[Establishment of a predictive nomogram for clinical pregnancy rate in patients with endometriosis undergoing fresh embryo transfer].

Shih‐Chun Pan, S Pan, Y Li, Ying Li, Z Wu, Zhaohua Wu, Y Mao, C Wang
Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2024 · vol. 44(7) , pp. 1407–1415 · doi:10.12122/j.issn.1673-4254.2024.07.21 · PMID:39051087 · PMC11270668 · W4401041474
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Abstract

OBJECTIVE: To establish a nomogram model for predicting clinical pregnancy rate in patients with endometriosis undergoing fresh embryo transfer. METHODS: We retrospectively collected the data of 464 endometriosis patients undergoing fresh embryo transfer, who were randomly divided into a training dataset (60%) and a testing dataset (40%). Using univariate analysis, multiple logistic regression analysis, and LASSO regression analysis, we identified the factors associated with the fresh transplantation pregnancy rate in these patients and developed a nomogram model for predicting the clinical pregnancy rate following fresh embryo transfer. We employed an integrated learning approach that combined GBM, XGBOOST, and MLP algorithms for optimization of the model performance through parameter adjustments. RESULTS: : 0.675-0.761) in the validation dataset. CONCLUSIONS: The established prediction model in this study can help in prediction of clinical pregnancy rates following fresh embryo transfer in patients with endometriosis.

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Condition tags

endometriosis

MeSH descriptors

Embryo Transfer Embryo Transfer Embryo Transfer Embryo Transfer Embryo Transfer Embryo Transfer Embryo Transfer Embryo Transfer Embryo Transfer Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Nomograms Nomograms Nomograms

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