Factors affecting embryo viability and uterine receptivity: insights from an analysis of the UK registry data

ARTICLE IN PRESS Reproductive BioMedicine Online (2015) ■■, ■■–■■

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ARTICLE

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Factors affecting embryo viability and uterine receptivity: insights from an analysis of the UK registry data

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Stephen A Roberts a,*, Mark Hann a, Daniel R Brison b

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Centre for Biostatistics, Institute of Population Health, Manchester Academic Health Sciences Centre, University of Manchester, Oxford Road, Manchester, UK; b Department of Reproductive Medicine, Old St Mary’s Hospital, Manchester Academic Health Sciences Centre, Central Manchester University Hospitals NHS Foundation Trust, Oxford Road, Manchester, UK Q2 * Corresponding author. E-mail address: [email protected] (SA Roberts). Steve Roberts is an applied biostatistician and senior lecturer in the Centre for Biostatistics at Manchester University. He leads the Biostatistics Unit at Central Manchester and Manchester Children’s University Hospitals (NHS) Trust. His work has ranged over wide areas of biomedical research, including laboratory studies, observational studies, clinical trials and health service research. His specific current research interests include the application of statistical modelling methodologies to routinely collected data to address clinical questions, with particular application to reproductive health.

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Many studies have identified prognostic factors for IVF treatment outcome; however, little information is available on the mechanism of their action. Embryo–uterus models have the potential to distinguish between factors acting on the embryo directly and those acting through the uterine environment. Here we apply embryo–uterus models to a comprehensive UK registry data from two periods, 2000–2005 and 2007–2011, containing 139,444 and 226,542 embryo transfer cycles, respectively. Given this large dataset, the embryo–uterus model is capable of distinguishing between uterine and embryo effects. Maternal age is the predominant predictor of live birth and acts on both the embryo and uterine components, but with larger effects on the embryo. Prolonged embryo culture is associated with greater embryo viability, reflecting the greater degree of selection, but is also associated with greater uterine receptivity. Cryopreserved embryos are less viable and were associated with poorer uterine receptivity. This work suggests that, in addition to the direct effects of in-vitro culture on the embryonic environment during the first few days of the embryo’s life, the delay in transfer after extended culture or cryopreservation can lead to an altered uterine environment for the embryo after transfer. Abstract

© 2015 Published by Elsevier Ltd on behalf of Reproductive Healthcare Ltd.

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KEYWORDS: embryo quality, embryo–uterus models, endometrial receptivity, IVF, statistical modelling

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http://dx.doi.org/10.1016/j.rbmo.2015.11.002 1472-6483/© 2015 Published by Elsevier Ltd on behalf of Reproductive Healthcare Ltd.

Please cite this article in press as: Stephen A. Roberts, Mark Hann, Daniel R. Brison, Factors affecting embryo viability and uterine receptivity: insights from an analysis of the UK registry data, Reproductive BioMedicine Online (2015), doi: 10.1016/j.rbmo.2015.11.002

ARTICLE IN PRESS 2 1

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SA Roberts et al.

Introduction

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After IVF treatment, the success of the subsequent transfer of the selected embryos to the potential mother depends on having a viable embryo and a receptive uterine environment. The role of the early embryonic environment has garnered significant interest both in its effect on IVF treatment success and on its potential effect on the long-term health of the resultant children. This environment has two components that are potentially influenced by IVF treatments: the in-vitro culture conditions and the in-vivo endometrial environment after embryo transfer. Over the years, understanding IVF success rates and how to improve them, and in particular how to minimize multiple births, has been limited by our inability to separate out effects on embryo viability from endometrial receptivity. In addition to the establishment of pregnancy, there are also implications for the long-term health of children arising from the Barker hypothesis (Barker, 1997), now formulated as the Developmental Origins of Health and Disease. Human epidemiological studies and use of animal models has established that maternal nutrition during pregnancy can programme fetal growth trajectory, birthweight, child growth and long-term health in the adult, in particular early onset of cardiovascular problems and type II diabetes. Recent work has highlighted the preimplantation phase of development as a window of particular vulnerability, as this is the point when remodelling of the embryonic genome, including the epigenome, occurs (Watkins et al., 2010). In support of this are well-established data showing that IVF singleton live births are of lower than normal birthweight (Pandey et al., 2012), and more recent data suggesting a possible association between early growth and some culture medium types (Kleijkers et al., 2014). One approach to the analysis of embryo implantation data is based on the embryo–uterus model (Speirs et al., 1983; Zhou and Weinberg, 1998), which explicitly assumes that treatment success (live birth after embryo transfer) requires both at least one viable transferred embryo and a receptive uterus. Separate logistic regression models are developed for each of these two components (embryo [E] and uterus [U]) as a function of patient and treatment characteristics. These E and U sub-models are then combined to estimate the probability of a successful outcome. As a simple clinical example, in doubleembryo transfer cycles, if clinical protocols result in embryo viability (E) being high but uterine receptivity (U) is low and rate limiting to success, then live birth rate will be determined by U, but the twin rate will be high, as either both embryos, or neither, will implant. Conversely, if E is low and U high, the live birth rate might be quite high (and dependent on E), but the twin rate will be relatively low, as implantation is possible in most cycles, but only rarely will two viable embryos be transferred. The EU approach was initially developed to enable a robust analysis of embryo factors and to solve the partial observability problem, where, unless all or none of the embryos develop, the embryo outcome is unknown (Roberts, 2007a). As outlined above, however, the biologically interpretable structure is of interest in itself, with the separate models for the two components providing the potential to identify the extent to which patient and treatment variables affect the embryo

