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

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.

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

RESEARCH QUESTION

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

DESIGN

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

RESULTS

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

CONCLUSIONS

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

Diagnostic or prognostic? Decoding the role of embryo selection on in vitro fertilization treatment outcomes

In this review, we take a fresh look at embryo assessment and selection methods from the perspective of diagnosis and prognosis. On the basis of a systematic search in the literature, we examined the evidence on the prognostic value of different embryo assessment methods, including morphological assessment, blastocyst culture, time-lapse imaging, artificial intelligence, and preimplantation genetic testing for aneuploidy.

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.

Prediction of pregnancy-related complications in women undergoing assisted reproduction, using machine learning methods

OBJECTIVE

To use machine learning methods to develop prediction models of pregnancy complications in women who conceived with assisted reproductive techniques (ART).

DESIGN

A nation-wide register-based cohort study with prospectively collected data.

SETTING

Swedish national registers and nationwide quality IVF register.

PATIENT(S)

all nulliparous women who achieved birth within the first 3 ART treatment cycles between 2008 and 2016 in Sweden.

INTERVENTION(S)

Characteristics before the use of ART, such as demographics and medical history, were considered potential predictors in the development of before treatment prediction models. ART treatment details were further included in after treatment prediction models.

MAIN OUTCOME MEASURE(S)

Potential diagnoses of preeclampsia, placental complications (previa, accreta, and abruption), and postpartum hemorrhage were identified using the International Classification of Diseases recorded in the Swedish Medical Birth and Patient registers, respectively. Multiple prediction model algorithms were performed and compared for each outcome and treatment cycle, including logistic regression, decision tree model, naïve Bayes classification, support vector machine, random forest, and gradient boosting. The performance of each model was assessed with C statistic, and nested cross-validation was used to aid model selection and hyperparameter tuning.

RESULT(S)

A total of 14,732 women gave birth after the first (N = 7,302), second (N = 4,688), or third (N = 2,742) ART cycle, representing birth rates of 24.1%, 23.8%, and 22.0%. Overall prediction performance did not vary much across the different methods used. In the first cycle, the before treatment prediction performance was at best 66%, 66%, and 60% for preeclampsia, placental complications, and postpartum hemorrhage, respectively. Inclusion of after treatment characteristics conferred slight improvement (approximately 1%-5%), as did prediction in later cycles (approximately 1%-5%). The top influential and consistent predictors included age, region of residence, infertility diagnosis, and type of embryo transfer (fresh or frozen) in the later (2nd and 3rd) cycles. Body mass index was a top predictor of preeclampsia and was also influential for placental complications but not for postpartum hemorrhage.

CONCLUSION(S)

The combined use of demographics, medical history, and ART treatment information was not enough to confidently predict serious pregnancy complications in women who conceived with ART. Future studies are needed to assess if additional longitudinal follow-up during pregnancy can improve the prediction to allow clinical protocol development.

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.

Interpretable artificial intelligence-assisted embryo selection improved single-blastocyst transfer outcomes: a prospective cohort study

RESEARCH QUESTION

What is the pregnancy and neonatal outcomes of an interpretable artificial intelligence (AI) model for embryo selection in a prospective clinical trial?

DESIGN

This single-centre prospective cohort study was carried out from October 2021 to March 2022. A total of 330 eligible patients were assigned to their preferred groups, with 250 patients undergoing a fresh single-blastocyst transfer cycle after the exclusion criteria had been applied. For the AI-assisted group (AAG), embryologists selected the embryos for transfer based on the ranking recommendations provided by an interpretable AI system, while with the manual group, embryologists used the Gardner grading system to make their decisions.

RESULTS

The implantation rate was significantly higher in the AAG than the manual group (80.87% versus 68.15%, P = 0.022). No significant difference was found in terms of monozygotic twin rate, miscarriage rate, live birth rate and ectopic pregnancy rate between the groups. Furthermore, there was no significant difference in terms of neonatal outcomes, including gestational weeks, premature birth rate, birth height, birthweight, sex ratio at birth and newborn malformation rate. The consensus rate between the AI and retrospective analysis by the embryologists was significantly higher for good-quality embryos (i.e. grade 4BB or higher) versus poor-quality embryos (i.e. less than 4BB) (84.71% versus 25%, P < 0.001).

CONCLUSIONS

These prospective trial results suggest that the proposed AI system could effectively help embryologists to improve the implantation rate with single-blastocyst transfer compared with traditional manual evaluation methods.

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.

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

STUDY QUESTION

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

SUMMARY ANSWER

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

WHAT IS KNOWN ALREADY

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

STUDY DESIGN, SIZE, DURATION

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

PARTICIPANTS/MATERIALS, SETTING, METHODS

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

MAIN RESULTS AND THE ROLE OF CHANCE

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

LIMITATIONS, REASONS FOR CAUTION

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

WIDER IMPLICATIONS OF THE FINDINGS

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

STUDY FUNDING/COMPETING INTEREST(S)

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

TRIAL REGISTRATION NUMBER

N/A.

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.

Comparative effectiveness of gonadotropins used for ovarian stimulation during assisted reproductive technologies (ART) in France: A real-world observational study from the French nationwide claims database (SNDS)

This comparative non-interventional study using data from the French National Health Database (Système National des Données de Santé) investigated real-world (cumulative) live birth outcomes following ovarian stimulation, leading to oocyte pickup with either originator recombinant human follicle-stimulating hormone (r-hFSH) products (alfa or beta), r-hFSH alfa biosimilars, or urinaries including mainly HP-hMG (menotropins), and marginally u-hFSH-HP (urofollitropin). Using data from 245,534 stimulations (153,600 women), biosimilars resulted in a 19% lower live birth (adjusted odds ratio (OR) 0.81, 95% confidence interval (CI) 0.76-0.86) and a 14% lower cumulative live birth (adjusted hazard ratio (HR) 0.86, 95% CI 0.82-0.89); and urinaries resulted in a 7% lower live birth (adjusted OR 0.93, 95% CI 0.90-0.96) and an 11% lower cumulative live birth (adjusted HR 0.89, 95% CI 0.87-0.91) versus originator r-hFSH alfa. Results were consistent across strata (age and ART strategy), sensitivity analysis using propensity score matching, and with r-hFSH alfa and beta as the reference group.

