Prediction Model For Live Birth Research Articles

Development and validation of a gonadotropin dose selection model for optimized ovarian stimulation in IVF/ICSI: an individual participant data meta-analysis.

The ovarian response to gonadotropin stimulation varies widely among women, and could impact the probability of live birth as well as treatment risks. Many studies have evaluated the impact of different gonadotropin starting doses, mainly based on predictive variables like ovarian reserve tests (ORT) including anti-Müllerian hormone (AMH), antral follicle count (AFC), and basal follicle-stimulating hormone (bFSH). A Cochrane systematic review revealed that individualizing the gonadotropin starting dose does not affect efficacy in terms of ongoing pregnancy/live birth rates, but may reduce treatment risks such as the development of ovarian hyperstimulation syndrome (OHSS). An individual patient data meta-analysis (IPD-MA) offers a unique opportunity to develop and validate a universal prediction model to help choose the optimal gonadotropin starting dose to minimize treatment risks without affecting efficacy. The objective of this IPD-MA is to develop and validate a gonadotropin dose-selection model to guide the choice of a gonadotropin starting dose in IVF/ICSI, with the purpose of minimizing treatment risks without compromising live birth rates. Electronic databases including MEDLINE, EMBASE, and CRSO were searched to identify eligible studies. The last search was performed on 13 July 2022. Randomized controlled trials (RCTs) were included if they compared different doses of gonadotropins in women undergoing IVF/ICSI, presented at least one type of ORT, and reported on live birth or ongoing pregnancy. Authors of eligible studies were contacted to share their individual participant data (IPD). IPD and information within publications were used to determine the risk of bias. Generalized linear mixed multilevel models were applied for predictor selection and model development. A total of 14 RCTs with data of 3455 participants were included. After extensive modeling, women aged 39 years and over were excluded, which resulted in the definitive inclusion of 2907 women. The optimal prediction model for live birth included six predictors: age, gonadotropin starting dose, body mass index, AFC, IVF/ICSI, and AMH. This model had an area under the curve (AUC) of 0.557 (95% confidence interval (CI) from 0.536 to 0.577). The clinically feasible live birth model included age, starting dose, and AMH and had an AUC of 0.554 (95% CI from 0.530 to 0.578). Two models were selected as the optimal model for combined treatment risk, as their performance was equal. One included age, starting dose, AMH, and bFSH; the other also included gonadotropin-releasing hormone (GnRH) analog. The AUCs for both models were 0.769 (95% CI from 0.729 to 0.809). The clinically feasible model for combined treatment risk included age, starting dose, AMH, and GnRH analog, and had an AUC of 0.748 (95% CI from 0.709 to 0.787). The aim of this study was to create a model including patient characteristics whereby gonadotropin starting dose was predictive of both live birth and treatment risks. The model performed poorly on predicting live birth by modifying theFSH starting dose. On the contrary, predicting treatment risks in terms of OHSS occurrence and management by modifying thegonadotropin starting dose was adequate. This dose-selection model, consisting of easily obtainable patient characteristics, aids in the choice of the optimal gonadotropin starting dose for each individual patient to lower treatment risks and potentially reduce treatment costs.

The details matter: personalized prediction of live birth after in vitro fertilization in women with polycystic ovary syndrome