directly, the uterine environment, or both. Such an analysis has been carried out for a large multicentre dataset with some success (Roberts et al., 2010b). Subsequent work has refined the criteria by which the evidence for the assignment of effects to the E or U sub-models can be judged (Stylianou et al., 2013). In this previous work, it was found that these assignments are not well identified, and large datasets are needed to provide any certainty. A replication of these findings is therefore needed in a larger cohort with a larger number of treatment centres. The UK register held by the Human Fertilisation and Embryology Authority (HFEA) provides a large repository of data on IVF treatments, albeit with limited data fields and a number of other issues limiting its historic usability (Roberts et al., 2010a). We have previously reported an analysis of a large cohort from this register for the period 2000–2005 (Hirst et al., 2011; Roberts et al., 2010a), concentrating on predictors of singleton and multiple birth after transfer. Others have presented analyses of predictors of whole-cycle IVF outcome (Nelson and Lawlor, 2011; Templeton et al., 1996) based on other cohorts from the HFEA register. Although no specific data are available on individual embryos, the large size of the dataset should nevertheless have the potential to identify and quantify embryo and uterine influences on outcome. Here, the primary analysis will be based on the previously described 2000–2005 dataset (Hirst et al., 2011), with a secondary analysis based on the more recent, but less detailed, publically available data for 2007–2011. The aim of this study was to use existing large registry datasets to elucidate the role of two exemplar environmental factors, embryo time in culture and cryopreservation, on the two components of treatment success: uterine environment and embryo viability. Secondary aims were to test the robustness of previous results inferring the effects of patient and treatment variables on the two components in a larger dataset and to explore the feasibility and utility of the EU modelling approach in datasets without embryo-specific variables.

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Materials and methods

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Data

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The primary dataset was derived from the UK HFEA register and comprises all IVF treatments in the UK between 2001 and 2005. This dataset was extensively cleaned and the derivation of the dataset has been described previously (Hirst et al., 2011; Roberts et al., 2010a). In the present analyses, where the time in culture is a primary focus, 394 cycles from the dataset used previously that were recorded as having the embryos transferred on the day of fertilization were excluded, as these either represent gamete intra-fallopian tube transfer cycles that are not relevant to the present study, or, equally likely, were erroneously recorded. For comparison, the analysis on the publically available data from the HFEA register for the most recent 5-year period (2007–2011) has been repeated (http://www.hfea.gov Q4 .uk/5874.html; downloaded: 27/1/2014 – 4/2/2014). This dataset provides less detailed data but provides validation with current practice, particularly the increased use of elective single embryo transfer after national policy changes and

Please cite this article in press as: Stephen A. Roberts, Mark Hann, Daniel R. Brison, Factors affecting embryo viability and uterine receptivity: insights from an analysis of the UK registry data, Reproductive BioMedicine Online (2015), doi: 10.1016/j.rbmo.2015.11.002

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ARTICLE IN PRESS Factors affecting embryo viability and uterine receptivity 1 2 3 4 5 6 7 8 9 10 11 12 13 14

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developments in the use of blastocyst transfer and cryopreservation. Fresh cycles with embryos recorded as being transferred on day 0 were again excluded (237 cycles). The use of intracytoplasmic sperm injection (ICSI) was excluded from models using frozen cycles in this dataset owing to large amounts of missing data (n = 21,854; 49% of frozen cycles). Similarly, insufficient data were available on the duration of infertility to include it in the models with this dataset. Because of confidentiality requirements, data on donated oocyte cycles were not available and these treatments are not included. This work used fully anonymized registry data and, as such, no ethical approval was deemed necessary under UK legislation (UK Departments of Health, 2011).

