Prediction of outcome in human in vitro fertilization based on follicular and stimulation response variables

Predictors of success after in vitro fertilization

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

The POPI-Plus tool: prediction model of outcome of pregnancy in in vitro fertilization from a large retrospective cohort

OBJECTIVE

To create a tool that accurately predicts live birth chances after a positive pregnancy test after elective single embryo transfer (ET).

DESIGN

Retrospective cohort.

SETTING

CHUM hospital and Ovo clinic in Montreal, Canada.

PATIENT(S)

Patients with a positive pregnancy test result who underwent their first single ET after in vitro fertilization (IVF) at the CHUM hospital and Ovo clinic in Montreal, Canada, from 2012 to 2016 were selected. A total of 1,995 patients were included in this study.

INTERVENTION(S)

The data from both centers were combined and divided into training (70%, n = 1,398) and validation (30%, n = 597) sets. The predictive model was developed using backward selection method for the following variables: age of patient at egg retrieval; log β-human chorionic gonadotropin (β-hCG) (β-hCG) 1; log β-hCG 2; and IVF treatment type. Moreover, the classification tree, random forest, and neural network models were generated.

MAIN OUTCOME MEASURE(S)

The measured outcomes were live birth (live fetus ≥24 weeks of gestation) and nonviable pregnancies. The performance of all models was evaluated by area under the receiver operating characteristic curve (AUC).

RESULT(S)

Advancing age was negatively correlated with live birth. The odds ratio (OR) of age of patient at the time of egg retrieval was 0.95 (95% confidence interval [CI], 0.91-0.99). The log β-hCG 1 and log β-hCG 2 were positively correlated with live birth in the univariate analysis (OR, 4.15 [95% CI, 3.19-5.39], and OR, 3.84 [95% CI, 2.99-4.93], respectively). The β-hCG 1 level needed for a successful pregnancy was lower in frozen ET and modified natural IVF than in simulated IVF (OR, 0.55 [95% CI, 0.34-0.91], and OR, 0.49 [95% CI, 0.26-0.95], respectively). The best performance in terms of the AUC was the updated logistic model: POPI-Plus. The AUC values were 0.76 (95% CI, 0.73-0.79) and 0.78 (95% CI, 0.74-0.82) for the training and validation data, respectively. The other models (classification tree, random forest, and neural network) also performed adequately, with an AUC of ≥0.7, but remained below POPI-Plus. An open-access calculator was generated and can be found on the website of the University of Montreal on the following link: https://deptobsgyn.umontreal.ca/departement/divisions/medecine-et-biologie-de-la-reproduction/the-popi-plus-tool/.

CONCLUSION(S)

The POPI-Plus tool offers individualized counseling for patients after an initial positive β-hCG test result. Future studies will assess its impact on patient anxiety while awaiting viability ultrasound and perform prospective validation on new patients.

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.

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.

Prediction models in in vitro fertilization; where are we? A mini review

Since the introduction of in vitro fertilization (IVF) in 1978, over five million babies have been born worldwide using IVF. Contrary to the perception of many, IVF does not guarantee success. Almost 50% of couples that start IVF will remain childless, even if they undergo multiple IVF cycles. The decision to start or pursue with IVF is challenging due to the high cost, the burden of the treatment, and the uncertain outcome. In optimal counseling on chances of a pregnancy with IVF, prediction models may play a role, since doctors are not able to correctly predict pregnancy chances. There are three phases of prediction model development: model derivation, model validation, and impact analysis. This review provides an overview on predictive factors in IVF, the available prediction models in IVF and provides key principles that can be used to critically appraise the literature on prediction models in IVF. We will address these points by the three phases of model development.

Predictive factors in in vitro fertilization (IVF): a systematic review and meta-analysis

BACKGROUND

Various models have been developed for the prediction of pregnancy after in vitro fertilization (IVF). These models differ from one another in the predictors they include. We performed a systematic review and meta-analysis to identify the most relevant predictors for success in IVF.

