based on Ultrasound Diagnostics

The goal of the study was to assess changes in parameters based on ultrasound examinations—these were Crown Rump Length (CRL), Nuchal Translucency (NT), Fetal Heart Rate (FHR), and Pulsatility Index for Ductus Venosus (DV-PI)—in the first trimester of pregnancy in women in which there was a natural initiation of the pregnancy due to spontaneous ovulation, women in which the pregnancy was initiated as a result of stimulated ovulation, as well as in the group in which pregnancy was achieved through the use of In-Vitro Fertilization (IVF)-assisted reproduction. A total of 1581 women became pregnant without the use of assisted reproduction methods. Out of 283 pregnancies, in 178 patients, induced ovulation was utilized. Next, 137 women had sexual intercourse and became pregnant; 41 of them became pregnant through Intrauterine Insemination (IUI) as a result of Artificial Insemination by Husband (AIH), and 13 became pregnant after Artificial Insemination by Donor (AID). The third group consisted of 105 women subjected to Controlled Ovarian Hyperstimulation (COH). In this group of pregnant women, 53 pregnancies were resultant of Intracytoplasmic Sperm Injection (ICSI), and 52 pregnancies were the result of Intracytoplasmic Morphologically selected Sperm Injection (IMSI). The obtained results did not indicate that the chosen method of fertilization or the chosen ovulation method had a statistically significant effect on the development risk of congenital heart or non-heart defects in the fetus.

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.

Nomogram to predict pregnancy outcomes of emergency oocyte freeze-thaw cycles

Background:

Existing clinical prediction models for in vitro fertilization are based on the fresh oocyte cycle, and there is no prediction model to evaluate the probability of successful thawing of cryopreserved mature oocytes. This research aims to identify and study the characteristics of pre-oocyte-retrieval patients that can affect the pregnancy outcomes of emergency oocyte freeze-thaw cycles.

Methods:

Data were collected from the Reproductive Center, Peking University Third Hospital of China. Multivariable logistic regression model was used to derive the nomogram. Nomogram model performance was assessed by examining the discrimination and calibration in the development and validation cohorts. Discriminatory ability was assessed using the area under the receiver operating characteristic curve (AUC), and calibration was assessed using the Hosmer–Lemeshow goodness-of-fit test and calibration plots.

Results:

The predictors in the model of “no transferable embryo cycles” are female age (odds ratio [OR] = 1.099, 95% confidence interval [CI] = 1.003–1.205, P = 0.0440), duration of infertility (OR = 1.140, 95% CI = 1.018–1.276, P = 0.0240), basal follicle-stimulating hormone (FSH) level (OR = 1.205, 95% CI = 1.051–1.382, P = 0.0084), basal estradiol (E2) level (OR = 1.006, 95% CI = 1.001–1.010, P = 0.0120), and sperm from microdissection testicular sperm extraction (MESA) (OR = 7.741, 95% CI = 2.905–20.632, P < 0.0010). Upon assessing predictive ability, the AUC for the “no transferable embryo cycles” model was 0.799 (95% CI: 0.722–0.875, P < 0.0010). The Hosmer–Lemeshow test (P = 0.7210) and calibration curve showed good calibration for the prediction of no transferable embryo cycles. The predictors in the cumulative live birth were the number of follicles on the day of human chorionic gonadotropin (hCG) administration (OR = 1.088, 95% CI = 1.030–1.149, P = 0.0020) and endometriosis (OR = 0.172, 95% CI = 0.035–0.853, P = 0.0310). The AUC for the “cumulative live birth” model was 0.724 (95% CI: 0.647–0.801, P < 0.0010). The Hosmer–Lemeshow test (P = 0.5620) and calibration curve showed good calibration for the prediction of cumulative live birth.

Conclusions:

The predictors in the final multivariate logistic regression models found to be significantly associated with poor pregnancy outcomes were increasing female age, duration of infertility, high basal FSH and E2 level, endometriosis, sperm from MESA, and low number of follicles with a diameter >10 mm on the day of hCG administration.

Cumulative live birth rates over multiple complete cycles of in vitro fertilisation cycles: 10-year cohort study of 20,687 women following freeze-all strategy from one single centre

PURPOSE

To explore the cumulative live birth rates (CLBRs) over multiple complete cycles of in vitro fertilization (IVF) among patients following freeze-all strategy METHODS: A retrospective cohort study was performed among 20,687 women undergoing their first and following IVF cycles from 2007 to 2016. The main outcomes of present study were live birth rate per cycle, conservative CLBR and optimal CLBR.