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

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

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

BACKGROUND

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

OBJECTIVE

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

STUDY DESIGN

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

RESULTS

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

CONCLUSION

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

New frontiers in embryo selection

Human infertility is a major global public health issue estimated to affect one out of six couples, while the number of assisted reproduction cycles grows impressively year over year. Efforts to alleviate infertility using advanced technology are gaining traction rapidly as infertility has an enormous impact on couples and the potential to destabilize entire societies if replacement birthrates are not achieved. Artificial intelligence (AI) technologies, leveraged by the highly advanced assisted reproductive technology (ART) industry, are a promising addition to the armamentarium of tools available to combat global infertility. This review provides a background for current methodologies in embryo selection, which is a manual, time-consuming, and poorly reproducible task. AI has the potential to improve this process (among many others) in both the clinician's office and the IVF laboratory. Embryo selection is evolving through digital methodologies into an automated procedure, with superior reliability and reproducibility, that is likely to result in higher pregnancy rates for patients. There is an emerging body of data demonstrating the utility of AI applications in multiple areas in the IVF laboratory. AI platforms have been developed to evaluate individual embryologist performance; to provide quality assurance for culture systems; to correlate embryologist's assessments and AI systems; to predict embryo ploidy, implantation, fetal heartbeat, and live birth outcome; and to replace the current "analogue" system of embryo selection with a digital paradigm. AI capability will distinguish high performing, high profit margin, low-cost, and highly successful IVF clinic business models. We think it will become the standard, "new normal" in IVF labs, as rapidly and thoroughly as vitrification, blastocyst culture, and intracytoplasmic sperm injection replaced their predecessor technologies. At the time of this review, the AI technology to automate embryo evaluation and selection has robustly matured, and therefore, it is the main focus of this review.

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

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

A Bayesian network model for prediction of low or failed fertilization in assisted reproductive technology based on a large clinical real-world data

STUDY QUESTION

To construct prediction models based on the Bayesian network (BN) learning method for the probability of fertilization failure (including low fertilization rate [LRF] and total fertilization failure [TFF]) in assisted reproductive technology (ART) treatment. A BN model was developed to predict TFF/LFR. The model showed relatively high calibration in external validation, which could facilitate the identification of risk factors for fertilization disorders and improve the efficiency of in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) treatment.

WHAT IS KNOWN ALREADY

The prediction of TFF/LFR is very complex. Although some studies attempted to construct prediction models for TFF/LRF, most of the reported models were based on limited variables and traditional regression-based models, which are unsuitable for analyzing real-world clinical data. Therefore, none of the reported models have been widely used in routine clinical practice. To date, BN modeling analysis is a prominent and increasingly popular machine learning method that is powerful in dealing with dynamic and complex real-world data.

STUDY DESIGN, SIZE, DURATION

A retrospective study was performed with 106,640 fresh embryo IVF/ICSI cycles from 2009 to 2019 in one of China's largest reproductive health centers.

PARTICIPANTS/MATERIALS, SETTING, METHODS

A total of 106, 640 cycles were included in this study, including 97,102 controls, 4,339 LFR cases, and 5,199 TFF cases. Twenty-four predictors were initially included, including 13 female-related variables, five male-related variables, and six variables related to IVF/ICSI treatment. BN modeling analysis with tenfold cross-validation was performed to construct the predictive model for TFF/LFR. The receiver operating characteristic (ROC) curves and the corresponding area under the curves (AUCs) were used to evaluate the performance of the BN model.

MAIN RESULTS AND THE ROLE OF CHANCE

All twenty-four predictors were first organized into seven hierarchical layers in a theoretical BN model, according to prior knowledge from previous literature and clinical practice. A machine-learning BN model was generated based on real-world clinical data, containing a total of eighteen predictors, of which the infertility type, ART method, and number of retrieved oocytes directly influence the probabilities of LFR/TFF. The prediction accuracy of the BN model was 91.7%. The AUC of the TFF versus control groups was 0.779 (95% CI: 0.766-0.791), with a sensitivity of 71.2% and specificity of 70.1%; the AUC of of TFF versus LFR groups was 0.807 (95% CI: 0.790-0.824), with a sensitivity of 49.0% and specificity of 99.0%.

LIMITATIONS, REASON FOR CAUTION

First, our study was based on clinical data from a single center, and the results of this study should be further verified by external data. In addition, some critical data (e.g., the detailed IVF laboratory parameters of the sperm and oocytes used for insemination) were not available in this study, which should be given full consideration when further improving the performance of the BN model.

WIDER IMPLICATIONS OF THE FINDINGS

Based on extensive clinical real-world data, we developed a BN model to predict the probabilities of fertilization failures in ART, which provides new clues for clinical decision-making support for clinicians in formulating personalized treatment plans and further improving ART treatment outcomes.

STUDY FUNDING/COMPETING INTEREST(S)

Dr. Y. Wang was supported by grants from the Beijing Municipal Science & Technology Commission (Z191100006619086). We declare that there are no conflicts of interest.

TRIAL REGISTRATION NUMBER

N/A.