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Statistical model development and analysis

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We used the EU modelling approach (Roberts, 2007a; Zhou and Weinberg, 1998). The EU model consists of two logistic regression sub-models modelling the effect of patient and treatment variables on the embryo viability and the uterine receptivity. The patient outcome is then computed assuming that, for each embryo to develop, both the embryo needs to be viable and the uterine environment receptive and, given conditional independence assumptions, the model can then be fitted to the data using maximum likelihood methods. Technical details of the model formulation have been published previously (Roberts, 2007b; Roberts et al, 2010b). Given the large size of the databases, all the available patient and treatment variables were included in both submodels with no selection. As the EU model is formally undetermined for single embryo transfers, a variable for the number of embryos transferred cannot be fitted in both sub-models simultaneously and so was fitted in the E sub-model only, this being strongly favoured using the Akaike Information Criterion. To test the evidence favouring effects on the embryo or uterine environment (the E or U sub-models), models were fitted with each variable in each of the four possible submodel combinations (E only, U only, neither or both E and U). An Akaike Information Criterion difference greater than 2, corresponding to greater than 75% chance of correct assignment, provides reasonable evidence for favouring one or other assignment (Stylianou et al., 2013). The model was developed for the more comprehensive 2000–2005 dataset for fresh cycles with the following parameters: maternal age, duration of infertility, infertility diagnoses (tubal disease, endometriosis, ovulatory disorder, idiopathic/unexplained cause, male factor: all yes/no), number of previous IVF cycles, previous live birth (yes/no), number of embryos created, day of embryo transfer, number of embryos transferred, donor sperm use (yes/no), use of ICSI (yes/no) and year of treatment. To enable an easy and consistent comparison, continuous variables were categorized after the aggregation used in the 2007–2011 dataset (the upper two age bands 43–44 years and 45–50 years were merged for analysis owing to the very small numbers in the upper band). The number of embryos available was included as a surrogate for embryo quality, reflecting the number of embryos from which the best one(s) could be selected and therefore the chances of there being a high-quality embryo for transfer. Models including fresh and frozen cycles could not include

3 this variable as no links were found in the datasets between the frozen cycle and the fresh cycle from which the embryos were derived. To investigate the effects of embryo freezing, models were fitted to the combined fresh and frozen data. Interactions between freezing and other covariates were included in both sub-models where these reached the 1% significance level in a likelihood-ratio test in the 2000–2005 dataset. Models were fitted using the maximum likelihood procedures in Stata v13 (StataCorp, 2013).

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Results

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The 2000–2005 dataset contains data on 139,444 IVF embryo transfer cycles (119,536 fresh and 19,908 frozen) from 85,160 patients (81,769 with a fresh cycle and 14,874 with a frozen cycle) in 84 centres. Data for the calendar years 2007–2011 were available on 226,542 cycles (182,196 fresh, 44,346 frozen). Other than for the excluded variables discussed above, rates of missing data on key modelling variables were small: 756 fresh cycles had missing data on one or more of day of embryo transfer, source of sperm (i.e. partner or donor) or treatment type. A small number of cycles (<0.25%) were excluded owing to data inconsistencies, for example, the number of times the patient had been pregnant through the use of IVF exceeded the number of previous IVF cycles. In all, 181,203 fresh and 44,326 frozen cycles were included in the analyses. The fully anonymized data available does not allow identification of the number of patients treated in this series. The clinical and treatment characteristics of the two datasets are presented in Table 1.

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The fitted models

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The odds ratios for each of the fitted parameters in each of the datasets for the two sub-models are shown in Figure 1. The model includes all the parameters shown plus the year of treatment, and the odds ratios represent the effect of each variable on the embryo viability (E sub-model) and uterine receptivity (U sub-model) respectively. The results of the tests to determine the specific influences on the embryo viability and uterine receptivity within the EU framework are presented in Table 2. Full details of the fitted models and their parameter estimates are provided in supplementary Tables 1 and 2. In general, the two datasets give similar parameter estimates. The fitted models have relatively low predictive power, with the area under the receiver-operator curve statistic for the three outcomes (twins, singleton, no baby) across the two models varying between 0.61 and 0.70 (see Supplementary Table S3 for details).

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Patient characteristics

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In fresh cycles, strong evidence is available that age affects both the embryo and uterine components, although the effect is considerably stronger on the embryo. Female-related

Please cite this article in press as: Stephen A. Roberts, Mark Hann, Daniel R. Brison, Factors affecting embryo viability and uterine receptivity: insights from an analysis of the UK registry data, Reproductive BioMedicine Online (2015), doi: 10.1016/j.rbmo.2015.11.002

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SA Roberts et al.

Table 1

Description of the two datasets.

