Prediction models in reproductive medicine: a critical appraisal

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

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

Development of an IVF prediction model for donor oocytes: a retrospective analysis of 10 877 embryo transfers

STUDY QUESTION

Can we develop a prediction model for the chance of a live birth following the transfer of an embryo created using donated oocytes?

SUMMARY ANSWER

Three primary models that included patient, past treatment, and cycle characteristics were developed using Australian data to predict the chance of a live birth following the transfer of an embryo created using donated oocytes; these models were well-calibrated to the population studied, achieved reasonable predictive power and generalizability when tested on New Zealand data.

WHAT IS KNOWN ALREADY

Nearly 9% of ART embryo transfer cycles performed globally use embryos created using donated oocytes. This percentage rises to one-quarter and one-half in same-sex couples and women aged over 45 years, respectively.

STUDY DESIGN, SIZE, DURATION

This study uses population-based Australian clinical registry data comprising 9384 embryo transfer cycles that occurred between 2015 and 2021 for model development, with an external validation cohort of 1493 New Zealand embryo transfer cycles.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Three prediction models were compared that incorporated patient characteristics, but differed in whether they considered use of prior autologous treatment factors and current treatment parameters. We internally validated the models on Australian data using grouped cross-validation and reported several measures of model discrimination and calibration. Variable importance was measured through calculating the change in predictive performance that resulted from variable permutation. The best-performing model was externally validated on data from New Zealand.

MAIN RESULTS AND THE ROLE OF CHANCE

The best-performing model had an internal validation AUC-ROC of 0.60 and Brier score of 0.20, and external validation AUC-ROC of 0.61 and Brier score of 0.23. While these results indicate ∼15% less discriminatory ability compared to models assessed on an autologous cohort from the same population the performance of the models was clearly statistically significantly better than random, demonstrated generalizability, and was well-calibrated to the population studied. The most important variables for predicting the chance of a live birth were the oocyte donor age, the number of prior oocyte recipient embryo transfer cycles, whether the transferred embryo was cleavage or blastocyst stage and oocyte recipient age. Of lesser importance were the oocyte-recipient parity, whether donor or partner sperm was used, the number of prior autologous embryo transfer cycles and the number of embryos transferred.

LIMITATIONS, REASONS FOR CAUTION

The models had relatively weak discrimination suggesting further features need to be added to improve their predictive power. Variation in donor oocyte cohorts across countries due to differences such as whether anonymous and compensated donation are allowed may necessitate the models be recalibrated prior to application in non-Australian cohorts.

WIDER IMPLICATIONS OF THE FINDINGS

These results confirm the well-established importance of oocyte age and ART treatment history as the key prognostic factors in predicting treatment outcomes. One of the developed models has been incorporated into a consumer-facing website (YourIVFSuccess.com.au/Estimator) to allow patients to obtain personalized estimates of their chance of success using donor oocytes.

STUDY FUNDING/COMPETING INTEREST(S)

This research was funded by the Australian government as part of the Medical Research Future Fund (MRFF) Emerging Priorities and Consumer Driven Research initiative: EPCD000007. L.R. declares personal consulting fees from Abbott and Merck, lecture fees from Abbott, receipt of an educational grant from Merck, past presidency of the Fertility Society of Australia & New Zealand and World Endometriosis Society and being a minor shareholder in Monash IVF Group (ASX:MVF). G.M.C. declares receipt of Australian government grant funding for the research study and the development and maintenance of the YourIVFSuccess website. O.F., J.N., and A.P. report no conflicts of interest.

TRIAL REGISTRATION NUMBER

N/A.

Development of a machine learning-based prediction model for clinical pregnancy of intrauterine insemination in a large Chinese population

PURPOSE

This study aimed to evaluate the effectiveness of a random forest (RF) model in predicting clinical pregnancy outcomes from intrauterine insemination (IUI) and identifying significant factors affecting IUI pregnancy in a large Chinese population.

METHODS

RESULTS: A total of 11 variables, including eight from female (age, body mass index, duration of infertility, prior miscarriage, and spontaneous abortion), hormone levels (anti-Müllerian hormone, follicle-stimulating hormone, luteinizing hormone), and three from male (smoking, semen volume, and sperm concentration), were identified as the significant variables associated with IUI clinical pregnancy in our Chinese dataset. The RF-based prediction model presents an area under the receiver operating characteristic curve (AUC) of 0.716 (95% confidence interval, 0.6914-0.7406), an accuracy rate of 0.6081, a sensitivity rate of 0.7113, and a specificity rate of 0.505. Importance analysis indicated that semen volume was the most vital variable in predicting IUI clinical pregnancy.

CONCLUSIONS

The machine learning-based IUI clinical pregnancy prediction model showed a promising predictive efficacy that could provide a potent tool to guide selecting targeted infertile couples beneficial from IUI treatment, and also identify which parameters are most relevant in IUI clinical pregnancy.

Follicular Fluid Metabolomics: Tool for Predicting IVF Outcomes of Different Infertility Causes

Infertility affects approximately 15% of couples at child-bearing ages and assisted reproductive technologies (ART), especially in vitro fertilization and embryo transfer (IVF-ET), provided infertile patients with an effective solution. The current paradox is that multiple embryo transfer that may leads to severe obstetric and perinatal complications seems to be the most valid measure to secure high success rate in the majority of clinic centers. Therefore, to avoid multiple transfer of embryos, it is urgent to explore biomarkers for IVF prognosis to select high-quality oocytes and embryos. Follicular fluid (FF), a typical biofluid constituted of the plasma effusion and granulosa-cell secretion, provides essential intracellular substances for oocytes maturation and its variation in composition reflects oocyte developmental competence and embryo viability. With the advances in metabolomics methodology, metabolomics, as an accurate and sensitive analyzing method, has been utilized to explore predictors in FF for ART success. Although FF metabolomics has provided a great possibility for screening markers with diagnostic and predictive value, its effectiveness is still doubted by some researchers. This may be resulted from the ignorance of the impact of sterility causes on the FF metabolomic profiles and thus its predictive ability might not be rightly illustrated. Therefore, in this review, we categorically demonstrate the study of FF metabolomics according to specific infertility causes, expecting to reveal the predicting value of metabolomics for IVF outcomes.

Evaluating prognosis in unexplained infertility

IMPORTANCE

The diagnosis of unexplained infertility presents a dilemma as it signifies both uncertainty about the cause of infertility and the potential for natural conception. Immediate treatment of all would result in overtreatment. Prediction models estimating the likelihood of natural conception and subsequent live birth can guide treatment decisions.

OBJECTIVE

To evaluate if in couples with unexplained infertility, prediction models are effective in guiding treatment decisions.

EVIDENCE REVIEW

This review examines 25 studies that assess prediction models for natural conception in couples with unexplained infertility in terms of derivation, validation, and impact analysis.

FINDINGS

The largest prediction models have been integrated in the synthesis models of Hunault, which includes female age and infertility duration, having been pregnant before and motile sperm percentage. Despite its limited discriminative capacity, this model demonstrates excellent calibration. Importantly, the impact of the Hunault model has been evaluated in randomized clinical trials, and shows that in couples with unexplained infertility and 12-month natural conception chances exceeding 30%, immediate treatment with intrauterine insemination (IUI) and controlled ovarian hyperstimulation is not better than expectant management for 6 months. Below the threshold of 30%, treatment with IUI is superior over expectant management, but immediate in vitro fertilization was not better than IUI.

CONCLUSION

In couples with unexplained infertility and a good prognosis for natural conception, treatment can be delayed, whereas in couples with a poor prognosis, immediate treatment (with IUI-controlled ovarian hyperstimulation) is warranted.