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

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

Opportunities and Limits of Conventional IVF versus ICSI: It Is Time to Come off the Fence

Conventional IVF (c-IVF) is one of the most practiced assisted reproductive technology (ART) approaches used worldwide. However, in the last years, the number of c-IVF procedures has dropped dramatically in favor of intracytoplasmic sperm injection (ICSI) in cases of non-male-related infertility. In this review, we have outlined advantages and disadvantages associated with c-IVF, highlighting the essential steps governing its success, its limitations, the methodology differences among laboratories and the technical progress. In addition, we have debated recent insights into fundamental questions, including indications regarding maternal age, decreased ovarian reserve, endometriosis, autoimmunity, single oocyte retrieval-cases as well as preimplantation genetic testing cycles. The “overuse” of ICSI procedures in several clinical situations of ART has been critically discussed. These insights will provide a framework for a better understanding of opportunities associated with human c-IVF and for best practice guidelines applicability in the reproductive medicine field.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

STUDY QUESTION

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

SUMMARY ANSWER

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

WHAT IS KNOWN ALREADY

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

STUDY DESIGN, SIZE, DURATION

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

PARTICIPANTS/MATERIALS, SETTING, METHODS

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

MAIN RESULTS AND THE ROLE OF CHANCE

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

LIMITATIONS, REASONS FOR CAUTION

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

WIDER IMPLICATIONS OF THE FINDINGS

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

STUDY FUNDING/COMPETING INTEREST(S)

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

TRIAL REGISTRATION NUMBER

N/A.

A descriptive appraisal of quality of reporting in a cohort of machine learning studies in anesthesiology

BACKGROUND

The field of machine learning is being employed more and more in medicine. However, studies have shown that the quality of published studies frequently lacks completeness and adherence to published reporting guidelines. This assessment has not been done in the subspecialty of anesthesiology.

METHODS

We appraised the quality of reporting of a convenience sample of 67 peer-reviewed publications sourced from the scoping review by Hashimoto et al. Each publication was appraised on the presence of reporting elements (reporting compliance) selected from 4 peer-reviewed guidelines for reporting on machine learning studies. Results are described in several cross sections, including by section of manuscript (e.g. abstract, introduction, etc.), year of publication, impact factor of journal, and impact of publication.

RESULTS

On average, reporting compliance was 64% ± 13%. There was marked heterogeneity of reporting based on section of manuscript. There was a mild trend towards increased quality of reporting with increasing impact factor of journal of publication and increasing average number of citations per year since publication, and no trend regarding recency of publication.

CONCLUSION

The quality of reporting of machine learning studies in anesthesiology is on par with other fields, but can benefit from improvement, especially in presenting methodology, results, and discussion points, including interpretation of models and pitfalls therein. Clinicians in today's learning health systems will benefit from skills in appraisal of evidence. Several reporting guidelines have been released, and updates to mainstream guidelines are under development, which we hope will usher in improvement in reporting quality.

Machine Learning Approach to Predict Clinical Pregnancy Potential in Women Undergoing IVF Program

Objective: Hidden knowledge could be discovered within a large practical data of in vitro fertilization (IVF) practice. In this study, Machine learning-based data mining techniques were utilized to construct a reliable prediction model for clinical pregnancy in IVF.

Study Design: A retrospective cohort multicenter study involving 4.570 IVF cycles. All patients underwent fresh embryo transfer at either the cleavage or blastocyst stage between January 2015 and December 2019. The experiment focused on utilizing tree-based classifiers to generate and compare the most effective prediction model that could predict a clinical pregnancy through clinical data. Additionally, each classifier is optimized via a genetic algorithm technique, along with the selection of variables.

Results: Both the decision tree and random forest showed similar performance that was much better than the gradient boost. The two superior classifiers achieved a balanced accuracy of roughly 0.62. Additionally, each prediction model was shown to work optimally with different combinations of variables, with some variables being consistently included, such as female age, and some consistently excluded, which provides an insight into the relationship between the variables and each prediction model.

Conclusion: Machine learning algorithm remains effective for the purpose of data mining and knowledge extraction in IVF clinical datasets through which a relatively reliable prediction system for clinical pregnancy could be constructed, provided the available data is sufficient.

Are we leaving money on the table in infertility RCTs? Trialists should statistically adjust for prespecified, prognostic covariates to increase power

Infertility randomized controlled trials (RCTs) are often too small to detect realistic treatment effects. Large observational studies have been proposed as a solution. However, this strategy threatens to weaken the evidence base further, because non-random assignment to treatments makes it impossible to distinguish effects of treatment from confounding factors. Alternative solutions are required. Power in an RCT can be increased by adjusting for prespecified, prognostic covariates when performing statistical analysis, and if stratified randomization or minimization has been used, it is essential to adjust in order to get the correct answer. We present data showing that this simple, free and frequently necessary strategy for increasing power is seldom employed, even in trials appearing in leading journals. We use this article to motivate a pedagogical discussion and provide a worked example. While covariate adjustment cannot solve the problem of underpowered trials outright, there is an imperative to use sound methodology to maximize the information each trial yields.

Prediction of Fertilization Disorders in the In Vitro Fertilization/Intracytoplasmic Sperm Injection: A Retrospective Study of 106,728 Treatment Cycles

Purpose

This study aimed to develop a risk prediction of fertilization disorders during the in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI).

Methods

A retrospective study was performed with 106,728 fresh embryo IVF/ICSI cycles from 2009 to 2019. Basic characteristics of patients, clinical treatment data, and laboratory parameters were involved. The associations between the selected variables and risks for low fertilization rate (LFR) and total fertilization failure (TFF) were investigated. Ordinal logistic regression and the receiver operating characteristic curves (ROCs) were used to construct and evaluate the prediction models.

Results

A total of 97,181 controls, 4,343 LFR and 5,204 TFF cases were involved in this study. The model based on clinical characteristics (the ages of the couples, women's BMI, types of infertility, ART failure history, the diminished ovarian reserve, sperm quality, insemination method, and the number of oocytes retrieved) had an AUC of 0.743 for TFF. The laboratory model showed that primary infertility, ART failure history, minimal-stimulation cycle/natural cycle, numbers of oocyte retrieved < 5, IVF, and Anti-Mullerian hormone (AMH) level < 1.1ng/ml are predictors of TFF, with an AUC of 0.742.