ObjectiveTo derive and internally validate a clinical prediction model for live birth in women with polycystic ovary syndrome (PCOS) undergoing in-vitro fertilization (IVF) DesignRetrospective cohort study SettingFour academic reproductive endocrinology clinics Patients207 women with PCOS confirmed by Rotterdam criteria undergoing their first fresh IVF cycle InterventionsNone Main Outcome MeasureThe primary outcome was cumulative live birth (LB) per IVF cycle start. This included any LB that resulted from either fresh embryo transfer or any subsequent frozen embryo transfer from embryos obtained at the index oocyte retrieval. A prediction model was derived using multivariable logistic regression. Covariates considered for inclusion in the prediction model included demographic characteristics, medical history, and prior fertility treatment. Predicted probabilities for live birth were calculated using the prediction model which included the 90% shrinkage factor for each adjusted OR. ResultsThe final model, based on maximization of the area under the receiver operating characteristic curve (AUROC), included age <35 years, White race, presence of polycystic ovaries on ultrasound (PCOM), normal BMI (<25 kg/m2), being metabolically healthy (no metabolic risk factors), and being a non-responder to ovulation induction (OI) agents including letrozole and clomiphene citrate. The AUROC for the model was 0.68 (95% CI: 0.60, 0.77). Predicted probabilities of live birth ranged from 8.1% (95% CI 2.8, 21.5) for a woman who had no favorable predictors to 74.2% (95% CI: 59.5, 84.9) for a woman who had all favorable predictors. ConclusionOur study demonstrates that in addition to anovulation, the underlying pathophysiology and associated co-morbidities alter the likelihood of a successful pregnancy in women with PCOS undergoing IVF. Further validation of this model is needed before it, can serve as a tool to personalize prediction estimates for probability of live birth in women with PCOS.

Using blastocyst re-expansion rate for deciding when to warm a new blastocyst for single vitrified–warmed blastocyst transfer

Research questionCan predictive post-warm parameters that support the decision to transfer a warmed blastocyst or to warm another blastocyst be identified in women with multiple frozen–vitrified blastocysts? DesignRetrospective single-centre observational cohort analysis. A total of 1092 single vitrified–warmed blastocyst transfers (SVBT) with known Gardner score, maternal age and live birth were used to develop live birth prediction models based on logistic regression, including post-warm re-expansion parameters. Time-lapse incubation was used for pre-vitrification and post-warm embryo culture. A dataset of 558 SVBT with the same inclusion criteria was used to validate the model, but with known clinical pregnancy outcome instead of live birth outcome. ResultsThree different logistic regression models were developed for predicting live birth based on post-warm blastocyst re-expansion. Different post-warm assessment times indicated that a 2-h post-warm culture period was optimal for live birth prediction (model 1). Adjusting for pre-vitrification Gardner score (model 2) and in combination with maternal age (model 3) further increased predictability (area under the curve [AUC] = 0.623, 0.633, 0.666, respectively). Model validation gave an AUC of 0.617, 0.609 and 0.624, respectively. The false negative rate and true negative rate for model 3 were 2.0 and 10.1 in the development dataset and 3.5 and 8.0 in the validation dataset. ConclusionsClinical application of a simple model based on 2 h of post-warm re-expansion data, pre-vitrification Gardner score and maternal age can support a standardized approach for deciding if warming another blastocyst may increase the likelihood of live birth in SVBT.

Development and evaluation of a live birth prediction model for evaluating human blastocysts: a retrospective study.

In infertility treatment, blastocyst morphological grading is commonly used in clinical practice for blastocyst evaluation and selection, but has shown limited predictive power on live birth outcomes of blastocysts. To improve live birth prediction, a number of artificial intelligence (AI) models have been established. Most existing AI models for blastocyst evaluation only used images for live birth prediction, and the area under the receiver operating characteristic (ROC) curve (AUC) achieved by these models has plateaued at ~0.65. This study proposed a multimodal blastocyst evaluation method using both blastocyst images and patient couple's clinical features (e.g., maternal age, hormone profiles, endometrium thickness, and semen quality) to predict live birth outcomes of human blastocysts. To utilize the multimodal data, we developed a new AI model consisting of a convolutional neural network (CNN) to process blastocyst images and a multilayer perceptron to process patient couple's clinical features. The data set used in this study consists of 17,580 blastocysts with known live birth outcomes, blastocyst images, and patient couple's clinical features. This study achieved an AUC of 0.77 for live birth prediction, which significantly outperforms related works in the literature. Sixteen out of 103 clinical features were identified to be predictors of live birth outcomes and helped improve live birth prediction. Among these features, maternal age, the day of blastocyst transfer, antral follicle count, retrieved oocyte number, and endometrium thickness measured before transfer are the top five features contributing to live birth prediction. Heatmaps showed that the CNN in the AI model mainly focuses on image regions of inner cell mass and trophectoderm (TE) for live birth prediction, and the contribution of TE-related features was greater in the CNN trained with the inclusion of patient couple's clinical features compared with the CNN trained with blastocyst images alone. The results suggest that the inclusion of patient couple's clinical features along with blastocyst images increases live birth prediction accuracy. Natural Sciences and Engineering Research Council of Canada and the Canada Research Chairs Program.

