Deep phenotyping to predict live birth outcomes in in vitro fertilization

Improving IVF Utilization with Patient-Centric Artificial Intelligence-Machine Learning (AI/ML): A Retrospective Multicenter Experience

Objectives: In vitro fertilization (IVF) has the potential to give babies to millions more people globally, yet it continues to be underutilized. We established a globally applicable and locally adaptable IVF prognostics report and framework to support patient-provider counseling and enable validated, data-driven treatment decisions. This study investigates the IVF utilization rates associated with the usage of machine learning, center-specific (MLCS) prognostic reports (the Univfy® report) in provider-patient pre-treatment and IVF counseling. Methods: We used a retrospective cohort comprising 24,238 patients with new patient visits (NPV) from 2016 to 2022 across seven fertility centers in 17 locations in seven US states and Ontario, Canada. We tested the association of Univfy report usage and first intra-uterine insemination (IUI) and/or first IVF usage (a.k.a. conversion) within 180 days, 360 days, and "Ever" of NPV as primary outcomes. Results: Univfy report usage was associated with higher direct IVF conversion (without prior IUI), with odds ratios (OR) 3.13 (95% CI 2.83, 3.46), 2.89 (95% CI 2.63, 3.17), and 2.04 (95% CI 1.90, 2.20) and total IVF conversion (with or without prior IUI), OR 3.41 (95% CI 3.09, 3.75), 3.81 (95% CI 3.49, 4.16), and 2.78 (95% CI 2.59, 2.98) in 180-day, 360-day, and Ever analyses, respectively; p < 0.05. Among patients with Univfy report usage, after accounting for center as a factor, older age was a small yet independent predictor of IVF conversion. Conclusions: Usage of a patient-centric, MLCS-based prognostics report was associated with increased IVF conversion among new fertility patients. Further research to study factors influencing treatment decision making and real-world optimization of patient-centric workflows utilizing the MLCS reports is warranted.

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.

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

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

Identifying individual predictive factors for treatment efficacy

Given the heterogeneous responses to therapy and the high cost of treatments, there is an increasing interest in identifying pretreatment predictors of therapeutic effect. Clearly, the success of such an endeavor will depend on the amount of information that the patient-specific variables convey about the individual causal treatment effect on the response of interest. In the present work, using causal inference and information theory, a strategy is proposed to evaluate individual predictive factors for cancer immunotherapy efficacy. In a first step, the methodology proposes a causal inference model to describe the joint distribution of the pretreatment predictors and the individual causal treatment effect. Further, in a second step, the so-called predictive causal information (PCI), a metric that quantifies the amount of information the pretreatment predictors convey on the individual causal treatment effects, is introduced and its properties are studied. The methodology is applied to identify predictors of therapeutic success for a therapeutic vaccine in advanced lung cancer. A user-friendly R library EffectTreat is provided to carry out the necessary calculations.

Embryos derived from donor or patient oocytes are not different for in vitro fertilization outcomes when PGT allows euploid embryo selection: a retrospective study

BACKGROUND

At our facilities, patients that received embryos using donor oocyte during in vitro fertilization (IVF), usually have had at least one failed attempt to produce at least one euploid embryo with their own oocytes; however, the current debate between using donor over patient oocytes remains inconclusive. We examined the aneuploidy rate and IVF clinical outcomes from embryos derived from either donor or patient oocytes.

METHODS

Retrospectively, 973 cycles were examined of patients who underwent a standard IVF protocol. Chromosomal content was determined using Pre-implantation Genetic Testing (PGT) by either microarray-comparative genomic hybridization or Next-generation sequencing from either Day 3 (blastocysts) or Day 5 (trophectoderm) embryo biopsies, respectively. Embryo implantation was confirmed by serum β-hCG (> 10 m IU/mL/Day 14), whereas clinical pregnancy by a fetal heartbeat (Week 6.5-8).

