Dataset Information

Deep phenotyping to predict live birth outcomes in in vitro fertilization.

ABSTRACT: Nearly 75% of in vitro fertilization (IVF) treatments do not result in live births and patients are largely guided by a generalized age-based prognostic stratification. We sought to provide personalized and validated prognosis by using available clinical and embryo data from prior, failed treatments to predict live birth probabilities in the subsequent treatment. We generated a boosted tree model, IVFBT, by training it with IVF outcomes data from 1,676 first cycles (C1s) from 2003-2006, followed by external validation with 634 cycles from 2007-2008, respectively. We tested whether this model could predict the probability of having a live birth in the subsequent treatment (C2). By using nondeterministic methods to identify prognostic factors and their relative nonredundant contribution, we generated a prediction model, IVF(BT), that was superior to the age-based control by providing over 1,000-fold improvement to fit new data (p<0.05), and increased discrimination by receiver-operative characteristic analysis (area-under-the-curve, 0.80 vs. 0.68 for C1, 0.68 vs. 0.58 for C2). IVFBT provided predictions that were more accurate for approximately 83% of C1 and approximately 60% of C2 cycles that were out of the range predicted by age. Over half of those patients were reclassified to have higher live birth probabilities. We showed that data from a prior cycle could be used effectively to provide personalized and validated live birth probabilities in a subsequent cycle. Our approach may be replicated and further validated in other IVF clinics.

SUBMITTER: Banerjee P

PROVIDER: S-EPMC2922227 | biostudies-literature | 2010 Aug

REPOSITORIES: biostudies-literature

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Deep phenotyping to predict live birth outcomes in in vitro fertilization.

Banerjee Prajna P Choi Bokyung B Shahine Lora K LK Jun Sunny H SH O'Leary Kathleen K Lathi Ruth B RB Westphal Lynn M LM Wong Wing H WH Yao Mylene W M MW

Proceedings of the National Academy of Sciences of the United States of America 20100719 31

Nearly 75% of in vitro fertilization (IVF) treatments do not result in live births and patients are largely guided by a generalized age-based prognostic stratification. We sought to provide personalized and validated prognosis by using available clinical and embryo data from prior, failed treatments to predict live birth probabilities in the subsequent treatment. We generated a boosted tree model, IVFBT, by training it with IVF outcomes data from 1,676 first cycles (C1s) from 2003-2006, followed ...[more]

PMID: 20643955

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Project description:ImportanceThe likelihood of achieving a live birth with repeat in vitro fertilization (IVF) is unclear, yet treatment is commonly limited to 3 or 4 embryo transfers.ObjectiveTo determine the live-birth rate per initiated ovarian stimulation IVF cycle and with repeated cycles.Design, setting, and participantsProspective study of 156,947 UK women who received 257,398 IVF ovarian stimulation cycles between 2003 and 2010 and were followed up until June 2012.ExposuresIn vitro fertilization, with a cycle defined as an episode of ovarian stimulation and all subsequent separate fresh and frozen embryo transfers.Main outcomes and measuresLive-birth rate per IVF cycle and the cumulative live-birth rates across all cycles in all women and by age and treatment type. Optimal, prognosis-adjusted, and conservative cumulative live-birth rates were estimated, reflecting 0%, 30%, and 100%, respectively, of women who discontinued due to poor prognosis and having a live-birth rate of 0 had they continued.ResultsAmong the 156,947 women, the median age at start of treatment was 35 years (interquartile range, 32-38; range, 18-55), and the median duration of infertility for all 257,398 cycles was 4 years (interquartile range, 2-6; range, <1-29). In all women, the live-birth rate for the first cycle was 29.5% (95% CI, 29.3%-29.7%). This remained above 20% up to and including the fourth cycle. The cumulative prognosis-adjusted live-birth rate across all cycles continued to increase up to the ninth cycle, with 65.3% (95% CI, 64.8%-65.8%) of women achieving a live birth by the sixth cycle. In women younger than 40 years using their own oocytes, the live-birth rate for the first cycle was 32.3% (95% CI, 32.0%-32.5%) and remained above 20% up to and including the fourth cycle. Six cycles achieved a cumulative prognosis-adjusted live-birth rate of 68.4% (95% CI, 67.8%-68.9%). For women aged 40 to 42 years, the live-birth rate for the first cycle was 12.3% (95% CI, 11.8%-12.8%), with 6 cycles achieving a cumulative prognosis-adjusted live-birth rate of 31.5% (95% CI, 29.7%-33.3%). For women older than 42 years, all rates within each cycle were less than 4%. No age differential was observed among women using donor oocytes. Rates were lower for women with untreated male partner-related infertility compared with those with any other cause, but treatment with either intracytoplasmic sperm injection or sperm donation removed this difference.Conclusions and relevanceAmong women in the United Kingdom undergoing IVF, the cumulative prognosis-adjusted live-birth rate after 6 cycles was 65.3%, with variations by age and treatment type. These findings support the efficacy of extending the number of IVF cycles beyond 3 or 4.

