A predictive model for women's assisted fecundity before starting the first IVF/ICSI treatment cycle

Predicting personalized cumulative live birth rate after a complete in vitro fertilization cycle: an analysis of 32,306 treatment cycles in China

BACKGROUND

The cumulative live birth rate (CLBR) has been regarded as a key measure of in vitro fertilization (IVF) success after a complete treatment cycle. Women undergoing IVF face great psychological pressure and financial burden. A predictive model to estimate CLBR is needed in clinical practice for patient counselling and shaping expectations.

METHODS

This retrospective study included 32,306 complete cycles derived from 29,023 couples undergoing IVF treatment from 2014 to 2020 at a university-affiliated fertility center in China. Three predictive models of CLBR were developed based on three phases of a complete cycle: pre-treatment, post-stimulation, and post-treatment. The non-linear relationship was treated with restricted cubic splines. Subjects from 2014 to 2018 were randomly divided into a training set and a test set at a ratio of 7:3 for model derivation and internal validation, while subjects from 2019 to 2020 were used for temporal validation.

RESULTS

Predictors of pre-treatment model included female age (non-linear relationship), antral follicle count (non-linear relationship), body mass index, number of previous IVF attempts, number of previous embryo transfer failure, type of infertility, tubal factor, male factor, and scarred uterus. Predictors of post-stimulation model included female age (non-linear relationship), number of oocytes retrieved (non-linear relationship), number of previous IVF attempts, number of previous embryo transfer failure, type of infertility, scarred uterus, stimulation protocol, as well as endometrial thickness, progesterone and luteinizing hormone on trigger day. Predictors of post-treatment model included female age (non-linear relationship), number of oocytes retrieved (non-linear relationship), cumulative Day-3 embryos live-birth capacity (non-linear relationship), number of previous IVF attempts, scarred uterus, stimulation protocol, as well as endometrial thickness, progesterone and luteinizing hormone on trigger day. The C index of the three models were 0.7559, 0.7744, and 0.8270, respectively. All models were well calibrated (p = 0.687, p = 0.468, p = 0.549). In internal validation, the C index of the three models were 0.7422, 0.7722, 0.8234, respectively; and the calibration P values were all greater than 0.05. In temporal validation, the C index were 0.7430, 0.7722, 0.8234 respectively; however, the calibration P values were less than 0.05.

CONCLUSIONS

This study provides three IVF models to predict CLBR according to information from different treatment stage, and these models have been converted into an online calculator ( https://h5.eheren.com/hcyc/pc/index.html#/home ). Internal validation and temporal validation verified the good discrimination of the predictive models. However, temporal validation suggested low accuracy of the predictive models, which might be attributed to time-associated amelioration of IVF practice.

Pretreatment prediction for IVF outcomes: generalized applicable model or centre-specific model?

STUDY QUESTION

What was the performance of different pretreatment prediction models for IVF, which were developed based on UK/US population (McLernon 2016 model, Luke model, Dhillon model, and McLernon 2022 model), in wider populations?

SUMMARY ANSWER

For a patient in China, the published pretreatment prediction models based on the UK/US population provide similar discriminatory power with reasonable AUCs and underestimated predictions.

WHAT IS KNOWN ALREADY

Several pretreatment prediction models for IVF allow patients and clinicians to estimate the cumulative probability of live birth in a cycle before the treatment, but they are mostly based on the population of Europe or the USA, and their performance and applicability in the countries and regions beyond these regions are largely unknown.

STUDY DESIGN, SIZE, DURATION

A total of 26 382 Chinese patients underwent oocyte pick-up cycles between January 2013 and December 2020.

PARTICIPANTS/MATERIALS, SETTING, METHODS

UK/US model performance was externally validated according to the coefficients and intercepts they provided. Centre-specific models were established with XGboost, Lasso, and generalized linear model algorithms. Discriminatory power and calibration of the models were compared as the forms of the AUC of the Receiver Operator Characteristic and calibration curves.

MAIN RESULTS AND THE ROLE OF CHANCE

The AUCs for McLernon 2016 model, Luke model, Dhillon model, and McLernon 2022 model were 0.69 (95% CI 0.68-0.69), 0.67 (95% CI 0.67-0.68), 0.69 (95% CI 0.68-0.69), and 0.67 (95% CI 0.67-0.68), respectively. The centre-specific yielded an AUC of 0.71 (95% CI 0.71-0.72) with key predictors including age, duration of infertility, and endocrine parameters. All external models suggested underestimation. Among the external models, the rescaled McLernon 2022 model demonstrated the best calibration (Slope 1.12, intercept 0.06).

LIMITATIONS, REASONS FOR CAUTION

The study is limited by its single-centre design and may not be representative elsewhere. Only per-complete cycle validation was carried out to provide a similar framework to compare different models in the sample population. Newer predictors, such as AMH, were not used.