Conclusion

We established a clinical and a laboratory prediction model for LFR/TFF. Both of the models showed relatively high AUCs.

Noninvasive embryo evaluation and selection by time-lapse monitoring vs. conventional morphologic assessment in women undergoing in vitro fertilization/intracytoplasmic sperm injection: a single-center randomized controlled study

OBJECTIVE

To determine whether time-lapse monitoring (TLM) for cleavage-stage embryo selection improves reproductive outcomes in comparison with conventional morphological assessment (CMA) selection.

DESIGN

Prospective randomized controlled trial.

SETTING

Single academic center.

PATIENTS

We randomly assigned 139 women who were undergoing their first in vitro fertilization or intracytoplasmic sperm injection cycle to undergo either fresh embryo transfer or first frozen embryo transfer (FET). Only 1 cleavage-stage embryo was transferred to each participant.

INTERVENTIONS

The patients were randomly assigned to either the CMA or the TLM group. In the CMA group, day 2 and day 3 embryos were observed. A good-quality cleavage-stage embryo was selected for transfer or freezing in both groups.

MAIN OUTCOME MEASURES

The primary and secondary outcomes were the clinical pregnancy rate (CPR) and the live birth rate (LBR), respectively, after the first embryo transfer (fresh embryo transfer or FET).

RESULTS

The CPR and LBR were significantly lower in the TLM group than in the CMA group (CPR: 49.18% vs. 70.42%; relative risk, 0.70; 95% confidence interval [CI], 0.52-0.94; LBR: 45.90% vs. 64.79%; relative risk, 0.71; 95% CI, 0.51-0.98). The CPR with fresh embryo transfer or FET did not significantly differ between the TLM and the CMA groups (fresh embryo transfer: 44.44% vs. 70.0%, relative risk, 0.63, 95% CI, 0.39-1.03; FET: 52.94% vs. 70.73%, relative risk, 0.75, 95% CI, 0.52-1.09). There was a significant difference in the LBR with fresh embryo transfer between the TLM and the CMA groups (40.74% vs. 66.67%; relative risk, 0.61; 95% CI, 0.36-1.03). The LBRs with FET were similar in the TLM and the CMA groups (50.0% vs. 63.41%; relative risk, 0.79; 95% CI, 0.52-1.19). The rates of early spontaneous abortion and ectopic pregnancy did not differ between the TLM and the CMA groups.

CONCLUSIONS

Elective single cleavage-stage embryo transfer with TLM-based selection did not have any advantages over CMA when day 2 and day 3 embryo morphology was combined in young women with a good ovarian reserve. Because of these results, we conclude that TLM remains an investigational procedure for in vitro fertilization practice.

CLINICAL TRIAL REGISTRATION NUMBER

ChiCTR1900021981.

Endorsement of the TRIPOD statement and the reporting of studies developing contrast-induced nephropathy prediction models for the coronary angiography/percutaneous coronary intervention population: a cross-sectional study

OBJECTIVE

Clear and specific reporting of a research paper is essential for its validity and applicability. Some studies have revealed that the reporting of studies based on the clinical prediction models was generally insufficient based on the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist. However, the reporting of studies on contrast-induced nephropathy (CIN) prediction models in the coronary angiography (CAG)/percutaneous coronary intervention (PCI) population has not been thoroughly assessed. Thus, the aim is to evaluate the reporting of the studies on CIN prediction models for the CAG/PCI population using the TRIPOD checklist.

DESIGN

A cross-sectional study.

METHODS

PubMed and Embase were systematically searched from inception to 30 September 2021. Only the studies on the development of CIN prediction models for the CAG/PCI population were included. The data were extracted into a standardised spreadsheet designed in accordance with the 'TRIPOD Adherence Assessment Form'. The overall completeness of reporting of each model and each TRIPOD item were evaluated, and the reporting before and after the publication of the TRIPOD statement was compared. The linear relationship between model performance and TRIPOD adherence was also assessed.

RESULTS

We identified 36 studies that developed CIN prediction models for the CAG/PCI population. Median TRIPOD checklist adherence was 60% (34%-77%), and no significant improvement was found since the publication of the TRIPOD checklist (p=0.770). There was a significant difference in adherence to individual TRIPOD items, ranging from 0% to 100%. Moreover, most studies did not specify critical information within the Methods section. Only 5 studies (14%) explained how they arrived at the study size, and only 13 studies (36%) described how to handle missing data. In the Statistical analysis section, how the continuous predictors were modelled, the cut-points of categorical or categorised predictors, and the methods to choose the cut-points were only reported in 7 (19%), 6 (17%) and 1 (3%) of the studies, respectively. Nevertheless, no relationship was found between model performance and TRIPOD adherence in both the development and validation datasets (r=-0.260 and r=-0.069, respectively).

CONCLUSIONS

The reporting of CIN prediction models for the CAG/PCI population still needs to be improved based on the TRIPOD checklist. In order to promote further external validation and clinical application of the prediction models, more information should be provided in future studies.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Risk assessment and prediction model of renal damage in childhood immunoglobulin A vasculitis

OBJECTIVES

To explore the risk factors for renal damage in childhood immunoglobulin A vasculitis (IgAV) within 6 months and construct a clinical model for individual risk prediction.

METHODS

We retrospectively analyzed the clinical data of 1,007 children in our hospital and 287 children in other hospitals who were diagnosed with IgAV. Approximately 70% of the cases in our hospital were randomly selected using statistical product service soltions (SPSS) software for modeling. The remaining 30% of the cases were selected for internal verification, and the other hospital's cases were reviewed for external verification. A clinical prediction model for renal damage in children with IgAV was constructed by analyzing the modeling data through single-factor and multiple-factor logistic regression analyses. Then, we assessed and verified the degree of discrimination, calibration and clinical usefulness of the model. Finally, the prediction model was rendered in the form of a nomogram.

