Development and Validation of a Clinical Pregnancy Failure Prediction Model for Poor Ovarian Responders During IVF/ICSI

Li, Fangyuan; Lu, Ruihui; Zeng, Cheng; X, Li; Xue, Qing

doi:10.3389/fendo.2021.717288

Cited by 4 publications

(1 citation statement)

References 58 publications

(66 reference statements)

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“…Some clinical prediction models have been developed to estimate the chances of IVF success in different populations over the last three decades ( 16 , 17 ). In a clinical pregnancy failure prediction model based on 281 POR patients, is still conducted according to the Bologna criteria, cannot be distinguished the biological heterogeneity of patients with POR ( 18 , 19 ). Another study found independent predictors of live birth in POR patients based on the POSEIDON criteria, but failed to develop a complete live birth predictive model ( 20 ).…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a live birth prediction model for expected poor ovarian response patients during IVF/ICSI

et al. 2023

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BackgroundA number of live birth predictive model during assisted reproductive technology treatment have been available in recent years, but few targeted evaluating the chances of live birth in poor ovarian response(POR) patients. The aim of this study was to develop a nomogram based on POSEIDON criteria to predict live birth in patients with expected POR.MethodsThis retrospective cohort study using clinical data from 657 patients in POSEIDON Groups 3 and 4 (antral follicle count [AFC] ≤5 and AMH <1.2 ng/ml) in the Center for Reproductive Medicine, First Affiliated Hospital of Xinjiang Medical University, and Construction a nomogram model tResultsAmong 657 expected POR patients, 111 (16.89%) had live births, and 546 (83.11%) did not have live births. These were divided into a training set(n=438) and a validation set (n=219). Multivariate logistic regression analysis showed that the age (OR = 0.91, 95% CI: 0.86–0.97), BMI (OR = 1.98, 95% CI: 1.09–3.67), AMH (OR = 3.48, 95% CI: 1.45–8.51), normal fertilized oocytes (OR = 1.40, 95% CI: 1.21–1.63), and the basal FSH (OR = 0.89, 95% CI: 0.80–0.98) of the female were independent factors predicting live birth in patients with expected POR. Then, an individualized nomogram prediction model was built from these five factors. The area under the ROC curve of the live birth prediction model was 0.820 in the training set and 0.879 in the validation set.ConclusionWe have developed a nomogram combining clinical and laboratory factors to predict the probability of live birth in patients with an expected POR during IVF/ICSI, which can helpful for clinician in decision-making. However, the data comes from the same center, needs a prospective multicenter study for further in-depth evaluation and validation of this prediction model.

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Section: Discussionmentioning

confidence: 99%

Development and validation of a live birth prediction model for expected poor ovarian response patients during IVF/ICSI

et al. 2023

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Interpretable machine learning models for predicting clinical pregnancies associated with surgical sperm retrieval from testes of different etiologies: a retrospective study

Cao,

Liu,

Song

et al. 2024

BMC Urol

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Background The relationship between surgical sperm retrieval of different etiologies and clinical pregnancy is unclear. We aimed to develop a robust and interpretable machine learning (ML) model for predicting clinical pregnancy using the SHapley Additive exPlanation (SHAP) association of surgical sperm retrieval from testes of different etiologies. Methods A total of 345 infertile couples who underwent intracytoplasmic sperm injection (ICSI) treatment with surgical sperm retrieval due to different etiologies from February 2020 to March 2023 at the reproductive center were retrospectively analyzed. The six machine learning (ML) models were used to predict the clinical pregnancy of ICSI. After evaluating the performance characteristics of the six ML models, the Extreme Gradient Boosting model (XGBoost) was selected as the best model, and SHAP was utilized to interpret the XGBoost model for predicting clinical pregnancies and to reveal the decision-making process of the model. Results Combining the area under the receiver operating characteristic curve (AUROC), accuracy, precision, recall, F1 score, brier score, and the area under the precision-recall (P-R) curve (AP), the XGBoost model has the best performance (AUROC: 0.858, 95% confidence interval (CI): 0.778–0.936, accuracy: 79.71%, brier score: 0.151). The global summary plot of SHAP values shows that the female age is the most important feature influencing the model output. The SHAP plot showed that younger age in females, bigger testicular volume (TV), non-tobacco use, higher anti-müllerian hormone (AMH), lower follicle-stimulating hormone (FSH) in females, lower FSH in males, the temporary ejaculatory disorders (TED) group, and not the non-obstructive azoospermia (NOA) group all resulted in an increased probability of clinical pregnancy. Conclusions The XGBoost model predicts clinical pregnancies associated with testicular sperm retrieval of different etiologies with high accuracy, reliability, and robustness. It can provide clinical counseling decisions for patients with surgical sperm retrieval of various etiologies.

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To Compare the Research Progress of AMH and bFSH in the Reflection of Ovarian Function and the Predictive Value of Assisted Reproductive Outcomes

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2022

ACM

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Development and Validation of a Clinical Pregnancy Failure Prediction Model for Poor Ovarian Responders During IVF/ICSI

Cited by 4 publications

References 58 publications

Development and validation of a live birth prediction model for expected poor ovarian response patients during IVF/ICSI

Development and validation of a live birth prediction model for expected poor ovarian response patients during IVF/ICSI

Interpretable machine learning models for predicting clinical pregnancies associated with surgical sperm retrieval from testes of different etiologies: a retrospective study

To Compare the Research Progress of AMH and bFSH in the Reflection of Ovarian Function and the Predictive Value of Assisted Reproductive Outcomes

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