RESULTS

The CLBR increased from 50.74% for the first complete cycle to 64.41% for the conservative estimate and 84.77% for the optimal estimate after seven complete cycles. The CLBRs varied by age. The conservative estimate of CLBR after five complete cycles declined from 77.11% for women younger than 31 years, to 8.63% for women older than 40 years. The optimal CLBRs were 91.82% and 13.74%, respectively. The predictors of live birth over multiple complete cycles for patients embarking on IVF following freeze-all strategy were women's age and causes of infertility. For patients finishing the first complete cycle, the number of oocytes retrieved at complete cycle one also played an important predictive role.

CONCLUSIONS

Among women undergoing IVF following freeze-all strategy, the CLBR after seven complete IVF cycles was 84.77% if there were not barriers to continue the IVF treatment, with variation by age. Two prediction models were developed to estimate their probability of having a baby over multiple complete IVF cycles with freeze-all strategy among patients before starting IVF and patients after the first complete cycle, which is critical for patients to make treatment decisions and preparations physically, emotionally, and financially.

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.

Unexplained infertility categorization based on female laparoscopy and total motile sperm count, and its impact on cumulative live-births after one in-vitro fertilization cycle. A retrospective cohort study involving 721 cycles

PURPOSE

To determine how subcategorizing unexplained infertility based on female laparoscopy and total-motile-sperm-count assessment would impact cumulative live-births after one in-vitro fertilization (IVF) cycle.

METHODS

Seven hundred twenty one IVF cycles from Jan 2014-April 2019 performed at a single-center were retrospectively analyzed. Couples with unexplained infertility having normal uterine and endometrial morphology were subcategorized into three cohorts, UI (1): those with no tuboperitoneal pathology on laparoscopy and total-motile-sperm-count (TMSC) ≧20 million: n = 103; UI (2): tuboperitoneal pathology on laparoscopy or TMSC <20 million, n = 86; and UI(3): tuboperitoneal status not known: n = 114. Controls were severe male factor, bilateral tubal block, and grade 3/4 endometriosis: n = 418. Primary Outcome was cumulative-live-birth-per-initiated-IVF cycle (CLBR). Odds ratio for live-births were adjusted for confounding factors.

RESULTS

The CLBR in UI1 cohort was significantly lower than controls (29.1% vs 39; OR = 0.62; 95%CI = 0.39-0.98; P = .04); but similar in UI2 and UI3 vs. controls. (37.2% vs 39.95%; OR = 0.89, 95%CI = 0.55-1.44; P = .89) and (38.6% vs 39.95%; OR = 0.98, 95%CI = 0.64-1.55; P = .98). After adjusting for age, infertility duration, past live-births, and AMH, the adjusted odds for CLBR in UI1 was 0.48 (95%CI = 0.28-0.82; P = .007).

CONCLUSIONS

Unexplained infertility when defined after a normal laparoscopy and TMSC significantly lowered cumulative-live-births-per-initiated-IVF cycle when compared with traditional diagnosis of tubal, endometriosis, or male factor infertility. In UI subcategory with abnormal laparoscopy or TMSC, CLBR remained unaffected. This information could be useful for counseling couples prior to IVF. Large-scale prospective studies are needed to confirm this observation.

Using Deep Learning in a Monocentric Study to Characterize Maternal Immune Environment for Predicting Pregnancy Outcomes in the Recurrent Reproductive Failure Patients