RESULTS

Embryos derived from donor oocytes presented with more monosomies than embryos derived from patient oocytes (41.2% vs. 25.4%, p < 0.05, respectively); however, only Trisomy 7 (0.4% vs. 2.3%, p < 0.05) and Trisomy in X (0.7% vs. 2.3%, p < 0.05) were significantly less present when compared to patient oocyte derived embryos. Interestingly, rates for embryo implantation (46.7% vs. 50.8%, p = 0.35), clinical pregnancy (38.5% vs. 43.1%, p = 0.30), and live birth (30.5% vs. 30.5%, p = 0.99) were similar for embryos derived from donor and patient oocytes. These results did not change when adjusted for the number of embryos implanted.

CONCLUSION

Here, we show no significant differences in achieving pregnancy when using donor oocytes. Taking into consideration that aneuploidy rates are > 30% in embryos, independent of the oocyte origin, PGT should be recommended with donor oocytes as well.

Uterine size and volume are associated with higher live birth rate in patients undergoing assisted reproduction technology: A prospective cohort study

To investigate how uterine size and volume are associated with live birth rate in patients undergoing assisted reproduction technology.This prospective cohort study was conducted at the Reproductive Medicine Centre from January 2010 to May 2017. Multivariate binary logistic regression was used to evaluate the relations between uterine size, total volume, and live birth outcomes, after they were adjusted for the main influencing factors.A total of 7320 women of clinical pregnancy were enrolled. Compared with uterine lengths of 50 to 59 mm (referent), women with uterine lengths ≥60 mm had a lower live birth rate (RR = 1.541). Compared with uterine widths of ≥50 mm (referent), women with uterine widths <30 mm had a lower live birth rate (RR = 1.430). Compared with uterine anteroposterior diameters of <30 mm (referent), women with uterine anteroposterior diameters ≥50 mm had a lower live birth rate (RR = 1.636). Compared with uterine volumes of 30 to 49 mL (referent), women with volumes <30 mL and ≥70 mL had lower live birth rates (RR = 1.368 and 1.742, respectively).Our findings indicate that uterine sizes and volumes that were too large or too small reduced the live birth rate.

An artificial neural network for the prediction of assisted reproduction outcome

PURPOSE

To construct and validate an efficient artificial neural network (ANN) based on parameters with statistical correlation to live birth, to be used as a comprehensive tool for the prediction of the clinical outcome for patients undergoing ART.

METHODS

Data from 257 infertile couples that underwent a total of 426 IVF/ICSI cycles from 2010 to 2017 was collected on an ensemble of 118 parameters for each cycle. Statistical correlation of the parameters with the outcome of live birth was performed, using either t test or χ² test, and the parameters that demonstrated statistical significance were used to construct the ANN. Cross-validation was performed by random separation of data and repeating the training-testing procedure by 10 times.

RESULTS

12 statistically significant parameters out of the initial ensemble were used for the ANN construction, which exhibited a cumulative sensitivity and specificity of 76.7% and 73.4%, respectively. During cross-validation, the system exhibited the following: sensitivity 69.2% ± 2.36%, specificity 69.19% ± 2.8% (OR 5.21 ± 1.27), PPV 36.96 ± 3.44, NPV 89.61 ± 1.09, and OA 69.19% ± 2.69%. A rather small standard deviation in the performance indices between the training and test sets throughout the validation process indicated a stable performance of the constructed ANN.

CONCLUSIONS

The constructed ANN is based on statistically significant variables with the outcome of live birth and represents a stable and efficient system with increased performance indices. Validation of the system allowed an insight of its clinical value as a supportive tool in medical decisions, and overall provides a reliable approach in the routine practice of IVF units in a user-friendly environment.

Time-lapse videography for embryo selection/de-selection: a bright future or fading star?

The recent clinical introduction of time-lapse videography into in vitro fertilization laboratories has offered a novel opportunity for embryologists to explore improved methods for embryo selection. While the concept of uninterrupted culture of embryos provided by such systems is welcomed, the current evidence does not support its full application in routine clinical practice. The issue of whether or not algorithms for embryo selection can be extrapolated between laboratories, which may represent a major hurdle to its wide application, is currently gaining increasing attention amongst embryologists worldwide. In this commentary issues identified in time-lapse embryo selection/de-selection algorithms, such as quantitative versus qualitative parameters, are discussed alongside the reference start point for the timing system, and types of datasets used for developing and validating time-lapse algorithms. Considering these factors, alternative future research directions which could potentially solve current issues are proposed.