O-006 Annotation-free embryo score calculated by iDAScore® correlated with live birth and has no correlation with neonatal outcomes after single vitrified-warmed blastocyst transfer

Abstract Study question Does the embryo score calculated by annotation-free embryo scoring system based on deep learning and time-lapse sequence images correlate with live birth (LB) and neonatal outcomes? Summary answer Annotation-free embryo score calculated by iDAScore correlates with decreased miscarriage and increased LB and has no correlation with neonatal outcomes. What is known already Embryo ranking models have recently been developed based on artificial intelligence (AI) and deep learning to rank embryos according to their potential for pregnancy. The practicability and usability of such models have been reported. And the previous report suggested iDAScore which is one of the deep learning models for embryo scoring was superior to traditional morphological assessment methods and morphokinetic embryo assessment models. However, few studies have used independent datasets to analyze the correlation between the score calculated by AI models, LB, and neonatal outcomes. Study design, size, duration A total of 3,010 single vitrified-warmed blastocyst transfer (SVBT) cycles were analyzed retrospectively. The quality and scoring of embryos were assessed using iDAScore v1.0 (iDAScore, Vitrolife, Sweden). The cohort was divided into four groups based on the iDAScore according to the percentile (9.9-9.3, 9.2-8.7, 8.6-7.3 and, 7.2-1.0). Participants/materials, setting, methods Scores were calculated using the iDAScore software module in the Vitrolife Technology Hub (Vitrolife, Gothenburg, Sweden). The correlation between iDAScore, LB rates and total miscarriage (TM), including 1st and 2nd trimester miscarriage, were analysed using a trend-test and multivariable logistic regression analysis. Furthermore, similarly, correlation between the iDAScore and neonatal outcomes were analysed. Main results and the role of chance LB rates decreased as the iDAScore decreased (P &amp;lt; 0.05), and a similar inverse trend was observed for the TM rates (P &amp;lt; 0.05). Additionally, multivariate logistic regression analysis showed that iDAScore significantly correlated with increased LB (adjusted odds ratio: 1.742, 95% CI: 1.601–1.904, P &amp;lt; 0.05) and decreased TM (adjusted odds ratio: 0.799, 95% CI: 0.706–0.905, P &amp;lt; 0.05). There was no significant correlation between iDAScore and neonatal outcomes, including congenital malformations, sex, gestational age, and birth weight. Multivariate logistic regression analysis, which included maternal and paternal age, maternal body mass index, parity, smoking, and the presence or absence of caesarean section as confounding factors, revealed no significant difference in any neonatal characteristics (low birth weight, small for gestation, large for gestation, preterm birth, male sex rates, and major congenital malformation). Limitations, reasons for caution SVBT was performed following minimal stimulation and natural cycle in vitro fertilisation. Therefore, only a few cycles of elective blastocyst transfer were available. However, there was no bias in selecting embryos for SVBT. Wider implications of the findings Objective embryo assessment using a completely automatic and annotation-free model, like iDAScore, showed a good correlation with increased LB and decreased TM. Furthermore, it did not correlate with neonatal outcomes. Therefore, iDAScore may be an optimal LB prediction model after SVBT without affecting neonatal outcomes. Trial registration number not applicable

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

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

An elective single cleavage embryo transfer strategy to minimize twin live birth rate based on a prediction model from double cleavage embryos transfer patients