METHODS

We systematically searched MEDLINE and EMBASE for studies evaluating IVF/ICSI outcome. Studies were included if they reported an unconditional odds ratio (OR) or whenever one could be calculated for one or more of the following factors: age, type of infertility, indication, duration of infertility, basal FSH, number of oocytes, fertilization method, number of embryos transferred and embryo quality.

RESULTS

Fourteen studies were identified. A summary OR could be calculated for five factors. We found negative associations between pregnancy and female age [OR: 0.95, 95% confidence interval (CI): 0.94-0.96], duration of subfertility (OR: 0.99, 95% CI: 0.98-1.00) and basal FSH (OR: 0.94, 95% CI: 0.88-1.00). We found a positive association with number of oocytes (OR 1.04, 95% CI: 1.02-1.07). Better embryo quality was associated with higher pregnancy chances. No significant association was found for the type of infertility and fertilization method. A summary OR for IVF indication and number of embryos transferred could not be calculated, because studies reporting on these used different reference categories.

CONCLUSIONS

Female age, duration of subfertility, bFSH and number of oocytes, all reflecting ovarian function, are predictors of pregnancy after IVF. Better quality studies are necessary, especially studies that focus on embryo factors that are predictive of success in IVF.

Prediction models in reproductive medicine: a critical appraisal

BACKGROUND

Prediction models have been developed in reproductive medicine to help assess the chances of a treatment-(in)dependent pregnancy. Careful evaluation is needed before these models can be implemented in clinical practice.

METHODS

We systematically searched the literature for papers reporting prediction models in reproductive medicine for three strategies: expectant management, intrauterine insemination (IUI) or in vitro fertilization (IVF). We evaluated which phases of development these models had passed, distinguishing between (i) model derivation, (ii) internal and/or external validation, and (iii) impact analysis. We summarized their performance at external validation in terms of discrimination and calibration.

RESULTS

We identified 36 papers reporting on 29 prediction models. There were 9 models for the prediction of treatment-independent pregnancy, 3 for the prediction of pregnancy after IUI and 17 for the prediction of pregnancy after IVF. All of the models had completed the phase of model derivation. For six models, the validity of the model was assessed only in the population in which it was developed (internal validation). For eight models, the validity was assessed in populations other than the one in which the model was developed (external validation), and only three of these showed good performance. One model had reached the phase of impact analysis.

CONCLUSIONS

Currently, there are three models with good predictive performance. These models can be used reliably as a guide for making decisions about fertility treatment, in patients similar to the development population. The effects of using these models in patient care have to be further investigated.

Intact follicle culture: what it can tell us about the roles of FSH glycoforms during follicle development

An important limiting factor in assisted reproduction treatment success rates is oocyte quality. In spite of improved results through several important innovations, the pregnancy rate per collected oocyte remains far too low. In order to improve this situation, it is necessary to learn more about fundamental factors modulating follicular development patterns. FSH is known to be the driving force for follicle development, but it is not yet understood how its multifarious functions are controlled and modulated. Evidence is accumulating that FSH glycoforms may be the key to this mystery. Intact follicle culture is a useful tool for the clarification of the actions of the different isoforms because the follicle unit is maintained and allowed to develop through several critical stages. Additionally important is the availability of the oocyte for functional evaluation. Because of these features, relationships can be uncovered that are not revealed with single cell test systems. The results so far obtained with this system suggest that follicle development pattern and oocyte quality is strongly influenced by FSH glycoform range, and that the requirements of the follicle may shift during progress through different stages of development. More studies are required, but these findings already suggest that the physiological shifts of circulating FSH glycoforms may indeed be important, and that attention should be paid to the glycoform distribution of exogenously applied FSH.