Recurrent reproductive failure (RRF), such as recurrent pregnancy loss and repeated implantation failure, is characterized by complex etiologies and particularly associated with diverse maternal factors. It is currently believed that RRF is closely associated with the maternal environment, which is, in turn, affected by complex immune factors. Without the use of automated tools, it is often difficult to assess the interaction and synergistic effects of the various immune factors on the pregnancy outcome. As a result, the application of Artificial Intelligence (A.I.) has been explored in the field of assisted reproductive technology (ART). In this study, we reviewed studies on the use of A.I. to develop prediction models for pregnancy outcomes of patients who underwent ART treatment. A limited amount of models based on genetic markers or common indices have been established for prediction of pregnancy outcome of patients with RRF. In this study, we applied A.I. to analyze the medical information of patients with RRF, including immune indicators. The entire clinical samples set (561 samples) was divided into two sets: 90% of the set was used for training and 10% for testing. Different data panels were established to predict pregnancy outcomes at four different gestational nodes, including biochemical pregnancy, clinical pregnancy, ongoing pregnancy, and live birth, respectively. The prediction models of pregnancy outcomes were established using sparse coding, based on six data panels: basic patient characteristics, hormone levels, autoantibodies, peripheral immunology, endometrial immunology, and embryo parameters. The six data panels covered 64 variables. In terms of biochemical pregnancy prediction, the area under curve (AUC) using the endometrial immunology panel was the largest (AUC = 0.766, accuracy: 73.0%). The AUC using the autoantibodies panel was the largest in predicting clinical pregnancy (AUC = 0.688, accuracy: 78.4%), ongoing pregnancy (AUC = 0.802, accuracy: 75.0%), and live birth (AUC = 0.909, accuracy: 89.7%). Combining the data panels did not significantly enhance the effect on prediction of all the four pregnancy outcomes. These results give us a new insight on reproductive immunology and establish the basis for assisting clinicians to plan more precise and personalized diagnosis and treatment for patients with RRF.

Early prediction of live birth for assisted reproductive technology patients: a convenient and practical prediction model

Live birth is the most important concern for assisted reproductive technology (ART) patients. Therefore, in the medical reproductive centre, obstetricians often need to answer the following question: "What are the chances that I will have a healthy baby after ART treatment?" To date, our obstetricians have no reference on which to base the answer to this question. Our research aimed to solve this problem by establishing prediction models of live birth for ART patients. Between January 1, 2010, and May 1, 2017, we conducted a retrospective cohort study of women undergoing ART treatment at the Reproductive Medicine Centre, Xiangya Hospital of Central South University, Hunan, China. The birth of at least one live-born baby per initiated cycle or embryo transfer procedure was defined as a live birth, and all other pregnancy outcomes were classified as no live birth. A live birth prediction model was established by stepwise multivariate logistic regression. All eligible subjects were randomly allocated to two groups: group 1 (80% of subjects) for the establishment of the prediction models and group 2 (20% of subjects) for the validation of the established prediction models. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of each prediction model at different cut-off values were calculated. The prediction model of live birth included nine variables. The area under the ROC curve was 0.743 in the validation group. The sensitivity, specificity, PPV, and NPV of the established model ranged from 97.9-24.8%, 7.2-96.3%, 44.8-83.8% and 81.7-62.5%, respectively, at different cut-off values. A stable, reliable, convenient, and satisfactory prediction model for live birth by ART patients was established and validated, and this model could be a useful tool for obstetricians to predict the live rate of ART patients. Meanwhile, it is also a reference for obstetricians to create good conditions for infertility patients in preparation for pregnancy.

Prediction of live birth probability after in vitro fertilization and intracytoplasmic sperm injection treatment: A multi-center retrospective study in Chinese population

AIM

To develop a prediction model to estimate the chances of live birth over multiple cycles of in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) treatment.

METHODS

A retrospective cohort study was launched in three reproductive centers including 10 824 couples who received 14 106 treatment cycles with known pregnancy outcomes by the end of 2016. Discrete time logistic regression was used to establish the model and a nomogram was developed to predict the chance of live birth on plain paper-based final predictors.

RESULTS

Among 10 824 couples, 5809 (53.7%) ended up with a live birth with several successive transplant cycles. What's more, we found that younger female age (p < 0.001), smaller cycle number (p < 0.001), female body mass index (p < 0.001), male factor (p < 0.001), ovulation disorder (p = 0.006), and higher endometrial thickness (p < 0.001) were significantly associated with increased live birth rate. Discrimination of the model expressed by area under the curve (AUC) was 0.66.

CONCLUSION

Our study will help shape couples' expectations of their ART outcome, allowing them to plan their treatments more efficiently and prepare emotionally and financially.

Significance of endometrial thickness change after human chorionic gonadotrophin triggering in modified natural cycles for frozen-thawed embryo transfer

Background

Peak endometrial thickness (EMT), measured on the end of follicular phase or early luteal phase, is the most widely used marker for endometrial receptivity during infertility treatment. However, the clinical significance of follicular-to-luteal EMT change remains unclear. We aimed to study whether the change of EMT between the day of human chorionic gonadotrophin (hCG) triggering and the day of frozen-thawed embryo transfer (FET) has any influence on pregnancy outcomes in modified natural cycles (mNCs).