Are computational applications the "crystal ball" in the IVF laboratory? The evolution from mathematics to artificial intelligence

Mathematics rules the world of science. Innovative technologies based on mathematics have paved the way for implementation of novel strategies in assisted reproduction. Ascertaining efficient embryo selection in order to secure optimal pregnancy rates remains the focus of the in vitro fertilization scientific community and the strongest driver behind innovative approaches. This scoping review aims to describe and analyze complex models based on mathematics for embryo selection, devices, and software most widely employed in the IVF laboratory and algorithms in the service of the cutting-edge technology of artificial intelligence. Despite their promising nature, the practicing embryologist is the one ultimately responsible for the success of the IVF laboratory and thus the one to approve embracing pioneering technologies in routine practice. Applied mathematics and computational biology have already provided significant insight into the selection of the most competent preimplantation embryo. This review describes the leap of evolution from basic mathematics to bioinformatics and investigates the possibility that computational applications may be the means to foretell a promising future for the IVF clinical practice.

Promoting the use of elective single embryo transfer in clinical practice

Background

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

Main body

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

Conclusions

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

Validating predictors of therapeutic success: A causal inference approach

In personalized medicine medical decisions, practices and/or products are tailored to the individual patient. The idea is to provide the right patient with the right drug at the right dose at the right time. However, our current lack of ability to predict an individual patient's treatment success for most diseases and conditions is a major challenge to achieve the goal of personalized medicine. In the present work, we argue that many of the techniques often used to evaluate predictors of therapeutic success may not be able to answer the relevant scientific questions and we propose a new validation strategy based on causal inference. The methodology is illustrated using data from a clinical trial in opiate/heroin addiction. The user-friendly R library EffectTreat is provided to carry out the necessary calculations.

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

OBJECTIVE

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

DESIGN

Retrospective development and temporal external validation of prediction model.

SETTING

Outpatient IVF clinic.

PATIENT(S)

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

INTERVENTION(S)

None.

MAIN OUTCOME MEASURE(S)

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

RESULT(S)

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

CONCLUSION(S)

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

Predictors of in vitro fertilization outcomes in women with highest follicle-stimulating hormone levels ≥ 12 IU/L: a prospective cohort study

Objective

The purpose of this study is to evaluate factors predictive of outcomes in women with highest follicle-stimulating hormone (FSH) levels ≥12 IU/L on basal testing, undergoing in vitro fertilization (IVF).

Methods

A prospective cohort study was conducted at Stanford University Hospital in the Reproductive Endocrinology and Infertility Center for 12 months. Women age 21 to 43 undergoing IVF with highest FSH levels on baseline testing were included. Donor/Recipient and frozen embryo cycles were excluded from this study. Prognostic factors evaluated in association with clinical pregnancy rates were type of infertility diagnosis and IVF stimulation parameters.

Results

The current study found that factors associated with clinical pregnancy were: increased number of mature follicles on the day of triggering, number of oocytes retrieved, number of Metaphase II oocytes if intracytoplasmic sperm injection was done, and number of embryos developed 24 hours after retrieval.

Conclusions

Our findings suggest that it would be beneficial for women with increased FSH levels to attempt a cycle of IVF. Results of ovarian stimulation, especially embryo quantity appear to be the best predictors of IVF outcomes and those can only be obtained from a cycle of IVF. Therefore, increased basal FSH levels should not discourage women from attempting a cycle of IVF.

External validation and calibration of IVFpredict: a national prospective cohort study of 130,960 in vitro fertilisation cycles

BACKGROUND

Accurately predicting the probability of a live birth after in vitro fertilisation (IVF) is important for patients, healthcare providers and policy makers. Two prediction models (Templeton and IVFpredict) have been previously developed from UK data and are widely used internationally. The more recent of these, IVFpredict, was shown to have greater predictive power in the development dataset. The aim of this study was external validation of the two models and comparison of their predictive ability.