RESULTS

Age, persistent cutaneous purpura, erythrocyte distribution width, complement C₃, immunoglobulin G and triglycerides were independent influencing factors of renal damage in IgAV. Based on these factors, the area under the curve (AUC) for the prediction model was 0.772; the calibration curve did not significantly deviate from the ideal curve; and the clinical decision curve was higher than two extreme lines when the prediction probability was ~15-82%. When the internal and external verification datasets were applied to the prediction model, the AUC was 0.729 and 0.750, respectively, and the Z test was compared with the modeling AUC, P > 0.05. The calibration curves fluctuated around the ideal curve, and the clinical decision curve was higher than two extreme lines when the prediction probability was 25~84% and 14~73%, respectively.

CONCLUSION

The prediction model has a good degree of discrimination, calibration and clinical usefulness. Either the internal or external verification has better clinical efficacy, indicating that the model has repeatability and portability.

CLINICAL TRIAL REGISTRATION

www.chictr.org.cn, identifier ChiCTR2000033435.

OUP accepted manuscript

BACKGROUND

Oocyte activation deficiency (OAD) is attributed to the majority of cases underlying failure of ICSI cycles, the standard treatment for male factor infertility. Oocyte activation encompasses a series of concerted events, triggered by sperm-specific phospholipase C zeta (PLCζ), which elicits increases in free cytoplasmic calcium (Ca²⁺) in spatially and temporally specific oscillations. Defects in this specific pattern of Ca²⁺ release are directly attributable to most cases of OAD. Ca²⁺ release can be clinically mediated via assisted oocyte activation (AOA), a combination of mechanical, electrical and/or chemical stimuli which artificially promote an increase in the levels of intra-cytoplasmic Ca²⁺. However, concerns regarding safety and efficacy underlie potential risks that must be addressed before such methods can be safely widely used.

OBJECTIVE AND RATIONALE

Recent advances in current AOA techniques warrant a review of the safety and efficacy of these practices, to determine the extent to which AOA may be implemented in the clinic. Importantly, the primary challenges to obtaining data on the safety and efficacy of AOA must be determined. Such questions require urgent attention before widespread clinical utilization of such protocols can be advocated.

SEARCH METHODS

A literature review was performed using databases including PubMed, Web of Science, Medline, etc. using AOA, OAD, calcium ionophores, ICSI, PLCζ, oocyte activation, failed fertilization and fertilization failure as keywords. Relevant articles published until June 2019 were analysed and included in the review, with an emphasis on studies assessing large-scale efficacy and safety.

OUTCOMES

Contradictory studies on the safety and efficacy of AOA do not yet allow for the establishment of AOA as standard practice in the clinic. Heterogeneity in study methodology, inconsistent sample inclusion criteria, non-standardized outcome assessments, restricted sample size and animal model limitations render AOA strictly experimental. The main scientific concern impeding AOA utilization in the clinic is the non-physiological method of Ca²⁺ release mediated by most AOA agents, coupled with a lack of holistic understanding regarding the physiological mechanism(s) underlying Ca²⁺ release at oocyte activation.

LIMITATIONS, REASONS FOR CAUTION

The number of studies with clinical relevance using AOA remains significantly low. A much wider range of studies examining outcomes using multiple AOA agents are required.

WIDER IMPLICATIONS

In addition to addressing the five main challenges of studies assessing AOA safety and efficacy, more standardized, large-scale, multi-centre studies of AOA, as well as long-term follow-up studies of children born from AOA, would provide evidence for establishing AOA as a treatment for infertility. The delivery of an activating agent that can more accurately recapitulate physiological fertilization, such as recombinant PLCζ, is a promising prospect for the future of AOA. Further to PLCζ, many other avenues of physiological oocyte activation also require urgent investigation to assess other potential physiological avenues of AOA.

STUDY FUNDING/COMPETING INTERESTS

D.G. was supported by Stanford University’s Bing Overseas Study Program. J.K. was supported by a Healthcare Research Fellowship Award (HF-14-16) made by Health and Care Research Wales (HCRW), alongside a National Science, Technology, and Innovation plan (NSTIP) project grant (15-MED4186-20) awarded by the King Abdulaziz City for Science and Technology (KACST). The authors have no competing interests to declare.

Development of a Model Predicting the Outcome of In Vitro Fertilization Cycles by a Robust Decision Tree Method

INTRODUCTION

Infertility is a worldwide problem. To evaluate the outcome of in vitro fertilization (IVF) treatment for infertility, many indicators need to be considered and the relation among indicators need to be studied.

OBJECTIVES

To construct an IVF predicting model by a robust decision tree method and find important factors and their interrelation.

METHODS

IVF and intracytoplasmic sperm injection (ICSI) cycles between January 2010 and December 2020 in a women's hospital were collected. Comprehensive evaluation and examination of patients, specific therapy strategy and the outcome of treatment were recorded. Variables were selected through the significance of 1-way analysis between the clinical pregnant group and the nonpregnant group and then were discretized. Then, gradient boosting decision tree (GBDT) was used to construct the model to compute the score for predicting the rate of clinical pregnancy.

RESULT

Thirty-eight variables with significant difference were selected for binning and thirty of them in which the pregnancy rate varied in different categories were chosen to construct the model. The final score computed by model predicted the clinical pregnancy rate well with the Area Under Curve (AUC) value achieving 0.704 and the consistency reaching 98.1%. Number of two-pronuclear embryo (2PN), age of women, AMH level, number of oocytes retrieved and endometrial thickness were important factors related to IVF outcome. Moreover, some interrelations among factors were found from model, which may assist clinicians in making decisions.

CONCLUSION

This study constructed a model predicting the outcome of IVF cycles through a robust decision tree method and achieved satisfactory prediction performance. Important factors related to IVF outcome and some interrelations among factors were found.

