Prediction of pregnancy outcomes of single vitrified-warmed blastocyst transfer using combination of an automatic classification algorithm applied on cleavage stage embryos and blastocyst morphological assessment: a single - centre, retrospective study

Prediction of pregnancy outcomes of single vitrified-warmed blastocyst transfer using combination of an automatic classification algorithm applied on cleavage stage embryos and blastocyst morphological assessment: a single - centre, retrospective study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prediction of pregnancy outcomes of single vitrified-warmed blastocyst transfer using combination of an automatic classification algorithm applied on cleavage stage embryos and blastocyst morphological assessment: a single - centre, retrospective study Hop Vu Dinh, Cuong An Manh, Anh Phi Thi Tu, Huong Nguyen Thi Lien, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4022641/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background To investigate a combination of the Early Embryo Viability Assessment (EEVA) system and blastocyst morphological assessment as a predictor of pregnancy outcomes of single vitrified-warmed blastocyst transfer, such as implantation and ongoing pregnancy. Methods The retrospective study was conducted in a single centre from 2020 to 2023 and included 511 single vitrified-warmed blastocyst transfer cycles. Blastocyst were selected for transfer based on conventional morphological assessment. Embryos Day 3 were evaluated using EEVA software. The correlation between the EEVA system alone, or a combination of the EEVA system and blastocyst morphological assessment, and pregnancy outcomes was qualified by generalized estimating equations (GEEs). Results The implantation rate and ongoing pregnancy were higher with lower scores generated by the EEVA software. A GEE model showed a negative association between a higher embryo score and lower odds of implantation and ongoing pregnancy. The OR of Score 3;4;5 vs. 1 were 0.350; 0.288; 0.282 (95%CI 0.201–0.607; 0.151–0.546; 0.125–0.636, p=0.000), respectively, for implantation. The OR of Score 3;4;5 vs. 1 were 0.321; 0256; 0.228 (95%CI 0.184-0.557; 0.129-0.505; 0.092-0.563, p=0.000), respectively, for ongoing pregnancy. The AUC of the model using the EEVA system for implantation and ongoing pregnancy potential is 0.651 and 0.655, respectively. The AUC of the model combining both systems for implantation and ongoing pregnancy potential is 0.730 and 0.726. The differences were statistically significant (p=0.0001). Conclusions The EEVA system can predict the success rates of assisted reproduction cycles, especially when combined with blastocyst morphological assessment in blastocyst selection for transfer. Early Embryo Viability Assessment (EEVA) Automated embryo assessment Timelapse Geri Incubator Blastocyst Morphology. Figures Figure 1 1. Introduction Currently, single blastocyst transfer is a trend at In vitro Fertilization (IVF) centre to avoid multiple pregnancies. Therefore, single vitrified-warmed blastocyst transfer (SVBT) is the main strategy in the embryo transfer policy of many centers [ 1 ]. The selection of embryos for transfer based on morphology is often subjective between embryologists [ 2 ], between different embryo morphology assessment systems, and between different times of assessment [3] . As a result, these techniques are not very precise, resulting in variable and sometimes inaccurate embryo ranking and selection for transfer [ 3 ]. To improve embryo culture and selection, the timelapse incubator system was introduced to allow continuous monitoring of embryos, which provides a lot of information about the embryo's development process for the assessment of embryologists without removing embryos outside of the stable culture environment of the incubator [ 2 ], [ 4 ], [ 5 ] [ 6 ]. Although the timelapse system has greatly improved embryo evaluation, it requires highly qualified embryologists and can be complex and time-consuming. Furthermore, although subjectivity is reduced, evaluation still requires manual annotation, especially when many new parameters are used [ 7 ][ 8 ]. Artificial intelligence (AI) can overcome these issues in selecting embryos for transfer. Recently, the benefits of AI in predicting pregnancy outcomes have been widely studied [ 9 ], [ 10 ], [ 11 ], [ 12 ]. The first automatic system for the analysis of images obtained through time-lapse was EEVA (Early Embryo Viability Assessment) [ 13 ]. This technology was the first practical application of artificial intelligence in an IVF laboratory, and it solves many of the previously described problems with the introduction of automation. EEVA is an automated embryo assessment software at the early embryo development stage, integrated exclusively into the GERI Plus timelapse incubator. This is the first software approved by the US Food and Drug Administration [ 14 ]. EEVA software records the development of each embryo with a cell-tracking system and predicts the likelihood (high, medium, or low) that an embryo will form a blastocyst based on automated detection and analysis of timelapse imaging information of the early cell division stage. EEVA software automatically detects the durations of the 2-cell (P2; t3–t2) and 3-cell (P3; t4–t3) stages and characteristics of the embryo during the first 3 days of embryonic development. This may avoid prolonging culture and save resources [ 15 ]. EEVA assesses embryos automatically and classifies them into 5 degrees: E1, E2, E3, E4, and E5, with a decreasing ability to develop into the blastocyst stage [ 14 ]. Although the EEVA Test was originally developed to predict embryos’ potential to reach the blastocyst stage at early developmental stages, strong associations were also found between the best EEVA categories and increased implantation rates in transferred embryos [ 13 ], [ 16 ]. However, there were also some studies that showed no difference in the selection of embryos for implantation when using EEVA software alone or when EEVA was combined with morphological assessment [ 5 ], [ 17 ], [ 18 ]. Some algorithms developed to predict implantation potential focus on late predictive parameters such as blastocyst morphology and/or timing of blastulation [ 19 ], [ 20 ], [ 21 ], KIDScore D5 (Vitrolife, Denmark), some algorithms also include early morphokinetic parameters [ 3 ]. On the other hand, the relationship between pregnancy outcomes and EEVA classification after single vitrified-warmed blastocyst transfer has not been evaluated. Therefore, the clinical application of EEVA still requires studies that analyse its correlation with pregnancy outcomes. The aim of this study was to investigate the EEVA system as a predictor of pregnancy outcomes, such as implantation and ongoing pregnancy. Furthermore, the aim was to compare the performance of the EEVA Test with the traditional morphological classification and with the combination of both systems for implantation and ongoing pregnancy potential. 2. Materials and methods 2.1. Study design The study was designed as retrospective research, with 511 single vitrified-warmed blastocyst transfer cycles from 2020 to 2023 in a single Assisted Reproductive Centre. All embryos were assessed by the EEVA Test on day 3, resulting in classification into scores from E1 (best) to E5 (worse) to predict the ability of blastocyst formation based on the embryo’s morphokinetic characteristics. Blastocysts were assessed morphologically before vitrification. Blastocysts were warmed before transfer for at least 1 hour. Blastocyst quality had not been reduced in this study. The embryologist evaluating blastocyst quality was blinded to the EEVA score. The correlation between univariate variables, including maternal age, endometrial thickness, BMI, embryonic age, embryonic expansion degree, ICM morphology, TE morphology, EEVA test, and pregnancy outcomes, was assessed by generalized estimating equations (GEEs). The GEE model using blastocyst morphological assessment only, the EEVA test only, and a combination of both systems for predicting implantation and ongoing pregnancy were compared to each other. 2.2. Oocytes and sperm handling Oocytes after retrieval were collected, washed, and incubated in pre-equilibrated G-IVF PLUS culture medium (Vitrolife, Sweden) at a benchtop BT37 incubator at 37°C using 7% CO2 and 5% O2 mixing gas. After incubation (approximately 3 hours), oocyte denudation was carried out by mechanical pipetting in G-IVF PLUS medium containing the enzyme hyaluronidase 80 IU/ml. After 2 h of culture in IVF PLUS medium, intracytoplasmic sperm injection (ICSI) was performed in gamete medium (Cook Medical, USA) supplemented with HEPES at 200× magnification using a Nikon inverted microscope with Hoffman optics. Sperm used for ICSI was filtered and washed by a concentration gradient method. All oocytes after ICSI were cultured in the continuous single culture NXC plate (Irvine Scientific). 2.3. Embryo culture and imaging Embryos were cultured in the Geri Dishes® with a capacity for up to 16 embryos sharing a common medium droplet of 80 µl of pre-equilibrated continuous single culture-NXC plate (Irvine Scientific) covered in 3.5 ml of mineral oil in the Geri+® timelapse incubator (Genea Biomed, Australia). Embryos were kept inside the incubator uninterruptedly from Days 1 to 3. The fertilization, Day 2, and D3 scores were performed on the GERI incubator’s screen. The embryos were assisted in hatching on day 3, and the culture medium was changed outside the incubator. After assisted hatching, the embryos were cultured uninterrupted until Day 5. The EEVA system 3.0 (Progyny Inc., CA, USA) was used to image embryonic development and predict blastocyst formation. The system consists of a microscope (EEVA scope), which was placed in a Geri+® time-lapse incubator, and a cell-tracking system for registering cell divisions. Every 5 minutes, a single, high-resolution, single-plane dark field illumination image was taken of each embryo. 2.4. Embryo assessment Evaluation and grading of embryo morphology were according to the Istanbul consensus workshop on embryo assessment: proceedings of an expert meeting, 2011. Fertilization was assessed at 17 ± h post-ICSI by the presence of two pronuclei and two polar bodies. The number of blastomeres on embryo day 3 was assessed at 68 ± 1 h post-ICSI. Blastocyst morphology was evaluated on Days 5 and 6 based on cavity expansion, quality of TE, and ICM (according to the scoring blastocyst by grading system of Gardner and Schoolcraft), at 116 ± 1 [ 22 ]. The EEVA assessment was based on day 3 embryo morphokinetics. EEVA software automatically recognized the durations of the 2-cell (P2; t3–t2) and 3-cell (P3; t4–t3) stages and characteristics of the embryo during the first 3 days of embryonic development. In addition, the number of blastomeres in the day 3 embryo of each embryo was put into the EEVA software, which then produced the EEVA grade automatically for each embryo. All embryos were classified by the EEVA Test (with Version 3.0) and classified with a numeric score from E1 (best) to E5 (worse) according to their likelihood of reaching the blastocyst stage. 2.5. Blastocyst vitrification and warming For vitrification on day 5 or 6, blastocysts were required to attain a blastocyst expansion degree of 3 (> BL2). These blastocysts were vitrified immediately according to the Cryotop method (Cryotech vitrification, Vitrolife, Sweden). If the developing embryo did not fulfill the criteria, it was cultured for a maximum of 7 days. When the patient is indicated for single vitrified-warmed blastocyst transfer, the blastocyst will be warmed on the day of embryo transfer using the Cryotop method (Cryotech warming, Vitrolife, Sweden). 2.6. Post-warming embryo culture and vitrified-warmed blastocyst transfer procedure transfer, and luteal support Embryos were cultured in the continuous single culture NXC plate (Irvine Scientific) after warming for at least 1 hour before embryo transfer. Blastocyst quality was assessed before transferring. Only blastocysts that maintain the same quality or develop better compared to blastocyst quality before vitrification were used in this study. A decreased-quality blastocyst will not be included in this study. The hormonal replacement therapy (HRT) protocol was initiated on the second day of the menstrual cycle for all patients. To ensure optimal endometrial thickness, patients were required to undergo ultrasound check-ups of the endometrium, uterine, and adnexal on days 10 and 14 of the menstrual cycle. Once the endometrial thickness reached a minimum of 7 mm, endometrial transformation was achieved using vaginal micronized progesterone at a daily dosage of 600–800 mg (Utrogestan, Besins, Thailand, or Cyclogest, Actavis U.K. Limited, United Kingdom), in addition to orally administered Dydrogesterone at a daily dose of 30mg (Duphaston, Abbott Biologicals B.V., Netherlands). Blastocyst embryos were transferred after approximately 120 +/- 3 hours. The embryo transfer procedure adhered to the 2017 guidelines by the American Society for Reproductive Medicine (ASRM). In situations where the catheter encountered difficulty passing through the cervical canal into the uterine cavity, resulting in prolonged transfer time and requiring the use of a catheter with a malleable mandrel or cervical clamp ponzi, the procedure was recorded as a difficult embryo transfer. 2.7. Pregnancy outcomes For clinical outcomes, we used the following definitions: pregnancy was confirmed (serum hCG > 5mIU/mL); implantation was confirmed at 8 weeks of pregnancy by observation of the gestational sac by ultrasound; clinical pregnancy, with a confirmed gestational sac at 6–7 weeks of pregnancy; ongoing pregnancy: defined as pregnancy that continued beyond 12 weeks of gestation; LB: live birth was confirmed by direct communication of the patients. 