Methods

This was a retrospective cohort study of 2,768 regular ovulatory women who underwent their first mNC-FET cycles from January 2011 to June 2015. Patients were divided into three groups according to the percentage change of EMT from the hCG triggering day to the FET day: >5% decrease (n=405), ±5% plateau (n=1,259) and >5% increase (n=1,104). The main outcome measure was live birth rate.

Results

Live birth rates were 41.9%, 39.8% [crude odds ratio (cOR) 0.91, 95% CI, 0.73–1.15) and 42.4% (cOR 1.02, 95% CI, 0.87–1.20) in the EMT plateau, decrease and increase groups, respectively (P=0.649). Multiple regression analysis did not alter the finding after controlling for a variety of confounders. Compared with the post-trigger EMT plateau group, the adjusted OR of live birth was 0.88 (95% CI, 0.69–1.12) in the decrease group and 1.05 (95% CI, 0.88–1.25) in the increase group. Similarly, no significant associations were observed before or after adjustment between EMT change and other pregnancy outcomes including positive hCG test, clinical pregnancy, early miscarriage and ongoing pregnancy.

Conclusions

EMT change from hCG triggering to embryo transfer was not associated with pregnancy chances in mNC-FET cycles. This reassuring finding should provide guidance for physicians and patients when confronted with EMT decrease on the transfer day.

Application of Artificial Intelligence Algorithms to Estimate the Success Rate in Medically Assisted Procreation

The aim of this study was to build an Artificial Neural Network (ANN) complemented by a decision tree to predict the chance of live birth after an In Vitro Fertilization (IVF)/Intracytoplasmic Sperm Injection (ICSI) treatment, before the first embryo transfer, using demographic and clinical data. Overall, 26 demographic and clinical data from 1193 cycles who underwent an IVF/ICSI treatment at Centro de Infertilidade e Reprodução Medicamente Assistida, between 2012 and 2019, were analyzed. An ANN was constructed by selecting experimentally the input variables which most correlated to the target through Pearson correlation. The final used variables were: woman’s age, total dose of gonadotropin, number of eggs, number of embryos and Antral Follicle Count (AFC). A decision tree was developed considering as an initial set the input variables integrated in the previous model. The ANN model was validated by the holdout method and the decision tree model by the 10-fold cross method. The ANN accuracy was 75.0% and the Area Under the Receiver Operating Characteristic (AUROC) curve was 75.2% (95% Confidence Interval (CI): 72.5–77.5%), whereas the decision tree model reached 75.0% and 74.9% (95% CI: 72.3–77.5%). These results demonstrated that both ANN and decision tree methods are fair for prediction the chance of conceive after an IVF/ICSI cycle.

Women from diverse minority ethnic or religious backgrounds desire more infertility education and more culturally and personally sensitive fertility care

STUDY QUESTION

What are the views, experiences and healthcare needs of infertile women from a minority ethnic or religious background living in Wales?

SUMMARY ANSWER

Women from ethnic and religious minority backgrounds consider that their communities have highly pronatalistic attitudes and stigmatize infertility, and express the need for more infertility education (for themselves and their communities), as well as more socio-culturally and interpersonally sensitive fertility care.

WHAT IS ALREADY KNOWN

Some people from minority ethnic or religious groups perceive pressure to conceive from their communities, experience social costs when they are unable to have children and stressful interactions with the fertility healthcare system while attempting to conceive.

STUDY DESIGN, SIZE, DURATION

This study was based on a one-day drawing workshop to collect visual (artwork produced by participants) and textual (all conversations and discussions during the workshop) data about the participants' views and experiences of infertility and their fertility care needs.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Participants were nine adult women with a minority ethnic or religious status living in Wales, UK, who were experiencing or had experienced infertility in the past. The workshop comprised five activities: (i) small and large group discussion of infertility-related drawings, (ii) lide-based lecture consisting of an introduction to the basics of drawing objects and people and (iii) thoughts and feelings, (iv) free drawing session and (v) group sharing. Audio recordings of the workshop were transcribed verbatim. Textual data was analysed with thematic analysis. Risk for bias was addressed via individual coding by two authors followed by joint presentation and discussion of results with the research team and participants.

