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Nomogram Model to Predict the Probability of Ovarian Hyperstimulation Syndrome in the Treatment of Patients With Polycystic Ovary Syndrome
OBJECTIVE: The objective of this study was to explore the risk factors of ovarian hyperstimulation syndrome (OHSS) in patients with polycystic ovary syndrome (PCOS) undergoing in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) and to establish a nomogram model evaluate the probabilit...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8377671/ https://www.ncbi.nlm.nih.gov/pubmed/34421814 http://dx.doi.org/10.3389/fendo.2021.619059 |
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author | Li, Fei Chen, Ying Niu, Aiqin He, Yajing Yan, Ying |
author_facet | Li, Fei Chen, Ying Niu, Aiqin He, Yajing Yan, Ying |
author_sort | Li, Fei |
collection | PubMed |
description | OBJECTIVE: The objective of this study was to explore the risk factors of ovarian hyperstimulation syndrome (OHSS) in patients with polycystic ovary syndrome (PCOS) undergoing in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) and to establish a nomogram model evaluate the probability of OHSS in PCOS patients. METHODS: We retrospectively analyzed clinical data from 4,351 patients with PCOS receiving IVF/ICSI in our reproductive medical center. The clinical cases were randomly divided into a modeling group (3,231 cases) and a verification group (1,120 cases) according to a ratio of about 3:1. The independent risk factors correlation with the occurrence of OHSS was identified by logistic regression analysis. Based on the selected independent risk factors and correlated regression coefficients, we established a nomogram model to predict the probability of OHSS in PCOS patients, and the predictive accuracy of the model was measured using the area under the receiver operating curve (AUC). RESULTS: Univariate and multivariate logistic regression analyses showed that FSH (OR, 0.901; 95% CI, 0.847–0.958; P<0.001), AMH (OR, 1.259; 95% CI, 1.206–1.315; P<0.001), E2 value on the day of hCG injection (OR, 1.122; 95% CI, 1.021–1.253; P<0.001), total dosage of Gn used (OR, 1.010; 95% CI, 1.002–1.016; P=0.041), and follicle number on the day of hCG injection (OR, 0.134; 95% CI, 1.020–1.261; P=0.020) are the independent risk factors for OHSS in PCOS patients. The AUC of the modeling group is 0.827 (95% CI, 0.795–0.859), and the AUC of the verification group is 0.757 (95% CI, 0.733–0.782). CONCLUSION: The newly established nomogram model has proven to be a novel tool that can effectively, easily, and intuitively predict the probability of OHSS in the patients with PCOS, by which the clinician can set up a better clinical management strategies for conducting a precise personal therapy. |
format | Online Article Text |
id | pubmed-8377671 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-83776712021-08-21 Nomogram Model to Predict the Probability of Ovarian Hyperstimulation Syndrome in the Treatment of Patients With Polycystic Ovary Syndrome Li, Fei Chen, Ying Niu, Aiqin He, Yajing Yan, Ying Front Endocrinol (Lausanne) Endocrinology OBJECTIVE: The objective of this study was to explore the risk factors of ovarian hyperstimulation syndrome (OHSS) in patients with polycystic ovary syndrome (PCOS) undergoing in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) and to establish a nomogram model evaluate the probability of OHSS in PCOS patients. METHODS: We retrospectively analyzed clinical data from 4,351 patients with PCOS receiving IVF/ICSI in our reproductive medical center. The clinical cases were randomly divided into a modeling group (3,231 cases) and a verification group (1,120 cases) according to a ratio of about 3:1. The independent risk factors correlation with the occurrence of OHSS was identified by logistic regression analysis. Based on the selected independent risk factors and correlated regression coefficients, we established a nomogram model to predict the probability of OHSS in PCOS patients, and the predictive accuracy of the model was measured using the area under the receiver operating curve (AUC). RESULTS: Univariate and multivariate logistic regression analyses showed that FSH (OR, 0.901; 95% CI, 0.847–0.958; P<0.001), AMH (OR, 1.259; 95% CI, 1.206–1.315; P<0.001), E2 value on the day of hCG injection (OR, 1.122; 95% CI, 1.021–1.253; P<0.001), total dosage of Gn used (OR, 1.010; 95% CI, 1.002–1.016; P=0.041), and follicle number on the day of hCG injection (OR, 0.134; 95% CI, 1.020–1.261; P=0.020) are the independent risk factors for OHSS in PCOS patients. The AUC of the modeling group is 0.827 (95% CI, 0.795–0.859), and the AUC of the verification group is 0.757 (95% CI, 0.733–0.782). CONCLUSION: The newly established nomogram model has proven to be a novel tool that can effectively, easily, and intuitively predict the probability of OHSS in the patients with PCOS, by which the clinician can set up a better clinical management strategies for conducting a precise personal therapy. Frontiers Media S.A. 2021-08-06 /pmc/articles/PMC8377671/ /pubmed/34421814 http://dx.doi.org/10.3389/fendo.2021.619059 Text en Copyright © 2021 Li, Chen, Niu, He and Yan https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Endocrinology Li, Fei Chen, Ying Niu, Aiqin He, Yajing Yan, Ying Nomogram Model to Predict the Probability of Ovarian Hyperstimulation Syndrome in the Treatment of Patients With Polycystic Ovary Syndrome |
title | Nomogram Model to Predict the Probability of Ovarian Hyperstimulation Syndrome in the Treatment of Patients With Polycystic Ovary Syndrome |
title_full | Nomogram Model to Predict the Probability of Ovarian Hyperstimulation Syndrome in the Treatment of Patients With Polycystic Ovary Syndrome |
title_fullStr | Nomogram Model to Predict the Probability of Ovarian Hyperstimulation Syndrome in the Treatment of Patients With Polycystic Ovary Syndrome |
title_full_unstemmed | Nomogram Model to Predict the Probability of Ovarian Hyperstimulation Syndrome in the Treatment of Patients With Polycystic Ovary Syndrome |
title_short | Nomogram Model to Predict the Probability of Ovarian Hyperstimulation Syndrome in the Treatment of Patients With Polycystic Ovary Syndrome |
title_sort | nomogram model to predict the probability of ovarian hyperstimulation syndrome in the treatment of patients with polycystic ovary syndrome |
topic | Endocrinology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8377671/ https://www.ncbi.nlm.nih.gov/pubmed/34421814 http://dx.doi.org/10.3389/fendo.2021.619059 |
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