Prediction of Fertilization Disorders in the In Vitro Fertilization/Intracytoplasmic Sperm Injection: A Retrospective Study of 106,728 Treatment Cycles

Indications affect neonatal outcomes following early rescue ICSI: a retrospective study

PURPOSE

To investigate the impact of heterogeneity in patient indications or insemination protocols on neonatal outcomes of singletons following early rescue ICSI (rICSI) treatments.

METHODS

A retrospective study was conducted. Propensity score matching and multivariable logistic regression were used to adjust for confounders and biases.

RESULTS

A total of 9095 IVF patients, 2063 ICSI patients, and 642 early rICSI patients were included in the study. No differences were detected in neonatal outcomes except small for gestational age (SGA) which increased in early rICSI patients compared with both unmatched and matched IVF groups with the risk ratio (RR) of 1.31 (95% CI: 1.05, 1.64) and 1.49 (95% CI: 1.05, 2.12). Further analysis showed that SGA increased significantly in partial fertilization failure (PFF) cycles with RRs of 1.56 (95% CI: 1.08, 2.27) and 1.78 (95% CI: 1.22, 2.59) compared with both unmatched and matched IVF patients but not in TFF patients. A positive association between fertilization rate via IVF and birth weight z-score was revealed in the PFF patients.

CONCLUSION

Early rICSI in patients with total fertilization failure (TFF) appeared to be safe in terms of neonatal outcomes. However, when expanding the indications of rICSI to PFF patients, the SGA in the offspring increased, suggesting a potential effect on long-term health. Since other treatment options, such as using only the IVF-origin embryos still exist for these patients, further studies were needed to confirm the optimal decision for these patients.

Total Fertilization Failure: A Single Center Analysis

Our main objective was to identify the male and female parameters associated with total fertilization failure (TFF) in IVF with nonmasculine indications. The present work, IRB equivalent INS 63209, is a case-control study that evaluated all cases with TFF after conventional IVF at the Center for Human Reproduction from January 2010 to December 2019 (n = 154). As a control group, we analyzed all patients who did not experience fertilization failure after conventional IVF in the same period (n = 475). We evaluated various parameters, both male and female, assessed during infertility treatment, and only cases without masculine etiology (normal seminal parameters) were included. Ages (female and male) were not different between the groups. Moreover, AMH (anti-Müllerian hormone), semen volume, preprocessing concentration and preprocessing motility were not significantly different (P > 0.05). However, the number of collected oocytes (study versus control groups, median [25-75 interquartile]: 2 [1-5] and 5 [3-8]); MII (2 [1-4] and 5 [2-7]); and postprocessing motility (85 [70-90] and 90 [80-95]) were significantly different between both groups (P < 0.05). Furthermore, a logistic regression analysis including all significant data demonstrated that the number of collected oocytes was significantly related to IVF failure. Patients with fewer than 5 oocytes had an OR of - 1.37 (- 0.938 to - 1.827) for TFF after conventional IVF. Our results showed that a lower follicular response to controlled ovarian stimulation, evidenced by a decreased number of collected oocytes, was the most important parameter associated with IVF failure in nonmasculine infertility.

A Bayesian network model for prediction of low or failed fertilization in assisted reproductive technology based on a large clinical real-world data

STUDY QUESTION

To construct prediction models based on the Bayesian network (BN) learning method for the probability of fertilization failure (including low fertilization rate [LRF] and total fertilization failure [TFF]) in assisted reproductive technology (ART) treatment. A BN model was developed to predict TFF/LFR. The model showed relatively high calibration in external validation, which could facilitate the identification of risk factors for fertilization disorders and improve the efficiency of in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) treatment.

WHAT IS KNOWN ALREADY

The prediction of TFF/LFR is very complex. Although some studies attempted to construct prediction models for TFF/LRF, most of the reported models were based on limited variables and traditional regression-based models, which are unsuitable for analyzing real-world clinical data. Therefore, none of the reported models have been widely used in routine clinical practice. To date, BN modeling analysis is a prominent and increasingly popular machine learning method that is powerful in dealing with dynamic and complex real-world data.

STUDY DESIGN, SIZE, DURATION

A retrospective study was performed with 106,640 fresh embryo IVF/ICSI cycles from 2009 to 2019 in one of China's largest reproductive health centers.

PARTICIPANTS/MATERIALS, SETTING, METHODS

A total of 106, 640 cycles were included in this study, including 97,102 controls, 4,339 LFR cases, and 5,199 TFF cases. Twenty-four predictors were initially included, including 13 female-related variables, five male-related variables, and six variables related to IVF/ICSI treatment. BN modeling analysis with tenfold cross-validation was performed to construct the predictive model for TFF/LFR. The receiver operating characteristic (ROC) curves and the corresponding area under the curves (AUCs) were used to evaluate the performance of the BN model.

MAIN RESULTS AND THE ROLE OF CHANCE

All twenty-four predictors were first organized into seven hierarchical layers in a theoretical BN model, according to prior knowledge from previous literature and clinical practice. A machine-learning BN model was generated based on real-world clinical data, containing a total of eighteen predictors, of which the infertility type, ART method, and number of retrieved oocytes directly influence the probabilities of LFR/TFF. The prediction accuracy of the BN model was 91.7%. The AUC of the TFF versus control groups was 0.779 (95% CI: 0.766-0.791), with a sensitivity of 71.2% and specificity of 70.1%; the AUC of of TFF versus LFR groups was 0.807 (95% CI: 0.790-0.824), with a sensitivity of 49.0% and specificity of 99.0%.