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2000–2005 dataset

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2007–2011 dataset

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Fresh cycles

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Frozen cycles

Fresh cycles

19,908 9783 5114 2542 1944 371 154 3489 2807 5200 5488 2924 5446 1105 2917 3877 8380 5206 1073 8827 4824 5184 494 8451 NA

181,959 77,474 44,735 29,295 23,992 5261 1202 NA

Frozen cycles

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Number of cycles, n (%) Age, years

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Duration of infertility, n (%)

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Diagnosis, n (%)

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Previous live birth, n (%) Previous IVF cycles, n (%)

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Donor sperm, n (%) ICSI, n (%) Number of embryos created, n (%)

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Embryos thawed and viable, n (%)

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Days in culture, n (%)

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Embryos Transferred, n (%)

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Treatment year, n (%)

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18–34 35–37 38–39 40–42 43–44 45–50 0–1 2 3–4 5–8 9–25 Tubal Endometriosis Ovulatory Idiopathic Male Yes 0 1 2 3–23 Yes Yes 1 2 3 4 5 6 7–8 9–13 14–45 1 2 3 4 5–21 1–2 3 4 5–7 1 2 3 2000 2001 2002 2003 2004 2005 2007 2008 2009 2010 2011

119,536 59,437 30,089 15,502 11,563 2342 603 13,012 16,211 38,233 34,240 17,840 29,019 8538 15,065 25,226 52,134 20,264 68,901 27,249 12,798 10,588 2670 56,549 6395 10,994 12,939 13,736 13,071 11,891 18,899 20,918 10,693 NA

83,291 31,560 746 3,939 10,110 91,956 17,470 17,245 18,783 19,135 19,796 21,972 22,605 NA NA NA NA NA

(49.7) (25.2) (13.0) (9.7) (2.0) (0.5) (10.9) (13.6) (32.0) (28.6) (14.9) (24.3) (7.1) (12.6) (21.1) (43.6) (17.0) (57.6) (22.8) (10.7) (8.9) (2.2) (47.3) (5.4) (9.2) (10.8) (11.5) (10.9) (9.9) (15.8) (17.5) (8.9)

(69.7) (26.4) (0.6) (3.3) (8.5) (76.9) (14.6) (14.4) (15.7) (16.0) (16.6) (18.4) (18.9)

(49.1) (25.7) (12.8) (9.8) (1.9) (0.8) (17.5) (14.1) (26.1) (27.6) (14.7) (27.4) (5.6) (14.7) (19.5) (42.1) (26.2) (5.4) (44.3) (24.2) (26.0) (2.5) (42.5)

2180 8565 4677 2302 2184 NA

(11.0) (43.0) (23.5) (11.6) (11.0)

3145 13,865 2898 1478 2694 3233 4001 4063 4439 NA NA NA NA NA

(15.8) (69.6) (14.6) (7.4) (13.5) (16.2) (20.1) (20.4) (22.3)

(42.6) (24.6) (16.1) (13.2) (2.9) (0.7)

30,269 11,822 20,525 52,416 75,910 14,989 94,905 40,403 22,219 24,432 7416 101,724 15,231 20,802 21,984 21,452 19,744 17,091 26,074 27,338 12,235 NA

(16.6) (6.5) (11.3) (28.8) (41.7) (8.2) (52.2) (22.2) (12.2) (13.4) (4.1) (55.9) (8.4)b (11.4) (12.1) (11.8) (10.9) (9.4) (14.3) (15.0) (6.7)

69,449 71,710 1946 38,343 43,573 129,109 9277 NA NA NA NA NA NA 31,455 33,901 37,027 38,972 40,604

(38.3)c (39.5) (1.1) (21.1) (23.9) (71.0) (5.1)

(17.3) (18.6) (20.3) (21.4) (22.3)

44,346 19,296 10,941 6947 5702 1107 353 NA

(43.5) (24.7) (15.7) (12.9) (2.5) (0.8)

8692 2531 6409 12,045 17,999 12,247 268 19,230 11,031 13,817 1657 11,371 NA

(19.6) (5.7) (14.5) (27.2) (40.6) (27.6) (0.6) (43.4) (24.9) (31.2) (3.7) (50.6)a

6423 15,059 8450 14,414

(14.5) (34.0) (19.1) (32.5)

NA

13,178 30,228 940 NA NA NA NA NA NA 7931 8077 8494 9615 10,229

(29.7) (68.2) (2.1)

(17.9) (18.2) (19.2) (21.7) (23.1)

a

Data on use of intracytoplasmic sperm injection not available for 21,854 frozen cycles. Data missing for eight cycles. c Data missing on culture time for 511 cycles. b

Please cite this article in press as: Stephen A. Roberts, Mark Hann, Daniel R. Brison, Factors affecting embryo viability and uterine receptivity: insights from an analysis of the UK registry data, Reproductive BioMedicine Online (2015), doi: 10.1016/j.rbmo.2015.11.002