Comparison of follicle steroidogenesis from normal and polycystic ovaries in women undergoing IVF: relationship between steroid concentrations, follicle size, oocyte quality and fecundability

Studies of ovarian stimulation for IVF have suggested a relationship between follicle size and pregnancy rates. Furthermore the follicular endocrine environment is correlated with oocyte quality. The aim of this study was first to verify the relationship between follicular steroid content, follicular size, oocyte maturity and fertilization outcome in women with normal ovaries following recombinant human FSH (rhFSH). Secondly this study was extended to women with polycystic ovarian syndrome (PCOS). Fifty-nine patients (31 normal, 28 PCOS) underwent conventional IVF with rhFSH induction. Follicular diameter was classified as small (8-13 mm) or large (>14 mm) and sex steroid content was analysed for each group. Oocyte maturity was studied according to nuclear maturation the day after fertilization. In both ovulation groups, 17 beta-oestradiol and progesterone concentration were significantly higher in large follicles with meiotically competent oocytes compared with those containing meiotically incompetent oocytes. Testosterone levels were increased in PCOS follicles compared with normal patients, with no difference between corresponding sub-groups of follicles with meiotically competent oocytes. The relationship between follicle size and embryo development showed that 14 mm could be a threshold value following rhFSH induction in normal or PCOS women.

Developmental failure in preimplantation human conceptuses

The majority of human conceptuses fertilized normally in vitro fail to establish a pregnancy following their replacement in utero. However, since conceptuses are usually transferred after only one or two cell divisions, their developmental outcome is not known. It has been found that a significant number of human oocytes which can be fertilized carry chromosomal abnormalities, even in the absence of ovarian stimulation. After fertilization, preimplantation-stage conceptuses developing in vitro display a high incidence of cellular abnormalities. Similar disruptions of cellular organization have also been noted in conceptuses fertilized in vivo. Thus, developmental abnormalities and the demise of the conceptus prior to the stage of implantation may stem from the poor quality of the oocyte. The conditions encountered in vitro have also been proposed to cause or contribute to the early demise of human conceptuses.

Hormone release from cultured luteinized-granulosa cells mimics differences seen in vivo in patients undergoing IVF-ET

OBJECTIVES

Previous research from this laboratory has suggested that a relationship exists between the increase in circulating progesterone concentrations at the time of hCG administration and cycle outcome in patients undergoing IVF. Progesterone (P) increases of threefold or better within the 24-h period surrounding hCG administration appeared to be associated with a higher pregnancy rate. These data suggest a functional difference in the luteinized-granulosa of patients undergoing IVF. To test this hypothesis:

DESIGN

A split-split plot arrangement of treatments with two cell sources under five hormonal stimulations at four time points.

METHODS

Luteinized-granulosa cells (LGC) were collected from patients with either a normal increase (> or = threefold = NC) in circulating P (n = 4) or those with lower P increases (AC; n = 4). The cells were isolated by Ficoll gradient centrifugation and then cultured in 24-well culture plates using a modified media 199 containing 100 mIU/ml of hMG, FSH, LH, hCG, or a nonhormonal control to stimulate steroid-hormone production. At time points 2, 4, 6, and 8 days, media from each well was frozen for later hormone assay and replaced with fresh media containing the same stimulating factor. After all the media had been collected, P and estradiol (E2) released into the media were measured using radioimmunoassay.

RESULTS

Results indicate a higher media concentration of P (P < 0.001), but not E2 (P = 0.254), from NC, regardless of hormone stimulation or time in culture, when compared to the media from AC. Media concentrations of E2 were affected by a cell source by hormone stimulation by time interaction (P < 0.001) with varying effects. Media from NC maintained a constant E2 of between 1000-3000 pg/ml over the 8-day period (P = 0.163). However, media from AC demonstrated a stimuli-dependent E2 release (P < 0.001) ranging from < 1000 to over 11,000 pg/ml.

CONCLUSIONS

These data appear to support the existence of functionally different populations of luteinized-granulosa cells from patients undergoing IVF-ET.