LIMITATIONS, REASON FOR CAUTION

First, our study was based on clinical data from a single center, and the results of this study should be further verified by external data. In addition, some critical data (e.g., the detailed IVF laboratory parameters of the sperm and oocytes used for insemination) were not available in this study, which should be given full consideration when further improving the performance of the BN model.

WIDER IMPLICATIONS OF THE FINDINGS

Based on extensive clinical real-world data, we developed a BN model to predict the probabilities of fertilization failures in ART, which provides new clues for clinical decision-making support for clinicians in formulating personalized treatment plans and further improving ART treatment outcomes.

STUDY FUNDING/COMPETING INTEREST(S)

Dr. Y. Wang was supported by grants from the Beijing Municipal Science & Technology Commission (Z191100006619086). We declare that there are no conflicts of interest.

TRIAL REGISTRATION NUMBER

N/A.

Predicting the unexpected total fertilization failure in conventional in vitro fertilization cycles: What is the role of semen quality?

Background: Male and female gametes factors might contribute to the total fertilization failure (TFF). In first in vitro fertilization (IVF) cycles, decision-making of insemination protocol was mainly based on semen quality for the contribution of female clinical characteristics to TFF remained obscure. The purpose of the study was to evaluate the role of semen quality in predicting unexpected TFF. Methods: A single-center retrospective cohort analysis was performed on 19539 cycles between 2013 and 2021. Two algorithms, a Least Absolute Shrinkage and Selection Operator (LASSO) and an Extreme Gradient Boosting (Xgboost) were used to create models with cycle characteristics parameters. By including semen parameters or not, the contribution of semen parameters to the performance of the models was evaluated. The area under the curve (AUC), the calibration, and the net reclassification index (NRI) were used to evaluate the performance of the models. Results: The prevalence of TFF were .07 (95%CI:0.07-0.08), and .08 (95%CI:0.07-0.09) respectively in the development and validation group. Including all characteristics, with the models of LASSO and Xgboost, TFF was predicted with the AUCs of .74 (95%CI:0.72-0.77) and .75 (95%CI:0.72-0.77) in the validation group. The AUCs with models of LASSO and Xgboost without semen parameters were .72 (95%CI:0.69-0.74) and .73 (95%CI:0.7-0.75). The models of LASSO and Xgboost with semen parameters only gave the AUCs of .58 (95%CI:0.55-0.61) and .57 (95%CI:0.55-0.6). For the overall validation cohort, the event NRI values were -5.20 for the LASSO model and -.71 for the Xgboost while the non-event NRI values were 10.40 for LASSO model and 0.64 for Xgboost. In the subgroup of poor responders, the prevalence was .21 (95%CI:0.18-0.24). With refitted models of LASSO and Xgboost, the AUCs were .72 (95%CI:0.67-0.77) and .69 (95%CI:0.65-0.74) respectively. Conclusion: In unselected patients, semen parameters contribute to limited value in predicting TFF. However, oocyte yield is an important predictor for TFF and the prevalence of TFF in poor responders was high. Because reasonable predicting power for TFF could be achieved in poor responders, it may warrant further study to prevent TFF in these patients.

Nomogram models to predict low fertilisation rate and total fertilisation failure in patients undergoing conventional IVF cycles

OBJECTIVES

To establish visualised prediction models of low fertilisation rate (LFR) and total fertilisation failure (TFF) for patients in conventional in vitro fertilisation (IVF) cycles.

DESIGN

A retrospective cohort study.

SETTING

Data from August 2017 to August 2021 were collected from the electronic records of a large obstetrics and gynaecology hospital in Sichuan, China.

PARTICIPANTS

A total of 11 598 eligible patients who underwent the first IVF cycles were included. All patients were randomly divided into the training group (n=8129) and the validation group (n=3469) in a 7:3 ratio.

PRIMARY OUTCOME MEASURE

The incidence of LFR and TFF.

RESULTS

Logistic regressions showed that ovarian stimulation protocol, primary infertility and initial progressive sperm motility were the independent predictors of LFR, while serum luteinising hormone and P levels before human chorionic gonadotropin injection and number of oocytes retrieved were the critical predictors of TFF. And these indicators were incorporated into the nomogram models. According to the area under the curve values, the predictive ability for LFR and TFF were 0.640 and 0.899 in the training set and 0.661 and 0.876 in the validation set, respectively. The calibration curves also showed good concordance between the actual and predicted probabilities both in the training and validation group.

CONCLUSION

The novel nomogram models provided effective methods for clinicians to predict LFR and TFF in traditional IVF cycles.

Opportunities and Limits of Conventional IVF versus ICSI: It Is Time to Come off the Fence

Conventional IVF (c-IVF) is one of the most practiced assisted reproductive technology (ART) approaches used worldwide. However, in the last years, the number of c-IVF procedures has dropped dramatically in favor of intracytoplasmic sperm injection (ICSI) in cases of non-male-related infertility. In this review, we have outlined advantages and disadvantages associated with c-IVF, highlighting the essential steps governing its success, its limitations, the methodology differences among laboratories and the technical progress. In addition, we have debated recent insights into fundamental questions, including indications regarding maternal age, decreased ovarian reserve, endometriosis, autoimmunity, single oocyte retrieval-cases as well as preimplantation genetic testing cycles. The “overuse” of ICSI procedures in several clinical situations of ART has been critically discussed. These insights will provide a framework for a better understanding of opportunities associated with human c-IVF and for best practice guidelines applicability in the reproductive medicine field.