Objective To minimize twin birth rate by establishing an elective single cleavage embryo transfer strategy based on a twin live birth prediction model from fresh double cleavage embryos transfer (cleavage DET) patients. Methods A total of 2478 patients underwent fresh cleavage DET in Nanjing Tower Hospital were enrolled to establish the twin live birth prediction model by logistic regression analysis and the cutoff value was calculated by ROC curve. Another 300 fresh cleavage DET patients and 550 cleavage single-embryo transfer (SET) patients were selected to testify the sensitivity, specificity and usefulness of this model. Results The twin live birth probability (TLBP) = eX /(eX + 1), e is a natural logarithm, X = −1.763 – (0.319 × female age) + (0.329 × endometrial thickness) + (0.282 × the number of transferred top embryos) – (0.314 × previous transfer times), and the cutoff value of TLBP was 24.2%. The sensitivity of this model for predicting twin live birth was 75.6%, while the specificity was 52.5% in the external validation of 300 DET patients. Furthermore, the validation of 550 SET patients showed that the live birth rate of TLBP value positive patients was significantly higher than that in negative patients (54.3% vs. 35.5%, p < .001). When adopted an elective single cleavage embryo transfer strategy, the patients with a positive TLBP value choose SET, while still undergo DET who with a negative TLBP value, the live birth rate would maintain as 56.7%; however, the twin birth rate would significantly decline to 7.4%. Conclusion Female age, endometrial thickness, the number of transferred top embryos and previous embryo transfer times were critical variables for the twin live birth prediction model. An elective single cleavage embryo transfer strategy according to this model can maintain the relatively high live birth rate, meanwhile get the acceptable low twin birth rate.

Trends in primeval β-hCG level increment after fresh and frozen-thawed IVF embryo transfer cycles

Mid-trimester beta-human chorionic gonadotropin (BHCG) levels are considerably higher in pregnancies resulting from frozen embryo transfer (FET) compared with fresh (FRET), leading to a higher false positive rate in aneuploidy screening tests. We aimed to investigate the dynamics of BHCG increment and its predictive value for cycle outcome. A retrospective analysis of FRET and FET cycles. BHCG values on days 14 and 16 post embryo transfer were compared and stratified according to the number of sacs demonstrated on US scan at six weeks gestation, and pregnancy outcome (biochemical pregnancy, ectopic pregnancy, spontaneous abortion, and a singleton or twin birth). A prediction model for live birth was built. A total of 430 treatment cycles were analyzed. The average BHCG levels were significantly higher in FET compared with FRET group in nonviable pregnancies on day 14, 450 vs. 183 IU/L, p < .05 and day 16, 348 vs. 735 IU/L, p < .05, respectively. The increment of BHCG was significantly steeper in the FET compared with FRET group in biochemical pregnancies (F = 6.485, p = .012*). Optimal cutoff level for live birth prediction in the FRET group was 211 IU/L (sensitivity 84%, specificity 76.2%) for day 14 and 440 IU/L (sensitivity 86.0% and specificity 72.5%) for day 16. The increment in BHCG differed significantly between the FRET and FET cycles in nonviable pregnancies. Nevertheless, the difference in BHCG levels observed in the second trimester in pregnancies conceived after FRET and FET cycle may begin as early as the fourth week of pregnancy.