2.8. Statistical analysis Statistical analysis was performed using the Statistical Package for the Social Sciences 26 (SPSS Inc.). To describe the data, descriptive statistics frequency analysis, and percentage analysis were used for categorical variables, and the mean and standard deviation (mean ± SD) were used for continuous variables. Statistical test for quantitative variables, when comparing the average value in two research groups, use the ANOVA test. Statistical test for qualitative variables, when comparing two proportions, use the Chi square test. Using the generalized estimation equation system (GEEs) to evaluate the relationship between the EEVA test, blastocyst morphological assessment, or a combination of the EEVA test and blastocyst morphological assessment, and implantation and ongoing pregnancy. Comparison of 3 methods to evaluate the results of predicting pregnancy outcomes using the area under the ROC curve (AUC): performed on the prediction probability scale of the model rule. The odds ratios (ORs) of the effect of the variables included in the GEEs on the outcome variables were expressed as 95% CI, and statistical significance was considered for P -values < 0.05. Receiver operating characteristic (ROC) curves were graphed from the probability values obtained by the GEE models, and the areas under the ROC curves (AUC) were calculated as model evaluation metrics. A comparison of AUCs was performed using a paired, two-tailed DeLong’s test. The 2 AUC was statistically significant different when p < 0.05. 2.9. Ethics statement This study was reviewed and approved by the Internal Committee for Ethics of the Tam Anh General Hospital, Vietnam. 3. Results 3.1. Demographic and clinical characteristics of patients We analyzed the pregnancy outcomes of 511 single vitrified-warmed blastocyst transfer cycles from 2020 to 2023. A general description of the study population, including cycle, demographic, and embryonic characteristics, was presented by the parameters in Table 1 . Table 1 General Descriptive and demographic characteristics of the cycles. Characteristic Cycles (n) 511 Maternal age (year) 30.36 ± 4.33 Endometrial thickness 9.5 ± 1.2 BMI (kg/m 2 ) 21.07 ± 2.54 - Embryo Day 5 - Embryo Day 6 - 460 - 51 - BL3 - BL4 - BL5 - BL6 - BL7 - 20 - 53 - 243 - 161 - 34 - ICM A - ICM B - ICM C - 352 - 113 - 46 - TE A - TE B - TE C - 312 - 157 - 42 - E1 - E2 - E3 - E4 - E5 - 220 - 134 - 76 - 52 - 29 Implantation rate (%) 67.1 Clinical pregnancy rate (%) 54.6 Ongoing pregnancy rate (%) 49.7 Live birth rate (%) 45 Continuous data are expressed as mean ± SD; categorical data are expressed as percentage (%). Categorical data are expressed as percentages (%), E: EEVA score, EEVA Early embryo viability assessment. BMI: BMI of the oocyte provider, ICM inner cell mass, TE trophectoderm, BL blastocyst expansion degree No differences were found in the maternal age, endometrial thickness, BMI, or embryo expansion degree between the implantation and non-implantation groups, ongoing pregnancy and non-ongoing pregnancy groups (p > 0.05). The rates of day 5 blastocysts, ICM A, TE A, and E1 were significantly higher in the implantation and ongoing pregnancy groups compared with the non-implantation and non-ongoing pregnancy groups, respectively (p < 0.05), as detailed in Table 2 . Table 2 Descriptive and Demographic characteristics in implantation and non-implantation groups; ongoing pregnancy and non-ongoing pregnancy group Characteristic Implantation Non-implantation P value Maternal age (year) 30.1 ± 4.2 30.9 ± 4.6 0.06 Endometrial thickness 9.5 ± 1.2 9.4 ± 1.4 0.456 BMI 21.1 ± 2.5 20.9 ± 2.5 0.44 - Embryo Day 5 - Embryo Day 6 - 319 (93%) - 24 (7%) - 141 (83.9%) - 27 (16.1%) 0.001 - BL3 - BL4 - BL5 - BL6 - BL7 - 12 (3.5%) - 35 (10.2%) - 167 (48.7%) - 110 (32.1%) - 19 (5.5%) - 8 (4.8%) - 18 (10.7%) - 76 (45.2%) - 51 (30.4%) - 15 (8.9%) 0.588 - ICM A - ICM B - ICM C - 271 (79%) - 58 (16.9%) - 14 (4.1%) - 81 (48.2%) - 55 (32.7%) - 32 (19.0%) 0.000 - TE A - TE B - TE C - 248 (72.3%) - 86 (25.1%) - 9 (2.6%) - 64 (38.1%) - 71 (42.3%) - 33 (19.6) 0.000 - E1 - E2 - E3 - E4 - E5 - 169 (49.3%) - 97 (28.3%) - 40 (11.7%) - 24 (7.0%) - 13 (3.8%) - 51 (30.4%) - 37 (22%) - 36 (21.4%) - 28 (16.7%) - 16 (9.5) 0.000 Ongoing pregnancy Non-ongoing pregnancy P value Maternal age (year) 29.7 ± 4.05 30.9 ± 4.5 0.052 Endometrial thickness 9.6 ± 1.2 9.4 ± 1.3 0.059 BMI 21.1 ± 2.5 21.1 ± 2.5 0.953 - Embryo Day 5 - Embryo Day 6 - 239 (94.1%) - 15 (5.9%) - 221 (86%) - 36 (14%) 0.002 - BL3 - BL4 - BL5 - BL6 - BL7 - 8 (3.1%) - 26 (10.2%) - 121 (47.6%) - 85 (33.5%) - 14 (5.5%) - 12 (4.7%) - 27 (10.5%) - 122 (47.5%) - 76 (29.6%) - 20 (7.8%) 0.669 - ICM A - ICM B - ICM C - 213 (83.9%) - 35 (13.8%) - 6 (2.4%) - 139 (54.1%) - 78 (30.4%) - 40 (15.6%) 0.000 - TE A - TE B - TE C - 199 (78.3%) - 52 (20.5%) - 3 (1.2%) - 113 (44%) - 105 (40.9%) - 39 (15.2%) 0.000 - E1 - E2 - E3 - E4 - E5 - 135 (53.1%) - 73 (28.7%) - 25 (9.8%) - 14 (5.5%) - 7 (2.8%) - 85 (33.1%) - 61 (23.7%) - 51 (19.8%) - 38 (14.8%) - 22 (8.6%) 0.000 Continuous data are expressed as mean ± SD; categorical data are expressed as percentage (%). Categorical data are expressed as percentages (%), E: EEVA score, EEVA Early embryo viability assessment. P < 0.05, statistical significance of the variable-outcome association. BMI: BMI of the oocyte provider, ICM inner cell mass, TE trophectoderm, BL blastocyst expansion degree 3.2. Association between univariate with implantation and ongoing pregnancy A GEEs model was built to quantify the odds of achieving implantation and ongoing pregnancy according to univariate factors including maternal age, endometrial thickness, BMI, embryo age, embryo expansion, ICM morphology, TE morphology, and EEVA score. Embryo age, ICM morphology, TE morphology, and EEVA score significantly correlated with implantation and ongoing pregnancy (p < 0.05), as presented in Table 3 . Table 3 Generalized estimating equations assessing the association of univariate with implantation and ongoing pregnancy Univariate analysis OR 95%CI P value Implantation Maternal age (year) - 0.069 Endometrial thickness - 0.455 BMI - 0.437 Embryo age (Day 6 vs Day 5) 0.393 0.219–0.705 0.002 - BL4 vs BL3 - BL5 vs BL3 - BL6 vs BL3 - BL7 vs BL3 - - - - 0.631 0.423 0.456 0.768 - ICM B vs ICM A - ICM C vs ICM A 0.315 0.131 0.202–0.492 0.067–0.257 0.000 0.000 - TE B vs TE A - TE C vs TE A 0.313 0.070 0.206–0.475 0.032–0.155 0.000 0.000 - E2 vs E1 - E3 vs E1 - E4 vs E1 - E5 vs E1 0.791 0.335 0.259 0.245 0.484–1.293 0.194–0.580 0.138–0.485 0.111–0.544 0.350 0.000 0.000 0.001 Ongoing pregnancy Maternal age (year) 0.938 0.899–0.977 0.052 Endometrial thickness - 0.059 BMI - 0.953 Embryo age (Day 6 vs Day 5) 0.385 0.205–0.723 0.003 - BL4 vs BL3 - BL5 vs BL3 - BL6 vs BL3 - BL7 vs BL3 - - - - 0.490 0.402 0.284 0.932 - ICM B vs ICM A - ICM C vs ICM A 0.293 0.098 0.186–0.460 0.040–0.237 0.000 0.000 - TE B vs TE A - TE C vs TE A 0.281 0.044 0.188–0.421 0.013–0.145 0.000 0.000 - E2 vs E1 - E3 vs E1 - E4 vs E1 - E5 vs E1 0.753 0.309 0.232 0.200 0.488–1.164 0.178–0.535 0.119–0.453 0.082–0.489 0.202 0.000 0.000 0.000 Continuous data are expressed as mean ± SD; categorical data are expressed as percentage (%). Categorical data are expressed as percentages (%), E: EEVA score, EEVA Early embryo viability assessment. BMI: BMI of the oocyte provider, ICM inner cell mass, TE trophectoderm, BL blastocyst expansion degree. OR odds ratio, CI confidence interval. P < 0.05, statistical significance of the variable-outcome association The predictive power of EEVA test classification in multivariate GEEs analyzed for implantation and ongoing pregnancy is shown in Table 4 . The EEVA score significantly correlated with implantation and ongoing pregnancy, in which the OR of each category was decreasingly lower. The ORs for implantation and ongoing pregnancy were significantly lower in E3, E4, and E5 when compared to E1 (p < 0.05), except those between E1 and E2 (Table 4 ). The embryonic age variable was not significantly different in the multivariate GEEs model (Table 4 ). 3.3. Association between EEVA score with implantation and ongoing pregnancy Table 4 Generalized estimating equations assessing the association of the EEVA score with implantation and ongoing pregnancy, alongside embryo age. Multivariate analysis OR 95% CI P value Implantation Embryo age (Day 6 vs Day 5) - 0.074 - E2 vs E1 - E3 vs E1 - E4 vs E1 - E5 vs E1 0.795 0.350 0.288 0.282 0.486–1.301 0.201–0.607 0.151–0.546 0.125–0.636 0.362 0.000 0.000 0.002 Ongoing pregnancy Embryo age (Day 6 vs Day 5) - 0.1 - E2 vs E1 - E3 vs E1 - E4 vs E1 - E5 vs E1 0.757 0.321 0.256 0.228 0.489–1.170 0.184–0.557 0.129–0.505 0.092–0.563 0.21 0.000 0.000 0.001 Continuous data are expressed as mean ± SD; categorical data are expressed as percentage (%). Categorical data are expressed as percentages (%), E: EEVA score, EEVA Early embryo viability assessment. BMI: BMI of the oocyte provider, ICM inner cell mass, TE trophectoderm, BL blastocyst expansion degree. OR odds ratio, CI confidence interval. P < 0.05, statistical significance of the variable-outcome association The predictive power of ICM morphology and TE morphology test classification in multivariate GEEs analyzed for implantation and ongoing pregnancy is shown in Table 5 . TE morphology significantly correlated with implantation and ongoing pregnancy, in which the OR of each category was decreasingly lower. The ORs for implantation and ongoing pregnancy were significantly lower in TE B and TE C when compared to TE A (p < 0.05), except those between E1 and E2 (Table 5 ). The embryonic age variable and ICM morphology were not significantly different in the multivariate GEEs model (Table 5 ). 3.4. Association between blastocyst morphology with implantation and ongoing pregnancy Table 5 Generalized estimating equations assessing the association of the morphology with implantation and ongoing pregnancy, alongside with embryo age. Multivariate analysis OR 95% CI P value Implantation Embryo age (Day 6 vs Day 5) - 0.759 - ICM B vs ICM A - ICM C vs ICM A - - 0.073 0.929 - TE B vs TE A - TE C vs TE A 0.405 0.072 0.240–0.683 0.017–0.293 0.001 0.000 Ongoing pregnancy Embryo age (Day 6 vs Day 5) - 0.703 - ICM B vs ICM A - ICM C vs ICM A - - 0.053 0.614 - TE B vs TE A - TE C vs TE A 0.371 0.058 0.226–0.611 0.010–0.318 0.000 0.001 Continuous data are expressed as mean ± SD; categorical data are expressed as percentage (%). Categorical data are expressed as percentages (%), E: EEVA score, EEVA Early embryo viability assessment. BMI: BMI of the oocyte provider, ICM inner cell mass, TE trophectoderm, BL blastocyst expansion degree. OR odds ratio, CI confidence interval. P < 0.05, statistical significance of the variable-outcome association The predictive power of EEVA test classification combined with ICM, TE morphology, and embryo age in multivariate GEEs analyzed for implantation and ongoing pregnancy is shown in Table 6 . EEVA score and TE morphology significantly correlated with implantation and ongoing pregnancy, in which the OR of each category was decreasingly lower. The ORs for implantation and ongoing pregnancy were significantly lower in TE B and TE C when compared to TE A (p < 0.05), in E3, E4, and E5 when compared to E1 (p < 0.05), except those between E1 and E2 (Table 6 ). The embryonic age variable and ICM morphology were not significantly different in the multivariate GEEs model (Table 6 ). 3.5. Association between combination of EEVA score and blastocyst morphology with implantation and ongoing pregnancy Table 6 Generalized estimating equations assessing the association of the Eeva scores and morphology together with implantation and ongoing pregnancy, alongside with embryo age. Multivariate analysis OR 95% CI P value Implantation Embryo age (Day 6 vs Day 5) - 0.559 - ICM B vs ICM A - ICM C vs ICM A - - 0.214 0.699 - TE B vs TE A - TE C vs TE A 0.436 0.065 0.256–0.742 0.016–0.267 0.002 0.000 - E2 vs E1 - E3 vs E1 - E4 vs E1 - E5 vs E1 0.928 0.514 0.402 0.338 0.552–1.563 0.282–0.935 0.199–0.810 0.258–1.591 0.780 0.029 0.011 0.017 Ongoing pregnancy Embryo age (Day 6 vs Day 5) - 0.473 - ICM B vs ICM A - ICM C vs ICM A - - 0.194 0.965 - TE B vs TE A - TE C vs TE A 0.400 0.052 0.240–0.666 0.010–0.288 0.000 0.001 - E2 vs E1 - E3 vs E1 - E4 vs E1 - E5 vs E1 0.874 0.463 0.364 0.266 0.551–1.385 0.256–0.839 0.174–0.762 0.207–1.544 0.566 0.011 0.007 0.007 Continuous data are expressed as mean ± SD; categorical data are expressed as percentage (%). Categorical data are expressed as percentages (%), E: EEVA score, EEVA Early embryo viability assessment. BMI: BMI of the oocyte provider, ICM inner cell mass, TE trophectoderm, BL blastocyst expansion degree. OR odds ratio, CI confidence interval. P < 0.05, statistical significance of the variable-outcome association 3.6. Comparison three models The ROC curves of three models are presented in Fig. 1. The AUC of the model using EEVA test only, blastocyst morphology only, or a combination of EEVA test and blastocyst morphology was 0.651 (95% CI 0.608–0.692), 0.703 (95% CI 0.662–0.743), and 0.730 (95% CI 0.688–0.767), respectively, for implantation; 0.655 (95% CI 0.612–0.696), 0.700 (95% CI 0.658–0.740), and 0.726 (95% CI 0.686–0.765), respectively, for ongoing pregnancy. The model with the highest AUC for implantation and ongoing pregnancy was always the one combining both the EEVA test and blastocyst morphological assessment, when compared with models using only the EEVA test or blastocyst morphological assessment. The differences were statistically significant (p < 0.05). The difference between the model using only the EEVA test and blastocyst morphology was not statistically significant. The dates are presented in Fig. 1. 