METHODS AND FINDINGS

130,960 IVF cycles undertaken in the UK in 2008-2010 were used to validate and compare the Templeton and IVFpredict models. Discriminatory power was calculated using the area under the receiver-operator curve and calibration assessed using a calibration plot and Hosmer-Lemeshow statistic. The scaled modified Brier score, with measures of reliability and resolution, were calculated to assess overall accuracy. Both models were compared after updating for current live birth rates to ensure that the average observed and predicted live birth rates were equal. The discriminative power of both methods was comparable: the area under the receiver-operator curve was 0.628 (95% confidence interval (CI): 0.625-0.631) for IVFpredict and 0.616 (95% CI: 0.613-0.620) for the Templeton model. IVFpredict had markedly better calibration and higher diagnostic accuracy, with calibration plot intercept of 0.040 (95% CI: 0.017-0.063) and slope of 0.932 (95% CI: 0.839-1.025) compared with 0.080 (95% CI: 0.044-0.117) and 1.419 (95% CI: 1.149-1.690) for the Templeton model. Both models underestimated the live birth rate, but this was particularly marked in the Templeton model. Updating the models to reflect improvements in live birth rates since the models were developed enhanced their performance, but IVFpredict remained superior.

CONCLUSION

External validation in a large population cohort confirms IVFpredict has superior discrimination and calibration for informing patients, clinicians and healthcare policy makers of the probability of live birth following IVF.

Prognostic factors associated with clinical pregnancy in in vitro fertilization using pituitary down-regulation with depot and daily low-dose luteal phase gonadotropin releasing hormone agonists: A single center's experience

AIM:

To review the experience on depot-dose, and daily low-dose gonadotropin releasing hormone agonist (GnRHa) long protocols and identify prognostic factors.

SETTING AND DESIGN:

A chart review was conducted on 2106 depot and 1299 daily low-dose cycles at a university hospital.

METHODS:

Clinical parameters were summarized, and prognostic factors of clinical pregnancy for each protocol were identified by logistic regressions. Missing data were imputed using multiple imputations (MI) and the regression models were rerun after MI.

RESULTS:

Clinical pregnancy rate was 57.5% and 46.9% in the depot and daily low-dose groups, respectively. Logistic regressions with MI identified age (odds ratio [OR]: 0.95, 95% confidence interval [CI]: 0.92–0.98), serum progesterone (OR: 0.62, 95% CI: 0.45–0.84) and endometrial thickness (OR: 1.06, 95% CI: 1.02–1.12) on human chorionic gonadotropin (hCG) day, number of oocytes retrieved (OR: 1.04, 95% CI: 1.01–1.06), fertilization rate (OR: 2.66, 95% CI: 1.46–4.87) and ratio of good-quality D3 embryos (OR: 4.31, 95% CI: 2.79–6.67) as prognostic factors in the depot group. Age (OR: 0.95, 95% CI: 0.92–0.98), endometrial thickness on hCG day (OR: 1.09, 95% CI: 1.03–1.15), ratio of good quality D3 embryos (OR: 2.56, 95% CI: 1.59–4.13) and the number of cryopreserved embryos (OR: 1.07, 95% CI: 1.003–1.15) are prognostic for the daily low-dose protocol. Some regression coefficients that are significant under model-wise deletion become nonsignificant after MI.

CONCLUSIONS:

Age, embryo quality and endometrial thickness on hCG day are important prognostic factors for both 1.0/1.3 mg depot and 0.05/0.1 mg daily low-dose luteal phase GnRHa long protocols. MI is a valuable tool to gauge and address bias caused by missing data in reproductive medicine.

Application of the Gradient Boosted method in randomised clinical trials: Participant variables that contribute to depression treatment efficacy of duloxetine, SSRIs or placebo

BACKGROUND

Randomised, placebo-controlled trials of treatments for depression typically collect outcomes data but traditionally only analyse data to demonstrate efficacy and safety. Additional post-hoc statistical techniques may reveal important insights about treatment variables useful when considering inter-individual differences amongst depressed patients. This paper aims to examine the Gradient Boosted Model (GBM), a statistical technique that uses regression tree analyses and can be applied to clinical trial data to identify and measure variables that may influence treatment outcomes.

METHODS

GBM was applied to pooled data from 12 randomised clinical trials of 4987 participants experiencing an acute depressive episode who were treated with duloxetine, an SSRI or placebo to predict treatment remission. Additional analyses were conducted on the same dataset using the logistic regression model for comparison between these two methods.