RELEVANCE

These data indicate that in couples with unexplained infertility, integration of prediction models into clinical decision making can optimize treatment selection and maximize fertility outcomes while limiting unnecessary treatment.

Progress of the application clinical prediction model in polycystic ovary syndrome

Clinical prediction models play an important role in the field of medicine. These can help predict the probability of an individual suffering from disease, complications, and treatment outcomes by applying specific methodologies. Polycystic ovary syndrome (PCOS) is a common disease with a high incidence rate, huge heterogeneity, short- and long-term complications, and complex treatments. In this systematic review study, we reviewed the progress of clinical prediction models in PCOS patients, including diagnosis and prediction models for PCOS complications and treatment outcomes. We aimed to provide ideas for medical researchers and clues for the management of PCOS. In the future, models with poor accuracy can be greatly improved by adding well-known parameters and validations, which will further expand our understanding of PCOS in terms of precision medicine. By developing a series of predictive models, we can make the definition of PCOS more accurate, which can improve the diagnosis of PCOS and reduce the likelihood of false positives and false negatives. It will also help discover complications earlier and treatment outcomes being known earlier, which can result in better outcomes for women with PCOS.

Novel predictors for livebirth delivery rate in patients with idiopathic non-obstructive azoospermia based on the clinical prediction model

Objective

To build a prediction model for live birth delivery per intracytoplasmic sperm injection (ICSI) in iNOA patients by obtaining sperm by microdissection testicular sperm extraction (mTESE).

Methods

A retrospective cohort study of 377 couples with iNOA male partners treated with 519 mTESE-ICSI cycles was conducted from September 2013 to July 2021 at the Reproductive Medical Centre of Peking University Third Hospital. Following exclusions, 377 couples with iNOA male partners treated with 482 mTESE-ICSIs were included. A prediction model for live birth delivery per ICSI cycle was built by multivariable logistic regression and selected by 10-fold cross-validation. Discrimination was evaluated by c-statistics and calibration was evaluated by the calibration slope.

Results

The live birth delivery rate per mTESE-ICSI cycle was 39.21% (189/482) in these couples. The model identified that the presence of motile sperm during mTESE, bigger testes, higher endometrial thickness on the day of human chorionic gonadotrophin (hCG) administration (ET-hCG), and higher quality embryos are associated with higher live birth delivery success rates. The results of the model were exported based on 10-fold cross-validation. In addition, the area under the mean ROC curve was 0.71 ± 0.05 after 10-fold cross-validation, indicating that the prediction model had certain prediction precision. A calibration plot with an estimated intercept of -1.653 (95% CI: -13.403 to 10.096) and a slope of 1.043 (95% CI: 0.777 to 1.308) indicated that the model was well-calibrated.

Conclusion

Our prediction model will provide valuable information about the chances of live birth delivery in couples with iNOA male partners who have a plan for mTESE-ICSI treatment. Therefore, it can improve and personalize counseling for the medical treatment of these 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.

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.

Validation and update of a prediction model for risk of relapse after cessation of anti-TNF treatment in Crohn's disease

BACKGROUND

Anti-tumor necrosis factor (TNF) therapy is effective for the treatment of Crohn's disease. Cessation may be considered in patients with a low risk of relapse. We aimed to externally validate and update our previously developed prediction model to estimate the risk of relapse after cessation of anti-TNF therapy.

METHODS

We performed a retrospective cohort study in 17 Dutch hospitals. Crohn's disease patients in clinical, biochemical or endoscopic remission were included after anti-TNF cessation. Primary outcome was a relapse necessitating treatment. Discrimination and calibration of the previously developed model were assessed. After external validation, the model was updated. The performance of the updated prediction model was assessed in internal-external validation and by using decision curve analysis.

RESULTS

486 patients were included with a median follow-up of 1.7 years. Relapse rates were 35 and 54% after 1 and 2 years. At external validation, the discriminative ability of the prediction model was equal to that found at the development of the model [c-statistic 0.58 (95% confidence interval (CI) 0.54-0.62)], though the model was not well-calibrated on our cohort [calibration slope: 0.52 (0.28-0.76)]. After an update, a c-statistic of 0.60 (0.58-0.63) and calibration slope of 0.89 (0.69-1.09) were reported in internal-external validation.

CONCLUSION

Our previously developed and updated prediction model for the risk of relapse after cessation of anti-TNF in Crohn's disease shows reasonable performance. The use of the model may support clinical decision-making to optimize patient selection in whom anti-TNF can be withdrawn. Clinical validation is ongoing in a prospective randomized trial.

Predicting success of intrauterine insemination using a clinically based scoring system

Purpose

To develop a predictive score for the success of intrauterine insemination (IUI) based on clinical parameters.

Methods

We performed a retrospective cohort study evaluating the homologous IUI cycles performed at a single university-based reproductive medical center between 2009 and 2017. The primary outcome measure was pregnancy, defined as positive serum human chorionic gonadotropin (hCG) 12–14 days after IUI. Predictive factors for pregnancy after IUI were identified, and a predictive score was developed using a multivariable continuation ratio model.

Results

Overall, 1437 IUI cycles in 758 couples were evaluated. We found a per cycle pregnancy rate of 10.9% and a cumulative pregnancy rate of 19.4%. In a multivariable analysis, the probability of pregnancy was negatively associated with female age ≥ 35 years (OR 0.63, 95% CI 0.41–0.97, p = 0.034), endometriosis, unilateral tubal factor, or anatomical alteration (OR 0.54, 95% CI 0.33–0.89, p = 0.016), anti-Mullerian hormone (AMH) < 1 ng/ml (OR 0.50, 95% CI 0.29–0.87, p = 0.014), and total progressive motile sperm count (TPMSC) < 5 mil (OR 0.47, 95% CI 0.19–0.72, p = 0.004). We developed a predictive clinical score ranging from 0 to 5. Following 3 cycles, couples in our cohort with a score of 5 had a cumulative probability of achieving pregnancy of nearly 45%. In contrast, couples with a score of 0 had a cumulative probability of only 5%.

Conclusion

IUI success rates vary widely depending on couples’ characteristics. A simple to use score could be used to estimate a couple’s chance of achieving pregnancy via IUI, facilitating individualized counseling and decision-making.

Developing machine learning-based models to predict intrauterine insemination (IUI) success by address modeling challenges in imbalanced data and providing modification solutions for them

Background

This study sought to provide machine learning-based classification models to predict the success of intrauterine insemination (IUI) therapy. Additionally, we sought to illustrate the effect of models fitting with balanced data vs original data with imbalanced data labels using two different types of resampling methods. Finally, we fit models with all features against optimized feature sets using various feature selection techniques.

Methods

The data for the cross-sectional study were collected from 546 infertile couples with IUI at the Fatemehzahra Infertility Research Center, Babol, North of Iran. Logistic regression (LR), support vector classification, random forest, Extreme Gradient Boosting (XGBoost) and, Stacking generalization (Stack) as the machine learning classifiers were used to predict IUI success by Python v3.7. We employed the Smote-Tomek (Stomek) and Smote-ENN (SENN) resampling methods to address the imbalance problem in the original dataset. Furthermore, to increase the performance of the models, mutual information classification (MIC-FS), genetic algorithm (GA-FS), and random forest (RF-FS) were used to select the ideal feature sets for model development.

Results

In this study, 28% of patients undergoing IUI treatment obtained a successful pregnancy. Also, the average age of women and men was 24.98 and 29.85 years, respectively. The calibration plot in this study for IUI success prediction by machine learning models showed that between feature selection methods, the RF-FS, and among the datasets used to fit the models, the balanced dataset with the Stomek method had well-calibrating predictions than other methods. Finally, the brier scores for the LR, SVC, RF, XGBoost, and Stack models that were fitted utilizing the Stomek dataset and the chosen feature set using the Random Forest technique obtained equal to 0.202, 0.183, 0.158, 0.129, and 0.134, respectively. It showed duration of infertility, male and female age, sperm concentration, and sperm motility grading score as the most predictable factors in IUI success.

