Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Wang Q

PROVIDER: S-EPMC9703318 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Publications

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

Wang Qiaofeng Q Wan Qi Q Bu Xiaoqing X Feng Qian Q Li Tian T Lv Xingyu X Meng Xiangqian X Chen Mingxing M Qian Yue Y Yang Yin Y Geng Lihong L Zhong Zhaohui Z Tang Xiaojun X Ding Yubin Y

BMJ open 20221125 11

<h4>Objectives</h4>To establish visualised prediction models of low fertilisation rate (LFR) and total fertilisation failure (TFF) for patients in conventional in vitro fertilisation (IVF) cycles.<h4>Design</h4>A retrospective cohort study.<h4>Setting</h4>Data from August 2017 to August 2021 were collected from the electronic records of a large obstetrics and gynaecology hospital in Sichuan, China.<h4>Participants</h4>A total of 11 598 eligible patients who underwent the first IVF cycles were in ...[more]

PMID: 36428025

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Project description: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.

Dataset Information

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

Objectives

Design

Setting

Participants

Primary outcome measure

Results

Conclusion

Publications

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

Similar Datasets

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