RESULTS

With GBM, there were noticeable differences between treatments when identifying which and to what extent variables were associated with remission. A single logistic regression only revealed a decreasing or increasing relationship between predictors and remission while GBM was able to reveal a complex relationship between predictors and remission.

LIMITATIONS

These analyses were conducted post-hoc utilising clinical trials databases. The criteria for constructing the analyses data were based on the characteristics of the clinical trials.

CONCLUSIONS

GBM can be used to identify and quantify patient variables that predict remission with specific treatments and has greater flexibility than the logistic regression model. GBM may provide new insights into inter-individual differences in treatment response that may be useful for selecting individualised treatments.

TRIAL REGISTRATION

IMPACT clinical trial number 3327; IMPACT clinical trial number 4091; IMPACT clinical trial number 4689; IMPACT clinical trial number 4298; NCT00071695; NCT00062673; NCT00036335; NCT00067912; NCT00073411; NCT00489775; NCT00536471; NCT00666757 (note that trials with IMPACT numbers predate mandatory clinical trial registration requirements).

The phenotype of an IVF child is associated with peri-conception measures of follicular characteristics and embryo quality

STUDY QUESTION

Are childhood measures of phenotype associated with peri-conception parental, IVF treatment and/or embryonic characteristics of IVF children?

SUMMARY ANSWER

Birthweight, childhood body mass index (BMI) and height of pre-pubertal IVF children were strongly associated with peri-conception factors, including follicular and embryonic characteristics.

WHAT IS KNOWN ALREADY

A growing number of studies have identified a range of phenotypic differences between IVF and naturally conceived pre-pubertal children; for example, birthweights are lower following a fresh compared with a thawed embryo transfer.

STUDY DESIGN, SIZE, DURATION

This retrospective cohort study included IVF children (n = 96) born at term (>37 weeks) after a singleton pregnancy from the transfer of either fresh or thawed embryos in New Zealand. Between March 2004 and November 2008, these children were subjected to clinical assessment before puberty.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Clinical assessment provided anthropometric measures of children aged 3.5-11 years old. Peri-conception factors (n = 36) derived retrospectively from parental, treatment, laboratory and embryonic variables (n = 69) were analysed using multiple stepwise regression with respect to standard deviation scores (SDSs) of the birthweight, mid-parental corrected BMI and height of the IVF children. Data from children conceived from fresh (n = 60) or thawed (n = 36) embryos, that met inclusion criteria and had high-quality data with >90% completeness, were analysed.

MAIN RESULTS AND THE ROLE OF CHANCE

Embryo treatment at transfer was identified as a predictor of birthweight with thawed embryos resulting in heavier birthweights than fresh embryos [P = 0.02, 95% confidence interval (CI) fresh minus thawed: -1.047 to -0.006]. Birthweight SDS was positively associated with mid-parental corrected BMI SDS (P = 0.003, slope 0.339 ± 0.100). Four factors were related (P < 0.05) to mid-parental corrected height SDS. In particular, child height was inversely associated with the diameter of lead follicles at oocyte retrieval (P < 0.0001, slope -0.144 ± 0.040) and with the quality score of embryos at transfer (P = 0.0008, slope -0.425 ± 0.157), and directly associated with the number of follicles retrieved (P = 0.05, slope 1.011 ± 0.497). Child height was also positively associated with the transfer of a fresh as opposed to thawed embryo (P < 0.001, 95% CI 0.275-0.750).

LIMITATIONS, REASONS FOR CAUTION

More than one embryo was transferred in most cycles so mean development and quality data were used. The large number of variables measured was on a relatively small sample size. Large cohorts from multiple clinics using a variety of treatment protocols and embryology methods are needed to confirm the associations identified and ultimately to test these factors as possible predictors of phenotype.

WIDER IMPLICATIONS OF THE FINDINGS

This is the first study to directly associate peri-conception measures of IVF treatment with a pre-pubertal child's phenotype. Demonstration that peri-conception measures relate to a pre-pubertal child's phenotype extends the range of factors that may influence growth and development. These findings, if corroborated by larger studies, would provide invaluable information for practitioners, who may want to consider the impact of ovarian stimulation protocols as well as the quality of the embryo transferred on a child's growth and development, in addition to their impact on pregnancy rate.