WIDER IMPLICATIONS OF THE FINDINGS

Existing pretreatment prediction models for IVF may be used to provide useful discriminatory power in populations different from those on which they were developed. However, models based on newer more relevant datasets may provide better calibrations.

STUDY FUNDING/COMPETING INTEREST(S)

This work was supported by the National Natural Science Foundation of China [grant number 22176159], the Xiamen Medical Advantage Subspecialty Construction Project [grant number 2018296], and the Special Fund for Clinical and Scientific Research of Chinese Medical Association [grant number 18010360765].

TRIAL REGISTRATION NUMBER

N/A.

Using feature optimization and LightGBM algorithm to predict the clinical pregnancy outcomes after in vitro fertilization

Background

According to a recent report by the WHO, approximately 17.5\% (about one-sixth) of the global adult population is affected by infertility. Consequently, researchers worldwide have proposed various machine learning models to improve the prediction of clinical pregnancy outcomes during IVF cycles. The objective of this study is to develop a machine learning(ML) model that predicts the outcomes of pregnancies following in vitro fertilization (IVF) and assists in clinical treatment.

Methods

This study conducted a retrospective analysis on provincial reproductive centers in China from March 2020 to March 2021, utilizing 13 selected features. The algorithms used included XGBoost, LightGBM, KNN, Naïve Bayes, Random Forest, and Decision Tree. The results were evaluated using performance metrics such as precision, recall, F1-score, accuracy and AUC, employing five-fold cross-validation repeated five times.

Results

Among the models, LightGBM achieved the best performance, with an accuracy of 92.31%, recall of 87.80%, F1-score of 90.00\%, and an AUC of 90.41%. The model identified the estrogen concentration at the HCG injection(etwo), endometrium thickness (mm) on HCG day(EM TNK), years of infertility(Years), and body mass index(BMI) as the most important features.

Conclusion

This study successfully demonstrates the LightGBM model has the best predictive effect on pregnancy outcomes during IVF cycles. Additionally, etwo was found to be the most significant predictor for successful IVF compared to other variables. This machine learning approach has the potential to assist fertility specialists in providing counseling and adjusting treatment strategies for patients.

Prolactin Relationship with Fertility and In Vitro Fertilization Outcomes-A Review of the Literature

Hyperprolactinemia is a known cause of amenorrhea and infertility. However, there is an increasing body of evidence suggesting that prolactin is involved in multiple physiological aspects of normal reproduction. Thus, the present paper aims to review the current literature regarding the relationship between serum prolactin level and in vitro fertilization (IVF)/intracytoplasmic sperm injection outcome and the role of dopamine agonists treatment in IVF success. Moreover, the mechanisms by which prolactin may exert its role in fertility and infertility were summarized. Although not all studies agree, the available evidence suggests that higher prolactin levels in follicular fluid are associated with increased oocytes competence, but also with positive effects on corpus luteum formation and survival, endometrial receptivity, blastocyst implantation potential and survival of low-motile sperm. Transient hyperprolactinemia found in IVF cycles was reported in most of the studies not to be related to IVF outcome, although a few reports suggested that it may be associated with higher implantation and pregnancy rates, and better-cumulated pregnancy outcomes. Administration of dopamine agonists for hyperprolactinemia preceding IVF treatment does not seem to negatively impact the IVF results, while treatment of transient hyperprolactinemia during IVF might be beneficial in terms of fertilization rates and conception rates. Due to limited available evidence, future studies are necessary to clarify the optimal level of circulating prolactin in patients performing IVF and the role of dopamine agonist treatment.

Predictors of live birth in women with a history of biochemical pregnancies after assisted reproduction treatment

OBJECTIVES

To ascertain whether women with a history of biochemical pregnancies (BPs) in in-vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI) treatment cycles have decreased chances of live birth (LB); and (2) to build a predictive model for LB in this particular population of infertile women.

METHODS

In order to achieve the first objective, data from 1536 women that had a LB using autologous fresh or frozen embryos, or dropped out of further IVF/ICSI treatments after completing one to three unsuccessful treatment cycles were retrospectively analyzed. A subpopulation of 90 women that experienced one or more BPs in our assisted reproduction unit were selected to build a predictive logistic regression model for LB.

RESULTS

LB percentages significantly decreased from a value of 55.3 % in women with no history of previous BPs to 30.9 % and 11.1 % in women that displayed a history of one or more than one BP, respectively. Three out of 35 selected potential predictors were finally included into the model: "number of the last embryo transfer cycle resulting in a BP", "women's age", and "oligo-, astheno-, and/or teratozoospermia". The value of the c-statistic was 0.819 (asymptotic 95 % CI: 0.724-0.913). The model adequately fitted the data with no significant over or underestimation of predictor effects.

CONCLUSION

(1) A history of BPs is negatively associated with later chance of LB in women undergoing a series of IVF/ICSI treatment cycles; and (2) LB probability of women with a history of BPs can be predicted using a model with excellent discriminatory capacity.