A Novel Nomogram for Individualized Gonadotropin Starting Dose in GnRH Antagonist Protocol

Clinical data-based modeling of IVF live birth outcome and its application

BACKGROUND

The low live birth rate and difficult decision-making of the in vitro fertilization (IVF) treatment regimen bring great trouble to patients and clinicians. Based on the retrospective clinical data of patients undergoing the IVF cycle, this study aims to establish classification models for predicting live birth outcome (LBO) with machine learning methods.

METHODS

The historical data of a total of 1405 patients undergoing IVF cycle were first collected and then analyzed by univariate and multivariate analysis. The statistically significant factors were identified and taken as input to build the artificial neural network (ANN) model and supporting vector machine (SVM) model for predicting the LBO. By comparing the model performance, the one with better results was selected as the final prediction model and applied in real clinical applications.

RESULTS

Univariate and multivariate analysis shows that 7 factors were closely related to the LBO (with P < 0.05): Age, ovarian sensitivity index (OSI), controlled ovarian stimulation (COS) treatment regimen, Gn starting dose, endometrial thickness on human chorionic gonadotrophin (HCG) day, Progesterone (P) value on HCG day, and embryo transfer strategy. By taking the 7 factors as input, the ANN-based and SVM-based LBO models were established, yielding good prediction performance. Compared with the ANN model, the SVM model performs much better and was selected as the final model for the LBO prediction. In real clinical applications, the proposed ANN-based LBO model can predict the LBO with good performance and recommend the embryo transfer strategy of potential good LBO.

CONCLUSIONS

The proposed model involving all essential IVF treatment factors can accurately predict LBO. It can provide objective and scientific assistance to clinicians for customizing the IVF treatment strategy like the embryo transfer strategy.

Algorithm vs. clinical experience: controlled ovarian stimulations with follitropin-delta and individualised doses of follitropin-alpha/beta

In the registrational trials, follitropin delta was compared with a fixed dose of 150 UI of follitropin alpha/beta, finding higher chances to reach a target response of 8-14 oocytes compared to controls. For this reason, follitropin delta is marketed as particularly useful in expected hyper-responder patients. The main outcome of this study is to report if comparable results are reached in a real-life scenario with follitropin alpha/beta personalized doses, based on patients' characteristics. This is a retrospective study performed in two public fertility centres. All first cycles from January 2020 to June 2022 with either follitropin delta (cases) or alpha/beta (controls) in patients with antiMüllerian hormone >2.5 ng/ml were compared by an inverse probability weighting approach based on propensity score. The follitropin total dose was higher in controls (1179.06 ± 344.93 vs. 1668.67 ± 555.22 IU, p<0.001). The target response of 8-14 oocytes was reached by 40.2% of cases and 40.7% of controls (odds ratio (OR) 0.99, 95% confidence interval (CI) 0.65-1.53, p=0.98). Fewer than 8 oocytes were collected in 24.1% of cases and 22% of controls (OR 1.10, 95% CI 0.71-1.69, p=0.67); more than 14 oocytes in 35.7% of cases and 37.3% of controls (OR 0.83, 95% CI 0.54-1.28, p=0.40). Our experience did not find worse results in term of proportion of patients who reached the target response with an algorithm-chosen dose of follitropin delta compared to a personalised starting dose of follitropin alpha/beta, with follitropin delta having the advantage of objectivity. Larger numbers are needed to confirm these results.

Development and validation of a nomogram for predicting ongoing pregnancy in single vitrified-warmed blastocyst embryo transfer cycles

Introduction

The global adoption of the "freeze-all strategy" has led to a continuous increase in utilization of single vitrified-warmed blastocyst embryo transfer (SVBT) owing to its clinical effectiveness. Accurate prediction of clinical pregnancy is crucial from a patient-centered perspective. However, this remains challenging, with inherent limitations due to the absence of precise and user-friendly prediction tools. Thus, this study primarily aimed to develop and assess a nomogram based on quantitative clinical data to optimize the efficacy of personalized prognosis assessment.

Materials and methods

We conducted a retrospective cohort analysis of ongoing pregnancy data from 658 patients with infertility who underwent SVBT at our center between October 17, 2017, and December 18, 2021. Patients were randomly assigned to the training (n=461) or validation (n=197) cohort for nomogram development and testing, respectively. A nomogram was constructed using the results of the multivariable logistic regression (MLR), which included clinical covariates that were assessed for their association with ongoing pregnancy.

Results

The MLR identified eight significant variables that independently predicted ongoing pregnancy outcomes in the study population. These predictors encompassed maternal physiology, including maternal age at oocyte retrieval and serum anti-Müllerian hormone levels; uterine factors, such as adenomyosis; and various embryo assessment parameters, including the number of fertilized embryos, blastocyst morphology, blastulation day, blastocyst re-expansion speed, and presence of embryo string. The area under the receiver operating characteristic curve in our prediction model was 0.675 (95% confidence interval [CI], 0.622-0.729) and 0.656 (95% CI, 0.573-0.739) in the training and validation cohorts, respectively, indicating good discrimination performance in both cohorts.

Conclusions

Our individualized nomogram is a practical and user-friendly tool that can provide accurate and useful SVBT information for patients and clinicians. By offering this model to patients, clinical stakeholders can alleviate uncertainty and confusion about fertility treatment options and enhance patients' confidence in making informed decisions.

Algorithm-based individualization methodology of the starting gonadotropin dose in IVF/ICSI and the freeze-all strategy prevent OHSS equally in normal responders: a systematic review and network meta-analysis of the evidence

PURPOSE

Ovarian hyperstimulation syndrome (OHSS) represents a rare but dangerous condition associated with controlled ovarian stimulation (COS) in IVF/ICSI. Over the last decades, many strategies have been introduced into clinical practice with the objective of preventing this potentially life-threatening condition. Among these, the freeze-all policy has gained great popularity, thanks to improvements in vitrification. Nevertheless, not all clinics have adequate skills in vitrification procedures and patients may be dissatisfied with a longer time to pregnancy.

METHODS

This study is a systematic review and network meta-analysis of randomized controlled trials comparing different strategies of ovarian stimulation in IVF/ICSI cycles (freeze-all policy, algorithm-based individualization of the starting dose, experience-based individualization of the starting dose, standard dose) in terms of reduction of OHSS, in normal responders.

RESULTS

The results indicate that only the algorithm-based individualization of the starting gonadotropin dose reduces OHSS similarly to the freeze-all strategy.

CONCLUSION

Albeit in the era of the freeze-all policy, the personalization of the starting gonadotropin dose obtained by the use of algorithms should be pursued as a valid and safe option for IVF.