STUDY FUNDING/COMPETING INTERESTS

This work was supported by grants from the National Research Centre of Growth and Development New Zealand (grant 3682065) and the Australasian Paediatric Endocrine Group (APEG; grant 3621994), as well as a fellowship from Fertility Associates New Zealand awarded to M.P.G. In terms of competing interest, J.C.P is a shareholder of Fertility Associates. M.P.G. currently holds the position of Merck Serono Lecturer in Reproductive Biology. W.S.C. and P.L.H. have also received grants and non-financial support from Novo Nordisk, as well as personal fees from Pfizer that are unrelated to the current study. The other authors have no conflict of interest to declare.

Defining a comprehensive verotype using electronic health records for personalized medicine

The burgeoning adoption of electronic health records (EHR) introduces a golden opportunity for studying individual manifestations of myriad diseases, which is called 'EHR phenotyping'. In this paper, we break down this concept by: relating it to phenotype definitions from Johannsen; comparing it to cohort identification and disease subtyping; introducing a new concept called 'verotype' (Latin: vere = true, actually) to represent the 'true' population of similar patients for treatment purposes through the integration of genotype, phenotype, and disease subtype (eg, specific glucose value pattern in patients with diabetes) information; analyzing the value of the 'verotype' concept for personalized medicine; and outlining the potential for using network-based approaches to reverse engineer clinical disease subtypes.

Reducing multiples: a mathematical formula that accurately predicts rates of singletons, twins, and higher-order multiples in women undergoing in vitro fertilization

OBJECTIVE

To develop a mathematical formula that accurately predicts the probability of a singleton, twin, and higher-order multiple pregnancy according to implantation rate and number of embryos transferred.

DESIGN

A total of 12,003 IVF cycles from a single center resulting in ET were analyzed. Using mathematical modeling we developed a formula, the Combined Formula, and tested for the ability of this formula to accurately predict outcomes.

SETTING

Academic hospital.

PATIENT(S)

Patients undergoing IVF.

INTERVENTION(S)

None.

MAIN OUTCOME MEASURE(S)

Goodness of fit of data from our center and previously published data to the Combined Formula and three previous mathematical models.

RESULT(S)

The Combined Formula predicted the probability of singleton, twin, and higher-order pregnancies more accurately than three previous formulas (1.4% vs. 2.88%, 4.02%, and 5%, respectively) and accurately predicted outcomes from five previously published studies from other centers. An online applet is provided (https://secure.ivf.org/ivf-calculator.html).

CONCLUSION(S)

The probability of pregnancy with singletons, twins, and higher-order multiples according to number of embryos transferred is predictable and not random and can be accurately modeled using the Combined Formula. The embryo itself is the major predictor of pregnancy outcomes, but there is an influence from "barriers," such as the endometrium and collaboration between embryos (embryo-embryo interaction). This model can be used to guide the decision regarding number of embryos to transfer after IVF.

Predicting personalized multiple birth risks after in vitro fertilization-double embryo transfer

OBJECTIVE

To report and evaluate the performance and utility of an approach to predicting IVF-double embryo transfer (DET) multiple birth risks that is evidence-based, clinic-specific, and considers each patient's clinical profile.

DESIGN

Retrospective prediction modeling.

SETTING

An outpatient university-affiliated IVF clinic.

PATIENT(S)

We used boosted tree methods to analyze 2,413 independent IVF-DET treatment cycles that resulted in live births. The IVF cycles were retrieved from a database that comprised more than 33,000 IVF cycles.

INTERVENTION(S)

None.

MAIN OUTCOME MEASURE(S)

The performance of this prediction model, MBP-BIVF, was validated by an independent data set, to evaluate predictive power, discrimination, dynamic range, and reclassification.

RESULT(S)

Multiple birth probabilities ranging from 11.8% to 54.8% were predicted by the model and were significantly different from control predictions in more than half of the patients. The prediction model showed an improvement of 146% in predictive power and 16.0% in discrimination over control. The population standard error was 1.8%.