Which factors are most predictive for live birth after in vitro fertilization and intracytoplasmic sperm injection (IVF/ICSI) treatments? Analysis of 100 prospectively recorded variables in 8,400 IVF/ICSI single-embryo transfers

To construct a prediction model for live birth after invitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) treatment and single-embryo transfer (SET) after 2days of embryo culture. Prospective observational cohort study. University-affiliated private infertility center. SET in 8,451 IVF/ICSI treatments in 5,699 unselected consecutive couples during 1999-2014. A total of 100 basal patient characteristics and treatment data were analyzed for associations with live birth after IVF/ICSI (adjusted for repeated treatments) and subsequently combined for prediction model construction. Live birth rate (LBR) and performance of live birth prediction model. Embryo score, treatment history, ovarian sensitivity index (OSI; number of oocytes/total dose of FSH administered), female age, infertility cause, endometrial thickness, and female height were all independent predictors of live birth. A prediction model (training data set; n = 5,722) based on these variables showed moderate discrimination, but predicted LBR with high accuracy in subgroups of patients, with LBR estimates ranging from <10% to >40%. Outcomes were similar in an internal validation data set (n = 2,460). Based on 100 variables prospectively recorded during a 15-year period, a model for live birth prediction after strict SET was constructed and showed excellent calibration in internal validation. For the first time, female height qualified as a predictor of live birth after IVF/ICSI.

Physician prediction model for live birth and multiple births after assisted reproductive technology

Physician prediction model for live birth and multiple births after assisted reproductive technology

Prediction model for live birth in ICSI using testicular extracted sperm.

Which parameters have a predictive value for live birth in couples undergoing ICSI after successful testicular sperm extraction (TESE-ICSI)? Female age, a first or subsequent started TESE-ICSI cycle, male LH, male testosterone, motility of the spermatozoa during the ICSI procedure and the initial male diagnosis before performing TESE were identified as relevant and independent parameters for live birth after TESE-ICSI. In reproductive medicine prediction models are used frequently to predict treatment success, but no prediction model currently exists for live birth after TESE-ICSI. A retrospective cohort study between 2007 and 2015 in two academic hospitals including 1559 TESE-ICSI cycles. The prediction model was developed using data from one centre and validation was performed with data from the second centre. We included couples undergoing ICSI treatment with surgically retrieved sperm from the testis for the first time. In the development set we included 526 couples undergoing 1006 TESE-ICSI cycles. In the validation set we included 289 couples undergoing 553 TESE-ICSI cycles. Multivariable logistic regression models were constructed in a stepwise fashion (P < 0.2 for entry). The external validation was based on discrimination and calibration. We included 224 couples (22.3%) with a live birth in the development set. The occurrence of a live birth was associated with lower female age, first TESE-ICSI cycle, lower male LH, higher male testosterone, the use of motile spermatozoa for ICSI and having obstructive azoospermia as an initial suspected diagnosis. The area under the receiver operating characteristic (ROC) curve was 0.62. From validation data, the model had moderate discriminative capacity (c-statistic 0.67, 95% confidence interval: 0.62-0.72) but calibrated well, with a range from 0.06 to 0.56 in calculated probabilities. We had a lack of data about the motility of spermatozoa during TESE, therefore, we used motility of the spermatozoa used for ICSI after freeze-thawing, information which is only available during treatment. We had to exclude data on paternal BMI in the model because too many missing values in the validation data hindered testing. We did not include a histologic diagnosis, which would have made our data set less heterogeneous and, finally, our model may not be applicable in centres which have a different policy for the indication for performing sperm extraction. The prognostic value of the model is limited because of a low 'area under the curve'. This model enables the differentiation between couples with a low or high chance to reach a live birth using TESE-ICSI. As such it can aid in the counselling of patients and in clinical decision-making. This study was partly supported by an unconditional grant from Merck Serono (to D.D.M.B. and K.F.) and by the Department of Obstetrics and Gynaecology of Radboud University Medical Center, Nijmegen, The Netherlands, the Department of Obstetrics and Gynaecology, Jeroen Bosch Hospital, Den Bosch, The Netherlands, and the Department of Obstetrics and Gynaecology, Academic Medical Center, Amsterdam, The Netherlands. Merck Serono had no influence in concept, design, nor elaboration of this study. Not applicable.

Competence Classification of Cumulus and Granulosa Cell Transcriptome in Embryos Matched by Morphology and Female Age.