Conclusion

The results of this study with the XGBoost prediction model can be used to foretell the individual success of IUI for each couple before initiating therapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-01974-8.

Nomogram prediction for the prediction of clinical pregnancy in Freeze-thawed Embryo Transfer

BACKGROUND

This study aimed to identify multiple endometrial receptivity related factors by applying non-invasive, repeatable multimodal ultrasound methods. Combined with basic clinical data, we further established a practical prediction model for early clinical outcomes in Freeze-thawed Embryo Transfer (FET).

METHODS

Retrospective analysis of clinical data of infertility patients undergoing FET cycle in our Center from January 2017 to September 2019. Receiver operating characteristic (ROC) curve and decision curve analyses were performed by 500 bootstrap resamplings to assess the determination and clinical value of the nomogram, respectively.

RESULTS

A total of 2457 FET cycles were included. We developed simple nomograms that predict the early clinical outcomes in FET cycles by using the parameters of age, BMI, type and number of embryos transferred, endometrial thickness, FI, RI, PI and number of endometrial and sub-endometrial blood flow. In the training cohort, the area under the ROC curve (AUC) showed statistical accuracy (AUC = 0.698), and similar results were shown in the subsequent validation cohort (AUC = 0.699). Decision curve analysis demonstrated the clinical value of this nomogram.

CONCLUSIONS

Our nomogram can predict clinical outcomes and it can be used as a simple, affordable and widely implementable tool to provide guidance and treatment recommendations for FET patients.

Prognostic model for predicting survival in very preterm infants: an external validation study

OBJECTIVE

To perform a temporal and geographical validation of a prognostic model, considered of highest methodological quality in a recently published systematic review, for predicting survival in very preterm infants admitted to the neonatal intensive care unit. The original model was developed in the UK and included gestational age, birthweight and gender.

DESIGN

External validation study in a population-based cohort.

SETTING

Dutch neonatal wards.

POPULATION OR SAMPLE

All admitted white, singleton infants born between 23⁺⁰ and 32⁺⁶  weeks of gestation between 1 January 2015 and 31 December 2019. Additionally, the model's performance was assessed in four populations of admitted infants born between 24⁺⁰ and 31⁺⁶  weeks of gestation: white singletons, non-white singletons, all singletons and all multiples.

METHODS

The original model was applied in all five validation sets. Model performance was assessed in terms of calibration and discrimination and, if indicated, it was updated.

MAIN OUTCOME MEASURES

Calibration (calibration-in-the-large and calibration slope) and discrimination (c statistic).

RESULTS

Out of 6092 infants, 5659 (92.9%) survived. The model showed good external validity as indicated by good discrimination (c statistic 0.82, 95% CI 0.79-0.84) and calibration (calibration-in-the-large 0.003, calibration slope 0.92, 95% CI 0.84-1.00). The model also showed good external validity in the other singleton populations, but required a small intercept update in the multiples population.

CONCLUSIONS

A high-quality prognostic model predicting survival in very preterm infants had good external validity in an independent, nationwide cohort. The accurate performance of the model indicates that after impact assessment, implementation of the model in clinical practice in the neonatal intensive care unit could be considered.

TWEETABLE ABSTRACT

A high-quality model predicting survival in very preterm infants is externally valid in an independent cohort.

Estimation Methods for Infertility Treatment Success: Comparison of Four Methods

Objective: To analyze and compare four methods for estimating the chance of treatment success in infertile couples.

Materials and methods: In a retrospective cohort study, information on demographic and clinical features, including age, body mass index (BMI), duration of infertility, semen analysis, previous history of treatment and clinical examination of infertile couples were analyzed. Treatment success (childbearing) was calculated with four methods as live birth ratio, conditional probability and survival analysis (life table and Kaplan-Meyer method) and results are compared.

Results: The fertility ratio for the first treatment cycle was 29.72% which decreased to 23.13% by total treatment cycles. The success rate was 75.4%. With conditional probability calculation at the end of the five treatment cycles. With the life table method in a five-year period, the probability for live birth was 78% and by Kaplan-Meyer method 73.1% and the median of treatment time was 562 days.

Conclusion: Calculation of infertility treatment success rate by only simple live birth ratio of childbearing couples is associated with underestimation. Using the conditional probability method reduces that underestimation, but it is not considered the censored cases in the treatments. It seems life table (as a proxy of survival analysis) presents the closest estimation to clinical facts with considering the repetition of the treatment cycle and the duration of treatment.

Development of a multivariable prediction model for congenital unilateral absence of the vas deferens in male partners of infertile couples

BACKGROUND

Congenital unilateral absence of vas deferens has been diagnosed in fertile and normozoospermic males and is associated with the risk of unilateral renal absence or cystic fibrosis transmembrane conductance regulator mutations; but no prediction model currently exists to diagnose this condition.

OBJECTIVES

The study aims to identify clinical and biological variables that may have a predictive value for the diagnosis of congenital unilateral absence of vas deferens in male partners of infertile couples MATERIALS AND METHODS: We designed a retrospective, cross-sectional, case-control study on electronic health records of a single tertiary-care andrological centre collected between 1998 and 2018. We included all subjects diagnosed with congenital unilateral absence of vas deferens using combined scrotal and transrectal ultrasounds. Controls were confirmed free of congenital unilateral absence of vas deferens by the same way. Both groups received standardised exploration procedures. Multivariable logistic regression model was built in a backward stepwise manner. Model performance and calibration were assessed. The study is reported according to TRIPOD statement.

RESULTS

We included 69 congenital unilateral absence of vas deferens cases and 78 controls. Cases had a lower semen volume than controls. The congenital unilateral absence of vas deferens risk was associated with history of cryptorchidism and both levels of semen fructose and α-glucosidase. These predictors were confirmed by a random forest algorithm. The area under the curve was 0.886 (95% interval: 0.81-0.92). Calibration was performed with the Hosmer-Lemeshow test (p = 0.88).

DISCUSSION AND CONCLUSION

History of cryptorchidism, semen fructose and α-glucosidase were identified as relevant and independent predictors for the diagnosis of congenital unilateral absence of vas deferens. The model enables to identify male patients with a high risk of congenital unilateral absence of vas deferens to whom a transrectal ultrasounds would be proposed to confirm the diagnosis, whatever their semen parameters. It will also help to address the risks of unilateral renal absence and of cystic fibrosis transmembrane conductance regulator mutations carrying during the management of infertile couples.

Chance of live birth: a nationwide, registry-based cohort study

STUDY QUESTION

Does the sequence of prior pregnancy events (pregnancy losses, live births, ectopic pregnancies, molar pregnancy and still birth), obstetric complications and maternal age affect chance of live birth in the next pregnancy and are prior events predictive for the outcome?

SUMMARY ANSWER

The sequence of pregnancy outcomes is significantly associated with chance of live birth; however, pregnancy history and age are insufficient to predict the outcome of an individual woman's next pregnancy.

WHAT IS KNOWN ALREADY

Adverse pregnancy outcomes decrease the chance of live birth in the next pregnancy, whereas the impact of prior live births is less clear.

STUDY DESIGN, SIZE, DURATION

Nationwide, registry-based cohort study of 1 285 230 women with a total of 2 722 441 pregnancies from 1977 to 2017.

PARTICIPANTS/MATERIALS, SETTING, METHODS

All women living in Denmark in the study period with at least one pregnancy in either the Danish Medical Birth Registry or the Danish National Patient Registry. Data were analysed using logistic regression with a robust covariance model to account for women with more than one pregnancy. Model discrimination and calibration were ascertained using 20% of the women in the cohort randomly selected as an internal validation set.