Project description:In-vitro fertilization (IVF) is a popular method of resolving complications such as endometriosis, poor egg quality, a genetic disease of mother or father, problems with ovulation, antibody problems that harm sperm or eggs, the inability of sperm to penetrate or survive in the cervical mucus and low sperm counts, resulting human infertility. Nevertheless, IVF does not guarantee success in the fertilization. Choosing IVF is burdensome for the reason of high cost and uncertainty in the result. As the complications and fertilization factors are numerous in the IVF process, it is a cumbersome task for fertility doctors to give an accurate prediction of a successful birth. Artificial Intelligence (AI) has been employed in this study for predicting the live-birth occurrence. This work mainly focuses on making predictions of live-birth occurrence when an embryo forms from a couple and not a donor. Here, we compare various AI algorithms, including both classical Machine Learning, deep learning architecture, and an ensemble of algorithms on the publicly available dataset provided by Human Fertilisation and Embryology Authority (HFEA). Insights on data and metrics such as confusion matrices, F1-score, precision, recall, receiver operating characteristic (ROC) curves are demonstrated in the subsequent sections. The training process has two settings Without feature selection and With feature selection to train classifier models. Machine Learning, Deep learning, ensemble models classification paradigms have been trained in both settings. The Random Forest model achieves the highest F1-score of 76.49% in without feature selection setting. For the same model, the precision, recall, and area under the ROC Curve (ROC AUC) scores are 77%, 76%, and 84.60%, respectively. The success of the pregnancy depends on both male and female traits and living conditions. This study predicts a successful pregnancy through the clinically relevant parameters in In-vitro fertilization. Thus artificial intelligence plays a promising role in decision making process to support the diagnosis, prognosis, treatment etc.

Project description:BackgroundWe aimed to develop an accurate model to predict live birth for patients receiving in vitro fertilization and embryo transfer (IVF-ET) treatment.MethodsThis is a prospective nested case-control study. Women aged between 18 and 38 years, whose body mass index (BMI) were between the range of 18.5-24 kg/m2, who had an endometrium of ≥ 8 mm at the thickest were enrolled from 2018/9 to 2020/8. All patients received IVF-ET treatment and were followed up until Jan. 2022 when they had reproductive outcomes. Endometrial samples during the window of implantation (LH + 6 to 9 days) were subjected to analyze specific endometrial receptivity genes' expression using real-time PCR (RT-PCR). Patients were divided into live birth group and non-live birth group based on IVF-ET outcomes. Clinical signatures relevant to live birth were collected, analyzed, and used to establish a predictive model for live birth by univariate analysis (clinical model). Specific endometrial receptivity genes' expression was analyzed, selected, and used to construct a predictive model for live birth by The Least Absolute Shrinkage and Selection Operator (LASSO) analysis (gene model). Finally, significant clinical factors and genes were used to construct a combined model for predicting live birth using multivariate logistical regression (combined model). Different models' Area Under Curve (AUC) were compared to identify the most predictive model.ResultsThirty-nine patients were enrolled in the study, twenty-four patients had live births, fifteen did not. In univariate analysis, the odds of live birth for women with ovulation dysfunction was 4 times higher than that for women with other IVF-ET indications (OR = 4.0, 95% CI: 1.125 - 8.910, P = 0.018). Age, body mass index, duration of infertility, primary infertility, repeated implantation failure, antral follicle counting, ovarian sensitivity index, anti-Mullerian hormone, controlled ovarian hyperstimulation protocol and duration, total dose of FSH/hMG, number of oocytes retrieved, regiment of endometrial preparation, endometrium thickness before embryo transfer, type of embryo transferred were not associated with live birth (P > 0.05). Only ovulation dysfunction was used to construct the clinical model and its AUC was 0.688. In lasso analysis, GAST, GPX3, THBS2 were found to promote the risk of live birth. AUCs for GAST, GPX3, THBS2 reached to 0.736, 0.672, and 0.678, respectively. The gene model was established based on these three genes and its AUC was 0.772. Ovulation dysfunction, GAST, GPX3, and THBS2 were finally used to construct the combined model, reaching the highest AUC (AUC = 0.842).ConclusionsCompared to the single model, the combined model of clinical (Ovulation dysfunction) and specific genes (GAST, GPX3, THBS2) was more accurate to predict live birth for IVF-ET patients.

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Deep phenotyping to predict live birth outcomes in in vitro fertilization.

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Deep phenotyping to predict live birth outcomes in in vitro fertilization.

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