ObjectiveBy focussing on differences in the mural granulosa cell (MGC) and cumulus cell (CC) transcriptomes from follicles resulting in competent (live birth) and non-competent (no pregnancy) oocytes the study aims on defining a competence classifier expression profile in the two cellular compartments. Design: A case-control study. Setting: University based facilities for clinical services and research. Patients: MGC and CC samples from 60 women undergoing IVF treatment following the long GnRH-agonist protocol were collected. Samples from 16 oocytes where live birth was achieved and 16 age- and embryo morphology matched incompetent oocytes were included in the study.MethodsMGC and CC were isolated immediately after oocyte retrieval. From the 16 competent and non-competent follicles, mRNA was extracted and expression profile generated on the Human Gene 1.0 ST Affymetrix array. Live birth prediction analysis using machine learning algorithms (support vector machines) with performance estimation by leave-one-out cross validation and independent validation on an external data set.ResultsWe defined a signature of 30 genes expressed in CC predictive of live birth. This live birth prediction model had an accuracy of 81%, a sensitivity of 0.83, a specificity of 0.80, a positive predictive value of 0.77, and a negative predictive value of 0.86. Receiver operating characteristic analysis found an area under the curve of 0.86, significantly greater than random chance. When applied on 3 external data sets with the end-point outcome measure of blastocyst formation, the signature resulted in 62%, 75% and 88% accuracy, respectively. The genes in the classifier are primarily connected to apoptosis and involvement in formation of extracellular matrix. We were not able to define a robust MGC classifier signature that could classify live birth with accuracy above random chance level.ConclusionWe have developed a cumulus cell classifier, which showed a promising performance on external data. This suggests that the gene signature at least partly include genes that relates to competence in the developing blastocyst.

81: The effect of transferring fewer embryos over more cycles: a prediction model for live birth and multiple births with in vitro fertilization

81: The effect of transferring fewer embryos over more cycles: a prediction model for live birth and multiple births with in vitro fertilization

A prediction model for live birth and multiple births within the first three cycles of assisted reproductive technology

To develop a model predictive of live-birth rates (LBR) and multiple birth rates (MBR) for an individual considering assisted reproduction technology (ART) using linked cycles from Society for Assisted Reproductive Technology Clinic Outcome Reporting System (SART CORS) for 2004-2011. Longitudinal cohort. Clinic-based data. 288,161 women with an initial autologous cycle, of whom 89,855 did not become pregnant and had a second autologous cycle and 39,334 did not become pregnant in the first and second cycles and had a third autologous cycle, with an additional 33,598 women who had a cycle using donor oocytes (first donor cycle). None. LBRs and MBRs modeled by woman's age, body mass index, gravidity, prior full-term births, infertility diagnoses by oocyte source, fresh embryos transferred, and cycle, using backward-stepping logistic regression with results presented as adjusted odds ratios (AORs) and 95% confidence intervals. The LBRs increased in all models with prior full-term births, number of embryos transferred; in autologous cycles also with gravidity, diagnoses of male factor, and ovulation disorders; and in donor cycles also with the diagnosis of diminished ovarian reserve. The MBR increased in all models with number of embryos transferred and in donor cycles also with prior full-term births. For both autologous and donor cycles, transferring two versus one embryo greatly increased the probability of a multiple birth (AOR 27.25 and 38.90, respectively). This validated predictive model will be implemented on the Society for Assisted Reproductive Technology Web site (www.sart.org) so that patients considering initiating a course of ART can input their data on the Web site to generate their expected outcomes.

A prediction model for live birth after assisted reproductive technology

A prediction model for live birth after assisted reproductive technology

Live birth prediction in polycystic ovary syndrome

Anovulatory infertility is common in polycystic ovary syndrome (PCOS). Clinicians who provide first-line treatment have to decide how best to induce ovulation with the desired outcome of live birth, a decision often complicated by the phenotypic heterogeneity of PCOS. Could a new live birth prediction model of different ovulation induction methods developed from basic clinical parameters help select infertility treatments?