MAIN RESULTS AND THE ROLE OF CHANCE

Forty-one themes were identified and grouped into eight distinct higher order themes. These themes described the emotional, relational and social burden of infertility experienced by women, which they perceived to result from their communities' highly pronatalistic attitudes and stigmatization of infertility. Themes also captured women's adaptive coping strategies and critical attitude towards pronatalist ideologies. Lastly, themes captured their overall positive evaluation of their fertility health care, their desire for more infertility education (for themselves and their communities) and for culturally competent and interpersonally sensitive care.

LIMITATIONS, REASONS FOR CAUTION

Our participants were a small, non-random sample recruited in collaboration with a local charity, which may mean that all participants were well integrated in their communities. Analysis focused on capturing commonalities in participants' experiences and this may sometimes result in homogenising diverse experiences.

WIDER IMPLICATIONS OF THE FINDINGS

More education about the infertility experiences of minority ethnic and religious groups at the community and healthcare delivery level may translate into lessened negative attitudes towards infertility and more culturally competent care, which can be beneficial for women.

STUDY FUNDING/COMPETING INTEREST(S)

This research was funded by Welsh Crucible. The authors have no conflict of interests to declare.

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.

Association between Vitamin D Levels and Fertility Outcomes in Patients Undergoing IVF/ICSI

Background: Several studies have demonstrated that vitamin D (vitD) might play an important role in the reproductive system due to expression of vitD receptor and vitD-metabolizing enzymes in many reproductive tissues. VitD deficiency has been associated with increased risk of obstetric complications. However, the effect of vitD levels on in vitro fertilization (IVF)/ICSI outcomes is not fully understood. Evidence shows that women with adequate vitD levels might have higher pregnancy rates. This study evaluated the association between serum vitD levels and IVF/ICSI outcomes.

Methods: This multicenter, retrospective cohort study was conducted at IVFMD, My Duc Hospital and IVFMDPN, My Duc Phu Nhuan Hospital, Ho Chi Minh City, Vietnam between November 2017 and July 2019. Vietnamese patients aged 18–40 years with serum vitD (25(OH)D) samples collected before starting controlled ovarian stimulation and undergoing embryo transfer were eligible. Patients were divided into four groups based on 25(OH)D levels: <10 ng/mL, 10 to <20 ng/mL, 20 to <30 ng/mL, and [Formula: see text]30 ng/mL. The primary outcome was ongoing pregnancy rate.

Results: Of 3779 patients recruited, 25(OH)D levels were <10 ng/mL in 564 (14.9%), 10 to <20 ng/mL in 436 (11.5%), 20 to <30 in 1,142 (30.2%), and [Formula: see text]30 ng/mL in 1,637 (43.3%). Ongoing pregnancy rates were similar across the four subgroups (36%, 40%, 36%, and 36%, respectively; p = 0.409). The number of oocytes retrieved, embryos, clinical pregnancy, implantation, and miscarriage rates did not differ significantly between subgroups.

Conclusions: In this analysis, serum vitD levels did not appear to be correlated with pregnancy outcomes in patients undergoing IVF/ICSI.

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.

Impact of Gonadotropin-Releasing Hormone Agonist Pre-treatment on the Cumulative Live Birth Rate in Infertile Women With Adenomyosis Treated With IVF/ICSI: A Retrospective Cohort Study

Introduction: Although pre-treatment with a GnRH agonist can reduce the size of adenomyosis lesions, the supra-physiological hormone level induced by controlled ovarian hyperstimulation (COH) may negate the usefulness of the GnRH agonist in patients with adenomyosis lesions, leading to continued poor outcomes in fresh embryo transfer cycles during in vitro fertilization (IVF). It is unclear whether GnRH agonist pre-treatment before starting the long GnRH agonist protocol for IVF/ICSI (intracytoplasmic sperm injection) can improve cumulative live birth rate (CLBR) of infertile women with adenomyosis. Method: In this retrospective cohort study, a total of 374 patients diagnosed as adenomyosis (477 cycles) underwent IVF/ICSI with long GnRH agonist protocol with or without GnRH agonist pre-treatment between January 2009 and June 2018. Logistic regression was used to assess the association between GnRH agonist pre-treatment and pregnancy outcome after adjusting for confounding factors. Results: The live birth rate in fresh embryo transfer cycles was higher in the non-pre-treatment group than in the GnRH agonist pre-treatment group (37.7 vs. 21.2%, P = 0.028); the adjusted odds ratio (OR) for the long agonist protocol without pre-treatment was 1.966 (95% CI: 0.9-4.296, P = 0.09). The CLBR was higher in the non-pre-treatment group than in the GnRH agonist pre-treatment group (40.50 vs. 27.90%, P = 0.019); the adjusted OR for the long agonist protocol without pre-treatment was 1.361 (95% CI: 0.802-2.309, P = 0.254). Conclusion: Our results indicated that GnRH agonist pre-treatment before starting the long GnRH agonist protocol does not improve the live birth rate in fresh embryo transfer cycles or CLBR in infertile women with adenomyosis after IVF/ICSI treatment when compared to that in non-pre-treated patients. A subsequent prospective randomized controlled study is needed to confirm these results.