CONCLUSION(S)

We showed that IVF patients have inherently different risks of multiple birth, even when DET is specified, and this risk can be predicted before ET. The use of clinic-specific prediction models provides an evidence-based and personalized method to counsel patients.

The influence of patient and cohort parameters on the incidence and developmental potential of embryos with poor quality traits for use in human embryonic stem cell derivation

BACKGROUND

Human embryonic stem cells (hESCs) are most commonly derived from the inner cell mass (ICM) of blastocyst stage embryos. While the majority of hESC lines originate from good-quality embryos donated after cryogenic storage, poor-quality embryos (PQEs) not suitable for clinical use have also been shown to generate hESC. This provides a newfound function for embryos that would otherwise be discarded following IVF or ICSI. Owing to their lack of clinical importance, however, data on the poorest embryos in a cohort go largely unreported in the literature. It is therefore of interest to better understand the availability of PQEs from IVF/ICSI cycles and to determine their ability to develop into blastocysts with good-quality ICMs for use in hESC derivation. In this study, we investigate the influence of patient parameters and embryo cohort on PQE incidence, blastocyst development, ICM quality and successful hESC derivation from donated PQEs.

METHODS

PQEs from 736 patient cycles that did not meet our clinical criteria for transfer or cryopreservation were cultured until Day 6 of development and assessed for blastocyst formation and ICM quality. A subset of blastocysts with good-quality ICMs were then used for hESC derivation attempts. Anonymous patient data such as maternal age, embryo history and cohort parameters were then retrospectively compiled and analysed.

RESULTS

PQEs made up 46.8% of two pronucleate embryos created from IVF/ICSI. Including embryos with abnormal fertilization, a mean of 3.6 ± 2.8 embryos were donated per cycle with 32.6% developing to the blastocyst stage. Good-quality ICM were produced in 13.9% of PQEs cultured. Of good-quality ICM, 15.4% of those used in hESC derivation attempts resulted in a novel line. The PQEs that originated from older patients (>37 year) or from cycles that did not result in pregnancy had significantly diminished blastocyst development and ICM quality. Maternal age was also shown to further influence the ability of good-quality ICMs to generate hESC.

CONCLUSIONS

PQEs are an abundant source of embryos capable of developing to blastocysts with good-quality ICMs and subsequently generating novel hESC. We have shown that prognostic variables used to predict IVF/ICSI outcome can also help predict which PQEs have the best hESC developmental potential. Owing to the diversity of PQE origin, experiments designed to compare hESC derivation techniques or efficiency using PQEs should consider clinical IVF/ICSI parameters to establish groups with equal developmental competence. Additional investigation is needed to determine if these results are applicable to hESC derivation using good-quality embryos.

Cumulative live-birth rates per total number of embryos needed to reach newborn in consecutive in vitro fertilization (IVF) cycles: a new approach to measuring the likelihood of IVF success

OBJECTIVE

To report the use of cumulative live-birth rates (CLBRs) per ovarian stimulation cycle to measure the success of IVF is proving to be the most accurate method for advising couples who failed to conceive, although the accuracy yielded is relatively low, and cycle outcome is highly dependent on the number of embryos replaced. Our aim with this work is to report the CLBRs of IVF as a function of the number of embryos required to reach a live birth (EmbR), considering age, day of ET, and infertility etiology.

DESIGN

Survival curves and Kaplan-Meier methods to analyze CLBR in a retrospective cohort with respect to the number of EmbR.

SETTING

University-affiliated infertility center.

PATIENT(S)

Infertile couples undergoing IVF using own oocytes.

INTERVENTION(S)

None.

MAIN OUTCOME MEASURE(S)

CLBR per embryo transferred.

RESULT(S)

CLBRs increase rapidly between 1 and 5 EmbR, moderately between 5 and 15, and slowly thereafter. Live-birth rates rise more slowly when embryos are transferred on days 2-3 rather than on days 5-6, with comparable long-term results. Women's age is a negative factor from 35 to 37 years old, with a dramatic decrease in live-birth rates beyond age 40 years. In addition, there are significant worse results in endometriosis patients.

CONCLUSION(S)

The relationship between CLBR and number of EmbR provides realistic and precise information regarding IVF success and can be used to guide couples and practitioners.