MAIN RESULTS AND THE ROLE OF CHANCE

Obstetric complications, still birth, ectopic pregnancies and pregnancy losses had a negative effect on the chance of live birth in the next pregnancy. Consecutive, identical pregnancy outcomes (pregnancy losses, live births or ectopic pregnancies) immediately preceding the next pregnancy had a larger impact than the total number of any outcome. Model discrimination was modest (C-index = 0.60, positive predictive value = 0.45), but the models were well calibrated.

LIMITATIONS, REASONS FOR CAUTION

While prior pregnancy outcomes and their sequence significantly influenced the chance of live birth, the discriminative abilities of the predictive models demonstrate clearly that pregnancy history and maternal age are insufficient to reliably predict the outcome of a given pregnancy.

WIDER IMPLICATIONS OF THE FINDINGS

Prior pregnancy history has a significant impact on the chance of live birth in the next pregnancy. However, the results emphasize that only taking age and number of losses into account does not predict if a pregnancy will end as a live birth or not. A better understanding of biological determinants for pregnancy outcomes is urgently needed.

STUDY FUNDING/COMPETING INTEREST(S)

The work was supported by the Novo Nordisk Foundation, Ole Kirk Foundation and Rigshospitalet's Research Foundation. The authors have no financial relationships that could appear to have influenced the work.

TRIAL REGISTRATION NUMBER

N/A.

Prognostic Models Predicting Mortality in Preterm Infants: Systematic Review and Meta-analysis

CONTEXT

Prediction models can be a valuable tool in performing risk assessment of mortality in preterm infants.

OBJECTIVE

Summarizing prognostic models for predicting mortality in very preterm infants and assessing their quality.

DATA SOURCES

Medline was searched for all articles (up to June 2020).

STUDY SELECTION

All developed or externally validated prognostic models for mortality prediction in liveborn infants born <32 weeks' gestation and/or <1500 g birth weight were included.

DATA EXTRACTION

Data were extracted by 2 independent authors. Risk of bias (ROB) and applicability assessment was performed by 2 independent authors using Prediction model Risk of Bias Assessment Tool.

RESULTS

One hundred forty-two models from 35 studies reporting on model development and 112 models from 33 studies reporting on external validation were included. ROB assessment revealed high ROB in the majority of the models, most often because of inadequate (reporting of) analysis. Internal and external validation was lacking in 41% and 96% of these models. Meta-analyses revealed an average C-statistic of 0.88 (95% confidence interval [CI]: 0.83-0.91) for the Clinical Risk Index for Babies score, 0.87 (95% CI: 0.81-0.92) for the Clinical Risk Index for Babies II score, and 0.86 (95% CI: 0.78-0.92) for the Score for Neonatal Acute Physiology Perinatal Extension II score.

LIMITATIONS

Occasionally, an external validation study was included, but not the development study, because studies developed in the presurfactant era or general NICU population were excluded.

CONCLUSIONS

Instead of developing additional mortality prediction models for preterm infants, the emphasis should be shifted toward external validation and consecutive adaption of the existing prediction models.

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

STUDY QUESTION

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

SUMMARY ANSWER

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

WHAT IS KNOWN ALREADY

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

STUDY DESIGN, SIZE, DURATION

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

PARTICIPANTS/MATERIALS, SETTING, METHODS

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

MAIN RESULTS AND THE ROLE OF CHANCE

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

LIMITATIONS, REASONS FOR CAUTION

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

WIDER IMPLICATIONS OF THE FINDINGS

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

STUDY FUNDING/COMPETING INTEREST(S)

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

REGISTRATION NUMBER

N/A.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

AIM

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

METHODS

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

RESULTS

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

CONCLUSION

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

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.

What is the prognosis for a live birth after unexplained recurrent implantation failure following IVF/ICSI?

STUDY QUESTION

What is the cumulative incidence of live birth and mean time to pregnancy (by conception after IVF/ICSI or natural conception) in women experiencing unexplained recurrent implantation failure (RIF) following IVF/ICSI treatment?

SUMMARY ANSWER

In 118 women who had experienced RIF, the reported cumulative incidence of live birth during a maximum of 5.5 years follow-up period was 49%, with a calculated median time to pregnancy leading to live birth of 9 months after diagnosis of RIF.

WHAT IS KNOWN ALREADY

Current definitions of RIF include failure to achieve a pregnancy following IVF/ICSI and undergoing three or more fresh embryo transfer procedures of one or two high quality embryos or more than 10 embryos transferred in fresh or frozen cycles. The causes and optimal management of this distressing condition remain uncertain and a range of empirical and often expensive adjuvant therapies is often advocated. Little information is available regarding the long-term prognosis for achieving a pregnancy.

STUDY DESIGN, SIZE, DURATION

Two hundred and twenty-three women under 39 years of age who had experienced RIF without a known cause after IVF/ICSI treatment in two tertiary referral university hospitals between January 2008 and December 2012 were invited to participate in this retrospective cohort follow up study.

PARTICIPANTS/MATERIALS, SETTING, METHODS

All eligible women were sent a letter requesting their consent to the anonymous use of their medical file data and were asked to complete a questionnaire enquiring about treatments and pregnancies subsequent to experiencing RIF. Medical files and questionnaires were examined and results were analysed to determine the subsequent cumulative incidence of live birth and time to pregnancy within a maximum 5.5 year follow-up period using Kaplan Meier analysis. Clinical predictors for achieving a live birth were investigated using a Cox hazard model.

MAIN RESULTS AND THE ROLE OF CHANCE

One hundred and twenty-seven women responded (57%) and data from 118 women (53%) were available for analysis. During the maximum 5.5 year follow up period the overall cumulative incidence of live birth was 49% (95% CI 39-59%). Among women who gave birth, the calculated median time to pregnancy was 9 months after experiencing RIF, where 18% arose from natural conceptions.

LIMITATIONS, REASONS FOR CAUTION

Since only 57% of the eligible study cohort completed the questionnaire, the risk of response bias limits the applicability of the study findings.

WIDER IMPLICATIONS OF THE FINDINGS

This study reports a favorable overall prognosis for achieving live birth in women who have previously experienced RIF, especially in those who continue with further IVF/ICSI treatments. However since 51% did not achieve a live birth during the follow-up period, there is a need to distinguish those most likely to benefit from further treatment. In this study, no clinical factors were found to be predictive of those achieving a subsequent live birth.

STUDY FUNDING/COMPETING INTEREST(S)

This study was funded by the University Medical Center Utrecht, in Utrecht and the Academic Medical Centre, in Amsterdam. NSM has received consultancy and speaking fees and research funding from Ferring, MSD, Merck Serono, Abbott, IBSA, Gedion Richter, and Clearblue. During the most recent 5-year period BCJMF has received fees or grant support from the following organizations (in alphabetic order); Actavis/Watson/Uteron, Controversies in Obstetrics & Gynecology (COGI), Dutch Heart Foundation, Dutch Medical Research Counsel (ZonMW), Euroscreen/Ogeda, Ferring, London Womens Clinic (LWC), Merck Serono, Myovant, Netherland Genomic Initiative (NGI), OvaScience, Pantharei Bioscience, PregLem/Gedeon Richter/Finox, Reproductive Biomedicine Online (RBMO), Roche, Teva, World Health Organisation (WHO).None of the authors have disclosures to make in relation to this manuscript.

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.

The predicted probability of live birth in In Vitro Fertilization varies during important stages throughout the treatment: analysis of 114,882 first cycles

RESEARCH QUESTION

How much the variability in patients' response during in vitro fertilization (IVF) may add to the initial predicted prognosis based only on patients' basal characteristics?