4. Discussion To our knowledge, this is the first study where an embryo selection model, which is a combination of the EEVA test, an automatic classification algorithm applied to cleavage stage embryos, and the conventional blastocyst morphological assessment, is tested on a single vitrified-warmed blastocyst transfer for implantation and ongoing pregnancy as the end points. The ability of the EEVA test is to identify on Day 3 embryos with the highest potential to reach blastocyst stage. The results of our study confirm that the EEVA test is also useful in embryo selection on Day 5 and 6 for single vitrified-warmed blastocyst transfer, especially when the EEVA test is used in combination with the conventional blastocyst morphological assessment. As for predicting the implantation potential of the EEVA test, Vermilyea et al. (2014) showed that embryos with a low score of EEVA had a higher implantation rate than those with a high score of EEVA [ 13 ]. In the external validation performed by Aparicio-Ruiz et al. (2016), the study confirmed the predictive power of the EEVA test for implantation, in which embryos from oocyte donations with a low score of EEVA also had higher implantation than those with a higher score of EEVA [ 16 ]. The results of our study showed the ability of the EEVA test to identify embryos with higher implantation potential. Transferred vitrified-warmed blastocysts with the high EEVA score (E5) had 0.282 times lower odds of successful implantation than blastocysts with the low EEVA score (E1), p < 0.05 (Table 4 ). Our study is also the first to test single vitrified-warmed blastocyst transfer for ongoing pregnancy as the end point. Transferred vitrified-warmed blastocysts with the high EEVA score (E5) had 0.228 times lower odds of a successful ongoing pregnancy than blastocysts with the low EEVA score (E1), p < 0.05 (Table 4 ). Prolonging the culture of the cleavage embryo stage to the blastocyst stage has now become a routine practice in many IVF laboratories. Consequently, new selection algorithms tend to use later morphokinetic events, or a combination of algorithms and morphology assessment [ 23 ]. Among the algorithms for blastocyst selection, Motato et al. (2016) used two morphokinetic parameters: the synchrony in divisions of the third cell cycle (s3) and the time to reach the expanded blastocyst stage (tEB), with an AUC = 0.602 (95% CI 0.559–0.645), but it has not been externally validated [ 24 ]. Goodman et al. (2016) used some parameters and the start of blastulation time (tSB) as a positively scoring variable [ 20 ]. However, this model did not mention AUC. An external validation of another predictive algorithm, KIDScore D5 version 3, used for selection of embryos at the blastocyst stage, available for EmbryoScope and EmbryoScope Plus incubators, was presented by Bori et al. (2022), with an AUC = 0.633 for implantation [ 8 ]. The KIDScore D5 was designed as an embryo classification system independent of the conventional blastocyst morphological assessment, as it already includes trophectoderm and inner cell mass morphology as predictive variables in the algorithm. Contrary to KIDScore D5, the EEVA test was developed as a complementary tool to the conventional morphological evaluation of day 3 embryo to predict blastocyst formation [ 25 ]. Depending on the type of embryo transfer cycle, many embryo selection algorithms based on automatic annotations have been reported to achieve AUCs of approximately 0.650–0.700, achieving a statistically significant ability to predict implantation, but only at a relatively high level. None of these models showed a statistically significant improvement compared to blastocyst morphological assessment [ 26 ]. In the study of Valera et al. (2023), EEVA classification performed similarly to traditional blastocyst morphological assessment for implantation prediction. The combination of the EEVA system and the traditional blastocyst morphological assessment tended to have better prognostic value than EEVA classification alone, AUCs = 0.636 (95% CI 0.584–0.660); 0.622 (95% CI 0.598–0.673), respectively. However the difference was not statistically significant (p > 0.05) [ 26 ]. In our study, the EEVA classification also performed similarly to the traditional blastocyst morphological assessment for implantation prediction with an AUC = 0.651 (95% CI 0.608–0.692) vs. 0.703 (95% CI 0.662–0.743), respectively, and for ongoing pregnancy prediction with an AUC = 0.655 (95% CI 0.612–0.696) vs. 0.700 (95% CI 0.658–0.740), respectively. The differences were not statistically significant (p > 0.05) (Fig. 1). Further analysis of the GEE model using a combination of both methods, including the EEVA test and the blastocyst morphological assessment, showed that both predictors were statistically significant for implantation and ongoing pregnancy prediction, and their predictive values were independent of each other. Indeed, in this study, the best AUCs for both outcomes were found when combining both the evaluation systems of the EEVA test and the blastocyst morphological assessment compared with the EEVA test alone or the blastocyst morphological assessment alone, the AUC = 0.730 (95% CI 0.688–0.767), 0.651 (95% CI 0.608–0.692), and 0.703 (95% CI 0.662–0.743) for implantation, respectively; the AUC = 0.726 (95% CI 0.686–0.765), 0.655 (95% CI 0.612–0.696) and 0.700 (95% CI 0.658–0.740) for ongoing pregnancy, respectively. The differences of pairwise comparison of ROC curves were statistically significant, p < 0.05 (Fig. 1). Our study used GEEs for statistical analysis. GEEs consider several events as intra-patient variables. This way, the model considers the relationship between embryos that share the same patient background characteristics, adding one more layer to consideration of the intra-data context of measured confounders. We only selected blastocysts whose quality did not decrease after warming. Therefore, the vitrification procedure did not adversely affect the quality of the transferred embryo. In this study, we analyzed univariate data using the GEEs model; only statistically significant variables were included in the multivariate model. The age of the transferred embryo was included in the three GEEs models using the EEVA test only, blastocyst morphological assessment only, and a combination of both. However, in all 3 models, the age of the transferred embryo was not statistically significant when combined with the variables (p > 0.05). The limitation of the study was that the details of the algorithm and some morphological features used in the EEVA system were hidden by the manufacturer, which prevented our later conclusions because we did not know how to calculate the score. The EEVA score is automatically obtained from the software. The performance of automatic annotations of morphokinetic event timings provided by the EEVA system may be unclear. In this study, the automatic annotations were not validated, as our aim was to analyze the performance of the whole system. In addition, it should be noted that our study was a retrospective study and therefore may have limitations. Therefore, in future studies, randomised controlled trials may be required. In conclusion, our results confirm the efficacy of the EEVA algorithm for implantation and ongoing pregnancy outcome prediction. The highest AUC was achieved when combining the EEVA test and blastocyst morphological assessment, and the improvement over the EEVA test alone or morphological assessment alone was statistically significant. Abbreviations IVF In vitro Fertilization SVBT single vitrified-warmed blastocyst transfer AI Artificial intelligence EEVA Early Embryo Viability Assessment ICSI Intracytoplasmic sperm injection GEEs Generalized estimating equations ROC Receiver operating characteristic AUC Area under the ROC curve OR Odds ratios CI Confidence intervals BMI Body mass index ICM Inner cell mass TE Trophectoderm BL Blastocyst expansion degree Declarations Acknowledgements The authors would like to thank the members of the IVF laboratory Tam Anh for supporting all the lab’s work during the group's research. Specifically, we would like to thank Tam Anh General Hospital for allowing and facilitating us to complete the study. Funding The authors did not receive support from any organization for the submitted work. Authors’ contribution statement Hop Vu Dinh, Huong Nguyen Thi Lien, Hanh Van Nguyen, Hoang Le conceived, designed, interpreted the data, coordinated the work. Hop Vu Dinh wrote the manuscript. Cuong An Manh Anh Phi Thi Tu, collected, double checked the data, participated in interpreting the data. Huong Nguyen Thi Lien, Hanh Nguyen Van, Hoang Le reviewed the manuscript. All authors jointly contributed to the final version of the manuscript. Ethics approval This study was reviewed and approved by the Internal Committee for Ethics of the Tam Anh General Hospital, Vietnam. Consent for publication Not applicable. Consent to participate Not applicable. Competing interests The authors declare that they have no competing interests Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request References K. Kato et al. , “Comparison of pregnancy outcomes following fresh and electively frozen single blastocyst transfer in natural cycle and clomiphene-stimulated IVF cycles,” Hum. Reprod. Open , vol. 2018, no. 3, p. hoy006, May 2018, doi: 10.1093/hropen/hoy006. B. Aparicio, M. Cruz, and M. Meseguer, “Is morphokinetic analysis the answer?,” Reprod. Biomed. Online , vol. 27, no. 6, pp. 654–663, Dec. 2013, doi: 10.1016/j.rbmo.2013.07.017. R. D. Gallego, J. Remohí, and M. Meseguer, “Time-lapse imaging: the state of the art†,” Biol. Reprod. , vol. 101, no. 6, pp. 1146–1154, Dec. 2019, doi: 10.1093/biolre/ioz035. C. Wong, A. A. Chen, B. Behr, and S. Shen, “Time-lapse microscopy and image analysis in basic and clinical embryo development research,” Reprod. Biomed. Online , vol. 26, no. 2, pp. 120–129, Feb. 2013, doi: 10.1016/j.rbmo.2012.11.003. D. J. Kaser and C. Racowsky, “Clinical outcomes following selection of human preimplantation embryos with time-lapse monitoring: a systematic review,” Hum. Reprod. Update , vol. 20, no. 5, pp. 617–631, 2014, doi: 10.1093/humupd/dmu023. L. Sundvall, H. J. Ingerslev, U. Breth Knudsen, and K. Kirkegaard, “Inter- and intra-observer variability of time-lapse annotations,” Hum. Reprod. Oxf. Engl. , vol. 28, no. 12, pp. 3215–3221, Dec. 2013, doi: 10.1093/humrep/det366. “Good practice recommendations for the use of time-lapse technology† | Human Reproduction Open | Oxford Academic.” Accessed: Jan. 31, 2024. [Online]. Available: https://academic.oup.com/hropen/article/2020/2/hoaa008/5809428 L. Bori et al. , “Novel and conventional embryo parameters as input data for artificial neural networks: an artificial intelligence model applied for prediction of the implantation potential,” Fertil. Steril. , vol. 114, no. 6, pp. 1232–1241, Dec. 2020, doi: 10.1016/j.fertnstert.2020.08.023. L. Bori et al. , “An artificial intelligence model based on the proteomic profile of euploid embryos and blastocyst morphology: a preliminary study,” Reprod. Biomed. Online , vol. 42, no. 2, pp. 340–350, Feb. 2021, doi: 10.1016/j.rbmo.2020.09.031. C. L. Bormann et al. , “Performance of a deep learning based neural network in the selection of human blastocysts for implantation,” eLife , vol. 9, p. e55301, Sep. 2020, doi: 10.7554/eLife.55301. J. Swain et al. , “AI in the treatment of fertility: key considerations,” J. Assist. Reprod. Genet. , vol. 37, no. 11, pp. 2817–2824, Nov. 2020, doi: 10.1007/s10815-020-01950-z. N. Zaninovic and Z. Rosenwaks, “Artificial intelligence in human in vitro fertilization and embryology,” Fertil. Steril. , vol. 114, no. 5, pp. 914–920, Nov. 2020, doi: 10.1016/j.fertnstert.2020.09.157. M. D. VerMilyea et al. , “Computer-automated time-lapse analysis results correlate with embryo implantation and clinical pregnancy: a blinded, multi-centre study,” Reprod. Biomed. Online , vol. 29, no. 6, pp. 729–736, Dec. 2014, doi: 10.1016/j.rbmo.2014.09.005. M. P. Diamond et al. , “Using the Eeva Test TM adjunctively to traditional day 3 morphology is informative for consistent embryo assessment within a panel of embryologists with diverse experience,” J. Assist. Reprod. Genet. , vol. 32, no. 1, pp. 61–68, Jan. 2015, doi: 10.1007/s10815-014-0366-1. A. Revelli et al. , “Impact of the addition of Early Embryo Viability Assessment to morphological evaluation on the accuracy of embryo selection on day 3 or day 5: a retrospective analysis,” J. Ovarian Res. , vol. 12, no. 1, p. 73, Aug. 2019, doi: 10.1186/s13048-019-0547-8. B. Aparicio-Ruiz, N. Basile, S. Pérez Albalá, F. Bronet, J. Remohí, and M. Meseguer, “Automatic time-lapse instrument is superior to single-point morphology observation for selecting viable embryos: retrospective study in oocyte donation,” Fertil. Steril. , vol. 106, no. 6, pp. 1379-1385.e10, Nov. 2016, doi: 10.1016/j.fertnstert.2016.07.1117. D. C. Kieslinger et al. , “Embryo selection using time-lapse analysis (Early Embryo Viability Assessment) in conjunction with standard morphology: a prospective two-center pilot study,” Hum. Reprod. Oxf. Engl. , vol. 31, no. 11, pp. 2450–2457, Nov. 2016, doi: 10.1093/humrep/dew207. L. Yang et al. , “Single embryo transfer by Day 3 time-lapse selection versus Day 5 conventional morphological selection: a randomized, open-label, non-inferiority trial,” Hum. Reprod. Oxf. Engl. , vol. 33, no. 5, pp. 869–876, May 2018, doi: 10.1093/humrep/dey047. N. Desai et al. , “Delayed blastulation, multinucleation, and expansion grade are independently associated with live-birth rates in frozen blastocyst transfer cycles,” Fertil. Steril. , vol. 106, no. 6, pp. 1370–1378, Nov. 2016, doi: 10.1016/j.fertnstert.2016.07.1095. L. R. Goodman, J. Goldberg, T. Falcone, C. Austin, and N. Desai, “Does the addition of time-lapse morphokinetics in the selection of embryos for transfer improve pregnancy rates? A randomized controlled trial,” Fertil. Steril. , vol. 105, no. 2, pp. 275-285.e10, Feb. 2016, doi: 10.1016/j.fertnstert.2015.10.013. Y. Mizobe et al. , “Selection of human blastocysts with a high implantation potential based on timely compaction,” J. Assist. Reprod. Genet. , vol. 34, no. 8, pp. 991–997, Aug. 2017, doi: 10.1007/s10815-017-0962-y. D. K. Gardner and W. B. Schoolcraft, “Culture and transfer of human blastocysts,” Curr. Opin. Obstet. Gynecol. , vol. 11, no. 3, pp. 307–311, Jun. 1999, doi: 10.1097/00001703-199906000-00013. B. Carrasco et al. , “Selecting embryos with the highest implantation potential using data mining and decision tree based on classical embryo morphology and morphokinetics,” J. Assist. Reprod. Genet. , vol. 34, no. 8, pp. 983–990, Aug. 2017, doi: 10.1007/s10815-017-0955-x. Y. Motato, M. J. de los Santos, M. J. Escriba, B. A. Ruiz, J. Remohí, and M. Meseguer, “Morphokinetic analysis and embryonic prediction for blastocyst formation through an integrated time-lapse system,” Fertil. Steril. , vol. 105, no. 2, pp. 376-384.e9, Feb. 2016, doi: 10.1016/j.fertnstert.2015.11.001. J. Conaghan et al. , “Improving embryo selection using a computer-automated time-lapse image analysis test plus day 3 morphology: results from a prospective multicenter trial,” Fertil. Steril. , vol. 100, no. 2, pp. 412-419.e5, Aug. 2013, doi: 10.1016/j.fertnstert.2013.04.021. M. A. Valera, B. Aparicio-Ruiz, S. Pérez-Albalá, L. Romany, J. Remohí, and M. Meseguer, “Clinical validation of an automatic classification algorithm applied on cleavage stage embryos: analysis for blastulation, euploidy, implantation, and live-birth potential,” Hum. Reprod. Oxf. Engl. , vol. 38, no. 6, pp. 1060–1075, Jun. 2023, doi: 10.1093/humrep/dead058. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4022641","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":277321397,"identity":"37847168-a200-404a-9af4-13fb6a974b65","order_by":0,"name":"Hop Vu Dinh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYBACAxDBUwFmszGDSQYGxgOEtZwxQNIC1ENYC28bkhYGQlrM2XuPSbyd90det/3ssccFFXV5fPLNDw4wtt3BqcWy51ya5NxtBobbzuSlG884c7iYjY3NAKjlGW6H3cgxNubdZsC47UCOmTRv24HENjYGgwMMZw4T0DLHwH7b+TcgLXVALewfCGkxfMzbYJC47QbYFmagFh6gLRV4tJw5l/hwzjHj5G03gLbwgP2SU3AgAZ+W470HDrypkbPddh5oCw8wxOSbj2988MEAtxZgPKJyE5BIkrSMglEwCkbBKEACAFMiV76prBQzAAAAAElFTkSuQmCC","orcid":"","institution":"Tam Anh General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Hop","middleName":"Vu","lastName":"Dinh","suffix":""},{"id":277321398,"identity":"3c7f67b9-690b-482f-956c-39507440c8e7","order_by":1,"name":"Cuong An Manh","email":"","orcid":"","institution":"Tam Anh General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Cuong","middleName":"An","lastName":"Manh","suffix":""},{"id":277321399,"identity":"068302d9-35ae-42ab-bf23-c50893f643bc","order_by":2,"name":"Anh Phi Thi Tu","email":"","orcid":"","institution":"Tam Anh General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Anh","middleName":"Phi Thi","lastName":"Tu","suffix":""},{"id":277321400,"identity":"8e2d9ccb-d006-48a0-8bd1-0ecbcadc7c6a","order_by":3,"name":"Huong Nguyen Thi Lien","email":"","orcid":"","institution":"Tam Anh General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Huong","middleName":"Nguyen Thi","lastName":"Lien","suffix":""},{"id":277321401,"identity":"731f290e-e450-46f3-b177-e50b3526f33f","order_by":4,"name":"Hoang Le","email":"","orcid":"","institution":"Tam Anh General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hoang","middleName":"","lastName":"Le","suffix":""},{"id":277321402,"identity":"ea21e0e8-ba0d-4832-a2fc-04a5bdd00c38","order_by":5,"name":"Hanh Nguyen Van","email":"","orcid":"","institution":"Graduate University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Hanh","middleName":"Nguyen","lastName":"Van","suffix":""}],"badges":[],"createdAt":"2024-03-07 02:51:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4022641/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4022641/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52498976,"identity":"e98fc209-7c77-4637-9307-cd5e87732fd9","added_by":"auto","created_at":"2024-03-12 09:15:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":55455,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePerformance metrics for the generalized estimating equations (GEEs) for implantation and ongoing pregnancy prediction. \u003c/strong\u003eReceiver operating characteristic (ROC) curves and area under the ROC curves (AUCs) of the GEE are modeled for assessing the performance of the three classification systems: the Eeva Test and blastocyst morphological assessment, as well as a combination of both, for implantation and ongoing pregnancy prediction. P\u0026lt;0.05, statistical significance\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4022641/v1/22657167a007b09339b0c4ac.png"},{"id":52814907,"identity":"a269149e-2a0d-4f82-b4a7-d83fef6ecc70","added_by":"auto","created_at":"2024-03-16 12:07:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":951473,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4022641/v1/14fc8455-c36b-4867-9c31-793550422296.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prediction of pregnancy outcomes of single vitrified-warmed blastocyst transfer using combination of an automatic classification algorithm applied on cleavage stage embryos and blastocyst morphological assessment: a single - centre, retrospective study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCurrently, single blastocyst transfer is a trend at In vitro Fertilization (IVF) centre to avoid multiple pregnancies. Therefore, single vitrified-warmed blastocyst transfer (SVBT) is the main strategy in the embryo transfer policy of many centers [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The selection of embryos for transfer based on morphology is often subjective between embryologists [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], between different embryo morphology assessment systems, and between different times of assessment \u003csup\u003e[3]\u003c/sup\u003e. As a result, these techniques are not very precise, resulting in variable and sometimes inaccurate embryo ranking and selection for transfer [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo improve embryo culture and selection, the timelapse incubator system was introduced to allow continuous monitoring of embryos, which provides a lot of information about the embryo's development process for the assessment of embryologists without removing embryos outside of the stable culture environment of the incubator [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough the timelapse system has greatly improved embryo evaluation, it requires highly qualified embryologists and can be complex and time-consuming. Furthermore, although subjectivity is reduced, evaluation still requires manual annotation, especially when many new parameters are used [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e][\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Artificial intelligence (AI) can overcome these issues in selecting embryos for transfer.\u003c/p\u003e \u003cp\u003eRecently, the benefits of AI in predicting pregnancy outcomes have been widely studied [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The first automatic system for the analysis of images obtained through time-lapse was EEVA (Early Embryo Viability Assessment) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This technology was the first practical application of artificial intelligence in an IVF laboratory, and it solves many of the previously described problems with the introduction of automation. EEVA is an automated embryo assessment software at the early embryo development stage, integrated exclusively into the GERI Plus timelapse incubator. This is the first software approved by the US Food and Drug Administration [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. EEVA software records the development of each embryo with a cell-tracking system and predicts the likelihood (high, medium, or low) that an embryo will form a blastocyst based on automated detection and analysis of timelapse imaging information of the early cell division stage. EEVA software automatically detects the durations of the 2-cell (P2; t3\u0026ndash;t2) and 3-cell (P3; t4\u0026ndash;t3) stages and characteristics of the embryo during the first 3 days of embryonic development. This may avoid prolonging culture and save resources [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. EEVA assesses embryos automatically and classifies them into 5 degrees: E1, E2, E3, E4, and E5, with a decreasing ability to develop into the blastocyst stage [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough the EEVA Test was originally developed to predict embryos\u0026rsquo; potential to reach the blastocyst stage at early developmental stages, strong associations were also found between the best EEVA categories and increased implantation rates in transferred embryos [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, there were also some studies that showed no difference in the selection of embryos for implantation when using EEVA software alone or when EEVA was combined with morphological assessment [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Some algorithms developed to predict implantation potential focus on late predictive parameters such as blastocyst morphology and/or timing of blastulation [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], KIDScore D5 (Vitrolife, Denmark), some algorithms also include early morphokinetic parameters [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. On the other hand, the relationship between pregnancy outcomes and EEVA classification after single vitrified-warmed blastocyst transfer has not been evaluated. Therefore, the clinical application of EEVA still requires studies that analyse its correlation with pregnancy outcomes.\u003c/p\u003e \u003cp\u003eThe aim of this study was to investigate the EEVA system as a predictor of pregnancy outcomes, such as implantation and ongoing pregnancy. Furthermore, the aim was to compare the performance of the EEVA Test with the traditional morphological classification and with the combination of both systems for implantation and ongoing pregnancy potential.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study design\u003c/h2\u003e \u003cp\u003eThe study was designed as retrospective research, with 511 single vitrified-warmed blastocyst transfer cycles from 2020 to 2023 in a single Assisted Reproductive Centre. All embryos were assessed by the EEVA Test on day 3, resulting in classification into scores from E1 (best) to E5 (worse) to predict the ability of blastocyst formation based on the embryo\u0026rsquo;s morphokinetic characteristics. Blastocysts were assessed morphologically before vitrification. Blastocysts were warmed before transfer for at least 1 hour. Blastocyst quality had not been reduced in this study. The embryologist evaluating blastocyst quality was blinded to the EEVA score.\u003c/p\u003e \u003cp\u003eThe correlation between univariate variables, including maternal age, endometrial thickness, BMI, embryonic age, embryonic expansion degree, ICM morphology, TE morphology, EEVA test, and pregnancy outcomes, was assessed by generalized estimating equations (GEEs). The GEE model using blastocyst morphological assessment only, the EEVA test only, and a combination of both systems for predicting implantation and ongoing pregnancy were compared to each other.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Oocytes and sperm handling\u003c/h2\u003e \u003cp\u003eOocytes after retrieval were collected, washed, and incubated in pre-equilibrated G-IVF PLUS culture medium (Vitrolife, Sweden) at a benchtop BT37 incubator at 37\u0026deg;C using 7% CO2 and 5% O2 mixing gas. After incubation (approximately 3 hours), oocyte denudation was carried out by mechanical pipetting in G-IVF PLUS medium containing the enzyme hyaluronidase 80 IU/ml. After 2 h of culture in IVF PLUS medium, intracytoplasmic sperm injection (ICSI) was performed in gamete medium (Cook Medical, USA) supplemented with HEPES at 200\u0026times; magnification using a Nikon inverted microscope with Hoffman optics.\u003c/p\u003e \u003cp\u003eSperm used for ICSI was filtered and washed by a concentration gradient method. All oocytes after ICSI were cultured in the continuous single culture NXC plate (Irvine Scientific).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Embryo culture and imaging\u003c/h2\u003e \u003cp\u003eEmbryos were cultured in the Geri Dishes\u0026reg; with a capacity for up to 16 embryos sharing a common medium droplet of 80 \u0026micro;l of pre-equilibrated continuous single culture-NXC plate (Irvine Scientific) covered in 3.5 ml of mineral oil in the Geri+\u0026reg; timelapse incubator (Genea Biomed, Australia). Embryos were kept inside the incubator uninterruptedly from Days 1 to 3. The fertilization, Day 2, and D3 scores were performed on the GERI incubator\u0026rsquo;s screen. The embryos were assisted in hatching on day 3, and the culture medium was changed outside the incubator. After assisted hatching, the embryos were cultured uninterrupted until Day 5.\u003c/p\u003e \u003cp\u003eThe EEVA system 3.0 (Progyny Inc., CA, USA) was used to image embryonic development and predict blastocyst formation. The system consists of a microscope (EEVA scope), which was placed in a Geri+\u0026reg; time-lapse incubator, and a cell-tracking system for registering cell divisions. Every 5 minutes, a single, high-resolution, single-plane dark field illumination image was taken of each embryo.