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.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

A large observational data study supporting the PROsPeR score classification in poor ovarian responders according to live birth outcome

STUDY QUESTION

Can the Poor Responder Outcome Prediction (PROsPeR) score identify live birth outcomes in subpopulations of patients with poor ovarian response (POR) defined according to the ESHRE Bologna criteria (female age, anti-Müllerian hormone (AMH), number of oocytes retrieved during the previous cycle (PNO) after treatment with originator recombinant human follitropin alfa?

SUMMARY ANSWER

The PROsPeR score discriminated the probability of live birth in patients with POR using observational data with fair discrimination (AUC ≅ 70%) and calibration, and the AUC losing less than 5% precision compared with a model developed using the observational data.

WHAT IS KNOWN ALREADY

Although scoring systems for the likelihood of live birth after ART have been developed, their accuracy may be insufficient, as they have generally been developed in the general population with infertility and were not validated for patients with POR. The PROsPeR score was developed using data from the follitropin alfa (GONAL-f; Merck KGaA, Darmstadt, Germany) arm of the Efficacy and Safety of Pergoveris in Assisted Reproductive Technology (ESPART) randomized controlled trial (RCT) and classifies women with POR as mild, moderate or severe, based upon three variables: female age, serum AMH level and number of oocytes retrieved during the previous cycle (PNO).

STUDY DESIGN, SIZE, DURATION

The external validation of the PROsPeR score was completed using data derived from eight different centres in France. In addition, the follitropin alfa data from the ESPART RCT, originally used to develop the PROsPeR score, were used as reference cohort. The external validation of the PROsPeR score l was assessed using AUC. A predetermined non-inferiority limit of 0.10 compared with a reference sample and calibration (Hosmer-Lemeshow test) were the two conditions required for evaluation.

PARTICIPANTS/MATERIALS, SETTING, METHODS

The observational cohort included data from 8085 ART treatment cycles performed with follitropin alfa in patients with POR defined according to the ESHRE Bologna criteria (17.6% of the initial data set). The ESPART cohort included 477 ART treatment cycles with ovarian stimulation performed with follitropin alfa in patients with POR.

MAIN RESULTS AND THE ROLE OF CHANCE

The external validation of the PROsPeR score to identify subpopulations of women with POR with different live birth outcomes was shown in the observational cohort (AUC = 0.688; 95% CI: 0.662, 0.714) compared with the ESPART cohort (AUC = 0.695; 95% CI: 0.623, 0.767). The AUC difference was -0.0074 (95% CI: -0.083, 0.0689). This provided evidence, with 97.5% one-sided confidence, that there was a maximum estimated loss of 8.4% in discrimination between the observational cohort and the ESPART cohort, which was below the predetermined margin of 10%. The Hosmer-Lemeshow test did not reject the calibration when comparing observed and predicted data (Hosmer-Lemeshow test = 1.266688; P = 0.260).

LIMITATIONS, REASONS FOR CAUTION

The study was based on secondary use of data that had not been collected specifically for the analysis reported here and the number of characteristics used to classify women with POR was limited to the available data. The data were from a limited number of ART centres in a single country, which may present a bias risk; however, baseline patient data were similar to other POR studies.

WIDER IMPLICATIONS OF THE FINDINGS

This evaluation of the PROsPeR score using observational data supports the notion that the likelihood of live birth may be calculated with reasonable precision using three readily available pieces of data (female age, serum AMH and PNO). The PROsPeR score has potential to be used to discriminate expected probability of live birth according to the degree of POR (mild, moderate, severe) after treatment with follitropin alfa, enabling comparison of performance at one centre over time and the comparison between centres.

STUDY FUNDING/COMPETING INTEREST(S)

This analysis was funded by Merck KGaA, Darmstadt, Germany. P.L. received grants from Merck KGaA, outside of the submitted work. N.M. reports grants, personal fees and non-financial support from Merck KGaA outside the submitted work. T.D.H. is Vice President and Head of Global Medical Affairs Fertility, Research and Development at Merck KGaA, Darmstadt, Germany. P.A. has received personal fees from Merck KGaA, Darmstadt, Germany, outside the submitted work. C.R. has received grants and personal fees from Gedeon Richter and Merck Serono S.A.S., France, an affiliate of Merck KGaA, Darmstadt, Germany, outside the submitted work. P.S. reports congress support from Merck Serono S.A.S., France (an affiliate of Merck KGaA, Darmstadt, Germany), Gedeon Richter, TEVA and MDS outside the submitted work. C.A., J.P., G.P. and R.W. declare no conflict of interest.

TRIAL REGISTRATION NUMBER

N/A.

Prioritizing IVF treatment in the post-COVID 19 era: a predictive modelling study based on UK national data

STUDY QUESTION

Can we use prediction modelling to estimate the impact of coronavirus disease 2019 (COVID 19) related delay in starting IVF or ICSI in different groups of women?

SUMMARY ANSWER

Yes, using a combination of three different models we can predict the impact of delaying access to treatment by 6 and 12 months on the probability of conception leading to live birth in women of different age groups with different categories of infertility.

WHAT IS KNOWN ALREADY

Increased age and duration of infertility can prejudice the chances of success following IVF, but couples with unexplained infertility have a chance of conceiving naturally without treatment whilst waiting for IVF. The worldwide suspension of IVF could lead to worse outcomes in couples awaiting treatment, but it is unclear to what extent this could affect individual couples based on age and cause of infertility.

STUDY DESIGN, SIZE, DURATION

A population based cohort study based on national data from all licensed clinics in the UK obtained from the Human Fertilisation and Embryology Authority Register. Linked data from 9589 women who underwent their first IVF or ICSI treatment in 2017 and consented to the use of their data for research were used to predict livebirth numbers.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Three prediction models were used to estimate the chances of livebirth associated with immediate treatment versus a delay of 6 and 12 months in couples about to embark on IVF or ICSI.