DESIGN

Anonymous data were obtained from the Human Fertilization and Embryology Authority (HFEA). Data involving 114,882 stimulated fresh IVF cycles were retrospectively analyzed. Logistic regression was used to develop the models.

RESULTS

Prediction of live birth was feasible with moderate accuracy in all of the three models; discrimination of the model based only on basal patients' characteristics (AUROC 0.61) was markedly improved adding information of number of embryos (AUROC 0.65) and, mostly, number of oocytes (AUROC 0.66).

CONCLUSIONS

The addition to prediction models of parameters such as the number of embryos obtained and especially the number of oocytes retrieved can statistically significantly improve the overall prediction of live birth probabilities when based on only basal patients' characteristics. This seems to be particularly true for women after the first IVF cycle. Since ovarian response affects the probability of live birth in IVF, it is highly recommended to add markers of ovarian response to models based on basal characteristics to increase their predictive ability.

Prediction model for clinical pregnancy for ICSI after surgical sperm retrieval in different types of azoospermia

STUDY QUESTION

Can a counselling tool be developed for couples with different types of azoospermia to predict the probability of clinical pregnancy in ICSI after surgical sperm retrieval?

SUMMARY ANSWER

A prediction model for clinical pregnancy in ICSI after surgical sperm retrieval in different types of azoospermia was created and clinical type of azoospermia, testicular size, male FSH, male LH, male testosterone, female age, female antral follicle count (AFC) and female anti-Müllerian hormone (AMH) were used as predictors.

WHAT IS KNOWN ALREADY

Prediction models are used frequently to predict treatment success in reproductive medicine; however, there are few prediction models only for azoospermia couples who intend to conceive through surgical sperm retrieval and ICSI. Furthermore, no specific clinical types of azoospermia have been reported as predictors.

STUDY DESIGN, SIZE, DURATION

A cohort study of 453 couples undergoing ICSI was conducted between 2016 and 2019 in an academic teaching hospital.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Couples undergoing ICSI with surgically retrieved sperm were included, with 302 couples included in the development set and 151 couples included in the validation set. We constructed a prediction model using multivariable logistic regression analysis. The internal validation was based on discrimination and calibration.

MAIN RESULTS AND THE ROLE OF CHANCE

We found that for male patients involved in our model, different clinical types of azoospermia are associated with different clinical pregnancy outcomes after ICSI. Considering the clinical type of azoospermia, larger testicular volume and higher levels of FSH, LH and testosterone in the body are associated with higher clinical pregnancy success rates. For women involved in our model, younger age and higher AFC and AMH levels are associated with higher clinical pregnancy success rates. In the development set, the AUC was 0.891 (95% CI 0.849–0.934), indicating that the model had good discrimination. The slope of the calibration plot was 1.020 (95% CI 0.899–1.142) and the intercept of the calibration plot was −0.015 (95% CI −0.112 to 0.082), indicating that the model was well-calibrated. From the validation set, the model had good discriminative capacity (AUC 0.866, 95% CI 0.808–0.924) and calibrated well, with a slope of 1.015 (95% CI 0.790–1.239) and an intercept of −0.014 (95% CI −0.180 to 0.152) in the calibration plot.

LIMITATIONS, REASONS FOR CAUTION

We found that BMI was not an effective indicator for predicting clinical pregnancy, which was inconsistent with some other studies. We lacked data about the predictors that reflected sperm characteristics, therefore, we included the clinical type of azoospermia instead as a predictor because it is related to sperm quality. We found that almost all patients did not have regular alcohol consumption, so we did not use alcohol consumption as a possible predictor, because of insufficient data on drinking habits. We acknowledge that our development set might not be a perfect representation of the population, although this is a common limitation that researchers often encounter when developing prediction models. The number of non-obstructive azoospermia patients that we could include in the analysis was limited due to the success rate of surgical sperm retrieval, although this did not affect the establishment and validation of our model. Finally, this prediction model was developed in a single centre. Although our model was validated in an independent dataset from our centre, validation for different clinical populations belonging to other centres is required before it can be exported.

WIDER IMPLICATIONS OF THE FINDINGS

This model enables the differentiation between couples with a low or high chance of reaching a clinical pregnancy through ICSI after surgical sperm retrieval. As such it can provide couples dealing with azoospermia a new approach to help them choose between surgical sperm retrieval with ICSI and the use of donor sperm.

STUDY FUNDING/COMPETING INTEREST(S)

This work was supported by a grant from the National Natural Science Foundations of China (81501246 and 81501020 and 81671443). The authors declare no competing interest.

TRIAL REGISTRATION NUMBER

N/A.

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

STUDY QUESTION

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

SUMMARY ANSWER

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

WHAT IS KNOWN ALREADY

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

STUDY DESIGN, SIZE, DURATION

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

PARTICIPANTS/MATERIALS, SETTING, METHODS

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

MAIN RESULTS AND THE ROLE OF CHANCE

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

LIMITATIONS, REASONS FOR CAUTION

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

WIDER IMPLICATIONS OF THE FINDINGS

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

STUDY FUNDING/COMPETING INTEREST(S)

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

Use of Artificial Neural Networks and PCA to Predict Results of Infertility Treatment in the ICSI Method

Background:

Intracytoplasmic sperm injection (ICSI) or microinjection is one of the most commonly used assisted reproductive technologies (ART) in the treatment of patients with infertility problems. At each stage of this treatment cycle, many dependent and independent variables may affect the results, according to which, estimating the accuracy of fertility rate for physicians will be difficult

Objective:

This study aims to evaluate the efficiency of artificial neural networks (ANN) and principal component analysis (PCA) to predict results of infertility treatment in the ICSI method

Material and Methods:

In the present research that is an analytical study, multilayer perceptron (MLP) artificial neural networks were designed and evaluated to predict results of infertility treatment using the ICSI method. In addition, the PCA method was used before the process of training the neural network for extracting information from data and improving the efficiency of generated models. The network has 11 to 17 inputs and 2 outputs.

Results:

The area under ROC curve (AUC) values were derived from modeling the results of the ICSI technique for the test data and the total data. The AUC for total data vary from 0.7670 to 0.9796 for two neurons, 0.9394 to 0.9990 for three neurons and 0.9540 to 0.9906 for four neurons in hidden layers

Conclusion:

The proposed MLP neural network can model the specialist performance in predicting treatment results with a high degree of accuracy and reliability

Prediction Model for Massive Transfusion in Placenta Previa during Cesarean Section

PURPOSE

Recently, obstetric massive transfusion protocols have shifted toward early intervention. This study aimed to develop a prediction model for transfusion of ≥5 units of packed red blood cells (PRBCs) during cesarean section in women with placenta previa.

MATERIALS AND METHODS

We conducted a cohort study including 287 women with placenta previa who delivered between September 2011 and April 2018. Univariate and multivariate logistic regression analyses were used to test the association between clinical factors, ultrasound factors, and massive transfusion. For the external validation set, we obtained data (n=50) from another hospital.

RESULTS

We formulated a scoring model for predicting transfusion of ≥5 units of PRBCs, including maternal age, degree of previa, grade of lacunae, presence of a hypoechoic layer, and anterior placentation. For example, total score of 223/260 had a probability of 0.7 for massive transfusion. Hosmer-Lemeshow goodness-of-fit test indicated that the model was suitable (p>0.05). The area under the receiver operating characteristics curve (AUC) was 0.922 [95% confidence interval (CI) 0.89-0.95]. In external validation, the discrimination was good, with an AUC value of 0.833 (95% CI 0.70-0.92) for this model. Nomogram calibration plots indicated good agreement between the predicted and observed outcomes, exhibiting close approximation between the predicted and observed probability.

CONCLUSION

We constructed a scoring model for predicting massive transfusion during cesarean section in women with placenta previa. This model may help in determining the need to prepare an appropriate amount of blood products and the optimal timing of blood transfusion.