ARTICLE IN PRESS Factors affecting embryo viability and uterine receptivity

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E sub−model

U sub−model

18−34 35−37 38−39 40−42 43−50 0−1y Duration of infertility 2y 3−4y 5−8y 9−25y Tubal Diagnosis Endometriosis Idiopathic Ovulatory Male factor Previous live birth 0 Previous cycles 1 2 3−23 Donor sperm Intracytoplasmic sperm injection 1 Embryos created 2 3 4 5 6 7−8 9−13 14−45 Days in culture 1−2 3 4 5−7 Embryos transferred 1 2 3 Age

2000−2005 2007−2011 0.2

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0.5 1.0 OR

2.0

0.2

0.5 1.0 OR

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Figure 1 Odds ratios and 95% confidence intervals for embryo viability and uterine receptivity for each of the fitted parameters from the “E” and “U” components of the fitted model to data from fresh cycles. Reference levels are omitted for binary indicators or shown as odds ratios of 1.0 without error bars.

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infertility diagnoses only have a weak effect on uterine receptivity and a non-significant effect on embryo viability. Couples with male factor infertility or using donor sperm show an increased uterine receptivity, reflecting the fact that these patients are less likely to have female-related pathology. Patients who had undergone previous IVF cycles have less receptive uteri, generally more so with increasing attempts. In contrast, no evidence was available for an effect on the embryo in the 2000–2005 data, but some evidence for a weak effect in the more recent data. A modest association of poorer prognosis with increased duration of infertility exists, which acts predominantly through the uterine component.

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Treatment

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The use of ICSI has only a very small effect and acts through the embryo component. Some differences over time reflect

an improvement in outcomes over the 2000–2005 period, but less clear trends are apparent in the more recent data. Interestingly, these predominantly affect the uterine component, with no evidence for changes in embryo viability (Supplementary Table S1).

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Embryo quality The number of embryos created is strongly associated with improved viability of the transferred embryos; with more embryos created there is a greater chance of having a good quality embryo for transfer. Having more embryos is also strongly associated with greater uterine receptivity (Figure 1). In the older 2000–2005 dataset, before the widespread advocacy of elective single embryo transfer, embryos from single embryo transfer and double embryo transfer had similar

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SA Roberts et al.

Table 2 Difference in Akaike Information Criterion between the presented model and models with the patient and treatment effects allocated to different sub-models, E, U, neither or both. Zero indicates the presented model; negative values represent better fits to the data and positive values worse fits. Differences of two or less can be interpreted as not providing evidence in favour of either model (Stylianou et al., 2013). The best fitting model is indicated by an asterix.

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Increases in Akaike Information Criterion from “Base Model”

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2007–11 dataset

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Age Duration of infertility Tubal diagnosis Endometriosis Ovululatory diagnosis Idiopathic diagnosis Male factor Previous live birth Previous IVF cycles Donor sperm ICSI Embryos created Days in culture Embryos Transferred Year

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ICSI, intracytoplasmic sperm injection. a The embryo–uterus model with “E” and “U” in both components is not identified for single-mbryo transfers. b Data not available in 2007-2011 dataset. c Best fitting model.

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2780.6 209.7 81.3 −3.7c 1.3 0.5 15.0 261.2 298.0 31.0 3.6 1138.4 259.4 373.8 73.6

419.6 −0.8c −0.3c −1.7 −0.2c −0.4c 2.1 52.2 0.2 −1.7c −0.2 126.7 72.7 177.7 −6.0c

5.5 91.4 19.7 −1.9 2.9 2.2 14.1 10.5 72.4 11.4 −1.7c 51.2 18.9 0c 23.5

0c 0 0 0 0 0 0c 0c 0c 0 0 0c 0c NAa 0

5,511.8 NAb 54.0 0.5 8.2 2.0 9.4 483.5 604.7 45.4 36.9 1,710.1 1,298.1 306.0 12.4

1,003.5 NAb –0.9c 2.5 −1.9c −2.0c −2.0c 14.7 19.2 −0.8c 15.9 170.7 294.6 13.7 −2.4c

23.4 NAb 10.2 1.3 3.5 0.3 3.3 115.4 112.6 10.0 -1.6c 32.4 52.7 0c 3.8

0c NAb 0 0c 0 0 0 0c 0c 0 0 0c 0c NAa 0

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viability, whereas, in the more recent 2007–2011 data, embryos transferred singly had a greater viability, reflecting the choice of embryos for single embryo transfer. In both datasets, embryos transferred as part of a triple embryo transfer showed poorer viability, as would be expected given that this option was only recommended in exceptional circumstances throughout the period of both cohorts.