Calibration: the Achilles heel of predictive analytics

BACKGROUND

The assessment of calibration performance of risk prediction models based on regression or more flexible machine learning algorithms receives little attention.

MAIN TEXT

Herein, we argue that this needs to change immediately because poorly calibrated algorithms can be misleading and potentially harmful for clinical decision-making. We summarize how to avoid poor calibration at algorithm development and how to assess calibration at algorithm validation, emphasizing balance between model complexity and the available sample size. At external validation, calibration curves require sufficiently large samples. Algorithm updating should be considered for appropriate support of clinical practice.

CONCLUSION

Efforts are required to avoid poor calibration when developing prediction models, to evaluate calibration when validating models, and to update models when indicated. The ultimate aim is to optimize the utility of predictive analytics for shared decision-making and patient counseling.

Computational prediction of implantation outcome after embryo transfer

The aim of this study is to develop a computational prediction model for implantation outcome after an embryo transfer cycle. In this study, information of 500 patients and 1360 transferred embryos, including cleavage and blastocyst stages and fresh or frozen embryos, from April 2016 to February 2018, were collected. The dataset containing 82 attributes and a target label (indicating positive and negative implantation outcomes) was constructed. Six dominant machine learning approaches were examined based on their performance to predict embryo transfer outcomes. Also, feature selection procedures were used to identify effective predictive factors and recruited to determine the optimum number of features based on classifiers performance. The results revealed that random forest was the best classifier (accuracy = 90.40% and area under the curve = 93.74%) with optimum features based on a 10-fold cross-validation test. According to the Support Vector Machine-Feature Selection algorithm, the ideal numbers of features are 78. Follicle stimulating hormone/human menopausal gonadotropin dosage for ovarian stimulation was the most important predictive factor across all examined embryo transfer features. The proposed machine learning-based prediction model could predict embryo transfer outcome and implantation of embryos with high accuracy, before the start of an embryo transfer cycle.

A predictive model for women's assisted fecundity before starting the first IVF/ICSI treatment cycle

PURPOSE

To introduce a prognostic model for women's assisted fecundity before starting the first IVF/ICSI treatment cycle.

METHODS

In contrast to previous predictive models, we analyze two groups of women at the extremes of prognosis. Specifically, 708 infertile women that had either a live birth (LB) event in the first autologous IVF/ICSI cycle ("high-assisted-fecundity women", n = 458) or did not succeed in having a LB event after completing three autologous IVF/ICSI cycles ("low-assisted-fecundity women", n = 250). The initial sample of 708 women was split into two sets in order to develop (n = 531) and internally validate (n = 177) a predictive logistic regression model using a forward-stepwise variable selection.

RESULTS

Seven out of 32 initially selected potential predictors were included into the model: women's age, presence of multiple female infertility factors, number of antral follicles, women's tobacco smoking, occurrence of irregular menstrual cycles, and basal levels of prolactin and LH. The value of the c-statistic was 0.718 (asymptotic 95% CI 0.672-0.763) in the development set and 0.649 (asymptotic 95% CI: 0.560-0.738) in the validation set. The model adequately fitted the data with no significant over or underestimation of predictor effects.

CONCLUSION

Women's assisted fecundity may be predicted using a relatively small number of predictors. This approach may complement the traditional procedure of estimating cumulative and cycle-specific probabilities of LB across multiple complete IVF/ICSI cycles. In addition, it provides an easy-to-apply methodology for fertility clinics to develop and actualize their own predictive models.