Precise Personalized Medicine in Gynecology Cancer and Infertility

Since the conception of precision medicine has been put forward in oncology, this idea has been popularized and applied in many specialties. Significant progress has been made toward personalizing the entire process, including diagnosis, treatment planning, and embryo identification, and combining large-scale genetic information data and knowledge discovery can offer better prospects in reproductive medicine. This work reviews the application of precision medicine and possibilities in reproductive medicine and gynecologic cancer diagnosis and treatment. The limitations and challenges of precision medicine in this area remain to be discussed.

The Relationship between Fatigue, Health-Promoting Behavior, and Depression among Infertile Women

PURPOSE

As the number of infertile couples has grown, many infertile women have experienced depression during the diagnosis and treatment of their infertility. This study aimed to identify the factors related to depression in infertile women who underwent reproductive treatments.

METHODS

The study subjects were 149 infertile women who underwent reproductive treatments. The data were collected by self-administered questionnaires from August 1 to December 24, 2018. The questionnaire consisted of questions about fatigue, health-promoting behavior, and depression. Analyses of the descriptive statistics, t-tests, one-way analysis of variance, correlation, and multiple regression were conducted using the SPSS 25.0 Windows program.

RESULTS

Thirty-six of the women in the study (24.2%) were in the probably depressed group and 113 (75.8%) were in the definitely depressed group and 100% of the subjects experienced symptoms of depression. Depression was positively correlated with fatigue and negatively correlated with health-promoting behavior. Multiple regression analysis revealed that fatigue and interpersonal relationships were factors significantly related to depression in the model (p<.001), with an explanatory power of 42.6%.

CONCLUSION

The results confirmed that fatigue and interpersonal relationships, which is a subfactor of health-promoting behavior, were factors related to depression. To alleviate depression in infertile women, efforts should be made to identify and reduce psychological and physical fatigue. In addition, minimizing relational difficulties that they experience during an infertility diagnosis and treatment and strengthening positive interpersonal relationships can be positive strategies to alleviate depression.

The use of rigorous methods was strongly warranted among prognostic prediction models for obstetric care

OBJECTIVE

The objective of the study was to examine methodological characteristics about the design and conduct in prognostic prediction models used for obstetric care.

STUDY DESIGN AND SETTING

We searched PubMed for studies on prognostic prediction models for obstetric care, published in top general medicine or major specialty journals between January 2011 and February 2018. Teams of method-trained investigators independently screened titles and abstracts and collected data using a prespecified, pilot-tested, structured questionnaire.

RESULTS

In total, 91 studies were eligible, of which two were published in top general medicine journals, 20 (22.0%) involved an epidemiologist or statistician, 18 (19.4%) published study protocols, 53 (58.2%) did not include any model validation, 20 (22.0%) did not clearly state the intended timing of use, 23 (25.3%) had no eligibility criteria, 15 (16.5%) did not use clear criteria for ascertaining outcome, and 69 (75.82%) did not apply blinding to outcome assessment. Among those models, 11 (12.1%) included participants fewer than 200 events, 41 (48.8%) had fewer than 100 events, and 19 (24.7%) had fewer than 10 events per variable.

CONCLUSION

The prognostic prediction models have important limitations in design and conduct. Substantial efforts are needed to strengthen the production of reliable prognostic prediction models for obstetric care.

External validation of a dynamic prediction model for repeated predictions of natural conception over time

STUDY QUESTION

How well does a previously developed dynamic prediction model perform in an external, geographical validation in terms of predicting the chances of natural conception at various points in time?

SUMMARY ANSWER

The dynamic prediction model performs well in an external validation on a Scottish cohort.

WHAT IS KNOWN ALREADY

Prediction models provide information that can aid evidence-based management of unexplained subfertile couples. We developed a dynamic prediction model for natural conception (van Eekelen model) that is able to update predictions of natural conception when couples return to their clinician after a period of unsuccessful expectant management. It is not known how well this model performs in an external population.

STUDY DESIGN, SIZE, DURATION

A record-linked registry study including the long-term follow-up of all couples who were considered unexplained subfertile following a fertility workup at a Scottish fertility clinic between 1998 and 2011. Couples with anovulation, uni/bilateral tubal occlusion, mild/severe endometriosis or impaired semen quality according to World Health Organization criteria were excluded.

PARTICIPANTS/MATERIALS, SETTING, METHODS

The endpoint was time to natural conception, leading to an ongoing pregnancy (defined as reaching a gestational age of at least 12 weeks). Follow-up was censored at the start of treatment, at the change of partner or at the end of study (31 March 2012). The performance of the van Eekelen model was evaluated in terms of calibration and discrimination at various points in time. Additionally, we assessed the clinical utility of the model in terms of the range of the calculated predictions.

MAIN RESULTS AND THE ROLE OF CHANCE

Of a total of 1203 couples with a median follow-up of 1 year and 3 months after the fertility workup, 398 (33%) couples conceived naturally leading to an ongoing pregnancy. Using the dynamic prediction model, the mean probability of natural conception over the course of the first year after the fertility workup was estimated at 25% (observed: 23%). After 0.5, 1 and 1.5 years of expectant management after the completion of the fertility workup, the average probability of conceiving naturally over the next year was estimated at 18% (observed: 15%), 14% (observed: 14%) and 12% (observed: 12%). Calibration plots showed good agreement between predicted chances and the observed fraction of ongoing pregnancy within risk groups. Discrimination was moderate with c statistics similar to those in the internal validation, ranging from 0.60 to 0.64. The range of predicted chances was sufficiently wide to distinguish between couples having a good and poor prognosis with a minimum of zero at all times and a maximum of 55% over the first year after the workup, which decreased to maxima of 43% after 0.5 years, 34% after 1 year and 29% after 1.5 years after the fertility workup.

LIMITATIONS, REASONS FOR CAUTION

The model slightly overestimated the chances of conception by ~2-3% points on group level in the first-year post-fertility workup and after 0.5 years of expectant management, respectively. This is likely attributable to the fact that the exact dates of completion of the fertility workup for couples were missing and had to be estimated.

WIDER IMPLICATIONS OF THE FINDINGS

The van Eekelen model is a valid and robust tool that is ready to use in clinical practice to counsel couples with unexplained subfertility on their individualized chances of natural conception at various points in time, notably when couples return to the clinic after a period of unsuccessful expectant management.

STUDY FUNDING/COMPETING INTEREST(S)

This work was supported by a Chief Scientist Office postdoctoral training fellowship in health services research and health of the public research (ref PDF/12/06). There are no conflicts of interest.

IVF for unexplained subfertility; whom should we treat?

STUDY QUESTION

Which couples with unexplained subfertility can expect increased chances of ongoing pregnancy with IVF compared to expectant management?

SUMMARY ANSWER

For couples in which the woman is under 40 years of age, IVF is associated with higher chances of conception than expectant management.

WHAT IS KNOWN ALREADY

The clinical indications for IVF have expanded over time from bilateral tubal blockage to include unexplained subfertility in which there is no identifiable barrier to conception. Yet, there is little evidence from randomized controlled trials that IVF is effective in these couples.

STUDY DESIGN, SIZE, DURATION

We compared outcomes in British couples with unexplained subfertility undergoing IVF (n = 40 921) from registry data to couples with the same type of subfertility on expectant management. Those couples on expectant management (defined as no intervention aside from the advice to have intercourse) comprised a prospective nation-wide Dutch cohort (n = 4875) and a retrospective regional cohort from Aberdeen, Scotland (n = 975). We excluded couples who had tried for <1 year to conceive and also those with anovulation, uni- or bilateral tubal occlusion, mild or severe endometriosis or male subfertility i.e. impaired semen quality according to World Health Organization criteria.