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Embryo culture time

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In the embryo sub-model, long-term (blastocyst) culture (5–7 days) yields embryos with greater viability as far as live birth is concerned (odds ratio [OR] for day 5–7 versus day 3 transfers 1.71 (1.52–1.92) in the 2000–2005 data and 1.64 (1.55–1.74) in the more recent data). Most (95%) blastocyst transfers were carried out on day 5 in both datasets, with the remainder on day 6, and only a very few recorded with a longer culture time of 7 days (16 [0.41%] in the 2000–2005 and 58 [0.15%] in the 2007–2011 datasets). Blastocyst transfer represents embryos that survived the longer time in culture and are, therefore, a subset of the embryos that would have been transferred earlier in the same cycles if culture had not been extended. Blastocyst culture was quite rare in the earlier time period (only 3939 cycles [3.3%]) but increased in the later dataset (38,343 cycles [21%]). The U component shows a more modest increase with blastocyst culture, but suggests that extended culture time also favourably affects uterine environment (OR for day 5–7 versus

day 3 transfers 1.24 [1.09–1.41] in the 2000–2005 data and 1.21 [1.14–1.30] in the more recent data).

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Effect of embryo cryopreservation

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Overall, frozen embryos have a lower viability compared with fresh embryos (OR averaged over age 0.69 [0.62–0.77] in the 2000–2005 data and 0.89 [0.83–0.94] in the 2007–2011 data). The model including both fresh and frozen cycles shows a significant interaction for age by freezing in both sub-models (P = 0.049 in U and P = 0.024 in E for the 2000–2005 dataset and P < 0.001 for both E and U in the 2007–2011 data). Embryo viability and uterine receptivity as a function of age at time of transfer for fresh and frozen embryo transfers is shown in Figure 2. The effect of age on embryo viability is less pronounced in frozen cycles; however, we are considering maternal age at the time of embryo transfer (age at time of egg recovery is not available in these datasets), with a large proportion of the embryos transferred in the older women having been generated when they were younger. The uterine receptivity in frozen transfers was, overall, poorer (2000–2005 data: OR = 0.79 [0.71–0.88]; 2007–2011 data: OR = 0.61 [0.57–0.65]) and again frozen transfers showed a weaker age effect. Significant (interaction P < 0.01) differences were also found in the effects of previous treatments and numbers of embryos transferred between fresh and frozen cycles. As these simply reflect differences in population and clinical practice,

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ARTICLE IN PRESS Factors affecting embryo viability and uterine receptivity

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1.0 Odds ratio 0.5 0.2 18−34

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Figure 2 The effect of age on the two components of IVF success in fresh and frozen cycles. Odds ratios with 95% confidence intervals are shown relative to a reference of fresh cycles in 35–37-year old women in the respective cohorts. The reference age (35–37 years) is shown without error bars.

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they were included in the model, but not presented in detail. Full model details are available in Supplementary Table S2.

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Discussion

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The EU model was developed with sub-models for embryo viability and uterine receptivity to enable the analysis of variables measured on embryos and to account for the lack of full knowledge of the outcomes of individual embryos. The work here has demonstrated that the EU modelling approach can be used in the absence of embryo-level data and, with large datasets, can yield both novel and intuitively reasonable results. The use of EU modelling has significant implications for analysis of IVF success rates, and understanding of multiple birth minimization strategies, as it provides an understanding of the separate roles of embryo viability and endometrial receptivity. The effect of maternal age on IVF outcome is well established, and the EU modelling shows this is predominantly an effect of embryo (oocyte) ageing, with effects on uterine environment being rather weaker (but still present). This interpretation is broadly consistent with evidence from donor egg treatments (Legro et al., 1995; Stolwijk et al., 1997; Toner et al., 2002) and from frozen transfers (see below); however, our use of large-scale EU data modelling allows these relative contributions of maternal age to be quantified. In the datasets analysed here, cryopreservation is associated with poorer IVF outcomes, with both poorer embryo viability and lower uterine receptivity compared with fresh cycles. The poorer embryo viability could be attributed to less efficient cryopreservation (recent innovations, such as vitrification, have increased embryo survival) and that, for most treatments, the best quality embryos were transferred in a fresh cycle, leaving less viable embryos for freezing. Some centres chose to freeze on day 1, before embryo grading, and some cycles where the woman was deemed to have been overstimulated had all the embryos frozen (freeze-all), with no