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Embryo assessment\u003c/h2\u003e \u003cp\u003eEvaluation and grading of embryo morphology were according to the Istanbul consensus workshop on embryo assessment: proceedings of an expert meeting, 2011. Fertilization was assessed at 17\u0026thinsp;\u0026plusmn;\u0026thinsp;h post-ICSI by the presence of two pronuclei and two polar bodies. The number of blastomeres on embryo day 3 was assessed at 68\u0026thinsp;\u0026plusmn;\u0026thinsp;1 h post-ICSI. Blastocyst morphology was evaluated on Days 5 and 6 based on cavity expansion, quality of TE, and ICM (according to the scoring blastocyst by grading system of Gardner and Schoolcraft), at 116\u0026thinsp;\u0026plusmn;\u0026thinsp;1 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe EEVA assessment was based on day 3 embryo morphokinetics. EEVA software automatically recognized the durations of the 2-cell (P2; t3\u0026ndash;t2) and 3-cell (P3; t4\u0026ndash;t3) stages and characteristics of the embryo during the first 3 days of embryonic development. In addition, the number of blastomeres in the day 3 embryo of each embryo was put into the EEVA software, which then produced the EEVA grade automatically for each embryo. All embryos were classified by the EEVA Test (with Version 3.0) and classified with a numeric score from E1 (best) to E5 (worse) according to their likelihood of reaching the blastocyst stage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Blastocyst vitrification and warming\u003c/h2\u003e \u003cp\u003eFor vitrification on day 5 or 6, blastocysts were required to attain a blastocyst expansion degree of 3 (\u0026gt;\u0026thinsp;BL2). These blastocysts were vitrified immediately according to the Cryotop method (Cryotech vitrification, Vitrolife, Sweden). If the developing embryo did not fulfill the criteria, it was cultured for a maximum of 7 days.\u003c/p\u003e \u003cp\u003eWhen the patient is indicated for single vitrified-warmed blastocyst transfer, the blastocyst will be warmed on the day of embryo transfer using the Cryotop method (Cryotech warming, Vitrolife, Sweden).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Post-warming embryo culture and vitrified-warmed blastocyst transfer procedure transfer, and luteal support\u003c/h2\u003e \u003cp\u003eEmbryos were cultured in the continuous single culture NXC plate (Irvine Scientific) after warming for at least 1 hour before embryo transfer. Blastocyst quality was assessed before transferring. Only blastocysts that maintain the same quality or develop better compared to blastocyst quality before vitrification were used in this study. A decreased-quality blastocyst will not be included in this study.\u003c/p\u003e \u003cp\u003eThe hormonal replacement therapy (HRT) protocol was initiated on the second day of the menstrual cycle for all patients. To ensure optimal endometrial thickness, patients were required to undergo ultrasound check-ups of the endometrium, uterine, and adnexal on days 10 and 14 of the menstrual cycle. Once the endometrial thickness reached a minimum of 7 mm, endometrial transformation was achieved using vaginal micronized progesterone at a daily dosage of 600\u0026ndash;800 mg (Utrogestan, Besins, Thailand, or Cyclogest, Actavis U.K. Limited, United Kingdom), in addition to orally administered Dydrogesterone at a daily dose of 30mg (Duphaston, Abbott Biologicals B.V., Netherlands). Blastocyst embryos were transferred after approximately 120 +/- 3 hours. The embryo transfer procedure adhered to the 2017 guidelines by the American Society for Reproductive Medicine (ASRM). In situations where the catheter encountered difficulty passing through the cervical canal into the uterine cavity, resulting in prolonged transfer time and requiring the use of a catheter with a malleable mandrel or cervical clamp ponzi, the procedure was recorded as a difficult embryo transfer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Pregnancy outcomes\u003c/h2\u003e \u003cp\u003eFor clinical outcomes, we used the following definitions: pregnancy was confirmed (serum hCG\u0026thinsp;\u0026gt;\u0026thinsp;5mIU/mL); implantation was confirmed at 8 weeks of pregnancy by observation of the gestational sac by ultrasound; clinical pregnancy, with a confirmed gestational sac at 6\u0026ndash;7 weeks of pregnancy; ongoing pregnancy: defined as pregnancy that continued beyond 12 weeks of gestation; LB: live birth was confirmed by direct communication of the patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using the Statistical Package for the Social Sciences 26 (SPSS Inc.).\u003c/p\u003e \u003cp\u003eTo describe the data, descriptive statistics frequency analysis, and percentage analysis were used for categorical variables, and the mean and standard deviation (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD) were used for continuous variables. Statistical test for quantitative variables, when comparing the average value in two research groups, use the ANOVA test. Statistical test for qualitative variables, when comparing two proportions, use the Chi square test.\u003c/p\u003e \u003cp\u003eUsing the generalized estimation equation system (GEEs) to evaluate the relationship between the EEVA test, blastocyst morphological assessment, or a combination of the EEVA test and blastocyst morphological assessment, and implantation and ongoing pregnancy. Comparison of 3 methods to evaluate the results of predicting pregnancy outcomes using the area under the ROC curve (AUC): performed on the prediction probability scale of the model rule.\u003c/p\u003e \u003cp\u003eThe odds ratios (ORs) of the effect of the variables included in the GEEs on the outcome variables were expressed as 95% CI, and statistical significance was considered for \u003cem\u003eP\u003c/em\u003e-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Receiver operating characteristic (ROC) curves were graphed from the probability values obtained by the GEE models, and the areas under the ROC curves (AUC) were calculated as model evaluation metrics. A comparison of AUCs was performed using a paired, two-tailed DeLong\u0026rsquo;s test. The 2 AUC was statistically significant different when p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9. Ethics statement\u003c/h2\u003e \u003cp\u003e This study was reviewed and approved by the Internal Committee for Ethics of the Tam Anh General Hospital, Vietnam.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Demographic and clinical characteristics of patients\u003c/h2\u003e\n \u003cp\u003eWe analyzed the pregnancy outcomes of 511 single vitrified-warmed blastocyst transfer cycles from 2020 to 2023. A general description of the study population, including cycle, demographic, and embryonic characteristics, was presented by the parameters in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGeneral Descriptive and demographic characteristics of the cycles.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCycles (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e511\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal age (year)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.36\u0026thinsp;\u0026plusmn;\u0026thinsp;4.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEndometrial thickness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (kg/m\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.07\u0026thinsp;\u0026plusmn;\u0026thinsp;2.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- \u003cstrong\u003eEmbryo Day 5\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eEmbryo Day 6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- 460\u003c/p\u003e\n \u003cp\u003e- 51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- \u003cstrong\u003eBL3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eBL4\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eBL5\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eBL6\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eBL7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- 20\u003c/p\u003e\n \u003cp\u003e- 53\u003c/p\u003e\n \u003cp\u003e- 243\u003c/p\u003e\n \u003cp\u003e- 161\u003c/p\u003e\n \u003cp\u003e- 34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- \u003cstrong\u003eICM A\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eICM B\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eICM C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- 352\u003c/p\u003e\n \u003cp\u003e- 113\u003c/p\u003e\n \u003cp\u003e- 46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- \u003cstrong\u003eTE A\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eTE B\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eTE C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- 312\u003c/p\u003e\n \u003cp\u003e- 157\u003c/p\u003e\n \u003cp\u003e- 42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- \u003cstrong\u003eE1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eE2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eE3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eE4\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eE5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- 220\u003c/p\u003e\n \u003cp\u003e- 134\u003c/p\u003e\n \u003cp\u003e- 76\u003c/p\u003e\n \u003cp\u003e- 52\u003c/p\u003e\n \u003cp\u003e- 29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eImplantation rate (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical pregnancy rate (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOngoing pregnancy rate (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLive birth rate (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003eContinuous data are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD; categorical data are expressed as percentage (%). Categorical data are expressed as percentages (%), E: EEVA score, EEVA Early embryo viability assessment. BMI: BMI of the oocyte provider, ICM inner cell mass, TE trophectoderm, BL blastocyst expansion degree\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eNo differences were found in the maternal age, endometrial thickness, BMI, or embryo expansion degree between the implantation and non-implantation groups, ongoing pregnancy and non-ongoing pregnancy groups (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The rates of day 5 blastocysts, ICM A, TE A, and E1 were significantly higher in the implantation and ongoing pregnancy groups compared with the non-implantation and non-ongoing pregnancy groups, respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as detailed in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive and Demographic characteristics in implantation and non-implantation groups; ongoing pregnancy and non-ongoing pregnancy group\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eImplantation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-implantation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal age (year)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEndometrial thickness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.456\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- \u003cstrong\u003eEmbryo Day 5\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eEmbryo Day 6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- 319 (93%)\u003c/p\u003e\n \u003cp\u003e- 24 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- 141 (83.9%)\u003c/p\u003e\n \u003cp\u003e- 27 (16.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- \u003cstrong\u003eBL3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eBL4\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eBL5\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eBL6\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eBL7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- 12 (3.5%)\u003c/p\u003e\n \u003cp\u003e- 35 (10.2%)\u003c/p\u003e\n \u003cp\u003e- 167 (48.7%)\u003c/p\u003e\n \u003cp\u003e- 110 (32.1%)\u003c/p\u003e\n \u003cp\u003e- 19 (5.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- 8 (4.8%)\u003c/p\u003e\n \u003cp\u003e- 18 (10.7%)\u003c/p\u003e\n \u003cp\u003e- 76 (45.2%)\u003c/p\u003e\n \u003cp\u003e- 51 (30.4%)\u003c/p\u003e\n \u003cp\u003e- 15 (8.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.588\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- \u003cstrong\u003eICM A\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eICM B\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eICM C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- 271 (79%)\u003c/p\u003e\n \u003cp\u003e- 58 (16.9%)\u003c/p\u003e\n \u003cp\u003e- 14 (4.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- 81 (48.2%)\u003c/p\u003e\n \u003cp\u003e- 55 (32.7%)\u003c/p\u003e\n \u003cp\u003e- 32 (19.