PARTICIPANTS/MATERIALS, SETTING, METHODS

We matched couples who received IVF and couples on expectant management based on their characteristics to control for confounding. We fitted a Cox proportional hazards model including patient characteristics, IVF treatment and their interactions to estimate the individualized chance of conception over 1 year—either following IVF or expectant management for all combinations of patient characteristics. The endpoint was conception leading to ongoing pregnancy, defined as a foetus reaching a gestational age of at least 12 weeks.

MAIN RESULTS AND THE ROLE OF CHANCE

The adjusted 1-year chance of conception was 47.9% (95% CI: 45.0–50.9) after IVF and 26.1% (95% CI: 24.2–28.0) after expectant management. The absolute difference in the average adjusted 1-year chances of conception was 21.8% (95%CI: 18.3–25.3) in favour of IVF. The effectiveness of IVF was influenced by female age, duration of subfertility and previous pregnancy. IVF was effective in women under 40 years, but the 1-year chance of an IVF conception declined sharply in women over 34 years. In contrast, in woman over 40 years of age, IVF was less effective, with an absolute difference in chance compared to expectant management of 10% or lower. Regardless of female age, IVF was also less effective in couples with a short period of secondary subfertility (1 year) who had chances of natural conception of 30% or above.

LIMITATIONS, REASONS FOR CAUTION

The 1-year chances of conception were based on three cohorts with different sampling mechanisms. Despite adjustment for the three most important prognostic patient characteristics, namely female age, duration of subfertility and primary or secondary subfertility, our estimates might not be free from residual confounding.

WIDER IMPLICATIONS OF THE FINDINGS

IVF should be used selectively based on judgements on gain compared to continuing expectant management for a given couple. Our results can be used by clinicians to counsel couples with unexplained subfertility, to inform their expectations and facilitate evidence-based, shared decision making.

STUDY FUNDING/COMPETING INTEREST(S)

This work was supported by Tenovus Scotland [grant G17.04]. Travel for RvE was supported by the Amsterdam Reproduction & Development Research Group [grant V.000296]. SB reports acting as editor-in-chief of HROpen. Other authors have no conflicts.

Predicting the cumulative chance of live birth over multiple complete cycles of in vitro fertilization: an external validation study

STUDY QUESTION

Are the published pre-treatment and post-treatment McLernon models, predicting cumulative live birth rates (LBR) over multiple complete IVF cycles, valid in a different context?

SUMMARY ANSWER

With minor recalibration of the pre-treatment model, both McLernon models accurately predict cumulative LBR in a different geographical context and a more recent time period.

WHAT IS KNOWN ALREADY

Previous IVF prediction models have estimated the chance of a live birth after a single fresh embryo transfer, thereby excluding the important contribution of embryo cryopreservation and subsequent IVF cycles to cumulative LBR. In contrast, the recently developed McLernon models predict the cumulative chance of a live birth over multiple complete IVF cycles at two certain time points: (i) before initiating treatment using baseline characteristics (pre-treatment model) and (ii) after the first IVF cycle adding treatment related information to update predictions (post-treatment model). Before implementation of these models in clinical practice, their predictive performance needs to be validated in an independent cohort.

STUDY DESIGN, SIZE, DURATION

External validation study in an independent prospective cohort of 1515 Dutch women who participated in the OPTIMIST study (NTR2657) and underwent their first IVF treatment between 2011 and 2014. Participants underwent a total of 2881 complete treatment cycles, with a complete cycle defined as all fresh and frozen thawed embryo transfers resulting from one episode of ovarian stimulation. The follow up duration was 18 months after inclusion, and the primary outcome was ongoing pregnancy leading to live birth.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Model performance was externally validated up to three complete treatment cycles, using the linear predictor as described by McLernon et al. to calculate the probability of a live birth. Discrimination was expressed by the c-statistic and calibration was depicted graphically in a calibration plot. In contrast to the original model development cohort, anti-Müllerian hormone (AMH), antral follicle count (AFC) and body weight were available in the OPTIMIST cohort, and evaluated as potential additional predictors for model improvement.

MAIN RESULTS AND THE ROLE OF CHANCE

Applying the McLernon models to the OPTIMIST cohort, the c-statistic of the pre-treatment model was 0.62 (95% CI: 0.59-0.64) and of the post-treatment model 0.71 (95% CI: 0.69-0.74). The calibration plot of the pre-treatment model indicated a slight overestimation of the cumulative LBR. To improve calibration, the pre-treatment model was recalibrated by subtracting 0.35 from the intercept. The post-treatment model calibration plot revealed accurate cumulative LBR predictions. After addition of AMH, AFC and body weight to the McLernon models, the c-statistic of the updated pre-treatment model improved slightly to 0.66 (95% CI: 0.64-0.68), and of the updated post-treatment model remained at the previous level of 0.71 (95% CI: 0.69-0.73). Using the recalibrated pre-treatment model, a woman aged 30 years with 2 years of primary infertility who starts ICSI treatment for male factor infertility has a chance of 40% of a live birth from the first complete cycle, increasing to 72% over three complete cycles. If this woman weighs 70 kg, has an AMH of 1.5 ng/mL and an AFC of 10 measured at the beginning of her treatment, the updated pre-treatment model revises the estimated chance of a live birth to 30% in the first complete cycle and 59% over three complete cycles. If this woman then has five retrieved oocytes, no embryos cryopreserved and a single fresh cleavage stage embryo transfer in her first ICSI cycle, the post-treatment model estimates the chances of a live birth at 28 and 58%, respectively.

LIMITATIONS, REASONS FOR CAUTION

Two randomized controlled trials (RCT) evaluating the effectiveness of gonadotropin dose individualization on basis of the AFC were nested within the OPTIMIST study. The strict dosing regimens, the RCT in- and exclusion criteria and the limited follow up time of 18 months might have influenced model performance in this independent cohort. Also, consistent with the original model development study, external validation was performed using the optimistic assumption that the cumulative LBR in couples who discontinue treatment without a live birth would have been equal to that of those who continue treatment.

WIDER IMPLICATIONS OF THE FINDINGS

After national recalibration to account for geographical differences in IVF treatment, the McLernon prediction models can be introduced as new counselling tools in clinical practice to inform patients and to complement clinical reasoning. These models are the first to offer an objective and personalized estimate of the cumulative probability of a live birth over multiple complete IVF cycles.

STUDY FUNDING/COMPETING INTEREST(S)

No external funds were obtained for this study. M.J.C.E., D.J.M. and S.B. have nothing to disclose. J.A.L, S.C.O, T.C.v.T. and H.LT. received an unrestricted personal grant from Merck BV. B.W.M. is supported by a NHMRC Practitioner Fellowship (GNT1082548) and reports consultancy for ObsEva, Merck and Guerbet. F.J.M.B. receives monetary compensation as a member of the external advisory board for Merck BV (the Netherlands) and Ferring pharmaceutics BV (the Netherlands), for consultancy work for Gedeon Richter (Belgium) and Roche Diagnostics on automated AMH assay development, and for a research cooperation with Ansh Labs (USA).

TRIAL REGISTRATION NUMBER

Not applicable.

PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration

Prediction models in health care use predictors to estimate for an individual the probability that a condition or disease is already present (diagnostic model) or will occur in the future (prognostic model). Publications on prediction models have become more common in recent years, and competing prediction models frequently exist for the same outcome or target population. Health care providers, guideline developers, and policymakers are often unsure which model to use or recommend, and in which persons or settings. Hence, systematic reviews of these studies are increasingly demanded, required, and performed. A key part of a systematic review of prediction models is examination of risk of bias and applicability to the intended population and setting. To help reviewers with this process, the authors developed PROBAST (Prediction model Risk Of Bias ASsessment Tool) for studies developing, validating, or updating (for example, extending) prediction models, both diagnostic and prognostic. PROBAST was developed through a consensus process involving a group of experts in the field. It includes 20 signaling questions across 4 domains (participants, predictors, outcome, and analysis). This explanation and elaboration document describes the rationale for including each domain and signaling question and guides researchers, reviewers, readers, and guideline developers in how to use them to assess risk of bias and applicability concerns. All concepts are illustrated with published examples across different topics. The latest version of the PROBAST checklist, accompanying documents, and filled-in examples can be downloaded from www.probast.org.