fresh transfer; unfortunately, these are relatively small numbers and not readily identified in these datasets. Poorer uterine receptivity in frozen cycles is harder to interpret, and we note that this was predominantly an effect in younger women. Women receiving frozen transfers include those who had had a treatment failure in the fresh cycle so it is possible that these women have a worse prognosis and lower receptivity. Unfortunately, it is not possible in these datasets to link the frozen cycles to their corresponding fresh cycles. Some recent data and opinion suggests that, with modern freezing methods, frozen transfer cycles may be superior and some have advocated an elective “freeze-all” policy, despite the lack of good-quality evidence to support this (Cohen and Alikani, 2013). A randomized controlled trial of fresh versus frozen blastocyst transfers (Shapiro et al., 2011) found a higher live birth rate after frozen transfers, and concluded that ovarian stimulation had impaired uterine receptivity. Our data does not provide any evidence to support this opinion, although, as with all such studies, the effects of patient and embryo selection are heavily confounded. Although the evidence base is currently weak, the possibility of an altered uterine environment associated with frozen transfers and, in particular, data suggesting that frozen transfer babies may have higher birthweights compared with fresh transfer babies (Pinborg et al., 2014; Wennerholm et al., 2013) is an important consideration, and further data from clinical trials are required. We note that a multicentre clinical trial of elective embryo freeze-all in clinical IVF treatment has recently been funded in the UK by the National Institutes of Health Research and will start in 2015. Extended embryo culture to blastocyst stage increased significantly in usage in the more recent cohort and clearly acts to increase per-transfer success rates. This occurs largely by an effect on apparent embryo viability through selection effects, as significant numbers of embryos do not survive the prolonged culture to blastocyst. This agrees with extensive data from prospective clinical trials in IVF (Glujovsky et al., 2012). It cannot be determined, however, whether these embryos would or would not have developed successfully if

Please cite this article in press as: Stephen A. Roberts, Mark Hann, Daniel R. Brison, Factors affecting embryo viability and uterine receptivity: insights from an analysis of the UK registry data, Reproductive BioMedicine Online (2015), doi: 10.1016/j.rbmo.2015.11.002

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transferred earlier. Further data from randomized controlled trials suggest that a significant proportion of these “lost” embryos would have implanted, as the same Cochrane meta-analysis shows that cumulative live birth rates per egg collection are higher using day 3 transfer and freezing compared with blastocyst transfer and freezing (Glujovsky et al., 2012). Embryo selection by blastocyst culture, therefore, acts to increase the immediate chance of success in the fresh cycle and to decrease the overall success rate from fresh and frozen transfer. Our data also show an apparent association between prolonged culture and improved uterine receptivity, suggestive of an improved uterine environment, potentially related to an increased recovery time after ovarian stimulation, an increase in embryonic or maternal synchrony, or both. This is of interest because it suggests that, as well as directly altering the embryo’s environment by in-vitro culture, extended culture time also indirectly affects uterine environment (favourably as far as treatment success is concerned), leading to a second mechanism whereby time in culture could affect child outcomes beyond direct in-vitro effects on the embryo. Given the potential for selection and population characteristics to influence the U component (see discussion in Roberts et al., 2010b), this result could be artefactual, but we have failed to identify any specific confounding mechanism that could account for this result. Nevertheless, these data do demonstrate that extended culture may indirectly affect the uterine receptivity via a biologically plausible mechanism. As seen previously (Roberts et al., 2010b), infertility diagnosis has only a modest effect on outcome, with a tubal diagnosis having a negative effect on the uterine receptivity. This is counter-intuitive as patients with tubal problems are thought to be a subset that are otherwise reproductively normal, whereas our finding may suggest an interesting association between tubal problems and more widespread fertility. A male factor diagnosis shows a positive effect on uterine receptivity and only weak evidence to support any effect on embryo viability. This is readily accounted for by the fact that it is the couple that is infertile, and a male cause will therefore be associated with a better female prognosis. Duration of infertility is known to be strongly associated with decreased chances of success (Roberts et al., 2010b), and our data show that this effect is predominantly mediated via reduced uterine receptivity, rather than reduced embryo viability. This is particularly interesting as it has been previously assumed that a long duration of infertility selected a subpopulation of couples with poor-quality gametes. The number of previous cycles also has a strong adverse effect on success, weakly via E and predominantly via U, suggesting that repeat failed IVF cycles selects for a subpopulation of patients with reduced receptivity rather than reduced gamete/ embryo quality. The number of embryos created is positively correlated with outcome as expected from previous analyses of UK national data (Hirst et al., 2011; Nelson and Lawlor, 2012; Templeton et al., 1996). Interestingly however, this is mediated not just through embryo viability, as previously assumed, as more embryos are available for selection, but also women who generate more embryos are more receptive. This is somewhat unexpected, given the apparent adverse effect of ovarian stimulation on receptivity (see above). The fact that that uterine receptivity is positively associated with the ovarian