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- \u003cstrong\u003eTE A\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eTE B\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eTE C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- 248 (72.3%)\u003c/p\u003e\n \u003cp\u003e- 86 (25.1%)\u003c/p\u003e\n \u003cp\u003e- 9 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- 64 (38.1%)\u003c/p\u003e\n \u003cp\u003e- 71 (42.3%)\u003c/p\u003e\n \u003cp\u003e- 33 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- \u003cstrong\u003eE1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eE2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eE3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eE4\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eE5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- 169 (49.3%)\u003c/p\u003e\n \u003cp\u003e- 97 (28.3%)\u003c/p\u003e\n \u003cp\u003e- 40 (11.7%)\u003c/p\u003e\n \u003cp\u003e- 24 (7.0%)\u003c/p\u003e\n \u003cp\u003e- 13 (3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- 51 (30.4%)\u003c/p\u003e\n \u003cp\u003e- 37 (22%)\u003c/p\u003e\n \u003cp\u003e- 36 (21.4%)\u003c/p\u003e\n \u003cp\u003e- 28 (16.7%)\u003c/p\u003e\n \u003cp\u003e- 16 (9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOngoing pregnancy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-ongoing pregnancy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal age (year)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEndometrial thickness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.953\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- \u003cstrong\u003eEmbryo Day 5\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eEmbryo Day 6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- 239 (94.1%)\u003c/p\u003e\n \u003cp\u003e- 15 (5.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- 221 (86%)\u003c/p\u003e\n \u003cp\u003e- 36 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- \u003cstrong\u003eBL3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eBL4\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eBL5\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eBL6\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eBL7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- 8 (3.1%)\u003c/p\u003e\n \u003cp\u003e- 26 (10.2%)\u003c/p\u003e\n \u003cp\u003e- 121 (47.6%)\u003c/p\u003e\n \u003cp\u003e- 85 (33.5%)\u003c/p\u003e\n \u003cp\u003e- 14 (5.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- 12 (4.7%)\u003c/p\u003e\n \u003cp\u003e- 27 (10.5%)\u003c/p\u003e\n \u003cp\u003e- 122 (47.5%)\u003c/p\u003e\n \u003cp\u003e- 76 (29.6%)\u003c/p\u003e\n \u003cp\u003e- 20 (7.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.669\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- \u003cstrong\u003eICM A\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eICM B\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eICM C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- 213 (83.9%)\u003c/p\u003e\n \u003cp\u003e- 35 (13.8%)\u003c/p\u003e\n \u003cp\u003e- 6 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- 139 (54.1%)\u003c/p\u003e\n \u003cp\u003e- 78 (30.4%)\u003c/p\u003e\n \u003cp\u003e- 40 (15.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- \u003cstrong\u003eTE A\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eTE B\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eTE C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- 199 (78.3%)\u003c/p\u003e\n \u003cp\u003e- 52 (20.5%)\u003c/p\u003e\n \u003cp\u003e- 3 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- 113 (44%)\u003c/p\u003e\n \u003cp\u003e- 105 (40.9%)\u003c/p\u003e\n \u003cp\u003e- 39 (15.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- \u003cstrong\u003eE1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eE2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eE3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eE4\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eE5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- 135 (53.1%)\u003c/p\u003e\n \u003cp\u003e- 73 (28.7%)\u003c/p\u003e\n \u003cp\u003e- 25 (9.8%)\u003c/p\u003e\n \u003cp\u003e- 14 (5.5%)\u003c/p\u003e\n \u003cp\u003e- 7 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- 85 (33.1%)\u003c/p\u003e\n \u003cp\u003e- 61 (23.7%)\u003c/p\u003e\n \u003cp\u003e- 51 (19.8%)\u003c/p\u003e\n \u003cp\u003e- 38 (14.8%)\u003c/p\u003e\n \u003cp\u003e- 22 (8.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eContinuous data are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD; categorical data are expressed as percentage (%). Categorical data are expressed as percentages (%), E: EEVA score, EEVA Early embryo viability assessment. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, statistical significance of the variable-outcome association. BMI: BMI of the oocyte provider, ICM inner cell mass, TE trophectoderm, BL blastocyst expansion degree\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Association between univariate with implantation and ongoing pregnancy\u003c/h2\u003e\n \u003cp\u003eA GEEs model was built to quantify the odds of achieving implantation and ongoing pregnancy according to univariate factors including maternal age, endometrial thickness, BMI, embryo age, embryo expansion, ICM morphology, TE morphology, and EEVA score. Embryo age, ICM morphology, TE morphology, and EEVA score significantly correlated with implantation and ongoing pregnancy (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as presented in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGeneralized estimating equations assessing the association of univariate with implantation and ongoing pregnancy\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eUnivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003eImplantation\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal age (year)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEndometrial thickness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmbryo age (Day 6 vs Day 5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.219\u0026ndash;0.705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- \u003cstrong\u003eBL4 vs BL3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eBL5 vs BL3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eBL6 vs BL3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eBL7 vs BL3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.631\u003c/p\u003e\n \u003cp\u003e0.423\u003c/p\u003e\n \u003cp\u003e0.456\u003c/p\u003e\n \u003cp\u003e0.768\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- \u003cstrong\u003eICM B vs ICM A\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eICM C vs ICM A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.202\u0026ndash;0.492\u003c/p\u003e\n \u003cp\u003e0.067\u0026ndash;0.257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- \u003cstrong\u003eTE B vs TE A\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eTE C vs TE A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.206\u0026ndash;0.475\u003c/p\u003e\n \u003cp\u003e0.032\u0026ndash;0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- \u003cstrong\u003eE2 vs E1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eE3 vs E1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eE4 vs E1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eE5 vs E1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.791\u003c/p\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003cp\u003e0.259\u003c/p\u003e\n \u003cp\u003e0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.484\u0026ndash;1.293\u003c/p\u003e\n \u003cp\u003e0.194\u0026ndash;0.580\u003c/p\u003e\n \u003cp\u003e0.138\u0026ndash;0.485\u003c/p\u003e\n \u003cp\u003e0.111\u0026ndash;0.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.350\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eOngoing pregnancy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal age (year)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.899\u0026ndash;0.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEndometrial thickness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.953\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmbryo age (Day 6 vs Day 5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.205\u0026ndash;0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- \u003cstrong\u003eBL4 vs BL3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eBL5 vs BL3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eBL6 vs BL3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eBL7 vs BL3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003cp\u003e0.402\u003c/p\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- \u003cstrong\u003eICM B vs ICM A\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eICM C vs ICM A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.186\u0026ndash;0.460\u003c/p\u003e\n \u003cp\u003e0.040\u0026ndash;0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- \u003cstrong\u003eTE B vs TE A\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eTE C vs TE A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.188\u0026ndash;0.421\u003c/p\u003e\n \u003cp\u003e0.013\u0026ndash;0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- \u003cstrong\u003eE2 vs E1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eE3 vs E1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eE4 vs E1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eE5 vs E1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003cp\u003e0.309\u003c/p\u003e\n \u003cp\u003e0.232\u003c/p\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.488\u0026ndash;1.164\u003c/p\u003e\n \u003cp\u003e0.178\u0026ndash;0.535\u003c/p\u003e\n \u003cp\u003e0.119\u0026ndash;0.453\u003c/p\u003e\n \u003cp\u003e0.082\u0026ndash;0.489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eContinuous data are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD; categorical data are expressed as percentage (%). Categorical data are expressed as percentages (%), E: EEVA score, EEVA Early embryo viability assessment. BMI: BMI of the oocyte provider, ICM inner cell mass, TE trophectoderm, BL blastocyst expansion degree. OR odds ratio, CI confidence interval. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, statistical significance of the variable-outcome association\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe predictive power of EEVA test classification in multivariate GEEs analyzed for implantation and ongoing pregnancy is shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. The EEVA score significantly correlated with implantation and ongoing pregnancy, in which the OR of each category was decreasingly lower. The ORs for implantation and ongoing pregnancy were significantly lower in E3, E4, and E5 when compared to E1 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), except those between E1 and E2 (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The embryonic age variable was not significantly different in the multivariate GEEs model (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Association between EEVA score with implantation and ongoing pregnancy\u003c/h2\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGeneralized estimating equations assessing the association of the EEVA score with implantation and ongoing pregnancy, alongside embryo age.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003eImplantation\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmbryo age (Day 6 vs Day 5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- \u003cstrong\u003eE2 vs E1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eE3 vs E1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eE4 vs E1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eE5 vs E1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.795\u003c/p\u003e\n \u003cp\u003e0.350\u003c/p\u003e\n \u003cp\u003e0.288\u003c/p\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.486\u0026ndash;1.301\u003c/p\u003e\n \u003cp\u003e0.201\u0026ndash;0.607\u003c/p\u003e\n \u003cp\u003e0.151\u0026ndash;0.546\u003c/p\u003e\n \u003cp\u003e0.125\u0026ndash;0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.362\u003c/p\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eOngoing pregnancy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmbryo age (Day 6 vs Day 5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- \u003cstrong\u003eE2 vs E1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eE3 vs E1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eE4 vs E1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eE5 vs E1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.757\u003c/p\u003e\n \u003cp\u003e0.321\u003c/p\u003e\n \u003cp\u003e0.256\u003c/p\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.489\u0026ndash;1.170\u003c/p\u003e\n \u003cp\u003e0.184\u0026ndash;0.557\u003c/p\u003e\n \u003cp\u003e0.129\u0026ndash;0.505\u003c/p\u003e\n \u003cp\u003e0.092\u0026ndash;0.563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eContinuous data are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD; categorical data are expressed as percentage (%). Categorical data are expressed as percentages (%), E: EEVA score, EEVA Early embryo viability assessment. BMI: BMI of the oocyte provider, ICM inner cell mass, TE trophectoderm, BL blastocyst expansion degree. OR odds ratio, CI confidence interval. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, statistical significance of the variable-outcome association\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe predictive power of ICM morphology and TE morphology test classification in multivariate GEEs analyzed for implantation and ongoing pregnancy is shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. TE morphology significantly correlated with implantation and ongoing pregnancy, in which the OR of each category was decreasingly lower. The ORs for implantation and ongoing pregnancy were significantly lower in TE B and TE C when compared to TE A (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), except those between E1 and E2 (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). The embryonic age variable and ICM morphology were not significantly different in the multivariate GEEs model (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. Association between blastocyst morphology with implantation and ongoing pregnancy\u003c/h2\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGeneralized estimating equations assessing the association of the morphology with implantation and ongoing pregnancy, alongside with embryo age.