MAIN RESULTS AND THE ROLE OF CHANCE

We estimated that a 6-month delay would reduce livebirths by 0.4%, 2.4%, 5.7%, 9.5% and 11.8% in women aged <30, 30-35, 36-37, 38-39 and 40-42 years, respectively, while corresponding values associated with a delay of 12 months were 0.9%, 4.9%, 11.9%, 18.8% and 22.4%, respectively. In women with known causes of infertility, worst case (best case) predicted chances of livebirth after a delay of 6 months in women aged <30, 30-35, 36-37, 38-39 and 40-42 years varied between 31.6% (35.0%), 29.0% (31.6%), 23.1% (25.2%), 17.2% (19.4%) and 10.3% (12.3%) for tubal infertility and 34.3% (39.2%), 31.6% (35.3%) 25.2%(28.5%) 18.3% (21.3%), and 11.3% (14.1%) for male factor infertility. The corresponding values in those treated immediately were 31.7%, 29.8%, 24.5%, 19.0% and 11.7% for tubal factor and 34.4%, 32.4%, 26.7%, 20.2% and 12.8% in male factor infertility. In women with unexplained infertility the predicted chances of livebirth after a delay of 6 months followed by one complete IVF cycle were 41.0%, 36.6%, 29.4%, 22.4% and 15.1% in women aged <30, 30-35, 36-37, 38-39 and 40-42 years, respectively, compared to 34.9%, 32.5%, 26.9%, 20.7% and 13.2% in similar groups of women treated without any delay. The additional waiting period, which provided more time for spontaneous conception, was predicted to increase the relative number of babies born by 17.5%, 12.6%, 9.1%, 8.4% and 13.8%, in women aged <30, 30-35, 36-37, 38-39 and 40-42 years, respectively. A 12-month delay showed a similar pattern in all subgroups.

LIMITATIONS, REASONS FOR CAUTION

Major sources of uncertainty include the use of prediction models generated in different populations and the need for a number of assumptions. Although the models are validated and the bases for the assumptions are robust, it is impossible to eliminate the possibility of imprecision in our predictions. Therefore, our predicted live birth rates need to be validated in prospective studies to confirm their accuracy.

WIDER IMPLICATIONS OF THE FINDINGS

A delay in starting IVF reduces success rates in all couples. For the first time, we have shown that while this results in fewer babies in older women and those with a known cause of infertility, it has a less detrimental effect on couples with unexplained infertility, some of whom conceive naturally whilst waiting for treatment. Post COVID 19, clinics planning a phased return to normal clinical services should prioritise older women and those with a known cause of infertility.

STUDY FUNDING/COMPETING INTEREST(S)

No external funding was received for this study. B.W.M. is supported by an NHMRC Practitioner Fellowship (GNT1082548) and reports consultancy work for ObsEva, Merck, Merck KGaA, Guerbet and iGenomics. SB is Editor-in-Chief of Human Reproduction Open. None of the other authors declare any conflicts of interest.

Multivariate prediction of mixed, multilevel, sequential outcomes arising from in vitro fertilisation

In vitro fertilisation (IVF) comprises a sequence of interventions concerned with the creation and culture of embryos which are then transferred to the patient's uterus. While the clinically important endpoint is birth, the responses to each stage of treatment contain additional information about the reasons for success or failure. As such, the ability to predict not only the overall outcome of the cycle, but also the stage-specific responses, can be useful. This could be done by developing separate models for each response variable, but recent work has suggested that it may be advantageous to use a multivariate approach to model all outcomes simultaneously. Here, joint analysis of the sequential responses is complicated by mixed outcome types defined at two levels (patient and embryo). A further consideration is whether and how to incorporate information about the response at each stage in models for subsequent stages. We develop a case study using routinely collected data from a large reproductive medicine unit in order to investigate the feasibility and potential utility of multivariate prediction in IVF. We consider two possible scenarios. In the first, stage-specific responses are to be predicted prior to treatment commencement. In the second, responses are predicted dynamically, using the outcomes of previous stages as predictors. In both scenarios, we fail to observe benefits of joint modelling approaches compared to fitting separate regression models for each response variable.

Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis

Background

Predictions in pregnancy care are complex because of interactions among multiple factors. Hence, pregnancy outcomes are not easily predicted by a single predictor using only one algorithm or modeling method.

Objective

This study aims to review and compare the predictive performances between logistic regression (LR) and other machine learning algorithms for developing or validating a multivariable prognostic prediction model for pregnancy care to inform clinicians’ decision making.

Methods

Research articles from MEDLINE, Scopus, Web of Science, and Google Scholar were reviewed following several guidelines for a prognostic prediction study, including a risk of bias (ROB) assessment. We report the results based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies were primarily framed as PICOTS (population, index, comparator, outcomes, timing, and setting): Population: men or women in procreative management, pregnant women, and fetuses or newborns; Index: multivariable prognostic prediction models using non-LR algorithms for risk classification to inform clinicians’ decision making; Comparator: the models applying an LR; Outcomes: pregnancy-related outcomes of procreation or pregnancy outcomes for pregnant women and fetuses or newborns; Timing: pre-, inter-, and peripregnancy periods (predictors), at the pregnancy, delivery, and either puerperal or neonatal period (outcome), and either short- or long-term prognoses (time interval); and Setting: primary care or hospital. The results were synthesized by reporting study characteristics and ROBs and by random effects modeling of the difference of the logit area under the receiver operating characteristic curve of each non-LR model compared with the LR model for the same pregnancy outcomes. We also reported between-study heterogeneity by using τ² and I².

Results

Of the 2093 records, we included 142 studies for the systematic review and 62 studies for a meta-analysis. Most prediction models used LR (92/142, 64.8%) and artificial neural networks (20/142, 14.1%) among non-LR algorithms. Only 16.9% (24/142) of studies had a low ROB. A total of 2 non-LR algorithms from low ROB studies significantly outperformed LR. The first algorithm was a random forest for preterm delivery (logit AUROC 2.51, 95% CI 1.49-3.53; I²=86%; τ²=0.77) and pre-eclampsia (logit AUROC 1.2, 95% CI 0.72-1.67; I²=75%; τ²=0.09). The second algorithm was gradient boosting for cesarean section (logit AUROC 2.26, 95% CI 1.39-3.13; I²=75%; τ²=0.43) and gestational diabetes (logit AUROC 1.03, 95% CI 0.69-1.37; I²=83%; τ²=0.07).