response to stimulation independently of the effects of age, diagnosis and treatment history included in the model implies a decoupling of the response to stimulation (eggs retrieved), which is associated with improved receptivity from the adverse effects of stimulation itself, which is abrogated by increasing the delay before embryo transfer. This can be readily understood if the response to stimulation is more strongly controlled by maternal factors rather than the actual amount of stimulation drug. Our comparative data analysis incorporating two almost consecutive time periods also highlights a number of important changes in the clinical IVF patient population and practice, in particular after the introduction of a multiple births policy by the HFEA (Human Fertilisation and Embryology Authority, 2008). The number of patients with a previous live birth has decreased by one-half in the most recent cohort, suggesting that use of IVF is being extended to wider patient populations. The use of donor sperm has increased threefold in absolute terms, reflecting factors including an increased presentation of male factor infertility, an increased uptake by single women or female–female couples and increased availability of donor sperm sourced from international sperm banks. Similarly, the proportion of cycles using ICSI has increased slightly in relative terms, but doubled in absolute terms. Interestingly, the number of embryos created has decreased, with more cycles generating three or fewer embryos and fewer cycles generating more than six embryos in the most recent cohort. Although the databases do not contain details of stimulation regimens, as fertilization rates have remained static over this time period, this decrease potentially reflects changes to milder stimulation in Europe (Fauser et al., 2010) resulting in collection of fewer oocytes. Embryo culture practice has changed as noted above, with a dramatic increase in blastocyst stage culture and transfer, almost entirely day 5–6 with very few later transfers, but also a notable shift away from early (day 1–2) transfer towards day 3. Finally, the second of the two datasets spans a period in which a substantive effort led by the professional bodies and the HFEA was undertaken to increase the use of singleembryo transfer in order to reduce the incidence of twin pregnancies. Our data confirm this with the proportion of cycles with single-embryo transfer increasing and the number of three-embryo transfers decreasing substantially in the second cohort. Significantly, patients receiving single-embryo transfer show a greater embryo viability (compared with double-embryo transfer) in the later cohort, which confirms the appropriateness of performing elective single-embryo transfer with good quality embryos. An interesting contrast is seen in the earlier cohort, where single-embryo transfer showed lower embryo viability. This reflects the pre-elective singleembryo transfer era, when most single-embryo-transfers were carried out when only a single (often poor quality) embryo was available. The fact that the similar results are obtained from two independent series suggests that the model is robust. Indeed similar results were obtained from another UK dataset (Roberts et al., 2010b) in which embryo-level data were available. The EU model, however, does rely on fairly strong assumptions around the independence of the E and U components (after allowing for measured covariates), and it is not yet known if these assumptions are fully justified in real data. The

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assignment of variable effects on to the E or U sub-model is only weakly identified through differential covariate effects on singleton and twin outcomes from cycles with multiple embryos transferred, so requires large datasets and leads to concern that these assignments may be sensitive to subtle biases. Second, it the national data has limitations in both the range of variables collected and in its accuracy and completeness (Roberts et al., 2010a). In addition, the data will contain couples with repeat treatments, which cannot be identified in the anonymized data. Previous work has suggested that ignoring the correlations between these repeat cycles does not lead to significant biases in the EU model estimates (Roberts et al., 2010b), and the influence and interpretation of such correlations within the EU model framework have been explored in detail elsewhere (Roberts and Stylianou, 2012). The fact that the two large cohorts here yield consistent results suggests that these limitations may not be of concern in practice, but further theoretical work and analysis of large datasets from other settings are needed to confirm this. In conclusion, we have shown that it is possible to use the EU modelling framework without embryo-specific variables providing that we have large datasets. These models enable identification of the effects of patient and treatment variables on the two components of IVF success: the embryo and the uterine environment, although some care is needed in interpretation of observational data with strong selection effects. Of particular note, this study suggests that the effects of embryo culture may well be two-fold: first, direct effects of the in-vitro environment occur during the first few days of the embryo’s life; but second, the use of transfer delayed by extended culture may well lead to a more normal uterine environment after transfer. The use of EU modelling has significant implications for analysis of IVF success rates and understanding of multiple birth minimization strategies, as it provides understanding of the separate roles of embryo viability and endometrial receptivity.

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The work presented here was conducted as part of the EU FP7 EpiHealth project FP7-HEALTH-2011-278418. SAR and DRB are Q5 supported by Central Manchester University Hospitals NHS Foundation Trust. SAR and DRB conceived the study; SAR and MH undertook the data analysis; all authors contributed to the writing of the manuscript and approved the final version.

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Appendix: Supplementary material

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Supplementary data to this article can be found online at doi:10.1016/j.rbmo.2015.11.002.

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References

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Declaration: The authors report no financial or commercial conflicts of interest.

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Received 16 July 2015; refereed 2 November 2015; accepted 4 November 2015.

Please cite this article in press as: Stephen A. Roberts, Mark Hann, Daniel R. Brison, Factors affecting embryo viability and uterine receptivity: insights from an analysis of the UK registry data, Reproductive BioMedicine Online (2015), doi: 10.1016/j.rbmo.2015.11.002

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