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003eImplantation\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmbryo age (Day 6 vs Day 5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.759\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- \u003cstrong\u003eICM B vs ICM A\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eICM C vs ICM A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003cp\u003e0.929\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- \u003cstrong\u003eTE B vs TE A\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eTE C vs TE A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.405\u003c/p\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.240\u0026ndash;0.683\u003c/p\u003e\n \u003cp\u003e0.017\u0026ndash;0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eOngoing pregnancy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmbryo age (Day 6 vs Day 5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.703\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- \u003cstrong\u003eICM B vs ICM A\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eICM C vs ICM A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003cp\u003e0.614\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- \u003cstrong\u003eTE B vs TE A\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eTE C vs TE A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.371\u003c/p\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.226\u0026ndash;0.611\u003c/p\u003e\n \u003cp\u003e0.010\u0026ndash;0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eContinuous data are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD; categorical data are expressed as percentage (%). Categorical data are expressed as percentages (%), E: EEVA score, EEVA Early embryo viability assessment. BMI: BMI of the oocyte provider, ICM inner cell mass, TE trophectoderm, BL blastocyst expansion degree. OR odds ratio, CI confidence interval. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, statistical significance of the variable-outcome association\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe predictive power of EEVA test classification combined with ICM, TE morphology, and embryo age in multivariate GEEs analyzed for implantation and ongoing pregnancy is shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. EEVA score and TE morphology significantly correlated with implantation and ongoing pregnancy, in which the OR of each category was decreasingly lower. The ORs for implantation and ongoing pregnancy were significantly lower in TE B and TE C when compared to TE A (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), in E3, E4, and E5 when compared to E1 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), except those between E1 and E2 (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). The embryonic age variable and ICM morphology were not significantly different in the multivariate GEEs model (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5. Association between combination of EEVA score and blastocyst morphology with implantation and ongoing pregnancy\u003c/h2\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGeneralized estimating equations assessing the association of the Eeva scores and morphology together with implantation and ongoing pregnancy, alongside with embryo age.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003eImplantation\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmbryo age (Day 6 vs Day 5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.559\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- \u003cstrong\u003eICM B vs ICM A\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eICM C vs ICM A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.214\u003c/p\u003e\n \u003cp\u003e0.699\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- \u003cstrong\u003eTE B vs TE A\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eTE C vs TE A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.436\u003c/p\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.256\u0026ndash;0.742\u003c/p\u003e\n \u003cp\u003e0.016\u0026ndash;0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- \u003cstrong\u003eE2 vs E1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eE3 vs E1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eE4 vs E1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eE5 vs E1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.928\u003c/p\u003e\n \u003cp\u003e0.514\u003c/p\u003e\n \u003cp\u003e0.402\u003c/p\u003e\n \u003cp\u003e0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.552\u0026ndash;1.563\u003c/p\u003e\n \u003cp\u003e0.282\u0026ndash;0.935\u003c/p\u003e\n \u003cp\u003e0.199\u0026ndash;0.810\u003c/p\u003e\n \u003cp\u003e0.258\u0026ndash;1.591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.780\u003c/p\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eOngoing pregnancy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmbryo age (Day 6 vs Day 5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- \u003cstrong\u003eICM B vs ICM A\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eICM C vs ICM A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003cp\u003e0.965\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- \u003cstrong\u003eTE B vs TE A\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eTE C vs TE A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.240\u0026ndash;0.666\u003c/p\u003e\n \u003cp\u003e0.010\u0026ndash;0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e- \u003cstrong\u003eE2 vs E1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eE3 vs E1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eE4 vs E1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eE5 vs E1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.874\u003c/p\u003e\n \u003cp\u003e0.463\u003c/p\u003e\n \u003cp\u003e0.364\u003c/p\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.551\u0026ndash;1.385\u003c/p\u003e\n \u003cp\u003e0.256\u0026ndash;0.839\u003c/p\u003e\n \u003cp\u003e0.174\u0026ndash;0.762\u003c/p\u003e\n \u003cp\u003e0.207\u0026ndash;1.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.566\u003c/p\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eContinuous data are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD; categorical data are expressed as percentage (%). Categorical data are expressed as percentages (%), E: EEVA score, EEVA Early embryo viability assessment. BMI: BMI of the oocyte provider, ICM inner cell mass, TE trophectoderm, BL blastocyst expansion degree. OR odds ratio, CI confidence interval. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, statistical significance of the variable-outcome association\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6. Comparison three models\u003c/h2\u003e\n \u003cp\u003eThe ROC curves of three models are presented in Fig.\u0026nbsp;1. The AUC of the model using EEVA test only, blastocyst morphology only, or a combination of EEVA test and blastocyst morphology was 0.651 (95% CI 0.608\u0026ndash;0.692), 0.703 (95% CI 0.662\u0026ndash;0.743), and 0.730 (95% CI 0.688\u0026ndash;0.767), respectively, for implantation; 0.655 (95% CI 0.612\u0026ndash;0.696), 0.700 (95% CI 0.658\u0026ndash;0.740), and 0.726 (95% CI 0.686\u0026ndash;0.765), respectively, for ongoing pregnancy. The model with the highest AUC for implantation and ongoing pregnancy was always the one combining both the EEVA test and blastocyst morphological assessment, when compared with models using only the EEVA test or blastocyst morphological assessment. The differences were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The difference between the model using only the EEVA test and blastocyst morphology was not statistically significant. The dates are presented in Fig.\u0026nbsp;1.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eTo our knowledge, this is the first study where an embryo selection model, which is a combination of the EEVA test, an automatic classification algorithm applied to cleavage stage embryos, and the conventional blastocyst morphological assessment, is tested on a single vitrified-warmed blastocyst transfer for implantation and ongoing pregnancy as the end points. The ability of the EEVA test is to identify on Day 3 embryos with the highest potential to reach blastocyst stage. The results of our study confirm that the EEVA test is also useful in embryo selection on Day 5 and 6 for single vitrified-warmed blastocyst transfer, especially when the EEVA test is used in combination with the conventional blastocyst morphological assessment.\u003c/p\u003e \u003cp\u003eAs for predicting the implantation potential of the EEVA test, Vermilyea et al. (2014) showed that embryos with a low score of EEVA had a higher implantation rate than those with a high score of EEVA [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In the external validation performed by Aparicio-Ruiz et al. (2016), the study confirmed the predictive power of the EEVA test for implantation, in which embryos from oocyte donations with a low score of EEVA also had higher implantation than those with a higher score of EEVA [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The results of our study showed the ability of the EEVA test to identify embryos with higher implantation potential. Transferred vitrified-warmed blastocysts with the high EEVA score (E5) had 0.282 times lower odds of successful implantation than blastocysts with the low EEVA score (E1), p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Our study is also the first to test single vitrified-warmed blastocyst transfer for ongoing pregnancy as the end point. Transferred vitrified-warmed blastocysts with the high EEVA score (E5) had 0.228 times lower odds of a successful ongoing pregnancy than blastocysts with the low EEVA score (E1), p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eProlonging the culture of the cleavage embryo stage to the blastocyst stage has now become a routine practice in many IVF laboratories. Consequently, new selection algorithms tend to use later morphokinetic events, or a combination of algorithms and morphology assessment [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Among the algorithms for blastocyst selection, Motato et al. (2016) used two morphokinetic parameters: the synchrony in divisions of the third cell cycle (s3) and the time to reach the expanded blastocyst stage (tEB), with an AUC\u0026thinsp;=\u0026thinsp;0.602 (95% CI 0.559\u0026ndash;0.645), but it has not been externally validated [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Goodman et al. (2016) used some parameters and the start of blastulation time (tSB) as a positively scoring variable [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, this model did not mention AUC. An external validation of another predictive algorithm, KIDScore D5 version 3, used for selection of embryos at the blastocyst stage, available for EmbryoScope and EmbryoScope Plus incubators, was presented by Bori et al. (2022), with an AUC\u0026thinsp;=\u0026thinsp;0.633 for implantation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The KIDScore D5 was designed as an embryo classification system independent of the conventional blastocyst morphological assessment, as it already includes trophectoderm and inner cell mass morphology as predictive variables in the algorithm. Contrary to KIDScore D5, the EEVA test was developed as a complementary tool to the conventional morphological evaluation of day 3 embryo to predict blastocyst formation [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDepending on the type of embryo transfer cycle, many embryo selection algorithms based on automatic annotations have been reported to achieve AUCs of approximately 0.650\u0026ndash;0.700, achieving a statistically significant ability to predict implantation, but only at a relatively high level. None of these models showed a statistically significant improvement compared to blastocyst morphological assessment [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In the study of Valera et al. (2023), EEVA classification performed similarly to traditional blastocyst morphological assessment for implantation prediction. The combination of the EEVA system and the traditional blastocyst morphological assessment tended to have better prognostic value than EEVA classification alone, AUCs\u0026thinsp;=\u0026thinsp;0.636 (95% CI 0.584\u0026ndash;0.660); 0.622 (95% CI 0.598\u0026ndash;0.673), respectively. However the difference was not statistically significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our study, the EEVA classification also performed similarly to the traditional blastocyst morphological assessment for implantation prediction with an AUC\u0026thinsp;=\u0026thinsp;0.651 (95% CI 0.608\u0026ndash;0.692) vs. 0.703 (95% CI 0.662\u0026ndash;0.743), respectively, and for ongoing pregnancy prediction with an AUC\u0026thinsp;=\u0026thinsp;0.655 (95% CI 0.612\u0026ndash;0.696) vs. 0.700 (95% CI 0.658\u0026ndash;0.740), respectively. The differences were not statistically significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;1). Further analysis of the GEE model using a combination of both methods, including the EEVA test and the blastocyst morphological assessment, showed that both predictors were statistically significant for implantation and ongoing pregnancy prediction, and their predictive values were independent of each other. Indeed, in this study, the best AUCs for both outcomes were found when combining both the evaluation systems of the EEVA test and the blastocyst morphological assessment compared with the EEVA test alone or the blastocyst morphological assessment alone, the AUC\u0026thinsp;=\u0026thinsp;0.730 (95% CI 0.688\u0026ndash;0.767), 0.651 (95% CI 0.608\u0026ndash;0.692), and 0.703 (95% CI 0.662\u0026ndash;0.743) for implantation, respectively; the AUC\u0026thinsp;=\u0026thinsp;0.726 (95% CI 0.686\u0026ndash;0.765), 0.655 (95% CI 0.612\u0026ndash;0.696) and 0.700 (95% CI 0.658\u0026ndash;0.740) for ongoing pregnancy, respectively. The differences of pairwise comparison of ROC curves were statistically significant, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eOur study used GEEs for statistical analysis. GEEs consider several events as intra-patient variables. This way, the model considers the relationship between embryos that share the same patient background characteristics, adding one more layer to consideration of the intra-data context of measured confounders. We only selected blastocysts whose quality did not decrease after warming. Therefore, the vitrification procedure did not adversely affect the quality of the transferred embryo. In this study, we analyzed univariate data using the GEEs model; only statistically significant variables were included in the multivariate model. The age of the transferred embryo was included in the three GEEs models using the EEVA test only, blastocyst morphological assessment only, and a combination of both. However, in all 3 models, the age of the transferred embryo was not statistically significant when combined with the variables (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eThe limitation of the study was that the details of the algorithm and some morphological features used in the EEVA system were hidden by the manufacturer, which prevented our later conclusions because we did not know how to calculate the score. The EEVA score is automatically obtained from the software. The performance of automatic annotations of morphokinetic event timings provided by the EEVA system may be unclear. In this study, the automatic annotations were not validated, as our aim was to analyze the performance of the whole system. In addition, it should be noted that our study was a retrospective study and therefore may have limitations. Therefore, in future studies, randomised controlled trials may be required.\u003c/p\u003e \u003cp\u003eIn conclusion, our results confirm the efficacy of the EEVA algorithm for implantation and ongoing pregnancy outcome prediction. The highest AUC was achieved when combining the EEVA test and blastocyst morphological assessment, and the improvement over the EEVA test alone or morphological assessment alone was statistically significant.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIVF\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIn vitro Fertilization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSVBT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esingle vitrified-warmed blastocyst transfer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArtificial intelligence\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eEEVA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEarly Embryo Viability Assessment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eICSI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntracytoplasmic sperm injection\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGEEs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneralized estimating equations\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eROC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAUC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea under the ROC curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds ratios\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence intervals\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eICM\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInner cell mass\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTE\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTrophectoderm\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBlastocyst expansion degree\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the members of the IVF laboratory Tam Anh for supporting all the lab’s work during the group's research. Specifically, we would like to thank Tam Anh General Hospital for allowing and facilitating us to complete the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors did not receive support from any organization for the submitted work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHop Vu Dinh, Huong Nguyen Thi Lien, Hanh Van Nguyen, Hoang Le conceived, designed, interpreted the data, coordinated the work. Hop Vu Dinh wrote the manuscript. Cuong An Manh Anh Phi Thi Tu, collected, double checked the data, participated in interpreting the data. Huong Nguyen Thi Lien, Hanh Nguyen Van, Hoang Le reviewed the manuscript. All authors jointly contributed to the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was reviewed and approved by the Internal Committee for Ethics of the Tam Anh General Hospital, Vietnam.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eK. 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Engl.\u003c/em\u003e, vol. 28, no. 12, pp. 3215\u0026ndash;3221, Dec. 2013, doi: 10.1093/humrep/det366.\u003c/li\u003e\n\u003cli\u003e\u0026ldquo;Good practice recommendations for the use of time-lapse technology\u0026dagger; | Human Reproduction Open | Oxford Academic.\u0026rdquo; Accessed: Jan. 31, 2024. [Online]. Available: https://academic.oup.com/hropen/article/2020/2/hoaa008/5809428\u003c/li\u003e\n\u003cli\u003eL. Bori \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Novel and conventional embryo parameters as input data for artificial neural networks: an artificial intelligence model applied for prediction of the implantation potential,\u0026rdquo; \u003cem\u003eFertil. Steril.\u003c/em\u003e, vol. 114, no. 6, pp. 1232\u0026ndash;1241, Dec. 2020, doi: 10.1016/j.fertnstert.2020.08.023.\u003c/li\u003e\n\u003cli\u003eL. 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Genet.\u003c/em\u003e, vol. 32, no. 1, pp. 61\u0026ndash;68, Jan. 2015, doi: 10.1007/s10815-014-0366-1.\u003c/li\u003e\n\u003cli\u003eA. Revelli \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Impact of the addition of Early Embryo Viability Assessment to morphological evaluation on the accuracy of embryo selection on day 3 or day 5: a retrospective analysis,\u0026rdquo; \u003cem\u003eJ. Ovarian Res.\u003c/em\u003e, vol. 12, no. 1, p. 73, Aug. 2019, doi: 10.1186/s13048-019-0547-8.\u003c/li\u003e\n\u003cli\u003eB. Aparicio-Ruiz, N. Basile, S. P\u0026eacute;rez Albal\u0026aacute;, F. Bronet, J. Remoh\u0026iacute;, and M. Meseguer, \u0026ldquo;Automatic time-lapse instrument is superior to single-point morphology observation for selecting viable embryos: retrospective study in oocyte donation,\u0026rdquo; \u003cem\u003eFertil. Steril.\u003c/em\u003e, vol. 106, no. 6, pp. 1379-1385.e10, Nov. 2016, doi: 10.1016/j.fertnstert.2016.07.1117.\u003c/li\u003e\n\u003cli\u003eD. C. Kieslinger \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Embryo selection using time-lapse analysis (Early Embryo Viability Assessment) in conjunction with standard morphology: a prospective two-center pilot study,\u0026rdquo; \u003cem\u003eHum. Reprod. Oxf. Engl.\u003c/em\u003e, vol. 31, no. 11, pp. 2450\u0026ndash;2457, Nov. 2016, doi: 10.1093/humrep/dew207.\u003c/li\u003e\n\u003cli\u003eL. Yang \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Single embryo transfer by Day 3 time-lapse selection versus Day 5 conventional morphological selection: a randomized, open-label, non-inferiority trial,\u0026rdquo; \u003cem\u003eHum. Reprod. Oxf. Engl.\u003c/em\u003e, vol. 33, no. 5, pp. 869\u0026ndash;876, May 2018, doi: 10.1093/humrep/dey047.\u003c/li\u003e\n\u003cli\u003eN. Desai \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Delayed blastulation, multinucleation, and expansion grade are independently associated with live-birth rates in frozen blastocyst transfer cycles,\u0026rdquo; \u003cem\u003eFertil. Steril.\u003c/em\u003e, vol. 106, no. 6, pp. 1370\u0026ndash;1378, Nov. 2016, doi: 10.1016/j.fertnstert.2016.07.1095.\u003c/li\u003e\n\u003cli\u003eL. R. Goodman, J. Goldberg, T. Falcone, C. Austin, and N. Desai, \u0026ldquo;Does the addition of time-lapse morphokinetics in the selection of embryos for transfer improve pregnancy rates? A randomized controlled trial,\u0026rdquo; \u003cem\u003eFertil. Steril.\u003c/em\u003e, vol. 105, no. 2, pp. 275-285.e10, Feb. 2016, doi: 10.1016/j.fertnstert.2015.10.013.\u003c/li\u003e\n\u003cli\u003eY. Mizobe \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Selection of human blastocysts with a high implantation potential based on timely compaction,\u0026rdquo; \u003cem\u003eJ. Assist. Reprod. Genet.\u003c/em\u003e, vol. 34, no. 8, pp. 991\u0026ndash;997, Aug. 2017, doi: 10.1007/s10815-017-0962-y.\u003c/li\u003e\n\u003cli\u003eD. K. Gardner and W. B. Schoolcraft, \u0026ldquo;Culture and transfer of human blastocysts,\u0026rdquo; \u003cem\u003eCurr. Opin. Obstet. Gynecol.\u003c/em\u003e, vol. 11, no. 3, pp. 307\u0026ndash;311, Jun. 1999, doi: 10.1097/00001703-199906000-00013.\u003c/li\u003e\n\u003cli\u003eB. Carrasco \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Selecting embryos with the highest implantation potential using data mining and decision tree based on classical embryo morphology and morphokinetics,\u0026rdquo; \u003cem\u003eJ. Assist. Reprod. Genet.\u003c/em\u003e, vol. 34, no. 8, pp. 983\u0026ndash;990, Aug. 2017, doi: 10.1007/s10815-017-0955-x.\u003c/li\u003e\n\u003cli\u003eY. Motato, M. J. de los Santos, M. J. Escriba, B. A. Ruiz, J. Remoh\u0026iacute;, and M. Meseguer, \u0026ldquo;Morphokinetic analysis and embryonic prediction for blastocyst formation through an integrated time-lapse system,\u0026rdquo; \u003cem\u003eFertil. Steril.\u003c/em\u003e, vol. 105, no. 2, pp. 376-384.e9, Feb. 2016, doi: 10.1016/j.fertnstert.2015.11.001.\u003c/li\u003e\n\u003cli\u003eJ. Conaghan \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Improving embryo selection using a computer-automated time-lapse image analysis test plus day 3 morphology: results from a prospective multicenter trial,\u0026rdquo; \u003cem\u003eFertil. Steril.\u003c/em\u003e, vol. 100, no. 2, pp. 412-419.e5, Aug. 2013, doi: 10.1016/j.fertnstert.2013.04.021.\u003c/li\u003e\n\u003cli\u003eM. A. Valera, B. Aparicio-Ruiz, S. P\u0026eacute;rez-Albal\u0026aacute;, L. Romany, J. Remoh\u0026iacute;, and M. Meseguer, \u0026ldquo;Clinical validation of an automatic classification algorithm applied on cleavage stage embryos: analysis for blastulation, euploidy, implantation, and live-birth potential,\u0026rdquo; \u003cem\u003eHum. Reprod. Oxf. Engl.\u003c/em\u003e, vol. 38, no. 6, pp. 1060\u0026ndash;1075, Jun. 2023, doi: 10.1093/humrep/dead058.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Early Embryo Viability Assessment (EEVA), Automated embryo assessment, Timelapse Geri Incubator, Blastocyst, Morphology.","lastPublishedDoi":"10.21203/rs.3.rs-4022641/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4022641/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate a combination of the Early Embryo Viability Assessment (EEVA) system and blastocyst morphological assessment as a predictor of pregnancy outcomes of single vitrified-warmed blastocyst transfer, such as implantation and ongoing pregnancy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe retrospective study was conducted in a single centre from 2020 to 2023 and included 511 single vitrified-warmed blastocyst transfer cycles. Blastocyst were selected for transfer based on conventional morphological assessment. Embryos Day 3 were evaluated using EEVA software. The correlation between the EEVA system alone, or a combination of the EEVA system and blastocyst morphological assessment, and pregnancy outcomes was qualified by generalized estimating equations (GEEs).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe implantation rate and ongoing pregnancy were higher with lower scores generated by the EEVA software. A GEE model showed a negative association between a higher embryo score and lower odds of implantation and ongoing pregnancy. The OR of Score 3;4;5 vs. 1 were 0.350; 0.288; 0.282 (95%CI 0.201–0.607; 0.151–0.546; 0.125–0.636, p=0.000), respectively, for implantation. The OR of Score 3;4;5 vs. 1 were 0.321; 0256; 0.228 (95%CI 0.184-0.557; 0.129-0.505; 0.092-0.563, p=0.000), respectively, for ongoing pregnancy. The AUC of the model using the EEVA system for implantation and ongoing pregnancy potential is 0.651 and 0.655, respectively. The AUC of the model combining both systems for implantation and ongoing pregnancy potential is 0.730 and 0.726. The differences were statistically significant (p=0.0001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe EEVA system can predict the success rates of assisted reproduction cycles, especially when combined with blastocyst morphological assessment in blastocyst selection for transfer.\u003c/p\u003e","manuscriptTitle":"Prediction of pregnancy outcomes of single vitrified-warmed blastocyst transfer using combination of an automatic classification algorithm applied on cleavage stage embryos and blastocyst morphological assessment: a single - centre, retrospective study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-12 09:15:40","doi":"10.21203/rs.3.rs-4022641/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b152b3d7-1c21-496e-a950-53bdef019b2e","owner":[],"postedDate":"March 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-16T11:59:27+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-12 09:15:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4022641","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4022641","identity":"rs-4022641","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}