IUI: review and systematic assessment of the evidence that supports global recommendations

BACKGROUND

IUI with or without ovarian stimulation (OS) has become a first-line treatment option for many infertile couples, worldwide. The appropriate treatment modality for couples and their clinical management through IUI or IUI/OS cycles must consider maternal and perinatal outcomes, most notably the clinical complication of higher-order multiple pregnancies associated with IUI-OS. With a current global emphasis to continue to decrease maternal and perinatal mortality and morbidity, the World Health Organization (WHO) had established a multi-year project to review the evidence for the establishment of normative guidance for the implementation of IUI as a treatment to address fertility problems, and to consider its cost-effectiveness for lower resource settings.

OBJECTIVE AND RATIONALE

The objective of this review is to provide a review of the evidence of 13 prioritized questions that cover IUI with and without OS. We provide summary recommendations for the development of global, evidence-based guidelines based upon methodology established by the WHO.

SEARCH METHODS

We performed a comprehensive search using question-specific relevant search terms in May 2015. For each PICO (Population, Intervention, Comparison and Outcomes) drafted by WHO, specific search terms were used to find the available evidence in MEDLINE (1950 to May 2015) and The Cochrane Library (until May 2015). After presentation to an expert panel, a further hand search of references in relevant reviews was performed up to January 2017. Articles that were found to be relevant were read and analysed by two investigators and critically appraised using the Cochrane Collaboration's tool for assessing risk of bias, and AMSTAR in case of systematic reviews. The quality of the evidence was assessed using the GRADE system. An independent expert review process of our analysis was conducted in November 2016.

OUTCOMES

This review provides an assessment and synthesis of the evidence that covers 13 clinical questions including the indications for the use of IUI versus expectant management, the sperm parameters required, the best and optimal method of timing and number of inseminations per cycle, prevention strategies to decrease multiple gestational pregnancies, and the cost-effectiveness of IUI versus IVF. We provide an evidence-based formulation of 20 recommendations, as well as two best practice points that address the integration of methods for the prevention of infection in the IUI laboratory. The quality of the evidence ranges from very low to high, with evidence that may be decades old but of high quality, however, we further discuss where critical research gaps in the evidence remain.

WIDER IMPLICATIONS

This review presents an evidence synthesis assessment and includes recommendations that will assist health care providers worldwide with their decision-making when considering IUI treatments, with or without OS, for their patients presenting with fertility problems.

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

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

ABBREVIATIONS

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

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

The current definition of infertility acknowledges the importance of duration of pregnancy seeking but fails to recognize the prevalent negative impact of female age. In fact, the diagnosis of unexplained infertility increases with women's age because of our incapacity to discern between age-related infertility and real unexplained infertility. Physicians' response to the pressures of increased female age has been to take prompt refuge in assisted reproduction despite the lack of robust evidence and the inherent risks and costs of these procedures. Moreover, the prioritization of immediate health gains over those in the future, preference for accessing active treatment rapidly and reluctance to wait for spontaneous pregnancy expose patients to additional risks of overtreatment. Solutions are not simple to find but an alternative and innovative vision of infertility based on prognosis may be a valid solution. The availability of validated dynamic models based on real-life data that could predict both natural and ART-mediated conceptions may be of benefit. They could facilitate patients' counselling and could optimize the chances of success without exposing patients to unnecessary, expensive and demanding treatments.

A predictive neonatal mortality score for women with premature rupture of membranes after 22-27 weeks of gestation

OBJECTIVE

Premature rupture of the membranes (PROM) remains a leading cause of neonatal morbidity. The objectives of the present study were to analyze the outcomes of pregnancies complicated by PROM between 22 and 27⁺⁶ weeks of gestation (WG) and to study antepartum risk factors that might predict neonatal death.

PATIENTS AND METHODS

One hundred and seven pregnancies were analyzed over a 3-year period in a tertiary maternity hospital. The collected maternal and neonatal data were used to model and predict the outcome of PROM.

RESULTS

Prevalence of PROM (for live births) was 1.08%, and the overall survival rate was 59.8%. From preselected candidate variables, gestational age (GA) at PROM (p = .0002), a positive vaginal culture for pathogenic bacteria (p = .01), primiparity (p = .02), and the quantity of amniotic fluid (p = .03) were included in a multivariable logistic regression analysis. The corresponding adjusted odds ratios [95% confidence interval] were, respectively, 0.91 [0.87-0.96], 11.08 [1.65-74.42], 0.55 [0.33-0.91], and 0.97 [0.95-0.99]. These parameters were used to build a predictive score for neonatal death.

CONCLUSIONS

The survival rate after PROM at 22-27⁺⁶ weeks of gestation was 59.8%. Our predictive model (built using multivariable logistic regression) may be of value for obstetricians and neonatologists counseling couples after PROM.

The effectiveness of intrauterine insemination: A matched cohort study

OBJECTIVE

To study the effectiveness of an intrauterine insemination (IUI) program compared to no treatment in subfertile couples with unexplained subfertility and a poor prognosis on natural conception.

STUDY DESIGN

A retrospective matched cohort study in which ongoing pregnancy rates in 72 couples who voluntarily dropped out of treatment with IUI were compared to ongoing pregnancy rates in 144 couples who continued treatment with IUI. Couples with unexplained subfertility, mild male subfertility or cervical factor subfertility who started treatment with IUI between January 2000 and December 2008 were included. Couples were matched on hospital, age, duration of subfertility, primary or secondary subfertility and diagnosis. Primary outcome was cumulative ongoing pregnancy rate after three years. Time to pregnancy was censored at the moment couples were lost to follow up or when their child wish ended and, for the no-treatment group, when couples re-started treatment.

RESULTS

After three years, there were 18 pregnancies in the stopped treatment group (25%) versus 41 pregnancies in the IUI group (28%) (RR 1.1 (0.59-2.2)(p=0.4)). The cumulative pregnancy rate after three years was 40% in both groups, showing no difference in time to ongoing pregnancy (shared frailty model p=0.86).

CONCLUSIONS

In couples with unexplained subfertility and a poor prognosis for natural conception, treatment with IUI does not to add to expectant management. There is need for a randomized clinical trial comparing IUI with expectant management in these couples.

Natural conception: repeated predictions over time

STUDY QUESTION

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

SUMMARY ANSWER

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

WHAT IS KNOWN ALREADY

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

STUDY DESIGN, SIZE, DURATION

A prospective cohort studying the long-term follow-up of subfertile couples included in 38 centres in the Netherlands between January 2002 and February 2004. Couples with bilateral tubal occlusion, anovulation or a total motile sperm count <1 × 10⁶ were excluded.

PARTICIPANTS/MATERIALS, SETTING, METHODS

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

MAIN RESULTS AND THE ROLE OF CHANCE

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

LIMITATIONS, REASONS FOR CAUTION

The dynamic prediction model needs to be validated in an external population.

WIDER IMPLICATIONS OF THE FINDINGS

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

STUDY FUNDING/COMPETING INTERESTS

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

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

OBJECTIVE

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

DESIGN

 Population based cohort study.

SETTING

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

PARTICIPANTS

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

MAIN OUTCOME MEASURE

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

RESULTS

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

CONCLUSIONS

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