Conclusions

Prediction models with the best performances across studies were not necessarily those that used LR but also used random forest and gradient boosting that also performed well. We recommend a reanalysis of existing LR models for several pregnancy outcomes by comparing them with those algorithms that apply standard guidelines.

Trial Registration

PROSPERO (International Prospective Register of Systematic Reviews) CRD42019136106; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=136106

Predicting the chance on live birth per cycle at each step of the IVF journey: external validation and update of the van Loendersloot multivariable prognostic model

OBJECTIVE

To study the performance of the 'van Loendersloot' prognostic model for our clinic's in vitro fertilisation (IVF) in its original version, the refitted version and in an adapted version replacing previous by current cycle IVF laboratory variables.

METHODS

This retrospective cohort study in our academic tertiary fertility clinic analysed 1281 IVF cycles of 591 couples, who completed at least one 2nd-6th IVF cycle with own fresh gametes after a previous IVF cycle with the same partner in our clinic between 2010 and 2018. The outcome of interest was the chance on a live birth after one complete IVF cycle (including all fresh and frozen embryo transfers from the same episode of ovarian stimulation). Model performance was expressed in terms of discrimination (c-statistics) and calibration (calibration model, comparison of prognosis to observed ratios of five disjoint groups formed by the quintiles of the IVF prognoses and a calibration plot).

RESULTS

A total of 344 live births were obtained (26.9%). External validation of the original van Loendersloot model showed a poor c-statistic of 0.64 (95% CI: 0.61 to 0.68) and an underestimation of IVF success. The refitted and the adapted models showed c-statistics of respectively 0.68 (95% CI: 0.65 to 0.71) and 0.74 (95% CI: 0.70 to 0.77). Similar c-statistics were found with cross-validation. Both models showed a good calibration model; refitted model: intercept=0.00 (95% CI: -0.23 to 0.23) and slope=1.00 (95% CI: 0.79 to 1.21); adapted model: intercept=0.00 (95% CI: -0.18 to 0.18) and slope=1.00 (95% CI: 0.83 to 1.17). Prognoses and observed success rates of the disjoint groups matched well for the refitted model and even better for the adapted model.

CONCLUSION

External validation of the original van Loendersloot model indicated that model updating was recommended. The good performance of the refitted and adapted models allows informing couples about their IVF prognosis prior to an IVF cycle and at the time of embryo transfer. Whether this has an impact on couple's expected success rates, distress and IVF discontinuation can now be studied.

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.

Machine learning vs. classic statistics for the prediction of IVF outcomes

PURPOSE

To assess whether machine learning methods provide advantage over classic statistical modeling for the prediction of IVF outcomes.

METHODS

The study population consisted of 136 women undergoing a fresh IVF cycle from January 2014 to August 2016 at a tertiary, university-affiliated medical center. We tested the ability of two machine learning algorithms, support vector machine (SVM) and artificial neural network (NN), vs. classic statistics (logistic regression) to predict IVF outcomes (number of oocytes retrieved, mature oocytes, top-quality embryos, positive beta-hCG, clinical pregnancies, and live births) based on age and BMI, with or without clinical data.

RESULTS

Machine learning algorithms (SVM and NN) based on age, BMI, and clinical features yielded better performances in predicting number of oocytes retrieved, mature oocytes, fertilized oocytes, top-quality embryos, positive beta-hCG, clinical pregnancies, and live births, compared with logistic regression models. While accuracies were 0.69 to 0.9 and 0.45 to 0.77 for NN and SVM, respectively, they were 0.34 to 0.74 using logistic regression models.

CONCLUSIONS

Our findings suggest that machine learning algorithms based on age, BMI, and clinical data have an advantage over logistic regression for the prediction of IVF outcomes and therefore can assist fertility specialists' counselling and their patients in adjusting the appropriate treatment strategy.

Artificial intelligence in the IVF laboratory: overview through the application of different types of algorithms for the classification of reproductive data

Over the past years, the assisted reproductive technologies (ARTs) have been accompanied by constant innovations. For instance, intracytoplasmic sperm injection (ICSI), time-lapse monitoring of the embryonic morphokinetics, and PGS are innovative techniques that increased the success of the ART. In the same trend, the use of artificial intelligence (AI) techniques is being intensively researched whether in the embryo or spermatozoa selection. Despite several studies already published, the use of AI within assisted reproduction clinics is not yet a reality. This is largely due to the different AI techniques that are being proposed to be used in the daily routine of the clinics, which causes some uncertainty in their use. To shed light on this complex scenario, this review briefly describes some of the most frequently used AI algorithms, their functionalities, and their potential use. Several databases were analyzed in search of articles where applied artificial intelligence algorithms were used on reproductive data. Our focus was on the classification of embryonic cells and semen samples. Of a total of 124 articles analyzed, 32 were selected for this review. From the proposed algorithms, most have achieved a satisfactory precision, demonstrating the potential of a wide range of AI techniques. However, the evaluation of these studies suggests the need for more standardized research to validate the proposed models and their algorithms. Routine use of AI in assisted reproduction clinics is just a matter of time. However, the choice of AI technique to be used is supported by a better understanding of the principles subjacent to each technique, that is, its robustness, pros, and cons. We provide some current (although incipient) and potential uses of AI on the clinic routine, discussing how accurate and friendly it could be. Finally, we propose some standards for AI research on the selection of the embryo to be transferred and other future hints. For us, the imminence of its use is evident, providing a revolutionary milestone that will impact the ART.